Leveraging Open Data

𝐎𝐩𝐞𝐧 𝐃𝐚𝐭𝐚 𝐂𝐥𝐢𝐦𝐚𝐭𝐞 𝐑𝐢𝐬𝐤 𝐀𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭 𝐓𝐨𝐨𝐥𝐬 – Deep Dive Last week, I shared a post on open data tools for climate risk assessment and their role in climate adaptation. Since it sparked some interest, here’s a follow-up: a closer look at some of the best tools out there. 🦍 UN Biodiversity Lab 🦍 Hosts an amazing 269 datasets on biodiversity, from habitat intactness and ecosystem resilience to socio-economic indicators. – Great extra: national biodiversity statistics for 193 countries. – One highlight (which is integrated into many tools): The „GLC_FCS30“ land-cover map with an incredible 30x30m resolution. ⛈️ WESR Climate ⛈️ I like the tool by the UN Environment Programme because it offers a great framework for analyzing climate change variables: “Drivers” and “Pressures” (what drives climate change), “States” (how it alters Earth's systems), “Impacts” (resulting societal risks) and even “Responses” (what do we do to mitigate them). 🏭 Global Infrastructure Risk Model and Resilience Index (GIRI) 🏭 A collection by the Coalition for Disaster Resilient Infrastructure of an incredible 113 up-to-date and granular datasets on climate risks to buildings and infrastructures. – Great extra: Country-level statistics on average annual losses by climate hazards and infrastructure category. 🏚️ GIS-ImmoRisk 🏚️ Not flashy, but the only tool I know that lets you export building-specific climate risk PDF reports. It even factors in asset details (size, roof shape, windows, …) to assess likely damages by climate hazards. (Covers only Germany.) ❗ Where can you find these and other open climate and nature risk tools? – Click "resources" on the UN Environment Programme's World Environment Situation Room’s website. – Have a look at the MapX tool examples by UNEP/GRID-Geneva. – See the partially free KanataQ tool list. (Thank you, Nawar!) – Check out the tools and resources list of the NOAA. (Thank you, Douglas!) ❗ I’d appreciate hearing your opinion on the tools in this post, which tools you'd recommend, and where to find more. Link to last week's post: https://lnkd.in/dv_GKW83

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

🌍 A new era of open data has arrived 🌍 On 1 October 2025, European Centre for Medium-Range Weather Forecasts - ECMWF made its entire Real-time Catalogue open to all, under a CC-BY-4.0 licence. This is one of the largest meteorological datasets in the world, now freely accessible for science, innovation and entrepreneurship. This moment feels very much like when Landsat data was opened years ago — a decision that unlocked billions in economic value, empowering entrepreneurs, local governments, and innovators to build solutions that no one had imagined at the time. Now, with open meteorological data: 🔹 Local businesses can create new weather-driven services — from agriculture optimisation and insurance models to logistics and retail planning. 🔹 Entrepreneurs and startups gain access to world-class data to train AI/ML models, develop predictive tools, and build new digital products without prohibitive licensing barriers. 🔹 Local governments can improve urban planning, resilience strategies, and climate adaptation measures by tapping into global-scale forecasts at local resolution. 🔹 Communities worldwide benefit from better preparedness, aligning with the UN’s Early Warnings for All initiative — protecting lives and livelihoods. Innovation often begins when barriers to data fall away. With ECMWF opening the gates, we can expect new industries, smarter decisions, and stronger climate resilience to emerge — just as we saw with the Landsat revolution. 💡 The question is: who will be the first to harness this opportunity and turn open forecasts into open futures? https://lnkd.in/e5SEt-dP #OpenData #ClimateResilience #Innovation #Entrepreneurship #WeatherData #ECMWF #AI #Geospatial

A breakthrough in climate data accessibility: meet CRA5 ERA5 is one of the most important global reanalysis datasets for weather and climate research — but in raw float32 form it reaches around 400 TB, which is a major barrier for storage, sharing and AI workflows. CRA5 tackles this by compressing ERA5 to just 0.85 TB using the neural-network framework Aeolus — a 470× reduction. Despite the extreme compression, the dataset preserves key climatological patterns, power spectra and extreme-weather structures, with a reported mean absolute temperature error of only 0.17 K across 37 vertical levels. Why it matters: CRA5 makes high-resolution atmospheric data far more portable and accessible, lowering infrastructure barriers for researchers, smaller teams and AI-based weather forecasting. Code, pretrained models and dataset links are available on GitHub. This could be a real game-changer for climate and weather research. https://lnkd.in/dB4NedpS

Want to work with real satellite data-for free? Here's how. Remote sensing is more accessible than ever. Whether you're a student just starting out, a researcher tackling climate questions, or part of an NGO addressing environmental challenges, free satellite data is a goldmine of information waiting to be explored. But many people ask: Where do I get this data? How do I actually use it? That’s why I created this visual guide-to walk you through both: How to access satellite data from trusted platforms like: • USGS EarthExplorer (for Landsat) - https://lnkd.in/efDVuVa2 • Copernicus Open Access Hub (for Sentinel data) - https://lnkd.in/eBhwDG2D • NASA's LAADS DAAC, Earthdata Search, and more - https://lnkd.in/e9ZqjjJi How to use the data once you have it: • Preprocessing in SNAP or QGIS or ENVI • Time series and analysis with Google Earth Engine • Visualization and decision-making using geospatial tools Whether you're monitoring vegetation change, mapping urban growth, or analyzing water quality, these open datasets empower you to explore the Earth at scale-with nothing more than an internet connection and the right tools. Let’s democratize geospatial science, one open dataset at a time. Have a favorite open platform or tool for working with satellite data? Drop it in the comments. #RemoteSensing #EarthObservation #GIS #OpenData #SatelliteImagery #Copernicus #Landsat #ClimateTech #EnvironmentalMonitoring #Geospatial #GoogleEarthEngine #QGIS #StudentsInSTEM #DataForGood

I am delighted to announce the launch of Data For India's Climate vertical, with our first piece, on temperature trends in India, linked in the comments. At Data For India, three of the things we are committed to are: - the use of raw public data (and preferably not opaque reports or models built on top of that data), - starting with the absolute foundational stuff, even if it seems obvious, and - explaining how things are defined, caluclated, measured. This piece rests on all three of these pillars. Using IMD data, my colleague Juhi Chatterjee plots long-term historical and current-day data on temperatures in India to show precisely what has been happening with warming in India. This is foundational - even basic - work, but it doesn't exist in the public sphere, and is essential to do and say more. It's also work that we hope will be useful for others to build on as well. I hear a lot from the data-using community that they'd like more data on climate, but you've got to start right from the bottom, and we've now made that start. Everything else builds up from these basics. On measurement, the piece explains precisely which baseline is being used to capture deviations from the "norm", how it is calculated, and how the choice of baseline affects findings on warming. In fact, you can interact with the charts to see how temperature deviations change depending on the choice of baseline. This is Piece #1 of our climate vertical, but using these same principles, we look forward to sharing work on temperature and precipitation, emissions, the energy transition, agriculture and food security in this vertical. Do read, share and let us know what you think. We'd be happy to hear from others in the climate/ climate data-using space if they find this useful.

🌧️ 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

>380,000 data points on climate policy data. This is the culmination of >2 years work with a great team on the OECD - OCDE Climate Actions and Policies Measurement Framework (CAPMF). Still cannot believe that the CAPMF data is finally publicly available.   All >380,000 data points are publicly available. Ready to be explored by you and your colleagues to analyse 👉 climate policy trends 👉 which policies worked and which did not 👉 differences in climate policy approaches across countries and across time 👉 any climate policy-related question that you may have      Link to the database: https://oe.cd/dx/capmf Please like, comment, share, and - above all - USE!   #climatepolicy, #mitigation, #climatedata, #climatechange, #sustainability

🚀 25 years in the making—#OpenCEDA is live! When I released the very first version of the Comprehensive Environmental Data Archive (CEDA) back in 2000 as a PhD student at Leiden, I imagined a world where rigorous, transparent Scope 3 data would be available to anyone tackling climate change. Today that vision becomes reality. CEDA is now free and open to the public at openceda.org—unlocking >95 % of global GDP/GHG coverage, 400 industry sectors across 148 countries and regions, and tens of thousands of up-to-date emissions factors, refreshed annually. This milestone is the work of an incredible community. Deep gratitude to Mo Li, Ph.D., Cheng Lin, Yohanna Maldonado, Michael Steffen, Jake Feintzeig, Jonathan Gidden, Gizem Ilayda Dinç Liston Witherill, Christian Anderson—and every researcher, practitioner, and customer who has shaped CEDA since its 2000 debut. Whether you’re a start-up calculating your footprint, a Fortune 500 driving supply-chain decarbonization, or a researcher pushing LCA boundaries—this data is yours. Dive in, build, question, and tag me with what you create. Let’s accelerate climate action together! #Scope3 #LCA #GHGAccounting #OpenData #Sustainability #ClimateTech

Mapping a Decade of Rainfall Trends in Bangladesh with Python and Google Earth Engine. Climate data becomes far more meaningful when transformed into spatial insights. In this recent project, I explored annual precipitation patterns and precipitation-change trends across Bangladesh using NASA GPM IMERG satellite rainfall datasets. The analysis included: 1. Annual precipitation maps (2015–2025) 2. Multi-year precipitation change analysis 3. Spatial anomaly visualization 4. Comparative yearly rainfall assessment The workflow combined cloud-based geospatial computing and scientific Python tools: Google Earth Engine, Xarray, Xee, Numpy, GeoPandas, Matplotlib and Raster-based spatial analytics Dataset used: NASA GPM IMERG Monthly V07 Key findings from the visualization: The northeastern and southeastern regions continue to experience comparatively higher precipitation intensity. Some regions demonstrate persistent negative rainfall anomalies over multiple years and spatial precipitation change patterns may have implications for flood risk, agriculture, groundwater recharge, and climate adaptation planning. As climate variability increases, satellite-based environmental monitoring can play an important role in improving regional planning and climate resilience strategies. One of the most exciting parts of this work was integrating Earth Engine datasets directly into Xarray for scalable climate analysis workflows in Python. This project was a great opportunity to combine: a. Climate resilience research b. Water-resource management c. Disaster-risk assessment d. Environmental monitoring e. Seasonal trend analysis f. Extreme rainfall detection g. Flood susceptibility assessment h. Machine learning-based climate prediction #GIS #ClimateData #RemoteSensing #Python #GoogleEarthEngine #Hydrology #GeoAI #ClimateScience #Bangladesh #EarthObservation #SpatialAnalysis #DataVisualization