Author(s)

Wenfan Wang¹, Jiemin Zhan¹, Dehui Chen², Zitong Chen², Yanxia Zhang², Qing Fan^1*, Guangfeng Dai², Yingying Luo¹, Aifen Ye²

¹Department of Applied Mechanics and Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China.
²Guangzhou Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction, CMA, Guangzhou, China.

In this paper, the CMA-TRAMS tropical high-resolution system was used to forecast a typical hot weather process in Guangdong, China with different horizontal resolutions and surface coverage. The results of resolutions of 0.02° and 0.06° were presented with the same surface coverage of the GlobeLand30 V2020, companies with the results of resolution 0.02° with the USGS global surface coverage. The results showed that, on the overall assessment the 2 km model performed better in forecasting 2 m temperature, while the 6 km model was more accurate in predicting 10 m wind speed. In the evaluation of representative stations, the 2 km model performed better in forecasting 2 m temperature and 2 m relative humidity at the coastal stations, and the 2 km model was also better in forecasting 2 m pressure at the representative stations. However, the 6 km model performed better in forecasting 10 m wind speed at the representative stations. Furthermore, the 2 km model, owing to its higher horizontal resolution, presented a more detailed stratification of various meteorological field maps, allowing for a more pronounced simulation of local meteorological element variations. And the use of the surface coverage data of the GlobeLand30 V2020 improved the forecasting of 2 m temperature, and 10 m wind speed compared to the USGS surface coverage data.

High-Resolution Numerical Model, CMA-TRAMS Model, Test Evaluation, Surface Coverage, Numerical Simulation

Wang, W. , Zhan, J. , Chen, D. , Chen, Z. , Zhang, Y. , Fan, Q. , Dai, G. , Luo, Y. and Ye, A. (2024) Preliminary Model Study for Forecasting a Hot Weather Process in Guangdong Province Using CMA-TRAMS. Atmospheric and Climate Sciences, 14, 101-117. doi: 10.4236/acs.2024.141006.