Connecting field data to climate science

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

Game-changer for Geospatial AI & Climate Science Google DeepMind just introduced AlphaEarth Foundations — a “virtual satellite” that fuses petabytes of Earth-observation data into a unified planetary embedding at ~10 m resolution, now available in Google Earth Engine. Huge step forward for mapping, monitoring, and actionable climate insights. So what? Treat AlphaEarth as your canonical geospatial feature store, then fuse it with near-real-time streams to move from maps ➜ decisions: Fuse with near-real-time data • Aerial & drone imagery for sub-10 cm change detection (post-event damage, coastal erosion, illegal mining). • SAR/Optical satellite nowcasts for all-weather flood, landslide, wildfire front tracking. • Geosensing/IoT (river gauges, air-quality, flux towers, soil moisture, tide gauges) for ground truth & model calibration. • Numerical forecasts (weather, waves, wildfire spread) to turn “what’s happening” into “what happens next.” Pipeline pattern Base layer: AlphaEarth annual embeddings (2017–2024) as consistent, compact features. Streaming layer: Real-time imagery + sensor feeds (Kafka/Kinesis). Reasoning & prediction: Spatiotemporal models + LLM agents for triage/explanations. Ops & action: Geofenced alerts, playbooks, and digital twins feeding emergency response, insurers, utilities, and cities. Impact examples • Wildfire: early-warning + resource allocation. • Floods: road passability & cross-border impact in hours, not weeks. • Agriculture: field-level yield & water stress forecasting. • Biodiversity: continuous habitat change & restoration ROI. • Urban heat: micro-zone cooling plans and grid load shaping. If you’re building climate decision systems, this is a foundational layer worth piloting. https://lnkd.in/e5zgYpTT #GeospatialAI #EarthObservation #ClimateTech #DigitalTwins #RemoteSensing #AIforGood #Sustainability #DisasterResponse #Insurance #ESG

✨ I’m constantly inspired by what’s possible when open data meets open minds. 🌍 Collaboration is the real climate solution—and the learning never stops! 💡 If you’re digging into climate data, automating geospatial workflows, or just curious about this tech, let’s connect, swap stories, and build something impactful together. 🚀 Recently, I have been handling extensive CMIP6 climate model NetCDF datasets as part of our project’s rainfall simulation and climate impact analysis work. Using Python (xarray, rioxarray, geopandas) on Ubuntu WSL2, I was able to: ✅ Automate the merging of 100+ NetCDF files ✅ Clip model rainfall data to our study site using shapefiles ✅ Deliver clean CSV outputs, ready for downstream analysis and visualization If you’re working on similar projects or have questions about automating climate and geospatial workflows, let’s connect and share insights! Screenshots below show the real workflow: 1️⃣ Terminal and code (workflow in action) 2️⃣ Inspecting NetCDF model metadata and rainfall variables 3️⃣ Visualizing global rainfall from IITM-ESM SSP245 scenario 🌦️ Next up: Turning global climate data into local action! I’ll be sharing my workflow for downscaling and bias-correcting CMIP6 rainfall datasets—stay tuned! 🔎 #Python #ClimateChange #CMIP6 #NetCDF #Geospatial #DataScience #GIS #OpenSource #WSL #xarray #rioxarray #geopandas

From Past Patterns to Future Warnings: My Climate ML Project Over the last few weeks, I built a Machine Learning–powered early warning system for monsoon temperature anomalies — turning decades of climate data into actionable insights. Here’s what I did: Pulled historical seasonal temperature data from a MySQL database Engineered climate-specific features: rolling trends, seasonal contrasts, lag variables Used Isolation Forest to detect past unusual years — no labels required Leveraged Prophet to forecast the next 10 years Flagged future anomaly risks to help plan for agriculture, water management & disaster readiness Visualized past vs. future anomalies for crystal-clear decision-making Why it matters: In India, a single unusual monsoon can disrupt food supply chains, impact millions of livelihoods, and cost billions in losses. This project transforms raw climate records into predictive intelligence that ministries, researchers, and communities can act on. Next step: Integrating rainfall & atmospheric pressure to build a multi-factor climate risk model — and deploying it as a real-time API for decision-makers. If you work in climate tech, agriculture, disaster management, or AI for social impact, I’d love to connect and exchange ideas. #MachineLearning #ClimateChange #AI #DataScience #Forecasting #Prophet #IsolationForest #AgricultureTech #SustainableDevelopment #EarlyWarningSystem #AI #MachineLearning #DeepLearning #DataScience #AIResearch #NeuralNetworks #AIAutomation #BigData #AIInnovation #AIEthics #AIRevolution