Editorial Type:
Article
Article Type:
Research Article

Daytime Precipitation Estimation Using Bispectral Cloud Classification System

Ali Behrangi Center for Hydrometeorology and Remote Sensing, and Department of Civil and Environmental Engineering, Henry Samueli School of Engineering, University of California, Irvine, Irvine, California

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Koulin Hsu Center for Hydrometeorology and Remote Sensing, and Department of Civil and Environmental Engineering, Henry Samueli School of Engineering, University of California, Irvine, Irvine, California

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Bisher Imam Center for Hydrometeorology and Remote Sensing, and Department of Civil and Environmental Engineering, Henry Samueli School of Engineering, University of California, Irvine, Irvine, California

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Soroosh Sorooshian Center for Hydrometeorology and Remote Sensing, and Department of Civil and Environmental Engineering, Henry Samueli School of Engineering, University of California, Irvine, Irvine, California

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Print Publication:
01 May 2010
Collections:
International Precipitation Working Group (IPWG)
DOI:
https://doi.org/10.1175/2009JAMC2291.1
Page(s):
1015–1031
Restricted access

Abstract

Two previously developed Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) algorithms that incorporate cloud classification system (PERSIANN-CCS) and multispectral analysis (PERSIANN-MSA) are integrated and employed to analyze the role of cloud albedo from Geostationary Operational Environmental Satellite-12 (GOES-12) visible (0.65 μm) channel in supplementing infrared (10.7 mm) data. The integrated technique derives finescale (0.04° × 0.04° latitude–longitude every 30 min) rain rate for each grid box through four major steps: 1) segmenting clouds into a number of cloud patches using infrared or albedo images; 2) classification of cloud patches into a number of cloud types using radiative, geometrical, and textural features for each individual cloud patch; 3) classification of each cloud type into a number of subclasses and assigning rain rates to each subclass using a multidimensional histogram matching method; and 4) associating satellite gridbox information to the appropriate corresponding cloud type and subclass to estimate rain rate in grid scale. The technique was applied over a study region that includes the U.S. landmass east of 115°W. One reference infrared-only and three different bispectral (visible and infrared) rain estimation scenarios were compared to investigate the technique’s ability to address two major drawbacks of infrared-only methods: 1) underestimating warm rainfall and 2) the inability to screen out no-rain thin cirrus clouds. Radar estimates were used to evaluate the scenarios at a range of temporal (3 and 6 hourly) and spatial (0.04°, 0.08°, 0.12°, and 0.24° latitude–longitude) scales. Overall, the results using daytime data during June–August 2006 indicate that significant gain over infrared-only technique is obtained once albedo is used for cloud segmentation followed by bispectral cloud classification and rainfall estimation. At 3-h, 0.04° resolution, the observed improvement using bispectral information was about 66% for equitable threat score and 26% for the correlation coefficient. At coarser 0.24° resolution, the gains were 34% and 32% for the two performance measures, respectively.

* Current affiliation: Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

Corresponding author address: Ali Behrangi, Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, MS 183-301, Pasadena, CA 91109. Email: ali.behrangi@jpl.nasa.gov

This article included in the International Precipitation Working Group (IPWG) special collection.

Abstract

Two previously developed Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) algorithms that incorporate cloud classification system (PERSIANN-CCS) and multispectral analysis (PERSIANN-MSA) are integrated and employed to analyze the role of cloud albedo from Geostationary Operational Environmental Satellite-12 (GOES-12) visible (0.65 μm) channel in supplementing infrared (10.7 mm) data. The integrated technique derives finescale (0.04° × 0.04° latitude–longitude every 30 min) rain rate for each grid box through four major steps: 1) segmenting clouds into a number of cloud patches using infrared or albedo images; 2) classification of cloud patches into a number of cloud types using radiative, geometrical, and textural features for each individual cloud patch; 3) classification of each cloud type into a number of subclasses and assigning rain rates to each subclass using a multidimensional histogram matching method; and 4) associating satellite gridbox information to the appropriate corresponding cloud type and subclass to estimate rain rate in grid scale. The technique was applied over a study region that includes the U.S. landmass east of 115°W. One reference infrared-only and three different bispectral (visible and infrared) rain estimation scenarios were compared to investigate the technique’s ability to address two major drawbacks of infrared-only methods: 1) underestimating warm rainfall and 2) the inability to screen out no-rain thin cirrus clouds. Radar estimates were used to evaluate the scenarios at a range of temporal (3 and 6 hourly) and spatial (0.04°, 0.08°, 0.12°, and 0.24° latitude–longitude) scales. Overall, the results using daytime data during June–August 2006 indicate that significant gain over infrared-only technique is obtained once albedo is used for cloud segmentation followed by bispectral cloud classification and rainfall estimation. At 3-h, 0.04° resolution, the observed improvement using bispectral information was about 66% for equitable threat score and 26% for the correlation coefficient. At coarser 0.24° resolution, the gains were 34% and 32% for the two performance measures, respectively.

* Current affiliation: Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

Corresponding author address: Ali Behrangi, Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, MS 183-301, Pasadena, CA 91109. Email: ali.behrangi@jpl.nasa.gov

This article included in the International Precipitation Working Group (IPWG) special collection.

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