Evaluation of the Performance of Three Satellite Precipitation Products over Africa

Abstract

Abstract:We present an evaluation of daily estimates from three near real-time quasi-global Satellite Precipitation Products-Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Climate Prediction Center (CPC) Morphing Technique (CMORPH)-over the African continent, using the Global Precipitation Climatology Project one Degree Day (GPCP-1dd) as a reference dataset for years 2001 to 2013. Different types of errors are characterized for each season as a function of spatial classifications (latitudinal bands, climatic zones and topography) and in relationship with the main rain-producing mechanisms in the continent: the Intertropical Convergence Zone (ITCZ) and the East African Monsoon. A bias correction of the satellite estimates is applied using a probability density function (pdf) matching approach, with a bias analysis as a function of rain intensity, season and latitude. The effects of bias correction on different error terms are analyzed, showing an almost elimination of the mean and variance terms in most of the cases. While raw estimates of TMPA show higher efficiency, all products have similar efficiencies after bias correction. PERSIANN consistently shows the smallest median errors when it correctly detects precipitation events. The areas with smallest relative errors and other performance measures follow the position of the ITCZ oscillating seasonally over the equator, illustrating the close relationship between satellite estimates and rainfall regime.

Spatial assessment of the performance of multiple high‐resolution satellite‐based precipitation data sets over the Middle East

“…The very low bias (0.05 mm) can probably be explained by the summed effect of false alarms (overestimations) and missed events (underestimations) of daily rainfall, which tend to balance out. While both CMORPH‐BLD and PERSIANN‐CDR assimilate information from infrared (IR) and passive micro‐waves (PMW) sensors, TMPA extends its retrieval synthesis further to include information from TRMMs orbital radar, which seems to improve the quality of estimates compared to the other two products (Serrat‐Capdevila et al ., ). Overall, the validation metrics suggest that TRMM‐3B42 can be useful to reproduce the observed rainfall characteristics aggregated at coarser timescales (i.e., monthly and seasonally).…”

Evaluation of remotely sensed rainfall products over Central Africa

“…Via a multiple comparison ranking, and by taking GPCC (Global Precipitation Climatology Centre) data over entire Africa and rain-gauge observations over the Greater Horn of Africa as references, it was found that PERSIANN had the highest signal-to-noise ratio, followed by ARC v2, TMPA, CMORPH, TAMSAT and GSMaP. A similar T A B L E 1 Inventory of satellite rainfall estimates datasets listed in the literature review or used in the study (upper part of the intercomparison, taking into account a smaller number of datasets but at a finer temporal resolution (daily) was carried out by Serrat-Capdevila et al (2016). TMPA was found to have the smallest bias in Central Africa compared to the other products, underestimating daily rainfall on average by less than 10%, and PERSIANN has the smallest median errors of rainfall amounts when it correctly detects precipitation events.…”

Applications of Satellite-Based Rainfall Estimates in Flood Inundation Modeling—A Case Study in Mundeni Aru River Basin, Sri Lanka

“…In addition, the RRI model has been utilized with GSMaP to simulate the 2008 flood event in Pakistan [20]. In the meantime, SRE products have been compared with each other and their characteristics and accuracy have also been evaluated [21][22][23]. However, evaluation among multiple SREs in terms of flood inundation reproducibility has not been done yet.…”