Leveraging Open Data

🌧️ CHIRPS Rainfall Downscaling & Seasonal Precipitation Mapping (Oct 2023 – Jan 2024) Source Code = https://lnkd.in/dMFgrp3K 📡 Google Earth Engine–based rainfall analysis over an Indian region of Haryana. 🚀 Completed a seasonal precipitation analysis using CHIRPS daily rainfall data, including spatial downscaling and visualization, to better understand rainfall distribution patterns during the post-monsoon to winter period (Oct 2023 – Jan 2024). 🎯 THE CHALLENGE 🌧️ CHIRPS rainfall is natively coarse in spatial resolution 🗺️ High-resolution rainfall is required for regional hydrology & agro-climatic studies 📉 Visualizing spatial rainfall gradients clearly for interpretation 🗺️ MY APPROACH ✅ Defined AOI using India administrative boundary (OBJECTID = 5) ✅ CHIRPS Daily precipitation → filtered for 3-month seasonal period ✅ Aggregated daily rainfall into total seasonal precipitation ✅ Used Sentinel-2 surface reflectance as a spatial reference ✅ Downscaled CHIRPS rainfall using bilinear resampling ✅ Reprojected rainfall to 100 m spatial resolution ✅ Visualized both original and downscaled rainfall layers ✅ Added an intuitive rainfall legend (Very Low → Very High) 🚀 KEY INSIGHTS 📍 Clear spatial variation in cumulative rainfall across the study region 📊 Downscaled rainfall preserves spatial patterns while improving visual clarity 🌧️ High-rainfall and low-rainfall pockets become more distinguishable at finer scale 💡 WHY IT MATTERS 🌾 Supports district-level and local agro-climatic assessments 💧 Useful for hydrology, drought monitoring, and water resource planning 📈 Enhances rainfall interpretation for decision-support systems 🛰️ Demonstrates practical downscaling of climate datasets in GEE 💻 TECH STACK 🌐 Google Earth Engine – Data processing & visualization 🌧️ CHIRPS Daily – Precipitation dataset 🛰️ Sentinel-2 SR – Spatial projection reference 🧮 Bilinear resampling & reprojection techniques 📂 OUTPUTS 🌧️ Total seasonal precipitation map (Oct 2023 – Jan 2024) 🗺️ Downscaled rainfall layer at 100 m resolution 🎨 Rainfall classification map (Very Low → Very High) 📌 Interactive map legend for clear interpretation #GoogleEarthEngine #GEE #RemoteSensing #RainfallAnalysis #CHIRPS #Precipitation #ClimateAnalysis #Hydrology #MonsoonStudy #WeatherData #Geospatial #GIS #EarthObservation #SatelliteData #SpatialAnalysis #Downscaling #ClimateMonitoring #WaterResources #EnvironmentalSciencen #GeoData #RasterAnalysis #TimeSeriesAnalysis #IndiaGIS #ClimateChange #Hydroclimatology #Geoinformatics #MapVisualization #DataScience #OpenSourceGIS #Sentinel2 #RainfallMapping #DroughtMonitoring #FloodAnalysis #ClimateResearch #SpatialData #GISDeveloper #GeoAnalytics #MapLegend #EarthEngineCode

The Python Climate Stack Just Got a Major Upgrade! Gone are the days of wrestling with NetCDF files and manual regridding. The 2024 Python ecosystem for climate and weather science has evolved into something truly powerful. Here are 5 modern packages changing how we work with Earth system data: 🔹 xgcm - The grid-aware analysis powerhouse for next-gen ocean/climate models (MOM6, MPAS). Finally, sensible operations on staggered grids without the headache. 🔹 climetlab - ECMWF's official Python package that makes accessing ERA5, seasonal forecasts, and C3S data as easy as a single function call. ML-ready formats included! 🔹 Intake-ESM - Your intelligent interface to the entire CMIP6/ESGF universe. Query petabytes of climate model output like a database, then lazy-load only what you need. 🔹 SciKit-GStat - Modern geostatistics meets climate science. Machine-learning enhanced kriging, anisotropy detection, and spatio-temporal analysis in one clean API. 🔹 PyQGIS in Jupyter - The ultimate GIS-climate bridge. Run professional geospatial operations directly on your climate data without leaving your Python workflow. The paradigm shift: We've moved from "how do I open this data?" to "what climate question can I answer today?" These tools are abstracting away the data plumbing so scientists can focus on science. Whether you're downscaling regional projections, analyzing multi-model ensembles, or building climate services - the modern Python stack has you covered. #ClimateScience #Python #DataScience #Geospatial #WeatherTech #ClimateTech #OpenScience #MachineLearning #EarthObservation #Pangeo

Good morning Atmospheric Scientists and Meteorologists around the world, Today I’m sharing a Python feature I’ve started leaning on heavily in my WRF post-processing: concurrent.futures.ProcessPoolExecutor. If you’ve ever waited far too long for CAPE, lifted indices, WBGT, or other diagnostics to finish, you already know the pain point: WRF can run on dozens of cores, but our post-processing scripts often run on just one. ProcessPoolExecutor lets us push those calculations back onto multiple CPU cores by running functions and file reads in parallel processes, rather than in a single serial loop. For WRF users, this means you can: Process multiple wrfout files at the same time (e.g., whole forecast cycles in parallel). Spread expensive diagnostics (parcel profiles, radiation balances, etc.) across several workers. Keep your existing scientific logic, while only restructuring how the work is dispatched. Used carefully—with a fixed number of workers (for example, max_workers=4) and per-file worker functions—it can dramatically reduce wall-clock time without sacrificing readability or accuracy. Below is an article I wrote that walks through the concepts, common patterns (files, time steps, grid chunks), and practical pitfalls when applying ProcessPoolExecutor to WRF workflows. Thanks and happy coding.

🌍 Improving Climate Model Accuracy Using Machine Learning: A Multi-Model Ensemble Approach 📢 Just wrapped up an exciting project where I used Bayesian Optimization + XGBoost to compute a Multi-Model Ensemble (MME) of Global Climate Models (GCMs). 🧠 The Goal: Climate models vary widely. Instead of relying on a single GCM, I combined outputs from multiple models—CESM2-WACCM, INM-CM4-8, and EC-Earth3—to better match observed record. 🔧 The Process: ✅ Data Preprocessing ✔ Cleaned + normalized GCM & observed data ✔ Filled missing values and ensured time-consistent splits ✅ Bayesian Optimization Used scikit-optimize to find optimal hyperparameters for an XGBoost model, accelerating convergence with smart probabilistic search. ✅ Grid Search Refinement Fine-tuned the best Bayesian result using a local Grid Search for extra precision. ✅ Evaluation 📊 Metrics: R², RMSE, and NSE 📈 Visuals: Time series comparison + residual analysis 🔍 Why It Matters: MMEs are crucial for reducing uncertainty in climate predictions. By integrating machine learning with GCM outputs, we can boost reliability for real-world decision-making—from water resource management to climate adaptation strategies. 🚀 Youtube video link🌱 https://lnkd.in/d5k3wFrx #ClimateChange #MachineLearning #XGBoost #BayesianOptimization #GCM #EnvironmentalScience #AI4Climate #Hydrology #DataScience #ClimateModeling #Python #TimeSeries #MME

When it comes to climate data science, the good news is there’s massive amounts of data. The bad news? There’s massive amounts of data. Downloading decades of climate data can mean gigabytes (or terabytes), plus serious compute power just to do simple analysis. But what if you didn’t need to download anything at all? Yesterday, after smelling wildfire smoke here in Gainesville (Florida), I got curious about when dry conditions are more common in Florida. So instead of the usual workflow, I tried something different: What I did: → Queried 25 years of ERA5 soil moisture data → Processed it directly on Google Cloud → Ran the analysis in a Colab notebook → Generated plots for Florida 𝟭𝟬𝟬% 𝗰𝗹𝗼𝘂𝗱-𝗯𝗮𝘀𝗲𝗱, 𝘇𝗲𝗿𝗼 𝗹𝗼𝗰𝗮𝗹 𝗱𝗮𝘁𝗮 No local storage. No heavy computing. Just cloud-native analysis. You can see the results in the plots below showing the seasonal soil moisture patterns, and monthly spatial variation across Florida. The results suggest that April and May are among the driest months in Florida. This aligns with recent reports, where Rick Dolan, director of the Florida Forest Service, noted: “The peak fire season is not here yet. That’s usually toward the end of May.” [1] I’m sharing the full notebook below if you want to try it yourself or build on it. Notebook: https://lnkd.in/eBCfvTHs ♻️ Repost this, someone in your network working on climate data will thank you! P.S. [1] https://lnkd.in/e3r9HqZi

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