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    Daytime Precipitation Estimation Using Bispectral Cloud Classification System

    Source: Journal of Applied Meteorology and Climatology:;2009:;volume( 049 ):;issue: 005::page 1015
    Author:
    Behrangi, Ali
    ,
    Hsu, Koulin
    ,
    Imam, Bisher
    ,
    Sorooshian, Soroosh
    DOI: 10.1175/2009JAMC2291.1
    Publisher: 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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      Daytime Precipitation Estimation Using Bispectral Cloud Classification System

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4209921
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    • Journal of Applied Meteorology and Climatology

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    contributor authorBehrangi, Ali
    contributor authorHsu, Koulin
    contributor authorImam, Bisher
    contributor authorSorooshian, Soroosh
    date accessioned2017-06-09T16:28:01Z
    date available2017-06-09T16:28:01Z
    date copyright2010/05/01
    date issued2009
    identifier issn1558-8424
    identifier otherams-68371.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4209921
    description abstractTwo 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.
    publisherAmerican Meteorological Society
    titleDaytime Precipitation Estimation Using Bispectral Cloud Classification System
    typeJournal Paper
    journal volume49
    journal issue5
    journal titleJournal of Applied Meteorology and Climatology
    identifier doi10.1175/2009JAMC2291.1
    journal fristpage1015
    journal lastpage1031
    treeJournal of Applied Meteorology and Climatology:;2009:;volume( 049 ):;issue: 005
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian