阅读说明：本技术 基于旬倾向和物理模态建模的次季节气候预测方法与系统 (Method and system for predicting sub-season climate based on ten-day tendency and physical modal modeling ) 是由 杨修群 陶凌峰 孙旭光 房佳蓓 张志琦 王昱 于 2021-07-23 设计创作，主要内容包括：本发明公开了一种基于旬倾向和物理模态建模的次季节气候预测方法与系统。利用历史资料计算大气要素场和预测变量的旬倾向距平；通过奇异值分解,提取决定预测变量旬倾向距平的前期热带大气向外长波辐射场和中高纬大气500hPa位势高度场旬倾向距平变异的主要物理模态,并作为预测因子；利用多元回归方法,构建预测变量旬倾向距平与前期主要物理模态关系的预测模型,并通过历史回报确定最优物理模态；将前期观测的最优物理模态代入预测模型,从而实现对预测变量的旬倾向距平预测；将预测的旬倾向距平叠加前一旬的观测或预测距平,从而得到本旬的距平预测。本发明建立的基于旬倾向距平和物理模态建模的方法与系统可有效提高次季节气候预测准确率。(The invention discloses a method and a system for predicting sub-season climate based on ten-day tendency and physical modal modeling. Calculating the ten-day tendency of the atmospheric element field and the predictive variable by using historical data; extracting main physical modes of the early tropical atmosphere outward long-wave radiation field and the medium and high latitude atmosphere 500hPa potential altitude field which determine the inclination of the predictive variable to be flat in ten days through singular value decomposition, and taking the main physical modes as predictive factors; constructing a prediction model of the relation between the ten-day tendency pitch of the prediction variable and the early main physical mode by using a multivariate regression method, and determining the optimal physical mode through historical return; substituting the optimal physical mode observed in the early stage into the prediction model, thereby realizing the prediction of the ten-day tendency of the prediction variable; and superposing the predicted ten-day inclination distance on the observed or predicted distance in the previous ten-day to obtain the distance prediction in the current ten-day. The method and the system based on the Laetian dip and the physical mode modeling can effectively improve the accuracy of climate prediction in the next season.)

1. The method for predicting the sub-season climate based on the ten-day tendency and the physical modal modeling is characterized in that: the method comprises the following steps:

(1) respectively calculating and converting an atmospheric outward long wave radiation (OLR) field in the early tropical region, a 500hPa potential height (Z500) field in the middle and high latitude region outside the early tropical region and a predictive variable field into respective ten-day inclined distance flat fields;

(2) singular Value Decomposition (SVD) is respectively carried out on the early OLR and Z500 ten-day inclination distance flat field and the prediction variable ten-day inclination distance flat field, and the time series of main physical modes which are obtained by decomposition and determine the early OLR and Z500 ten-day inclination distance flat variation of the prediction variable ten-day inclination distance flat are standardized to be used as prediction factors of the prediction variable ten-day inclination distance flat;

(3) selecting different numbers of the predictive factors in the step (2), and establishing a statistical prediction model of the relation between the ten-day tendency pitch of the predictive variable and the early main physical mode by using a multivariate linear regression method;

(4) historical return of the ten-day tendency is carried out, and an optimal prediction factor and an optimal prediction model are determined through comparison with observation;

(5) respectively projecting the early-stage OLR tendency distance flat field and the Z500 tendency distance flat field of the predicted ten days to the spatial physical mode in the step (2), substituting the obtained time sequence into the optimal prediction model, and calculating to obtain the ten-day tendency distance of the predicted ten days of the predicted variable;

(6) adding the ten-day inclination distance of the predicted ten-day obtained in the step (5) to the observed or predicted ten-day distance of the last ten-day to obtain the ten-day distance of the predicted ten-day; and adding the ten-day distance of the predicted ten-day with the climate state of the predicted ten-day to obtain the total value of the predicted ten-day of the predicted variable.

2. The method for sub-season climate prediction based on ten-day tendencies and physical modal modeling according to claim 1, wherein: the ten-day tendency pitch in the step (1) is specifically calculated as follows:

wherein A is the average value in ten days of a certain variable,the climate mean value of the variable A is shown, delta A is the climate trend of the variable A, delta A is the climate trend of the variable A, t is a certain ten-day of 36 ten-day of the year, and t-1 is the last ten-day of the ten-day.

3. The method for sub-season climate prediction based on ten-day tendencies and physical modal modeling according to claim 1, wherein: the statistical prediction model in the step (3) is as follows:

wherein, δ Δ A_fcstTo predict the flat tendencies of a variable in ten days,andtime coefficients corresponding to the ith SVD mode and the jth SVD mode of the Z500 are respectively set,andweights of the ith and jth SVD modes of OLR, M and N are SVD modes of selected OLRThe number of states and the SVD modes of Z500, x is a station or a lattice point, t is a forecast ten-day, and t-n is a forecast ten-day.

4. The method for sub-season climate prediction based on ten-day tendencies and physical modal modeling according to claim 1, wherein: in the step (4), an optimal prediction model is selected by adopting independent sample inspection, historical return of the ten-day tendency is carried out, the return results of a plurality of prediction models and the spatial correlation coefficient of observation data are compared, the combination of prediction factors corresponding to the maximum value of the correlation coefficient, namely the OLR field SVD mode and the Z500 field SVD mode, is taken as an optimal prediction factor, and the corresponding prediction model is taken as an optimal prediction model.

5. The method for sub-season climate prediction based on ten-day tendencies and physical modal modeling according to claim 1, wherein: the method can be used for predicting rainfall or temperature in 6 th ten days from any 36 th ten days in the year, and can provide corresponding prediction of the atmospheric circulation field.

6. The system for predicting the sub-season climate based on the ten-day tendency and the physical modal modeling is characterized in that: the system comprises the following modules:

the ten-day tendency conversion module is used for respectively calculating and converting an atmospheric outward long-wave radiation (OLR) field in the early tropical zone, a 500hPa potential height (Z500) field in the early tropical zone and the medium and high latitude zone and a prediction variable field into respective ten-day tendency distance flat fields;

an SVD module, configured to perform Singular Value Decomposition (SVD) on the ten-day tendency distance flat field of the early OLR and Z500 and the ten-day tendency distance flat field of the predictor, respectively, and normalize the decomposed time series of the main physical modalities determining the early OLR and Z500-day tendency distance variation of the predictor, as a predictor of the ten-day tendency distance;

the ten-day tendency prediction model building module is used for selecting prediction factors with different numbers and building a statistical prediction model of the relation between the ten-day tendency distance of a prediction variable and the early main physical modal by utilizing a multivariate linear regression method; historical return of the ten-day tendency is carried out, and an optimal prediction factor and an optimal prediction model are determined through comparison with observation;

the ten-day tendency prediction module is used for respectively projecting the early-stage OLR tendency distance flat field and the Z500 tendency distance flat field of the predicted ten-day onto a spatial physical mode in the SVD decomposition module, and substituting the obtained time sequence into the optimal prediction model to calculate the ten-day tendency distance of the predicted ten-day of the predicted variable;

the result processing module is used for adding the obtained ten-day inclination distance of the predicted ten-day with the observed or predicted ten-day distance of the last ten-day to obtain the ten-day distance of the predicted ten-day; and adding the ten-day distance of the predicted ten-day with the climate state of the predicted ten-day to obtain the total value of the predicted ten-day of the predicted variable.

7. The ten-day-bias and physical modality modeling based sub-season climate prediction system according to claim 6, wherein: the ten-day tendency pitch is specifically calculated as follows:

wherein A is the average value in ten days of a certain variable,the climate mean value of the variable A is shown, delta A is the climate trend of the variable A, delta A is the climate trend of the variable A, t is a certain ten-day of 36 ten-day of the year, and t-1 is the last ten-day of the ten-day.

8. The ten-day-bias and physical modality modeling based sub-season climate prediction system according to claim 1, wherein: the statistical prediction model is as follows:

wherein, δ Δ A_fcstTo predict the flat tendencies of a variable in ten days,andtime coefficients corresponding to the ith SVD mode and the jth SVD mode of the Z500 are respectively set,andthe weights are respectively the ith SVD mode of the OLR and the jth SVD mode of the Z500, M and N are respectively the number of the SVD modes of the OLR and the SVD modes of the Z500, x is a site or a lattice point, t is a predicted ten-day, and t-N is a predicted ten-day.

9. A computer system comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein: the computer program when loaded into a processor implements the sub-seasonal climate prediction method based on ten-day tendencies and physical modal modeling according to any one of claims 1-5.

Technical Field

The invention relates to establishment and application of a weather statistical prediction model of a sub-seasonal scale, and particularly can be used for predicting meteorological elements such as precipitation in ten days of an extended period and air temperature in ten days in weather and climate prediction business.

Background

The extended period prediction is weather prediction of 10-90 days between day-by-day weather prediction and seasonal weather prediction, has important significance for weather disaster prevention and reduction, economic and social development and national security guarantee, is a difficult problem of international seamless weather prediction at present, and is an important task of attacking and closing urgently needed to be solved by the national weather service department. At present, the theoretical basis of the domestic and overseas extension period prediction is weak, corresponding prediction technology is still very lack, and the business application is still in the preliminary stage and can not meet the requirements of the country, the society and the people.

At present, the main extending-period weather prediction technical methods at home and abroad mainly comprise dynamic mode prediction and statistical method prediction. The power mode prediction mainly depends on a power equation to construct a weather and climate numerical mode for prediction, and a prediction result has a large error and strong mode dependence and initial value dependence: on the one hand, the prediction results calculated by different numerical modes may be completely different, and on the other hand, the prediction results given by the same numerical mode at different time of issuance may also be very different. The statistical method is mainly used for predicting based on the historical evolution statistical rule of the strong gas signal, and a statistical model is built for prediction. At the present stage, the selection of the prediction factors in the statistical prediction model cannot effectively give physical explanation, the method has great arbitrariness, and the prediction result is still very unstable.

Disclosure of Invention

The purpose of the invention is as follows: aiming at the defects of the existing extended period prediction, the invention aims to provide a method and a system for predicting the secondary season climate based on ten-day tendency and physical modal modeling, wherein the method comprises the steps of converting an early atmospheric circulation field into a ten-day tendency distance flat field, completely considering high latitude abnormal signals in tropical and northern hemispheres, extracting the early atmospheric circulation modes which are most closely related to Chinese secondary season prediction variables through SVD (singular value decomposition), using a multiple linear regression method to establish a secondary season prediction model with an internal physical relation, and predicting the ten-day tendency distance of the prediction variables, thereby realizing more accurate and more stable prediction of the Chinese extended period meteorological elements.

The technical scheme is as follows: in order to achieve the purpose of the invention, the technical scheme adopted by the invention is as follows:

the method for predicting the sub-season climate based on the ten-day tendency distance and the physical modal modeling comprises the following steps of:

(1) respectively calculating and converting an atmospheric outward long wave radiation (OLR) field in the early tropical region, a 500hPa potential height field (Z500) in the middle and high latitude region outside the early tropical region and a predictive variable field into respective ten-day inclined distance flat fields;

(2) singular Value Decomposition (SVD) is respectively carried out on the early OLR and Z500 ten-day inclination distance flat field and the prediction variable ten-day inclination distance flat field, and the time series of main physical modes which are obtained by decomposition and determine the early OLR and Z500 ten-day inclination distance flat variation of the prediction variable ten-day inclination distance flat are standardized to be used as prediction factors of the prediction variable ten-day inclination distance flat;

(3) selecting different numbers of the predictive factors in the step (2), and establishing a statistical prediction model of the relation between the ten-day tendency pitch of the predictive variable and the early main physical mode by using a multivariate linear regression method;

(4) historical return of the ten-day tendency is carried out, and an optimal prediction factor and an optimal prediction model are determined through comparison with observation;

(5) respectively projecting the early-stage OLR tendency distance flat field and the Z500 tendency distance flat field of the predicted ten days to the spatial physical mode in the step (2), substituting the obtained time sequence into the optimal prediction model, and calculating to obtain the ten-day tendency distance of the predicted ten days of the predicted variable;

(6) adding the ten-day inclination distance of the predicted ten-day obtained in the step (5) to the observed or predicted ten-day distance of the last ten-day to obtain the ten-day distance of the predicted ten-day; and adding the ten-day distance of the predicted ten-day with the climate state of the predicted ten-day to obtain the total value of the predicted ten-day of the predicted variable.

The ten-day tendency pitch in the step (1) is calculated as:

wherein A is the average value in ten days of a certain variable,the climate mean value of the variable A is shown, delta A is the climate trend of the variable A, delta A is the climate trend of the variable A, t is a certain ten-day of 36 ten-day of the year, and t-1 is the last ten-day of the ten-day.

The statistical prediction model in the step (3) is as follows:

wherein, δ Δ A_fcstTo predict the Laetian Trend of a variable, F_i ^olrAndtime coefficients corresponding to the ith SVD mode and the jth SVD mode of the Z500 are respectively set,andweights of the ith SVD mode and the jth SVD mode of Z500 of OLR are respectively provided, and M and N are respectively the SVD mode of the selected OLR and the SV of Z500The number of D modes, x is a station or a lattice point, t is a forecast ten-day, and t-n is a forecast ten-day.

In the step (4), an optimal prediction model is selected by adopting independent sample inspection, historical return of the ten-day tendency is carried out, the return results of a plurality of prediction models and the spatial correlation coefficient of observation data are compared, the combination of prediction factors corresponding to the maximum value of the correlation coefficient, namely the OLR field SVD mode and the Z500 field SVD mode, is taken as an optimal prediction factor, and the corresponding prediction model is taken as an optimal prediction model.

Based on the same inventive concept, the sub-season prediction system based on the ten-day tendency pitch and the physical modal modeling comprises the following modules:

the ten-day tendency conversion module is used for respectively calculating and converting the early OLR field, the Z500 field and the predictive variable field into respective ten-day tendency distance flat fields;

the SVD module is used for respectively carrying out SVD on the ten-day tendency distance flat field of the early OLR and Z500 and the ten-day tendency distance flat field of the predictive variable, and standardizing time series of main physical modes which are obtained by decomposition and determine the early OLR of the ten-day tendency distance flat of the predictive variable and the Z500-day tendency distance flat variation to serve as a predictive factor of the ten-day tendency distance flat of the predictive variable;

the ten-day tendency prediction model building module is used for selecting prediction factors with different numbers and building a statistical prediction model of the relation between the ten-day tendency distance of a prediction variable and the early main physical modal by utilizing a multivariate linear regression method; historical return of the ten-day tendency is carried out, and an optimal prediction factor and an optimal prediction model are determined through comparison with observation;

the ten-day tendency prediction module is used for respectively projecting the early-stage OLR tendency distance flat field and the Z500 tendency distance flat field of the predicted ten-day onto a spatial physical mode in the SVD decomposition module, and substituting the obtained time sequence into the optimal prediction model to calculate the ten-day tendency distance of the predicted ten-day of the predicted variable;

the result processing module is used for adding the obtained ten-day inclination distance of the predicted ten-day with the observed or predicted ten-day distance of the last ten-day to obtain the ten-day distance of the predicted ten-day; and adding the ten-day distance of the predicted ten-day with the climate state of the predicted ten-day to obtain the total value of the predicted ten-day of the predicted variable.

Based on the same inventive concept, the present invention provides a computer system, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program, when loaded into the processor, implements the sub-seasonal prediction method based on the ten-day tendency distance and the physical modality modeling.

Has the advantages that: compared with the prior art, the invention has the following advantages:

(1) direct prediction of the predicted elements is converted into indirect prediction of their ten-day tendencies. The benefit of this approach is that the sub-seasonal signature is highlighted by considering only the ten-day differences of the elements. The longer time signal was introduced as a persistent background by the last ten days. Therefore, the long-time signal does not need to be predicted, and the prediction accuracy is effectively improved.

(2) And carrying out statistical modeling on the main circulation modes and the prediction variables in the early atmospheric sub-season through SVD (singular value decomposition), and extracting the optimal prediction factor quantity combination by using return independent sample inspection. These ensure that the predictor is the most closely linked previous atmospheric mode with the chinese seasonal predictor variable.

(3) The method can also give the corresponding prediction of the atmospheric circulation element field in addition to the predicted ten-day rainfall and the ten-day temperature, which is favorable for understanding the intrinsic physical mechanism between the atmospheric circulation field and the prediction variable.

Drawings

FIG. 1 is a flow chart of a method of an embodiment of the present invention;

FIG. 2 is a schematic representation of OLR and precipitation SVD first modality time coefficients and anisotropic regression fields in an example of the present invention; specifically, the time coefficients and the respective anisotropic regression fields correspond to a first modal of the tropical OLR ten-day tendency of 30 days in total from 1 st of 10 months to 3 rd of 10 months in 2004 to 2013 and the 328 station precipitation ten-day tendency SVD in the widely-growing triangular region from 3 rd of 10 months to 2 nd of 11 months in 30 days;

FIG. 3 is a schematic representation of Z500 and precipitation SVD first modality time coefficients and an opposite sex regression field in an example of the present invention; specifically, the time coefficients and the respective anisotropic regression fields correspond to a first modal of the medium-high latitude Z500 tendency of 30 days in total from 10 st 1 to 3 rd 3 of 10 months in 2004 to 2013 and the 328 station precipitation tendencies SVD of the widely-growing triangular region from 3 rd to 2 nd 2 of 10 months in 30 days;

FIG. 4 is a distribution diagram of a rainfall trend prediction return spatial correlation coefficient varying with a combination of modal groups in the first 10 th month to the second 11 th month in the extended trigonal area on average from 2014 to 2018;

FIG. 5 is a graph illustrating the predicted precipitation tendency in the middle of 11 months in 2019 of the extended-length trigonal area;

FIG. 6 is a graph illustrating the predicted values of the precipitation in mid-11 months of 2019 in a triquetrum according to an example of the present invention;

FIG. 7 is a graph illustrating predicted temperature trends in the middle of 11 months in 2019 in the extended triangular region in accordance with an exemplary embodiment of the present invention;

FIG. 8 is a graph of the predicted outcome of 850hPa wind field inclination in the middle of 11 Yue in 2019 in accordance with an example of the present invention;

FIG. 9 is a graph comparing the predicted values and observed values in 11 months of 2019 in the extended triangular region in accordance with an example of the present invention.

Detailed Description

The invention is further elucidated with reference to the drawing and the specific application example. It is to be understood that these examples are intended to illustrate and not to limit the scope of the invention, which is defined by the claims appended hereto, and that modifications of various equivalent forms to the invention by those skilled in the art will be readily apparent to those of skill in the art after reading this disclosure.

Specifically, as shown in fig. 1, the method for predicting the sub-season climate based on ten-day tendency and physical modal modeling, disclosed by the embodiment of the invention, includes the following steps:

1. pre-processing of data

Removing the climatic states of the atmospheric outward long-wave radiation (OLR) field in the tropical region, the atmospheric 500hPa potential height (Z500) field in the tropical region and the medium and high latitude region in the tropical region and the predictive variable field respectively to obtain a ten-day distance, and then calculating the difference between the ten-day distance and the last ten-day distance to obtain the ten-day inclination distance of the ten-day distance.

The specific calculation formula of the ten-day tendency pitch is as follows:

wherein A is the average value in ten days of a certain variable,the climate mean value of the variable A is shown, delta A is the climate trend of the variable A, delta A is the climate trend of the variable A, t is a certain ten-day of 36 ten-day of the year, and t-1 is the last ten-day of the ten-day.

2. Selection of a predictor

2.1, supposing that the last ten days is predicted in advance by n, firstly, the OLR ten-day inclination distance flat field of the t-n th day, the Z500 th day inclination distance flat field and the predictive variable ten-day inclination distance flat field of each year of the total ten years from the last 5 years to the last 15 years obtained in the step 1 are expanded for one day from the front to the back on the basis of each ten day, namely, the OLR ten-day inclination distance flat field of the t-n-1 th day to the t-n +1 th day and the Z500 th day inclination distance flat field and the predictive variable ten-day inclination distance flat field of the t-1 st day to the t +1 th day are extracted for each year, and the total thirty days.

2.2, performing SVD decomposition on the extended OLR ten-day tendency flat field and Z500 ten-day tendency flat field obtained in step 2.1 and the predicted variable ten-day tendency flat field, and normalizing the time series corresponding to the first 20 OLR modalities and the first 20Z 500 modalities obtained by decomposition respectively (i.e. unifying the time series into a series with a standard deviation of 1 and an average value of 0) to be used as prediction factors.

3. Establishing a statistical prediction model

3.1 selecting the prediction factor combinations with different numbers in the step 2.2, and establishing a statistical prediction model with a plurality of prediction variables having a flat tendency by utilizing multivariate linear regression, wherein the specific formula of the statistical prediction model is as follows:

wherein, δ Δ A_fcstTo predict the Laetian Trend of a variable, F_i ^olrAndtime coefficients corresponding to the ith SVD mode and the jth SVD mode of the Z500 are respectively set,andthe weights are respectively the ith SVD mode of the OLR and the jth SVD mode of the Z500, M and N are respectively the number of the SVD modes of the OLR and the SVD modes of the Z500, x is a site or a lattice point, t is a predicted ten-day, and t-N is a predicted ten-day.

3.2 returning the ten-day trend of the last 5 years, comparing the returned results of the plurality of prediction models with the spatial correlation coefficients of the observation data, and taking the time coefficients corresponding to the M OLR field SVD modes and the N Z500 SVD modes corresponding to the maximum value of the correlation coefficients as optimal prediction factors, wherein the corresponding prediction models are taken as optimal prediction models.

4. Prediction using optimal prediction model

4.1 projecting the inclination distance flat field of atmospheric OLR ten days and the inclination distance flat field of Z500 ten days of the predicted year to the respective spatial physical modes in the step 2.2 to obtain corresponding time coefficients.

And 4.2, substituting the time coefficient obtained in the step 4.1 into the optimal prediction model, and calculating to obtain the inclination distance of the prediction variable in the last ten days.

4.3, adding the inclination distance of the t th ten days obtained in the step 4.2 to the distance between the t-1 th ten days to obtain the distance between the t th ten days. Further, adding the interval of the last ten days to the climate state of the last ten days to obtain the predicted average value of the last ten days.

5. For other predictive variables, the step 1 can be repeated to obtain the flat trend of the predictive variable. And (4) correspondingly giving an optimal model of the predictive variable by using the optimal predictive factor obtained in the step (3). And repeating the step 4 to obtain the prediction result of the prediction variable.

The detailed process and effect of the method of the present invention will be described below with the following column of the precipitation and temperature predictions of the method of the present invention applied to the widely growing triangular region in the first 11 th month of 2019.

1. Pre-processing of data

Removing the climatic states of the OLR field in tropical areas from 2004 to 2018, the Z500 field in middle and high latitude areas outside the tropical areas and the rainfall field in the extensive triangular area respectively to obtain the ten-day interval, and then calculating the difference between the ten-day interval and the last ten-day interval to obtain the ten-day inclination interval of the ten-day.

2. Selection of a predictor

2.1 forecast 11 month 1 st and 2 nd days 2 and 3 th ahead respectively, taking 2 th ahead as an example: the OLR ten-day inclination distance flat field, the Z500 ten-day inclination distance flat field and the precipitation ten-day inclination distance flat field in the 1 st of 11 months of each year from 2004 to 2013 obtained in the step 1 are extended from the front to the back on the basis of the respective ten days, namely the OLR ten-day inclination distance flat field in the 1 st to 3 rd of 10 months and the Z500 ten-day inclination distance flat field and the precipitation ten-day inclination distance flat field in the 3 rd to 2 nd of 10 months are extracted from each year, and the total number of the OLR ten-day inclination distance flat fields and the Z500 ten-day inclination distance flat fields are thirty days.

2.2, performing SVD decomposition on the extended OLR ten-day inclination distance flat field, the Z500 ten-day inclination distance flat field and the precipitation ten-day inclination distance flat field obtained in the step 2.1 (as shown in fig. 2 and 3), and normalizing the time series corresponding to the first 20 OLR modalities and the first 20Z 500 modalities obtained by decomposition respectively to obtain prediction factors.

3. Establishing a statistical prediction model

3.1, selecting the forecasting factor combinations with different numbers in the step 2.2, and establishing a plurality of statistical forecasting models of the rainfall ten-day tendency by utilizing multivariate linear regression.

3.2 reporting the trend of ten days from 2014 to 2018, comparing the reported results of the plurality of prediction models with the spatial correlation coefficients of the observation data, and taking the combination of the prediction factors corresponding to the maximum value of the correlation coefficients in the OLR field SVD modalities (for example, fig. 4, 17 should be selected) and the SVD modalities (for example, fig. 4, 7 should be selected) in the Z500 as the optimal prediction factors, and taking the corresponding prediction model as the optimal prediction model.

4. Prediction using a ten day prediction model

4.1 projecting the atmospheric OLR ten-day tendency field and the Z500 ten-day tendency field in 2 nd month 10 of 2019 onto the respective spatial physical modes in step 2.2 to obtain corresponding time coefficients.

And 4.2, substituting the time coefficient obtained by the step 4.1 into the optimal prediction model, and calculating to obtain the inclination distance of the 11 st 1 st day of the precipitation (as shown in the figure 5).

4.3 adding the inclination of the last 1 st of 11 month obtained in the step 4.2 to the last 3 rd of 10 month to obtain the last 1 st of 11 month. Further, add the interval of 1 st ten days of 11 months to the climate state of the corresponding ten days to obtain the total predicted value of the corresponding ten days (see fig. 6).

5. For other predicted variables, such as air temperature, wind field, OLR field, Z500 field, etc., step 1 may be repeated to obtain the flat-trend prediction of the predicted variable. And (4) correspondingly giving an optimal model of the predictive variable by using the optimal predictive factor obtained in the step (3). Repeating the step 4 to obtain the prediction result of the predictor variable (as shown in figure 7 and figure 8). Therefore, the prediction of the corresponding atmospheric circulation element field can be obtained, and the understanding of the internal physical mechanism between the atmospheric circulation field and the prediction variable is facilitated.

Fig. 9 is a comparison between the forecast of rainfall in the middle of 11 th month in 2019 and the actual observed value in the widely-long triangular region (the time of initial reporting is the third ten th month in 2019), which shows that the spatial distribution of the forecast rainfall tendency in ten days is basically consistent with the spatial distribution of the actually observed rainfall tendency in ten days, and the forecast value is basically consistent with the actual observed value.

Based on the same inventive concept, the sub-season climate prediction system based on ten-day tendency and physical modal modeling provided by the embodiment of the invention comprises: the ten-day tendency conversion module is used for respectively calculating and converting an OLR field in the early tropical zone, a Z500 field in the middle-high latitude area outside the early tropical zone and a predictive variable field into respective ten-day tendency distance flat fields; the SVD module is used for respectively carrying out SVD on the ten-day tendency distance flat fields of the early OLR and Z500 and the ten-day tendency distance flat field of the predictive variable, and standardizing time series of main physical modes which are obtained by decomposition and determine the early OLR of the ten-day tendency distance flat of the predictive variable and the Z500-day tendency distance flat variation to be used as a predictive factor of the ten-day tendency distance flat of the predictive variable; the ten-day tendency prediction model building module is used for selecting prediction factors with different numbers and building a statistical prediction model of the relation between the ten-day tendency distance of a prediction variable and the early main physical modal by utilizing a multivariate linear regression method; historical return of the ten-day tendency is carried out, and an optimal prediction factor and an optimal prediction model are determined through comparison with observation; the ten-day tendency prediction module is used for respectively projecting the early-stage OLR tendency distance flat field and the Z500 tendency distance flat field of the predicted ten-day onto a spatial physical mode in the SVD decomposition module, and substituting the obtained time sequence into the optimal prediction model to calculate the ten-day tendency distance of the predicted ten-day of the predicted variable; the result processing module is used for adding the obtained ten-day inclination distance of the predicted ten-day with the observed or predicted ten-day distance of the last ten-day to obtain the ten-day distance of the predicted ten-day; and adding the ten-day distance of the predicted ten-day with the climate state of the predicted ten-day to obtain the total value of the predicted ten-day of the predicted variable. For details of the specific implementation of each module, reference is made to the above method embodiments, which are not described herein again.

Based on the same inventive concept, an embodiment of the present invention provides a computer system, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when loaded into the processor, implements the sub-season climate prediction method based on the ten-day tendency and the physical modality modeling.