This study examines the impacts of land-use data on the simulation of surface air temperature in Northwest China by the Weather Research and Forecasting (WRF) model. International Geosphere-Biosphere Program (IGBP) landuse data with 500-m spatial resolution are generated from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products. These data are used to replace the default U.S. Geological Survey (USGS) land-use data in the WRF model. Based on the data recorded by national basic meteorological observing stations in Northwest China, results are compared and evaluated. It is found that replacing the default USGS land-use data in the WRF model with the IGBP data improves the ability of the model to simulate surface air temperature in Northwest China in July and December 2015. Errors in the simulated daytime surface air temperature are reduced, while the results vary between seasons. There is some variation in the degree and range of impacts of land-use data on surface air temperature among seasons. Using the IGBP data, the simulated daytime surface air temperature in July 2015 improves at a relatively small number of stations, but to a relatively large degree; whereas the simulation of daytime surface air temperature in December 2015 improves at almost all stations, but only to a relatively small degree (within 1 degrees C). Mitigation of daytime surface air temperature overestimation in July 2015 is influenced mainly by the change in ground heat flux. The modification of underestimated temperature comes mainly from the improvement of simulated net radiation in December 2015.

Based on the U.S. Weather Research and Forecasting (WRF) numerical model, this study has developed the Northwest Mesoscale Numerical Prediction Service and Experimental System (NW-MNPS). Surface and sounding data assimilation has been introduced for this system. Effects of model vertical layers and land-use data replacement have been assessed. A year-long forecast validation and analysis have been performed. The following results have been obtained: (1) Data assimilation can improve the performance of regional numerical forecasting. (2) Compared to simulations with 40 vertical layers, simulations with 55 vertical layers are more accurate. The average absolute error and root-mean-square error of the 48 h surface element forecast decrease. The analysis of threat score (TS) and equitable threat score (ETS) shows that there are higher TS and ETS values for various precipitation intense levels, in particular for heavy rainfall when comparing a 55-vertical-layer test with a 40-vertical-layer test. (3) Updating the database to include vegetation coverage can more accurately reflect actual surface conditions. The updated land-use data reduce prediction errors in all domains of the NW-MNPS.

Soil temperature is a crucial parameter in surface emissions of carbon, water, and energy exchanges. This study utilized the soil temperature of 836 national basic meteorological observing stations over China to evaluate three soil temperature products. Soil temperature data from the China Meteorology Administration Land Data Assimilation System (CLDAS), European Centre for Medium-Range Weather Forecasts (ERA-Interim), and Global Land Data Assimilation System (GLDAS) during 2017 are evaluated. The results showed that soil temperature reanalysis datasets display a significant north-to-south difference over eastern China with generally underestimated magnitudes. CLDAS data perform soil temperature assessment best at different depths and can be reproduced well in most areas of China. CLDAS slightly overestimates soil temperature in summer. The most significant deviation of ERA-Interim (GLDAS) appears in summer (summer and autumn). As soil depth increases, the soil temperature errors of all three datasets increase. The CLDAS represents the soil temperature over China but owns a more considerable bias in barren or sparsely vegetated croplands. ERA-Interim performs poorest in urban and built-up and barren or sparsely vegetated areas. GLDAS overall owns an enormous bias at the mixed forest, grassland, and croplands areas, which should be improved, especially in summer. However, it performs better in open shrublands and barren or sparsely vegetated areas. The ST of mixed forests shows better results in the south region than the north region. For grasslands, smaller MEs are located in the north and northwest regions. The ST of croplands shows the poorest performance over the northwest region.

Wire icing is a prevalent challenge in both industrial and scientific domains, and it is widely acknowledged that terrain and water vapor are significant contributing factors in the formation of wire icing. Consequently, the identification of terrains that are prone to inducing water vapor uplift serves as the scientific foundation for predicting ice accumulation on power lines. Yunnan Province, a mountainous province in China, features a large elevation difference. In winter, this region is prone to wire, pole and tower icing, which can affect power transmission and cause economic and property losses. Therefore, it is necessary to conduct research on the identification of typical terrain patterns in this region. In previous terrain studies, more attention has been focused on slope and aspect, watershed analysis and terrain profile analysis. When the purpose of the terrain identification is to analyze which terrains are more prone to collecting water vapor, we hope to obtain slightly larger terrain blocks and analyze the water vapor sources for different terrains in order to identify typical terrains that are conducive to icing formation. A new technology for identifying terrain patterns based on the K-means clustering method is proposed in this study to explore the typical terrain in Yunnan province. Additionally, the influences of different terrain patterns on water vapor movement are also analyzed. The results indicate that the typical terrains in Yunnan are “Valley-Air Channel”, “Topographic Uplifting”, “Ravine”, “Mountain Pass” and “Alpine Divide” patterns. The results show that the identified typical terrain is consistent with observations from satellite images, which verifies the effectiveness of this identification method. Among these five typical terrains, the “Valley-Air Channel”, the “Topographic Uplifting” and the “Mountain Pass” terrains are prone to collecting water vapor and forming ice cover. The “Alpine Divide” terrain is also prone to accumulating water vapor on both sides to form ice cover. The identified typical terrain demonstrates that typical terrain patterns near water bodies are more prone to the occurrence of wire, pole and tower icing because these areas are abundant in water vapor, and the extensive water vapor is easily condensed under the effects of terrain uplifting and cooling. In these key areas, existing wires and towers, as well as those to be constructed in the future, deserve our special attention.

In this study, we evaluate the impacts of different datasets (e.g., NCEP global forecast system (GFS) and ERA5) that are used to derive the initial and boundary conditions, various planetary parameterization boundary layer (PBL) schemes and radiation parameterization schemes on wind speed simulations over wind farms near Dunhuang in Northwest of China. The mesoscale community Weather Research and Forecasting (WRF) model is employed to simulate the wind speeds in March of 2014. The sensitivity of numerical simulations to different PBL schemes, including the Yonsei University (YSU), the Asymmetric Convective Model (ACM2) and the Mellor–Yamada–Janjic (MYJ) scheme are examined. Besides, simulations with different radiation parameterization schemes, including the Rapid Radiative Transfer Model for general circulation model (GCM) applications (RRTMG) and the Fu–Liou–Gu radiative transfer scheme (FLG), are compared. Based on hourly observation data from three national basic meteorological observing stations and an anemometer tower in Dunhuang, the simulation results are evaluated. Results show that, using the GFS data as the initial data, the simulation error of 10-m wind speed is rather smaller under the combination of the YSU and FLG. When using the ERA5 data as the initial data, the error of the 2-m temperature simulation is smaller, and it is also less than that of the 10-m wind speed simulation. The simulation results show significant differences at different altitudes. The relative error of wind speed is larger at higher altitude. In the vertical direction, the wind speed is smaller at a lower height and so is the simulation error. In terms of wind speed from the anemometer tower, the error of the wind speed is related to the magnitude of the observed wind speed. Therefore, according to specific conditions of the simulated area, selecting an appropriate combination of initial data and parameterization schemes can effectively reduce the errors of simulated wind speed.