Aiming at a significant heavy rainfall event that occurred in Inner Mongolia in July 2021, the paper conducfed a set of convection-allowing ensemble prediction (CAEP) experiments to evaluate its forecasting capability for intense precipitation processes, and compared the results with the global ensemble forecasts from the European Center for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction Global Ensemble Forecast System (NCEP-GEFS), and the China Meteorological Administration Regional Ensemble Prediction System (CMA-REPS). The results show that the ensemble mean of global ensemble forecasts tended to underestimate the intensity of heavy precipitation centers, although ECMWF provided relatively accurate predictions of their locations. Both CMA-REPS and CAEP precipitation intensities forecasts close to observations but with some positional deviations, whereas NCEP-GEFS performed poorly in forecasting both the location and intensity of heavy rainfall. The Probability Matching Ensemble Mean (PM) effectively improved the simulated precipitation intensity compared with the traditional ensemble mean, leading to a notable increase in the threat score (TS), particularly for ECMWF and CAEP. The CAEP outperformed both global and regional ensemble forecasts in predicting the magnitude and temporal evolution of single-station precipitation. Objective verification indicated that ECMWF, CMA-REPS, and CAEP ensemble members exhibited certain forecasting capability for 25 mm·(6 h)^-1 precipitation, while NCEP-GEFS performed poorly. For 60 mm·(6 h)^-1 precipitation, CAEP achieved the highest TS, the lowest Brier score, and the highest AROC score among the ensemble systems, demonstrating its superior capability in forecasting heavy rainfall over the Inner Mongolia region.

In the extreme arid region of Northwest China, the Gobi desert is widely distributed, with strong winds and abundant sand, leading to frequent disasters. A deep understanding of the basic laws of the aeolian sand-dust movement over Gobi surfaces is an important prerequisite for disaster warning and scientific prevention. Given the current difficulty in accurately predicting the instantaneous aeolian sediment transport rate, exploring the statistical laws of airflow and sand-dust characteristic physical quantities in wind-sand events, and then conducting statistical forecasting, may be a feasible way to establish a quantitative relationship between wind and sand on time scales of seconds or less. This study borrows the ideas and methods from the statistical theory of turbulence, and analyzes four field observation datasets of Gobi sand-dust movement using the Hilbert-Huang transform. The results indicate that the Hilbert marginal spectra of the time series of wind speed, kinetic energy and number of saltation sand particle, and dust concentration in the aeolian events all follow the power scaling law. The scaling exponents of variables, characterizing aeolian sand and dust motions, and wind speed range from 0.78 to 1.51 and 0.59 to 1.47, respectively.

Spring snowmelt flood simulation and forecasting in the mountainous region has been a difficulty of cold region hydrological study. Current forecasting studies mainly use complex snow melt energy balance models, and take into account underlying surfaces such as frozen soil, vegetation, and runoff processes, resulting in complex model structures. This method needs a lot of data support and has a great uncertainty in prediction, which leads to difficulties in application in operational forecasting. In this paper, a simple and effective spring snowmelt flood forecasting method is established by using statistical methods and the snow depth observation data in winter of meteorological stations as well as a variety of snow remote sensing data in the Xiying River Basin, combined with the flood observation data of Jiutiaoling Hydrology Station on its control section. The study shows that the snow water equivalent information of the basin from MODIS snow cover products in March and April and the integration of microwave remote sensing snow depth products can well reflect the magnitude of spring snowmelt flood at Jiutiaoling Hydrological Station in the Xiying River Basin. This method provides a useful reference for snowmelt flood forecasting in other stable snow cover areas.

Based on Jiuhua Mountain meteorological stations at different altitudes and regional automatic weather station data， the comparative analysis was made about climate characteristics of fog in mountainous area and flat area，and fog formation mechanism was analyzed also． The results show that the annual change of fog in mountainous area decreased year by year，but fog in flat area presented slowly increasing trend，the peak time of fog occurrence in mountain area was at about 05: 00，which was one hours earlier than that in the flat area，but the highest frequency of fog occurrence at 08: 00，which was one hours later than that in the flat area． The percentage of fog days occurred when temperature cooling at night was more than or equal to 6 ℃ was 74． 4%，and the percentage of fog days occurred when the daily temperature range was more than or equal to 7 ℃ was 80． 9%． When daily mean wind speed was less than 3 m/s， fog weather would appeared，and on 83． 9% fog days the inversion layer existed near the ground，and fog days was positively related to inversion intensity． The convergence role of the bell mouth topographic made water vapor in the flared bottom area to reach saturation and formed fog，and breeze conditions was advantageous to radiation fog formation，if there was wind field convergence near surface，it  was more conducive to the formation and maintenance of the fog，and forest microclimate effects also contribute to fog formation．