Improving the Regional Applicability of Satellite Precipitation Products by Ensemble Algorithm

Abstract

Satellite-based precipitation products (e.g., Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) and its predecessor, Tropical Rainfall Measuring Mission (TRMM)) are a critical source of precipitation estimation, particularly for a region with less, or no, hydrometric networking. However, the inconsistency in the performance of these products has been observed in different climatic and topographic diverse regions, timescales, and precipitation intensities and there is still room for improvement. Hence, using a projected ensemble algorithm, the regional precipitation estimate (RP) is introduced here. The RP concept is mainly based on the regional performance weights derived from the Mean Square Error (MSE) and the precipitation estimate from the TRMM product, that is, TRMM 3B42 (TR), real-time (late) (IT) and the research (post-real-time) (IR) products of IMERG. The overall results of the selected contingency table (e.g., Probability of detection (POD)) and statistical indices (e.g., Correlation Coefficient (CC)) signposted that the proposed RP product has shown an overall better potential to capture the gauge observations compared with the TR, IR, and IT in five different climatic regions of Pakistan from January 2015 to December 2016, at a diurnal time scale. The current study could be the first research providing preliminary feedback from Pakistan for global precipitation measurement researchers by highlighting the need for refinement in the IMERG.

An Appraisal of Dynamic Bayesian Model Averaging-based Merged Multi-Satellite Precipitation Datasets Over Complex Topography and the Diverse Climate of Pakistan

“…However, SPPs observations are subjected to uncertainties resulting from retrieval algorithms. To reduce these uncertainties and to obtain high-quality precipitation estimates, researchers have focused on merging individual SPPs [16,22,26,70].…”

“…It accounts for both spatial and temporal variability in the performances of the merging members. The DBMA-MSPD was found superior to both SMA and OOR merging methods [26].Very limited literature is available on developing and evaluating the MSPDs across Pakistan [16,22,48]. The results demonstrated significant improvements of MSPDs overall merging members.…”

“…The applications of SPPs are complicated, and the proper selection of one SPP over another is debatable. Therefore, the evaluation of each SPP against the in-situ observations before the application is extremely important and considered a standard process [14,15].Various studies have evaluated different SPPs against the GPGs both on regional and global scales at daily [16][17][18][19][20][21][22], monthly [17,19,23,24], and annual [17,20] temporal resolutions. It is concluded from the previous studies that all the SPPs have inevitable errors based on different statistical indices, including mean error, correlation with GPGs, and other categorical indices (probability of detection, critical success index, and false alarm ratio).…”

“…The results demonstrated significant improvements of MSPDs overall merging members. However, most of the literature used the fixed weight techniques [16,22] or the dynamic weight algorithms to merge different SPPs in each climate region [48], and the spatial variation of these weights are not fully considered. The present MSPD considers both spatial and temporal variation in weights for different SPPs over complex topography and diverse climate of Pakistan.The key objectives of the current study are to develop a MSPD using the Dynamic Bayesian Model Averaging (DBMA-MSPD) algorithm for whole Pakistan, to evaluate and compare the performance of DBMA-MSPD against the individual members at seasonal and topographical scales.…”

“…Very limited literature is available on developing and evaluating the MSPDs across Pakistan [16,22,48]. The results demonstrated significant improvements of MSPDs overall merging members.…”