Subnational climate data analysis methods

ERA5-Land 2m Temperature Analysis for South Asia (2024) I have applied machine learning and geospatial analysis techniques to explore monthly 2-meter air temperature trends across South Asia in 2024 using ECMWF’s ERA5-Land reanalysis data. Data & Tools: 1. Source: ERA5-Land monthly aggregated temperature data (2m above ground) 2. Platform: Google Earth Engine 3. Language: Python with geemap and Cartopy for spatial processing and visualization Key Findings: 1. Seasonal Temperature Variation: Clear month-by-month temperature variation, with cold winters in the Himalayan region and intense heat peaks during summer across the Indian subcontinent. 2. Spatial Insights: Temperature gradients reflect diverse climate zones, critical for environmental and climate impact studies. Methodology: 1. Extracted temperature data for each month in 2024. 2. Converted Kelvin to Celsius for meaningful interpretation. 3. Generated detailed spatial maps using Python visualization libraries with Cartopy projections. 4. Created a comprehensive multi-panel figure showcasing monthly variations for easy comparison. Significance: This analysis demonstrates how integrating open climate data with cloud-based geospatial tools and machine learning enables high-quality, reproducible climate monitoring. These insights can support regional planning, agriculture, disaster preparedness, and climate resilience initiatives. #ClimateScience #DataScience #GeospatialAnalysis #MachineLearning #GoogleEarthEngine #ClimateChange #uthAsia

🌍 Unveiling Climate Patterns in Bangladesh🇧🇩: Correlation Analysis Using Python API (Xee) & Google Earth Engine 🌍 In recent years, Bangladesh has experienced shifting climate patterns, with increasing concerns about temperature variability and precipitation changes. To better understand these dynamics, I embarked on a geospatial analysis project leveraging Google Earth Engine (GEE) and the Python API (Xee). By utilizing vast climate datasets such as MODIS and TerraClimate, I explored how annual temperature and precipitation interact over time. This type of correlation analysis is essential for identifying climate trends and their potential impacts on agriculture, water resources, and disaster preparedness. I want to express my heartfelt gratitude to Qiusheng Wu and Amirhossein Ahrari for their incredible guidance and teaching. Their unique approach to #remotesensing and #GEE #Python has transformed my learning experience that help to dive my project. 🔍 Why Bangladesh? Bangladesh faces severe climate challenges—rising temperatures, erratic rainfall, floods, and droughts. By mapping correlations, we can identify regions where temperature increases may exacerbate precipitation deficits (or surpluses), aiding policymakers and environmental planners. 🔎 Key Insights from the Analysis: ✅ Climate Trends: Understanding the relationship between temperature and precipitation helps predict potential droughts, floods, and shifts in monsoon patterns. ✅ Advanced Data Processing: Using Python libraries like xarray, matplotlib, and pandas, I processed and visualized climate data efficiently. ✅ Correlation Mapping: By computing correlation coefficients, I identified regional variations in temperature-precipitation interactions, which can guide policy decisions and climate adaptation strategies. ✅ Practical Applications: This approach can aid in designing resilient agricultural practices, optimizing water resource management, and strengthening climate change mitigation efforts. 🔹 Why is This Important? With climate change intensifying, data-driven insights are crucial for decision-makers, researchers, and stakeholders in Bangladesh. This type of analysis can support early warning systems, infrastructure planning, and sustainable development strategies. 💡 The possibilities with Earth Engine and Python are endless! If you’re interested in geospatial data analysis, let's connect and collaborate. Check out the interactive correlation maps and climate visualizations below! Let me know your thoughts or any recommendations for further analysis. 🚀 #ClimateData #GeospatialAnalysis #PythonAPI #Xee #EarthEngine #Bangladesh #ClimateChange #DataScience #Sustainability #EnvironmentalResearch #DataVisualization #ClimateResilience