Land surface physics-based downscaling approach for agricultural meteorological prediction: Applicability for tropical-monsoon region, the Red River Delta, Vietnam

Abstract

This study represents a pioneering effort to establish a downscaling framework named “land-surface-physics-based downscaling” (LSP-DS) for agricultural meteorological prediction in the tropical-monsoon region of the Red River Delta, Vietnam. The primary focus of this article is to evaluate the performance of LSP-DS on meteorological variables, specifically temperature and humidity. In details, LSP-DS, which is based on the NCAR's Noah Multi-Parameterizations land surface model, incorporated by recently developed land use/cover data for Vietnam released by JAXA, is forced by ERA5 data for 2013; and the results are compared with ground-based station observations. The results exhibit excellent performance of LSP-DS versus observations with consistently high correlation coefficient between the two, highlighting the high potential of using LSP-DS for real-time operational forecast. The LSP-DS performance varies with different sub land use/cover categories, implying that the proper parameter settings could be key point for improvement. The findings of this research underscore the dual strengths of the LSP-DS approach: its computational efficiency and its remarkable efficacy in predicting spatial heterogeneity of local climates. These attributes render it well-suited for agrometeorological forecasting in a tropical monsoon climate, exemplified by the Red River Delta in Vietnam.