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Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions

Lorenz, Christof ORCID iD icon 1; Portele, Tanja C. 1; Laux, Patrick 1; Kunstmann, Harald 1
1 Institut für Meteorologie und Klimaforschung – Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT)

Abstract:

Seasonal forecasts have the potential to substantially improve water management particularly in water-scarce regions. However, global seasonal forecasts are usually not directly applicable as they are provided at coarse spatial resolutions of at best 36 km and suffer from model biases and drifts. In this study, we therefore apply a bias-correction and spatial-disaggregation (BCSD) approach to seasonal precipitation, temperature and radiation forecasts of the latest long-range seasonal forecasting system SEAS5 of the European Centre for Medium-Range Weather Forecasts (ECMWF). As reference we use data from the ERA5-Land offline land surface rerun of the latest ECMWF reanalysis ERA5. Thereby, we correct for model biases and drifts and improve the spatial resolution from 36 km to 0.1∘. This is performed for example over four predominately semi-arid study domains across the world, which include the river basins of the Karun (Iran), the São Francisco River (Brazil), the Tekeze–Atbara river and Blue Nile (Sudan, Ethiopia and Eritrea), and the Catamayo–Chira river (Ecuador and Peru). Compared against ERA5-Land, the bias-corrected and spatially disaggregated forecasts have a higher spatial resolution and show reduced biases and better agreement of spatial patterns than the raw forecasts as well as remarkably reduced lead-dependent drift effects. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000134589
Veröffentlicht am 01.07.2021
Originalveröffentlichung
DOI: 10.5194/essd-13-2701-2021
Scopus
Zitationen: 5
Web of Science
Zitationen: 5
Dimensions
Zitationen: 8
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung – Atmosphärische Umweltforschung (IMK-IFU)
Zukunftscampus (CAMPUS)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 1866-3516
KITopen-ID: 1000134589
HGF-Programm 12.11.33 (POF IV, LK 01) Regional Climate and Hydrology
Erschienen in Earth system science data
Verlag Copernicus Publications
Band 13
Heft 6
Seiten 2701–2722
Vorab online veröffentlicht am 15.06.2021
Nachgewiesen in Dimensions
Web of Science
Scopus
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