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  • Author or Editor: Marlon Maranan x
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Marlon Maranan
,
Andreas H. Fink
,
Peter Knippertz
,
Leonard K. Amekudzi
,
Winifred A. Atiah
, and
Martin Stengel

Abstract

Using a two-year dataset (2016–17) from 17 one-minute rain gauges located in the moist forest region of Ghana, the performance of Integrated Multisatellite Retrievals for GPM, version 6b (IMERG), is evaluated based on a subdaily time scale, down to the level of the underlying passive microwave (PMW) and infrared (IR) sources. Additionally, the spaceborne cloud product Cloud Property Dataset Using SEVIRI, edition 2 (CLAAS-2), available every 15 min, is used to link IMERG rainfall to cloud-top properties. Several important issues are identified: 1) IMERG’s proneness to low-intensity false alarms, accounting for more than a fifth of total rainfall; 2) IMERG’s overestimation of the rainfall amount from frequently occurring weak convective events, while that of relatively rare but strong mesoscale convective systems is underestimated, resulting in an error compensation; and 3) a decrease of skill during the little dry season in July and August, known to feature enhanced low-level cloudiness and warm rain. These findings are related to 1) a general oversensitivity for clouds with low ice and liquid water path and a particular oversensitivity for low cloud optical thickness, a problem which is slightly reduced for direct PMW overpasses; 2) a pronounced negative bias for high rain intensities, strongest when IR data are included; and 3) a large fraction of missed events linked with rainfall out of warm clouds, which are inherently misinterpreted by IMERG and its sources. This paper emphasizes the potential of validating spaceborne rainfall products with high-resolution rain gauges on a subdaily time scale, particularly for the understudied West African region.

Open access
Simon Ageet
,
Andreas H. Fink
,
Marlon Maranan
,
Jeremy E. Diem
,
Joel Hartter
,
Andrew L. Ssali
, and
Prosper Ayabagabo

Abstract

Rain gauge data sparsity over Africa is known to impede the assessments of hydrometeorological risks and of the skill of numerical weather prediction models. Satellite rainfall estimates (SREs) have been used as surrogate fields for a long time and are continuously replaced by more advanced algorithms and new sensors. Using a unique daily rainfall dataset from 36 stations across equatorial East Africa for the period 2001–18, this study performs a multiscale evaluation of gauge-calibrated SREs, namely, IMERG, TMPA, CHIRPS, and MSWEP (v2.2 and v2.8). Skills were assessed from daily to annual time scales, for extreme daily precipitation, and for TMPA and IMERG near-real-time (NRT) products. Results show that 1) the SREs reproduce the annual rainfall pattern and seasonal rainfall cycle well, despite exhibiting biases of up to 9%; 2) IMERG is the best for shorter temporal scales while MSWEPv2.2 and CHIRPS perform best at the monthly and annual time steps, respectively; 3) the performance of all the SREs varies spatially, likely due to an inhomogeneous degree of gauge calibration, with the largest variation seen in MSWEPv2.2; 4) all the SREs miss between 79% (IMERG-NRT) and 98% (CHIRPS) of daily extreme rainfall events recorded by the rain gauges; 5) IMERG-NRT is the best regarding extreme event detection and accuracy; and 6) for return values of extreme rainfall, IMERG, and MSWEPv2.2 have the least errors while CHIRPS and MSWEPv2.8 cannot be recommended. The study also highlights improvements of IMERG over TMPA, the decline in performance of MSWEPv2.8 compared to MSWEPv2.2, and the potential of SREs for flood risk assessment over East Africa.

Open access