Malaria Suitability Indicator

1.1 Area of interest

In this vignette, the malaria indicator between 2010 and 2020 will be computed for the Iberian Peninsula.

• Which countries?
• What coordinates?

Wikipedia image

1.2 Data extraction

To compute the malaria indicator, we will use reanalysis data from the ERA5-Land repository. These data are produced by the Copernicus service and extracted and curated by the Earth Sciences department from the Barcelona Supercomputing Center. The function CST_Load from the CSTools package allows the user to access the processed data and to work with it using function from the CSIndicators package.

In order to extract data using CST_Load, there are some requirements to be met. First, the path to the files is introduced using whitecards delimited by dollar signs, which are useful for iterating over variables. Second, the temporal extent and resolution of the output is inputted with sdates, which takes a vector of all necessary dates. The spatial extent is insputed within lonmax, lonmin, latmax and latmin.

# Path to file locations using whitecards
path_ERA5_CDS <- list(path='/esarchive/recon/ecmwf/era5land/$STORE_FREQ$_mean/$VAR_NAME$_f1h/$VAR_NAME$_$YEAR$$MONTH$.nc')

# Temporal extent and resolution:
# We will work with all months from 2010 to 2020
year_in <- 2020 # initial year
year_fi <- 2021 # last year
month_in <- 1 # first month
month_fi <- 12 # last month

sdates <- paste0(year_in:year_fi, '0101')

# Extract data
vars <- c('tas', 'tdps', 'prlr')

out <- NULL
for(var in vars){
  out[[var]] <- CST_Load_s2dv(var = var,
                           exp = NULL, # We only require observved data
                           obs = list(path_ERA5_CDS),
                           sdates = sdates,
                           lonmax = 355, lonmin = 350,
                           latmax = 45, latmin = 43,
                           storefreq = 'monthly',
                           leadtimemin = month_in, 
                           leadtimemax = month_fi,
                           output = "lonlat")  
}

This function creates s2dv_cube objects, defined as XXXX. For further information about these objects visit Data retrieval and storage.

As an example, we will show the dimensions of near surface temperature

dim(out$tas$data)

## dataset  member   sdate   ftime     lat     lon 
##       1       1       2      12      21      51

Near surface temperature is made up of 10 years with 12 months per year distributed over a grid of XXxXX.

2.1 Near surface temperature and near surface dew point temperature

The first step before working with temperature is transforming their unit from Kelvin (K) to Degrees Celsius (C).

summary(out$tas$data)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   272.5   281.8   284.7   284.9   288.7   292.9   19680

for(var in c('tas', 'tdps')){
  out[[var]]$data <- out[[var]]$data - 273.15
}

summary(out$tas$data)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   -0.67    8.61   11.56   11.77   15.56   19.78   19680

2.2 Precipitation

summary(out$prlr$data)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##       0       0       0       0       0       0   19680

out$prlr$data <- out$prlr$data*3600*24*30.41*1000

summary(out$prlr$data)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    2.22   61.84   99.72  107.85  142.38  395.53   19680

2.3 Relative humidity

out$rh <- out$tas

out$rh$data <- 100*(exp((17.625*out$tdps$data)/(243.04+out$tdps$data)) / 
                      exp((17.625*out$tas$data) / (243.04+out$tas$data)))

summary(out$rh$data)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   67.55   79.30   81.90   81.88   83.97  116.08   19680

2.4 Land use

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