Enhancing Precipitation Estimates Through the Fusion of Weather Radar, Satellite Retrievals, and Surface Parameters

Youssef Wehbe,Marouane Temimi,Robert F Adler

doi:10.3390/rs12081342

Abstract

Accurate and timely monitoring of precipitation remains a challenge, particularly in hyper-arid regions such as the United Arab Emirates (UAE). The aim of this study is to improve the accuracy of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission’s latest product release (IMERG V06B) locally over the UAE. Two distinct approaches, namely, geographically weighted regression (GWR), and artificial neural networks (ANNs) are tested. Daily soil moisture retrievals from the Soil Moisture Active Passive (SMAP) mission (9 km), terrain elevations from the Advanced Spaceborne Thermal Emission and Reflection digital elevation model (ASTER DEM, 30 m) and precipitation estimates (0.5 km) from a weather radar network are incorporated as explanatory variables in the proposed GWR and ANN model frameworks. First, the performances of the daily GPM and weather radar estimates are assessed using a network of 65 rain gauges from 1 January 2015 to 31 December 2018. Next, the GWR and ANN models are developed with 52 gauges used for training and 13 gauges reserved for model testing and seasonal inter-comparisons. GPM estimates record higher Pearson correlation coefficients (PCC) at rain gauges with increasing elevation (z) and higher rainfall amounts (PCC = 0.29 z0.12), while weather radar estimates perform better for lower elevations and light rain conditions (PCC = 0.81 z−0.18). Taylor diagrams indicate that both the GWR- and the ANN-adjusted precipitation products outperform the original GPM and radar estimates, with the poorest correction obtained by GWR during the summer period. The incorporation of soil moisture resulted in improved corrections by the ANN model compared to the GWR, with relative increases in Nash–Sutcliffe efficiency (NSE) coefficients of 56% (and 25%) for GPM estimates, and 34% (and 53%) for radar estimates during summer (and winter) periods. The ANN-derived precipitation estimates can be used to force hydrological models over ungauged areas across the UAE. The methodology is expandable to other arid and hyper-arid regions requiring improved precipitation monitoring.

Highlights

Despite the widely reported inconsistencies of precipitation products over the Arabian Peninsula [1,2,3,4], a limited number of studies have attempted to improve precipitation monitoring over the progressively water-stressed region
Sources of precipitation estimates can be broadly grouped into three classes, namely: (i) ground-based rain gauge and radar observations, (ii) satellite precipitation retrievals, and (iii) reanalysis products fused from numerical weather predictions (NWP) models and observations
This study provides the first attempt of multivariate nonlinear precipitation estimation over the United Arab Emirates (UAE) by correcting the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission’s latest daily product release (IMERG V06B) overland using ancillary data and explanatory variables

Summary

Introduction

Despite the widely reported inconsistencies of precipitation products over the Arabian Peninsula [1,2,3,4], a limited number of studies have attempted to improve precipitation monitoring over the progressively water-stressed region. Satellite products continue to be the most widely used precipitation data sources These include products from the Tropical Rainfall Measurement Mission (TRMM) [11] and its successor the Global Precipitation Measurement (GPM) mission [12], the Global Precipitation Climate Center (GPCC) [13], the Climate Research Unit (CRU) [14], and the Climate Prediction Center morphing (CMORPH) technique [15], among others. Despite their widespread applications, their uncertainties remain high, especially over arid regions with absolute and relative biases reaching 100 mm and 300%, respectively [16,17]. The sparse distribution of rain gauges and inhomogeneity of observations hamper the calibration of such products for improved water resource management with rapidly expanding urbanization across the Arabian Peninsula [5]

Objectives

Methods

Results

Conclusion