ERA5 data analysis for climate action

🌍 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.

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);

🔍 Detecting Heatwaves in ERA5 Data using GEE and Google Colab🔥  With climate extremes on the rise, understanding and detecting severe heatwaves is more important than ever. Using ERA5 reanalysis data from Google Earth Engine + Python in Google Colab, I built a workflow to: ✅ Extract daily max temperature (1979–2023) ✅ Identify severe heatwaves (≥45°C) lasting ≥5, 7, and 10 days ✅ Visualize these events over time with clear shaded spans ✅ Export results and plots for further reporting and analysis 📊 The result? A scalable, transparent way to assess how extreme heat has changed over decades — using fully open-source tools and free data. 🌍 This workflow can be applied to any location globally — for research, early warning, or resilience planning. 🛠️ Tools used: Google Earth Engine (ERA5) Python (Pandas, Seaborn, Matplotlib) Google Colab CSV Export for analysis Youtube video link: https://lnkd.in/gXgQu3s3 #ClimateData #ERA5 #GoogleEarthEngine #Python #Heatwaves #ClimateChange #DataScience #Geospatial #OpenData

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