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| Titel |
Characterization of precipitation product errors across the United States using multiplicative triple collocation |
| VerfasserIn |
S. H. Alemohammad, K. A. McColl, A. G. Konings, D. Entekhabi, A. Stoffelen |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 19, no. 8 ; Nr. 19, no. 8 (2015-08-10), S.3489-3503 |
| Datensatznummer |
250120785
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| Publikation (Nr.) |
 copernicus.org/hess-19-3489-2015.pdf |
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| Zusammenfassung |
| Validation of precipitation estimates from various products is a challenging
problem, since the true precipitation is unknown. However, with the increased
availability of precipitation estimates from a wide range of instruments
(satellite, ground-based radar, and gauge), it is now possible to apply the
triple collocation (TC) technique to characterize the uncertainties in each
of the products. Classical TC takes advantage of three collocated data
products of the same variable and estimates the mean squared error of each,
without requiring knowledge of the truth. In this study, triplets among
NEXRAD-IV, TRMM 3B42RT, GPCP 1DD, and GPI products are used to quantify the
associated spatial error characteristics across a central part of the
continental US. Data are aggregated to biweekly accumulations from January
2002 through April 2014 across a 2° × 2° spatial grid.
This is the first study of its kind to explore precipitation estimation
errors using TC across the US. A multiplicative (logarithmic) error model is
incorporated in the original TC formulation to relate the precipitation
estimates to the unknown truth. For precipitation application, this is more
realistic than the additive error model used in the original TC derivations,
which is generally appropriate for existing applications such as in the case
of wind vector components and soil moisture comparisons. This study provides
error estimates of the precipitation products that can be incorporated into
hydrological and meteorological models, especially those used in data
assimilation. Physical interpretations of the error fields (related to
topography, climate, etc.) are explored. The methodology presented in this
study could be used to quantify the uncertainties associated with
precipitation estimates from each of the constellations of GPM satellites.
Such quantification is prerequisite to optimally merging these estimates. |
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