Validation of satellite daily rainfall estimates in complex terrain of Bali Island, Indonesia.

DOI

Satellite rainfall products have different performances in different geographic regions under different physical and climatological conditions. In this study, the objective was to select the most reliable and accurate satellite rainfall products for specific, environmental conditions of Bali Island. The performances of four spatio-temporal satellite rainfall products, i.e., CMORPH25, CMORPH8, TRMM, and PERSIANN, were evaluated at the island, zonation (applying elevation and climatology as constraints), and pixel scales, using (i) descriptive statistics and (ii) categorical statistics, including bias decomposition. The results showed that all the satellite products had low accuracy because of spatial scale effect, daily resolution and the island complexity. That accuracy was relatively lower in (i) dry seasons and dry climatic zones than in wet seasons and wet climatic zones; (ii) pixels jointly covered by sea and mountainous land than in pixels covered by land or by sea only; and (iii) topographically diverse than uniform terrains. CMORPH25, CMORPH8, and TRMM underestimated and PERSIANN overestimated rainfall when comparing them to gauged rain. The CMORPH25 had relatively the best performance and the PERSIANN had the worst performance in the Bali Island. The CMORPH25 had the lowest statistical errors, the lowest miss, and the highest hit rainfall events; it also had the lowest miss rainfall bias and was relatively the most accurate in detecting, frequent in Bali, ? 20 mm day?1 rain events. Lastly, the CMORPH25 coarse grid better represented rainfall events from coastal to inlands areas than other satellite products, including finer grid CMORPH8.

Files not yet migrated to Data Station. Files for this dataset can be found at https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:157272.

Identifier
DOI https://doi.org/10.17026/dans-zs4-veur
Metadata Access https://phys-techsciences.datastations.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.17026/dans-zs4-veur
Provenance
Creator N. Rahmawati
Publisher DANS Data Station Phys-Tech Sciences
Contributor M Th Koelen; M.W. Lubczynski (Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente)
Publication Year 2020
Rights DANS Licence; info:eu-repo/semantics/openAccess; https://doi.org/10.17026/fp39-0x58
OpenAccess true
Contact M Th Koelen (Faculty of Geo-Information Science and Earth Observation)
Representation
Resource Type Dataset
Format application/zip
Size 17447
Version 1.0
Discipline Earth and Environmental Science; Environmental Research; Geosciences; Natural Sciences