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    Combining TRMM and Surface Observations of Precipitation: Technique and Validation over South America

    Source: Weather and Forecasting:;2010:;volume( 025 ):;issue: 003::page 885
    Author:
    Rozante, José Roberto
    ,
    Moreira, Demerval Soares
    ,
    de Goncalves, Luis Gustavo G.
    ,
    Vila, Daniel A.
    DOI: 10.1175/2010WAF2222325.1
    Publisher: American Meteorological Society
    Abstract: The measure of atmospheric model performance is highly dependent on the quality of the observations used in the evaluation process. In the particular case of operational forecast centers, large-scale datasets must be made available in a timely manner for continuous assessment of model results. Numerical models and surface observations usually work at distinct spatial scales (i.e., areal average in a regular grid versus point measurements), making direct comparison difficult. Alternatively, interpolation methods are employed for mapping observational data to regular grids and vice versa. A new technique (hereafter called MERGE) to combine Tropical Rainfall Measuring Mission (TRMM) satellite precipitation estimates with surface observations over the South American continent is proposed and its performance is evaluated for the 2007 summer and winter seasons. Two different approaches for the evaluation of the performance of this product against observations were tested: a cross-validation subsampling of the entire continent and another subsampling of only areas with sparse observations. Results show that over areas with a high density of observations, the MERGE technique?s performance is equivalent to that of simply averaging the stations within the grid boxes. However, over areas with sparse observations, MERGE shows superior results.
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      Combining TRMM and Surface Observations of Precipitation: Technique and Validation over South America

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4213346
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    contributor authorRozante, José Roberto
    contributor authorMoreira, Demerval Soares
    contributor authorde Goncalves, Luis Gustavo G.
    contributor authorVila, Daniel A.
    date accessioned2017-06-09T16:38:34Z
    date available2017-06-09T16:38:34Z
    date copyright2010/06/01
    date issued2010
    identifier issn0882-8156
    identifier otherams-71452.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4213346
    description abstractThe measure of atmospheric model performance is highly dependent on the quality of the observations used in the evaluation process. In the particular case of operational forecast centers, large-scale datasets must be made available in a timely manner for continuous assessment of model results. Numerical models and surface observations usually work at distinct spatial scales (i.e., areal average in a regular grid versus point measurements), making direct comparison difficult. Alternatively, interpolation methods are employed for mapping observational data to regular grids and vice versa. A new technique (hereafter called MERGE) to combine Tropical Rainfall Measuring Mission (TRMM) satellite precipitation estimates with surface observations over the South American continent is proposed and its performance is evaluated for the 2007 summer and winter seasons. Two different approaches for the evaluation of the performance of this product against observations were tested: a cross-validation subsampling of the entire continent and another subsampling of only areas with sparse observations. Results show that over areas with a high density of observations, the MERGE technique?s performance is equivalent to that of simply averaging the stations within the grid boxes. However, over areas with sparse observations, MERGE shows superior results.
    publisherAmerican Meteorological Society
    titleCombining TRMM and Surface Observations of Precipitation: Technique and Validation over South America
    typeJournal Paper
    journal volume25
    journal issue3
    journal titleWeather and Forecasting
    identifier doi10.1175/2010WAF2222325.1
    journal fristpage885
    journal lastpage894
    treeWeather and Forecasting:;2010:;volume( 025 ):;issue: 003
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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