ERA5 data analysis for climate action

You don’t often get second chances in project development.   But here is a challenge: our wind farms are designed for a lifetime over 25 years. However, we typically only have a few years of wind measurement data… are those years representative? So, we blend real measurement data with modelled data from historical weather models.   At RWE, we wanted to better understand the reliability of the modelled data. Thanks to a digitalisation and automation initiative from our Smart Data Pipeline team, colleagues Sam Williams and Gibson Kersting led one of the most thorough benchmarks of modelled wind data in our industry.   We tested 9 datasets, including reanalysis, mesoscale and large-eddy simulation (LES), against 370+ wind measurements across 190+ sites in every major wind market. Each dataset was standardised, cleaned through our Smart Data Pipeline, and assessed using robust statistical metrics.   The results provided valuable insight:   ERA5, the most widely used reanalysis dataset, performed more reliably than often assumed, particularly offshore and in simple terrain. Mesoscale models offer added resolution and typically improve significantly on reanalysis, but accuracy varies by provider and setup. LES (as shown in the animation, the generated winds which capture the complex atmospheric phenomenon that govern the weather), demonstrates clear benefits in modelling large offshore clusters, complex onshore sites where small-scale atmospheric effects become decisive, and high‑quality turbulence estimates. However, for simpler sites, the added value is limited.   This wasn’t an academic exercise. It was about understanding the tools we depend on, knowing when a model is good enough and when it isn’t.   Modelled wind data is incredibly powerful, but like any tool, its value depends on how and where it’s applied. With this benchmarking, we’ve taken a major step toward using it with greater precision and confidence across our global portfolio.   In a data-driven industry, precision isn’t a luxury. It’s a competitive edge. And that edge depends not just on having more data but on understanding it deeply.

Reanalysis ≠ observations. ERA5 is incredibly useful, but it is not the same thing as what was measured at the weather station down the road. That distinction gets ignored all the time in climate risk work, which is how you can end up with missed exposures and false alarms in important decisions. Climate risk screening often asks: “𝗔𝗿𝗲 𝘄𝗲 𝗲𝘅𝗽��𝘀𝗲𝗱?” Adaptation and engineering ask: “𝗪𝗵𝗮𝘁 𝗱𝗼 𝘄𝗲 𝗯𝘂𝗶𝗹𝗱?” Different question. Different accuracy bar. New paper out from Degree Day: "Multivariate bias correction of ERA5 using in-situ observations for planning and engineering". We recalibrated ERA5 daily data from 1979–2025 against in-situ measurements from 16,000+ weather stations worldwide, covering temperature, humidity, surface pressure, and wind speed. The result: mean bias is virtually eliminated, RMSE drops at most sites, and the biggest improvements occur where raw ERA5 tends to struggle most. We also built a free web app where you can compare, station by station:  • local observations  • raw ERA5  • SCOPE-ERA5 (our dataset) You can explore four corrected variables, plus derived indicators like heat index, degree days, and days above user-defined thresholds. The biggest discrepancies tend to show up in nighttime temperatures and in tropical, coastal, and alpine regions. The app also shows how local climate indicators have changed across the full record. SCOPE-ERA5 is not perfect, and station calibration does not solve every site-specific data problem. But in many places, it is a major improvement over using raw ERA5 directly. If you build, insure, finance, or model anything that depends on day-to-day temperature, humidity, pressure, or wind speed, check how ERA5 actually performs near your assets. Stay tuned for more engineering-grade climate risk tools and research releases from Degree Day. 🔗 Paper: https://lnkd.in/gcNrDjrp 🔗 Web App: https://lnkd.in/g9isJ4CS Free. Open access. No login. No ads.

🌍 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

🗺️ Mapping Heatwave Frequency & Intensity with ERA5-Land in Google Earth Engine (GEE) I recently conducted an analysis using the ERA5-Land reanalysis dataset in Google Earth Engine to detect and visualize heatwave frequency and intensity across Tanzania. The workflow involved: ✔️ Preprocessing ERA5-Land temperature data (2000–2025) ✔️ Computing the 90th percentile (P90) baseline temperature ✔️ Identifying months exceeding P90 to quantify heatwave frequency ✔️ Calculating heatwave intensity (Σ exceedance °C·months) ✔️ Visualizing hotspots with custom palettes ✔️ Exporting results and running QA histograms for deeper insights 📍 Why this matters With global temperatures rising, heatwaves are becoming more frequent and intense. Mapping these events is essential for: 🌡️ Climate scientists – monitoring long-term climate patterns 🏙️ Urban planners – guiding adaptation strategies in heat-prone areas 🏥Public health experts – mitigating heat-related health risks 🌿 Environmental managers – protecting ecosystems under stress This type of analysis helps decision-makers design climate adaptation and risk management strategies, ensuring communities are better prepared for extreme temperature events.

🌍 Visualising Global Surface Air Temperatures: 2023–2025 Climate Pulse 🔥 I'm excited to share a Python adaptation of the Climate Pulse visualisation originally developed by Stjepan Dekanic, which analyses daily global temperature extremes using Copernicus ERA5 data. Key highlights: • Tracks global daily average temperatures over 3 years (2023–2025) • Compares recent data against a historical baseline (1991–2020) to reveal warming trends • Visualises frequency of temperature occurrences and deviations beyond normal variability • Utilises transparent, reproducible Python code with pandas & matplotlib for flexible analysis (see linked gist for details) This approach helps illustrate the increasing temperature anomalies relative to the past 30-year climate norm — essential for understanding ongoing climate change impacts. 🔗 Inspired by the original R project: Climate Pulse R Version 📊 Data sourced from ERA5 reanalysis via Copernicus Climate Change Service 📈  The code is modular, making it easy to adapt for regional or other climate data analyses (note: these results show global averages, not regional details). 🔍 Key Insight from Climate Pulse Analysis — First Half of 2025 Our temperature anomaly analysis for the first half of 2025 reveals a nuanced trend: ➡️ While 2025 was less warm compared to the same period in 2024, ➡️ It remains significantly warmer than any comparable period in the past 30 years globally. By quantifying the temperature deviation above historical variability (using polygon area analysis on ERA5 daily temperature data), we see that the “warmth intensity” for: • Jan–Jun 2023 — area: ~23.9 • Jan–Jun 2024 — area: ~73.9 • Jan–Jun 2025 — area: ~60.8 ➡️ This demonstrates that despite some short-term fluctuations, the overall warming trend persists well beyond natural variability. Visualising these periods side-by-side highlights how recent years consistently exceed the 30-year baseline upper bounds, underscoring the ongoing impacts of climate change (additional illustration in the comment). Feel free to reach out if you're interested in the code or collaboration on climate data projects! #ClimateChange #DataScience #Python #ClimatePulse #Copernicus #ERA5 #DataVisualization #EnvironmentalData #ClimateAction code: https://lnkd.in/edhbgmhN

This code uses Google Earth Engine to analyze temperature trends over Punjab, Pakistan, by utilizing ERA5 reanalysis data. It computes the monthly mean temperature and annual temperature anomalies (temperature change) from 1979 to 2023, helping to assess how the region’s temperature has evolved due to climate change. The data is visualized with a color palette to represent temperature variations, and a time series chart is generated to track temperature changes over the years. This analysis is useful for understanding the long-term climate impacts on water resources, agriculture, and public health in Punjab, enabling better climate adaptation strategies, resource planning, and policy decisions. It also provides insights into the region's vulnerability to extreme temperature events and their implications for resilience-building. // Load the boundary of Punjab, Pakistan from your uploaded shapefile var punjab = ee.FeatureCollection('projects/ee-shahidiqbal/assets/Punjab'); // Center the map on Punjab, Pakistan Map.centerObject(punjab, 6); // Load ERA5 temperature data for the past decades var era5 = ee.ImageCollection("ECMWF/ERA5/DAILY")  .filterDate('1979-01-01', '2023-12-31') // Adjust date range as needed  .filterBounds(punjab) // Filter for Punjab region  .select('mean_2m_air_temperature'); // Select 2-meter air temperature // Compute monthly mean temperature for the whole period var monthlyTemperature = era5.mean().clip(punjab); // Average temperature for all years // Compute the annual temperature anomalies (Temperature change) var annualTemperature = era5  .filter(ee.Filter.calendarRange(1, 12, 'month')) // Annual data  .map(function(image) {   var year = image.date().get('year');   return image.set('year', year);  })  .mean()  .clip(punjab); // Visualization parameters var visParams = {  min: 270, // Adjust based on data range  max: 310, // Adjust based on data range  palette: ['blue', 'green', 'yellow', 'red'], }; // Add layers to the map for visualization Map.addLayer(monthlyTemperature, visParams, 'Monthly Mean Temperature'); Map.addLayer(annualTemperature, visParams, 'Annual Mean Temperature'); // Define a time series chart for temperature change over time var chart = ui.Chart.image.seriesByRegion({  imageCollection: era5,  band: 'mean_2m_air_temperature',  regions: punjab,  reducer: ee.Reducer.mean(),  scale: 5000,  xProperty: 'system:time_start' }).setOptions({  title: 'Temperature Change Over Punjab (1979-2023)',  vAxis: {title: 'Temperature (K)'},  hAxis: {title: 'Year'},  lineWidth: 1,  pointSize: 3 }); print(chart);

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

🌐🌐Relative Humidity (RH) using ERA5-Land 🌐🌐 Source Code = https://lnkd.in/d75-aque 🌐 Introduction Monitoring atmospheric humidity is essential for understanding weather conditions, forest health, and ecosystem dynamics. Relative Humidity (RH) plays a vital role in influencing evapotranspiration, fire risk, disease spread, and overall environmental stress. This study utilizes ERA5-Land Hourly reanalysis data in Google Earth Engine (GEE) to generate temporal and spatial RH maps over Chandrapur Division (Central India) for April 2024. 🎯 Objective To compute and visualize Relative Humidity over time using ERA5 reanalysis data. To generate a time series chart showing RH trends. To map spatial variation of RH for the latest available date using a 10-class color palette. 📌 Importance Provides insights into microclimatic variations. Helps in identifying humidity stress zones, critical for forest and crop health. Supports planning in fire-prone regions where RH plays a role in fire ignition and spread. Aids climate resilience and early warning system development. ✅ Benefits Real-time and historical analysis using global, validated ERA5 data. Automated daily/hourly RH computation without the need for ground stations. Visual interpretation with clear 10-class color segmentation for RH intensity. Useful for forestry, agriculture, water resource management, and climate studies. 📤 Output Relative Humidity Time Series Chart (April 2024) X-axis: Dates (1–10 April 2024) Y-axis: Mean RH (%) over the AOI Output: Interactive GEE chart showing humidity trends RH Map (Last Available Date) RH (%) raster image over AOI Color-coded using 10 distinct bands (0–10%, 11–20%, … 91–100%) Output: Visual map layer of spatial RH variation #GEE #GoogleEarthEngine #BuildupAreaExpansion #GeospatialAnalytics #RemoteSensing #UrbanExpansion #Geospatial #GoogleEarthEngine #GIS #SustainableDevelopment #Sentinel2 #GeospatialTech #PhD #Agriculture #ClimateSmart #GIS #DeepLearning #ClimateSmartAgriculture #CropHealthMonitoring #DroughtMonitoring #SustainableFarming #Sentinel2 #GoogleEarthEngine #NDVI #LandsatData #GISMapping #GeospatialAnalysis #AIinAgriculture #EarthObservation #AgricultureMapping #RemoteSensin #SatelliteImagery