Mode rainfall forecast correction method and system based on weather situation field classification

Abstract

The invention discloses a mode rainfall forecast correction method and system based on weather situation field classification, and the method comprises the steps: carrying out the division of a flood season and a non-flood season of a sample based on the position of a target region, carrying out the classification of weather situation field types of the sample based on the forecast value of the sample, and carrying out the prediction of the weather situation field. Then, respectively constructing a mode rainfall forecast intensity correction model and a mode rainfall forecast falling area correction model of different weather situation field types in a flood season and a non-flood season; and selecting the corresponding mode rainfall forecast strength correction model and the mode rainfall forecast falling area correction model to carry out mode rainfall forecast correction, so that a more accurate mode rainfall forecast correction result can be obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of precipitation forecasting, and in particular relates to a method and system for correcting a precipitation forecast model based on weather situation field classification. Background Art

[0002] Quantitative precipitation forecast (QPF) is considered one of the most difficult challenges of numerical model forecasting due to the uncertainty of its spatiotemporal distribution and intensity changes. In recent years, with the improvement of the spatial resolution of the model, the enhancement of the data assimilation capability of the initial field and boundary field, and the improvement of the sub-grid physical process parameterization scheme, its quantitative precipitation forecasting capability has been continuously improved, but the systematic errors of the spatiotemporal distribution and intensity forecast of heavy precipitation based on models with different dynamic frameworks have also become increasingly obvious. Therefore, it is necessary to post-process and correct the model precipitation forecast results.

[0003] At present, the correction of model precipitation forecast products is mainly reflected in two aspects: First, subjective correction. Forecasters first count the weather situation and meteorological element field characteristics of historical heavy precipitation cases, and then summarize the correction strategy of model precipitation forecast products. This method mainly relies on the subjective experience of forecasters, and lacks a unified identification standard when counting weather situation and meteorological element field characteristics. Second, objective correction. Statistical methods are used to fit the multi-factor forecast data output by the model and the actual observation data, and the forecast model of related elements is constructed to obtain objective precipitation forecast results. For example, the model output statistical method (MOS) constructs a regression equation based on specific forecast factors and precipitation at the station or grid point, but this method can only perform simple deviation correction on precipitation forecast, and cannot correct the results of clear and rainy weather. The heavy precipitation level forecast based on the "batch method" (also known as the "component method") is to establish a corresponding forecast equation based on the physical connection between the precipitation forecast and the relevant indicator, and forecast the possibility of heavy precipitation. Compared with the MOS method, this method has gotten rid of the simple dependence on regression analysis and has a clear physical meaning. However, it is mostly used for qualitative judgment of heavy precipitation events and is difficult to make quantitative corrections. In recent years, target score threshold correction methods such as frequency matching method (FM) and optimal TS score correction method (OTS) have been gradually promoted and applied due to their advantages of quantitative correction, low computing resource consumption, and stable operation. The frequency matching method (FM) uses the frequency distribution curve of actual observed precipitation at various levels to correct the curve of model forecast precipitation, so that the distribution type of forecast precipitation is closer to the actual result and the forecast error is reduced. The optimal TS score correction method (OTS) uses historical precipitation observations and model precipitation forecasts to build a training model, and adjusts the model precipitation forecast to achieve the TS score optimization goal, thereby correcting the deviation of precipitation forecast. However, this type of method is highly dependent on actual and model historical sample data in terms of heavy precipitation forecasting. When the historical samples are too few, correction anomalies are prone to occur. Moreover, this method mainly corrects the deviation of precipitation intensity, but does not make sufficient corrections for the deviation of precipitation area. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the present invention proposes a method and system for correcting model precipitation forecast based on weather situation field classification.

[0005] The technical solution of the present invention is as follows:

[0006] A method for correcting model precipitation forecast based on weather situation field classification, comprising:

[0007] Acquire historical model precipitation forecast data of a target area and historical actual precipitation data that matches the time of the historical model precipitation forecast data, and form a plurality of samples having observation values ​​corresponding to the historical actual precipitation data and forecast values ​​corresponding to the historical model precipitation forecast data;

[0008] Based on the location of the target area, the samples are divided into flood season and non-flood season, and based on the forecast values ​​of the samples, the samples are classified into the weather situation field types of weak precipitation type, typhoon-controlled type controlled by typhoon circulation, subtropical high-controlled type controlled by subtropical high pressure, trough-front type, trough-back type and trough-bottom type which are not controlled by typhoon circulation and subtropical high pressure and are divided by different relative positions of corresponding precipitation areas and high-altitude short-wave troughs;

[0009] Constructing respectively the model precipitation forecast intensity correction model and the model precipitation forecast area correction model corresponding to different weather situation field types in the flood season and the non-flood season, and using the observed value of the sample as the model output value and the predicted value of the sample as the model input value, the model precipitation forecast intensity correction model and the model precipitation forecast area correction model are trained;

[0010] Based on the season and weather situation field corresponding to the real-time model precipitation forecast data, the corresponding model precipitation forecast intensity correction model and model precipitation forecast location correction model that have been trained are selected to perform model precipitation forecast correction.

[0011] Furthermore, the classification method of the weak precipitation type, typhoon control type and subtropical high control type in the weather situation field type includes:

[0012] For a sample at a certain moment, the target area is gridded with a set resolution, the model precipitation forecast data in the sample is interpolated to the meteorological stations in the target area, and the number of stations in each grid whose model precipitation forecast is greater than or equal to the first precipitation threshold is counted; if the number of stations corresponding to each grid is 0, the weather situation field type of the sample at this moment is weak precipitation type;

[0013] If the number of stations is not all 0, determine whether the typhoon center position in the typhoon message corresponding to the moment is located in the target area. If so, the weather situation field type of the sample at this moment is typhoon controlled; otherwise, based on the number of stations corresponding to the target area grid, screen out the precipitation center grid of the target area, calculate the average longitude and latitude of all stations in the precipitation center grid whose model precipitation forecast is greater than or equal to the first precipitation threshold, and use the average longitude and latitude as the precipitation center position of the model precipitation forecast at this moment, and calculate the average potential height forecast field of the sample at this moment with the subtropical high characteristic line and the subtropical high peripheral characteristic line; if the precipitation center position of the sample is within the range of the subtropical high characteristic line, the weather situation field type of the sample at this moment is subtropical high controlled.

[0014] Furthermore, the specific steps of selecting the precipitation center grid of the target area based on the number of stations corresponding to the grids in the target area include: dividing the target area into precipitation areas, and for each precipitation area, obtaining the number of heavy precipitation adjacent grids corresponding to each grid therein, wherein the heavy precipitation adjacent grids are grids corresponding to which the number of stations whose model precipitation forecast is greater than or equal to the first precipitation threshold in the grids adjacent to each grid is greater than 0; taking the grid with the highest number of heavy precipitation adjacent grids as the precipitation center grid of each precipitation area; taking the precipitation center grid with the highest number of corresponding heavy precipitation adjacent grids in different precipitation areas as the precipitation center grid of the target area, and in the case where there are multiple grids with the highest and consistent numbers of corresponding heavy precipitation adjacent grids, calculating the total precipitation forecast in each grid, and taking the grid with the largest total precipitation forecast as the precipitation center grid;

[0015] The specific method for dividing the target area into precipitation zones includes: obtaining all grids in the target area whose corresponding station numbers are not 0, calculating the number of interval grids between any two grids in all the obtained grids, and dividing two grids whose interval grid number is less than or equal to 1 into the same precipitation zone.

[0016] Furthermore, the classification method of the weather situation field types into the trough front type, the trough back type and the trough bottom type includes:

[0017] For samples whose weather situation field is not classified as weak precipitation type, typhoon controlled type controlled by typhoon circulation, or subtropical high controlled type controlled by subtropical high pressure, the latitudinal deviation of the mean geopotential height of the sample at that moment is calculated and compared with the latitudinal deviation threshold of the mean geopotential height. If the latitudinal deviation of the mean geopotential height is less than the latitudinal deviation threshold of the mean geopotential height, the weather situation field type of the sample at that moment is the front trough type; otherwise, the latitude difference between the first intersection point of the subtropical high outer characteristic line and the first judgment meridian and the second intersection point of the second judgment meridian in the mean geopotential height forecast field of the sample is determined, and compared with the latitude difference threshold. If the latitude difference is greater than the latitude difference threshold, the weather situation field type of the sample at that moment is the back trough type; otherwise, the weather situation field type of the sample at that moment is the bottom trough type.

[0018] Furthermore, the formula for the latitudinal deviation of the mean potential height is as follows:

[0019]

[0020] In the formula, is the latitudinal deviation of the average potential height corresponding to longitude lon_i and latitude lat_j, lon_i is longitude, lon_i∈[lon_i_min,lon_i_max], lat_j is latitude, lat_j∈[lat_j_min,lat_j_max]; lon_i_min is the first judgment longitude; lon_i_max is the second judgment longitude; lat_j_min is the first judgment latitude; lat_j_max is the second judgment latitude;

[0021] is the average potential height corresponding to longitude lon_i and latitude lat_j;

[0022] is the average value of the mean potential height corresponding to latitude lat_j at different longitudes.

[0023] Furthermore, the specific method for constructing and training the model for the correction of precipitation forecast intensity of different weather situation fields in the flood season and the non-flood season includes:

[0024] The model precipitation forecast correction models of typhoon-controlled type, subtropical high-controlled type, trough-front type, trough-back type and trough-bottom type in the flood season, and the model precipitation forecast correction models of subtropical high-controlled type, trough-front type, trough-back type and trough-bottom type in the non-flood season are respectively constructed; the subtropical high-controlled type, trough-front type and trough-back type in the flood season and non-flood season adopt the scaling coefficient correction model; the typhoon-controlled type and trough-bottom type in the flood season adopt the optimal TB score correction model; the trough-bottom type in the non-flood season adopts the scaling coefficient correction model;

[0025] The corresponding model precipitation forecast correction model is trained by taking the precipitation in the observed values ​​of samples of typhoon-controlled type, subtropical high-controlled type, trough-front type, trough-back type and trough-bottom type during the flood season as the model output value and taking the precipitation in the predicted value of the samples as the input value of the model. The corresponding model precipitation forecast correction model is trained by taking the precipitation in the observed values ​​of samples of subtropical high-controlled type, trough-front type, trough-back type and trough-bottom type during the non-flood season as the model output value and taking the precipitation in the predicted value of the samples as the input value of the model.

[0026] Furthermore, the expression of the scaling factor correction model is:

[0027] y=xF

[0028] Where y is the corrected value of the model precipitation forecast, i.e., the output of the scaling factor correction model; x is the original value of the model precipitation forecast, i.e., the input of the scaling factor correction model; F is the scaling factor;

[0029] The expression of the optimal TB score correction model is:

[0030]

[0031] In the formula, O k is the actual precipitation level of the kth level; O k+1 is the actual precipitation level of the k+1th level; E1 is the predicted precipitation level threshold of the 1st level; E k is the k-th level forecast precipitation level threshold; E k+1 is the k+1th level forecast precipitation level threshold; E n is the forecast precipitation level threshold of the nth level; k∈[1,n-1], n is the total number of precipitation levels;

[0032] The optimal TB score correction model is trained by adjusting the k-th level forecast precipitation level threshold E k The TB score of the actual precipitation level of level k and above is maximized. The calculation formula of the TB score is as follows:

[0033] TB=TS-B

[0034]

[0035]

[0036] Where TS is the TS score of the model forecast for precipitation above a certain level, 0≤TS≤1; B is the BIAS deviation of the model forecast for precipitation above a certain level, B≥0; NA is the station with correct forecast; NB is the station with missed forecast; and NC is the station with missed forecast.

[0037] Furthermore, the specific method for constructing and training the model for correcting the precipitation forecast area of ​​different weather situation fields in the flood season and the non-flood season includes:

[0038] The model precipitation forecast location correction models for the weather situation field types of trough front, trough back and trough bottom in the flood season and non-flood season are respectively constructed. The latitude difference between the center of the model precipitation position corresponding to the forecast value of the sample and the actual precipitation position corresponding to the observation value is used as the model output value, and the latitude difference between the model precipitation forecast position corresponding to the forecast value of the sample and the outer characteristic line of the subtropical high is used as the input value of the model to train the corresponding model precipitation forecast correction model.

[0039] Furthermore, the model precipitation forecast area correction model adopts a univariate linear regression model, and the expression is as follows:

[0040] Y=a×X+b

[0041] Where X is the latitude difference between the model precipitation center and the outer characteristic line of the subtropical high; Y is the latitude difference between the model precipitation center and the actual precipitation center; a is the slope of the univariate linear regression model; b is the intercept of the univariate linear regression model.

[0042] Furthermore, the specific steps of obtaining historical model precipitation forecast data of the target area and historical actual precipitation data matching the time of the historical model precipitation forecast data, and forming a plurality of samples having observation values ​​corresponding to the historical actual precipitation data and forecast values ​​corresponding to the historical model precipitation forecast data include:

[0043] A model precipitation forecast classification database is constructed, and the acquired historical model precipitation forecast data of the target area and the historical actual precipitation data that matches the time of the historical model precipitation forecast data are stored in the database. Each sample in the database includes the observation value of the corresponding historical actual precipitation data and the forecast value of the corresponding historical model precipitation forecast data.

[0044] A model precipitation forecast correction system based on weather situation field classification, including a sample acquisition module, a sample division module, a model construction module and a correction module;

[0045] The sample acquisition module is used to acquire historical model precipitation forecast data of the target area and historical actual precipitation data that matches the time of the historical model precipitation forecast data, and form a plurality of samples having observation values ​​corresponding to the historical actual precipitation data and forecast values ​​corresponding to the historical model precipitation forecast data;

[0046] The sample classification module is used to classify the samples into flood season and non-flood season based on the location of the target area, and classify the samples into weak precipitation type, typhoon-controlled type controlled by typhoon circulation, subtropical high-controlled type controlled by subtropical high pressure, trough-front type, trough-back type and trough-bottom type that are not controlled by typhoon circulation and subtropical high pressure and have different latitudinal deviations of average potential height based on the forecast values ​​of the samples;

[0047] The model building module is used to respectively build a model precipitation forecast intensity correction model and a model precipitation forecast area correction model corresponding to different weather situation field types in the flood season and the non-flood season, and train the model precipitation forecast intensity correction model and the model precipitation forecast area correction model using the observed value of the sample as the model output value and the predicted value of the sample as the model output value;

[0048] The correction module is used to select the corresponding trained model precipitation forecast intensity correction model and model precipitation forecast location correction model to perform model precipitation forecast correction based on the season and weather situation field corresponding to the real-time model precipitation forecast data.

[0049] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0050] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention proposes a model precipitation forecast correction method and system based on weather situation field classification. The method divides samples into flood season and non-flood season based on the location of the target area, and classifies the weather situation field types of the samples based on the forecast values ​​of the samples, and then constructs model precipitation forecast intensity correction models and model precipitation forecast location correction models for different weather situation field types in flood season and non-flood season respectively. Finally, based on the season and weather situation field corresponding to the real-time model precipitation forecast data, the corresponding model precipitation forecast intensity correction model and model precipitation forecast location correction model are selected to perform model precipitation forecast correction, thereby obtaining a more accurate model precipitation forecast correction result.

[0053] In classifying the weather situation field types of the samples according to the method of the present invention, the weak precipitation type and the "heavy precipitation type" are first divided, and then the samples belonging to the "heavy precipitation type" are divided into the typhoon-controlled type, the subtropical high-controlled type, the trough-front type, the trough-back type and the trough-bottom type in turn. Since the weak precipitation type samples are eliminated in advance, this design is equivalent to eliminating the noise interference in the samples, so the subsequent calculation of the average potential height forecast field is more accurate, thereby making the classification of the weather situation field of the present invention more accurate.

[0054] The method of the present invention adopts the optimal TB score correction model to correct the model precipitation forecast. Compared with the traditional correction method based on TS score, the present invention takes into account the forecast BIAS deviation amplitude, weakens the influence of false alarms and missed alarms on the forecast precipitation level threshold of the traditional method, makes the precipitation level deviation smaller, and is closer to the observation result.

[0055] The input of the model precipitation forecast fall area correction model in the method of the present invention is the latitude difference between the model precipitation center position and the outer characteristic line of the subtropical high, and the output is the latitude difference between the model precipitation center position and the actual precipitation center position. The design introduces the outer characteristic line of the subtropical high to make the fall area correction easier to implement, avoiding the problem that the existing method directly uses the model precipitation center position as input and the actual precipitation center position as output, which causes the model to be difficult to converge.

[0056] The method of the present invention adopts a model precipitation forecast area correction model to correct the precipitation area. Compared with the existing method of subjectively correcting the precipitation area based on artificial experience, the model precipitation forecast area correction model adopted by the method of the present invention is based on historical data and constructed using a data fitting approach, and is therefore more objective and scientific. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flow chart of the method for correcting the model precipitation forecast based on the classification of the weather situation field in the embodiment;

[0058] Figure 2 An example diagram for selecting a precipitation center grid in the embodiment;

[0059] Figure 3 It is a scatter distribution diagram of the latitude difference (abscissa) between the model precipitation center and the 584 line and the latitude difference (ordinate) between the model rainstorm center and the actual center at the model start time 08:00 in the embodiment;

[0060] Figure 4 It is a scatter distribution diagram of the latitude difference (abscissa) between the model precipitation center and the 584 line and the latitude difference (ordinate) between the model rainstorm center and the actual center at the model start time 08:00 and 20:00;

[0061] Figure 5 It is a scatter distribution diagram of the latitude difference between the model precipitation center and the 584 line (abscissa) and the latitude difference between the model rainstorm center and the actual center (ordinate) at the model start time 20 o'clock in the embodiment;

[0062] Figure 6 It is a scatter distribution diagram of the latitude difference (abscissa) between the model precipitation center and the 584 line corresponding to the trough back type in the embodiment and the latitude difference (ordinate) between the model rainstorm center and the actual center at the model start time 20 o'clock;

[0063] Figure 7 This is a schematic diagram of solving the scaling factor F in the embodiment;

[0064] Figure 8 It is a schematic diagram for comparing the test results in the embodiments. DETAILED DESCRIPTION

[0065] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent modifications of the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0066] Embodiment 1:

[0067] The present invention provides a method for correcting precipitation forecast based on weather situation field classification, such as Figure 1 As shown, the specific steps are as follows:

[0068] S1. Obtain historical model precipitation forecast data of the target area and historical actual precipitation data that matches the time of the historical model precipitation forecast data, and form a number of samples having observation values ​​corresponding to the historical actual precipitation data and forecast values ​​corresponding to the historical model precipitation forecast data;

[0069] S2. Based on the location of the target area, the samples are divided into flood season and non-flood season, and based on the forecast values ​​of the samples, several samples are classified into weak precipitation type, typhoon-controlled type controlled by typhoon circulation, subtropical high-controlled type controlled by subtropical high pressure, trough-front type, trough-back type and trough-bottom type which are not controlled by typhoon circulation and subtropical high pressure and are divided by the different relative positions of the corresponding precipitation area and the high-altitude short-wave trough;

[0070] S3. Construct the model precipitation forecast intensity correction model and the model precipitation forecast area correction model corresponding to different weather situation field types in the flood season and the non-flood season respectively, and use the sample observation value as the model output value and the sample forecast value as the model input value to train the model precipitation forecast intensity correction model and the model precipitation forecast area correction model;

[0071] S4. Based on the season corresponding to the real-time model precipitation forecast data, the samples are judged as being in the flood season and the non-flood season. Based on the weather situation field corresponding to the real-time model precipitation forecast data, the corresponding trained model precipitation forecast intensity correction model and model precipitation forecast fall area correction model are selected to correct the model precipitation forecast.

[0072] Embodiment 2:

[0073] This embodiment is further designed on the basis of the first embodiment in that the classification method of the weather situation field type in this example includes:

[0074] For samples at a certain moment, the target area is gridded with a set resolution, and the model precipitation forecast data in the sample is interpolated to the meteorological station in the target area. The meteorological station can be a "national station", and the number of stations in each grid whose model precipitation forecast is greater than or equal to the first precipitation threshold is counted; if the number of stations corresponding to each grid is 0, the weather situation field type of the sample at this moment is weak precipitation type; the above resolution can be 0.5°×0.5° or other resolutions. The above first precipitation threshold can be 3mm. If the number of stations is not all 0, it is a "heavy precipitation type", which includes typhoon control type, subtropical high control type, trough front type, trough back type and trough bottom type. The specific division method is as follows:

[0075] If the number of stations is not all 0, determine whether the typhoon center position in the typhoon message corresponding to the moment is located in the target area. If so, the weather situation field type of the sample at this moment is typhoon controlled; otherwise, based on the number of stations corresponding to the target area grid, screen out the precipitation center grid of the target area, calculate the average longitude and latitude of all stations in the precipitation center grid whose model precipitation forecast is greater than or equal to the first precipitation threshold, and use the average longitude and latitude as the precipitation center position of the model precipitation forecast at this moment, and calculate the average potential height forecast field of the sample at this moment with the characteristic line of the subtropical high and the peripheral characteristic line of the subtropical high; if the precipitation center position of the sample is within the range of the characteristic line of the subtropical high, the weather situation field type of the sample at this moment is subtropical high controlled.

[0076] Furthermore, the above-mentioned subtropical high characteristic line can be the 588dagpm line, and the typhoon message can adopt the typhoon message issued by the Central Meteorological Observatory.

[0077] Embodiment three:

[0078] This embodiment is further designed on the basis of the second embodiment in that the specific steps of selecting the precipitation center grid of the target area based on the number of stations corresponding to the grids in the target area in this example include: dividing the target area into precipitation areas, and for each precipitation area, obtaining the number of heavy precipitation adjacent grids corresponding to each grid therein, wherein the heavy precipitation adjacent grids are grids corresponding to the number of stations in the grids adjacent to each grid whose model precipitation forecast is greater than or equal to the first precipitation threshold value is greater than 0; taking the grid with the highest number of heavy precipitation adjacent grids as the precipitation center grid of each precipitation area; taking the precipitation center grid with the highest number of corresponding heavy precipitation adjacent grids in different precipitation areas as the precipitation center grid of the target area, and in the case where there are multiple grids with the highest and consistent numbers of corresponding heavy precipitation adjacent grids, calculating the total precipitation forecast in each grid, and taking the grid with the largest total precipitation forecast as the precipitation center grid;

[0079] The specific method for dividing the target area into precipitation zones includes: obtaining all grids whose corresponding station numbers in the target area are not 0, calculating the number of interval grids between any two grids in all the obtained grids, and dividing two grids whose interval grid number is less than or equal to 1 into the same precipitation zone;

[0080] The following is an example to illustrate the above process. Figure 2 As shown, there are three precipitation areas in this example, namely the yellow area, the green area and the orange area. There are 8 grids adjacent to heavy precipitation in the yellow area and the green area. Therefore, the total amount of precipitation forecast in these grids should be calculated separately, and the grid with the largest total amount of precipitation forecast is taken as the precipitation center grid.

[0081] Embodiment 4:

[0082] This embodiment is further designed on the basis of the second embodiment in that the classification method of the weather situation field type in this embodiment includes:

[0083] For samples whose weather situation field is not classified as weak precipitation type, typhoon controlled type controlled by typhoon circulation, or subtropical high controlled type controlled by subtropical high pressure, the latitudinal deviation of the mean geopotential height of the sample at that moment is calculated and compared with the latitudinal deviation threshold of the mean geopotential height. If the latitudinal deviation of the mean geopotential height is less than the latitudinal deviation threshold of the mean geopotential height, the weather situation field type of the sample at that moment is the trough front type; otherwise, the latitude difference between the first intersection of the subtropical high outer characteristic line and the first judgment meridian and the second intersection of the second judgment meridian in the mean geopotential height forecast field of the sample is determined and compared with the latitude difference threshold. If the latitude difference is greater than the latitude difference threshold, the weather situation field type of the sample at that moment is the trough back type; otherwise, the weather situation field type of the sample at that moment is the trough bottom type. The above-mentioned latitudinal deviation threshold of the mean geopotential height is 0, and the above-mentioned latitude difference threshold is 0.5°.

[0084] Embodiment five:

[0085] This embodiment is further designed on the basis of the fourth embodiment in that the formula for the latitudinal deviation of the average potential height in this embodiment is as follows:

[0086]

[0087] In the formula, is the latitudinal deviation of the average potential height corresponding to longitude lon_i and latitude lat_j, lon_i is longitude, lon_i∈[lon_i_min,lon_i_max], lat_j is latitude, lat_j∈[lat_j_min,lat_j_max]; lon_i_min is the first judgment longitude; lon_i_max is the second judgment longitude; lat_j_min is the first judgment latitude; lat_j_max is the second judgment latitude;

[0088] is the average potential height corresponding to longitude lon_i and latitude lat_j;

[0089] is the average value of the mean potential height corresponding to latitude lat_j at different longitudes.

[0090] Embodiment six:

[0091] This embodiment is further designed on the basis of the first embodiment in that the specific method of constructing and training the model for the correction of precipitation forecast intensity of different weather situation field types in the flood season and the non-flood season in this embodiment includes:

[0092] The model precipitation forecast correction models of typhoon-controlled type, subtropical high-controlled type, trough-front type, trough-back type and trough-bottom type in the flood season, and the model precipitation forecast correction models of subtropical high-controlled type, trough-front type, trough-back type and trough-bottom type in the non-flood season are constructed respectively; the subtropical high-controlled type, trough-front type and trough-back type in the flood season and non-flood season adopt the scaling coefficient correction model; the typhoon-controlled type and trough-bottom type in the flood season adopt the optimal TB score correction model; the trough-bottom type in the non-flood season adopts the scaling coefficient correction model; the weak precipitation type in the flood season and non-flood season and the typhoon type in the non-flood season are not corrected. The correction models for the flood season and non-flood season are shown in Table 2 and Table 3 respectively:

[0093] Table 2

[0094]

[0095] Table 3

[0096]

[0097]

[0098] The inspection levels in Tables 2 and 3 can be adjusted according to the timeliness of precipitation forecast.

[0099] The corresponding model precipitation forecast correction model is trained by taking the precipitation in the observed values ​​of samples of typhoon-controlled type, subtropical high-controlled type, trough-front type, trough-back type and trough-bottom type during the flood season as the model output value and taking the precipitation in the predicted value of the samples as the input value of the model. The corresponding model precipitation forecast correction model is trained by taking the precipitation in the observed values ​​of samples of subtropical high-controlled type, trough-front type, trough-back type and trough-bottom type during the non-flood season as the model output value and taking the precipitation in the predicted value of the samples as the input value of the model.

[0100] Embodiment seven:

[0101] This embodiment is further designed on the basis of the sixth embodiment in that the expression of the scaling coefficient correction model in this embodiment is:

[0102] y=xF

[0103] Where y is the correction value of the model precipitation forecast, i.e., the output of the scaling factor correction model; x is the original value of the model precipitation forecast, i.e., the input of the scaling factor correction model; F is the scaling factor; the goal of this design is to minimize the BIAS deviation of the model for precipitation forecasts above a certain level by adjusting the scaling factor F during sample training. For details, see Figure 7 .

[0104] The expression of the optimal TB score correction model is:

[0105]

[0106] In the formula, O k is the actual precipitation level of the kth level; O k+1 is the actual precipitation level of the k+1th level; E1 is the predicted precipitation level threshold of the 1st level; E k is the k-th level forecast precipitation level threshold; E k+1 is the k+1th level forecast precipitation level threshold; E n is the forecast precipitation level threshold of the nth level; k∈[1,n-1], n is the total number of precipitation levels;

[0107] The optimal TB score correction model is trained by adjusting the k-th level forecast precipitation level threshold E k The TB score of the actual precipitation level of level k and above is the highest. The TB score is used to evaluate the forecasting ability of the model for precipitation above a certain level. The specific calculation formula is as follows:

[0108] TB=TS-B

[0109]

[0110]

[0111] In the formula, TS is the TS score of the model forecast for precipitation above a certain magnitude, 0≤TS≤1, the higher the score, the higher the proportion of correct model forecast precipitation; B is the BIAS deviation amplitude of the model forecast for precipitation above a certain magnitude, B≥0, the smaller the deviation, the closer the model forecast precipitation range is to the actual situation; NA is the station number with correct forecast; NB is the station number with empty forecast; NC is the station number with missed forecast. As can be seen from the formula, the higher the TB score, the higher the proportion of correct model precipitation forecast, and the closer the model precipitation forecast range is to the actual situation, and the stronger the overall forecasting ability is.

[0112] Compared with the OTS algorithm, the method of the present invention can maximize the correct number NA of model precipitation forecasts while controlling its precipitation forecast range to a certain extent, thereby avoiding the rapid increase of the number of false reports NB or the number of missed reports NC that may be caused in the process.

[0113] Embodiment eight:

[0114] This embodiment is further designed on the basis of the first embodiment in that the specific method of constructing and training the model of precipitation forecast area correction for different weather situation field types in the flood season and the non-flood season in this embodiment includes:

[0115] The model precipitation forecast location correction models for the weather situation field types of trough front, trough back and trough bottom in the flood season and non-flood season are respectively constructed. The latitude difference between the center of the model precipitation position corresponding to the forecast value of the sample and the actual precipitation position corresponding to the observation value is used as the model output value, and the latitude difference between the model precipitation forecast position corresponding to the forecast value of the sample and the outer characteristic line of the subtropical high is used as the input value of the model to train the corresponding model precipitation forecast correction model.

[0116] Embodiment nine:

[0117] This embodiment is further designed on the basis of the first embodiment in that the model precipitation forecast fall area correction model in this embodiment adopts a univariate linear regression model, and the expression is as follows:

[0118] Y=a×X+b

[0119] Where X is the latitude difference between the model precipitation center and the outer characteristic line of the subtropical high; Y is the latitude difference between the model precipitation center and the actual precipitation center. If Y>0, it means that the model precipitation center is biased to the north compared with the actual precipitation center, otherwise it means that it is biased to the south; a is the slope of the univariate linear regression model; b is the intercept of the univariate linear regression model, and both the slope and intercept are constants.

[0120] Embodiment ten:

[0121] This embodiment is further designed on the basis of the first embodiment in that, in this embodiment, the historical model precipitation forecast data of the target area and the historical actual precipitation data matching the time of the historical model precipitation forecast data are obtained, and the specific steps of forming a plurality of samples having observation values ​​corresponding to the historical actual precipitation data and forecast values ​​corresponding to the historical model precipitation forecast data include:

[0122] A model precipitation forecast classification database is constructed, and the historical model precipitation forecast data of the target area and the historical actual precipitation data that matches the time of the historical model precipitation forecast data are stored in the database. Each sample in the database includes the observation value of the corresponding historical actual precipitation data and the forecast value of the corresponding historical model precipitation forecast data.

[0123] The existing method can be used to divide the samples into flood season and non-flood season based on the location of the target area. For the division of flood season and non-flood season in Anhui Province, it can be obtained based on the 30-year climate compilation data of the province (1971-2000). Heavy rain may occur in Anhui in all months, but heavy rain (daily precipitation ≥ 50 mm) is mainly concentrated in May-August, especially in June-July. The distribution pattern of heavy rain (daily precipitation ≥ 100 mm) is basically similar to that of heavy rain, and the number of occurrences is also more in June-July, and heavy rain has never occurred in winter. Extremely heavy rain (daily precipitation ≥ 250 mm) only occurs in summer (June-August), and is still mostly in June-July. Judging from the frequency of heavy rain in Anhui in each month, more than 50% of the heavy rain occurs in June-July, and more than 80% of the heavy rain occurs in May-August. Therefore, June-August is the flood season, and other months are the non-flood season. Other existing methods can also be used for division, such as the method recorded in "Basics and Practice of Anhui Weather Forecast Business".

[0124] Embodiment eleven:

[0125] The present invention provides a model precipitation forecast correction system based on weather situation field classification, comprising a sample acquisition module, a sample division module, a model construction module and a correction module;

[0126] A sample acquisition module is used to acquire historical model precipitation forecast data of a target area and historical actual precipitation data that matches the time of the historical model precipitation forecast data, and form a number of samples having observation values ​​corresponding to the historical actual precipitation data and forecast values ​​corresponding to the historical model precipitation forecast data;

[0127] The sample classification module is used to classify the samples into flood season and non-flood season based on the location of the target area, and classify the weather situation field types of the samples into weak precipitation type, typhoon-controlled type controlled by typhoon circulation, subtropical high-controlled type controlled by subtropical high pressure, trough-front type, trough-back type and trough-bottom type that are not controlled by typhoon circulation and subtropical high pressure and have different latitudinal deviations of average potential height based on the forecast values ​​of several samples;

[0128] The model building module is used to respectively build the model precipitation forecast intensity correction model and the model precipitation forecast area correction model corresponding to different weather situation field types in the flood season and the non-flood season, and train the model precipitation forecast intensity correction model and the model precipitation forecast area correction model using the sample observation value as the model output value and the sample forecast value as the model output value;

[0129] The correction module is used to select the corresponding trained model precipitation forecast intensity correction model and model precipitation forecast location correction model to correct the model precipitation forecast based on the season and weather situation field corresponding to the real-time model precipitation forecast data.

[0130] Embodiment 12:

[0131] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method described in any of the above embodiments.

[0132] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any of the above embodiments are implemented.

[0133] Application examples:

[0134] In this example, Anhui Province and its surrounding areas (114.7°-119.7°E, 29°-35°N) are taken as the target area, and the 3-hour precipitation forecast for the target area in the 015-036 forecast hours under the ECMWF model from June to August 2024 is corrected. The specific process is as follows:

[0135] First, based on the objective identification algorithm of precipitation weather situation field, the ECMWF model 3h precipitation forecast classification database is constructed as follows:

[0136] Step 101, objective determination of strong and weak precipitation and precipitation center, is as follows:

[0137] First, the target area is divided into 10 (longitudinal) × 12 (latitudinal) grids in units of 0.5° × 0.5°, and the ECMWF model 3h precipitation forecast is interpolated to all national stations in the area. Then, the number of stations with model 3h precipitation forecast ≥ 3mm in each grid is calculated. If the number of stations with ≥ 3mm in all grids is 0, the ECMWF model 3h precipitation forecast in Anhui Province and its surrounding areas at this moment is considered to be "weak precipitation type", otherwise it is considered to have a "heavy precipitation" forecast. Next, the precipitation center grid and precipitation center position of the area are obtained according to the method of the present invention.

[0138] Step 102, objective identification of precipitation weather situation field, is as follows:

[0139] Statistics of all the moments with "heavy precipitation" forecasts from 015 to 036, and calculation of the ECMWF model 500hPa average potential height forecast field And the average position of the precipitation center. Then, determine whether the precipitation process is controlled by the typhoon circulation or the subtropical high pressure. Taking Anhui Province and its surrounding areas (114.7°-119.7°E, 29°-35°N) as an example, the method for determining whether it is controlled by the typhoon circulation is: According to the typhoon message issued by the Central Meteorological Observatory every day, if the 24-hour forecast typhoon center position is within the range of 113°-123°E, 25°-37°N, then it is determined that the precipitation process belongs to "typhoon type". The method for determining whether it is controlled by the subtropical high pressure: Based on the 500hPa average potential height field If the average position of the precipitation center is within the range of the 588 dagpm line (hereinafter referred to as the 588 line) and is not affected by the typhoon circulation, it is judged to be "subtropical high-controlled type". Finally, for heavy precipitation processes that are not "typhoon-type" or "subtropical high-controlled", the 500 hPa average potential height field is used to determine the precipitation process. The weather situation field is divided into "trough front type", "trough back type" and "trough bottom type". The determination method of "trough front type" is: the latitudinal deviation of the average potential height in the area (108°-120°E, 35°-38°N) and (108°-115°E, 30°-33°N) or (108°-115°E, 33°-35°N) mean geopotential height latitudinal deviation The formula for the latitudinal deviation of mean geopotential height is as follows:

[0140]

[0141] Where lon_i is the longitude (value range: 90°-140°E), and lat_j is the latitude (value range: 26°-38°N). is the mean potential height field Average value between 90°-140°E at latitude lat_j.

[0142] The method for determining the “trough-back type” is: the latitude difference between line 584 at 115°E and 120°E is greater than 0.5°.

[0143] The method for determining "groove bottom type" is: except for the above two categories, the rest are classified as groove bottom type.

[0144] Step 103, constructing the ECMWF model 3h precipitation forecast classification database, as follows:

[0145] Using the 3-hour multi-factor forecast data from 015 to 036 reported by the ECMWF global model at 08:00 and 20:00 daily starting from January 1, 2017, a 3-hour precipitation forecast classification database including "typhoon type", "subtropical high controlled type", "trough front type", "trough back type", "trough bottom type" and "weak precipitation type" was constructed.

[0146] Second, the ECMWF model precipitation forecast area correction based on different weather conditions is as follows:

[0147] A large number of previous studies have shown that there is a correlation between the rainstorm area and the 500hPa potential height during the plum rain season in the Jianghuai region. Most of the rainstorm areas are concentrated in the area of ​​582-586dagpm, and the main rain belts are located near the 584 line. This example uses the position of the 584 line in the 500hPa potential height field of the ECMWF model as a reference to carry out precipitation forecast area correction for rainstorm processes under different weather conditions. The specific steps are as follows:

[0148] Step 201, using the ECMWF model 24h cumulative precipitation forecast data (015-036) and the corresponding actual precipitation observation data from June to August 2011 to 2023, using the method of determining whether there is a heavy precipitation forecast in the model for 3 hours, all rainstorm processes in Anhui Province and its surrounding areas (24h cumulative precipitation forecast ≥ 50mm) are counted, and the forecast value Lat_m and observation value Lat_o of the center latitude of the rainstorm, as well as the ECMWF model 500hPa average potential height field are calculated. The latitude forecast value of the 584 line within the range of 115°-120°E is Lat_584.

[0149] Step 202, classify the rainstorm process according to different weather conditions and model start time, and analyze the relationship between the forecast value Lat_m of the rainstorm center latitude, the observed value Lat_o and the forecast value Lat_584 of the 584 line latitude. The results show that for the "trough-front" and "trough-bottom" rainstorm processes reported at 08:00 in the model, and the "trough-front", "trough-back" and "trough-bottom" rainstorm processes reported at 20:00, there is a univariate linear regression relationship between the latitude difference between the model rainstorm center and the 584 line (Lat_m-Lat_584) and the latitude difference between the model rainstorm center and the actual center (Lat_m-Lat_o), and the 95% confidence test ( Figure 1-4 ), the specific formula is as follows:

[0150] Y=a×X+b

[0151] Where Y represents the latitude difference between the model heavy rain center and the actual center (Lat_m-Lat_o), Y>0 means that the model heavy rain center is biased to the north compared with the actual forecast, and Y<0 means that the forecast is biased to the south; X represents the latitude difference between the model heavy rain center and the 584 line (Lat_m-Lat_584); a and b are the slope and intercept of the univariate linear regression equation (both are constants).

[0152] Step 203, based on the above regression relationship, the ECMWF model 3h precipitation forecast falling area under different starting times and different weather conditions from June to August is corrected. The specific method is: when there is a 3h heavy precipitation forecast in the ECMWF model within the 015-036 forecast period, first use the model's 24h cumulative precipitation forecast to determine whether there is a rainstorm center. If so, calculate Lat_m-Lat_584. Then calculate Lat_m-Lat_o with reference to the corresponding regression equation. Finally, the falling area of ​​the model's 3h precipitation forecast is translated according to Lat_m-Lat_o, thereby realizing the correction of the precipitation falling area.

[0153] Step 204, using this method, the 24-hour precipitation areas of the ECMWF model for all rainstorm processes in Anhui Province from June to August from 2011 to 2023 are corrected, and compared with the original model data. The results are as follows: Figure 3 to Figure 6 As shown in Table 4. The results show that after correction by this method, the TS scores of the model for heavy rain (50 mm) and heavy rain (25 mm) and above are improved by 7.5% and 2.5% respectively compared with the original forecast, and the corresponding BIAS scores are still maintained near 1, which proves that this method has a certain effect on improving the model's heavy rain forecast.

[0154]

[0155]

[0156] 20:00 Revised forecast Original forecast ts50 0.249 0.233 bias50 0.847 0.849 ts25 0.423 0.409 bias25 1.177 1.174

[0157] Third, the ECMWF model precipitation forecast intensity is revised based on different weather conditions, as follows:

[0158] In this example, according to the season (flood season: June-August, non-flood season: other months) and the weather situation background field of the current ECMWF model 3h precipitation forecast, the corresponding historical precipitation forecast samples are selected from the 3h precipitation forecast classification database, and the intensity correction is performed using the method of the present invention.

[0159] Since this example is to correct the 3-h precipitation forecast, the precipitation level used in June-August is 10 mm / 3 h, and that in other months is 5 mm / 3 h. Figure 7 A schematic diagram of solving the scaling factor F in the actual calculation is given. In the sample training process, if the BIAS deviation of y=1.5x for precipitation above 10mm reaches the minimum when the scaling factor F is 1.5, then y=1.5x is considered to be the revised result of the model precipitation forecast.

[0160] Fourth, the results are tested as follows:

[0161] like Figure 8As shown in the figure, the revised product was compared with the original ECMWF precipitation forecast and other model precipitation forecasts. From the test results of the 24-hour cumulative precipitation forecast, it can be seen that the TS scores of the revised product are 10% and 19% higher than the original forecasts of various models, whether for heavy rain (25mm) or for rainstorms (50mm).

[0162] The above description is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for correcting model precipitation forecast based on weather situation field classification, characterized in that: include: Acquire historical model precipitation forecast data of a target area and historical actual precipitation data that matches the time of the historical model precipitation forecast data, and form a plurality of samples having observation values ​​corresponding to the historical actual precipitation data and forecast values ​​corresponding to the historical model precipitation forecast data; Based on the location of the target area, the samples are divided into flood season and non-flood season, and based on the forecast values ​​of the samples, the samples are classified into the weather situation field types of weak precipitation type, typhoon-controlled type controlled by typhoon circulation, subtropical high-controlled type controlled by subtropical high pressure, trough-front type, trough-back type and trough-bottom type which are not controlled by typhoon circulation and subtropical high pressure and are divided by different relative positions of corresponding precipitation areas and high-altitude short-wave troughs; Constructing respectively the model precipitation forecast intensity correction model and the model precipitation forecast area correction model corresponding to different weather situation field types in the flood season and the non-flood season, and using the observed value of the sample as the model output value and the predicted value of the sample as the model input value, the model precipitation forecast intensity correction model and the model precipitation forecast area correction model are trained; Based on the season and weather situation field corresponding to the real-time model precipitation forecast data, the corresponding model precipitation forecast intensity correction model and model precipitation forecast location correction model that have been trained are selected to perform model precipitation forecast correction.

2. The method for correcting model precipitation forecast based on weather situation field classification according to claim 1 is characterized in that: The classification methods of the weak precipitation type, typhoon control type and subtropical high control type in the weather situation field types include: For a sample at a certain moment, the target area is gridded with a set resolution, the model precipitation forecast data in the sample is interpolated to the meteorological stations in the target area, and the number of stations in each grid whose model precipitation forecast is greater than or equal to the first precipitation threshold is counted; if the number of stations corresponding to each grid is 0, the weather situation field type of the sample at this moment is weak precipitation type; If the number of stations is not all 0, determine whether the typhoon center position in the typhoon message corresponding to the moment is located in the target area. If so, the weather situation field type of the sample at this moment is typhoon controlled; otherwise, based on the number of stations corresponding to the target area grid, screen out the precipitation center grid of the target area, calculate the average longitude and latitude of all stations in the precipitation center grid whose model precipitation forecast is greater than or equal to the first precipitation threshold, and use the average longitude and latitude as the precipitation center position of the model precipitation forecast at this moment, and calculate the average potential height forecast field of the sample at this moment with the subtropical high characteristic line and the subtropical high peripheral characteristic line; if the precipitation center position of the sample is within the range of the subtropical high characteristic line, the weather situation field type of the sample at this moment is subtropical high controlled.

3. The method for correcting model precipitation forecast based on weather situation field classification according to claim 2 is characterized in that: The specific steps of selecting the precipitation center grid of the target area based on the number of stations corresponding to the grids in the target area include: dividing the target area into precipitation areas, and for each precipitation area, obtaining the number of heavy precipitation adjacent grids corresponding to each grid, wherein the heavy precipitation adjacent grids are grids corresponding to which the number of stations whose model precipitation forecast is greater than or equal to the first precipitation threshold in the grids adjacent to each grid is greater than 0; taking the grid with the highest number of heavy precipitation adjacent grids as the precipitation center grid of each precipitation area; taking the precipitation center grid with the highest number of heavy precipitation adjacent grids in different precipitation areas as the precipitation center grid of the target area, and in the case where there are multiple grids with the highest and consistent numbers of heavy precipitation adjacent grids, calculating the total amount of precipitation forecast in each grid, and taking the grid with the largest total amount of precipitation forecast as the precipitation center grid; The specific method for dividing the target area into precipitation zones includes: obtaining all grids in the target area whose corresponding station numbers are not 0, calculating the number of interval grids between any two grids in all the obtained grids, and dividing two grids whose interval grid number is less than or equal to 1 into the same precipitation zone.

4. The method for correcting model precipitation forecast based on weather situation field classification according to claim 2 is characterized in that: The classification method of the trough front type, trough back type and trough bottom type in the weather situation field type includes: For samples whose weather situation field is not classified as weak precipitation type, typhoon controlled type controlled by typhoon circulation, or subtropical high controlled type controlled by subtropical high pressure, the latitudinal deviation of the mean geopotential height of the sample at that moment is calculated and compared with the latitudinal deviation threshold of the mean geopotential height. If the latitudinal deviation of the mean geopotential height is less than the latitudinal deviation threshold of the mean geopotential height, the weather situation field type of the sample at that moment is the front trough type; otherwise, the latitude difference between the first intersection point of the subtropical high outer characteristic line and the first judgment meridian and the second intersection point of the second judgment meridian in the mean geopotential height forecast field of the sample is determined, and compared with the latitude difference threshold. If the latitude difference is greater than the latitude difference threshold, the weather situation field type of the sample at that moment is the back trough type; otherwise, the weather situation field type of the sample at that moment is the bottom trough type.

5. The method for correcting model precipitation forecast based on weather situation field classification according to claim 4 is characterized in that: The formula for the latitudinal deviation of the mean geopotential height is as follows: In the formula, is the latitudinal deviation of the average potential height corresponding to longitude lon_i and latitude lat_j, lon_i is longitude, lon_i∈[lon_i_min,lon_i_max], lat_j is latitude, lat_j∈[lat_j_min,lat_j_max]; lon_i_min is the first judgment longitude; lon_i_max is the second judgment longitude; lat_j_min is the first judgment latitude; lat_j_max is the second judgment latitude; is the average potential height corresponding to longitude lon_i and latitude lat_j; is the average value of the mean potential height corresponding to latitude lat_j at different longitudes.

6. The method for correcting model precipitation forecast based on weather situation field classification according to claim 1 is characterized in that: The specific method of constructing and training the correction model of the precipitation forecast intensity of the model for different weather situation field types in the flood season and the non-flood season includes: The model precipitation forecast correction models of typhoon-controlled type, subtropical high-controlled type, trough-front type, trough-back type and trough-bottom type in the flood season, and the model precipitation forecast correction models of subtropical high-controlled type, trough-front type, trough-back type and trough-bottom type in the non-flood season are respectively constructed; the subtropical high-controlled type, trough-front type and trough-back type in the flood season and non-flood season adopt the scaling coefficient correction model; the typhoon-controlled type and trough-bottom type in the flood season adopt the optimal TB score correction model; the trough-bottom type in the non-flood season adopts the scaling coefficient correction model; The corresponding model precipitation forecast correction model is trained by taking the precipitation in the observed values ​​of samples of typhoon-controlled type, subtropical high-controlled type, trough-front type, trough-back type and trough-bottom type during the flood season as the model output value and taking the precipitation in the predicted value of the samples as the input value of the model. The corresponding model precipitation forecast correction model is trained by taking the precipitation in the observed values ​​of samples of subtropical high-controlled type, trough-front type, trough-back type and trough-bottom type during the non-flood season as the model output value and taking the precipitation in the predicted value of the samples as the input value of the model.

7. The method for correcting model precipitation forecast based on weather situation field classification according to claim 6 is characterized in that: The expression of the scaling factor correction model is: y=xF Where y is the corrected value of the model precipitation forecast, i.e., the output of the scaling factor correction model; x is the original value of the model precipitation forecast, i.e., the input of the scaling factor correction model; F is the scaling factor; The expression of the optimal TB score correction model is: In the formula, O k is the actual precipitation level of the kth level; O k+1 is the actual precipitation level of the k+1th level; E1 is the predicted precipitation level threshold of the 1st level; E k is the k-th level forecast precipitation level threshold; E k+1 is the k+1th level forecast precipitation level threshold; E n is the forecast precipitation level threshold of level n; n is the total number of precipitation levels; The optimal TB score correction model is trained by adjusting the k-th level forecast precipitation level threshold E k The TB score of the actual precipitation level of level k and above is maximized. The calculation formula of the TB score is as follows: TB=TS-B Where TS is the TS score of the model forecast for precipitation above a certain level, 0≤TS≤1; B is the BIAS deviation of the model forecast for precipitation above a certain level, B≥0; NA is the station with correct forecast; NB is the station with missed forecast; and NC is the station with missed forecast.

8. The method for correcting model precipitation forecast based on weather situation field classification according to claim 1 is characterized in that: The specific method for constructing and training the model for the precipitation forecast area correction of different weather situation field types in the flood season and the non-flood season includes: The model precipitation forecast location correction models for the weather situation field types of trough front, trough back and trough bottom in the flood season and non-flood season are respectively constructed. The latitude difference between the center of the model precipitation position corresponding to the forecast value of the sample and the actual precipitation position corresponding to the observation value is used as the model output value, and the latitude difference between the model precipitation forecast position corresponding to the forecast value of the sample and the outer characteristic line of the subtropical high is used as the input value of the model to train the corresponding model precipitation forecast correction model.

9. The method for correcting model precipitation forecast based on weather situation field classification according to claim 8 is characterized in that: The model precipitation forecast area correction model adopts a univariate linear regression model, and the expression is as follows: Y=a×X+b Where X is the latitude difference between the model precipitation center and the outer characteristic line of the subtropical high; Y is the latitude difference between the model precipitation center and the actual precipitation center; a is the slope of the univariate linear regression model; b is the intercept of the univariate linear regression model.

10. The method for correcting model precipitation forecast based on weather situation field classification according to claim 1 is characterized in that: The specific steps of obtaining historical model precipitation forecast data of a target area and historical actual precipitation data matching the time of the historical model precipitation forecast data, and forming a plurality of samples having observation values ​​corresponding to the historical actual precipitation data and forecast values ​​corresponding to the historical model precipitation forecast data include: A model precipitation forecast classification database is constructed, and the acquired historical model precipitation forecast data of the target area and the historical actual precipitation data that matches the time of the historical model precipitation forecast data are stored in the database. Each sample in the database includes the observation value of the corresponding historical actual precipitation data and the forecast value of the corresponding historical model precipitation forecast data.

11. A model precipitation forecast correction system based on weather situation field classification, characterized in that: It includes sample acquisition module, sample division module, model building module and correction module; The sample acquisition module is used to acquire historical model precipitation forecast data of the target area and historical actual precipitation data that matches the time of the historical model precipitation forecast data, and form a plurality of samples having observation values ​​corresponding to the historical actual precipitation data and forecast values ​​corresponding to the historical model precipitation forecast data; The sample classification module is used to classify the samples into flood season and non-flood season based on the location of the target area, and classify the samples into weak precipitation type, typhoon-controlled type controlled by typhoon circulation, subtropical high-controlled type controlled by subtropical high pressure, trough-front type, trough-back type and trough-bottom type that are not controlled by typhoon circulation and subtropical high pressure and have different latitudinal deviations of average potential height based on the forecast values ​​of the samples; The model building module is used to respectively build a model precipitation forecast intensity correction model and a model precipitation forecast area correction model corresponding to different weather situation field types in the flood season and the non-flood season, and train the model precipitation forecast intensity correction model and the model precipitation forecast area correction model using the observed value of the sample as the model output value and the predicted value of the sample as the model output value; The correction module is used to select the corresponding trained model precipitation forecast intensity correction model and model precipitation forecast location correction model to perform model precipitation forecast correction based on the season and weather situation field corresponding to the real-time model precipitation forecast data.

Images