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| Titel |
Evaluation of high-resolution satellite precipitation products using rain gauge observations over the Tibetan Plateau |
| VerfasserIn |
Y. C. Gao, M. F. Liu |
| 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 ; 17, no. 2 ; Nr. 17, no. 2 (2013-02-28), S.837-849 |
| Datensatznummer |
250018807
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| Publikation (Nr.) |
 copernicus.org/hess-17-837-2013.pdf |
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| Zusammenfassung |
| High-resolution satellite precipitation products are very attractive for
studying the hydrologic processes in mountainous areas where rain gauges are
generally sparse. Four high-resolution satellite precipitation products are
evaluated using gauge measurements over different climate zones of the
Tibetan Plateau (TP) within a 6 yr period from 2004 to 2009. The four
satellite-based precipitation data sets are: Tropical Rainfall Measuring
Mission (TRMM) Multisatellite Precipitation Analysis 3B42 version 6 (TMPA)
and its Real Time version (TMPART), Climate Prediction Center Morphing
Technique (CMOPRH) and Precipitation Estimation from Remotely Sensed
Information using Artificial Neural Network (PERSIANN). TMPA and CMORPH,
with higher correlation coefficients and lower root mean square errors
(RMSEs), show overall better performance than PERSIANN and TMPART. TMPA has
the lowest biases among the four precipitation data sets, which is likely due
to the correction process against the monthly gauge observations from global
precipitation climatology project (GPCP). TMPA also shows large improvement
over TMPART, indicating the importance of gauge-based correction on accuracy
of rainfall. The four products show better agreement with gauge measurements
over humid regions than that over arid regions where correlation
coefficients are less than 0.5. Moreover, the four precipitation products
generally tend to overestimate light rainfall (0–10 mm) and underestimate
moderate and heavy rainfall (>10 mm). Moreover, this study
extracts 24 topographic variables from a DEM (digital elevation model) and uses a linear regression
model to explore the bias–topography relationship. Results show that biases
of TMPA and CMORPH present weak dependence on topography. However, biases of
TMPART and PERSIANN present dependence on topography and variability of
elevation and surface roughness plays important roles in explaining their
biases. |
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