Journal
JOURNAL OF HYDROLOGY
Volume 608, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.jhydrol.2022.127656
Keywords
Satellite precipitation product; Bias correction; Deep neural network; Ratio bias correction; Cumulative distribution function; Statistical evaluation
Funding
- Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering at Nanjing Hydraulic Research Institute, China [2018nkzd01]
- National Key R&D Program of China [2019YFC1805102]
- Ministry of Higher Education, Malaysia under Long-term Research Grant Scheme project 2 [LRGS/1/2020/UKM-USM/01/6/2]
- National Natural Science Foundation of China [41830863, 51679144]
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This study aims to establish a bidirectional long short-term memory recurrent network (Bi-LSTM) framework for the bias correction of satellite precipitation products (SPPs) in tropical regions. After optimization and statistical comparison, Bi-LSTM with the covariates of daily maximum and minimum temperature (Bi-LSTM-T) was determined to be the best model for bias correction. Bi-LSTM-T significantly outperformed two benchmark methods, ratio bias correction (RBC) and cumulative distribution function (CDF) matching, in correcting the bias of SPPs in tropical regions.
The high spatiotemporal variability of rainfall in tropical regions has posed a great challenge for generating satisfactory satellite precipitation products (SPPs). Most of previous studies have found a modest performance of various SPPs in estimating daily rainfall in tropical regions such as Malaysia. In-depth research on effective ways to correct the bias of SPPs in the tropical region is urgently needed. This study aims to establish a bidirectional long short-term memory recurrent network (Bi-LSTM) framework for the bias correction of SPPs, and apply it to correct daily rainfall estimates of the early runs of Integrated Multisatellite Retrievals for the Global Precipitation Measurement (IMERG-E) from 2010 to 2016 in the Kelantan River Basin, Malaysia. After optimization and statistical comparison, Bi-LSTM with the covariates of daily maximum and minimum temperature (Bi-LSTM-T) was determined to be the best model for bias correction. Annually, Bi-LSTM-T could raise the correlation coefficient (CC) of IMERG-E by 26.7%, while reducing its root mean square error (RMSE) and mean absolute error (MAE) by 23.9% and 21.7% in the Kelantan River Basin. In the four seasons, it increased the CC of IMERG-E by 19.7-27.5%, while decreasing its RMSE and MAE by 18.4-30.0% and 20.9-23.2%. Multiple statistical tests confirmed that Bi-LSTM-T significantly outperformed two benchmark methods, namely ratio bias correction (RBC) and cumulative distribution function (CDF) matching, in correcting the bias of IMERG-E for all four seasons. This suggests that the Bi-LSTM-T model may work as a promising framework of great potentials for correcting the bias of SPPs in tropical regions, where adequate precipitation data are in great need for diverse purposes such as water-related disaster prevention and mitigation.
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