Dynamic statistics combined sub-season prediction method based on low-frequency increment space-time coupling

Abstract

The invention discloses a dynamic statistics combined sub-seasonal prediction method based on low-frequency increment space-time coupling, and the method comprises the steps: selecting tropical and tropical outside atmosphere abnormal signals as a prediction factor variable, taking the low-frequency increment of the variable as a prediction object and a prediction factor, and eliminating the interference of a weather change rate and a seasonal change rate. On one hand, a synchronous physical relationship between a forecast factor and a forecast quantity increment is considered, a singular value decomposition statistical method is utilized to find a high coupling mode of a synchronous forecast factor increment and a forecast quantity increment, and a multiple linear regression method is adopted to establish a sub-season prediction model based on a physical mechanism. On the other hand, by means of the advantage that the dynamic mode has a good forecasting effect on the sub-season tropical and extra-tropical atmosphere abnormal modes, the time coefficient (namely, the forecasting factor) of the tropical and extra-tropical atmosphere abnormal high coupling modes predicted by the dynamic mode is substituted into the forecasting model, and a power-statistics combined sub-season prediction model is further constructed to predict meteorological elements.

Description

Technical field: [0001] The invention relates to a subseasonal prediction method based on low-frequency incremental time-space coupling combined with dynamic statistics, belonging to the field of atmospheric science. Background technique: [0002] Sub-season forecast refers to the forecast of weather anomalies several weeks in advance. It is the gap between weather (less than 10 days)-climate (more than three months) forecast, and is an important part of seamless forecast. It is very important for the formulation of disaster prevention and mitigation policies. play an important role. However, even with the continuous improvement of short- and medium-term weather and climate forecasting capabilities, subseasonal forecasting skills are still low, which has become a difficulty in scientific research and operational applications in recent years. In response to this difficulty, Xu Bangqi, Zhu Zhiwei and others have developed a statistical forecasting model STPM (Spatial Temporal...

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Problems solved by technology

Because the previous forecast factors may change during the evolution process, they may not be able to accurately reflect the subsequent changes in the forecast field, resulting in errors in the forecast results

At the same time, if the statistical relationship between the previous predictors and the predictors is relatively weak, the forecasting skills of statistical modeling will also be very low; secondly, the models considering the purely statistical relationship often fail to fully clarify the physical mechanism between the predictors and the predictors , and existing studies have shown that the use of predictors with physical meaning can effectively improve forecasting skills

Third, although similar methods have a certain ability to predict extreme temperatures and heavy precipitation, this purely statistical model is less effective in forecasting the intensity of disaster weather, and it is also more difficult for process forecasting

However, previous studies have found that the S2S model has limited ability to directly forecast meteorological elements (temperature, precipitation, etc.) at the subseasonal scale, but has better forecasting skills for large-scale subseasonal atmospheric circulation modes [7-8] How to effectively improve the subseasonal forecasting skills of meteorological elements (temperature, precipitation) and extreme events is still a difficult point in today's business

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