A utility to search, download global level PERSIANN precipitation data from CHRS Data Portal
Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine (UCI) provides several precipitation(rainfall) related datasets at a global scale that can be utilised for rainfall data analysis.
CHRS developed a system called Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) to compute an estimate of rainfall data at each 0.25° x 0.25° pixel of the infrared brightness temperature image provided by geostationary satellites useing neural network function classification/approximation procedures.
Data Period: March 2000 - Present
Coverage: 60°S to 60°N
Resolution: 0.25° x 0.25°
HTTP Download (full globe): hourly, 3-hourly, 6-hourly, daily, monthly, yearly
FTP also available: ftp://persiann.eng.uci.edu/CHRSdata/PERSIANN
Latest Update: Near real-time with 2 day delay
PERSIANN-Cloud Classification System (PERSIANN-CCS) is a real-time global high resolution (0.04° x 0.04° or 4km x 4km;) satellite precipitation product. PERSIANN-CCS system enables the categorization of cloud-patch features based on cloud height, areal extent, and variability of texture estimated from satellite imagery.
Data Period: January 2003 - Present
Coverage: 60°S to 60°N
Resolution: 0.04° x 0.04°
HTTP Download (full globe): hourly, 3-hourly, 6-hourly, daily, monthly, yearly
FTP also available: ftp://persiann.eng.uci.edu/CHRSdata/PERSIANN-CCS
Latest Update: Real-time
PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record) provides daily rainfall estimates (near-global 37+ year high-resolution precipitation) at 0.25 deg for the latitude band 60N-60S over the period of 01/01/1983 to 12/31/2015 (delayed present). PERSIANN-CDR is aimed at addressing the need for a consistent, long-term, high-resolution and global precipitation dataset for studying the changes and trends in daily precipitation, especially extreme precipitation events, due to climate change and natural variability. PERSIANN-CDR is generated from the PERSIANN algorithm using GridSat-B1 infrared data and adjusted using the Global Precipitation Climatology Project (GPCP) monthly product to maintain consistency of the two datasets at 2.5 deg monthly scale throughout the entire record.
Data Period: January 1983 - Present
Coverage: 60°S to 60°N
Resolution: 0.25° x 0.25°
HTTP Download (full globe): daily, monthly, yearly
FTP also available: ftp://persiann.eng.uci.edu/CHRSdata/PERSIANN-CDR
Latest Update: September 2020
The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Dynamic Infrared Rain Rate near real-time (PDIR-Now) is a real-time global high resolution (0.04° x 0.04° or = 4km x 4km;) satellite precipitation product. PDIR-Now has been implemented on the UCI CHRS global real-time satellite precipitation monitoring system - iRain.
PDIR-Now relies on the high frequency sampled IR imagery; consequently, the latency of PDIR-Now from the time of rainfall occurrence is very short (15-60 mins). Additionally, PDIR-Now accounts for the errors and uncertainties that result from the use of IR imagery by adopting a variety of techniques most notable is the dynamic shifting of (Tb-R) curves using rainfall climatology. The short latency of PDIR-Now renders the dataset well-suited for near-real time hydrologic applications such as flood forecasting and developing flood inundation maps.
Data Period: March 1st 2000 - Present
Coverage: 60°S to 60°N
Resolution: 0.04° x 0.04°
HTTP Download (full globe): hourly, 3-hourly, 6-hourly, daily, monthly, yearly
FTP also available: ftp://persiann.eng.uci.edu/CHRSdata/PDIRNow
You can install this module using the following methods;
python setup.py installor, you could go the project directory that contains the setup.py and run the pip command as below.
pip install .If you encounter a bug, please file an issue with steps to reproduce it on Github. Please use the same for any feature requests, enhancements or suggestions.
Instructions for Parameters:
"""
start (str): start date in 'yyyymmddHH' format
HH for 1hrly - 00, 01, ---, 23
HH for 3hrly - 00, 03, 06, 09, 12, 15, 18, 21
HH for 6hrly - 00, 06, 12, 18
end (str): end date in 'yyyymmddHH' format
HH for 1hrly - 00, 01, ---, 23
HH for 3hrly - 00, 03, 06, 09, 12, 15, 18, 21
HH for 6hrly - 00, 06, 12, 18
mailid (str): Mail Id of the user, requesting/placing an order for
the CHRS Persiann Data
download_path (str): local path on the system where the file is
downloaded.
file_format (str, optional): File format for the data to be downloaded.
options:
ArcGrid,
Tif,
NetCDF
Defaults to 'Tif'.
timestep (str, optional): Time step/interval for the subsequent data
files in the time period.
options:
1hrly,
3hrly,
6hrly,
daily,
monthly,
yearly,
Defaults to 'monthly'.
compression (str, optional): Download file format.
options:
zip,
tar (in development)
Defaults to 'zip'.
"""Example Usage -
from chrs_persiann import CHRS
params = {
'start': '2021010100',
'end': '2021010300',
'mailid': '[email protected]',
'download_path': '~/Downloads',
'file_format': 'Tif',
'timestep': 'daily',
'compression': 'zip'
}
dl = CHRS()
# PERSIANN
dl.get_persiann(**params)
# PERSIANN CCS
dl.get_persiann_ccs(**params)
# PERSIANN CDR
dl.get_persiann_cdr(**params)
# PDIR-Now
dl.get_pdir(**params)You could alternatively, try running the test_run.py in the project root directory as
an example run.
python ./test_run.py
Querying data & Placing the order...
Query Params:
start date - 2021010100
end date - 2021010300
time step - daily
data type - PERSIANN
file format - Tif
compression format - zip
download path - ~/Downloads
Query complete.
Order Details - User IP: 12345678, File: 2022-02-19111423pm
Generating Data url...
File url Generated - https://chrsdata.eng.uci.edu/userFile/12345678/temp/PERSIANN/PERSIANN_2022-02-19111423pm.zip
Downloading compressed data file - /Users/user-name/Downloads/PERSIANN_2022-02-19111423pm.zip
Download Complete ------------------------------------------Nikhil S Hubballi