CRU_Climate data:: Automatic Downloading, Processing, and Visualizing Climate Data in R

🌍 CRU_Climate data:: Automatic Downloading, Processing, and Visualizing Climate Data in R In this project, I demonstrate a complete workflow for downloading, processing, and analyzing long-term climate data using the CRU TS v4.09 dataset. The analysis focuses on precipitation, minimum temperature, and maximum temperature, covering more than 120 years (1901–2024). Using R, I downloaded the dataset directly from CRU, extracted and processed the NetCDF files, and then cropped and masked them to the Missouri state boundary. I generated monthly climate maps and computed drought indices including: SPI-3 (Meteorological Drought) SPEI-3 (Agricultural Drought) SPI-12 (Hydrological Drought) These drought indices help in understanding: ✔ Long-term climate variability ✔ Seasonal drought severity ✔ Hydro-climatic pattern shifts ✔ Potential climate risk to agriculture and water resources This workflow can support researchers, planners, and climate scientists working on drought monitoring, environmental risk assessment, and regional climate studies. 📁 Data Source: https://crudata.uea.ac.uk/cru/data/hrg/#current 🧠 Rscript: https://github.com/alihassan720/Geo_Solution12/blob/main/CRU_Climate%20data%20Automatic%20Downloading%2C%20Processing%2C%20and%20Visualizing%20Climate%20Data%20in%20R.txt #rstudio #rstats #gfas #copernicus #wildfire #wildfires #wildfirenews #wildfireimpact #wildfiremanagement #remotesensing #airquality #airqualityindex #airqualitymonitor #gis #consultation #datascience #climatechange #climatedata #geospatialtechnology #geospatial #rprogrammingforbeginners #california #californiarealestate #ucdavis #washu #ucsb #usa #climatedata #gis #remotesensing #DroughtMonitoring #RStats #CRUData #Hydroclimate #ClimateChange #EnvironmentalResearch #DataScience #GeospatialAnalysis #Missouri #SPEI #SPI #TimeSeriesAnalysis