Critical climate datasets for scientific tracking

🌬️ Wind Droughts Are a Growing Climate Risk for the power sector ⚡ A new study reveals a critical challenge: prolonged low-wind events (“wind droughts”) are projected to become up to 15% longer by the end of the century across much of the Northern Hemisphere. These extended calm periods, already observed in Europe, the US, China, and beyond, could threaten energy security and push power prices to record highs—as seen during the 2024–25 winter in Germany. 🔍 This is where World Meteorological Organization’s data and climate services become indispensable: ✅ #ERA5 reanalysis—used in the study—maps long-term wind behavior and identifies historical drought patterns. ✅ #CMIP6 climate projections, developed under the WMO-coordinated system, help us understand how wind resources may shift in a warming world. ✅ Operational wind forecasts and seasonal outlooks from #WMO and its partners can help grid operators and developers prepare for variability and avoid overreliance on a single energy source. ⚡ As we scale wind energy (projected to reach 6,000 GW by 2050), planning for these climate-induced shifts is non-negotiable. 💡 Diversifying with solar, hydro, storage, and smarter interconnections is key—but it all starts with robust climate intelligence. 📊 WMO’s wind and climate datasets are more than technical tools—they’re our compass in the transition to resilient, reliable, and decarbonized energy systems, learn more here: https://lnkd.in/e-6g3USz Read the scientific article 👇 https://lnkd.in/eCW6CfMb

🌾New Dataset Out 🌾 🌾 How do we make crop monitoring truly climate-aware at scale? In many EO/ML pipelines, we can model #crop dynamics reasonably well — but linking them consistently with #weather variability, drought, and #climate extremes across large geographies is still difficult. A major reason is simple: 👉 the community still lacks large-scale, multimodal, ML-ready datasets that unify satellites + climate signals + agricultural outcomes. So my PhD student Adrian Höhl (Technical University of Munich) built one. 📢 Very excited to share our new #ScientificData paper introducing #CropClimateX: a large-scale, multi-task, multi-sensory dataset for climate-aware crop monitoring in the contiguous US (2018–2022). 🔍 What makes CropClimateX different? ✅ 15,500 “minicubes” (each 12×12 km) spanning 1,527 counties ✅ Multi-source EO inputs (including Sentinel-1/2, Landsat-8, MODIS) ✅ Climate + extremes context (e.g., Daymet, U.S. Drought Monitor, heat/cold wave indicators) ✅ Supporting multi-task learning targets such as crop yield and broader crop monitoring applications To keep the dataset representative yet scalable, we use an optimized sampling strategy (Sliding Grid + Genetic Algorithm), reducing redundancy while retaining broad cropland coverage. 🚀 Why we hope this helps CropClimateX is designed to support research on: 🌱 climate-aware crop modeling 🛰️ multi-sensor fusion & spatiotemporal learning 🌍 generalizable EO foundation models for agriculture If you’re working on crop monitoring, climate resilience, or geospatial ML, take a look at CropClimateX. 🔗 Link to paper: https://lnkd.in/d3W3mnFZ 🔗 Link to dataset: https://lnkd.in/dVp4s-Mh 🔗 Link to Github: https://lnkd.in/d8DvYkMD This is a collaboration with Stella Ofori-Ampofo, Miguel Ángel Fernández Torres (Universidad Carlos III de Madrid), and Rıdvan Salih (German Aerospace Center (DLR)). The project is funded by the Deutsche Raumfahrtagentur im DLR in the framework of #ML4Earth (project page: ml4earth.de) #RemoteSensing #EarthObservation #GeospatialAI #ClimateAI #AgTech #CropMonitoring #Datasets #MachineLearning International Future AI4EO Lab, TUM School of Engineering and Design (ED)

We know that land and plants help us absorb carbon during photosynthesis. We also know the amount and rate of this change varies across ecosystems and during the season. This leads to unpredictable carbon fluctuations in our climate models, and make it harder to adequately finance projects that support land restoration. 💡 That's why the new data released from Land & Carbon Lab and the Global Pasture Watch research consortium is so impactful. For the first time, farmers, land managers, policymakers and scientists can track land productivity worldwide at 30-meter resolution — with a consistent bi-monthly record spanning more than two decades (2000–2024). ➡️ Explore the data, methodology and applications in link below: This new Gross Primary Productivity (GPP) data set shows the most detailed global view yet of the rate at which plants take in carbon during photosynthesis. Validated with more than 500 flux towers (ping Miles Austin), the data capture fine-scale variation in ecosystem productivity that coarser products have missed - especially in underrepresented landscapes like grasslands and rangelands. 🌱 Why it matters: - Critical metric for understanding land carbon dynamics. - Sharper insights into land health, restoration potential and responses to climate change. - Assessment of seasonality and long-term trends in vegetation productivity. Hats off to World Resources Institute and Bezos Earth Fund with help of Google Meta and University of Maryland - supporting the Land & Carbon Lab - as well as the Global Pasture Watch research consortium and partners who made this possible: OpenGeoHub Foundation Lapig - Laboratório de Sensoriamento Remoto e Geoprocessamento CMCC Foundation - Centro Euro Mediterraneo sui Cambiamenti Climatici CIFOR-ICRAF Alliance of Bioversity International and CIAT. Sean DeWitt Craig Mills Liza LePage Nick Martin For my carbon friends - will this impact any of the Verra Gold Standard or ACR at Winrock International methodologies? Andrew Copenhaver Palak Sharma Dr. Ruth Dagan Heather McEwan Patricia McCall Brad Kahn Vasco van Roosmalen Kerry Constabile Owen Hewlett Sarah Leugers Alexia Kelly Tom Walker Cecil Alexander Díaz Alex Logan #EcosystemMonitoring #ClimateData #OpenData

🌍 Exciting news for climate researchers and enthusiasts! 🌦️ The ECMWF ERA5 reanalysis dataset is revolutionizing our understanding of global climate patterns. 🔄 ERA5, the fifth generation of ECMWF atmospheric reanalysis, combines cutting-edge model data with observations from around the world, creating a comprehensive and consistent dataset. It's a significant upgrade from its predecessor, ERA-Interim reanalysis. One of the remarkable offerings of ERA5 is the ERA5 DAILY dataset, which provides aggregated daily values for seven key climate parameters, including 2m air temperature, total precipitation, and wind components. Daily aggregates such as mean sea level pressure and surface pressure offer valuable insights into daily weather patterns. For researchers and data enthusiasts, ERA5 DAILY opens up avenues for exploring climate trends and understanding weather phenomena on a global scale. From tracking changes in precipitation patterns to studying wind dynamics, the possibilities are endless. 📊 And here's where the magic happens: utilizing tools like Google Earth Engine, we can harness the power of ERA5 data for localized analysis and visualization. Check out this code snippet using ERA5 DAILY to analyze precipitation patterns in the Ceará region of Brazil! See code bellow: // Defining the region of interest var gaul1 = ee.FeatureCollection("FAO/GAUL/2015/level1"); var brazilStates = gaul1.filter(ee.Filter.eq('ADM0_NAME', 'Brazil')); var roi = brazilStates.filter(ee.Filter.eq('ADM1_NAME', 'Ceara')); // Setting the study area Map.centerObject(roi); Map.addLayer(roi); // Setting the time interval var starting = '2010-01-01'; var ending = '2023-01-01'; // Applying unit conversion var eraPrec = ee.ImageCollection("ECMWF/ERA5_LAND/DAILY_AGGR") .filterDate(starting, ending) .filterBounds(roi); // Printing the collection print('Collection:', eraPrec); print('Number of images:', eraPrec.size()); // Function to convert m to mm and add property to the collection var Precipitation = function(img){ // Precipitation units are depth in meters: divide to get m / mm var bands = img.select('total_precipitation_sum').multiply(1000).clip(roi); return bands.rename('total_precipitation_sum') .set('date', img.date().format('YYYY-MM-dd')) .copyProperties(img,['system:time_start','system:time_end']); }; var eraPrecConverted = eraPrec.map(Precipitation); rest of the code down in the comments #ERA5 #ClimateData #ClimateResearch #DataScience #ECMWF #EarthObservation #ClimateChange #WeatherPatterns #GoogleEarthEngine #DataVisualization #Copernicus #ClimateAction #javascript #codetutorial #remotesensing

In 2022, I was part of a research team that took on a critical challenge: 𝐡𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐚𝐬𝐬𝐞𝐬𝐬 𝐫𝐚𝐢𝐧𝐟𝐚𝐥𝐥 𝐢𝐧 𝐫𝐞𝐠𝐢𝐨𝐧𝐬 𝐰𝐡𝐞𝐫𝐞 𝐫𝐚𝐢𝐧 𝐠𝐚𝐮𝐠𝐞𝐬 𝐚𝐫𝐞 𝐬𝐜𝐚𝐫𝐜𝐞? Our focus was Sudan, a country where climate-driven decisions are crucial, but data gaps make it difficult to plan and prepare. We turned to satellite and gridded rainfall datasets, tools that have become essential in modern hydroclimatic research. What we found was both promising and eye-opening. 📍 While most rainfall products showed a tendency to underestimate rainfall, especially on annual and monthly scales, two stood out: 𝐂𝐇𝐈𝐑𝐏𝐒 𝐚𝐧𝐝 𝐂𝐑𝐔 𝐝𝐚𝐭𝐚𝐬𝐞𝐭𝐬 consistently performed best, especially in the western and southern regions of Sudan. 🌧️ We discovered that summer rainfall (the main rainy season) is captured more accurately than annual totals, especially in mountainous areas. And when we explored deeper, we noticed a significant link between rainfall trends and the Atlantic Multidecadal Oscillation (AMO), with some regions showing correlations as high as 90%. This was more than just data and numbers; it was a reflection of how remote sensing, when used wisely, can support 𝐜𝐥𝐢𝐦𝐚𝐭𝐞 𝐫𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐜𝐞, 𝐟𝐨𝐨𝐝 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲, 𝐚𝐧𝐝 𝐫𝐢𝐬𝐤 𝐩𝐫𝐞𝐩𝐚𝐫𝐞𝐝𝐧𝐞𝐬𝐬 in the very places that need it most. 🔍 The study underscored the value of CHIRPS data for monitoring rainfall variability and extreme events, and why it should be a go-to resource for decision-makers in data-scarce environments. Being part of this work reminded me of the power of science and collaboration in driving evidence-based action. It’s research like this that fuels real-world solutions, and I’m proud to have contributed to it.  Richard Anyah Mohamed Abdallah Ahmed Alriah, PhD Aslak Grinsted Hans Hersbach Lorenz Ewers Victor Ongoma Nixon Mutai #ClimateResearch #RainfallData #RemoteSensing #Sudan #ClimateResilience #CHIRPS #Hydroclimatology #SatelliteData #ClimateScience #DataForDevelopment

Weather radar has been a cornerstone of operational meteorology for decades, mainly for detecting precipitation and issuing warnings for severe weather such as hail, floods, and tornadoes. But radar data are increasingly becoming valuable not only for real-time monitoring and nowcasting, but also for long-term climate research. A review article in the Bulletin of the American Meteorological Society (2019) explores how weather radar archives can be used for climatological studies of precipitation and convective storms. Radar networks now cover most densely populated regions of the world and generate enormous high-resolution datasets, often reaching terabytes of data per day. This spatial and temporal detail allows scientists to analyze the structure, intensity, and frequency of precipitation in ways that traditional rain-gauge networks cannot capture. Radar data make it possible to study phenomena such as convective storm modes, hail occurrence, and the spatial organization of heavy rainfall with kilometer-scale resolution. At the same time, the paper highlights important challenges. Radar instruments are often replaced or upgraded every 10–20 years, which can introduce inconsistencies in long time series. Without proper metadata, calibration, and quality control, these changes can lead to misleading conclusions when studying climate trends. The key message is clear: the radar observations we archive today will become an essential climate dataset for future generations. Preserving high-quality radar data – together with detailed metadata – is critical for understanding long-term changes in precipitation extremes and convective storms in a warming climate. Figure description: Global map of weather radar coverage shown in Robinson projection. The areas “illuminated” by individual radars were calculated using the open-source Python library wradlib, assuming a maximum radar range of 200 km regardless of bandwidth, polarization, or terrain effects. Most radar locations were retrieved from the WMO radar database, although the database does not include all operational radars. Additional locations were manually added for China, the Philippines, Vietnam, and Myanmar based on publicly available sources and national meteorological service information. Paper: https://lnkd.in/djxw6S-9 #Meteorology #WeatherRadar #ClimateScience #Precipitation #AtmosphericScience

Day 12 – #30DayMapChallenge 🗺 India: Monthly Precipitation Analysis Using GLDAS (2000–Present) Today’s map visualizes long-term monthly precipitation patterns using NASA’s GLDAS (Global Land Data Assimilation System) dataset. This time-enabled layer provides a continuous historical record of rainfall and snowfall (as snow-water equivalent) from March 2000 onward, offering valuable insights into India’s hydro-climatic variability. Powered by the Noah Land Surface Model, the data integrates satellite observations and ground-based measurements at 0.25° spatial resolution, aggregated into monthly means. Such datasets are critical for understanding: 🔹 Seasonal and annual rainfall variability 🔹 Drought-prone and high-intensity rainfall zones 🔹 Water resource planning and watershed management 🔹 Climate-driven changes in precipitation patterns This analysis strengthens my experience in hydro-climatic modeling, remote sensing, and large-scale environmental data interpretation—demonstrating the value of geospatial technologies in monitoring long-term water availability. Data Source: NASA GLDAS-1 (Noah Model), Esri Living Atlas #30DayMapChallenge #RemoteSensing #Hydrology #Precipitation #GLDAS #NASA #ClimateData #EnvironmentalAnalysis #GIS #SpatialDataScience

Climate TRACE is a free and open global database of the emissions from over 745 million locations covering every country in the world. Data is independently calculated using satellite data, remote sensing, and machine learning, and is released on a monthly basis with over 10 years of history. It is used by governments, investment funds, corporates, banks, insurers, even the United Nations to understand what the emissions are at any location, city, municipality, region, or country. The data breaks down into a range of different gases from greenhouse gases to particulate matter 2.5 (PM2.5), and gives information on warming potential as well as health risks. The Climate TRACE website presents three major tools, the global emissions map where you can find any major source of emissions; the plumes map where you can see a visualisation of where emitted PM2.5 pollution lands under different conditions and therefore who is most effected; and an emissions reduction strategiser that shows for any country, city, or even facility, what actions will reduce the most emissions. If you need more granular insights, you can download the entire global dataset for every sector and analyse it offline. There is no longer any excuse for inaction, no claims of ignorance, we can see where emissions come from and we know how to reduce them. There is nowhere to hide. Every boardroom, every investment committee, every underwriter, every government should be using Climate TRACE at every meeting to understand what actions to take and what effect those actions have had to course correct as needed. With the UK's own intelligence report on the impacts of climate change highlighting that there is a fair chance that Britain will not be able to feed its population under the expected conditions of 2°C warming, it is past time to act.