A high-resolution dataset for lower atmospheric process studies over the Tibetan Plateau from 1981 to 2020

Abstract

A high-resolution meteorological dataset (≤10 km) over the Tibetan Plateau (TP) is the foundation for investigating and predicting the weather and climate over Asia. The TP Subregional Dynamical Downscaling (TPSDD) dataset is a newly developed high-spatial-temporal resolution gridded dataset for land‒air exchange processes and lower atmospheric structure studies over the whole TP region, taking the climate characteristics of each TP subregion into consideration. The dataset spans from 1981 to 2020, covering the TP with a temporal resolution of 2 hr and spatial resolution of 10 km. Meteorological elements of the dataset include near-surface land–air exchange parameters, such as downward/upward longwave/shortwave radiation flux, sensible heat flux, latent heat flux, etc. In addition, the vertical distributions of 3-dimensional wind, temperature, humidity, and pressure from the surface to the lower stratosphere are also included. Independent evaluations were conducted to verify the performance of the TPSDD dataset by comparing TPSDD/reanalysis with surface and vertical observations through the calculation of statistical parameters. The results demonstrate the accuracy and superiority of this dataset against reanalysis data, which provides great potential for future climate change research.

Keywords

• Tibetan Plateau
• WRF model
• dynamical downscaling
• land‒air interaction
• the lower atmosphere