Estimating Rainfall with Multi-Resource Data over East Asia Based on Machine Learning

Abstract

The lack of accurate estimation of intense precipitation is a universal limitation in precipitation retrieval. Therefore, a new rainfall retrieval technique based on the Random Forest (RF) algorithm is presented using the Advanced Himawari Imager-8 (Himawari-8/AHI) infrared spectrum data and the NCEP operational Global Forecast System (GFS) forecast information. And the gauge-calibrated rainfall estimates from the Global Precipitation Measurement (GPM) product served as the ground truth to train the model. The two-step RF classification model was established for (1) rain area delineation and (2) precipitation grades’ estimation to improve the accuracy of moderate rain and heavy rain. In view of the imbalance categories’ distribution in the datasets, the resampling technique including the Random Under-sampling algorithm and Synthetic Minority Over-sampling Technique (SMOTE) was implemented throughout the whole training process to fully learn the characteristics among the samples. Among the features used, the contributions of meteorological variables to the trained models were generally greater than those of infrared information; in particular, the contribution of precipitable water was the largest, indicating the sufficient necessity of water vapor conditions in rainfall forecasting. The simulation results by the RF model were compared with the GPM product pixel-by-pixel. To prove the universality of the model, we used independent validation sets which are not used for training and two independent testing sets with different periods from the training set. In addition, the algorithm was validated against independent rain gauge data and compared with GFS model rainfall. Consequently, the RF model identified rainfall areas with a Probability Of Detection (POD) of around 0.77 and a False-Alarm Ratio (FAR) of around 0.23 for validation, as well as a POD of 0.60–0.70 and a FAR of around 0.30 for testing. To estimate precipitation grades, the value of classification was 0.70 in validation and in testing the accuracy was 0.60 despite a certain overestimation. In summary, the performance on the validation and test data indicated the great adaptability and superiority of the RF algorithm in rainfall retrieval in East Asia. To a certain extent, our study provides a meaningful range division and powerful guidance for quantitative precipitation estimation.

Opposite Changes in Tropical Cyclone Rain Rate During the Recent El Niño and La Niña Years

“…Because the rain rate in weak TCs is usually small (Lonfat et al., 2004), tropical depressions (<35 knots) are excluded. Following previous works (Guzman & Jiang, 2021; Hu et al., 2017; Rehman et al., 2018; Zhang et al., 2021), rain rate values <0.1 mm hr −1 are considered as non‐rain, so that only pixel rain rates ≥0.1 mm hr −1 are considered in this work. We adopt the same approach of Tu et al.…”

“…Because the rain rate in weak TCs is usually small (Lonfat et al, 2004), tropical depressions (<35 knots) are excluded. Following previous works (Guzman & Jiang, 2021;Hu et al, 2017;Rehman et al, 2018;Zhang et al, 2021), rain rate values <0.1 mm hr −1 are considered as non-rain, so that only pixel rain rates ≥0.1 mm hr −1 are considered in this work. We adopt the same approach of Tu et al (2021) to calculate the average rain rate in each 25-km annulus from the TC center (such as 0-25, 25-50, 50-75 km, …) to obtain the radial distribution of TC rain rate (hereafter, for simplicity, the term "rain rate" will refer to TC rain rate unless otherwise stated).…”

Above‐ground biomass retrieval with multi‐source data: Prediction and applicability analysis in Eastern Mongolia

“…For instance, a recent study on salinity intrusion mapping in Vietnam's Mekong Delta, lacked training points at two cities in the north (Nguyen et al, 2021). Another example is a recent study on mapping rainfall in Eastern Asia, where no training data are included from outside of China (Zhang et al, 2021), but predictions are still made for these areas. In this case, how reliable is the accuracy of prediction in such areas without training points?…”

A Neural Network‐Based Scale‐Adaptive Cloud‐Fraction Scheme for GCMs

“…All yield promising outcomes. For the same reasons, the networks with complex architectures exhibit superiorities in building tools for specific predictions such as El Niño (Ham et al., 2019; Nooteboom et al., 2018), precipitation (G. Chen & Wang, 2022; Ravuri et al., 2021; Shi et al., 2015) and clouds (J. Zhang et al., 2018), and for data processing (Kim et al., 2021; Leinonen et al., 2021; Pan et al., 2019, 2021, 2022; Rasp & Lerch, 2018; Y. Zhang et al., 2021).…”