Editorial Type:
Article
Article Type:
Research Article

Evaluation of PERSIANN System Satellite-Based Estimates of Tropical Rainfall

Soroosh Sorooshian Department of Hydrology and Water Resources, The University of Arizona, Tucson, Arizona

Search for other papers by Soroosh Sorooshian in
Current site
Google Scholar
PubMed Close
,
Kuo-Lin Hsu Department of Hydrology and Water Resources, The University of Arizona, Tucson, Arizona

Search for other papers by Kuo-Lin Hsu in
Current site
Google Scholar
PubMed Close
,
Xiaogang Gao Department of Hydrology and Water Resources, The University of Arizona, Tucson, Arizona

Search for other papers by Xiaogang Gao in
Current site
Google Scholar
PubMed Close
,
Hoshin V. Gupta Department of Hydrology and Water Resources, The University of Arizona, Tucson, Arizona

Search for other papers by Hoshin V. Gupta in
Current site
Google Scholar
PubMed Close
,
Bisher Imam Department of Hydrology and Water Resources, The University of Arizona, Tucson, Arizona

Search for other papers by Bisher Imam in
Current site
Google Scholar
PubMed Close
, and
Dan Braithwaite Department of Hydrology and Water Resources, The University of Arizona, Tucson, Arizona

Search for other papers by Dan Braithwaite in
Current site
Google Scholar
PubMed Close
Online Publication:
01 Sep 2000
Print Publication:
01 Sep 2000
DOI:
https://doi.org/10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2
Page(s):
2035–2046
Full access

We are aware of a technical issue preventing figures and tables from showing in some newly published articles in the full-text HTML view.
While we are resolving the problem, please use the online PDF version of these articles to view figures and tables.

PERSIANN, an automated system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, has been developed for the estimation of rainfall from geosynchronous satellite longwave infared imagery (GOES-IR) at a resolution of 0.25° × 0.25° every half-hour. The accuracy of the rainfall product is improved by adaptively adjusting the network parameters using the instantaneous rain-rate estimates from the Tropical Rainfall Measurement Mission (TRMM) microwave imager (TMI product 2A12), and the random errors are further reduced by accumulation to a resolution of 1° × 1° daily. The authors' current GOES-IR-TRMM TMI based product, named PERSIANN-GT, was evaluated over the region 30°S–30°N, 90°E–30°W, which includes the tropical Pacific Ocean and parts of Asia, Australia, and the Americas. The resulting rain-rate estimates agree well with the National Climatic Data Center radar-gauge composite data over Florida and Texas (correlation coefficient p > 0.7). The product also compares well (p ~ 0.77–0.90) with the monthly World Meteorological Organization gauge measurements for 5° × 5° grid locations having high gauge densities. The PERSIANN-GT product was evaluated further by comparing it with current TRMM products (3A11, 3B31, 3B42, 3B43) over the entire study region. The estimates compare well with the TRMM 3B43 1° × 5 1° monthly product, but the PERSIANN-GT products indicate higher rainfall over the western Pacific Ocean when compared to the adjusted geosynchronous precipitation index–based TRMM 3B42 product.

Corresponding author address: Soroosh Sorooshian, Department of Hydrology and Water Resources, The University of Arizona, Building 11, P.O. Box 210011, Tucson, AZ 85721-1422. E-mail: soroosh@hwr.arizona.edu

PERSIANN, an automated system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, has been developed for the estimation of rainfall from geosynchronous satellite longwave infared imagery (GOES-IR) at a resolution of 0.25° × 0.25° every half-hour. The accuracy of the rainfall product is improved by adaptively adjusting the network parameters using the instantaneous rain-rate estimates from the Tropical Rainfall Measurement Mission (TRMM) microwave imager (TMI product 2A12), and the random errors are further reduced by accumulation to a resolution of 1° × 1° daily. The authors' current GOES-IR-TRMM TMI based product, named PERSIANN-GT, was evaluated over the region 30°S–30°N, 90°E–30°W, which includes the tropical Pacific Ocean and parts of Asia, Australia, and the Americas. The resulting rain-rate estimates agree well with the National Climatic Data Center radar-gauge composite data over Florida and Texas (correlation coefficient p > 0.7). The product also compares well (p ~ 0.77–0.90) with the monthly World Meteorological Organization gauge measurements for 5° × 5° grid locations having high gauge densities. The PERSIANN-GT product was evaluated further by comparing it with current TRMM products (3A11, 3B31, 3B42, 3B43) over the entire study region. The estimates compare well with the TRMM 3B43 1° × 5 1° monthly product, but the PERSIANN-GT products indicate higher rainfall over the western Pacific Ocean when compared to the adjusted geosynchronous precipitation index–based TRMM 3B42 product.

Corresponding author address: Soroosh Sorooshian, Department of Hydrology and Water Resources, The University of Arizona, Building 11, P.O. Box 210011, Tucson, AZ 85721-1422. E-mail: soroosh@hwr.arizona.edu
Save
Cover Bulletin of the American Meteorological Society
Bulletin of the American Meteorological Society
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 4063 1339 353
PDF Downloads 2709 612 41