Efficient climate data processing

🌍 Climate scientists often face a trade-off: Global Climate Models (GCMs) are essential for long-term climate projections — but they operate at coarse spatial resolution, making them too crude for regional or local decision-making. To get fine-scale data, researchers use Regional Climate Models (RCMs). These add crucial spatial detail, but come at a very high computational cost, often requiring supercomputers to run for months. ➡️ A new paper introduces EnScale — a machine learning framework that offers an efficient and accurate alternative to running full RCM simulations. Instead of solving the complex physics from scratch, EnScale "learns" the relationship between GCMs and RCMs by training on existing paired datasets. It then generates high-resolution, realistic, and diverse regional climate fields directly from GCM inputs. What makes EnScale stand out? ✅ It uses a generative ML model trained with a statistically principled loss (energy score), enabling probabilistic outputs that reflect natural variability and uncertainty ✅ It is multivariate – it learns to generate temperature, precipitation, radiation, and wind jointly, preserving spatial and cross-variable coherence ✅ It is computationally lightweight – training and inference are up to 10–20× faster than state-of-the-art generative approaches ✅ It includes an extension (EnScale-t) for generating temporally consistent time series – a must for studying events like heatwaves or prolonged droughts This approach opens the door to faster, more flexible generation of regional climate scenarios, essential for risk assessment, infrastructure planning, and climate adaptation — especially where computational resources are limited. 📄 Read the full paper: EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules ---> https://lnkd.in/dQr5rmWU (code: https://lnkd.in/dQk_Jv8g) 👏 Congrats to the authors — a strong step forward for ML-based climate modeling! #climateAI #downscaling #generativeAI #machinelearning #climatescience #EnScale #RCM #GCM #ETHZurich #climatescenarios

🌀 Automated Wind Data Extraction & Visualization Over India Using Python🐍 🔍 How can we automate extracting and visualizing regional wind patterns from complex climate datasets using open-source Python tools? In this project, I automated the entire workflow to extract, clip, analyze, and animate 10m wind components (u10 & v10) over India using ERA5 NetCDF data and India’s administrative boundary shapefile. 💡 Workflow Highlights: ✅ Loaded ERA5 10m wind component data (#u10, #v10) from NetCDF ✅ Clipped wind data precisely to India’s shapefile boundary (#geopandas, #shapely) ✅ Computed wind speed and direction fields (#numpy) ✅ Created static wind speed maps and vector field plots (#matplotlib, #cartopy) ✅ Generated high-quality PNG outputs for reports and presentations ✅ 🎞️ Created animated wind streamline maps in GIF format for temporal analysis (#matplotlibanimation, #imageio) ✅ Easily customizable for other regions or datasets 📍 Why automate wind data extraction & visualization? Handling large climate datasets manually can be overwhelming. This automated workflow enables seamless processing, visualization, and animation with reproducible and customizable Python code — perfect for #climateanalysts, #GISprofessionals, #researchers, and #students. 📌 Tools & Libraries Used: 🔹 #xarray, #netCDF4 – NetCDF data handling 🔹 #geopandas, #shapely – Shapefile operations and clipping 🔹 #matplotlib, #cartopy – Geospatial plotting and mapping 🔹 #numpy – Data processing 🔹 #matplotlibanimation, #imageio – Creating animations 📂 Outputs: 🖼️ Static wind speed and vector field PNG maps 🎞️ Animated wind streamline GIF showing temporal changes 📍 Precise clipping of wind data to India boundary 🔗 Want to explore more? 📂 GitHub Repo: [https://lnkd.in/dMW3_HpS] 🌬️ #Meteorology | 🌍 #ClimateResearch | 🌪️ #DisasterPreparedness | 🎓 #AcademicResearch | 🛰️ #EnvironmentalModeling For more insights, feel free to connect with me: 🔹 GitHub: [https://github.com/Roysubh] 🔹 ResearchGate: [https://lnkd.in/dzPz52m3] 🔹 Google Scholar: [https://lnkd.in/dZUUJN-4] 🔹 ORCiD: [https://lnkd.in/gRXKaz74] #Python #DataScience #OpenSource #Geospatial #GIS #WindAnalysis #ERA5 #NetCDF #ClimateData #Visualization #Animation #India #EnvironmentalScience #RemoteSensing #BigData #DataVisualization #ClimateChange #Sustainability #Weather #Atmosphere #DataProcessing #ScientificComputing #TechForGood #Research #EarthScience #SpatialAnalysis #Mapping #GIScience #Programming #TechInnovation

𝗔𝗜 𝗳𝗼𝗿 𝗚𝗢𝗢𝗗: 𝗡𝗔𝗦𝗔 𝗮𝗻𝗱 𝗜𝗕𝗠 𝗹𝗮𝘂𝗻𝗰𝗵 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗔𝗜 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝘄𝗲𝗮𝘁𝗵𝗲𝗿 𝗮𝗻𝗱 𝗰𝗹𝗶𝗺𝗮𝘁𝗲 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴! 🌍 (𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝗴𝗲𝘁 𝗺𝗼𝗿𝗲 𝘀𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁 𝗽𝗹𝗲𝗮𝘀𝗲 𝗮𝗻𝗱 𝗡𝗢𝗧 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗪𝗿𝗮𝗽𝗽𝗲𝗿!) In collaboration with NASA, IBM just launched Prithvi WxC an open-source, general-purpose AI model for weather and climate-related applications. And the truly remarkable part is that this model can run on a desktop computer. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗸𝗻𝗼𝘄: ⬇️ → The Prithvi WxC model (2.3-billion parameter) can create six-hour-ahead forecasts as a “zero-shot” skill – meaning it requires no tuning and runs on readily available data. → This AI model is designed to be customized for a variety of weather applications, from predicting local rainfall to tracking hurricanes or improving global climate simulations. → The model was trained using 40 years of NASA’s MERRA-2 data and can now be quickly tuned for specific use cases. And unlike traditional climate models that require massive supercomputers, this one operates on a desktop. Uniqueness lies in the ability to generalize from a small, high-quality sample of weather data to entire global forecasts. → This AI-powered model outperforms traditional numerical weather prediction methods in both accuracy and speed, producing global forecasts up to 10 days in advance within minutes instead of hours. → This model has immense potential for various applications, from downscaling high-resolution climate data to improving hurricane forecasts and capturing gravity waves. It could also help estimate the extent of past floods, forecast hurricanes, and infer the intensity of past wildfires from burn scars. It will be exciting to see what downstream apps, use cases, and potential applications emerge. What’s clear is that this AI foundation model joins a growing family of open-source tools designed to make NASA’s vast collection of satellite, geospatial, and Earth observational data faster and easier to analyze. With decades of observations, NASA holds a wealth of data, but its accessibility has been limited — until recently. This model is a big step toward democratizing data and making it more accessible to all. 𝗔𝗻𝗱 𝘁𝗵𝘀 𝗶𝘀 𝘆𝗲𝘁 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝗽𝗿𝗼𝗼𝗳 𝘁𝗵𝗮𝘁 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 𝗶𝘀 𝗼𝗽𝗲𝗻, 𝗱𝗲𝗰𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱, 𝗮𝗻𝗱 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗮𝘁 𝘁𝗵𝗲 𝗲𝗱𝗴𝗲. 🌍 🔗 Resources: Download the models from the Hugging Face repository: https://lnkd.in/gp2zmkSq Blog post: https://ibm.co/3TDul9a Research paper: https://ibm.co/3TAILXG #AI #ClimateScience #WeatherForecasting #OpenSource #NASA #IBMResearch

✨ 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

🔍 GeoAI: Elevating Rainfall Time-Series Analysis with Zarr-Based Geospatial Stacking in Python 🌧️🌍 As an Advanced Geospatial Data Scientist, I’m excited to share my recent work on GEOAI-Driven Rainfall Time-Series Extraction, where Python meets geospatial intelligence to streamline high-resolution rainfall data extraction and stacking — with Zarr format at the core. ✅ Why Zarr over traditional GeoTIFF? Zarr files enable efficient, chunked, compressed, and scalable storage of geospatial arrays. Compared to static .tif files, Zarr formats are 🌐 Cloud-native and web-compatible ♻️ Reusable and reproducible without re-running extraction scripts ⚙️ Ideal for automation pipelines and large-scale batch processing 💡 Adaptable to real-time and on-demand analytics scenarios 💻 This work demonstrates a seamless GIS-Python workflow, including: Extracting centroid from DEMs (GeoTIFF and Zarr) Aligning and masking IMD’s 30-year gridded rainfall datasets Generating time-series rainfall data specific to catchments or watersheds Saving the output as a clean Zarr file, ready for advanced modeling 🚀 The code is publicly available here: 🔗 https://lnkd.in/g8TXYwAq 📄 PDF with annotated Python snippets included in the post. I sincerely thank Ashis Kumar Saha for his guidance during the initial brainstorming phase of this work. This project is a step toward more scalable, automated, and cloud-adaptable hydrological modeling pipelines, enabling advanced data-driven insights for climate and terrain interactions. Last but not the least, I am thankful to be surrounded by critical thinkers around me like Gopal Chowdhury Nilanjan Bal Arup Baidya Arkaprovo Das and ADITI ROY, who have not only guided me with technical and research problems but also helped in motivating and helping each other with fruitful solutions. 🔖 #GEOAI #PythonForGIS #Zarr #Hydrology #GeospatialAI #DataScience #ClimateTech #RasterProcessing #OpenSourceGIS #ScalableAnalytics #RemoteSensing #RainfallModeling #MachineLearning #GeoPython #GitHubScience