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    Validation of Satellite Precipitation Estimates over the Congo Basin

    Source: Journal of Hydrometeorology:;2019:;volume 020:;issue 004::page 631
    Author:
    Nicholson, S. E.
    ,
    Klotter, D.
    ,
    Zhou, L.
    ,
    Hua, W.
    DOI: 10.1175/JHM-D-18-0118.1
    Publisher: American Meteorological Society
    Abstract: AbstractThis paper evaluates nine satellite rainfall products and the Global Precipitation Centre Climatology (GPCC) gauge dataset over the Congo basin. For the evaluation the reference dataset is a newly created, gridded gauge dataset based on a gauge network that is more complete than that of GPCC in recent years. It is termed NIC131-gridded. Gridding was achieved via a climatic reconstruction method based on principal components, so that reliable estimates of rainfall are available even in the data-sparse central basin. The satellite products were evaluated for two locations, the Congo basin and areas on its eastern and western periphery (termed the ?east plus west? sector). The station density was notably higher in the latter region. Two time periods were also considered: 1983?94, when station density was relatively high, and 1998?2010, when station density was much lower than during the earlier period. Several products show excellent agreement with the NIC131-gridded reference dataset. These include CHIRPS2, PERSIANN-CDR, GPCP 2.3, TRMM 3B43, and, to a lesser extent, GPCC V7. RMSE for the period 1983?94 in the east plus west sector is on the order of 20 mm month?1 for GPCC V7 and 20?30 mm month?1 for the other products. The compares with 40?60 mm month?1 for the most poorly performing products, African Rainfall Climatology version 2 (ARCv2) and CMAP. Over the Congo basin, RMSE for those two products is about the same as in the east plus west sector but is on the order of 30?40 mm month?1 for the better-performing products. In all cases, the performance of the 10 products evaluated is notably poorer in recent years (1998?2010), when the station network is sparse, than during the period 1983?94, when the dense station network provides reliable estimates of rainfall. For the more recent period RMSE is on the order of 30?40 mm month?1 for the best-performing products in the east plus west sector but only slightly higher over the Congo basin. All products do reasonably well in reproducing the seasonal cycle and the latitudinal gradients of rainfall. Estimates of interannual variability show more scatter among the various products and are less reliable. Overall, the most important results of the study are to demonstrate the strong impact that actual gauge data have on the various products and the need to have access to such gauge data, in order to produce reliable rainfall estimates from satellites.
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      Validation of Satellite Precipitation Estimates over the Congo Basin

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4263340
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    contributor authorNicholson, S. E.
    contributor authorKlotter, D.
    contributor authorZhou, L.
    contributor authorHua, W.
    date accessioned2019-10-05T06:45:45Z
    date available2019-10-05T06:45:45Z
    date copyright4/1/2019 12:00:00 AM
    date issued2019
    identifier otherJHM-D-18-0118.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4263340
    description abstractAbstractThis paper evaluates nine satellite rainfall products and the Global Precipitation Centre Climatology (GPCC) gauge dataset over the Congo basin. For the evaluation the reference dataset is a newly created, gridded gauge dataset based on a gauge network that is more complete than that of GPCC in recent years. It is termed NIC131-gridded. Gridding was achieved via a climatic reconstruction method based on principal components, so that reliable estimates of rainfall are available even in the data-sparse central basin. The satellite products were evaluated for two locations, the Congo basin and areas on its eastern and western periphery (termed the ?east plus west? sector). The station density was notably higher in the latter region. Two time periods were also considered: 1983?94, when station density was relatively high, and 1998?2010, when station density was much lower than during the earlier period. Several products show excellent agreement with the NIC131-gridded reference dataset. These include CHIRPS2, PERSIANN-CDR, GPCP 2.3, TRMM 3B43, and, to a lesser extent, GPCC V7. RMSE for the period 1983?94 in the east plus west sector is on the order of 20 mm month?1 for GPCC V7 and 20?30 mm month?1 for the other products. The compares with 40?60 mm month?1 for the most poorly performing products, African Rainfall Climatology version 2 (ARCv2) and CMAP. Over the Congo basin, RMSE for those two products is about the same as in the east plus west sector but is on the order of 30?40 mm month?1 for the better-performing products. In all cases, the performance of the 10 products evaluated is notably poorer in recent years (1998?2010), when the station network is sparse, than during the period 1983?94, when the dense station network provides reliable estimates of rainfall. For the more recent period RMSE is on the order of 30?40 mm month?1 for the best-performing products in the east plus west sector but only slightly higher over the Congo basin. All products do reasonably well in reproducing the seasonal cycle and the latitudinal gradients of rainfall. Estimates of interannual variability show more scatter among the various products and are less reliable. Overall, the most important results of the study are to demonstrate the strong impact that actual gauge data have on the various products and the need to have access to such gauge data, in order to produce reliable rainfall estimates from satellites.
    publisherAmerican Meteorological Society
    titleValidation of Satellite Precipitation Estimates over the Congo Basin
    typeJournal Paper
    journal volume20
    journal issue4
    journal titleJournal of Hydrometeorology
    identifier doi10.1175/JHM-D-18-0118.1
    journal fristpage631
    journal lastpage656
    treeJournal of Hydrometeorology:;2019:;volume 020:;issue 004
    contenttypeFulltext
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