Daytime Precipitation Estimation Using Bispectral Cloud Classification SystemSource: Journal of Applied Meteorology and Climatology:;2009:;volume( 049 ):;issue: 005::page 1015DOI: 10.1175/2009JAMC2291.1Publisher: American Meteorological Society
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 ?m) 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.
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contributor author | Behrangi, Ali | |
contributor author | Hsu, Koulin | |
contributor author | Imam, Bisher | |
contributor author | Sorooshian, Soroosh | |
date accessioned | 2017-06-09T16:28:01Z | |
date available | 2017-06-09T16:28:01Z | |
date copyright | 2010/05/01 | |
date issued | 2009 | |
identifier issn | 1558-8424 | |
identifier other | ams-68371.pdf | |
identifier uri | http://onlinelibrary.yabesh.ir/handle/yetl/4209921 | |
description 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 ?m) 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. | |
publisher | American Meteorological Society | |
title | Daytime Precipitation Estimation Using Bispectral Cloud Classification System | |
type | Journal Paper | |
journal volume | 49 | |
journal issue | 5 | |
journal title | Journal of Applied Meteorology and Climatology | |
identifier doi | 10.1175/2009JAMC2291.1 | |
journal fristpage | 1015 | |
journal lastpage | 1031 | |
tree | Journal of Applied Meteorology and Climatology:;2009:;volume( 049 ):;issue: 005 | |
contenttype | Fulltext |