Performance of precipitation products obtained from combinations of satellite and surface observations

ABSTRACT

Knowing the spatiotemporal distribution of precipitation is undoubtedly important for planning various economic/social activities, such as agriculture, livestock, and energy production. The coarse observation density over certain regions may significantly compromise the quality of precipitation products interpolated by only surface observations. To minimize the lack of observations over certain regions, the Centre for Weather Forecast and Climate Studies (CPTEC) of National Institute for Space Research (INPE) developed two types of blended precipitation products, namely, the Combined Scheme (CoSch) and MERGE, which combine observed precipitation data with satellite estimates on a daily scale. To understand how different blending methodologies impact the final results, a comparison of each algorithm with independent rain gauges was performed with a focus over the Brazilian territory. Both products were generated at a 10-km horizontal resolution using input data from the Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG-Early) for product (Version 5) in conjunction with surface observations from Surface Synoptic Observations (SYNOP), data collection platforms (DCPs) and data from regional meteorological centres. The cumulative 24-hour precipitation was evaluated for the period from June 2014 to June 2017. The results show that both products reliably characterize the precipitation regimes over most of the study regions, although MERGE and CoSch tend to over- and underestimate the amount of precipitation, respectively. However, the magnitude of the Bias achieved by MERGE is smaller than that achieved by CoSch. Overall, MERGE outperforms CoSch when analysing rain/no rain and light to moderate rainfall (0.5 to 20.0 mm). For heavy precipitation (>35.0 mm), the performance of both products is similar. The most significant differences between the two products occur over the Northeast Region of Brazil (R3 and R4), where CoSch tends to encounter difficulties characterizing the precipitation regime during the northeastern wet period (April – November). In R3 and R4, MERGE relies more on surface observations, whereas CoSch relies on GPM-IMERG-Early, which could be associated with the deficiency of GPM-IMERG-Early in estimating the amount of precipitation associated with warm clouds.

KEYWORDS:

• precipitation
• satellite
• TRMM
• GPM
• MERGE
• CoSch

Acknowledgements

The authors would like to acknowledge Fundação de Amparo à Pesquisa de São Paulo (FAPESP) - project 2018/11160-2 “Sistema de Monitoramento Hidrometeorológico (SMH) Baseado em Produtos de Sensoriamento Remoto”.

Disclosure statement

No potential conflict of interest was reported by the authors.