Research And Realization Of Heat Wave Forecasting And Early Warning System

Recent years, frequent high-intensity, long-period heat wave events happened in manyareas and cities over the world. Electricity, industry and public transport are beingseriously challenged. It has a serious impact on the production and living of the people,even some areas, tolls result casualties. At present, many countries and regions develop thedifferent standards of heat waves according to local conditions, with the accumulation ofexperience, weather staff come up with different heat wave indicators, and use numericalprediction and statistical forecasting methods to predict the hot weather, combined withmedical knowledge and improve the heat wave forecasting and early warning systems.Post development trend of the meteorological factors affect human health, as well as theprobability and time of the heat wave weather, and prevention advice for residents andfactories.At present, China has established a short-term climate prediction system of nationalextreme high-temperature. But heat wave indicators, monitoring, prediction and warning,as well as the impact of high temperatures on agriculture, water resources, energy, humanhealth is still lacking, not yet to submit the evaluation index system suited to China’s heatwave, nor the establishment of a complete heat wave monitoring, diagnosis, prediction,early warning and risk analysis, cannot meet the service needs of agriculture, water,electricity and economic industry.In this paper, the original data may not standardize, verify the data and pre-processing,use five forecasting methods such as Monthly Dynamical Extended mode, statistics downscales and so on to predict the number of hot days in a certain future, provide users hightemperature warning services with the type of graph, verify the predictions by methodssuch as cross-check, etc.The works done in this paper include: (1) In this paper there is a wide range of data sources and format diversity, facing theformat is not uniform, the data is missing or abnormal, adverse impact on thestable and effective implementation of the algorithm. Analyze and validateexternal original data, use Lagrange interpolation method to supplement themissing data. Extracted the intermediate data from the original data and processedthem to files.(2) Design percentile relative threshold index, obtain the relative extreme hightemperature days use this threshold value as a standard form monthly dynamicextended mode information; use statistical downscaling methods into reanalysisof the NCEP data and the DERF mode data, get number of hot days throughspace and time downscaling method; Select a similar factor as a characteristicvalue based on basic principles of physical similarity method to obtaincomprehensive similar year, calculate the highest temperature of the target date,analyse the feature of mean generating function and the neural network and applythem into the high temperature forecast. Predict the number of hot days in futurefrom five different directions.(3) Finally, use the cross-validation to test the predictions, ensure the reliability offorecast products, and improve the prediction and warning of heat waves andservice capabilities.