An Efficient Downscaling Scheme for High-Resolution Precipitation Estimates over a High Mountainous Watershed

Abstract

Satellites are capable of observing precipitation over large areas and are particularly suitable for estimating precipitation in high mountains and poorly gauged regions. However, the coarse resolution and relatively low accuracy of satellites limit their applications. In this study, a downscaling scheme was developed to obtain precipitation estimates with high resolution and high accuracy in the Heihe watershed. Shannon’s entropy, together with a semi-variogram, was applied to establish the optimal precipitation station network. A combination of the random forest (RF) method and the residual correction approach with the established rain gauge network was applied to downscale monthly precipitation products from Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). The results indicated that the RF model showed little improvement in the accuracy of IMERG-based precipitation downscaling. Including residual modification could improve the results of the RF model. The mean absolute error (MAE) and root mean square error (RMSE) values decreased by 19% and 21%, respectively, after residual corrections were added to the RF approach. Moreover, we found that enough rain gauge records are necessary for and remain an important component of tuning model performance. The application of more rain gauges improves the performance of the combined RF and residual modification methods, with the MAE and RMSE values reduced by 8% and 9%, respectively. Residual correction, together with enough precipitation stations, can effectively enhance the quality of the precipitation patterns and magnitudes obtained in the RF downscaling process. The proposed downscaling scheme is an effective tool for increasing the accuracy and spatial resolution of precipitation fields in the Heihe watershed.

Towards an Accurate and Reliable Downscaling Scheme for High-Spatial-Resolution Precipitation Data

“…However, some studies reported opposite results, indicating an improvement in precipitation data after residual correction. Zhao [69] illustrated that residual correction played an important role in RF-based downscaling. Residual correction greatly increased the accuracy of the precipitation estimation conducted by artificial neural networks [33].…”

Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI

“…However, careful optimization of the hyperparameters is necessary to achieve the best results. With the right approach, XGBoost enables data scientists to build accurate and reliable models that offer valuable insights into complex datasets 43 . XGBoost algorithm can be written aswhere f k is the leaf node's regular term of the kth classification tree, l y i , y i is the training error of sample xi, and Obj is the objective function 44 .…”

Clustering and assessment of precipitation climates using satellite precipitation products and improved machine learning precipitation models

“…Multisource high-performance satellite precipitation products along with geographical auxiliary data have also shown effective role to improve precipitation estimations in many researches Jia et al, 2011;Khan & Bhuiyan, 2021;Nosratpour et al, 2022;Retalis et al, 2017;Zhao, 2021). Retalis et al (2017) enhanced the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) retrievals for the years 1999-2010 by using altitude and NDVI in conjunction with an artificial neural network (ANN) model.…”