益精是什么意思

Question

I want to download the biweekly means of certain periods May-Sep of ERA5 data within a certain roi. (Mainly just Air/Soil Temp and Precip. The code I provided I just filtered for 2 weeks then applied .mean() in the visualization just to see how it looks. My first question is: if I just apply .mean() will it take a mean of all the days in the date I provided. My second question: should I use the hourly and take the mean? Or would the daily aggregate work just as well? Any advice would be helpful.

var roi = ee.FeatureCollection("projects/daniel-schartonproject/assets/roi")

var dataset = ee.ImageCollection('ECMWF/ERA5_LAND/DAILY_AGGR').filterDate("2025-08-04","2025-08-04");

var visualization = {
  bands: ['temperature_2m'],
  min: 250,
  max: 320,
  palette: [
    '000080', '0000d9', '4000ff', '8000ff', '0080ff', '00ffff',
    '00ff80', '80ff00', 'daff00', 'ffff00', 'fff500', 'ffda00',
    'ffb000', 'ffa400', 'ff4f00', 'ff2500', 'ff0a00', 'ff00ff',
  ]
};
Map.setCenter(70, 45, 3);
Map.addLayer(
    dataset.mean(), visualization, 'Air temperature (K) at 2m height', true, 0.8);

Answer 1

Since mean calculates the mean of all the observations in the collections (as mentioned in the comments sections), you need to create a loop to calculate the mean value in biweekly windows. Thus, you need to create a list containing the number of biweekly windows from your startDate to endDate and calculate the mean for each one. Here is the code that does precisely that.

// Set start and end Dates
var startDate = "2025-08-04"
var endDate = "2025-08-04"

var dataset = ee.ImageCollection('ECMWF/ERA5_LAND/DAILY_AGGR')
                .filterDate(startDate,endDate);

// Get difference in weeks from start to end dates
var difWeeks = ee.Date.parse("Y-M-d",endDate).difference(
ee.Date.parse("Y-M-d",startDate), "week").round();

// Create sequence of weeks in steps of 2 weeks
var weeks = ee.List.sequence(0, difWeeks, 2);

// Create loop base on the list of weeks
var biweekMean = weeks.map(function(week){
  // Create start date based on the startDate and advance 
  // the number of weeks in the current cycle
  var start = ee.Date.parse("Y-M-d", startDate)
                     .advance(week, "week");
  // Advance 2 weeks from start date                   
  var end = start.advance(2, "week");
  
  // Filter Image Collection based on start and end
  // Calculate mean value and set a time property
  var resul = dataset.filterDate(start, end)
                     .mean()
                     .set('system:time_start',
                          start);
  return resul;
});

// Collection with biweekly composites
biweekMean = ee.ImageCollection.fromImages(biweekMean);

print('biweekMean', biweekMean);

// Visualize
var visualization = {
  bands: ['temperature_2m'],
  min: 250,
  max: 320,
  palette: [
    '000080', '0000d9', '4000ff', '8000ff', '0080ff', '00ffff',
    '00ff80', '80ff00', 'daff00', 'ffff00', 'fff500', 'ffda00',
    'ffb000', 'ffa400', 'ff4f00', 'ff2500', 'ff0a00', 'ff00ff',
  ]
};
Map.setCenter(70, 45, 3);
Map.addLayer(biweekMean.first(), visualization, 'Air temperature (K) at 2m height', true, 0.8);