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Precipitation (PERSIANN)

PERSIANN Overview

PERSIANN-CDR is a daily quasi-global precipitation product that spans the period from 1983-01-01 to present. The data is produced quarterly, with a typical lag of three months. The product is developed by the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine (UC-IRVINE/CHRS) using Gridded Satellite (GridSat-B1) IR data that are derived from merging ISCCP B1 IR data, along with GPCP version 2.2.

Dataset Overview

Download Monthly PERSIANN PCP

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

import ee
import geemap
import os
import json


# 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 = 2023
lastyear = 2024

# Create output directories if they do not exist.
output_dir = 'PCP_persiann_Monthly'

if not os.path.exists(output_dir):
    os.makedirs(output_dir)


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

filenames =[]

for year in range(firstyear, lastyear + 1):
    for month in range(1,13):
        filename = f"pcp_persiann_{year}_{month:02d}"
        filenames.append(filename)

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

# Filter the collection by date and select the 'precipitation' band.
precipdata = ee.ImageCollection("NOAA/PERSIANN-CDR") \
    .filter(ee.Filter.date(f'{firstyear}-01-01', f'{lastyear}-12-31')) \
    .select('precipitation')

# Function to add Month and Year properties based on each image’s system:time_start.
def set_date_properties(img):
    date = ee.Date(img.get('system:time_start'))
    # Use the standard properties 'month' and 'year'
    return img.set({
        'Month': date.get('month'),
        'Year': date.get('year')
    })

pcpts_daily = precipdata.map(set_date_properties)

# --- Generate Monthly Precipitation Totals ---
def monthly_image(year):
    def process_month(m):
        # Sum all daily images for a given year and month.
        monthly_sum = pcpts_daily.filter(ee.Filter.eq('Year', year)) \
                                 .filter(ee.Filter.eq('Month', m)) \
                                 .sum()
        date = ee.Date.fromYMD(year, m, 1)
        return monthly_sum.set({
            'Year': year,
            'Month': m,
            'date': date,
            'system:time_start': date.millis()
        }).rename('PCP')
    # Create a list of monthly images for months 1 through 12.
    return ee.ImageCollection(ee.List.sequence(1, 12).map(process_month))

# Map over all years and flatten the results into one ImageCollection.
monthly_list = ee.List(years.map(lambda y: monthly_image(ee.Number(y)).toList(12))).flatten()

# print(monthly_list)
persiann_PCP_M = ee.ImageCollection(monthly_list)

count = persiann_PCP_M.size().getInfo()
print(f"đź“… Total images: {count}")

# print("persiann_PCP_M", persiann_PCP_M)


# def annual_image(year):
#     # Sum monthly images for a given year.
#     annual_sum = persiann_PCP_M.filter(ee.Filter.eq('Year', year)) \
#                                .select('PCP').sum()
#     date = ee.Date.fromYMD(year, 1, 1)
#     return annual_sum.set({
#         'year': year,
#         'date': date,
#         'system:time_start': date.millis()
#     })

# annual_list = years.map(annual_image)
# persiann_PCP_A = ee.ImageCollection(annual_list)

# print("persiann_PCP_A", persiann_PCP_A.getInfo())




# Here we use a global bounding box; adjust as needed.
roi = ee.Geometry.BBox(-180, -80, 180, 80)





# Export monthly images.
geemap.ee_export_image_collection(
    persiann_PCP_M,
    out_dir=output_dir,
    scale=27000,       # Adjust resolution (in meters) as needed.
    crs='EPSG:4326',
    region=roi,
    filenames=filenames
)



print("Export complete!")