Google Earth Engine Tutorial: Detect Monthly Precipitation Anomalies

Google Earth Engine Tutorial: Detect Monthly Precipitation Anomalies

Google Earth Engine (GEE) is a powerful platform for analyzing geospatial data with a variety of remote sensing datasets. In this tutorial, we’ll walk through how to detect monthly precipitation anomalies using GEE. Precipitation anomalies refer to deviations from the average, which can be used to identify droughts, floods, or unusual weather patterns. This example uses the Global Precipitation Measurement (GPM) dataset.

Data Sources

GEE provides access to datasets such as GOOGLE/CHIRPS/DAILY or NOAA/GFS for precipitation. For this example, we’ll use GOOGLE/GPM/IMERG (IMERG Final Run, 30-minute 0.1-degree precipitation data).

Steps to Detect Monthly Precipitation Anomalies

1. Import the Dataset:

var precipitation = ee.ImageCollection("GOOGLE/GPM/IMERG")
  .filterDate('2020-01-01', '2022-12-31');

2. Calculate Monthly Averages:

var monthly = precipitation.select('precipitation');
var monthlyAvg = monthly.map(function(img) {
  return img.set('month', img.date().get('month'));
});
var monthlyMean = monthlyAvg.reduce(ee.Reducer.mean()).rename('precipitation_mean');

3. Compute Standard Deviation for Anomaly Detection:

var monthlyStdDev = monthlyAvg.reduce(ee.Reducer.stdDev()).rename('precipitation_std');

4. Calculate Anomalies for a Specific Month:

var targetMonth = '2021-06-01';
var targetImage = precipitation.filterDate(targetMonth).first();
var anomaly = targetImage.select('precipitation').subtract(monthlyMean).divide(monthlyStdDev).rename('anomaly');

5. Visualize Anomalies:

Map.addLayer(anomaly, {min: -3, max: 3, palette: ['blue', 'white', 'red']}, 'Precipitation Anomalies');

Understanding the Output

The anomalies are calculated as a z-score, showing how many standard deviations the precipitation deviates from the historical average. A z-score of +2 indicates a +2σ deviation, while -2 suggests a -2σ deviation. This helps categorize anomalies as extreme, significant, or neutral.

Applications of Monthly Precipitation Anomalies

– Drought Monitoring: Identify areas with below-average rainfall.
– Flood Risk Assessment: Detect regions with unusually high precipitation.
– Climate Studies: Compare anomalous months to long-term trends.
– Agriculture Planning: Enable strategies for crop management.

FAQ

What datasets are best for detecting precipitation anomalies?

High-resolution datasets like GPM IMERG or CHIRPS (Climate Hazards Infrared Precipitation with Stations) are ideal for accuracy and global coverage.

How to adjust the analysis for a specific region?

Use geometry to filter the dataset. For example: precipitation.filter(ee.Filter.geometry(geometry)).

Why was the code not working with my date range?

Ensure the date range matches the dataset’s availability. Check the documentation for the specific dataset’s temporal coverage.

Can I analyze anomalies for other timeframes, like yearly or quarterly?

Yes. Modify the date filters (e.g., filterDate('2020-01-01', '2021-12-31')) or use reduce(ee.Reducer.mean().group()) for grouped statistics.

How long does the computation take in GEE?

Processing time depends on the dataset size and compute resources. Use evaluate() or getRegion() for smaller outputs.