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Precipitation (GSMaP)

GSMaP Overview

Global Satellite Mapping of Precipitation (GSMaP) provides a global hourly rain rate with a 0.1 x 0.1 degree resolution. GSMaP is a product of the Global Precipitation Measurement (GPM) mission, which provides global precipitation observations at three hour intervals. Values are estimated using multi-band passive microwave and infrared radiometers from the GPM Core Observatory satellite and with the assistance of a constellation of other satellites.

Dataset Overview

Download Daily GSMaP PCP

The following script downloads daily GSMaP PCP GeoTIFFs (in mm/day) for a given year range from Google Earth Engine:

import ee
import geemap
import os
import json
from datetime import date, timedelta

# Trigger the authentication flow.
ee.Authenticate()

# Initialize the library.
ee.Initialize(project='waterinfor') # <-- change to your GEE project

# Define the start and end years.
firstyear = 2024
lastyear = 2024

output_directory = 'GsMap_Daily'  # Change to your desired folder path.


years = ee.List.sequence(firstyear, lastyear)

start_date = date(firstyear, 1, 1)
end_date = date(lastyear, 12, 31)
filenames = []
dates = []

current_date = start_date
while current_date <= end_date:
    fname = f"pcp_gsmap_{current_date.year}_{current_date.month:02d}_{current_date.day:02d}"
    filenames.append(fname)
    dates.append(current_date.strftime("%Y-%m-%d"))
    current_date += timedelta(days=1)

print(f"🗂️ Total days to process: {len(filenames)}")

hourly_ic = ee.ImageCollection("JAXA/GPM_L3/GSMaP/v8/operational") \
    .filterDate(f'{firstyear}-01-01', f'{lastyear}-12-31') \
    .select('hourlyPrecipRateGC')


# Function to compute daily total
def make_daily_image(date_str):
    start = ee.Date(date_str)
    end = start.advance(1, 'day')

    daily_sum = hourly_ic.filterDate(start, end).sum() \
        .rename('PCP') \
        .set({
            'system:time_start': start.millis(),
            'date': start.format('YYYY_MM_dd')
        })

    return daily_sum 

daily_images = ee.ImageCollection(ee.List([make_daily_image(d) for d in dates]))


count = daily_images.size().getInfo()
print(f"📅 Total daily images: {count}")


geom = ee.Geometry.BBox(-180, -80, 180, 80)


feature = ee.Feature(geom, {})
roi = feature.geometry()



# Export all images to Google Drive
geemap.ee_export_image_collection(
    daily_images,
    out_dir=output_directory,
    region=roi,
    scale=15000,             # You can use 10000 or 15000
    crs='EPSG:4326',
    filenames=filenames

)

print("🚀 Export tasks created. Check Earth Engine Tasks tab or Code Editor.")

Aggregate Daily GeoTIFFs to Monthly

This script aggregate daily rasters into monthly precipitation maps by summing values.

import os
import numpy as np
import rasterio
from datetime import datetime

# ==== Paths ====
input_folder = "/Volumes/ExternalHDD/RS_Data/GsMap_Daily"      
output_folder = "/Volumes/ExternalHDD/RS_Data/GsMap_Monthly"   

firstyear = 2024
lastyear = 2024  # inclusive

# Create the output folder if it doesn't exist
os.makedirs(output_folder, exist_ok=True)

# Loop through years
for year in range(firstyear, lastyear + 1):
    for month in range(1, 13):
        sum_array = None
        profile = None

        # Loop through all possible days in month
        for day in range(1, 32):  # max days in a month
            try:
                # Validate date (skip invalid ones like Feb 30)
                date_obj = datetime(year, month, day)
            except ValueError:
                continue

            # File naming pattern — adjust if yours differs
            daily_filename = f"pcp_gsmap_{year}_{month:02d}_{day:02d}.tif"
            input_path = os.path.join(input_folder, daily_filename)

            if not os.path.exists(input_path):
                print(f"File {input_path} does not exist, skipping day {day}.")
                continue

            with rasterio.open(input_path) as src:
                data = src.read(1)

                if sum_array is None:
                    sum_array = np.zeros_like(data, dtype=np.float32)
                    profile = src.profile

                sum_array += data.astype(np.float32)

        # Save the monthly total
        if sum_array is not None:
            profile.update(dtype=rasterio.float32, count=1)
            output_path = os.path.join(output_folder, f"pcpm_gsmap_{year}_{month:02d}.tif")
            with rasterio.open(output_path, "w", **profile) as dst:
                dst.write(sum_array, 1)
            print(f"Monthly raster for {year}-{month:02d} saved to {output_path}")
        else:
            print(f"No daily rasters found for {year}-{month:02d}.")