Paired Satellite and NWP Precipitation for Global Flood Forecasting

Author:

Huang Zhijun12,Wu Huan123ORCID,Gu Guojun3,Li Xiaomeng12,Nanding Nergui4,Adler Robert F.3,Yilmaz Koray K.5,Alfieri Lorenzo67,Chen Sirong8

Affiliation:

1. a School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China

2. b Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Zhuhai, China

3. c Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

4. d School of Earth Sciences, Yunnan University, Kunming, China

5. e Department of Geological Engineering, Middle East Technical University, Ankara, Turkey

6. f Disaster Risk Management Unit, European Commission Joint Research Centre, Ispra, Italy

7. g CIMA Research Foundation, Savona, Italy

8. h Guangxi Climate Center, Nanning, China

Abstract

Abstract Precipitation data are known to be the key driver of hydrological simulations. Hence, reliable quantitative precipitation estimates and forecasts are vital for accurate hydrological forecasting. Satellite-based precipitation estimates from Integrated Multi-satellitE Retrievals for GPM Early Run (IMERG-E) and forecasted precipitation from NASA’s Goddard Earth Observing System Forward Processing (GEOS-FP) have shown values in global flood nowcasting and forecasting. However, few studies have comprehensively evaluated their hydrological performance let alone explored the potential value of combining them. Therefore, this study undertakes a quasi-global evaluation of their utility in real-time hydrological monitoring and 1–5-day forecasting with the Dominant River Tracing-Routing Integrated with Variable Infiltration Capacity (VIC) Environment (DRIVE) model. The gauge-corrected IMERG Final Run precipitation estimates and corresponding hydrological simulation are used as the references. Results showed that the hit bias is the dominant error source of IMERG-E, while the false precipitation is more noticeable in GEOS-FP. In terms of hydrological performance, the GEOS-FP-driven model (DRIVE-FP) performance is close to the IMERG-E-driven model (DRIVE-E) performance on day 1, indicating that GEOS-FP could nicely fill the gap of nowcasting caused by the IMERG-E time latency. For longer lead-time forecasts, the bias tends to diminish in most regions, likely because the under- or overestimation in IMERG-E is generally offset by the distinct types of misestimation in GEOS-FP. The skillful initial hydrological conditions present outperformed forecasts in most regions, except for tropical areas where the accuracy of GEOS-FP prevails. Overall, this study provides a valuable view of the combined use of IMERG-E and GEOS-FP precipitation in the context of hydrological nowcasts and forecasts.

Funder

National Natural Science Foundation of China

Program for Guangdong Introducing Innovative and Entrepreneurial Teams

Key R&D Program of Guangxi

Hainan R&D Program

Publisher

American Meteorological Society

Subject

Atmospheric Science

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