Evaluating the Benefits of Merging Near-Real-Time Satellite Precipitation Products: A Case Study in the Kinu Basin Region, Japan

Abstract

After the launch of the Global Precipitation Measurement (GPM) mission in 2014, many satellite precipitation products (SPPs) are available at finer spatiotemporal resolution and/or with reduced latency, potentially increasing the applicability of SPPs for near-real-time (NRT) applications. Therefore, there is a need to evaluate the NRT SPPs in the GPM era and investigate whether bias-correction techniques or merging of the individual products can increase the accuracy of these SPPs for NRT applications. This study utilizes five commonly used NRT SPPs, namely, CMOPRH RT, GSMaP NRT, IMERG EARLY, IMERG LATE, and PERSIANN-CCS. The evaluation is done for the Kinu basin region in Japan, an area that provides observed rainfall data with high accuracy in space and time. The selected bias correction techniques are the ratio bias correction and cumulative distribution function matching, while the merged products are derived with the error variance, inverse error variance weighting, and simple average merging techniques. Based on the results, all SPPs perform best for lower-intensity rainfall events and have challenges in providing accurate estimates for typhoon-induced rainfall (generally more than 50% underestimation) and at very fine temporal scales. Although the bias correction techniques successfully reduce the bias and improve the performance of the SPPs for coarse temporal scales, it is found that for shorter than 6-hourly temporal resolutions, both techniques are in general unable to bring improvements. Finally, the merging results in increased accuracy for all temporal scales, giving new perspectives in utilizing SPPs for NRT applications, such as flood and drought monitoring and early warning systems.

Comprehensive Evaluations on the Error Characteristics of the State‐of‐the‐Art Gridded Precipitation Products Over Jiangxi Province in 2019

“…The update frequency of ERA5 reanalysis precipitation data is about 1 day, while ERA5‐Land data is released monthly with a delay of about 3 months relatively to actual date, and IMERG‐Final has the latency of about 3.5 months. Benefiting from the short data release latency, ERA5 precipitation product has the potential to be used in the near‐real‐time hydrological and meteorological applications (Mastrantonas et al., 2019; Massari et al., 2019; Wang et al., 2019; Xu et al., 2019). As for the temporal coverage, the reanalysis precipitation products do have superiorities.…”

Assessment of Merged Satellite Precipitation Datasets in Monitoring Meteorological Drought over Pakistan

“…Furthermore, DML-MSPD also depicted better performance in capturing precipitation with a threshold of 1 mm/day, as compared to standalone SPDs. Mastrantonas et al [90] developed MSPD by merging five near real-time SPDs, including CMORPH, GSMaP, IMERG Early, IMERG Late, and PERSAINN SPDs using three techniques, i.e., Error Variance (EV), Inverse Error Variance Weighting (IEVW), and Simple Averaging (SA). Results showed that all SPDs have accurate precipitation estimates for lower intensity precipitation events, while they underestimated (more than 50%) the typhoon-induced rainfall.…”

Adequacy of Near Real-Time Satellite Precipitation Products in Driving Flood Discharge Simulation in the Fuji River Basin, Japan

“…Moreover, it is simple and easy to implement applicable. It has a wide range of applications in the field of SPPs correction [32,33] and the Inverse Distance Weighted (IDW) was selected as the interpolation method since it has the advantages of simplicity, computational speed, and good performance [47,48]. The main calculation steps are presented below:…”

“…Therefore, it is essential to introduce bias correction methods to improve NRT SPPs before their practical application [26]. As a result, several previous studies focused on reducing biases or deviations in NRT SPPs with different methods such as mean factor correction method [27,28], quantile mapping approach [29,30], Bayesian method [31], multiplicative bias correction [32], and ratio bias correction method [32,33]. Moreover, some new techniques like merging soil moisture with SPPs were introduced [34].…”