Identification

Title

PERSIANN-CCS Hourly Accumulated Precipitation

Alternative title(s)

d652000

Abstract

<p>The current operational PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) system uses neural network function classification/approximation procedures to compute an estimate of rainfall rate at each 0.25 degrees x 0.25 degrees pixel of the infrared brightness temperature image provided by geostationary satellites. An adaptive training feature facilitates updating of the network parameters whenever independent estimates of rainfall are available. The PERSIANN system was based on geostationary infrared imagery and later extended to include the use of both infrared and daytime visible imagery. The PERSIANN algorithm used here is based on the geostationary long wave infrared imagery to generate global rainfall. Rainfall product covers 60 degrees South to 60 degrees North globally.</p> <p>The system uses grid infrared images of global geosynchronous satellites (GOES-8, GOES-10, GMS-5, Metsat-6, and Metsat-7) provided by CPC, NOAA to generate 30-minute rain rates are aggregated to 6-hour accumulated rainfall. Model parameters are regularly updated using rainfall estimates from low-orbital satellites, including TRMM, NOAA-15, -16, -17, DMSP F13, F14, F15.</p> <p>Spectral Intervals and applicable satellites include the long wave infrared channel (10.2-11.2 micro-meters) from GOES-8, GOES-10, GMS-5, Meteosat-6, and Meteosat-7, and instantaneous rainfall estimates from TRMM, NOAA, and DMSP satellites.</p> <p>The PERSIANN Cloud Classification System (PERSIANN-CCS) is a real-time global high resolution (0.04 degrees x 0.04 degrees or 4km x 4km) satellite precipitation product developed by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine (UCI). The PERSIANN-CCS system enables the categorization of cloud-patch features based on cloud height, areal extent, and variability of texture estimated from satellite imagery. At the center of PERSIANN-CCS is the variable threshold cloud segmentation algorithm. In contrast with the traditional constant threshold approach, the variable threshold enables the identification and separation of individual patches of clouds. The individual patches can then be classified based on texture, geometric properties, dynamic evolution, and cloud top height. These classifications help in assigning rainfall values to pixels within each cloud based on a specific curve describing the relationship between rain-rate and brightness temperature.</p>

Resource type

dataset

Resource locator

https://gdex.ucar.edu/datasets/d652000/

protocol: https

name: Dataset Description

description: Related Link

function: information

https://gdex.ucar.edu/datasets/d652000/dataaccess/

protocol: https

name: Data Access

description: Related Link

function: download

Unique resource identifier

code

codeSpace

Dataset language

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

climatologyMeteorologyAtmosphere

Keywords

Keyword set

keyword value

dataset

originating controlled vocabulary

title

reference date

date type

revision

effective date

2021-03-30

Keyword set

keyword value

DMSP 5D-2/F13 > Defense Meteorological Satellite Program-F13

DMSP 5D-2/F14 > Defense Meteorological Satellite Program-F14

DMSP 5D-2/F15 > Defense Meteorological Satellite Program-F15

GMS > Japan Geostationary Meteorological Satellite

GOES-10 > Geostationary Operational Environmental Satellite 10

GOES-8

NOAA-15 > National Oceanic & Atmospheric Administration-15

NOAA-16 > National Oceanic & Atmospheric Administration-16

NOAA-17 > National Oceanic & Atmospheric Administration-17

TRMM > Tropical Rainfall Measuring Mission

originating controlled vocabulary

title

reference date

date type

revision

effective date

2026-02-13

Keyword set

keyword value

EARTH SCIENCE > ATMOSPHERE > PRECIPITATION > PRECIPITATION AMOUNT > HOURLY PRECIPITATION AMOUNT

originating controlled vocabulary

title

reference date

date type

revision

effective date

2026-02-13

Geographic location

West bounding longitude

-180.0

East bounding longitude

180.0

North bounding latitude

59.98

South bounding latitude

-59.98

Temporal reference

Temporal extent

Begin position

2003-01-01T0100+00

End position

2024-12-31T2300+00

Dataset reference date

date type

publication

effective date

2026-01-16

Frequency of update

irregular

Quality and validity

Lineage

Conformity

Data format

name of format

version of format

Constraints related to access and use

Constraint set

Use constraints

Creative Commons Attribution Non Commercial Share Alike 4.0 International License

Limitations on public access

None

Responsible organisations

Responsible party

organisation name

email address

datahelp@ucar.edu

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact

organisation name

NSF NCAR Geoscience Data Exchange

email address

datahelp@ucar.edu

web address

https://gdex.ucar.edu

name: NSF NCAR Geoscience Data Exchange

description: The Geoscience Data Exchange (GDEX), managed by the Computational and Information Systems Laboratory (CISL) at NSF NCAR, contains a large collection of meteorological, atmospheric composition, and oceanographic observations, and operational and reanalysis model outputs, integrated with NSF NCAR High Performance Compute services to support atmospheric and geosciences research.

function: download

responsible party role

pointOfContact

Metadata date

2026-02-21T17:20:59Z

Metadata language