Performance Assessment of Satellite Precipitation Products over Nigeria: A Compromise Programming Approach

Satellite precipitation products (SPPs) remain a good alternative to gauge based data, where data can be scarce or not available at long term. However, they can be characterized by uncertainties which can emanate from complexities like sampling, algorithmic and instrumental errors; sensitivity to topography; land use and land cover, contrasts in surface temperature and emissivity and climate. Therefore, the choice of an SPP for climate and hydrological study is crucial to its outcome. This study assesses the performances of five SPPs namely, CHIRPS, CMORPH, MSWEP, PERSIANN – CDR and TAMSAT in reproducing the properties of observed precipitation for annual and seasonal periods. Five statistical metrics namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Modified Index of Agreement (MD), Cohen’s Effect Size (D) and Skill Score (SS) were used. The scores from the metrics for each SPP were aggregated using compromise programming (CP) in order to determine the best performing SPP. In addition, Boxplot and Taylor diagram were used to assess the closeness of the SPPs to the observed precipitation at both seasonal and annual timescales. The study revealed that based on the statistical performances for the annual and seasonal periods at the different stations, TAMSAT ranked as the best performing SPP 104 times over MSWEP with 101, CHIRPS with 93, CMORPH with 26 and PERSIANN-CDR with only 1. However, TAMSAT was placed second to MSWEP after the aggregation of the statistical metrics using CP. The performances of the SPPs were affected by the climatic conditions with CHIRPS having the best performance at the Sahel & Sudan zone (SSZ), TAMSAT at the Guinea zone (GZ) and MSWEP at the Rainforest & Mangrove zone (RMZ) of the country for the annual evaluation. Performances also varies during the seasonal periods. PERSIANN-CDR has been found to perform poorly for the study area in this study.