Developing an Ensemble Precipitation Algorithm from Satellite Products and Its Topographical and Seasonal Evaluations Over Pakistan

Khalil Rahman,Jiang Li,Songhao Shang,Muhammad Shahid

doi:10.3390/rs10111835

Abstract

Accurate estimation of precipitation is critical for hydrological, meteorological, and climate models. This study evaluates the performance of satellite-based precipitation products (SPPs) including Global Precipitation Measurement (GPM)-based Integrated Multi-Satellite Retrievals for GPM (IMERG), Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA 3B43-v7), Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network (PERSIANN), and PERSIANN-CDR (Climate Data Record), over Pakistan based on Surface Precipitation Gauges (SPGs) at spatial and temporal scales. A novel ensemble precipitation (EP) algorithm is developed by selecting the two best SPPs using the Paired Sample t-test and Principal Component Analysis (PCA). The SPPs and EP algorithm are evaluated over five climate zones (ranging from glacial Zone-A to hyper-arid Zone-E) based on six statistical metrics. The result indicated that IMERG outperformed all other SPPs, but still has considerable overestimation in the highly elevated zones (+20.93 mm/month in Zone-A) and relatively small underestimation in the arid zone (−2.85 mm/month in Zone-E). Based on the seasonal evaluation, IMERG and TMPA overestimated precipitation during pre-monsoon and monsoon seasons while underestimating precipitation during the post-monsoon and winter seasons. However, the developed EP algorithm significantly reduced the errors both on spatial and temporal scales. The only limitation of the EP algorithm is relatively poor performance at high elevation as compared to low elevations.

Highlights

Precipitation is crucial input parameter of the global hydrological cycle [1,2] and an impetuous factor contributing to natural disasters like droughts and flooding [3]
This research is conducted to test the abilities of Integrated Multi-Satellite Retrievals for GPM (IMERG) over the complex topography of Pakistan by comparing with other satellite-based precipitation products (SPPs) (TMPA, PERSIANN, and PERSIANN-CDR) and against the Surface Precipitation Gauges (SPGs) data
It is a challenging task in the developing countries such as Pakistan having sparse SPG network and complex topography

Summary

Introduction

Precipitation is crucial input parameter of the global hydrological cycle [1,2] and an impetuous factor contributing to natural disasters like droughts and flooding [3]. The performance of different hydrological, meteorological, and climate models depends on the accuracy of precipitation inputs. These models are used in reliable modeling, monitoring, and quantification of floods, drought assessment, landslides, agricultural production, and sustainable water resource management. Accurate precipitation estimation with high spatiotemporal resolution on a regional scale is essential for significant hydrological predictions. This is still a challenging task for the developing countries like Pakistan because of the sparse surface precipitation gauge (SPG) network and highly complex topography [9,10,11]

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Conclusion