This study investigated the performance of the mesoscale Weather Research and Forecasting (WRF) model in predicting near-surface atmospheric temperature and wind for a complex underlying surface in Northwest China in June and December 2015. The spatial distribution of the monthly average bias errors in the forecasts of 2-m temperature and 10-m wind speed is analyzed first. It is found that the forecast errors for 2-m temperature and 10-m wind speed in June are strongly correlated with the terrain distribution. However, this type of correlation is not apparent in December, perhaps due to the inaccurate specification of the surface albedo and freezing–thawing process of frozen soil in winter in Northwest China in the WRF model. In addition, the WRF model is able to reproduce the diurnal variation in 2-m temperature and 10-m wind speed, although with weakened magnitude. Elevations and land-use types have strong influences on the forecast of near-surface variables with seasonal variations. The overall results imply that accurate specification of the complex underlying surface and seasonal changes in land cover is necessary for improving near-surface forecasts over Northwest China.

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.

The upper boundary condition of a numerical model can significantly influence the model prediction results. This study used the Weather Research and Forecasting model to introduce the Rayleigh damping layer in the complex terrain of southern Gansu, China. Sensitivity tests are conducted for a strong precipitation process that occurred in 2016. Meanwhile, the effects of the upper gravity-wave damping layer and vertical velocity damping term of different schemes are explored. The results show that the upper gravity-wave damping layer can effectively reduce the error caused by false gravity waves in the complex terrain of southern Gansu Province. Moreover, the applicability of this model in the complex terrain of northwestern China is discussed. Overal, prediction of the precipitation area and intensity is improved. Specifically, the introduction of an implicit gravity-wave damping layer has a more significant damping effect on the upwardly propagating gravity-wave.

Based on the daily precipitation observation data of 208 rain-gauge stations in Northwest China from 1961 to 2020, we use the statistical analysis method, the Mann-Kendall test method, the probability analysis and the ensemble empirical mode decomposition to analyze the spatio-temporal variation characteristics of precipitation duration and the contribution of the precipitation with different durations to the total precipitation. At the same time, the characteristics of precipitation duration in different regions (westerly region, monsoon transition region and monsoon region) are compared. The results show that the frequency, annual total precipitation and annual averaged precipitation intensity of the precipitation processes in Northwest China present a decreasing trend before the 1990s, but an increasing trend after 1991. Especially, the increasing trend is more obvious in the south-central and eastern parts of Northwest China. The occurrence frequency, total precipitation and average precipitation intensity of the precipitation processes with different durations all increase spatially from northwest to southeast, and this distribution is consistent with that of the climate zones in China. The distribution of the stations with a precipitation-increasing trend is closely related to the topography. In the north of large terrains, most stations present an increasing trend in precipitation frequency, total precipitation and precipitation intensity. The precipitation process in Northwest China is dominated by one-day precipitation process (short-duration precipitation process), and the frequency of the precipitation processes lasting for 3 days or more (long-duration precipitation process) is the least, but its precipitation intensity is the strongest. In the westerly region, the frequency and precipitation of one-day precipitation processes occupy the largest proportion. In the monsoon region, the frequency and precipitation of the precipitation processes lasting for 3 days or more occupy the largest proportion. In the past 60 years, the frequency, total precipitation and precipitation intensity of one-day precipitation processes show an increasing trend in all the three regions, while the two-day precipitation processes has no obvious change. For the precipitation processes lasting for 3 days or more, there is no obvious change or slight decrease in different regions. In general, the summer precipitation in Northwest China has an increasing trend. The results of this study may provide further understanding on the warming-wetting trend over Northwest China.