From 53a89414da039621f66b48c5a69eba78f13dc682 Mon Sep 17 00:00:00 2001 From: Chihchung Chou Date: Tue, 16 Mar 2021 04:17:09 +0100 Subject: [PATCH 01/15] PeriodAccumulation vignette draft added --- vignettes/PeriodAccumulation_vignette.Rmd | 130 ++++++++++++++++++++++ 1 file changed, 130 insertions(+) create mode 100644 vignettes/PeriodAccumulation_vignette.Rmd diff --git a/vignettes/PeriodAccumulation_vignette.Rmd b/vignettes/PeriodAccumulation_vignette.Rmd new file mode 100644 index 0000000..32476a3 --- /dev/null +++ b/vignettes/PeriodAccumulation_vignette.Rmd @@ -0,0 +1,130 @@ +--- +title: "Accumulation over a Period" +author: "Earth Sciences department, Barcelona Supercomputing Center (BSC)" +date: "16/03/2021" +output: rmarkdown::html_vignette +vignette: > +--- + + + +## Introduction + +Climate Services not only provide the forecasts of the Essential Climatic Variables, a variety of the sectoral indicators are often required for Climate Services people, including researchers, decision-makers and farmers, etc. + + +Two of the important indicators for the agricultural sector, especially for the grape industries, are **Spring Total Precipitation** and **Harvest Total Precipitation** that are the total precipitation from April 21th to June 21st and from August 21st to October 21st, respectively. + +A function which can compute the sum/accumulation of a variable (like precipitation here) in an adjustable period will be useful. Furthermore, parellel computation is also prefered when handling such a multi-dimensional big data. + +Therefore, `CST_PeriodAccumulation` and `AccumulationPeriod` (along with many other functions for indices computation) have been created to fulfill the needs. The former (with prefix _CST_) handle `s2dv_cube` object and a multidimensional array can be processed by the latter. + +When period selection is needed, the `start` and `end` have to be provided to cut out the portion in `time_dim`. Otherwise, the function will take the entire `time_dim`. + +## Preliminary setup + +In order to run the examples in this vignette, the *multiApply* package need to be loaded by running: + +```{r} +install.packages('multiApply') +library(multiApply) +``` + +### Acummulation over the whole period + +We use test data provided by CSTools to load a seasonal precipitation forecast: + +```{r} +exp <- CSTools::lonlat_prec +``` + +This gives us a CSTools object `exp`, containing an element `exp$data` with dimensions: + +```{r} +dim(exp$data) +#dataset member sdate ftime lat lon +# 1 6 3 31 4 4 +``` + +In this 's2dv_cube' object, there are 6 ensemble members available in the data set, 3 starting dates and 31 forecast times, which refer to daily values in the month of March following starting dates on November 1st in the years 2010, 2011, 2012. This sample spatial domain covers a small area of Northern Italy at resolution 1 degree lon=[6,9], lat=[44,47]. + +To compute the total precipitation in March for each starting dates, we can run: + +```{r} +TP <- CST_PeriodAccumulation(exp) +``` + +Without the `start` and `end` provided, the `CST_PeriodAccumulation` will use the entire `time_dim`. The result will have dimensions: + +```{r} +dim(TP$data) +#dataset member sdate lat lon +# 1 6 3 4 4 +``` + +### Computing Spring Total Precipitation (from April 21th to June 21st) + +To compute SprR with the period given, we use the sample data saved here. + +```{r} +load('/esarchive/scratch/cchou/MEDGOLD/grape/output/ECVs/data/temp/prlr/prlr_dv.RData') +``` + +This contains elements `prlr_dv$exp$data` and `prlr_dv$obs$data` with dimensions: + +```{r} +dim(prlr_dv$exp$data) +dataset member sdate ftime lat lon + 1 3 4 214 4 4 +dim(prlr_dv$obs$data) +dataset member sdate ftime lat lon + 1 1 4 214 4 4 +``` + +There are three ensemble members in the prediction data set, 4 starting dates and the entire 7-month forecast times, which refer to daily precipitation from April to October following starting dates on April 1st in the years from 2013-2016. The spatial domain cover parts of Douro Valley of Northern Portugal at resolution 0.25 degree lon=[352.25, 353], lat=[41, 41.75]. + +To compute SprR for each forecast years, we can run: + +```{r} +SprR <- CST_PeriodAccumulation(prlr_dv, start = list(21, 4), end = list(21, 6)) +``` + +The `start` and `end` are the initial and final dates and the day must be given before the month as above. They will be applied along the dimension `time_dim` (it is set to 'ftime' by default). + +As mentioned, these paramters are optional, the function will take the entire timeseries when the period is not specified in `start` and `end`. + +The dimensions of SprR are: + +```{r} +dim(SprR$data) +dataset member sdate lat lon + 1 3 4 4 4 +``` + +The forecast SprR for the 1st member from 2013-2016 of the 1st grid poinnt in mm are: + +```{r} +#SprR$data[1,1,,1,1] * 86400 * 1000 +#[1] 93.23205 230.41904 194.01412 226.52614 +``` + +### Computing Harvest Total Precipitation (from August 21st to October 21st) + +By using the same sample data loaded above, another important indicator, Harvest Total Preciptation, can be computed by running: + +```{r} +HarvestR <- CST_PeriodAccumulation(prlr_dv$exp, start = list(21, 8), end = list(21, 10)) +``` + +The forecast HarvestR for the 1st member from 2013-2016 of the 1st grid poinnt in mm are: + +```{r} +#HarvestR$data[1,1,,1,1] * 86400 * 1000 +#[1] 52.30026 42.88068 156.87961 32.18579 +``` + + -- GitLab From 013192c2fb9a0e7aea265d723977792469388f82 Mon Sep 17 00:00:00 2001 From: nperez Date: Tue, 16 Mar 2021 10:09:11 +0100 Subject: [PATCH 02/15] md format --- ...vignette.Rmd => AgriculturalIndicators.md} | 20 ++++++------------- 1 file changed, 6 insertions(+), 14 deletions(-) rename vignettes/{PeriodAccumulation_vignette.Rmd => AgriculturalIndicators.md} (80%) diff --git a/vignettes/PeriodAccumulation_vignette.Rmd b/vignettes/AgriculturalIndicators.md similarity index 80% rename from vignettes/PeriodAccumulation_vignette.Rmd rename to vignettes/AgriculturalIndicators.md index 32476a3..91bed65 100644 --- a/vignettes/PeriodAccumulation_vignette.Rmd +++ b/vignettes/AgriculturalIndicators.md @@ -1,5 +1,5 @@ --- -title: "Accumulation over a Period" +title: "Agricultural Indicators" author: "Earth Sciences department, Barcelona Supercomputing Center (BSC)" date: "16/03/2021" output: rmarkdown::html_vignette @@ -14,31 +14,23 @@ knitr::opts_chunk$set(eval = FALSE) ## Introduction -Climate Services not only provide the forecasts of the Essential Climatic Variables, a variety of the sectoral indicators are often required for Climate Services people, including researchers, decision-makers and farmers, etc. - +Besides forecasts of Essential Climate Vaiables, Climate Services also provides a variety of the sectoral indicators are often required for Climate Services people, including researchers, decision-makers and farmers, etc. Two of the important indicators for the agricultural sector, especially for the grape industries, are **Spring Total Precipitation** and **Harvest Total Precipitation** that are the total precipitation from April 21th to June 21st and from August 21st to October 21st, respectively. -A function which can compute the sum/accumulation of a variable (like precipitation here) in an adjustable period will be useful. Furthermore, parellel computation is also prefered when handling such a multi-dimensional big data. - -Therefore, `CST_PeriodAccumulation` and `AccumulationPeriod` (along with many other functions for indices computation) have been created to fulfill the needs. The former (with prefix _CST_) handle `s2dv_cube` object and a multidimensional array can be processed by the latter. - -When period selection is needed, the `start` and `end` have to be provided to cut out the portion in `time_dim`. Otherwise, the function will take the entire `time_dim`. +A function which can compute the sum/accumulation of a variable (like precipitation here) in an adjustable period is needed and CSIndicators provides the function (CST_)PeriodAccumulation. -## Preliminary setup +*Note: s2dv_cube and array classes can be handle by the functions in CSIndicators. See section 2 in vignette [Data retrieval and storage](https://cran.r-project.org/web/packages/CSTools/vignettes/Data_Considerations.html)from CSTools package for more information.* -In order to run the examples in this vignette, the *multiApply* package need to be loaded by running: +When period selection is needed, the `start` and `end` parameters have to be provided to cut out the portion in `time_dim`. Otherwise, the function will take the entire `time_dim`. -```{r} -install.packages('multiApply') -library(multiApply) -``` ### Acummulation over the whole period We use test data provided by CSTools to load a seasonal precipitation forecast: ```{r} +library(CSIndicators) exp <- CSTools::lonlat_prec ``` -- GitLab From f683b353083734eb445cedb3f8619dc8d2d0203c Mon Sep 17 00:00:00 2001 From: nperez Date: Tue, 16 Mar 2021 10:11:28 +0100 Subject: [PATCH 03/15] fix text --- vignettes/AgriculturalIndicators.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vignettes/AgriculturalIndicators.md b/vignettes/AgriculturalIndicators.md index 91bed65..30af124 100644 --- a/vignettes/AgriculturalIndicators.md +++ b/vignettes/AgriculturalIndicators.md @@ -14,7 +14,7 @@ knitr::opts_chunk$set(eval = FALSE) ## Introduction -Besides forecasts of Essential Climate Vaiables, Climate Services also provides a variety of the sectoral indicators are often required for Climate Services people, including researchers, decision-makers and farmers, etc. +Apart from forecasts of Essential Climate Vaiables, Climate Services also provides a variety of the sectoral indicators are often required for Climate Services people, including researchers, decision-makers and farmers, etc. Two of the important indicators for the agricultural sector, especially for the grape industries, are **Spring Total Precipitation** and **Harvest Total Precipitation** that are the total precipitation from April 21th to June 21st and from August 21st to October 21st, respectively. -- GitLab From 1cb5395eb5580337ff9a5a684e4f83c909b6ea77 Mon Sep 17 00:00:00 2001 From: Chihchung Chou Date: Thu, 25 Mar 2021 09:22:26 +0100 Subject: [PATCH 04/15] PeriodMean and PeriodAccumulation added --- vignettes/AgriculturalIndicators.md | 236 ++++++++++++++++++++++------ 1 file changed, 191 insertions(+), 45 deletions(-) diff --git a/vignettes/AgriculturalIndicators.md b/vignettes/AgriculturalIndicators.md index 30af124..01b6277 100644 --- a/vignettes/AgriculturalIndicators.md +++ b/vignettes/AgriculturalIndicators.md @@ -1,7 +1,7 @@ --- title: "Agricultural Indicators" author: "Earth Sciences department, Barcelona Supercomputing Center (BSC)" -date: "16/03/2021" +date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > --- @@ -16,57 +16,63 @@ knitr::opts_chunk$set(eval = FALSE) Apart from forecasts of Essential Climate Vaiables, Climate Services also provides a variety of the sectoral indicators are often required for Climate Services people, including researchers, decision-makers and farmers, etc. -Two of the important indicators for the agricultural sector, especially for the grape industries, are **Spring Total Precipitation** and **Harvest Total Precipitation** that are the total precipitation from April 21th to June 21st and from August 21st to October 21st, respectively. +In the project MEDGOLD, 10 indicators which were identified as critical indices for the three agricultural sectors - grape/wine, olive/olive oil and wheat/pasta - have been considered in this package CSIndicators. -A function which can compute the sum/accumulation of a variable (like precipitation here) in an adjustable period is needed and CSIndicators provides the function (CST_)PeriodAccumulation. +The computing functions and the corresponding indicators are listed as follows: -*Note: s2dv_cube and array classes can be handle by the functions in CSIndicators. See section 2 in vignette [Data retrieval and storage](https://cran.r-project.org/web/packages/CSTools/vignettes/Data_Considerations.html)from CSTools package for more information.* - -When period selection is needed, the `start` and `end` parameters have to be provided to cut out the portion in `time_dim`. Otherwise, the function will take the entire `time_dim`. +--- +1. **PeriodAccumulation.R -** Spring Total Precipitation (SprR) and Harvest Total Precipitation (HarvestR) +2. **PeriodMean.R -** Growing Season Temperature (GST) and Spring Mean Temperature Maximum (SPRTX) +3. **TotalTimeExceedingThreshold.R -** Number of Heat Stress Days - 35°C (SU35), 36°C (SU36), 40°C (SU40) and Spring Heat Stress Days - 32°C (Spr32) +4. **AccumulationExceedingThreshold.R -** Growing Degree Days (GDD) +5. **TotalSpellTimeExceedingThreshold.R -** Warm Spell Duration Index (WSDI) +--- +The above functions can take both multidimensional arrays and the s2dv_cube objects (see note below). Taking PeriodAccumulation an example, **CST_**PeriodAccumulation handles the latter and PeriodAccumulation without the prefix can compute multidimensional arrays. -### Acummulation over the whole period +*Note: s2dv_cube and array classes can be handle by the functions in CSIndicators. See section 2 in vignette [Data retrieval and storage](https://cran.r-project.org/web/packages/CSTools/vignettes/Data_Considerations.html) from CSTools package for more information.* -We use test data provided by CSTools to load a seasonal precipitation forecast: +There are some supplementary functions which must be called to smoothly run the above functions. -```{r} -library(CSIndicators) -exp <- CSTools::lonlat_prec -``` +--- +1. **SelectPeriodOnData.R -** to select the data in the requested period +2. **SelectPeriodOnDates.R -** to select the time dimension in the requested period +--- -This gives us a CSTools object `exp`, containing an element `exp$data` with dimensions: +When period selection is needed, the `start` and `end` parameters have to be provided to cut out the portion in `time_dim`. Otherwise, the function will take the entire `time_dim`. -```{r} -dim(exp$data) -#dataset member sdate ftime lat lon -# 1 6 3 31 4 4 -``` +The examples of computing the aforementioned indicators are given by functions as follows. -In this 's2dv_cube' object, there are 6 ensemble members available in the data set, 3 starting dates and 31 forecast times, which refer to daily values in the month of March following starting dates on November 1st in the years 2010, 2011, 2012. This sample spatial domain covers a small area of Northern Italy at resolution 1 degree lon=[6,9], lat=[44,47]. +### 1. PeriodAccumulation.R -To compute the total precipitation in March for each starting dates, we can run: +Load the required libraries, CSIndicators and CSTools, by running ```{r} -TP <- CST_PeriodAccumulation(exp) +library(CSIndicators) +library(CSTools) +library(s2dv) +library(s2dverification) ``` -Without the `start` and `end` provided, the `CST_PeriodAccumulation` will use the entire `time_dim`. The result will have dimensions: +Here, we load the daily precipitation (**prlr** given in `var`) data sets of ECMWF SEAS5 seasonal forecast and ERA5 reanalysis for the four starting dates 20130401-20160401 with the entire 7-month forecast time, April-October. -```{r} -dim(TP$data) -#dataset member sdate lat lon -# 1 6 3 4 4 -``` +The pathways of SEAS5 and ERA5 are given in the lists with some **whitecards** used to replace the variable name and iterative items such as year and month. See details of requirements in section 4 in vignette [Data retrieval and storage](https://cran.r-project.org/web/packages/CSTools/vignettes/Data_Considerations.html) from CSTools package. + +The spatial domain covers parts of Douro Valley of Northern Portugal lon=[352.25, 353], lat=[41, 41.75]. These four values are provided in `lonmin`, `lonmax`, `latmin` and `latmax`. -### Computing Spring Total Precipitation (from April 21th to June 21st) +With `grid` set to **r1440x721**, the SEAS5 forecast would be interpolated to the 0.25-degree ERA5 grid by using the **bicubic** method given in `method`. -To compute SprR with the period given, we use the sample data saved here. ```{r} -load('/esarchive/scratch/cchou/MEDGOLD/grape/output/ECVs/data/temp/prlr/prlr_dv.RData') +var <- 'prlr'; lonmax <- 353; lonmin <- 352.25; latmax <- 41.75; latmin <- 41 +cdoMethod <- 'bicubic'; leadtimemin <- 1; leadtimemax <- 214 +S5path_prlr <- list(path = '/esarchive/exp/ecmwf/system5c3s/original_files/chou/daily_mean/$VAR_NAME$_s0-24h/$VAR_NAME$_$YEAR$$MONTH$01.nc') +path_ERA5prlr_CDS <- list(path = '/esarchive/recon/ecmwf/era5/daily_mean/$VAR_NAME$_f1h-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc') +sdates <- paste0(2013:2016, '04', '01') +prlr_dv <- CST_Load(var = var, exp = list(S5path_prlr), obs = list(path_ERA5prlr_CDS), sdates = sdates, lonmax = lonmax, lonmin = lonmin, latmax = latmax, latmin = latmin, storefreq = 'daily', leadtimemin = leadtimemin, leadtimemax = leadtimemax, nmember = 3, output = "lonlat", grid = "r1440x721", method = cdoMethod) ``` -This contains elements `prlr_dv$exp$data` and `prlr_dv$obs$data` with dimensions: +The output contains elements `prlr_dv$exp$data` and `prlr_dv$obs$data` with dimensions: ```{r} dim(prlr_dv$exp$data) @@ -77,46 +83,186 @@ dataset member sdate ftime lat lon 1 1 4 214 4 4 ``` -There are three ensemble members in the prediction data set, 4 starting dates and the entire 7-month forecast times, which refer to daily precipitation from April to October following starting dates on April 1st in the years from 2013-2016. The spatial domain cover parts of Douro Valley of Northern Portugal at resolution 0.25 degree lon=[352.25, 353], lat=[41, 41.75]. - -To compute SprR for each forecast years, we can run: +To compute **SprR** of forecast and observation, we can run: ```{r} -SprR <- CST_PeriodAccumulation(prlr_dv, start = list(21, 4), end = list(21, 6)) +SprR_exp <- CST_PeriodAccumulation(prlr_dv$exp, start = list(21, 4), end = list(21, 6)) +SprR_obs <- CST_PeriodAccumulation(prlr_dv$obs, start = list(21, 4), end = list(21, 6)) ``` The `start` and `end` are the initial and final dates and the day must be given before the month as above. They will be applied along the dimension `time_dim` (it is set to 'ftime' by default). As mentioned, these paramters are optional, the function will take the entire timeseries when the period is not specified in `start` and `end`. -The dimensions of SprR are: +The dimensions of SprR forecasts and observations are: ```{r} -dim(SprR$data) +dim(SprR_exp$data) +dataset member sdate lat lon + 1 3 4 4 4 +dim(SprR_obs$data) dataset member sdate lat lon - 1 3 4 4 4 + 1 1 4 4 4 ``` -The forecast SprR for the 1st member from 2013-2016 of the 1st grid poinnt in mm are: +The forecast SprR for the 1st member from 2013-2016 of the 1st grid point in mm are: ```{r} -#SprR$data[1,1,,1,1] * 86400 * 1000 +#SprR_exp$data[1,1,,1,1] * 86400 * 1000 #[1] 93.23205 230.41904 194.01412 226.52614 ``` -### Computing Harvest Total Precipitation (from August 21st to October 21st) - -By using the same sample data loaded above, another important indicator, Harvest Total Preciptation, can be computed by running: +To compute **HarvestR**, run the following lines ```{r} -HarvestR <- CST_PeriodAccumulation(prlr_dv$exp, start = list(21, 8), end = list(21, 10)) +HarvestR_exp <- CST_PeriodAccumulation(prlr_dv$exp, start = list(21, 8), end = list(21, 10)) +HarvestR_obs <- CST_PeriodAccumulation(prlr_dv$obs, start = list(21, 8), end = list(21, 10)) ``` The forecast HarvestR for the 1st member from 2013-2016 of the 1st grid poinnt in mm are: ```{r} -#HarvestR$data[1,1,,1,1] * 86400 * 1000 +HarvestR_exp$data[1,1,,1,1] * 86400 * 1000 #[1] 52.30026 42.88068 156.87961 32.18579 ``` +To compute the 2013-2016 ensemble-mean bias of forecast HarvestR, run + +```{r} +fcst <- drop(HarvestR_exp$data) * 86400 * 1000 +obs <- drop(HarvestR_obs$data) * 86400 * 1000 + +Bias <- MeanDims((fcst - InsertDim(obs, 1, dim(fcst)['member'])), 'member') +``` +To plot the map of ensemble-mean bias of HarvestR forecast, run + +```{r} +lon <- prlr_dv$obs$lon; lat <- prlr_dv$obs$lat + +pname_root <- "/esarchive/scratch/cchou/MEDGOLD/" +# Load Douro Valley boundary +load(file = paste0(pname_root, "demo/input/DV_boundary.RData")) +douro$x <- douro$x - 360 +cols <- c('#b2182b', '#d6604d', '#f4a582', '#fddbc7', '#d1e5f0', '#92c5de', '#4393c3', '#2166ac') +brks <- seq(-60, 60, by = 20) +toptitle <- 'Ensemble-mean bias of HarvestR in 2013' + +PlotEquiMap(Bias[1,,], lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'mm', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, col_inf = cols[1], margin_scale = c(1, 1, 1, 1), cols = cols[2:7], col_sup = cols[8], brks = brks, toptitle = toptitle, colNA = 'white', bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2, file = '/esarchive/scratch/cchou/MEDGOLD/grape/output/HarvestR_Bias_2013.pdf') +``` +You will see the following maps of HarvestR bias in 2013. + +**/esarchive/scratch/cchou/MEDGOLD/grape/output/HarvestR_Bias_2013.pdf** + + +### 2. PeriodMean.R + +For the function PeriodMean, we use Growing Season Temperature (**GST**) as an example. + +Firstly, we prepare a sample data of daily mean temperature by running + +```{r} +var <- 'tas'; lonmax <- 353; lonmin <- 352.25; latmax <- 41.75; latmin <- 41 +cdoMethod <- 'bicubic'; leadtimemin <- 1; leadtimemax <- 214 +S5path <- list(path = '/esarchive/exp/ecmwf/system5c3s/daily_mean/$VAR_NAME$_f6h/$VAR_NAME$_$YEAR$$MONTH$01.nc') +ERA5path <- list(path = '/esarchive/recon/ecmwf/era5/daily_mean/$VAR_NAME$_f1h-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc') +sdates <- paste0(2013:2016, '04', '01') +tas_dv <- CST_Load(var = var, exp = list(S5path), obs = list(ERA5path), sdates = sdates, lonmax = lonmax, lonmin = lonmin, latmax = latmax, latmin = latmin, storefreq = 'daily', leadtimemin = leadtimemin, leadtimemax = leadtimemax, nmember = 3, output = "lonlat", grid = "r1440x721", method = cdoMethod) +``` + +The output contains observations `tas_dv$obs$data` and forecast `tas_dv$exp$data`, and their dimensions and summaries are like + +```{r} +dim(tas_dv$obs$data) +dataset member sdate ftime lat lon + 1 1 4 214 4 4 + +dim(tas_dv$exp$data) +dataset member sdate ftime lat lon + 1 3 4 214 4 4 + +summary(tas_dv$obs$data - 273.15) + Min. 1st Qu. Median Mean 3rd Qu. Max. + 3.63 14.38 17.89 17.65 21.24 30.21 + +summary(tas_dv$exp$data - 273.15) + Min. 1st Qu. Median Mean 3rd Qu. Max. + 0.54 11.65 16.56 16.50 21.25 31.41 + +``` +To compute the GST for both observation and forecast, run the following lines + +```{r} +tas_dv$exp$data <- tas_dv$exp$data - 273.15 +tas_dv$obs$data <- tas_dv$obs$data - 273.15 + +GST_exp <- CST_PeriodMean(tas_dv$exp, start = list(1, 4), end = list(31, 10)) +GST_obs <- CST_PeriodMean(tas_dv$obs, start = list(1, 4), end = list(31, 10)) +``` +The summaries and dimensions of the output are as follows: + +```{r} +summary(GST_exp$data) + Min. 1st Qu. Median Mean 3rd Qu. Max. + 14.23 15.78 16.50 16.50 17.17 18.70 + +summary(GST_obs$data) + Min. 1st Qu. Median Mean 3rd Qu. Max. + 15.34 16.85 17.72 17.65 18.41 19.60 + +dim(GST_exp$data) +dataset member sdate lat lon + 1 3 4 4 4 + +dim(GST_obs$data) +dataset member sdate lat lon + 1 1 4 4 4 +``` + +Here, we plot the 2013-2016 mean climatology of ERA5 GST by running + +```{r} +# compute ERA5 GST climatology +GST_Clim <- MeanDims(drop(GST_obs$data), 'sdate') + +# plot the map of climatology +cols <- c('#ffffd4','#fee391','#fec44f','#fe9929','#ec7014','#cc4c02','#8c2d04') +brks <- seq(16, 18.5, by = 0.5) +toptitle <- '2013-2016 mean ERA5 GST' + +PlotEquiMap(GST_Clim, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = '°C', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, col_inf = cols[1], margin_scale = c(1, 1, 1, 1), cols = cols[2:6], col_sup = cols[7], , brks = brks, toptitle = toptitle, colNA = 'white', bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2, file = '/esarchive/scratch/cchou/MEDGOLD/grape/output/GST_ERA5_Climatology.pdf') +``` + +The GST map is shown as below. + +**/esarchive/scratch/cchou/MEDGOLD/grape/output/GST_ERA5_Climatology.pdf** + + +### 3. TotalTimeExceedingThreshold.R + +For this function, **SU35** (Number of Heat Stress Days - 35°C) is taken as an example here. + +The daily temperature maximum of the entire 7-month forecast period is needed for this indicator. + +```{r} +var <- 'tasmax'; lonmax <- 353; lonmin <- 352.25; latmax <- 41.75; latmin <- 41 +cdoMethod <- 'bicubic'; leadtimemin <- 1; leadtimemax <- 214 +S5path <- list(path = '/esarchive/exp/ecmwf/system5c3s/daily/$VAR_NAME$/$VAR_NAME$_$YEAR$$MONTH$01.nc') +ERA5path <- list(path = '/esarchive/recon/ecmwf/era5/daily/$VAR_NAME$-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc') +sdates <- paste0(2013:2016, '04', '01') +tasmax_dv <- CST_Load(var = var, exp = list(S5path), obs = list(ERA5path), sdates = sdates, lonmax = lonmax, lonmin = lonmin, latmax = latmax, latmin = latmin, storefreq = 'daily', leadtimemin = leadtimemin, leadtimemax = leadtimemax, nmember = 3, output = "lonlat", grid = "r1440x721", method = cdoMethod) +``` + +Change the unit of temperature to from °C to K for the comparison with the threshold defined. + +After that, we can compute the SU35 by running + +```{r} +tasmax_dv$exp$data <- tasmax_dv$exp$data - 273.15 +tasmax_dv$obs$data <- tasmax_dv$obs$data - 273.15 + +threshold <- 35 + +SU35_exp <- CST_TotalTimeExceedingThreshold(tasmax_dv$exp, threshold = threshold, start = list(1, 4), end = list(31, 10)) +SU35_obs <- CST_TotalTimeExceedingThreshold(tasmax_dv$obs, threshold = threshold, start = list(1, 4), end = list(31, 10)) +``` -- GitLab From 5332faeeb9eb1828a7d563e07b25d1dd460fa4b2 Mon Sep 17 00:00:00 2001 From: Chihchung Chou Date: Mon, 29 Mar 2021 09:19:58 +0200 Subject: [PATCH 05/15] agro vig added --- vignettes/AgriculturalIndicators.md | 335 ++++++++++++++++++++++++++-- 1 file changed, 320 insertions(+), 15 deletions(-) diff --git a/vignettes/AgriculturalIndicators.md b/vignettes/AgriculturalIndicators.md index 01b6277..a1c772d 100644 --- a/vignettes/AgriculturalIndicators.md +++ b/vignettes/AgriculturalIndicators.md @@ -37,15 +37,20 @@ There are some supplementary functions which must be called to smoothly run the --- 1. **SelectPeriodOnData.R -** to select the data in the requested period 2. **SelectPeriodOnDates.R -** to select the time dimension in the requested period +3. **Threshold.R -** to convert absolute value/variable to its pecentile, e.g., Warm Spell Duration Index uses the 90th percentile corresponding to each day instead of a fixed threshold. See how this function is applied in Section 5. --- -When period selection is needed, the `start` and `end` parameters have to be provided to cut out the portion in `time_dim`. Otherwise, the function will take the entire `time_dim`. +When the period selection is required, the `start` and `end` parameters have to be provided to cut out the portion in `time_dim`. Otherwise, the function will take the **entire** `time_dim`. The examples of computing the aforementioned indicators are given by functions as follows. ### 1. PeriodAccumulation.R -Load the required libraries, CSIndicators and CSTools, by running +`PeriodAccumulation` (and `CST_PeriodAccumulation`) computes the sum of a given variable in a period. + +Here, two indicators are used to show how this function works: Spring Total Precipitation (SprR) and Harvest Total Precipitation (HarvestR). Both indices represent the total precipitation but in different periods, 21st April - 21st June for SprR and 21st August - 21st October for HarvestR. + +First, load the required libraries, CSIndicators, CSTools, etc by running ```{r} library(CSIndicators) @@ -54,9 +59,9 @@ library(s2dv) library(s2dverification) ``` -Here, we load the daily precipitation (**prlr** given in `var`) data sets of ECMWF SEAS5 seasonal forecast and ERA5 reanalysis for the four starting dates 20130401-20160401 with the entire 7-month forecast time, April-October. +To obtain the precipitation forecast and observation, we load the daily precipitation (**prlr** given in `var`) data sets of ECMWF SEAS5 seasonal forecast and ERA5 reanalysis for the four starting dates 20130401-20160401 (provided in `sdates`) with the entire 7-month forecast time, April-October (214 days in total given in parameter `leadtimemax`). -The pathways of SEAS5 and ERA5 are given in the lists with some **whitecards** used to replace the variable name and iterative items such as year and month. See details of requirements in section 4 in vignette [Data retrieval and storage](https://cran.r-project.org/web/packages/CSTools/vignettes/Data_Considerations.html) from CSTools package. +The pathways of SEAS5 and ERA5 are given in the lists with some **whitecards (inside two dollar signs)** used to replace the variable name and iterative items such as year and month. See details of requirements in section 4 in vignette [Data retrieval and storage](https://cran.r-project.org/web/packages/CSTools/vignettes/Data_Considerations.html) from CSTools package. The spatial domain covers parts of Douro Valley of Northern Portugal lon=[352.25, 353], lat=[41, 41.75]. These four values are provided in `lonmin`, `lonmax`, `latmin` and `latmax`. @@ -112,7 +117,15 @@ The forecast SprR for the 1st member from 2013-2016 of the 1st grid point in mm #[1] 93.23205 230.41904 194.01412 226.52614 ``` -To compute **HarvestR**, run the following lines +Dry springs will delay vegetative growth and reduce vigour and leaf area total surface. + +Fungal disease pressure will be lower and therefore there will be less need for protective and / or curative treatments, translating as less costs. + +Wet springs will promote higher vigour, increase the risk of fungal disease and disrupt vineyard operations as it may prevent machinery from getting in the vineyard due to mud. + +They are usually associated with higher costs. + +On the other hand, another moisture-related indicators, **HarvestR**, can be computed by using `PeriodAccumulation` as well, with the defined period as the following lines. ```{r} HarvestR_exp <- CST_PeriodAccumulation(prlr_dv$exp, start = list(21, 8), end = list(21, 10)) @@ -153,12 +166,22 @@ You will see the following maps of HarvestR bias in 2013. **/esarchive/scratch/cchou/MEDGOLD/grape/output/HarvestR_Bias_2013.pdf** +In 2013, the ensemble-mean SEAS5 seasonal forecast of HarvestR is underestimated by up to 60 mm over Douro Valley region (the central four grid points). + ### 2. PeriodMean.R -For the function PeriodMean, we use Growing Season Temperature (**GST**) as an example. +For the function `PeriodMean`, we use Growing Season Temperature (**GST**) as an example. + +GST is defined as the average of daily average temperatures between April 1st to October 31st in the northern hemisphere. + +It provides information onto which are the best suited varieties for a given site or inversely, which are the best places to grow a specific variety. + +For existing vineyards, GST also informs on the suitability of its varieties for the climate of specific years, explaining quality and production variation. -Firstly, we prepare a sample data of daily mean temperature by running +Many grapevine varieties across the world have been characterized in function of their GST optimum. + +Firstly, we prepare a sample data of daily mean temperature of SEAS5 and ERA5 data sets with the same starting dates, spatial domain, interpolation grid and method by running ```{r} var <- 'tas'; lonmax <- 353; lonmin <- 352.25; latmax <- 41.75; latmin <- 41 @@ -166,6 +189,7 @@ cdoMethod <- 'bicubic'; leadtimemin <- 1; leadtimemax <- 214 S5path <- list(path = '/esarchive/exp/ecmwf/system5c3s/daily_mean/$VAR_NAME$_f6h/$VAR_NAME$_$YEAR$$MONTH$01.nc') ERA5path <- list(path = '/esarchive/recon/ecmwf/era5/daily_mean/$VAR_NAME$_f1h-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc') sdates <- paste0(2013:2016, '04', '01') + tas_dv <- CST_Load(var = var, exp = list(S5path), obs = list(ERA5path), sdates = sdates, lonmax = lonmax, lonmin = lonmin, latmax = latmax, latmin = latmin, storefreq = 'daily', leadtimemin = leadtimemin, leadtimemax = leadtimemax, nmember = 3, output = "lonlat", grid = "r1440x721", method = cdoMethod) ``` @@ -192,12 +216,20 @@ summary(tas_dv$exp$data - 273.15) To compute the GST for both observation and forecast, run the following lines ```{r} +# change the unit of temperature from °C to K + tas_dv$exp$data <- tas_dv$exp$data - 273.15 tas_dv$obs$data <- tas_dv$obs$data - 273.15 +# compute GST + GST_exp <- CST_PeriodMean(tas_dv$exp, start = list(1, 4), end = list(31, 10)) GST_obs <- CST_PeriodMean(tas_dv$obs, start = list(1, 4), end = list(31, 10)) + ``` + +Since the period considered for GST is the entire period for starting month of April, in this case the start and end could be ignored. + The summaries and dimensions of the output are as follows: ```{r} @@ -223,6 +255,7 @@ Here, we plot the 2013-2016 mean climatology of ERA5 GST by running ```{r} # compute ERA5 GST climatology GST_Clim <- MeanDims(drop(GST_obs$data), 'sdate') +lon <- tas_dv$obs$lon; lat <- tas_dv$obs$lat # plot the map of climatology cols <- c('#ffffd4','#fee391','#fec44f','#fe9929','#ec7014','#cc4c02','#8c2d04') @@ -232,16 +265,40 @@ toptitle <- '2013-2016 mean ERA5 GST' PlotEquiMap(GST_Clim, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = '°C', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, col_inf = cols[1], margin_scale = c(1, 1, 1, 1), cols = cols[2:6], col_sup = cols[7], , brks = brks, toptitle = toptitle, colNA = 'white', bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2, file = '/esarchive/scratch/cchou/MEDGOLD/grape/output/GST_ERA5_Climatology.pdf') ``` -The GST map is shown as below. +The ERA5 GST climatology is shown as below. **/esarchive/scratch/cchou/MEDGOLD/grape/output/GST_ERA5_Climatology.pdf** +ERA5 GST ranges from 17-18.5°C over Douro Valley region for the period from 2013-2016 as shown in the figure. + ### 3. TotalTimeExceedingThreshold.R -For this function, **SU35** (Number of Heat Stress Days - 35°C) is taken as an example here. +For the function `TotalTimeExceedingThreshold`, **SU35** (Number of Heat Stress Days - 35°C) is taken as an example here. + +35°C is the average established threshold for photosynthesis to occur in the grapevine. + +Above this temperature, the plant closes its stomata. + +If this situation occurs after veraison, maturation will be arrested for as long as the situation holds, decreasing sugar, polyphenol and aroma precursor levels, all essential for grape and wine quality. -The daily temperature maximum of the entire 7-month forecast period is needed for this indicator. +The higher the index, the lower will be berry quality and aptitude to produce quality grapes. + +SU35 is defined as the Total count of days when daily maximum temperatures exceed 35°C in the seven months into the future. + +There are three indicators sharing the similar definition as SU35: SU36, SU40 and Spr32. Their definition are listed as follows. + +--- +1. **SU36**: Total count of days when daily maximum temperatures exceed 36°C between June 21st and September 21st +2. **SU40**: Total count of days when daily maximum temperatures exceed 40°C between June 21st and September 21st +3. **Spr32**: Total count of days when daily maximum temperatures exceed 32°C between April 21st and June 21st +--- + +These indicators can be computed as well by using the function `TotalTimeExceedingThreshold` with diferent thresholds and periods indicated. + +Here, we take SU35 as example, therefore the daily temperature maximum of the entire 7-month forecast period is needed for the computation of this indicator. + +Load SEAS5 and ERA5 daily temperature maximum by running ```{r} var <- 'tasmax'; lonmax <- 353; lonmin <- 352.25; latmax <- 41.75; latmin <- 41 @@ -249,20 +306,268 @@ cdoMethod <- 'bicubic'; leadtimemin <- 1; leadtimemax <- 214 S5path <- list(path = '/esarchive/exp/ecmwf/system5c3s/daily/$VAR_NAME$/$VAR_NAME$_$YEAR$$MONTH$01.nc') ERA5path <- list(path = '/esarchive/recon/ecmwf/era5/daily/$VAR_NAME$-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc') sdates <- paste0(2013:2016, '04', '01') -tasmax_dv <- CST_Load(var = var, exp = list(S5path), obs = list(ERA5path), sdates = sdates, lonmax = lonmax, lonmin = lonmin, latmax = latmax, latmin = latmin, storefreq = 'daily', leadtimemin = leadtimemin, leadtimemax = leadtimemax, nmember = 3, output = "lonlat", grid = "r1440x721", method = cdoMethod) -``` -Change the unit of temperature to from °C to K for the comparison with the threshold defined. +tasmax_dv <- CST_Load(var = var, exp = list(S5path), obs = list(ERA5path), sdates = sdates, lonmax = lonmax, lonmin = lonmin, latmax = latmax, latmin = latmin, storefreq = 'daily', leadtimemin = leadtimemin, leadtimemax = leadtimemax, nmember = 3, output = "lonlat", grid = "r1440x721", method = cdoMethod, nprocs = 1) +``` -After that, we can compute the SU35 by running +Change the unit of temperature to from °C to K for the comparison with the threshold defined (for example 35°C here). And the longitude and latitude are prepared for plotting the figure. ```{r} tasmax_dv$exp$data <- tasmax_dv$exp$data - 273.15 tasmax_dv$obs$data <- tasmax_dv$obs$data - 273.15 +lon <- tasmax_dv$obs$lon; lat <- tasmax_dv$obs$lat +``` -threshold <- 35 +Computing SU35 for forecast and observation by running +```{r} +threshold <- 35 SU35_exp <- CST_TotalTimeExceedingThreshold(tasmax_dv$exp, threshold = threshold, start = list(1, 4), end = list(31, 10)) SU35_obs <- CST_TotalTimeExceedingThreshold(tasmax_dv$obs, threshold = threshold, start = list(1, 4), end = list(31, 10)) ``` +The summaries of SU35 forecasts and observations are given below. + +```{r} +summary(SU35_exp$data) + Min. 1st Qu. Median Mean 3rd Qu. Max. + 0.00 2.00 5.00 7.12 12.00 26.00 + +summary(SU35_obs$data) + Min. 1st Qu. Median Mean 3rd Qu. Max. + 0.000 0.000 1.000 2.609 5.000 10.000 + +``` +As shown in the summaries, SEAS5 SU35 forecasts are overestimated by 5 days in terms of mean value. + +Therefore, `CST_BiasCorrection` is used to bias adjust the SU35 forecasts. + +```{r} +res <- CST_BiasCorrection(obs = SU35_obs, exp = SU35_exp) +SU35_exp_BC <- drop(res$data) +summary(SU35_exp_BC) + Min. 1st Qu. Median Mean 3rd Qu. Max. + -1.419 0.000 1.613 2.831 4.756 17.768 +``` + +Since there are negative values after bias adjustment, all negative data is converted to zero. + +```{r} +SU35_exp_BC[SU35_exp_BC < 0] <- 0 +summary(SU35_exp_BC) + Min. 1st Qu. Median Mean 3rd Qu. Max. + 0.000 0.000 1.613 2.943 4.756 17.768 +``` + +Plot the bias-adjusted SU35 forecast in 2016 by running + +```{r} +SU35_obs_Y2016 <- drop(SU35_obs$data)[4, , ] +SU35_exp_Y2016 <- MeanDims(drop(SU35_exp$data)[, 4, , ], 'member') +SU35_exp_BC_Y2016 <- MeanDims(SU35_exp_BC[, 4, , ], 'member') +cols <- c("#fee5d9", "#fcae91", "#fb6a4a", "#de2d26","#a50f15") +brks <- seq(0, 8, by = 2) + +toptitle <- 'ERA5 SU35 forecast in 2016' +PlotEquiMap(SU35_obs_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2, file = '/esarchive/scratch/cchou/MEDGOLD/grape/output/SU35_ERA5_Y2016.pdf') + +toptitle <- 'SU35 forecast in 2016' +PlotEquiMap(SU35_exp_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2, file = '/esarchive/scratch/cchou/MEDGOLD/grape/output/SU35_SEAS5_Y2016.pdf') + +toptitle <- 'Bias-adjusted SU35 forecast in 2016' +PlotEquiMap(SU35_exp_BC_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2, file = '/esarchive/scratch/cchou/MEDGOLD/grape/output/SU35_SEAS5_BC_Y2016.pdf') +``` +You can see the figure as below. + +**/esarchive/scratch/cchou/MEDGOLD/grape/output/SU35_ERA5_Y2016.pdf** +**/esarchive/scratch/cchou/MEDGOLD/grape/output/SU35_SEAS5_Y2016.pdf** +**/esarchive/scratch/cchou/MEDGOLD/grape/output/SU35_SEAS5_BC_Y2016.pdf** + +As seen above, the bias-adjusted SU35 forecasts is much close to the ERA5 result although differences remain. + +Beside of the original definition of SU35, here two supplementary functions in the package `CSIndicators` are demonstrated by computing its another definition with the percentile adjustment. + +--- +1. **AbsToProbs -**: to transform ensemble forecast into probabilities by using the Cumulative Distribution Function +2. **QThreshold -**: to transform an absolute threshold into probabilities. +--- + +The above two supplementary functions are required to compute SU35 with the percentile adjustment. The function `AbsToProbs` would be applied to forecast and the `QThreshold` would be used to convert the observations to its percentile based on the given threshold. + +The revised definition of SU35 is to reduce the potential influence induced by the fixed threshold of temperature defined for the index, instead of using the absolute value, the percentile corresponding to 35°C for observation is compared to the percentile corresponding to the predicted daily maximum temperature before being considered as a ‘heat stress’ day. + +As mentioned, the forecast is translated to its percentile by using the function `ABsToProbs` by running + +```{r} +exp_percentile <- AbsToProbs(tasmax_dv$exp$data) +S5txP <- aperm(drop(exp_percentile), c(2, 1, 3, 4, 5)) +``` + +After that, based on 35 of threshold, the percentile corresponding to each observational value can be calculated as follows. + +```{r} +obs_percentile <- QThreshold(tasmax_dv$obs$data, threshold = 35) +obs_percentile <- drop(obs_percentile) +``` + +After translating both forecasts and observations into probabilities, the comparison can then be done by running + +```{r} +SU35_exp_Percentile <- TotalTimeExceedingThreshold(S5txP, threshold = obs_percentile, time_dim = 'ftime') +``` + +Compute the same ensemble-mean SU35 **with percentile adjustment** in 2016 by running + +```{r} +SU35_exp_per_Y2016 <- MeanDims(SU35_exp_Percentile[, 4, , ], 'member') +``` + +Plot the same map for comparison + +```{r} +toptitle <- 'SU35 forecast with percentile adjustment in 2016' +PlotEquiMap(SU35_exp_per_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2, file = '/esarchive/scratch/cchou/MEDGOLD/grape/output/SU35_Percentile_SEAS5_Y2016.pdf') +``` + +As seen in **/esarchive/scratch/cchou/MEDGOLD/grape/output/SU35_Percentile_SEAS5_Y2016.pdf**, applying the percentile adjustment seems to implicitly adjust certain extent of bias which was observed in the non-bias-adjusted SEAS5 forecast. + +The performance of comparison of skills between two definitions requires further analysis such as the application of more skill metrics. + +### 4. AccumulationExceedingThreshold.R + +The function ´AccumulationExceedingThreshold´ can compute GDD (Growing Degree Days). + +The definition of GDD is the summation of daily differences between daily average temperatures and 10°C between April 1st and October 31st. + +Here, the tas (daily average temperature) used above (in Section 2. PeriodMean.R) is loaded again. + +Chang the unit from °C to K and prepare the longitude and latitude by running + +```{r} +tas_dv$exp$data <- tas_dv$exp$data - 273.15 +tas_dv$obs$data <- tas_dv$obs$data - 273.15 +lon <- tas_dv$obs$lon; lat <- tas_dv$obs$lat +``` + +As per the definition, `threshold` is set to 10 with `diff` set to TRUE so that the function will compute the differences between daily temperature and the threshold given before calculating summation. + +```{r} +GDD_exp <- CST_AccumulationExceedingThreshold(tas_dv$exp, threshold = 10, diff = TRUE) +GDD_obs <- CST_AccumulationExceedingThreshold(tas_dv$obs, threshold = 10, diff = TRUE) +``` + +The summaries of GDD are + +```{r} +summary(GDD_exp$data) + Min. 1st Qu. Median Mean 3rd Qu. Max. + 1021 1331 1480 1469 1596 1873 + +summary(GDD_obs$data) + Min. 1st Qu. Median Mean 3rd Qu. Max. + 1195 1504 1687 1660 1804 2055 +``` + +To compute the correlation coefficient for the period from 2013-2016, run the following lines + +```{r} +# reorder the dimension +fcst <- Reorder(GDD_exp$data, c(4, 3, 2, 1)) +obs <- Reorder(GDD_obs$data, c(3, 2, 1)) + +c <- veriApply('EnsCorr', fcst = fcst, obs = obs, ensdim = 4, tdim = 3) +GDD_Corr <- Reorder(c, c(2, 1)) +``` + +To plot the map of correlation coefficient of GDD for the 2013-2016 period. + +```{r} +cols <- c("#f7fcf5", "#e5f5e0", "#c7e9c0", "#a1d99b", "#74c476") +brks <- seq(0.5, 1, by = 0.1) +toptitle <- '2013-2016 correlation coefficient of GDD' + +PlotEquiMap(GDD_Corr, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'correlation', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols, brks = brks, toptitle = toptitle, bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2, file = '/esarchive/scratch/cchou/MEDGOLD/grape/output/GDD_SEAS5_Corr_Y13-16.pdf') +``` + +The map of correlation coefficient for the 2013-2016 period is shown as below. + +**/esarchive/scratch/cchou/MEDGOLD/grape/output/GDD_SEAS5_Corr_Y13-16.pdf** + +The 2013-2016 correlation coefficients of the SEAS5 forecasts of GDD in reference with ERA5 reanalysis over Douro Valley range between 0.6 and 0.8. + +### 5. TotalSpellTimeExceedingThreshold.R + +One of the critical agricultural indicators related to dry spell is **Warm Spell Duration Index (WSDI)** which is defined as the total count of days with at least 6 consecutive days when the daily temperature maximum exceeds its 90th percentile in the seven months into the future. + +The temperature maximum data used in Section 3. TotalTimeExceedingThreshold.R is loaded here again. + +Since the daily temperature maximum needs to compare to its 90th percentile, the function `Threshold` in the package `CSIndicators` is required to compute the percentile of observations used for each day. Here the same period (2013-2016) is considered. + +```{r} +tx_p <- CST_Threshold(tasmax_dv$obs, threshold = 0.9) +``` + +The output will be the 90th percentile of each day of each grid point derived by using the all the years in the data. + +See the dimension and summary as below. + +```{r} +dim(tx_p$data) +dataset ftime lat lon + 1 214 4 4 + +summary(tx_p$data) + Min. 1st Qu. Median Mean 3rd Qu. Max. + 287.0 295.2 299.2 299.4 303.9 309.9 +``` + +With the prepared threshold (90th percentile), the WSDI can be computed by running + +```{r} +WSDI_exp <- CST_TotalSpellTimeExceedingThreshold(tasmax_dv$exp, threshold = tx_p, spell = 6) +WSDI_obs <- CST_TotalSpellTimeExceedingThreshold(tasmax_dv$obs, threshold = tx_p, spell = 6) +``` + +After checking the summaries, compute the Fair Ranked Probability Skill Score (FRPSS) of WSDI by running the following lines + +```{r} +# Reorder the data +fcst <- Reorder(drop(WSDI_exp$data), c(4, 3, 2, 1)) +obs <- Reorder(drop(WSDI_obs$data), c(3, 2, 1)) + +# summaries of WSDI +summary(fcst) + Min. 1st Qu. Median Mean 3rd Qu. Max. + 0.00 13.00 28.00 30.65 42.25 82.00 + +summary(obs) + Min. 1st Qu. Median Mean 3rd Qu. Max. + 9.00 19.00 22.50 22.91 25.25 33.00 + +# compute FRPSS +f <- veriApply('FairRpss', fcst = fcst, obs = obs, ensdim = 4, tdim = 3, prob = 1:2/3)$skillscore +WSDI_FRPSS <- Reorder(f, c(2,1)) +``` + +Plot the map of WSDI FRPSS for the period from 2013-2016 + +```{r} +cols <- c("#edf8fb", "#ccece6", "#99d8c9", "#66c2a4") +brks <- seq(0, 0.9, by = 0.3) +toptitle <- 'SEAS5 WSDI FRPSS (2013-2016)' + +PlotEquiMap(WSDI_FRPSS, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'FRPSS', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:3], col_inf = 'white', col_sup = cols[4], brks = brks, toptitle = toptitle, bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2, file = '/esarchive/scratch/cchou/MEDGOLD/grape/output/WSDI_SEAS5_FRPSS_Y13-16.pdf') +``` + +The FRPSS map for 2013-2016 SEAS WSDI is shown as below. + +**/esarchive/scratch/cchou/MEDGOLD/grape/output/WSDI_SEAS5_FRPSS_Y13-16.pdf** + +As seen in the map, FRPSS in the eastern part of Douro Valley falls in 0.6-0.9 which are good enough to be useful when compared to observational climatology. + + +In additional to the grape/wine sector focused here, MEDGOLD also work on the other two sectors, olive/olive oil and durum wheat/pasta. Furthermore, the climate serives are also provided at the longer term (up to 30 years) by other project partners. + +Click on [MEDGOLD](https://www.med-gold.eu/climate-services/) for more information. + + -- GitLab From 2e51d31f080250cb8a49af3c38def6205c491f6c Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Mon, 12 Apr 2021 10:26:34 +0200 Subject: [PATCH 06/15] fixed typos --- vignettes/AgriculturalIndicators.md | 52 ++++++++++++++--------------- 1 file changed, 26 insertions(+), 26 deletions(-) diff --git a/vignettes/AgriculturalIndicators.md b/vignettes/AgriculturalIndicators.md index a1c772d..12d8ee4 100644 --- a/vignettes/AgriculturalIndicators.md +++ b/vignettes/AgriculturalIndicators.md @@ -14,9 +14,9 @@ knitr::opts_chunk$set(eval = FALSE) ## Introduction -Apart from forecasts of Essential Climate Vaiables, Climate Services also provides a variety of the sectoral indicators are often required for Climate Services people, including researchers, decision-makers and farmers, etc. +Apart from forecasts of Essential Climate Variables, Climate Services also provide a variety of the sectoral indicators that are often required for Climate Services people, including researchers, decision-makers, farmers, etc. -In the project MEDGOLD, 10 indicators which were identified as critical indices for the three agricultural sectors - grape/wine, olive/olive oil and wheat/pasta - have been considered in this package CSIndicators. +In the MEDGOLD project, 10 indicators which were identified as critical indices for the three agricultural sectors - grape/wine, olive/olive oil and wheat/pasta - have been considered in this CSIndicators package. The computing functions and the corresponding indicators are listed as follows: @@ -28,16 +28,16 @@ The computing functions and the corresponding indicators are listed as follows: 5. **TotalSpellTimeExceedingThreshold.R -** Warm Spell Duration Index (WSDI) --- -The above functions can take both multidimensional arrays and the s2dv_cube objects (see note below). Taking PeriodAccumulation an example, **CST_**PeriodAccumulation handles the latter and PeriodAccumulation without the prefix can compute multidimensional arrays. +The above functions can take both multidimensional arrays and the s2dv_cube objects (see note below). Taking PeriodAccumulation as example, **CST_**PeriodAccumulation handles the latter and PeriodAccumulation without the prefix can compute multidimensional arrays. -*Note: s2dv_cube and array classes can be handle by the functions in CSIndicators. See section 2 in vignette [Data retrieval and storage](https://cran.r-project.org/web/packages/CSTools/vignettes/Data_Considerations.html) from CSTools package for more information.* +*Note: s2dv_cube and array classes can be handled by the functions in CSIndicators. See Section 2 in vignette [Data retrieval and storage](https://cran.r-project.org/web/packages/CSTools/vignettes/Data_Considerations.html) from CSTools package for more information.* There are some supplementary functions which must be called to smoothly run the above functions. --- 1. **SelectPeriodOnData.R -** to select the data in the requested period 2. **SelectPeriodOnDates.R -** to select the time dimension in the requested period -3. **Threshold.R -** to convert absolute value/variable to its pecentile, e.g., Warm Spell Duration Index uses the 90th percentile corresponding to each day instead of a fixed threshold. See how this function is applied in Section 5. +3. **Threshold.R -** to convert absolute value/variable to its percentile, e.g., Warm Spell Duration Index uses the 90th percentile corresponding to each day instead of a fixed threshold. See how this function is applied in Section 5. --- When the period selection is required, the `start` and `end` parameters have to be provided to cut out the portion in `time_dim`. Otherwise, the function will take the **entire** `time_dim`. @@ -61,9 +61,9 @@ library(s2dverification) To obtain the precipitation forecast and observation, we load the daily precipitation (**prlr** given in `var`) data sets of ECMWF SEAS5 seasonal forecast and ERA5 reanalysis for the four starting dates 20130401-20160401 (provided in `sdates`) with the entire 7-month forecast time, April-October (214 days in total given in parameter `leadtimemax`). -The pathways of SEAS5 and ERA5 are given in the lists with some **whitecards (inside two dollar signs)** used to replace the variable name and iterative items such as year and month. See details of requirements in section 4 in vignette [Data retrieval and storage](https://cran.r-project.org/web/packages/CSTools/vignettes/Data_Considerations.html) from CSTools package. +The pathways of SEAS5 and ERA5 are given in the lists with some **whitecards (inside two dollar signs)** used to replace the variable name and iterative items such as year and month. See details of requirements in Section 4 in vignette [Data retrieval and storage](https://cran.r-project.org/web/packages/CSTools/vignettes/Data_Considerations.html) from CSTools package. -The spatial domain covers parts of Douro Valley of Northern Portugal lon=[352.25, 353], lat=[41, 41.75]. These four values are provided in `lonmin`, `lonmax`, `latmin` and `latmax`. +The spatial domain covers part of Douro Valley of Northern Portugal lon=[352.25, 353], lat=[41, 41.75]. These four values are provided in `lonmin`, `lonmax`, `latmin` and `latmax`. With `grid` set to **r1440x721**, the SEAS5 forecast would be interpolated to the 0.25-degree ERA5 grid by using the **bicubic** method given in `method`. @@ -97,7 +97,7 @@ SprR_obs <- CST_PeriodAccumulation(prlr_dv$obs, start = list(21, 4), end = list( The `start` and `end` are the initial and final dates and the day must be given before the month as above. They will be applied along the dimension `time_dim` (it is set to 'ftime' by default). -As mentioned, these paramters are optional, the function will take the entire timeseries when the period is not specified in `start` and `end`. +As mentioned, these parameters are optional, the function will take the entire timeseries when the period is not specified in `start` and `end`. The dimensions of SprR forecasts and observations are: @@ -113,7 +113,7 @@ dataset member sdate lat lon The forecast SprR for the 1st member from 2013-2016 of the 1st grid point in mm are: ```{r} -#SprR_exp$data[1,1,,1,1] * 86400 * 1000 +SprR_exp$data[1,1,,1,1] * 86400 * 1000 #[1] 93.23205 230.41904 194.01412 226.52614 ``` @@ -132,7 +132,7 @@ HarvestR_exp <- CST_PeriodAccumulation(prlr_dv$exp, start = list(21, 8), end = l HarvestR_obs <- CST_PeriodAccumulation(prlr_dv$obs, start = list(21, 8), end = list(21, 10)) ``` -The forecast HarvestR for the 1st member from 2013-2016 of the 1st grid poinnt in mm are: +The forecast HarvestR for the 1st member from 2013-2016 of the 1st grid point in mm are: ```{r} HarvestR_exp$data[1,1,,1,1] * 86400 * 1000 @@ -173,15 +173,15 @@ In 2013, the ensemble-mean SEAS5 seasonal forecast of HarvestR is underestimated For the function `PeriodMean`, we use Growing Season Temperature (**GST**) as an example. -GST is defined as the average of daily average temperatures between April 1st to October 31st in the northern hemisphere. +GST is defined as the average of daily average temperatures between April 1st to October 31st in the Northern Hemisphere. -It provides information onto which are the best suited varieties for a given site or inversely, which are the best places to grow a specific variety. +It provides information onto which are the best suited varieties for a given site or, inversely, which are the best places to grow a specific variety. For existing vineyards, GST also informs on the suitability of its varieties for the climate of specific years, explaining quality and production variation. Many grapevine varieties across the world have been characterized in function of their GST optimum. -Firstly, we prepare a sample data of daily mean temperature of SEAS5 and ERA5 data sets with the same starting dates, spatial domain, interpolation grid and method by running +Firstly, we prepare a sample data of daily mean temperature of SEAS5 and ERA5 data sets with the same starting dates, spatial domain, interpolation grid and method by running ```{r} var <- 'tas'; lonmax <- 353; lonmin <- 352.25; latmax <- 41.75; latmin <- 41 @@ -228,7 +228,7 @@ GST_obs <- CST_PeriodMean(tas_dv$obs, start = list(1, 4), end = list(31, 10)) ``` -Since the period considered for GST is the entire period for starting month of April, in this case the start and end could be ignored. +Since the period considered for GST is the entire period for starting month of April, in this case the `start` and `end` parameters could be ignored. The summaries and dimensions of the output are as follows: @@ -269,7 +269,7 @@ The ERA5 GST climatology is shown as below. **/esarchive/scratch/cchou/MEDGOLD/grape/output/GST_ERA5_Climatology.pdf** -ERA5 GST ranges from 17-18.5°C over Douro Valley region for the period from 2013-2016 as shown in the figure. +ERA5 GST ranges from 17-18.5°C over the Douro Valley region for the period from 2013-2016 as shown in the figure. ### 3. TotalTimeExceedingThreshold.R @@ -282,7 +282,7 @@ Above this temperature, the plant closes its stomata. If this situation occurs after veraison, maturation will be arrested for as long as the situation holds, decreasing sugar, polyphenol and aroma precursor levels, all essential for grape and wine quality. -The higher the index, the lower will be berry quality and aptitude to produce quality grapes. +The higher the index, the lower will be the berry quality and aptitude to produce quality grapes. SU35 is defined as the Total count of days when daily maximum temperatures exceed 35°C in the seven months into the future. @@ -294,7 +294,7 @@ There are three indicators sharing the similar definition as SU35: SU36, SU40 an 3. **Spr32**: Total count of days when daily maximum temperatures exceed 32°C between April 21st and June 21st --- -These indicators can be computed as well by using the function `TotalTimeExceedingThreshold` with diferent thresholds and periods indicated. +These indicators can be computed as well by using the function `TotalTimeExceedingThreshold` with different thresholds and periods indicated. Here, we take SU35 as example, therefore the daily temperature maximum of the entire 7-month forecast period is needed for the computation of this indicator. @@ -383,9 +383,9 @@ You can see the figure as below. **/esarchive/scratch/cchou/MEDGOLD/grape/output/SU35_SEAS5_Y2016.pdf** **/esarchive/scratch/cchou/MEDGOLD/grape/output/SU35_SEAS5_BC_Y2016.pdf** -As seen above, the bias-adjusted SU35 forecasts is much close to the ERA5 result although differences remain. +As seen above, the bias-adjusted SU35 forecasts are much closer to the ERA5 results, although differences remain. -Beside of the original definition of SU35, here two supplementary functions in the package `CSIndicators` are demonstrated by computing its another definition with the percentile adjustment. +Beside of the original definition of SU35, here two supplementary functions in the package `CSIndicators` are demonstrated by computing its another definition with the percentile adjustment. --- 1. **AbsToProbs -**: to transform ensemble forecast into probabilities by using the Cumulative Distribution Function @@ -441,7 +441,7 @@ The definition of GDD is the summation of daily differences between daily averag Here, the tas (daily average temperature) used above (in Section 2. PeriodMean.R) is loaded again. -Chang the unit from °C to K and prepare the longitude and latitude by running +Change the unit from °C to K and prepare the longitude and latitude by running ```{r} tas_dv$exp$data <- tas_dv$exp$data - 273.15 @@ -497,17 +497,17 @@ The 2013-2016 correlation coefficients of the SEAS5 forecasts of GDD in referenc ### 5. TotalSpellTimeExceedingThreshold.R -One of the critical agricultural indicators related to dry spell is **Warm Spell Duration Index (WSDI)** which is defined as the total count of days with at least 6 consecutive days when the daily temperature maximum exceeds its 90th percentile in the seven months into the future. +One of the critical agricultural indicators related to dry spell is the **Warm Spell Duration Index (WSDI)**, which is defined as the total count of days with at least 6 consecutive days when the daily maximum temperature exceeds its 90th percentile in the seven months into the future. -The temperature maximum data used in Section 3. TotalTimeExceedingThreshold.R is loaded here again. +The maximum temperature data used in Section 3. TotalTimeExceedingThreshold.R is loaded here again. -Since the daily temperature maximum needs to compare to its 90th percentile, the function `Threshold` in the package `CSIndicators` is required to compute the percentile of observations used for each day. Here the same period (2013-2016) is considered. +Since the daily maximum temperature needs to compare to its 90th percentile, the function `Threshold` in the `CSIndicators` package is required to compute the percentile of observations used for each day. Here the same period (2013-2016) is considered. ```{r} tx_p <- CST_Threshold(tasmax_dv$obs, threshold = 0.9) ``` -The output will be the 90th percentile of each day of each grid point derived by using the all the years in the data. +The output will be the 90th percentile of each day of each grid point derived by using all the years in the data. See the dimension and summary as below. @@ -563,10 +563,10 @@ The FRPSS map for 2013-2016 SEAS WSDI is shown as below. **/esarchive/scratch/cchou/MEDGOLD/grape/output/WSDI_SEAS5_FRPSS_Y13-16.pdf** -As seen in the map, FRPSS in the eastern part of Douro Valley falls in 0.6-0.9 which are good enough to be useful when compared to observational climatology. +As seen in the map, the FRPSS in the eastern part of Douro Valley falls in 0.6-0.9, which are good enough to be useful when compared to observational climatology. -In additional to the grape/wine sector focused here, MEDGOLD also work on the other two sectors, olive/olive oil and durum wheat/pasta. Furthermore, the climate serives are also provided at the longer term (up to 30 years) by other project partners. +In addition to the grape/wine sector focused here, the MEDGOLD project also works on the other two sectors: olive/olive oil and durum wheat/pasta. Furthermore, the climate services are also provided at the longer term (up to 30 years) by other project partners. Click on [MEDGOLD](https://www.med-gold.eu/climate-services/) for more information. -- GitLab From 7b7a16c8bc09e6715f32902382d829c21e07144d Mon Sep 17 00:00:00 2001 From: Chihchung Chou Date: Mon, 19 Apr 2021 03:56:19 +0200 Subject: [PATCH 07/15] fix figures --- vignettes/AgriculturalIndicators.md | 49 ++++++++++++++++++----------- 1 file changed, 30 insertions(+), 19 deletions(-) diff --git a/vignettes/AgriculturalIndicators.md b/vignettes/AgriculturalIndicators.md index 12d8ee4..ebc8a82 100644 --- a/vignettes/AgriculturalIndicators.md +++ b/vignettes/AgriculturalIndicators.md @@ -160,11 +160,11 @@ cols <- c('#b2182b', '#d6604d', '#f4a582', '#fddbc7', '#d1e5f0', '#92c5de', '#43 brks <- seq(-60, 60, by = 20) toptitle <- 'Ensemble-mean bias of HarvestR in 2013' -PlotEquiMap(Bias[1,,], lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'mm', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, col_inf = cols[1], margin_scale = c(1, 1, 1, 1), cols = cols[2:7], col_sup = cols[8], brks = brks, toptitle = toptitle, colNA = 'white', bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2, file = '/esarchive/scratch/cchou/MEDGOLD/grape/output/HarvestR_Bias_2013.pdf') +PlotEquiMap(Bias[1,,], lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'mm', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, col_inf = cols[1], margin_scale = c(1, 1, 1, 1), cols = cols[2:7], col_sup = cols[8], brks = brks, toptitle = toptitle, colNA = 'white', bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) ``` You will see the following maps of HarvestR bias in 2013. -**/esarchive/scratch/cchou/MEDGOLD/grape/output/HarvestR_Bias_2013.pdf** +PlotEquiMap(Bias[1,,], lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'mm', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, col_inf = cols[1], margin_scale = c(1, 1, 1, 1), cols = cols[2:7], col_sup = cols[8], brks = brks, toptitle = toptitle, colNA = 'white', bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) In 2013, the ensemble-mean SEAS5 seasonal forecast of HarvestR is underestimated by up to 60 mm over Douro Valley region (the central four grid points). @@ -262,12 +262,12 @@ cols <- c('#ffffd4','#fee391','#fec44f','#fe9929','#ec7014','#cc4c02','#8c2d04') brks <- seq(16, 18.5, by = 0.5) toptitle <- '2013-2016 mean ERA5 GST' -PlotEquiMap(GST_Clim, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = '°C', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, col_inf = cols[1], margin_scale = c(1, 1, 1, 1), cols = cols[2:6], col_sup = cols[7], , brks = brks, toptitle = toptitle, colNA = 'white', bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2, file = '/esarchive/scratch/cchou/MEDGOLD/grape/output/GST_ERA5_Climatology.pdf') +PlotEquiMap(GST_Clim, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = '°C', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, col_inf = cols[1], margin_scale = c(1, 1, 1, 1), cols = cols[2:6], col_sup = cols[7], , brks = brks, toptitle = toptitle, colNA = 'white', bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) ``` The ERA5 GST climatology is shown as below. -**/esarchive/scratch/cchou/MEDGOLD/grape/output/GST_ERA5_Climatology.pdf** +PlotEquiMap(GST_Clim, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = '°C', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, col_inf = cols[1], margin_scale = c(1, 1, 1, 1), cols = cols[2:6], col_sup = cols[7], , brks = brks, toptitle = toptitle, colNA = 'white', bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) ERA5 GST ranges from 17-18.5°C over the Douro Valley region for the period from 2013-2016 as shown in the figure. @@ -369,19 +369,25 @@ cols <- c("#fee5d9", "#fcae91", "#fb6a4a", "#de2d26","#a50f15") brks <- seq(0, 8, by = 2) toptitle <- 'ERA5 SU35 forecast in 2016' -PlotEquiMap(SU35_obs_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2, file = '/esarchive/scratch/cchou/MEDGOLD/grape/output/SU35_ERA5_Y2016.pdf') +PlotEquiMap(SU35_obs_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) toptitle <- 'SU35 forecast in 2016' -PlotEquiMap(SU35_exp_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2, file = '/esarchive/scratch/cchou/MEDGOLD/grape/output/SU35_SEAS5_Y2016.pdf') +PlotEquiMap(SU35_exp_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) toptitle <- 'Bias-adjusted SU35 forecast in 2016' -PlotEquiMap(SU35_exp_BC_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2, file = '/esarchive/scratch/cchou/MEDGOLD/grape/output/SU35_SEAS5_BC_Y2016.pdf') +PlotEquiMap(SU35_exp_BC_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) ``` You can see the figure as below. -**/esarchive/scratch/cchou/MEDGOLD/grape/output/SU35_ERA5_Y2016.pdf** -**/esarchive/scratch/cchou/MEDGOLD/grape/output/SU35_SEAS5_Y2016.pdf** -**/esarchive/scratch/cchou/MEDGOLD/grape/output/SU35_SEAS5_BC_Y2016.pdf** +toptitle <- 'ERA5 SU35 forecast in 2016' +PlotEquiMap(SU35_obs_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) + +toptitle <- 'SU35 forecast in 2016' +PlotEquiMap(SU35_exp_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) + +toptitle <- 'Bias-adjusted SU35 forecast in 2016' +PlotEquiMap(SU35_exp_BC_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) + As seen above, the bias-adjusted SU35 forecasts are much closer to the ERA5 results, although differences remain. @@ -426,10 +432,15 @@ Plot the same map for comparison ```{r} toptitle <- 'SU35 forecast with percentile adjustment in 2016' -PlotEquiMap(SU35_exp_per_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2, file = '/esarchive/scratch/cchou/MEDGOLD/grape/output/SU35_Percentile_SEAS5_Y2016.pdf') +PlotEquiMap(SU35_exp_per_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) ``` -As seen in **/esarchive/scratch/cchou/MEDGOLD/grape/output/SU35_Percentile_SEAS5_Y2016.pdf**, applying the percentile adjustment seems to implicitly adjust certain extent of bias which was observed in the non-bias-adjusted SEAS5 forecast. + +toptitle <- 'SU35 forecast with percentile adjustment in 2016' +PlotEquiMap(SU35_exp_per_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) + + +As seen in the figure above, applying the percentile adjustment seems to implicitly adjust certain extent of bias which was observed in the non-bias-adjusted SEAS5 forecast. The performance of comparison of skills between two definitions requires further analysis such as the application of more skill metrics. @@ -439,9 +450,9 @@ The function ´AccumulationExceedingThreshold´ can compute GDD (Growing Degree The definition of GDD is the summation of daily differences between daily average temperatures and 10°C between April 1st and October 31st. -Here, the tas (daily average temperature) used above (in Section 2. PeriodMean.R) is loaded again. +Here, the tas (daily average temperature) used above (in Section 2. PeriodMean.R) is loaded again (Please re-use the section of loading tas in Section 2). -Change the unit from °C to K and prepare the longitude and latitude by running +Change the unit, if necessary, from °C to K and prepare the longitude and latitude by running ```{r} tas_dv$exp$data <- tas_dv$exp$data - 273.15 @@ -486,12 +497,12 @@ cols <- c("#f7fcf5", "#e5f5e0", "#c7e9c0", "#a1d99b", "#74c476") brks <- seq(0.5, 1, by = 0.1) toptitle <- '2013-2016 correlation coefficient of GDD' -PlotEquiMap(GDD_Corr, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'correlation', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols, brks = brks, toptitle = toptitle, bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2, file = '/esarchive/scratch/cchou/MEDGOLD/grape/output/GDD_SEAS5_Corr_Y13-16.pdf') +PlotEquiMap(GDD_Corr, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'correlation', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols, brks = brks, toptitle = toptitle, bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) ``` The map of correlation coefficient for the 2013-2016 period is shown as below. -**/esarchive/scratch/cchou/MEDGOLD/grape/output/GDD_SEAS5_Corr_Y13-16.pdf** +PlotEquiMap(GDD_Corr, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'correlation', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols, brks = brks, toptitle = toptitle, bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) The 2013-2016 correlation coefficients of the SEAS5 forecasts of GDD in reference with ERA5 reanalysis over Douro Valley range between 0.6 and 0.8. @@ -499,7 +510,7 @@ The 2013-2016 correlation coefficients of the SEAS5 forecasts of GDD in referenc One of the critical agricultural indicators related to dry spell is the **Warm Spell Duration Index (WSDI)**, which is defined as the total count of days with at least 6 consecutive days when the daily maximum temperature exceeds its 90th percentile in the seven months into the future. -The maximum temperature data used in Section 3. TotalTimeExceedingThreshold.R is loaded here again. +The maximum temperature data used in Section 3. TotalTimeExceedingThreshold.R is loaded here again (If necessary re-use the codes above). Since the daily maximum temperature needs to compare to its 90th percentile, the function `Threshold` in the `CSIndicators` package is required to compute the percentile of observations used for each day. Here the same period (2013-2016) is considered. @@ -556,12 +567,12 @@ cols <- c("#edf8fb", "#ccece6", "#99d8c9", "#66c2a4") brks <- seq(0, 0.9, by = 0.3) toptitle <- 'SEAS5 WSDI FRPSS (2013-2016)' -PlotEquiMap(WSDI_FRPSS, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'FRPSS', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:3], col_inf = 'white', col_sup = cols[4], brks = brks, toptitle = toptitle, bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2, file = '/esarchive/scratch/cchou/MEDGOLD/grape/output/WSDI_SEAS5_FRPSS_Y13-16.pdf') +PlotEquiMap(WSDI_FRPSS, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'FRPSS', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:3], col_inf = 'white', col_sup = cols[4], brks = brks, toptitle = toptitle, bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) ``` The FRPSS map for 2013-2016 SEAS WSDI is shown as below. -**/esarchive/scratch/cchou/MEDGOLD/grape/output/WSDI_SEAS5_FRPSS_Y13-16.pdf** +PlotEquiMap(WSDI_FRPSS, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'FRPSS', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:3], col_inf = 'white', col_sup = cols[4], brks = brks, toptitle = toptitle, bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) As seen in the map, the FRPSS in the eastern part of Douro Valley falls in 0.6-0.9, which are good enough to be useful when compared to observational climatology. -- GitLab From e62b5dfe5554cfc5a993d6144653ba61b4d236df Mon Sep 17 00:00:00 2001 From: Chihchung Chou Date: Mon, 19 Apr 2021 10:06:21 +0200 Subject: [PATCH 08/15] figures added and vig revised --- vignettes/AgriculturalIndicators.md | 24 ++++++------------ vignettes/figures/GDD_SEAS5_Corr_Y13-16.pdf | Bin 0 -> 4770 bytes vignettes/figures/GST_ERA5_Climatology.pdf | Bin 0 -> 4811 bytes vignettes/figures/HarvestR_Bias_2013.pdf | Bin 0 -> 4865 bytes vignettes/figures/SU35_ERA5_Y2016.pdf | Bin 0 -> 4772 bytes .../figures/SU35_Percentile_SEAS5_Y2016.pdf | Bin 0 -> 4791 bytes vignettes/figures/SU35_SEAS5_BC_Y2016.pdf | Bin 0 -> 4780 bytes vignettes/figures/SU35_SEAS5_Y2016.pdf | Bin 0 -> 4771 bytes vignettes/figures/WSDI_SEAS5_FRPSS_Y13-16.pdf | Bin 0 -> 4760 bytes 9 files changed, 8 insertions(+), 16 deletions(-) create mode 100644 vignettes/figures/GDD_SEAS5_Corr_Y13-16.pdf create mode 100644 vignettes/figures/GST_ERA5_Climatology.pdf create mode 100644 vignettes/figures/HarvestR_Bias_2013.pdf create mode 100644 vignettes/figures/SU35_ERA5_Y2016.pdf create mode 100644 vignettes/figures/SU35_Percentile_SEAS5_Y2016.pdf create mode 100644 vignettes/figures/SU35_SEAS5_BC_Y2016.pdf create mode 100644 vignettes/figures/SU35_SEAS5_Y2016.pdf create mode 100644 vignettes/figures/WSDI_SEAS5_FRPSS_Y13-16.pdf diff --git a/vignettes/AgriculturalIndicators.md b/vignettes/AgriculturalIndicators.md index ebc8a82..45dbf8c 100644 --- a/vignettes/AgriculturalIndicators.md +++ b/vignettes/AgriculturalIndicators.md @@ -164,7 +164,7 @@ PlotEquiMap(Bias[1,,], lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6 ``` You will see the following maps of HarvestR bias in 2013. -PlotEquiMap(Bias[1,,], lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'mm', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, col_inf = cols[1], margin_scale = c(1, 1, 1, 1), cols = cols[2:7], col_sup = cols[8], brks = brks, toptitle = toptitle, colNA = 'white', bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) + In 2013, the ensemble-mean SEAS5 seasonal forecast of HarvestR is underestimated by up to 60 mm over Douro Valley region (the central four grid points). @@ -267,7 +267,7 @@ PlotEquiMap(GST_Clim, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, The ERA5 GST climatology is shown as below. -PlotEquiMap(GST_Clim, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = '°C', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, col_inf = cols[1], margin_scale = c(1, 1, 1, 1), cols = cols[2:6], col_sup = cols[7], , brks = brks, toptitle = toptitle, colNA = 'white', bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) + ERA5 GST ranges from 17-18.5°C over the Douro Valley region for the period from 2013-2016 as shown in the figure. @@ -379,15 +379,10 @@ PlotEquiMap(SU35_exp_BC_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, w ``` You can see the figure as below. -toptitle <- 'ERA5 SU35 forecast in 2016' -PlotEquiMap(SU35_obs_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) - -toptitle <- 'SU35 forecast in 2016' -PlotEquiMap(SU35_exp_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) - -toptitle <- 'Bias-adjusted SU35 forecast in 2016' -PlotEquiMap(SU35_exp_BC_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) + + + As seen above, the bias-adjusted SU35 forecasts are much closer to the ERA5 results, although differences remain. @@ -435,10 +430,7 @@ toptitle <- 'SU35 forecast with percentile adjustment in 2016' PlotEquiMap(SU35_exp_per_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) ``` - -toptitle <- 'SU35 forecast with percentile adjustment in 2016' -PlotEquiMap(SU35_exp_per_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) - + As seen in the figure above, applying the percentile adjustment seems to implicitly adjust certain extent of bias which was observed in the non-bias-adjusted SEAS5 forecast. @@ -502,7 +494,7 @@ PlotEquiMap(GDD_Corr, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, The map of correlation coefficient for the 2013-2016 period is shown as below. -PlotEquiMap(GDD_Corr, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'correlation', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols, brks = brks, toptitle = toptitle, bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) + The 2013-2016 correlation coefficients of the SEAS5 forecasts of GDD in reference with ERA5 reanalysis over Douro Valley range between 0.6 and 0.8. @@ -572,7 +564,7 @@ PlotEquiMap(WSDI_FRPSS, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = The FRPSS map for 2013-2016 SEAS WSDI is shown as below. -PlotEquiMap(WSDI_FRPSS, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'FRPSS', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:3], col_inf = 'white', col_sup = cols[4], brks = brks, toptitle = toptitle, bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) + As seen in the map, the FRPSS in the eastern part of Douro Valley falls in 0.6-0.9, which are good enough to be useful when compared to observational climatology. diff --git a/vignettes/figures/GDD_SEAS5_Corr_Y13-16.pdf b/vignettes/figures/GDD_SEAS5_Corr_Y13-16.pdf new file mode 100644 index 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vignettes/figures/HarvestR_Bias_2013-1.png | Bin 0 -> 21614 bytes vignettes/figures/SU35_ERA5_Y2016-1.png | Bin 0 -> 22152 bytes .../figures/SU35_Percentile_SEAS5_Y2016-1.png | Bin 0 -> 23552 bytes vignettes/figures/SU35_SEAS5_BC_Y2016-1.png | Bin 0 -> 22681 bytes vignettes/figures/SU35_SEAS5_Y2016-1.png | Bin 0 -> 20890 bytes vignettes/figures/WSDI_SEAS5_FRPSS_Y13-16-1.png | Bin 0 -> 22132 bytes 9 files changed, 8 insertions(+), 8 deletions(-) create mode 100644 vignettes/figures/GDD_SEAS5_Corr_Y13-16-1.png create mode 100644 vignettes/figures/GST_ERA5_Climatology-1.png create mode 100644 vignettes/figures/HarvestR_Bias_2013-1.png create mode 100644 vignettes/figures/SU35_ERA5_Y2016-1.png create mode 100644 vignettes/figures/SU35_Percentile_SEAS5_Y2016-1.png create mode 100644 vignettes/figures/SU35_SEAS5_BC_Y2016-1.png create mode 100644 vignettes/figures/SU35_SEAS5_Y2016-1.png create mode 100644 vignettes/figures/WSDI_SEAS5_FRPSS_Y13-16-1.png diff --git a/vignettes/AgriculturalIndicators.md b/vignettes/AgriculturalIndicators.md index 45dbf8c..661ecf7 100644 --- a/vignettes/AgriculturalIndicators.md +++ b/vignettes/AgriculturalIndicators.md @@ -164,7 +164,7 @@ PlotEquiMap(Bias[1,,], lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6 ``` You will see the following maps of HarvestR bias in 2013. - + In 2013, the ensemble-mean SEAS5 seasonal forecast of HarvestR is underestimated by up to 60 mm over Douro Valley region (the central four grid points). @@ -267,7 +267,7 @@ PlotEquiMap(GST_Clim, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, The ERA5 GST climatology is shown as below. - + ERA5 GST ranges from 17-18.5°C over the Douro Valley region for the period from 2013-2016 as shown in the figure. @@ -380,9 +380,9 @@ PlotEquiMap(SU35_exp_BC_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, w You can see the figure as below. - - - + + + As seen above, the bias-adjusted SU35 forecasts are much closer to the ERA5 results, although differences remain. @@ -430,7 +430,7 @@ toptitle <- 'SU35 forecast with percentile adjustment in 2016' PlotEquiMap(SU35_exp_per_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) ``` - + As seen in the figure above, applying the percentile adjustment seems to implicitly adjust certain extent of bias which was observed in the non-bias-adjusted SEAS5 forecast. @@ -494,7 +494,7 @@ PlotEquiMap(GDD_Corr, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, The map of correlation coefficient for the 2013-2016 period is shown as below. - + The 2013-2016 correlation coefficients of the SEAS5 forecasts of GDD in reference with ERA5 reanalysis over Douro 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The elements `prlr_exp$data` and `prlr_obs$data` have dimensions: -```{r} +``` dim(prlr_exp$data) #dataset member sdate ftime lat lon # 1 3 4 214 4 4 @@ -104,7 +105,7 @@ dim(prlr_obs$data) To compute **SprR** of forecast and observation, we can run: -```{r} +``` SprR_exp <- CST_PeriodAccumulation(prlr_exp, start = list(21, 4), end = list(21, 6)) SprR_obs <- CST_PeriodAccumulation(prlr_obs, start = list(21, 4), end = list(21, 6)) ``` @@ -115,7 +116,7 @@ As mentioned, these parameters are optional, the function will take the entire t The dimensions of SprR forecasts and observations are: -```{r} +``` dim(SprR_exp$data) #dataset member sdate lat lon # 1 3 4 4 4 @@ -126,7 +127,7 @@ dim(SprR_obs$data) The forecast SprR for the 1st member from 2013-2016 of the 1st grid point in mm are: -```{r} +``` SprR_exp$data[1,1,,1,1] * 86400 * 1000 #[1] 93.23205 230.41904 194.01412 226.52614 ``` @@ -135,21 +136,21 @@ Dry springs will delay vegetative growth and reduce vigour and leaf area total s On the other hand, another moisture-related indicators, **HarvestR**, can be computed by using `PeriodAccumulation` as well, with the defined period as the following lines. -```{r} +``` HarvestR_exp <- CST_PeriodAccumulation(prlr_exp, start = list(21, 8), end = list(21, 10)) HarvestR_obs <- CST_PeriodAccumulation(prlr_obs, start = list(21, 8), end = list(21, 10)) ``` The forecast HarvestR for the 1st member from 2013-2016 of the 1st grid point in mm are: -```{r} +``` HarvestR_exp$data[1,1,,1,1] * 86400 * 1000 #[1] 52.30026 42.88068 156.87961 32.18579 ``` To compute the 2013-2016 ensemble-mean bias of forecast HarvestR, run -```{r} +``` fcst <- drop(HarvestR_exp$data) * 86400 * 1000 obs <- drop(HarvestR_obs$data) * 86400 * 1000 @@ -158,7 +159,7 @@ Bias <- MeanDims((fcst - InsertDim(obs, 1, dim(fcst)['member'])), 'member') To plot the map of ensemble-mean bias of HarvestR forecast, run -```{r} +``` cols <- c('#b2182b', '#d6604d', '#f4a582', '#fddbc7', '#d1e5f0', '#92c5de', '#4393c3', '#2166ac') @@ -184,7 +185,7 @@ For the function `PeriodMean`, we use Growing Season Temperature (**GST**) as an Firstly, we prepare a sample data of daily mean temperature of SEAS5 and ERA5 data sets with the same starting dates, spatial domain, interpolation grid and method by running -```{r} +``` S5path <- list(path = '/esarchive/exp/ecmwf/system5c3s/daily_mean/$VAR_NAME$_f6h/$VAR_NAME$_$YEAR$$MONTH$01.nc') ERA5path <- list(path = '/esarchive/recon/ecmwf/era5/daily_mean/$VAR_NAME$_f1h-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc') @@ -199,7 +200,7 @@ c(tas_exp, tas_obs) %<-% CST_Load(var = 'tas', exp = list(S5path), obs = list(ER The output contains observations `tas_dv$obs$data` and forecast `tas_dv$exp$data`, and their dimensions and summaries are like -```{r} +``` dim(tas_obs$data) #dataset member sdate ftime lat lon # 1 1 4 214 4 4 @@ -219,7 +220,7 @@ summary(tas_exp$data - 273.15) ``` To compute the GST for both observation and forecast, run the following lines -```{r} +``` # change the unit of temperature from °C to K tas_exp$data <- tas_exp$data - 273.15 @@ -236,7 +237,7 @@ Since the period considered for GST is the entire period for starting month of A The summaries and dimensions of the output are as follows: -```{r} +``` summary(GST_exp$data) # Min. 1st Qu. Median Mean 3rd Qu. Max. # 14.23 15.78 16.50 16.50 17.17 18.70 @@ -256,7 +257,7 @@ dim(GST_obs$data) Here, we plot the 2013-2016 mean climatology of ERA5 GST by running -```{r} +``` # compute ERA5 GST climatology GST_Clim <- MeanDims(drop(GST_obs$data), 'sdate') @@ -296,7 +297,7 @@ Here, we take SU35 as example, therefore the daily temperature maximum of the en Load SEAS5 and ERA5 daily temperature maximum by running -```{r} +``` S5path <- list(path = '/esarchive/exp/ecmwf/system5c3s/daily/$VAR_NAME$/$VAR_NAME$_$YEAR$$MONTH$01.nc') ERA5path <- list(path = '/esarchive/recon/ecmwf/era5/daily/$VAR_NAME$-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc') @@ -311,14 +312,14 @@ c(tasmax_exp, tasmax_obs) %<-% CST_Load(var = 'tasmax', exp = list(S5path), Check the unit of temperature to from °C to K for the comparison with the threshold defined (for example 35°C here). -```{r} +``` tasmax_exp$data <- tasmax_exp$data - 273.15 tasmax_obs$data <- tasmax_obs$data - 273.15 ``` Computing SU35 for forecast and observation by running -```{r} +``` threshold <- 35 SU35_exp <- CST_TotalTimeExceedingThreshold(tasmax_exp, threshold = threshold, start = list(1, 4), end = list(31, 10)) @@ -328,7 +329,7 @@ SU35_obs <- CST_TotalTimeExceedingThreshold(tasmax_obs, threshold = threshold, The summaries of SU35 forecasts and observations are given below. -```{r} +``` summary(SU35_exp$data) # Min. 1st Qu. Median Mean 3rd Qu. Max. # 0.00 2.00 5.00 7.12 12.00 26.00 @@ -342,7 +343,7 @@ As shown in the summaries, SEAS5 SU35 forecasts are overestimated by 5 days in t Therefore, `CST_BiasCorrection` is used to bias adjust the SU35 forecasts. -```{r} +``` res <- CST_BiasCorrection(obs = SU35_obs, exp = SU35_exp) SU35_exp_BC <- drop(res$data) summary(SU35_exp_BC) @@ -352,7 +353,7 @@ summary(SU35_exp_BC) Since there are negative values after bias adjustment, all negative data is converted to zero. -```{r} +``` SU35_exp_BC[SU35_exp_BC < 0] <- 0 summary(SU35_exp_BC) # Min. 1st Qu. Median Mean 3rd Qu. Max. @@ -361,7 +362,7 @@ summary(SU35_exp_BC) Plot the bias-adjusted SU35 forecast in 2016 by running -```{r} +``` SU35_obs_Y2016 <- drop(SU35_obs$data)[4, , ] SU35_exp_Y2016 <- MeanDims(drop(SU35_exp$data)[, 4, , ], 'member') SU35_exp_BC_Y2016 <- MeanDims(SU35_exp_BC[, 4, , ], 'member') @@ -419,33 +420,33 @@ The revised definition of SU35 is to reduce the potential influence induced by t As mentioned, the forecast is translated to its percentile by using the function `ABsToProbs` by running -```{r} +``` exp_percentile <- AbsToProbs(tasmax_exp$data) S5txP <- aperm(drop(exp_percentile), c(2, 1, 3, 4, 5)) ``` After that, based on 35 of threshold, the percentile corresponding to each observational value can be calculated as follows. -```{r} +``` obs_percentile <- QThreshold(tasmax_obs$data, threshold = 35) obs_percentile <- drop(obs_percentile) ``` After translating both forecasts and observations into probabilities, the comparison can then be done by running -```{r} +``` SU35_exp_Percentile <- TotalTimeExceedingThreshold(S5txP, threshold = obs_percentile, time_dim = 'ftime') ``` Compute the same ensemble-mean SU35 **with percentile adjustment** in 2016 by running -```{r} +``` SU35_exp_per_Y2016 <- MeanDims(SU35_exp_Percentile[, 4, , ], 'member') ``` Plot the same map for comparison -```{r} +``` toptitle <- 'SU35 forecast with percentile adjustment in 2016' PlotEquiMap(SU35_exp_per_Y2016, lon = tasmax_obs$lon, lat = tasmax_obs$lat, intylat = 1, intxlon = 1, width = 6, height = 6, @@ -473,14 +474,14 @@ The definition of GDD is the summation of daily differences between daily averag *Note: The data is in degrees Celsiusi at this point* -```{r} +``` GDD_exp <- CST_AccumulationExceedingThreshold(tas_exp, threshold = 10, diff = TRUE) GDD_obs <- CST_AccumulationExceedingThreshold(tas_obs, threshold = 10, diff = TRUE) ``` The summaries of GDD are -```{r} +``` summary(GDD_exp$data) # Min. 1st Qu. Median Mean 3rd Qu. Max. # 1021 1331 1480 1469 1596 1873 @@ -492,7 +493,7 @@ summary(GDD_obs$data) To compute the correlation coefficient for the period from 2013-2016, run the following lines -```{r} +``` # reorder the dimension fcst <- Reorder(drop(GDD_exp$data), c(4, 3, 2, 1)) obs <- Reorder(drop(GDD_obs$data), c(3, 2, 1)) @@ -503,7 +504,7 @@ GDD_Corr <- Reorder(EnsCorr, c(2, 1)) To plot the map of correlation coefficient of GDD for the 2013-2016 period. -```{r} +``` cols <- c("#f7fcf5", "#e5f5e0", "#c7e9c0", "#a1d99b", "#74c476") toptitle <- '2013-2016 correlation coefficient of GDD' PlotEquiMap(GDD_Corr, lon = tas_obs$lon, lat = tas_obs$lat, @@ -527,13 +528,13 @@ One of the critical agricultural indicators related to dry spell is the **Warm S The maximum temperature data used in Section 3. Since the daily maximum temperature needs to compare to its 90th percentile, the function `Threshold` in the `CSIndicators` package is required to compute the percentile of observations used for each day. Here the same period (2013-2016) is considered. -```{r} +``` tx_p <- CST_Threshold(tasmax_obs, threshold = 0.9) ``` The output will be the 90th percentile of each day of each grid point derived by using all the years in the data.See the dimension and summary as below. -```{r} +``` dim(tx_p$data) #dataset ftime lat lon # 1 214 4 4 @@ -545,14 +546,14 @@ summary(tx_p$data) With the prepared threshold (90th percentile), the WSDI can be computed by running -```{r} +``` WSDI_exp <- CST_TotalSpellTimeExceedingThreshold(tasmax_exp, threshold = tx_p, spell = 6) WSDI_obs <- CST_TotalSpellTimeExceedingThreshold(tasmax_obs, threshold = tx_p, spell = 6) ``` After checking the summaries, compute the Fair Ranked Probability Skill Score (FRPSS) of WSDI by running the following lines -```{r} +``` # Reorder the data fcst <- Reorder(drop(WSDI_exp$data), c(4, 3, 2, 1)) obs <- Reorder(drop(WSDI_obs$data), c(3, 2, 1)) @@ -573,7 +574,7 @@ WSDI_FRPSS <- Reorder(f, c(2,1)) Plot the map of WSDI FRPSS for the period from 2013-2016 -```{r} +``` cols <- c("#edf8fb", "#ccece6", "#99d8c9", "#66c2a4") toptitle <- 'SEAS5 WSDI FRPSS (2013-2016)' -- GitLab From 02ffa5b3e6536beb84fb07f557b270e7c2345429 Mon Sep 17 00:00:00 2001 From: nperez Date: Thu, 29 Apr 2021 16:20:41 +0200 Subject: [PATCH 14/15] Change cran url --- vignettes/AgriculturalIndicators.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vignettes/AgriculturalIndicators.Rmd b/vignettes/AgriculturalIndicators.Rmd index 33cbecf..72473a0 100644 --- a/vignettes/AgriculturalIndicators.Rmd +++ b/vignettes/AgriculturalIndicators.Rmd @@ -31,7 +31,7 @@ The computing functions and the corresponding indicators are listed as follows: The above functions can take both multidimensional arrays and the s2dv_cube objects (see note below). Taking PeriodAccumulation as example, **CST_**PeriodAccumulation handles the latter and PeriodAccumulation without the prefix can compute multidimensional arrays. -*Note: s2dv_cube and array classes can be handled by the functions in CSIndicators. See Section 2 in vignette [Data retrieval and storage](https://cran.r-project.org/web/packages/CSTools/vignettes/Data_Considerations.html) from CSTools package for more information.* +*Note: s2dv_cube and array classes can be handled by the functions in CSIndicators. See Section 2 in vignette [Data retrieval and storage](https://cran.r-project.org/package=CSTools/vignettes/Data_Considerations.html) from CSTools package for more information.* There are some supplementary functions which must be called to smoothly run the above functions. -- GitLab From 7f30a2c313611458ffa0dfef818e1467318aa4ea Mon Sep 17 00:00:00 2001 From: nperez Date: Thu, 29 Apr 2021 16:28:59 +0200 Subject: [PATCH 15/15] Now fix url --- vignettes/AgriculturalIndicators.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vignettes/AgriculturalIndicators.Rmd b/vignettes/AgriculturalIndicators.Rmd index 72473a0..d72eed0 100644 --- a/vignettes/AgriculturalIndicators.Rmd +++ b/vignettes/AgriculturalIndicators.Rmd @@ -62,7 +62,7 @@ library(s2dv) To obtain the precipitation forecast and observation, we load the daily precipitation (**prlr** given in `var`) data sets of ECMWF SEAS5 seasonal forecast and ERA5 reanalysis for the four starting dates 20130401-20160401 (provided in `sdates`) with the entire 7-month forecast time, April-October (214 days in total given in parameter `leadtimemax`). -The pathways of SEAS5 and ERA5 are given in the lists with some **whitecards (inside two dollar signs)** used to replace the variable name and iterative items such as year and month. See details of requirements in Section 4 in vignette [Data retrieval and storage](https://cran.r-project.org/web/packages/CSTools/vignettes/Data_Considerations.html) from CSTools package. +The pathways of SEAS5 and ERA5 are given in the lists with some **whitecards (inside two dollar signs)** used to replace the variable name and iterative items such as year and month. See details of requirements in Section 4 in vignette [Data retrieval and storage](https://cran.r-project.org/package=CSTools/vignettes/Data_Considerations.html) from CSTools package. The spatial domain covers part of Douro Valley of Northern Portugal lon=[352.25, 353], lat=[41, 41.75]. These four values are provided in `lonmin`, `lonmax`, `latmin` and `latmax`. -- GitLab

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z@B)5fFs86!Jpkz^21PK1*MDPBH4Wx1$UiVB)6+jOmPRi7D*A7Ka2WiT{NUsn{~U!l>lRQh(c;BYt$qM%@8g@*hGhbIUj -- GitLab From 1ae470773081e9485145075878ac31b038439613 Mon Sep 17 00:00:00 2001 From: nperez Date: Wed, 28 Apr 2021 10:26:53 +0200 Subject: [PATCH 11/15] Format length lines, remove .R, add drop --- vignettes/AgriculturalIndicators.md | 368 +++++++++++++++------------- 1 file changed, 196 insertions(+), 172 deletions(-) diff --git a/vignettes/AgriculturalIndicators.md b/vignettes/AgriculturalIndicators.md index 661ecf7..ac311e1 100644 --- a/vignettes/AgriculturalIndicators.md +++ b/vignettes/AgriculturalIndicators.md @@ -21,11 +21,11 @@ In the MEDGOLD project, 10 indicators which were identified as critical indices The computing functions and the corresponding indicators are listed as follows: --- -1. **PeriodAccumulation.R -** Spring Total Precipitation (SprR) and Harvest Total Precipitation (HarvestR) -2. **PeriodMean.R -** Growing Season Temperature (GST) and Spring Mean Temperature Maximum (SPRTX) -3. **TotalTimeExceedingThreshold.R -** Number of Heat Stress Days - 35°C (SU35), 36°C (SU36), 40°C (SU40) and Spring Heat Stress Days - 32°C (Spr32) -4. **AccumulationExceedingThreshold.R -** Growing Degree Days (GDD) -5. **TotalSpellTimeExceedingThreshold.R -** Warm Spell Duration Index (WSDI) +1. **PeriodAccumulation -** Spring Total Precipitation (SprR) and Harvest Total Precipitation (HarvestR) +2. **PeriodMean -** Growing Season Temperature (GST) and Spring Mean Temperature Maximum (SPRTX) +3. **TotalTimeExceedingThreshold -** Number of Heat Stress Days - 35°C (SU35), 36°C (SU36), 40°C (SU40) and Spring Heat Stress Days - 32°C (Spr32) +4. **AccumulationExceedingThreshold -** Growing Degree Days (GDD) +5. **TotalSpellTimeExceedingThreshold -** Warm Spell Duration Index (WSDI) --- The above functions can take both multidimensional arrays and the s2dv_cube objects (see note below). Taking PeriodAccumulation as example, **CST_**PeriodAccumulation handles the latter and PeriodAccumulation without the prefix can compute multidimensional arrays. @@ -35,16 +35,16 @@ The above functions can take both multidimensional arrays and the s2dv_cube obje There are some supplementary functions which must be called to smoothly run the above functions. --- -1. **SelectPeriodOnData.R -** to select the data in the requested period -2. **SelectPeriodOnDates.R -** to select the time dimension in the requested period -3. **Threshold.R -** to convert absolute value/variable to its percentile, e.g., Warm Spell Duration Index uses the 90th percentile corresponding to each day instead of a fixed threshold. See how this function is applied in Section 5. +1. **SelectPeriodOnData -** to select the data in the requested period +2. **SelectPeriodOnDates -** to select the time dimension in the requested period +3. **Threshold -** to convert absolute value/variable to its percentile, e.g., Warm Spell Duration Index uses the 90th percentile corresponding to each day instead of a fixed threshold. See how this function is applied in Section 5. --- When the period selection is required, the `start` and `end` parameters have to be provided to cut out the portion in `time_dim`. Otherwise, the function will take the **entire** `time_dim`. The examples of computing the aforementioned indicators are given by functions as follows. -### 1. PeriodAccumulation.R +### 1. PeriodAccumulation `PeriodAccumulation` (and `CST_PeriodAccumulation`) computes the sum of a given variable in a period. @@ -55,8 +55,8 @@ First, load the required libraries, CSIndicators, CSTools, etc by running ```{r} library(CSIndicators) library(CSTools) +library(zeallot) library(s2dv) -library(s2dverification) ``` To obtain the precipitation forecast and observation, we load the daily precipitation (**prlr** given in `var`) data sets of ECMWF SEAS5 seasonal forecast and ERA5 reanalysis for the four starting dates 20130401-20160401 (provided in `sdates`) with the entire 7-month forecast time, April-October (214 days in total given in parameter `leadtimemax`). @@ -69,30 +69,44 @@ With `grid` set to **r1440x721**, the SEAS5 forecast would be interpolated to th ```{r} -var <- 'prlr'; lonmax <- 353; lonmin <- 352.25; latmax <- 41.75; latmin <- 41 -cdoMethod <- 'bicubic'; leadtimemin <- 1; leadtimemax <- 214 S5path_prlr <- list(path = '/esarchive/exp/ecmwf/system5c3s/original_files/chou/daily_mean/$VAR_NAME$_s0-24h/$VAR_NAME$_$YEAR$$MONTH$01.nc') + path_ERA5prlr_CDS <- list(path = '/esarchive/recon/ecmwf/era5/daily_mean/$VAR_NAME$_f1h-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc') + sdates <- paste0(2013:2016, '04', '01') -prlr_dv <- CST_Load(var = var, exp = list(S5path_prlr), obs = list(path_ERA5prlr_CDS), sdates = sdates, lonmax = lonmax, lonmin = lonmin, latmax = latmax, latmin = latmin, storefreq = 'daily', leadtimemin = leadtimemin, leadtimemax = leadtimemax, nmember = 3, output = "lonlat", grid = "r1440x721", method = cdoMethod) + +c(prlr_exp, prlr_obs) %<-% CST_Load(var = 'prlr', + exp = list(S5path_prlr), + obs = list(path_ERA5prlr_CDS), + sdates = sdates, + lonmax = 353, lonmin = 352.25, + latmax = 41.75, latmin = 41, + storefreq = 'daily', + leadtimemin = 1, leadtimemax = 214, + nmember = 3, output = "lonlat", + grid = "r1440x721", method = 'bicubic') ``` -The output contains elements `prlr_dv$exp$data` and `prlr_dv$obs$data` with dimensions: + +The output contains data and metadata for the experiment and the observations. The elements `prlr_exp$data` and `prlr_obs$data` have dimensions: + ```{r} -dim(prlr_dv$exp$data) -dataset member sdate ftime lat lon - 1 3 4 214 4 4 -dim(prlr_dv$obs$data) -dataset member sdate ftime lat lon - 1 1 4 214 4 4 +dim(prlr_exp$data) +#dataset member sdate ftime lat lon +# 1 3 4 214 4 4 +dim(prlr_obs$data) +#dataset member sdate ftime lat lon +# 1 1 4 214 4 4 ``` + To compute **SprR** of forecast and observation, we can run: + ```{r} -SprR_exp <- CST_PeriodAccumulation(prlr_dv$exp, start = list(21, 4), end = list(21, 6)) -SprR_obs <- CST_PeriodAccumulation(prlr_dv$obs, start = list(21, 4), end = list(21, 6)) +SprR_exp <- CST_PeriodAccumulation(prlr_exp, start = list(21, 4), end = list(21, 6)) +SprR_obs <- CST_PeriodAccumulation(prlr_obs, start = list(21, 4), end = list(21, 6)) ``` The `start` and `end` are the initial and final dates and the day must be given before the month as above. They will be applied along the dimension `time_dim` (it is set to 'ftime' by default). @@ -103,11 +117,11 @@ The dimensions of SprR forecasts and observations are: ```{r} dim(SprR_exp$data) -dataset member sdate lat lon - 1 3 4 4 4 +#dataset member sdate lat lon +# 1 3 4 4 4 dim(SprR_obs$data) -dataset member sdate lat lon - 1 1 4 4 4 +#dataset member sdate lat lon +# 1 1 4 4 4 ``` The forecast SprR for the 1st member from 2013-2016 of the 1st grid point in mm are: @@ -117,19 +131,13 @@ SprR_exp$data[1,1,,1,1] * 86400 * 1000 #[1] 93.23205 230.41904 194.01412 226.52614 ``` -Dry springs will delay vegetative growth and reduce vigour and leaf area total surface. - -Fungal disease pressure will be lower and therefore there will be less need for protective and / or curative treatments, translating as less costs. - -Wet springs will promote higher vigour, increase the risk of fungal disease and disrupt vineyard operations as it may prevent machinery from getting in the vineyard due to mud. - -They are usually associated with higher costs. +Dry springs will delay vegetative growth and reduce vigour and leaf area total surface. Fungal disease pressure will be lower and therefore there will be less need for protective and / or curative treatments, translating as less costs. Wet springs will promote higher vigour, increase the risk of fungal disease and disrupt vineyard operations as it may prevent machinery from getting in the vineyard due to mud. They are usually associated with higher costs. On the other hand, another moisture-related indicators, **HarvestR**, can be computed by using `PeriodAccumulation` as well, with the defined period as the following lines. ```{r} -HarvestR_exp <- CST_PeriodAccumulation(prlr_dv$exp, start = list(21, 8), end = list(21, 10)) -HarvestR_obs <- CST_PeriodAccumulation(prlr_dv$obs, start = list(21, 8), end = list(21, 10)) +HarvestR_exp <- CST_PeriodAccumulation(prlr_exp, start = list(21, 8), end = list(21, 10)) +HarvestR_obs <- CST_PeriodAccumulation(prlr_obs, start = list(21, 8), end = list(21, 10)) ``` The forecast HarvestR for the 1st member from 2013-2016 of the 1st grid point in mm are: @@ -147,20 +155,21 @@ obs <- drop(HarvestR_obs$data) * 86400 * 1000 Bias <- MeanDims((fcst - InsertDim(obs, 1, dim(fcst)['member'])), 'member') ``` + To plot the map of ensemble-mean bias of HarvestR forecast, run ```{r} -lon <- prlr_dv$obs$lon; lat <- prlr_dv$obs$lat +cols <- c('#b2182b', '#d6604d', '#f4a582', '#fddbc7', '#d1e5f0', + '#92c5de', '#4393c3', '#2166ac') -pname_root <- "/esarchive/scratch/cchou/MEDGOLD/" -# Load Douro Valley boundary -load(file = paste0(pname_root, "demo/input/DV_boundary.RData")) -douro$x <- douro$x - 360 -cols <- c('#b2182b', '#d6604d', '#f4a582', '#fddbc7', '#d1e5f0', '#92c5de', '#4393c3', '#2166ac') -brks <- seq(-60, 60, by = 20) -toptitle <- 'Ensemble-mean bias of HarvestR in 2013' - -PlotEquiMap(Bias[1,,], lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'mm', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, col_inf = cols[1], margin_scale = c(1, 1, 1, 1), cols = cols[2:7], col_sup = cols[8], brks = brks, toptitle = toptitle, colNA = 'white', bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) +PlotEquiMap(Bias[1,,], lon = prlr_obs$lon, lat = prlr_obs$lat, + intylat = 1, intxlon = 1, width = 6, height = 6, + filled.continents = FALSE, units = 'mm', title_scale = .8, + axes_label_scale = 1, axes_tick_scale = 1, col_inf = cols[1], + margin_scale = c(1, 1, 1, 1), cols = cols[2:7], col_sup = cols[8], + brks = seq(-60, 60, 20), colNA = 'white', + toptitle = 'Ensemble-mean bias of HarvestR in 2013', + bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) ``` You will see the following maps of HarvestR bias in 2013. @@ -169,48 +178,43 @@ You will see the following maps of HarvestR bias in 2013. In 2013, the ensemble-mean SEAS5 seasonal forecast of HarvestR is underestimated by up to 60 mm over Douro Valley region (the central four grid points). -### 2. PeriodMean.R - -For the function `PeriodMean`, we use Growing Season Temperature (**GST**) as an example. +### 2. PeriodMean -GST is defined as the average of daily average temperatures between April 1st to October 31st in the Northern Hemisphere. - -It provides information onto which are the best suited varieties for a given site or, inversely, which are the best places to grow a specific variety. - -For existing vineyards, GST also informs on the suitability of its varieties for the climate of specific years, explaining quality and production variation. - -Many grapevine varieties across the world have been characterized in function of their GST optimum. +For the function `PeriodMean`, we use Growing Season Temperature (**GST**) as an example. GST is defined as the average of daily average temperatures between April 1st to October 31st in the Northern Hemisphere. It provides information onto which are the best suited varieties for a given site or, inversely, which are the best places to grow a specific variety. For existing vineyards, GST also informs on the suitability of its varieties for the climate of specific years, explaining quality and production variation. Many grapevine varieties across the world have been characterized in function of their GST optimum. Firstly, we prepare a sample data of daily mean temperature of SEAS5 and ERA5 data sets with the same starting dates, spatial domain, interpolation grid and method by running ```{r} -var <- 'tas'; lonmax <- 353; lonmin <- 352.25; latmax <- 41.75; latmin <- 41 -cdoMethod <- 'bicubic'; leadtimemin <- 1; leadtimemax <- 214 S5path <- list(path = '/esarchive/exp/ecmwf/system5c3s/daily_mean/$VAR_NAME$_f6h/$VAR_NAME$_$YEAR$$MONTH$01.nc') ERA5path <- list(path = '/esarchive/recon/ecmwf/era5/daily_mean/$VAR_NAME$_f1h-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc') -sdates <- paste0(2013:2016, '04', '01') -tas_dv <- CST_Load(var = var, exp = list(S5path), obs = list(ERA5path), sdates = sdates, lonmax = lonmax, lonmin = lonmin, latmax = latmax, latmin = latmin, storefreq = 'daily', leadtimemin = leadtimemin, leadtimemax = leadtimemax, nmember = 3, output = "lonlat", grid = "r1440x721", method = cdoMethod) +c(tas_exp, tas_obs) %<-% CST_Load(var = 'tas', exp = list(S5path), obs = list(ERA5path), + sdates = sdates, lonmax = 353, lonmin = 352.25, + latmax = 41.75, latmin = 41, + storefreq = 'daily', + leadtimemin = 1, leadtimemax = 214, + nmember = 3, output = "lonlat", + grid = "r1440x721", method = 'bicubic') ``` The output contains observations `tas_dv$obs$data` and forecast `tas_dv$exp$data`, and their dimensions and summaries are like ```{r} -dim(tas_dv$obs$data) -dataset member sdate ftime lat lon - 1 1 4 214 4 4 +dim(tas_obs$data) +#dataset member sdate ftime lat lon +# 1 1 4 214 4 4 -dim(tas_dv$exp$data) -dataset member sdate ftime lat lon - 1 3 4 214 4 4 +dim(tas_exp$data) +#dataset member sdate ftime lat lon +# 1 3 4 214 4 4 -summary(tas_dv$obs$data - 273.15) - Min. 1st Qu. Median Mean 3rd Qu. Max. - 3.63 14.38 17.89 17.65 21.24 30.21 +summary(tas_obs$data - 273.15) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 3.63 14.38 17.89 17.65 21.24 30.21 -summary(tas_dv$exp$data - 273.15) - Min. 1st Qu. Median Mean 3rd Qu. Max. - 0.54 11.65 16.56 16.50 21.25 31.41 +summary(tas_exp$data - 273.15) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 0.54 11.65 16.56 16.50 21.25 31.41 ``` To compute the GST for both observation and forecast, run the following lines @@ -218,13 +222,13 @@ To compute the GST for both observation and forecast, run the following lines ```{r} # change the unit of temperature from °C to K -tas_dv$exp$data <- tas_dv$exp$data - 273.15 -tas_dv$obs$data <- tas_dv$obs$data - 273.15 +tas_exp$data <- tas_exp$data - 273.15 +tas_obs$data <- tas_obs$data - 273.15 # compute GST -GST_exp <- CST_PeriodMean(tas_dv$exp, start = list(1, 4), end = list(31, 10)) -GST_obs <- CST_PeriodMean(tas_dv$obs, start = list(1, 4), end = list(31, 10)) +GST_exp <- CST_PeriodMean(tas_exp, start = list(1, 4), end = list(31, 10)) +GST_obs <- CST_PeriodMean(tas_obs, start = list(1, 4), end = list(31, 10)) ``` @@ -234,20 +238,20 @@ The summaries and dimensions of the output are as follows: ```{r} summary(GST_exp$data) - Min. 1st Qu. Median Mean 3rd Qu. Max. - 14.23 15.78 16.50 16.50 17.17 18.70 +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 14.23 15.78 16.50 16.50 17.17 18.70 summary(GST_obs$data) - Min. 1st Qu. Median Mean 3rd Qu. Max. - 15.34 16.85 17.72 17.65 18.41 19.60 +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 15.34 16.85 17.72 17.65 18.41 19.60 dim(GST_exp$data) -dataset member sdate lat lon - 1 3 4 4 4 +#dataset member sdate lat lon +# 1 3 4 4 4 dim(GST_obs$data) -dataset member sdate lat lon - 1 1 4 4 4 +#dataset member sdate lat lon +# 1 1 4 4 4 ``` Here, we plot the 2013-2016 mean climatology of ERA5 GST by running @@ -255,14 +259,16 @@ Here, we plot the 2013-2016 mean climatology of ERA5 GST by running ```{r} # compute ERA5 GST climatology GST_Clim <- MeanDims(drop(GST_obs$data), 'sdate') -lon <- tas_dv$obs$lon; lat <- tas_dv$obs$lat -# plot the map of climatology cols <- c('#ffffd4','#fee391','#fec44f','#fe9929','#ec7014','#cc4c02','#8c2d04') -brks <- seq(16, 18.5, by = 0.5) -toptitle <- '2013-2016 mean ERA5 GST' - -PlotEquiMap(GST_Clim, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = '°C', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, col_inf = cols[1], margin_scale = c(1, 1, 1, 1), cols = cols[2:6], col_sup = cols[7], , brks = brks, toptitle = toptitle, colNA = 'white', bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) +PlotEquiMap(GST_Clim, lon = tas_obs$lon, lat = tas_obs$lat, + intylat = 1, intxlon = 1, width = 6, height = 6, + filled.continents = FALSE, units = '°C', title_scale = .8, + axes_label_scale = 1, axes_tick_scale = 1, col_inf = cols[1], + margin_scale = c(1, 1, 1, 1), cols = cols[2:6], col_sup = cols[7], + brks = seq(16, 18.5, 0.5), colNA = 'white', bar_label_scale = 1.5, + toptitle = '2013-2016 mean ERA5 GST', + bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) ``` The ERA5 GST climatology is shown as below. @@ -272,21 +278,11 @@ The ERA5 GST climatology is shown as below. ERA5 GST ranges from 17-18.5°C over the Douro Valley region for the period from 2013-2016 as shown in the figure. -### 3. TotalTimeExceedingThreshold.R - -For the function `TotalTimeExceedingThreshold`, **SU35** (Number of Heat Stress Days - 35°C) is taken as an example here. - -35°C is the average established threshold for photosynthesis to occur in the grapevine. - -Above this temperature, the plant closes its stomata. - -If this situation occurs after veraison, maturation will be arrested for as long as the situation holds, decreasing sugar, polyphenol and aroma precursor levels, all essential for grape and wine quality. +### 3. TotalTimeExceedingThreshold -The higher the index, the lower will be the berry quality and aptitude to produce quality grapes. +For the function `TotalTimeExceedingThreshold`, **SU35** (Number of Heat Stress Days - 35°C) is taken as an example here. 35°C is the average established threshold for photosynthesis to occur in the grapevine. Above this temperature, the plant closes its stomata. If this situation occurs after veraison, maturation will be arrested for as long as the situation holds, decreasing sugar, polyphenol and aroma precursor levels, all essential for grape and wine quality. The higher the index, the lower will be the berry quality and aptitude to produce quality grapes. -SU35 is defined as the Total count of days when daily maximum temperatures exceed 35°C in the seven months into the future. - -There are three indicators sharing the similar definition as SU35: SU36, SU40 and Spr32. Their definition are listed as follows. +SU35 is defined as the Total count of days when daily maximum temperatures exceed 35°C in the seven months into the future. There are three indicators sharing the similar definition as SU35: SU36, SU40 and Spr32. Their definition are listed as follows. --- 1. **SU36**: Total count of days when daily maximum temperatures exceed 36°C between June 21st and September 21st @@ -301,41 +297,45 @@ Here, we take SU35 as example, therefore the daily temperature maximum of the en Load SEAS5 and ERA5 daily temperature maximum by running ```{r} -var <- 'tasmax'; lonmax <- 353; lonmin <- 352.25; latmax <- 41.75; latmin <- 41 -cdoMethod <- 'bicubic'; leadtimemin <- 1; leadtimemax <- 214 S5path <- list(path = '/esarchive/exp/ecmwf/system5c3s/daily/$VAR_NAME$/$VAR_NAME$_$YEAR$$MONTH$01.nc') ERA5path <- list(path = '/esarchive/recon/ecmwf/era5/daily/$VAR_NAME$-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc') -sdates <- paste0(2013:2016, '04', '01') -tasmax_dv <- CST_Load(var = var, exp = list(S5path), obs = list(ERA5path), sdates = sdates, lonmax = lonmax, lonmin = lonmin, latmax = latmax, latmin = latmin, storefreq = 'daily', leadtimemin = leadtimemin, leadtimemax = leadtimemax, nmember = 3, output = "lonlat", grid = "r1440x721", method = cdoMethod, nprocs = 1) +c(tasmax_exp, tasmax_obs) %<-% CST_Load(var = 'tasmax', exp = list(S5path), + obs = list(ERA5path), sdates = sdates, + lonmax = 353, lonmin = 352.25, + latmax = 41.75, latmin = 41, storefreq = 'daily', + leadtimemin = 1, leadtimemax = 214, nmember = 3, + output = "lonlat", grid = "r1440x721", + method = 'bicubic', nprocs = 1) ``` -Change the unit of temperature to from °C to K for the comparison with the threshold defined (for example 35°C here). And the longitude and latitude are prepared for plotting the figure. +Check the unit of temperature to from °C to K for the comparison with the threshold defined (for example 35°C here). ```{r} -tasmax_dv$exp$data <- tasmax_dv$exp$data - 273.15 -tasmax_dv$obs$data <- tasmax_dv$obs$data - 273.15 -lon <- tasmax_dv$obs$lon; lat <- tasmax_dv$obs$lat +tasmax_exp$data <- tasmax_exp$data - 273.15 +tasmax_obs$data <- tasmax_obs$data - 273.15 ``` Computing SU35 for forecast and observation by running ```{r} threshold <- 35 -SU35_exp <- CST_TotalTimeExceedingThreshold(tasmax_dv$exp, threshold = threshold, start = list(1, 4), end = list(31, 10)) -SU35_obs <- CST_TotalTimeExceedingThreshold(tasmax_dv$obs, threshold = threshold, start = list(1, 4), end = list(31, 10)) +SU35_exp <- CST_TotalTimeExceedingThreshold(tasmax_exp, threshold = threshold, + start = list(1, 4), end = list(31, 10)) +SU35_obs <- CST_TotalTimeExceedingThreshold(tasmax_obs, threshold = threshold, + start = list(1, 4), end = list(31, 10)) ``` The summaries of SU35 forecasts and observations are given below. ```{r} summary(SU35_exp$data) - Min. 1st Qu. Median Mean 3rd Qu. Max. - 0.00 2.00 5.00 7.12 12.00 26.00 +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 0.00 2.00 5.00 7.12 12.00 26.00 summary(SU35_obs$data) - Min. 1st Qu. Median Mean 3rd Qu. Max. - 0.000 0.000 1.000 2.609 5.000 10.000 +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 0.000 0.000 1.000 2.609 5.000 10.000 ``` As shown in the summaries, SEAS5 SU35 forecasts are overestimated by 5 days in terms of mean value. @@ -346,8 +346,8 @@ Therefore, `CST_BiasCorrection` is used to bias adjust the SU35 forecasts. res <- CST_BiasCorrection(obs = SU35_obs, exp = SU35_exp) SU35_exp_BC <- drop(res$data) summary(SU35_exp_BC) - Min. 1st Qu. Median Mean 3rd Qu. Max. - -1.419 0.000 1.613 2.831 4.756 17.768 +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# -1.419 0.000 1.613 2.831 4.756 17.768 ``` Since there are negative values after bias adjustment, all negative data is converted to zero. @@ -355,8 +355,8 @@ Since there are negative values after bias adjustment, all negative data is conv ```{r} SU35_exp_BC[SU35_exp_BC < 0] <- 0 summary(SU35_exp_BC) - Min. 1st Qu. Median Mean 3rd Qu. Max. - 0.000 0.000 1.613 2.943 4.756 17.768 +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 0.000 0.000 1.613 2.943 4.756 17.768 ``` Plot the bias-adjusted SU35 forecast in 2016 by running @@ -366,16 +366,36 @@ SU35_obs_Y2016 <- drop(SU35_obs$data)[4, , ] SU35_exp_Y2016 <- MeanDims(drop(SU35_exp$data)[, 4, , ], 'member') SU35_exp_BC_Y2016 <- MeanDims(SU35_exp_BC[, 4, , ], 'member') cols <- c("#fee5d9", "#fcae91", "#fb6a4a", "#de2d26","#a50f15") -brks <- seq(0, 8, by = 2) toptitle <- 'ERA5 SU35 forecast in 2016' -PlotEquiMap(SU35_obs_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) +PlotEquiMap(SU35_obs_Y2016, lon = tasmax_obs$lon, lat = tasmax_obs$lat, + intylat = 1, intxlon = 1, width = 6, height = 6, + filled.continents = FALSE, units = 'day', title_scale = .8, + axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), + cols = cols[1:4], col_sup = cols[5], brks = seq(0, 8, 2), + toptitle = toptitle, + colNA = cols[1], bar_label_scale = 1.5, + bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) toptitle <- 'SU35 forecast in 2016' -PlotEquiMap(SU35_exp_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) +PlotEquiMap(SU35_exp_Y2016, lon = tasmax_obs$lon, lat = tasmax_obs$lat, + intylat = 1, intxlon = 1, width = 6, height = 6, + filled.continents = FALSE, units = 'day', title_scale = .8, + axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), + cols = cols[1:4], col_sup = cols[5], brks = seq(0, 8, 2), + toptitle = toptitle, + colNA = cols[1], bar_label_scale = 1.5, + bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) toptitle <- 'Bias-adjusted SU35 forecast in 2016' -PlotEquiMap(SU35_exp_BC_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) +PlotEquiMap(SU35_exp_BC_Y2016, lon = tasmax_obs$lon, lat = tasmax_obs$lat, + intylat = 1, intxlon = 1, width = 6, height = 6, + filled.continents = FALSE, units = 'day', title_scale = .8, + axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), + cols = cols[1:4], col_sup = cols[5], brks = seq(0, 8, 2), + toptitle = toptitle, + colNA = cols[1], bar_label_scale = 1.5, + bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) ``` You can see the figure as below. @@ -400,14 +420,14 @@ The revised definition of SU35 is to reduce the potential influence induced by t As mentioned, the forecast is translated to its percentile by using the function `ABsToProbs` by running ```{r} -exp_percentile <- AbsToProbs(tasmax_dv$exp$data) +exp_percentile <- AbsToProbs(tasmax_exp$data) S5txP <- aperm(drop(exp_percentile), c(2, 1, 3, 4, 5)) ``` After that, based on 35 of threshold, the percentile corresponding to each observational value can be calculated as follows. ```{r} -obs_percentile <- QThreshold(tasmax_dv$obs$data, threshold = 35) +obs_percentile <- QThreshold(tasmax_obs$data, threshold = 35) obs_percentile <- drop(obs_percentile) ``` @@ -427,69 +447,72 @@ Plot the same map for comparison ```{r} toptitle <- 'SU35 forecast with percentile adjustment in 2016' -PlotEquiMap(SU35_exp_per_Y2016, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'day', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:4], col_sup = cols[5], brks = brks, toptitle = toptitle, colNA = cols[1], bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) +PlotEquiMap(SU35_exp_per_Y2016, lon = tasmax_obs$lon, lat = tasmax_obs$lat, + intylat = 1, intxlon = 1, width = 6, height = 6, + filled.continents = FALSE, units = 'day', title_scale = .8, + axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), + cols = cols[1:4], col_sup = cols[5], brks = seq(0, 8, 2), + toptitle = toptitle, + colNA = cols[1], bar_label_scale = 1.5, + bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) + ``` + As seen in the figure above, applying the percentile adjustment seems to implicitly adjust certain extent of bias which was observed in the non-bias-adjusted SEAS5 forecast. The performance of comparison of skills between two definitions requires further analysis such as the application of more skill metrics. -### 4. AccumulationExceedingThreshold.R +### 4. AccumulationExceedingThreshold The function ´AccumulationExceedingThreshold´ can compute GDD (Growing Degree Days). -The definition of GDD is the summation of daily differences between daily average temperatures and 10°C between April 1st and October 31st. +The definition of GDD is the summation of daily differences between daily average temperatures and 10°C between April 1st and October 31st. Here, the tas (daily average temperature) used above (in Section 2. PeriodMean.R) is loaded again (Please re-use the section of loading tas in Section 2). As per the definition, `threshold` is set to 10 with `diff` set to TRUE so that the function will compute the differences between daily temperature and the threshold given before calculating summation. -Here, the tas (daily average temperature) used above (in Section 2. PeriodMean.R) is loaded again (Please re-use the section of loading tas in Section 2). - -Change the unit, if necessary, from °C to K and prepare the longitude and latitude by running - -```{r} -tas_dv$exp$data <- tas_dv$exp$data - 273.15 -tas_dv$obs$data <- tas_dv$obs$data - 273.15 -lon <- tas_dv$obs$lon; lat <- tas_dv$obs$lat -``` - -As per the definition, `threshold` is set to 10 with `diff` set to TRUE so that the function will compute the differences between daily temperature and the threshold given before calculating summation. +*Note: The data is in degrees Celsiusi at this point* ```{r} -GDD_exp <- CST_AccumulationExceedingThreshold(tas_dv$exp, threshold = 10, diff = TRUE) -GDD_obs <- CST_AccumulationExceedingThreshold(tas_dv$obs, threshold = 10, diff = TRUE) +GDD_exp <- CST_AccumulationExceedingThreshold(tas_exp, threshold = 10, diff = TRUE) +GDD_obs <- CST_AccumulationExceedingThreshold(tas_obs, threshold = 10, diff = TRUE) ``` The summaries of GDD are ```{r} summary(GDD_exp$data) - Min. 1st Qu. Median Mean 3rd Qu. Max. - 1021 1331 1480 1469 1596 1873 +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 1021 1331 1480 1469 1596 1873 summary(GDD_obs$data) - Min. 1st Qu. Median Mean 3rd Qu. Max. - 1195 1504 1687 1660 1804 2055 +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 1195 1504 1687 1660 1804 2055 ``` To compute the correlation coefficient for the period from 2013-2016, run the following lines ```{r} # reorder the dimension -fcst <- Reorder(GDD_exp$data, c(4, 3, 2, 1)) -obs <- Reorder(GDD_obs$data, c(3, 2, 1)) +fcst <- Reorder(drop(GDD_exp$data), c(4, 3, 2, 1)) +obs <- Reorder(drop(GDD_obs$data), c(3, 2, 1)) -c <- veriApply('EnsCorr', fcst = fcst, obs = obs, ensdim = 4, tdim = 3) -GDD_Corr <- Reorder(c, c(2, 1)) +EnsCorr <- veriApply('EnsCorr', fcst = fcst, obs = obs, ensdim = 4, tdim = 3) +GDD_Corr <- Reorder(EnsCorr, c(2, 1)) ``` To plot the map of correlation coefficient of GDD for the 2013-2016 period. ```{r} cols <- c("#f7fcf5", "#e5f5e0", "#c7e9c0", "#a1d99b", "#74c476") -brks <- seq(0.5, 1, by = 0.1) toptitle <- '2013-2016 correlation coefficient of GDD' - -PlotEquiMap(GDD_Corr, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'correlation', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols, brks = brks, toptitle = toptitle, bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) +PlotEquiMap(GDD_Corr, lon = tas_obs$lon, lat = tas_obs$lat, + intylat = 1, intxlon = 1, width = 6, height = 6, + filled.continents = FALSE, units = 'correlation', + title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, + margin_scale = c(1, 1, 1, 1), cols = cols, brks = seq(0.5, 1, 0.1), + toptitle = toptitle, bar_label_scale = 1.5, + bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) ``` The map of correlation coefficient for the 2013-2016 period is shown as below. @@ -502,33 +525,29 @@ The 2013-2016 correlation coefficients of the SEAS5 forecasts of GDD in referenc One of the critical agricultural indicators related to dry spell is the **Warm Spell Duration Index (WSDI)**, which is defined as the total count of days with at least 6 consecutive days when the daily maximum temperature exceeds its 90th percentile in the seven months into the future. -The maximum temperature data used in Section 3. TotalTimeExceedingThreshold.R is loaded here again (If necessary re-use the codes above). - -Since the daily maximum temperature needs to compare to its 90th percentile, the function `Threshold` in the `CSIndicators` package is required to compute the percentile of observations used for each day. Here the same period (2013-2016) is considered. +The maximum temperature data used in Section 3. Since the daily maximum temperature needs to compare to its 90th percentile, the function `Threshold` in the `CSIndicators` package is required to compute the percentile of observations used for each day. Here the same period (2013-2016) is considered. ```{r} -tx_p <- CST_Threshold(tasmax_dv$obs, threshold = 0.9) +tx_p <- CST_Threshold(tasmax_obs, threshold = 0.9) ``` -The output will be the 90th percentile of each day of each grid point derived by using all the years in the data. - -See the dimension and summary as below. +The output will be the 90th percentile of each day of each grid point derived by using all the years in the data.See the dimension and summary as below. ```{r} dim(tx_p$data) -dataset ftime lat lon - 1 214 4 4 +#dataset ftime lat lon +# 1 214 4 4 summary(tx_p$data) - Min. 1st Qu. Median Mean 3rd Qu. Max. - 287.0 295.2 299.2 299.4 303.9 309.9 +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 287.0 295.2 299.2 299.4 303.9 309.9 ``` With the prepared threshold (90th percentile), the WSDI can be computed by running ```{r} -WSDI_exp <- CST_TotalSpellTimeExceedingThreshold(tasmax_dv$exp, threshold = tx_p, spell = 6) -WSDI_obs <- CST_TotalSpellTimeExceedingThreshold(tasmax_dv$obs, threshold = tx_p, spell = 6) +WSDI_exp <- CST_TotalSpellTimeExceedingThreshold(tasmax_exp, threshold = tx_p, spell = 6) +WSDI_obs <- CST_TotalSpellTimeExceedingThreshold(tasmax_obs, threshold = tx_p, spell = 6) ``` After checking the summaries, compute the Fair Ranked Probability Skill Score (FRPSS) of WSDI by running the following lines @@ -540,12 +559,12 @@ obs <- Reorder(drop(WSDI_obs$data), c(3, 2, 1)) # summaries of WSDI summary(fcst) - Min. 1st Qu. Median Mean 3rd Qu. Max. - 0.00 13.00 28.00 30.65 42.25 82.00 +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 0.00 13.00 28.00 30.65 42.25 82.00 summary(obs) - Min. 1st Qu. Median Mean 3rd Qu. Max. - 9.00 19.00 22.50 22.91 25.25 33.00 +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 9.00 19.00 22.50 22.91 25.25 33.00 # compute FRPSS f <- veriApply('FairRpss', fcst = fcst, obs = obs, ensdim = 4, tdim = 3, prob = 1:2/3)$skillscore @@ -556,10 +575,15 @@ Plot the map of WSDI FRPSS for the period from 2013-2016 ```{r} cols <- c("#edf8fb", "#ccece6", "#99d8c9", "#66c2a4") -brks <- seq(0, 0.9, by = 0.3) toptitle <- 'SEAS5 WSDI FRPSS (2013-2016)' -PlotEquiMap(WSDI_FRPSS, lon = lon, lat = lat, intylat = 1, intxlon = 1, width = 6, height = 6, filled.continents = FALSE, units = 'FRPSS', title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), cols = cols[1:3], col_inf = 'white', col_sup = cols[4], brks = brks, toptitle = toptitle, bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) +PlotEquiMap(WSDI_FRPSS, lon = tasmax_obs$lon, lat = tasmax_obs$lat, + intylat = 1, intxlon = 1, width = 6, height = 6, + filled.continents = FALSE, units = 'FRPSS', title_scale = .8, + axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), + cols = cols[1:3], col_inf = 'white', col_sup = cols[4], + brks = seq(0, 0.9, 0.3), toptitle = toptitle, bar_label_scale = 1.5, + bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) ``` The FRPSS map for 2013-2016 SEAS WSDI is shown as below. -- GitLab From 6e98999be962e6a6d3dd9b9311e99f324bfb5131 Mon Sep 17 00:00:00 2001 From: nperez Date: Thu, 29 Apr 2021 11:40:05 +0200 Subject: [PATCH 12/15] Fix dimensions in AccumulationExcThre and compress png --- R/AccumulationExceedingThreshold.R | 3 +-- vignettes/AgriculturalIndicators.md | 4 ++-- vignettes/figures/GDD_SEAS5_Corr_Y13-16-1.png | Bin 23485 -> 9961 bytes vignettes/figures/GST_ERA5_Climatology-1.png | Bin 22458 -> 8885 bytes vignettes/figures/HarvestR_Bias_2013-1.png | Bin 21614 -> 8941 bytes vignettes/figures/SU35_ERA5_Y2016-1.png | Bin 22152 -> 8124 bytes .../figures/SU35_Percentile_SEAS5_Y2016-1.png | Bin 23552 -> 9212 bytes vignettes/figures/SU35_SEAS5_BC_Y2016-1.png | Bin 22681 -> 8836 bytes vignettes/figures/SU35_SEAS5_Y2016-1.png | Bin 20890 -> 7520 bytes .../figures/WSDI_SEAS5_FRPSS_Y13-16-1.png | Bin 22132 -> 8902 bytes 10 files changed, 3 insertions(+), 4 deletions(-) diff --git a/R/AccumulationExceedingThreshold.R b/R/AccumulationExceedingThreshold.R index a7cf94d..4d3e50e 100644 --- a/R/AccumulationExceedingThreshold.R +++ b/R/AccumulationExceedingThreshold.R @@ -170,11 +170,10 @@ AccumulationExceedingThreshold <- function(data, threshold, op = '>', } } if (diff == TRUE) { - dims <- dim(data) data <- Apply(list(data, threshold), target_dims = list(time_dim, NULL), fun = function(x, y) {x - y}, ncores = ncores)$output1 - dim(data) <- dims + dim(data) <- dim(data)[-length(dim(data))] threshold <- 0 } if (is.null(dim(threshold))) { diff --git a/vignettes/AgriculturalIndicators.md b/vignettes/AgriculturalIndicators.md index ac311e1..57dc63e 100644 --- a/vignettes/AgriculturalIndicators.md +++ b/vignettes/AgriculturalIndicators.md @@ -469,7 +469,7 @@ The performance of comparison of skills between two definitions requires further The function ´AccumulationExceedingThreshold´ can compute GDD (Growing Degree Days). -The definition of GDD is the summation of daily differences between daily average temperatures and 10°C between April 1st and October 31st. Here, the tas (daily average temperature) used above (in Section 2. PeriodMean.R) is loaded again (Please re-use the section of loading tas in Section 2). As per the definition, `threshold` is set to 10 with `diff` set to TRUE so that the function will compute the differences between daily temperature and the threshold given before calculating summation. +The definition of GDD is the summation of daily differences between daily average temperatures and 10°C between April 1st and October 31st. Here, the tas (daily average temperature) used above (in Section 2. PeriodMean) is loaded again (Please re-use the section of loading tas in Section 2). As per the definition, `threshold` is set to 10 with `diff` set to TRUE so that the function will compute the differences between daily temperature and the threshold given before calculating summation. *Note: The data is in degrees Celsiusi at this point* @@ -521,7 +521,7 @@ The map of correlation coefficient for the 2013-2016 period is shown as below. The 2013-2016 correlation coefficients of the SEAS5 forecasts of GDD in reference with ERA5 reanalysis over Douro Valley range between 0.6 and 0.8. -### 5. TotalSpellTimeExceedingThreshold.R +### 5. 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