sgosmyt2/climate-extremes-risk-analysis

An analysis of drought and snowfall using ERA5/CHIRPS data in Mongolia and Chad # Dzud and Drought, Assessing Climate Risk from Extreme Rainfall and Drought using Satellite Data ## Project Overview This project investigates how extreme weather, specifically drought, dzud and extreme rainfall, creates climate risk in semi‑arid and arid regions. Using satellite derived precipitation data, reanalysis data and geospatial analysis in Python, the project applies percentile based and persistence based frameworks to evaluate climate extremes relative to local climatology. The completed part of the analysis focuses on Chad during 2011, a year embedded within a multi year regional drought that led to severe food insecurity across the Sahel. Although national annual rainfall appeared near normal, this project demonstrates that seasonal timing, spatial heterogeneity, and persistence of dry conditions explain the severity of impacts. The continuation of the analysis will involve the analysis of Mongolia during 2009-2010, a 10 month time frame in which serious drought and low rainfall in the summer of 2009 set up for poor conditions going into the winter dzud that lasted well into 2010, resulting in serious food insecurity and mass migration to the urban centres by the rural subsistence farmers of Mongolia. This repository is intended as a portfolio‑quality demonstration of applied climate data science and geospatial analysis. **Note**: Visual themes for all visualisations and figures was selected off of a single accent colour. This colour was chosen for personal consistency and as a reminder to of explaining concepts out loud to someone who may not have knowledge in the area. ## 2011 Chad The climatological analysis of Chad in 2011 has been completed as of 29/12/2025 ### Methods This project combines temporal, seasonal, and spatial analyses: **Historical Baseline** - 29 year climatology (1981–2009) to represent pre drought conditions and provide context to the 2011 drought - Annual and wet season rainfall distributions - Anomalies, standardized z sc …