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

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.

On the analysis of a summertime convective event in a hyperarid environment

“…Consistent with the larger Arabian Peninsula rainfall regime, the majority of the precipitation occurs during the cold season from December to March, associated with midlatitude weather disturbances (Wehbe et al, 2018). Nevertheless, rain accumulations during the summer season in excess of 100 mm at isolated spots are recorded, and are mainly associated with short-lived mesoscale convective systems (MCSs) over the Al Hajar mountains (Branch et al, 2020;Wehbe et al, 2020). Despite their minimal contribution to the country's annual rainfall average, an understanding of the processes that favour the development of MCSs and an assessment of the capability of numerical models in representing them is crucial, for example, for the planning of cloud seeding activities that are regularly conducted in the eastern half of the country (Al Mazroui and Farrah, 2017;Branch et al, 2020).…”

“…Consistent with the larger Arabian Peninsula rainfall regime, the majority of the precipitation occurs during the cold season from December to March, associated with midlatitude weather disturbances (Wehbe et al ., 2018). Nevertheless, rain accumulations during the summer season in excess of 100 mm at isolated spots are recorded, and are mainly associated with short‐lived mesoscale convective systems (MCSs) over the Al Hajar mountains (Branch et al ., 2020; Wehbe et al ., 2020).…”

A Nonlinear Radiometric Normalization Model for Satellite Imgaes Time Series Based on Artificial Neural Networks and Greedy Algroithm

“…The linearity assumption of radiometric distortion in SITS is imprecise, and the nonlinear regression model may achieve more accurate normalization results of SITS. As a representative method of machine learning, ANN regression model has been widely used to solve nonlinear regression problems in remote sensing applications and has achieved significant results [24,25]. The schematic diagram of ANN [26] is shown in Figure 2.…”

Geographically Weighted Method Integrated with Logistic Regression for Analyzing Spatially Varying Accuracy Measures of Remote Sensing Image Classification

“…The focus of our attention on GWR is motivated by numerous studies which have demonstrated its potential in the investigation of spatially varying relationships, including climatology (Brunsdon et al 2001 ; Khosravi and Balyani 2019 ; Wehbe et al 2020 ), health (Lin and Wen 2011 ; Ehlkes et al 2014 ; Weber 2018 ; Hong et al 2018 ; Hasyim et al 2018 ), real estate management (Lu et al 2014 ; Liu et al 2016 ), urban studies (Faisal and Shaker 2017 ; Wang et al 2020 ) and land change analysis (Maimaitijiang et al 2015 ; Dadashpoor et al 2019 ). GWR can also be applied in combination with linear, logistic and poisson regression techniques for various applications and reported in many studies (Nakaya et al 2005 ; Ehlkes et al 2014 ; Mayfield et al 2018 ).…”