Sub-seasonal climate prediction method and system based on ten-day tendency and physical modal modeling

Abstract

The invention discloses a sub-seasonal climate prediction method and system based on ten-day tendency and physical modal modeling. The method includes calculating the ten-day tendency flatness of the atmospheric element field and the predictive variable by using historical data; through singular value decomposition, extracting main physical modes of early-stage tropical atmosphere outward long-wave radiation field and mid-high latitude atmosphere 500 hPa potential height field ten-day tendency bathymetric variation for determining predictive variable ten-day tendency bathymetric variation and taking same as predictive factors; utilizing a multiple regression method to construct a prediction model for predicting the relationship between the variable ten-day tendency distance and the early-stage main physical mode, and determining the optimal physical mode through historical return; substituting the optimal physical mode observed in the early stage into the prediction model, so that the 10-day tendency bay prediction of the prediction variable is realized; and superposing the predicted 10-day tendency flat distance with the observation or prediction flat distance of the previous 10-day so as to obtain the flat distance prediction of the current 10-day. The modeling method and system based on the ten-day tendency flatness and the physical mode established by the invention can effectively improve the sub-seasonal climate prediction accuracy.

Description

technical field [0001] The invention relates to the establishment and application of a climate statistical forecasting model on a sub-seasonal scale, which can be specifically used for forecasting meteorological elements such as precipitation and air temperature in extended periods in weather and climate forecasting business. Background technique [0002] Extended-period forecast is a 10-90-day meteorological forecast between daily weather forecast and seasonal climate forecast. It is of great significance to meteorological disaster prevention and mitigation, economic and social development, and national security. It is the current international seamless weather forecast. It is also a major research task that my country's meteorological business departments urgently need to solve. At present, the theoretical basis of extended period forecasting at home and abroad is relatively weak, the corresponding forecasting technology is still lacking, and the business application is sti...

AI Technical Summary

Problems solved by technology

At this stage, the selection of predictors in the statistical forecasting model cannot effectively give physical explanations, and is relatively arbitrary, and the forecasting results are still very unstable

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