China has been engaged in short-term climate prediction for nearly 70 years and was one of the earliest countries in the world to carry out short-term climate prediction and relevant researches. Since March 2021, the National Climate Center (NCC) of China has released officially the climate prediction for the next 15-30 days, months and seasons through its website. Short term climate forecasts are receiving increasing attention from society. In 2013, based on the operational prediction of short-term climate and the previous assessment methods, the Department of Forecasting and Networking of China Meteorological Administration issued a new method to assess operationally the short-term climate prediction. Using this new method, this paper analyzed the evaluation results for monthly climate prediction products during 1971-2020 released by the NCC. The results show that the prediction scores are lower in the winter half year than in the summer half year both for monthly temperature anomaly and precipitation anomaly percentage. The forecast skill of monthly temperature anomaly has been improved significantly in recent 50 years. The anomaly correlation coefficient between the forecast and observation of monthly temperature anomaly are positive in most parts of China throughout the year, except for December. The prediction level of monthly precipitation anomaly percentage in China in 50 years shows a trend of decreasing first and then increasing, especially in the past 30 years, it shows a relatively stable upward trend. The distribution of correlations between precipitation forecast and observation has three main patterns, which is much more complex than that of temperature, suggesting that the precipitation forecast is more challenging than temperature forecast.
To minimize the impact of windy weather on the construction of hydropower stations and power transmission in the lower reaches of the Jinsha River, this study aims to explore the relationship between wind speed variations and barometric pressure parameters in the canyon area, and to find effective indicators about early warning of windy weather through in-depth analysis of frequent windy events in the dry season. Numerical forecasting is limited in canyon areas due to the difficulty of wind speed forecasting, thus ground-based observations are particularly important, especially for barometric pressure variations. This paper takes the north-south longitudinal canyon at the junction of Sichuan and Yunnan in the lower reaches of the Jinsha River as the research object, and uses the data from the self-built observatory in the canyon area, combining with the empirical forecasts from the hydropower station dam area, to study the relationship between wind speed and barometric pressure related parameters in the canyon area during the gale events. The results show that, before the occurrence of gale, the barometric pressure at the station usually decreases significantly, and after the decrease exceeds 7.7 hPa and lasts for 3-6 hours, the possibility of wind speed increasing increases; if wind speed increases steadily for 1-6 hours, the wind can reach the level 7 gale. The pressure variation values of the stations are very important for wind speed warning, especially the 3-hour pressure change values. When the 3-hour negative pressure change value exceeds 2.0 hPa, the surface wind speed starts to increase. The Granger causality test method reveals that there is an obvious lag in the wind speed change in the upstream and downstream of the canyon, for example, Hulukou Bridge Station usually experiences gale 1-2 hours before the downstream. The difference in barometric pressure can be used as an important early warning indicator of gales, and when the difference in barometric pressure between Huangtian and Hulukou Bridge Station reaches 18.1 hPa, the possibility of gales increases significantly.
Geological hazards occur frequently along the transmission lines in Longnan, Gansu, which seriously threaten the safe and stable operation of power lines. In order to effectively improve the disaster prevention and mitigation ability of Longnan power grid, the precipitation data from May to October during 2018 to 2022 were selected to analyze the distribution characteristics of precipitation, and the disaster probability of effective rainfall was used to evaluate the risk of disaster-causing factors of geological hazards. The coupling model of information quantity and analytic hierarchy process was used to evaluate the exposure of pregnant environment, and the simplified evaluation model of geological disaster vulnerability was used to evaluate the vulnerability of disaster-bearing body. The effect of geological disaster risk early warning of power grid induced by heavy precipitation was tested using cases of geological disasters, taking Longnan ±800 kV Qingyu Line and Qishao Line as examples. The results show that the short-term heavy rainfall and rainstorm in Longnan have strong local characteristics, and their frequency increases from northwest to southeast. The disaster sites mostly occur in higher and high risk areas. Most of the disaster sites of geological disasters are located in the areas with higher elevation and steeper slope, and the north and south slope as well as convex slope are the main slopes. The geological disasters are easy to occur in the areas with dry land, moderate vegetation coverage and below, and 67.4% of the disaster sites is located in the areas with higher exposure. Longnan ±800 kV Qingyu Line and Qishao Line are not in the area of extremely high exposure degree and vulnerability. The meteorological risk model of geological disaster can capture the dense geological disaster events, and the probability of disaster caused by effective rainfall has a good effect on the early warning of geological disasters induced by precipitation.
In order to improve the fine defense ability of late frost disaster in the vineyards in the eastern foothills region of the Helan Mountain, the minimum temperature observation data of the vineyards from April to May during 2020-2023 were used to analyze the variation characteristics of minimum temperature, the occurrence frequency and regional distribution characteristics of late frost in the vineyards. Based on the European Centre for Medium-Range Weather Forecasts (ECMWF) model forecast products and the actual temperature of grid points in Ningxia, the radial basis function (RBF) neural network algorithm was used to construct the minimum temperature and frost prediction model in the vineyards in the eastern foot of the Helan Mountain. The results show that the light frost was the most common in the vineyards in the eastern foothills region of the Helan Mountain, followed by the medium frost. April was the main month for frost occurrence. The frost in D.F. Yuxing winery appeared most frequently, and the frost in Guanlan winery was the least. The verification results of the minimum temperature and frost forecast show that compared with the ECMWF model, the RBF model has improved the accuracy of the minimum temperature forecast in the Helan, Yongning and Hongsipu production areas, with the highest increase of 33.8%, and the average absolute error reduced by 0.20-1.50 °C. For the single station frost forecast, the RBF model has obvious advantages, the accuracy rate generally increased by 1.0%-14.0%, and the average absolute error reduced by 0.04-0.37 °C. For the average of the production areas, the RBF model has the highest accuracy of frost prediction in the Hongsipu production area, up to 13.0%. In the case analysis of frost, the RBF model has better prediction effect, especially for moderate frost prediction. Compared with the ECMWF model, the accuracy rate increased by 25.0%-50.0%, and the average absolute error reduced by 1.80-2.10 °C.
