| Title: | Generating Rainfall Rasters from IMD NetCDF Data |
|---|---|
| Description: | The developed function is a comprehensive tool for the analysis of India Meteorological Department (IMD) NetCDF rainfall data. Specifically designed to process high-resolution daily gridded rainfall datasets. It provides four key functions to process IMD NetCDF rainfall data and create rasters for various temporal scales, including annual, seasonal, monthly, and weekly rainfall. For method details see, Malik, A. (2019).<DOI:10.1007/s12517-019-4454-5>. It supports different aggregation methods, such as sum, min, max, mean, and standard deviation. These functions are designed for spatio-temporal analysis of rainfall patterns, trend analysis,geostatistical modeling of rainfall variability, identifying rainfall anomalies and extreme events and can be an input for hydrological and agricultural models. |
| Authors: | Nirmal Kumar [aut, cph], Nobin Chandra Paul [aut, cre], G.P. Obi Reddy [aut] |
| Maintainer: | Nobin Chandra Paul <[email protected]> |
| License: | GPL (>= 2.0) |
| Version: | 0.1.0 |
| Built: | 2024-12-06 06:41:57 UTC |
| Source: | CRAN |
Generating Annual rainfall raster from IMD NetCDF file
AnnualRF_raster(nc_data, output_dir = NULL, fun = "sum", year)AnnualRF_raster(nc_data, output_dir = NULL, fun = "sum", year)
nc_data |
Path to the IMD rainfall NetCDF file |
output_dir |
Directory to save the generated annual rainfall raster (Optional) |
fun |
Aggregation function ("sum", "min", "max", "mean", "sd")(Default is "sum") |
year |
Year for which to generate annual rainfall raster |
Annual rainfall raster in GeoTIFF format
1. Pai et al. (2014). Development of a new high spatial resolution (0.25° X 0.25°)Long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region, MAUSAM, 65(1),1-18. 2. Hijmans, R. J. (2022). raster: Geographic Data Analysis and Modeling. R package version 3.5-13. 3. Kumar et al. (2023). SpatGRID:Spatial Grid Generation from Longitude and Latitude List. R package version 0.1.0.
library(CLimd) # Example usage: nc_data <- system.file("extdata", "imd_RF_2022.nc", package = "CLimd") output_dir <- NULL fun<-"sum" year<-2022 # Calculate annual rainfall sum for 2022 annual_rainfall_sum<-AnnualRF_raster(nc_data, output_dir=NULL, fun="sum", year)library(CLimd) # Example usage: nc_data <- system.file("extdata", "imd_RF_2022.nc", package = "CLimd") output_dir <- NULL fun<-"sum" year<-2022 # Calculate annual rainfall sum for 2022 annual_rainfall_sum<-AnnualRF_raster(nc_data, output_dir=NULL, fun="sum", year)
Generating Monthly Rainfall Rasters from IMD NetCDF file
MonthRF_raster(nc_data, output_dir = NULL, fun = "sum", year)MonthRF_raster(nc_data, output_dir = NULL, fun = "sum", year)
nc_data |
Path to the IMD rainfall NetCDF file |
output_dir |
Directory to save the generated monthly rainfall raster (Optional) |
fun |
Aggregation function ("sum", "min", "max", "mean", "sd")(Default is "sum") |
year |
Year for which to generate monthly rainfall raster |
A list of monthly rainfall rasters in GeoTIFF format
1. Pai et al. (2014). Development of a new high spatial resolution (0.25° X 0.25°)Long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region, MAUSAM, 65(1),1-18. 2. Hijmans, R. J. (2022). raster: Geographic Data Analysis and Modeling. R package version 3.5-13. 3. Kumar et al. (2023). SpatGRID:Spatial Grid Generation from Longitude and Latitude List. R package version 0.1.0.
library(CLimd) # Example usage: nc_data <- system.file("extdata", "imd_RF_2022.nc", package = "CLimd") output_dir <- NULL fun<-"sum" year<-2022 # Calculate monthly rainfall sums for 2022 monthly_rainfall <-MonthRF_raster(nc_data, output_dir=NULL, fun="sum", year) # Calculate monthly rainfall means for 2022 fun<-"mean" monthly_rainfall_means <- MonthRF_raster(nc_data, output_dir=NULL, fun="mean", year)library(CLimd) # Example usage: nc_data <- system.file("extdata", "imd_RF_2022.nc", package = "CLimd") output_dir <- NULL fun<-"sum" year<-2022 # Calculate monthly rainfall sums for 2022 monthly_rainfall <-MonthRF_raster(nc_data, output_dir=NULL, fun="sum", year) # Calculate monthly rainfall means for 2022 fun<-"mean" monthly_rainfall_means <- MonthRF_raster(nc_data, output_dir=NULL, fun="mean", year)
Generating Seasonal rainfall rasters from IMD NetCDF file
SeasonalRF_raster(nc_data, output_dir = NULL, fun = "sum", year)SeasonalRF_raster(nc_data, output_dir = NULL, fun = "sum", year)
nc_data |
Path to the IMD rainfall NetCDF file |
output_dir |
Directory to save the generated seasonal rainfall rasters (Optional) |
fun |
Aggregation function ("sum", "min", "max", "mean", "sd")(Default is "sum") |
year |
Year for which to generate seasonal rainfall raster |
Returns a list containing the four seasonal rasters in GeoTIFF format
1. Pai et al. (2014). Development of a new high spatial resolution (0.25° X 0.25°)Long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region, MAUSAM, 65(1),1-18. 2. Hijmans, R. J. (2022). raster: Geographic Data Analysis and Modeling. R package version 3.5-13. 3. Kumar et al. (2023). SpatGRID:Spatial Grid Generation from Longitude and Latitude List. R package version 0.1.0.
library(CLimd) # Example usage: nc_data <- system.file("extdata", "imd_RF_2022.nc", package = "CLimd") output_dir <- NULL fun<-"sum" year<-2022 # Calculate seasonal rainfall sum for 2022 seasonal_rainfall <-SeasonalRF_raster(nc_data, output_dir=NULL, fun="sum", year)library(CLimd) # Example usage: nc_data <- system.file("extdata", "imd_RF_2022.nc", package = "CLimd") output_dir <- NULL fun<-"sum" year<-2022 # Calculate seasonal rainfall sum for 2022 seasonal_rainfall <-SeasonalRF_raster(nc_data, output_dir=NULL, fun="sum", year)
Generating weekly rainfall rasters from IMD NetCDF file
WeeklyRF_raster(nc_data, output_dir = NULL, fun = "sum", year)WeeklyRF_raster(nc_data, output_dir = NULL, fun = "sum", year)
nc_data |
Path to the IMD rainfall NetCDF file |
output_dir |
Directory to save the generated weekly rainfall rasters (Optional) |
fun |
Aggregation function ("sum", "min", "max", "mean", "sd")(Default is "sum") |
year |
Year for which to generate weekly rainfall raster |
A list of weekly rainfall rasters in GeoTIFF format
1. Pai et al. (2014). Development of a new high spatial resolution (0.25° X 0.25°)Long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region, MAUSAM, 65(1),1-18. 2. Hijmans, R. J. (2022). raster: Geographic Data Analysis and Modeling. R package version 3.5-13. 3. Kumar et al. (2023). SpatGRID:Spatial Grid Generation from Longitude and Latitude List. R package version 0.1.0.
library(CLimd) # Example usage: nc_data <- system.file("extdata", "imd_RF_2022.nc", package = "CLimd") output_dir <- NULL fun<-"sum" year<-2022 # Calculate weekly rainfall sum for 2022 weekly_rainfall_sum <-WeeklyRF_raster(nc_data, output_dir=NULL, fun="sum", year)library(CLimd) # Example usage: nc_data <- system.file("extdata", "imd_RF_2022.nc", package = "CLimd") output_dir <- NULL fun<-"sum" year<-2022 # Calculate weekly rainfall sum for 2022 weekly_rainfall_sum <-WeeklyRF_raster(nc_data, output_dir=NULL, fun="sum", year)