Abstract
The USGS (United States Geological Survey) land-use data used in the Weather Research and Forecasting (WRF) model have become obsolete as they are unable to accurately represent actual underlying surface features. Therefore, this study developed a new multi-satellite remote-sensing land-use dataset based on the latest GLC2015 (Global Land Cover, 2015) land-use data, which had 300 m spatial resolution. The new data were used to update the default USGS land-use dataset. Based on observational data from national meteorological observing stations in Xinjiang, northwest China, a comparison of the old USGS and new GLC2015 land-use datasets in the WRF model was performed for July 2018, where the simulated variables included the sensible heat flux (SHF), latent heat flux (LHF), surface skin temperature (Tsk), two-meter air temperature (T2), wind speed (Winds), specific humidity (Q2) and relative humidity (RH). The results indicated that there were significant differences between the two datasets. For example, our statistical verification results found via in situ observations made by the MET (model evaluation tools) illustrated that the bias of T2 decreased by 2.54%, the root mean square error (RMSE) decreased by 1.48%, the bias of Winds decreased by 10.46%, and the RMSE decreased by 6.77% when using the new dataset, and the new parameter values performed a net positive effect on land–atmosphere interactions. These results suggested that the GLC2015 land-use dataset developed in this study was useful in terms of improving the performance of the WRF model in the summer months.
Highlights
Land-use data are very important for atmospheric numerical modelling
A comparison of the old United States Geological Survey (USGS) and new GLC2015 land-use datasets in the Weather Research and Forecasting (WRF) model was performed for July 2018, where the simulated variables included the sensible heat flux (SHF), latent heat flux (LHF), surface skin temperature (Tsk), two-meter air temperature (T2), wind speed (Winds), specific humidity (Q2) and relative humidity (RH)
There are two sets of available land-use data for use in the WRF model: one consists of data produced by the United States Geological Survey (USGS) based on advanced very high-resolution radiation (AVHRR), which contains global imagery from April 1992 to March 1993 and which adopts the USGS’s 24 classification categories; the other is the dataset made by the University of Boston based on Moderate-resolution Imaging Spectroradiometer
Summary
Land-use data are very important for atmospheric numerical modelling Such data describe the properties of different types of land, including land-use categories characterized by six key physical parameters (e.g., albedo α, emissivity ε, roughness z0m , soil heat capacity C, surface thermal inertia λ and soil moisture availability M); each of these parameters plays an important role in land–atmosphere interactions [1,2]. These parameters regulate the exchanges of heat, moisture and momentum between the soil and the air, which in numerical models determine the calculations of meteorological variations (e.g., temperature, humidity) near the surface [3]. There are two sets of available land-use data for use in the WRF model: one consists of data produced by the United States Geological Survey (USGS) based on advanced very high-resolution radiation (AVHRR), which contains global imagery from April 1992 to March 1993 and which adopts the USGS’s 24 classification categories; the other is the dataset made by the University of Boston based on Moderate-resolution Imaging Spectroradiometer
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