diff --git a/vignettes/risk/risk_index.md b/vignettes/risk/risk_index.md index 5524776bf9aed1a81111d7abcb35c7236e46b4e8..3abd41d6c3475c41ae1d05aae944d66e38ef86ef 100644 --- a/vignettes/risk/risk_index.md +++ b/vignettes/risk/risk_index.md @@ -3,11 +3,11 @@ output: pdf_document: default html_document: default --- -Insurance Risk Indices +Extreme Indices ======================== -Insurance Risk Indices are an ensemble of indices relevant for insurance industry. These indices are based on Expert Team on Climate Change Detection Indices (ETCCDI). There are currently 5 available indices to compute for extreme: heat (tx90p), cold (tn10p), wind (wx), drought (ccd) and flooding (rx5day). The individual indices can be combined into a single index with or without weighting for each component. This combined index is roughly analogous to the Actuaris Climate Risk Index. +The extreme indices are an ensemble of indices based on the Expert Team on Climate Change Detection Indices (ETCCDI). There are currently 5 available indices to be computed: extreme heat (tx90p), extreme cold (tn10p), extreme wind (wx), drought (ccd) and flooding (rx5day). The individual indices can be combined into a single index with or without weighting for each component. This combined index is roughly analogous to the Actuaries Climate Risk Index (see http://actuariesclimateindex.org/home/). ### 1- Load dependencies @@ -155,7 +155,7 @@ names(dim(anomaly_data))[1] <- "time" ``` -This data can be detrended by applying `Trend` function from **s2dverification package**. In order to remove the trend from the `tmax_historical`, the correction is calculated by subtracting the `detrended_data` to the `anomaly_data`. +This data can be detrended by applying the `Trend` function from **s2dverification package**. In order to remove the trend from the `tmax_historical`, the correction is calculated by subtracting the `detrended_data` to the `anomaly_data`. ```r @@ -225,7 +225,7 @@ projection_index <- Climdex(data = tmax_projection, metric = 't90p', ``` -Its normalized with mean 10 and the standard deviation of the reference period. +It is normalized with mean 10 and the standard deviation of the reference period. ```r @@ -235,7 +235,7 @@ HeatExtremeIndex <- (projection_index$result - base_mean) / base_sd ``` -A spatial representation of the mean index values is obtained and save in PNG format in the working directory with the name: "SpatialExtremeHeatIndex.png". The matrix `masc` is build and shown as dots in the plot indicating wich pixels are considered land. +A spatial representation of the mean index values is obtained and saved in PNG format in the working directory with the name: "SpatialExtremeHeatIndex.png". The matrix `masc` is built and shown as dots in the plot indicating wich pixels are considered land. ```r @@ -288,7 +288,7 @@ dev.off() The Extreme Drought Index (*cdd*), which measures the maximum length of a dry spell, is defined as the maximum number of consecutive days with the daily precipitation amount lower than 1 mm. -To compute the Extreme Drought Index during the reference period and its standar deviation and mean: +To compute the Extreme Drought Index during the reference period and its standard deviation and mean: *Note: Precipitation data is not detrended. Furthermore, this index doesn't require to compute a threshold as `Climdex` function integrates the threshold of precipitation amount lower than 1 mm internally. However, this case requires the calculation of the mean.* @@ -434,7 +434,7 @@ dev.off() ### 6- Combining Indices -The individual indices can be combined into a single index with or without weighting for each component. This combined index is roughly analogous to the Actuaris Climate Risk Index (see http://actuariesclimateindex.org/home/). Extreme Indices should be saved in the same `list` object. +The individual indices can be combined into a single index with or without weighting for each component. This combined index is roughly analogous to the Actuaries Climate Risk Index. Extreme Indices should be saved in the same `list` object. ```r @@ -444,7 +444,7 @@ indices[[2]] <- DroughtExtremeIndex indices[[3]] <- FloodingExtremeIndex ``` -If the `weights` parameter is defined as `NULL`, all indices will be equally weighted if `operation` parameter is set as `mean` (by default). To define other `weights` a vector of length equal to the number of considered indices (5 in this example) and with total sum equal to 1. +If the `weights` parameter is defined as `NULL`, all indices will be equally weighted if the `operation` parameter is set as `mean` (by default). To define other `weights` a vector of length equal to the number of considered indices (5 in this example) and with total sum equal to 1. ```r @@ -452,7 +452,7 @@ aci <- CombineIndices(indices = indices, weights = NULL) ``` -A spatial visulitzation can be performs by executing: +A spatial visualization can be performed by executing: ```r @@ -465,4 +465,4 @@ PlotEquiMap(Mean1Dim(aci, which(names(dim(aci)) == "year")), lon = lon, ![Spatial distribution of Combined Indices](CombinedIndices.png) -*Note: This vignette shows the computation of three indices, however, five different indices can be computed with `Climdex` function. To consider other combination settings run `?CombinedIndices`.* +*Note: This vignette shows the computation of three indices, however, five different indices can be computed with the `Climdex` function. To consider other combination settings run `?CombinedIndices`.*