I am trying to export hourly wind speed data from ERA5 hourly dataset via GEE. I got a CSV file with system index and windspeed in separate columns. However, I think the values are incorrect, since they are very small considering there was a cyclone during this time in my AOI. This are my wind speed values so less!!!

enter image description here

This is the code I am using:

var ERA5 = ee.ImageCollection("ECMWF/ERA5_LAND/HOURLY")
  .filter(ee.Filter.date('2017-09-01', '2017-10-01'));

var ERA5DOM = ERA5.map(function(im){ 
  return im.clip(AOI);

var ERA5windspeed = ERA5DOM.map(function(image){
  var wind_10m = image.expression(
    'sqrt(u**2 + v**2)', {
      'u': image.select('u_component_of_wind_10m'),
      'v': image.select('v_component_of_wind_10m')
  var time = image.get('system:time_start');
  return wind_10m.set('system:time_start', time) } );

// Function to convert ImageCollection to FeatureCollection
var convertToFeatureCollection = function(image){
  return ee.Feature(null, {'windspeed': image.select('windspeed').reduceRegion(ee.Reducer.mean(), AOI).get('windspeed')});

// Convert ImageCollection to FeatureCollection
var ERA5windspeedFeatures = ERA5windspeed.map(convertToFeatureCollection);

// Export FeatureCollection to Drive
  collection: ERA5windspeedFeatures,
  description: 'DOM_mean_wind',
  fileFormat: 'CSV',
  selectors: ['system:index', 'windspeed']

What is the right formulae used in this code? Or if it is some other issue, can you guide me with that too?

1 Answer 1


Your formula and code look correct to me. There are a few factors that might make ERA5 wind speed lower than expected.

First, each ERA5 pixel represents average conditions over one hour over 121 km2. It looks like you're further averaging wind over your AOI. Those aggregations dramatically smooth out gusts. There's a disclaimer in the GEE data catalog specifically related to this:

Care should be taken when comparing this variable with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.

Second, underestimated wind speeds in ERA5 are well-recorded. For example:

  • Dulac et al., 2023

    TC [tropical cyclone] intensity is still strongly underestimated in ERA5...

  • Campos et al., 2022

    large RMSE and severe underestimation are found in tropical latitudes... The most extreme winds in tropical cyclones show the worst results...

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