A Dynamic Adjustment Method of Interpretation Model for Short-term Rainstorm Forecast

Abstract

The invention provides a dynamic adjustment method of a short-term rainstorm forecast interpretation model, which includes: calculating the spatial-temporal systematic deviation of the forecast data of the regional model and the optimal rainfall threshold parameter by using the optimal matching of the meso-α scale; On the basis of adjustment, use the optimal matching of β mesoscale to calculate the local optimal time and space offset parameters under β mesoscale on each forecast grid point; the above deviation parameters are dynamically calculated based on the historical data of the same time report In order to improve the hourly short-term heavy precipitation forecasting ability in the next 12 hours, the forecasted rainfall of the regional model is adjusted hourly in time and space, the intensity of rainfall, and the shape of the rain area. The method provided by the present invention can improve the application of multivariate data (radar, automatic rainfall station) combined in regional model interpretation, and further improve short-term forecast capabilities, providing technical support for better and seamless connection with short-term forecasts.

Description

technical field [0001] The invention relates to the technical field of weather forecast analysis, in particular to a dynamic adjustment method of a short-term rainstorm forecast interpretation model. Background technique [0002] At present, in the field of meteorology, the ability to forecast short-term torrential rain (definition: usually caused by strong convective weather, and the rainfall in one hour exceeds a certain threshold) is obviously insufficient. The existing technical deficiencies are summarized as follows: [0003] 1) Global numerical models (European Center, GFS, etc.) can make more accurate forecasts of the wind speed, humidity, air pressure and other situation fields that produce short-term heavy rain, but they cannot accurately predict the impact period and impact area of ​​short-term heavy precipitation and heavy rainfall levels. This is due to the defects of the integration method and physical parameterization scheme of this type of model. Some resea...

AI Technical Summary

Problems solved by technology

The regional model has a certain ability to forecast short-term rainstorm weather (better than the global numerical model), but there are still three problems: 1. The problem of high false alarm rate; 2. The impact of the forecast short-term rainstorm on time, space and There is a certain deviation in rainfall; 3. The problem of underreporting

The parameters of the model constructed by this type of statistical method are fixed, which can improve the accuracy of the short-term heavy precipitation forecast to a certain extent (relatively improve the rainfall deviation problem of problem 1, problem 3 and problem 2), but there are The problem is also obvious: A. For some sudden and extreme short-term heavy precipitation processes, there is a lack of real-time adjustment capabilities; B. Due to the wrong data in the training data set, the existence of wrong paired data, and the imbalance of positive and negative samples in the data set This will lead to greater uncertainty in the time, space, and precipitation level deviations of the training model forecast results; C) The forecast effect of this type of statistical model is closely related to the accuracy of the model forecast. If the regional There are too many time, space and forecast rainfall level deviations in the model forecast, and the interpretation effect of this type of statistical model cannot achieve the expected positive effect

[0005] 2) Short-term linear extrapolation based on radar and automatic station live observation can improve the accuracy of short-term heavy precipitation forecast for 0-30 minutes, but after 30 minutes, the forecasting ability drops rapidly

This is mainly because the development of strong convective weather is a complex nonlinear problem, and the existing extrapolation forecasts are based on the semi-Lagrangian extrapolation method. Therefore, this has led to the prediction of short-term heavy precipitation. With the prolongation of the forecast time, the forecast deviation of the falling area of ​​heavy precipitation also gradually increases.

In addition, the precipitation efficiency of extreme short-term precipitation also changes, and the traditional short-term extrapolation method assumes that the intensity remains unchanged for extrapolation. Therefore, this is another reason for the deviation of heavy precipitation forecasts

Aiming at the technical defects of short-imminent forecasting, some researchers tried to integrate the radar extrapolation and the precipitation forecasting results of the regional numerical model according to the time weight, so as to improve the short-imminent extrapolation forecasting effect of 0-2 hours, but overall , due to the limited ability of regional numerical values ​​to forecast short-term heavy precipitation and inappropriate weight fusion parameters, these make the improvement of the forecasting ability of fusion extrapolation forecasting technology for short-term heavy precipitation still relatively limited

[0006] In summary, the current short-term rainstorm forecast results are not accurate enough

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