The effects of the modified mosaic approach method on regional simulations of surface meteorological variables in western China

Abstract

In this article, a modified mosaic approach method (MMAM), which considers the subgrid‐scale effect of topographical height on atmospheric forcing, is introduced. Two experiments are designed to study the effects of MMAM on surface meteorological variables in Western China within the weather research and forecasting (WRF) modelling framework during June to August 2010. Results show that MMAM has obvious effects on surface 2 m temperature improvement. Simulations of the surface 2 m temperature with MMAM are closer to the observed values over a wide range of areas, particularly around the Qinghai‐Tibet Plateau and the Tianshan Mountains. However, MMAM does not show obvious improvements on 2 m relative humidity and on 10 m wind velocity. By investigations of two selected grid cells, we find that MMAM always shows a consistent deviation (positive or negative) on 2 m temperatures compared with the control experiment throughout the study period, which is related to the fact that the difference in topographical height within the model grid does not vary with time; the differences of surface energy illustrate that MMAM directly affects the upward longwave radiation due to a revision of the forcing temperature, resulting in a redistribution of surface energy.

Midsummer precipitation prediction over eastern China by the dynamic downscaling method

“…Previous research results show that the improvement of surface heat flux based on RCMs is helpful to the improvement of the temperature and precipitation prediction (Chen et al, 2019;Li et al, 2016). Li et al (2016) found that the intensity of simulated sensible heat flux over Asian continent in regional models can induce tropospheric temperature anomaly over land.…”

Towards an Optimal Representation of Sub‐Grid Heterogeneity in Land Surface Models

“…Assuming homogeneity of these processes within LSM grid cells neglects the nonlinear nature of the system; which can ultimately lead to various estimation issues (e.g., errors in estimation for development of the planetary boundary layer, initiation of shallow and deep convection, and cloud formation and precipitation) (Fisher & Koven, 2020; Simon et al., 2021; Tesfa et al., 2014; Vergopolan et al., 2022). Hence, correctly representing the effects of this physical heterogeneity in the LSM macroscale grid cells is vital to accurately represent weather and climate dynamics, as well as the hydrologic cycle (Chen et al., 2020; Li et al., 2013; Salmun and Molod, 2006).…”