Machine Learning of Weather Forecasting Rules from Large Meteorological Data Bases

Discovery of useful forecasting rules from observational weather data is an outstanding interesting topic.The traditional methods of acquiring forecasting knowledge are manual analysis and investigation performed by human scientists.This paper presents the experimental results of an automatic machine learning system which derives forecasting rules from real observational data.We tested the system on the two large real data sets from the areas of centra! China and Victoria of Australia.The experimental results show that the forecasting rules discovered by the system are very competitive to human experts.The forecasting accuracy rates are 86.4% and 78% of the two data sets respectively

Focus on Climate Impact Assessment and Forecast Service in Liaocheng City in 2015

Detailed statistics of characteristics of climatic factors in Liaocheng in 2015 were carried out. The main points of meteorological service＇ were summed up to im- prove the ability of meteorological forecast, thereby meeting the new requirements of the public for meteorological cause. The results showed that the annual average temperature in various Counties was in the range of 13.6-14.5 ℃, which was slight- ly higher than that in an average year and lower than that in the past year; the average annual precipitation of various counties ranged from 376.9 to 592.1 mm, which was slightly less than those in an average year and the past year; the aver- age sunshine duration across the city was 2 026 h, which was less than that in an average year and slightly more than that in the past year. The main climatic events included fog and haze, heavy snowfall, thunderstorm, gale, hail, high temperature, heavy rain and rainy sparse sunlight, which produced a negative impact on the agri- culture, tourism, transportation and people＇s daily life. This is the focus of future services.

Preface to the Special Issue on the"Forecast and Evaluation of Meteorological Disasters"(FEMD)

Meteorological disasters usually exert huge impacts on the development of both human society and the economy. According to statistics from the United Nations International Strategy for Disaster Reduction, the annual mean economic loss caused by meteorological disasters accounts for 3%-6% of the total amount of global GDP. China is a country that has been one of the most severely influenced by natural disasters.

Influences of Graupel Microphysics on CMA-GFS Simulation of Summer Regional Precipitation

The horizontal resolution of global numerical weather prediction models is continuously developing. However, due to the imperfect precipitation simulation/forecast of these models, the demand for considering riming particles in cloud microphysical schemes in these models is increasing. This study employed the latest versions of global atmospheric reanalysis data (ERA5), the satellite retrieval data of the Global Precipitation Observation Program (GPM),and station precipitation observations to explore the impacts of adding graupel to the cloud microphysical scheme in the China Meteorological Administration-Global Forecast System (CMA-GFS) on summer regional precipitation simulations in four Chinese climate zones. The results verify that the new graupel scheme can enable CMA-GFS to decently predict global graupel distribution, especially in tropical and midlatitude regions. The addition of graupel in the cloud microphysics increases the precipitation simulation in North China, while that in Southwest China is weakened and dispersed. Moreover, graupel scheme increases the precipitation simulations of almost all magnitudes.The increase in light rain is obvious, and the absolute value of heavy rain is strengthened. This may be because graupel quickly melts into rain after falling out of the zero-temperature layer due to its large mass and fast falling speed, increasing surface precipitation. In summary, the addition of graupel in the cloud microphysical scheme can improve CMA-GFS’s underestimation of strong precipitation.

New Version of the CMA-GFS Dynamical Core Based on the Predictor–Corrector Time Integration Scheme

The operational numerical weather prediction system established by the China Meteorological Administration(CMA),based on the Global/Regional Assimilation and Prediction System(GRAPES)model,adopts the classical semi-implicit semi-Lagrangian(SISL)time integration algorithm.This paper describes a major upgrade to the dynamical core of the CMA global forecast system(CMA-GFS),which was successfully incorporated into operation in 2020.In the upgrade,the classical SISL is further developed into a predictor–corrector scheme,a three-dimensional(3D)reference profile instead of the original isothermal reference profile is applied when implementing the semi-implicit algorithm,and a hybrid terrain-following vertical coordinate system is also applied.The new version of the dynamical core greatly improves the model performance,the time integration reaches second-order accuracy,the time step can be extended by 50%,and the efficiency is greatly improved(by approximately 30%).Atmospheric circulation simulation is systematically improved,and deviations in temperature,wind,and humidity are reduced.The new version of the dynamical core provides a solid foundation for further development of the entire operational system of the CMA.