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| Titel |
An algorithm for blending multiple satellite precipitation estimates with in situ precipitation measurements in Canada |
| VerfasserIn |
A. Lin, X. L. Wang |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250071545
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| Zusammenfassung |
| This study proposes an algorithm for blending multiple satellite precipitation estimates
(SPEs) with in situ gauge precipitation measurements in Canada. Depending on the
number of gauge stations in the target area, the algorithm employs gauge data alone or
blends gauge data with the corresponding SPEs that have been corrected for biases using a
novel bias removal procedure developed in this study. The performance of this
algorithm is evaluated in terms of root-mean-square error (RMSE), frequency bias
index, and Pierce skill score, using 10 year gauge data from southwestern Canada
where there are enough valid gauge stations to be split into a training data set and an
evaluation data set. Sensitivity of the algorithm to gauge density is assessed by using five
training data sets representing sparse to moderate gauge densities. The results show that,
in comparison with the SPEs and a kriging analysis of gauge data, the blended
analysis has the smallest RMSE and is least biased and most skillful in all seasons,
and that the lower the gauge density, the more superior the blended analysis is. When
gauge density is low, kriging analysis of gauge data is worse than bias-corrected SPEs. The
unadjusted SPEs are the worst by all measures considered, which indicate a need for a
proper correction of biases in the SPEs. The blending algorithm is promising for
producing a more realistic gridded precipitation, especially for gauge sparse regions, such
as northern Canada. A blended analysis of monthly precipitation is produced and
compared with several existing precipitation analyses. |
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