Sandstorm is a serious natural disaster in north China. It is of great significance to carry out relevant research to improve the forecast level of this kind of catastrophic weather. Based on the RegCM-dust model, an extended period numerical prediction analysis of a typical severe sandstorm process in north China is conducted, and the results are compared with NCEP reanalysis data and other analysis results. The results show that the regions with high sediment discharge simulated by the model are mainly located in southern Xinjiang, Mongolia and western Inner Mongolia. The model has a certain forecasting ability for 10 m wind speed, but the simulated wind speed is smaller than the reanalysis data. The changes of dust column content and total sedimentation simulated by the model can reflect the characteristics of the dust storm weather process. The simulated sand-dust mixing ratio has a certain correspondence with the urban pollution index, which indicates that the model has certain forecasting ability for the pollution weather caused by sand-dust.

Based on surface dead fuel data of Larix gmelinii (sunny slope) in Genhe City, mixed forest of Xylosma racemosum, Betula platyphylla and Betula dahurica (shady slope) in Oroqen Autonomous Banner, mixed forest of Betula platyphylla and Populus davidiana (sunny slope) in Yakeshi City and mixed forest of Betulaplatyphylla and Populus davidiana (shady slope) in Aershan City in great Xing’an Mountains forest and meteorological observation data during 2004-2019, the relationships between ground surface dead fuel moisture content and forest factors, meteorological factor inside and outside the forest were analyzed. The canopy density, surface dead fuel temperature and forest temperature had a negative correlation with ground surface dead fuel moisture, while the relative humidity had a positive correlation with ground surface dead fuel moisture content on sunny slope forest, and it was opposite on shady slope forest. The prediction models of ground surface dead fuel moisture content were established during the fire prevention period in different seasons by using stepwise regression method. The prediction model of sunny slope forest model was better than the shady slope model. The model was best in autumn, followed by spring and autumn, summer and it was worst during the total fire prevention period.

Based on 2-meter temperature data from the second generation monthly dynamic extended range forecast (DERF2.0) model, observational temperature data at 69 weather stations in Gansu Province, reanalysis data of NCEP/DOE and sea surface temperature data of NOAA，the forecast errors of January temperature in Gansu Province by DERF2.0 model from 1992 to 2013 and their relationship with external forcing were analyzed. The results are as follows: (1) The simulated effects of January temperature by DERF2.0 model in eastern Yellow River of Gansu (known as Hedong for short) were better than that in most regions of western Yellow River of Gansu (known as Hexi for short), especially  in Gannan, Linxia, Lanzhou, Dingxi, Pingliang and Qingyang, the average errors between forecast and observation were small and stable, and the linear tendency rates of forecasted temperature in January were consistent with observation from 1992 to 2013, while the average errors were bigger and unstable in most regions of Hexi, and the change trends of forecasted temperature were contrary to actual observation. (2) Although the model could well reflect the inter-annual variation and spatial distribution pattern of January temperature in Gansu, the abnormal centers and values of temperature change were significantly different from the observation. (3) The EOF1 of error field reflected consistent overestimate or underestimate to  January temperature, the EOF2 presented an opposite distribution pattern in Hedong and Hexi, while the EOF3 appeared a reverse phase distribution pattern in Gannan Plateau and other parts of Gansu Province. (4) The main modes of forecast error field were significantly correlated with circulation and sea surface temperature (SST) in key areas, which indicated that the response of model to circulation and SST anomalies was deficient. Therefore, it was partially possible to reduce forecast errors of January temperature in Gansu by adjusting the response ability of DERF2.0 model to circulation and SST in key areas.

:Based on the technology of 3D geographic information system (GIS) and mobile internet, Fujian artificial weather modification command system (B/S, 2D and 3D integrated) was designed to satisfy the “five-stage business”, which was universal at the provincial-municipal-county level. Intensive database and function flow of the whole province were also established. By using multi-source meteorological observation data, the system could mainly identify target-cloud automatically for early warning of operation point conditions. Dynamic artificial weather modification operation of the whole province and real-time navigation were visually displayed, and real-time decision-making command and efficient communication between the command center and  operating points were realized. Operation parameters were automatically designed and 3D simulated to test and revise by the combined use of layer overlap analysis technique, automatically identified safe area information of operation point, 3D radar-based data inversion and extrapolation technology, which was based on 3D GIS platform and 3D visualization technique. Also, information of weather modification operation was in the position to be reported and audited normatively and rapidly by means of App automatically gathering relevant data. The system had friendly interface and strong interaction, which effectively improved the ability of artificial weather modification command and the level of scientific decision-making.