Based on daily meteorological data from 55 meteorological stations in eastern Gansu from 1960 to 2017, the characteristics of the drought process and precipitation heterogeneity were analyzed, and the relationship between drought and precipitation heterogeneity was evaluated. Results showed that there were 1–3 drought processes in the study area every year. Drought processes in the eastern and north-central regions were more frequent than those in other regions. Droughts were mainly manifested as intra-seasonal droughts, especially across the spring and summer. PCD (Precipitation Concentration Degree, the concentration degree of the precipitation at a certain time) ranged from 0.2 to 0.7 in the area. PCD increased in spring and autumn but decreased in summer and winter for most regions from 1960 to 2017. PCP (Precipitation Concentration Period, the shortest time which the precipitation was concentrated in) was from late April to early May in spring, mid-to-late July in summer, mid-September in autumn, and late January in winter. In the last 58 years, PCP has remained consistent in most regions, varying by approximately 10 days. In addition to insignificant changes in winter, the days with light and moderate rain presented a declining trend, especially in summer and autumn. The larger the PCD, the fewer the days with light and moderate rain, and the stronger the drought intensity. However, in the east-central region, the larger the PCD in autumn, the weaker was the drought intensity. This difference is related to the PCP and the evapotranspiration. Additionally, the later the PCP, the stronger was the drought intensity, particularly in summer and autumn. When PCD was ≥0.5 in spring and ≥0.4 in summer, the PCP was after May and August in spring and summer, respectively. Droughts appeared in 28–56% of periods when seasonal precipitation was above normal. When PCD was ≥0.5 in autumn and PCP was in early and middle September, droughts appeared in 7% of periods when precipitation was above normal. Our results show that although less precipitation is the leading influencing factor of drought in the dry rain-fed agricultural areas, the influence of precipitation heterogeneity should be also considered for the prediction and diagnosis of seasonal drought.

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.