Method for Evaluating Regional Meteorological Element Forecast Error Based on Multidimensional Deviation

Abstract

The present invention provides a method for evaluating the prediction error of regional meteorological elements based on multi-dimensional deviation, belonging to the field of meteorological forecasting; it solves the problems of low efficiency in evaluating the prediction error of meteorological elements and poor correction function; specifically as follows: obtaining the original information and the prediction information; analyzing the original information and the prediction information to determine whether the error in the prediction information is within a reasonable range; if so, no processing is performed; if not, analyzing the change of meteorological factors in the prediction information over time and correcting it; if it cannot be corrected, obtaining the historical information, analyzing the change trends of temperature, humidity and wind force in the target area, and then establishing a piecewise probability model for precipitation or snowfall in the target area to re-predict the target area; through corresponding analysis, judgment and processing of the actual meteorological information and the predicted meteorological information of the target area, the present invention corrects the part with errors in the prediction information and improves the accuracy of meteorological element prediction evaluation.

Description

Technical Field

[0001] The present invention relates to a method for evaluating the prediction error of regional meteorological elements based on multi-dimensional deviation, and belongs to the field of meteorological prediction. Background Art

[0002] The existing methods for evaluating the prediction error of meteorological elements have the following deficiencies:

[0003] High data processing difficulty: The existing evaluation methods need to process multi-dimensional data such as space, time, and intensity simultaneously, and the data volume is too redundant; at the same time, the joint analysis of multi-dimensional deviation requires complex algorithm support, such as spatial interpolation, time series analysis, statistical modeling, etc., and the technical threshold is relatively high; moreover, the existing evaluation methods do not screen and process the data specifically during the calculation process, resulting in a large amount of calculation.

[0004] Strong data dependence: The accuracy of the existing error evaluation of meteorological element prediction depends on high-quality observation data. However, in some regions (such as the ocean, plateau, and remote areas), the observation data is sparse or of low quality, which will affect the evaluation results; at the same time, in order to achieve precise analysis of multi-dimensional deviation, it is necessary to efficiently assimilate the observation data and the model prediction data, which puts higher requirements on the performance of the data assimilation system.

[0005] Method limitations: Most of the existing evaluation methods use fixed threshold evaluation. When defining the deviation threshold, this evaluation method has corresponding subjectivity, and the threshold selection for different regions or different meteorological elements will also affect the final evaluation results. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a method for evaluating the prediction error of regional meteorological elements based on multi-dimensional deviation, aiming to solve the problems of low efficiency and poor correction function in the evaluation of meteorological element prediction error.

[0007] To achieve the above purpose, the present invention is realized through the following technical solutions: The method for evaluating the prediction error of regional meteorological elements based on multi-dimensional deviation includes:

[0008] Obtain the meteorological information and satellite cloud images of the target area in the past month as the original information;

[0009] Use the method to be tested to obtain the prediction information corresponding to the original information; analyze the original information and the prediction information, and judge whether the error of the method to be tested is within a reasonable range; if so, do not process; if not, analyze the change of meteorological factors in the prediction information over time, find out the meteorological factors with errors, and correct the method to be tested; if the method to be tested cannot be corrected, obtain the meteorological information of the corresponding month in the past three years of the target area as the historical information;

[0010] Extract the temperature and humidity data of the target area in the past three years based on historical information, analyze the changing trends of temperature and humidity in the target area, and reconstruct a linear model based on the changing trends as the prediction part of the temperature and humidity of the method to be tested; perform vector splitting on the wind speed in the target area according to historical information, and then predict the wind force change in the target area based on trigonometric function modeling; count the rainfall and snowfall frequencies in the target area, and establish a piecewise probability model for precipitation or snowfall in the target area with temperature, humidity, air pressure and other meteorological conditions as judgment conditions to predict the weather in the target area.

[0011] Further, the specific steps for determining whether the error of the method to be tested is within a reasonable range are as follows:

[0012] Prediction data representation: After using the method to be tested for prediction in the target area, the predicted temperature, predicted humidity, predicted wind direction, predicted wind speed and predicted weather are obtained;

[0013] Judge whether the difference in numerical information in the meteorological information is within ε, and judge whether the difference in text information in the meteorological information is within ε; ε represents the error determination coefficient;

[0014] If both the difference in numerical information and the difference in text information between the meteorological information and the prediction data are within ε, it means that the error of the method to be tested is within a reasonable range, the method to be tested is credible, and the subsequent steps are skipped;

[0015] If both the difference in numerical information and the difference in text information between the meteorological information and the prediction data are not within ε, it means that the error of the method to be tested is not within a reasonable range, and the method to be tested is not credible;

[0016] Take the months of the past month as the target months, obtain the meteorological information of the target area in the past three years within the target months as historical information, and do not perform the subsequent correction steps;

[0017] If the difference in numerical information between the meteorological information and the prediction data is within ε or the difference in text information is within ε, it means that part of the error of the method to be tested is reasonable. Analyze the meteorological factors with errors in the prediction information and correct the method to be tested.

[0018] Further, the specific steps for correcting the method to be tested are as follows:

[0019] If the difference in numerical information between the meteorological information and the prediction data is within ε, compare the magnitudes of the predicted temperature, predicted humidity and predicted wind speed in the prediction information with the temperature, humidity and wind speed in the meteorological information, find out the meteorological factors with errors in the prediction information as deviation factors; analyze the change of the deviation factors over time and correct the method to be tested;

[0020] If the difference in text information between the meteorological information and the prediction data is within ε, analyze the predicted wind speed and predicted weather in the prediction information.

[0021] Further, the specific steps for correcting the measurement method according to the change of the analysis deviation factor over time are as follows:

[0022] If the deviation factor is temperature, based on the temperature in the meteorological information, analyze the change of the predicted temperature in the prediction information with respect to temperature, and correct the temperature error in the measurement method to be measured;

[0023] If the deviation factor is humidity, repeat the same steps for correcting the temperature error in the measurement method to be measured to correct the humidity error in the measurement method to be measured;

[0024] If the deviation factor is wind speed, perform vector decomposition on the predicted wind speed in the prediction information according to the wind direction in the meteorological information to obtain the lateral predicted wind speed hfv （1） ~hfv （mn） and the longitudinal predicted wind speed zfv （1） ~zfv （mn） ;

[0025] Correct the predicted lateral wind speed and predicted longitudinal wind speed of the measurement method to be measured.

[0026] Further, the specific steps for correcting the temperature error in the measurement method to be measured are as follows:

[0027] Record the predicted temperature in the prediction information for the past month as ft （1,1） ~ft （mn,24） , and record the temperature in the original information as pt （1,1） ~pt （mn,24） ;

[0028] According to ft （1,1） ~ft （mn,24） and pt （1,1） ~pt （mn,24） Calculate the Kalman gain Kk （1） ~Kk （mn） and the observation mapping coefficient H （1） ~H （mn） ;

[0029] Calculate the average value aKk of Kk （1） ~Kk （mn） , and the average value aH of H （1） ~H （mn） ;

[0030] According to ft （1,1） ~ft （mn,24） and pt （1,1） ~pt （mn,24） Calculate the state transition matrix A from 1 to 2 o'clock in the past month （1，1-2） ~A （mn，1-2） ;

[0031] Similarly, the state transition matrix A from 23:00 to 24:00 （1，23-24） ~A （mn，23-24） ;

[0032] Calculate A （1，1-2） ~A （mn，1-2） The mean matrix of ~A （1-2） is used as the change matrix aA of the temperature from 1:00 to 2:00 （1，23-24） Similarly, calculate A （mn，23-24） ~A （23-24） ;

[0033] Let the actual temperature at time ti in the target area be t （ti） , and let the predicted temperatures at time ti and (ti + 1) predicted by the method to be tested be ft （ti） and ft （ti+1） ;

[0034] Let the change matrix from time ti to (ti + 1) be aA （ti-（ti+1）） , and the predicted temperature at (ti + 1) after correction be amt （ti+1） ;

[0035] Construct relationship A1:

[0036] ;

[0037] Correct the predicted temperature in the method to be tested according to relationship A1

[0038] Furthermore, the specific calculation steps of the Kk （1） and H （1） as well as the state transition matrix A （1，1-2） ~A （1，23-24） are as follows:

[0039] Construct matrix Mf （1-2） : ;

[0040] Construct matrix Mp （1-2） : ;

[0041] Calculate the state transition matrix A （1，1-2） :

[0042] ;

[0043] Calculate the process noise matrix wM （1-2） when matrix Mf （1-2） becomes matrix Mp （1-2） :

[0044] ;

[0045] Calculate the observation mapping coefficient H （1，1-2） :

[0046] ;

[0047] Calculate the covariance RM of the observation noise （1-2） :

[0048] ;

[0049] Calculate the covariance QM of the process noise （1-2） :

[0050] ;

[0051] Calculate the prediction error covariance Pk （1-2） :

[0052] ;

[0053] Calculate Kk （1，1-2） :

[0054] .

[0055] Furthermore, the specific steps to correct the predicted crosswind speed of the method to be measured are as follows:

[0056] Calculate hv （1） ~hv （mn） The average value ahv;

[0057] Calculate the white noise et from the 1st to the mnth day in the past month （1） ~et （mn） ;

[0058] Calculate hfv （1） ~hfv （mn） The first-order difference to obtain ▽hfv （2） ~▽hfv （mn） ;

[0059] Calculate hv （1） ~hv （mn） The first-order difference to obtain ▽hv （2） ~▽hv （mn） ;

[0060] Let the date be da, and the first-order difference of the crosswind speed on the date da is ▽hv （da） ;

[0061] The first-order difference of the predicted crosswind speed on the date (da + 1) is ▽hfv （da+1） , and the first-order difference of the crosswind speed is ▽hv（da+1） ;

[0062] The predicted lateral wind speed hfv on date da （da） , the lateral wind speed hv （da） ;

[0063] The white noise on the da-th day is et （da） , and the white noise on the (da + 1)-th day is et （da+1） ;

[0064] Construct relationship formula A2-1:

[0065] ;

[0066] φ represents the autoregressive coefficient, and θ represents the moving average coefficient;

[0067] Based on relationship formula A2-1, use the maximum likelihood estimation algorithm to calculate φ and θ;

[0068] Construct relationship formula A2-2:

[0069] ;

[0070] α and β represent the relationship coefficients of the predicted lateral wind speed with respect to the lateral wind speed;

[0071] ηt （da+1） represents the residual term on the (da + 1)-th day:

[0072] ;

[0073] Based on relationship formula A2-2, use the maximum likelihood estimation algorithm to calculate α and β;

[0074] Revise the lateral predicted wind speed in the method to be measured according to relationship formula A2-1 and relationship formula A2-2.

[0075] Furthermore, the specific steps for the predicted wind speed and predicted weather in the analysis and prediction information are as follows:

[0076] If the predicted wind speed in the prediction information does not match the wind speed in the meteorological information, then record the lateral predicted wind speed as uv and the longitudinal predicted wind speed as vv;

[0077] Calculate the combined wind speed wv: ;

[0078] Calculate the wind direction angle jv: ;

[0079] If jv is between 0 o ~90 o , then the wind direction is southwest; if jv is between 90 o ~180o If it is between them, the wind direction is southeast; if jv is at 180 o ~270 o If it is between them, the wind direction is northeast; if jv is at 270 o ~360 o If it is between them, the wind direction is northwest;

[0080] If the predicted weather in the prediction information does not match the weather in the meteorological information, the multi-spectral fusion algorithm is accessed for the method to be measured, and data of the infrared band, visible band, and short-wave infrared band of the corresponding cloud layer in the target area are obtained through the satellite, and the GPM algorithm is used to correct the weather in the target area.

[0081] Compared with the prior art, the beneficial effects of the present invention are:

[0082] Multi-dimensional analysis: In the time dimension, the present invention analyzes the errors of forecast elements in the time series, such as the change trends of humidity and temperature; at the same time, it quantifies the numerical deviations of meteorological elements, such as the differences between the predicted values and the actual values of temperature, humidity, and wind speed; through the joint analysis of multi-dimensional deviations, it can more comprehensively reflect the sources and characteristics of forecast errors and avoid the limitations of single indicators.

[0083] Forecast optimization: After completing the task of evaluating the meteorological element forecast errors of the method to be measured, the present invention provides a detailed optimization method for the method to be measured; by analyzing the changes of various meteorological factors in the target area over time and using various statistical regression methods, the meteorological changes in the target area are analyzed, and the prediction accuracy of the method to be measured is significantly improved.

[0084] Wide adaptability: All mathematical models in the present invention are based on the existing meteorological data analysis and construction in the target area. This processing method can make the present invention have strong adaptability for complex terrain areas (such as mountainous areas and coastal areas); through multi-dimensional deviation analysis, the systematic errors of forecasts in these areas can be revealed, and the forecast errors of the method to be measured can be eliminated; based on the error evaluation results and optimization schemes, the forecast method is improved to improve the accuracy of regional forecasts. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objects, and advantages of the present invention will become more apparent:

[0086] Figure 1 It is a schematic diagram of the method of the present invention;

[0087] Figure 2 It is a schematic diagram of the data of the present invention;

[0088] Figure 3 It is a schematic diagram of the wind direction of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0089] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0090] Please refer to Figure 1 and Figure 2 , the regional meteorological element forecast error evaluation method based on multi-dimensional deviation includes:

[0091] Step S1: Obtain the meteorological information and satellite cloud images of the target area in the past month as the original information;

[0092] The meteorological information includes: daily temperature, (air) humidity, wind direction, wind speed, and weather;

[0093] Step S2: Use the method to be tested to obtain the predicted information corresponding to the original information; analyze the original information and the predicted information to determine whether the error of the method to be tested is within a reasonable range; if so, do not process; if not, analyze the change of meteorological factors in the predicted information over time, find out the meteorological factors with errors, and correct the method to be tested; if the method to be tested cannot be corrected, obtain the meteorological information of the corresponding month in the past three years in the target area as historical information;

[0094] It should be noted that in the present invention, the "method to be tested" means: a method provided by a user or relevant technical personnel and evaluated for errors by the present invention (the regional meteorological element forecast error evaluation method based on multi-dimensional deviation);

[0095] The specific steps of Step S2 are as follows:

[0096] Step S21: Prediction data representation: within the past month, after the relevant department in the target area uses the method to be tested for prediction, the daily predicted temperature, predicted humidity, predicted wind direction, predicted wind speed, and predicted weather are obtained;

[0097] Judge whether the (numerical) difference in numerical information between the temperature, humidity, and wind speed in the meteorological information and the predicted temperature, predicted humidity, and predicted wind speed in the predicted data is within ε, and judge whether the difference in text information between the wind direction and weather in the meteorological information and the predicted wind direction and predicted weather in the predicted data is within ε (the difference in text information means: the mismatch rate of text information);

[0098] Among them, ε represents the error determination coefficient, and the value of ε is 20%; users or relevant technical personnel can adjust the value of ε according to actual needs;

[0099] If both the difference in numerical information and the difference in text information between the meteorological information and the predicted data are within ε, it means that the error of the method to be tested is within a reasonable range, the method to be tested is credible, and the subsequent steps S22 to step S23 and the subsequent step S3 are skipped;

[0100] If both the numerical information difference and the text information difference between the meteorological information and the prediction data are not within ε, it indicates that the error of the method to be tested is not within a reasonable range, and the method to be tested is not credible. Skip the subsequent steps S22 to S23;

[0101] Take the month nearly one month as the target month, obtain the meteorological information of the target area in the target month in the past three years as historical information, and enter the subsequent step S3;

[0102] The above "correction step" refers to steps S22 to step 23;

[0103] If the numerical information difference between the meteorological information and the prediction data is within ε or the text information difference is within ε, it indicates that part of the error of the method to be tested is reasonable and the method to be tested is partially credible. Skip the subsequent step S3, analyze the meteorological factors with errors in the prediction information, and enter steps S22 to S23;

[0104] Step S22: If the numerical information difference between the meteorological information and the prediction data is within ε, compare the predicted temperature, predicted humidity, and predicted wind speed in the prediction information with the temperature, humidity, and wind speed in the meteorological information, find out the meteorological factors with errors in the prediction information as deviation factors; analyze the change of the deviation factors over time and correct the method to be tested;

[0105] Step S221: If the deviation factor is temperature (that is, the numerical gap between the predicted temperature in the prediction information and the temperature in the meteorological information exceeds ε), then based on the temperature in the meteorological information, analyze the change of the predicted temperature in the prediction information with respect to temperature, and correct the temperature error in the method to be tested;

[0106] Step S2211: Denote the number of days in the nearly one month as mn; denote the predicted temperature in the prediction information for the nearly one month as ft （1,1） ~ft （mn,24） , and denote the temperature in the original information as pt （1,1） ~pt （mn,24） ;

[0107] According to ft （1,1） ~ft （mn,24） and pt （1,1） ~pt （mn,24） Calculate the Kalman gain Kk （1） ~Kk （mn） and the observation mapping coefficient H （1） ~H （mn） ;

[0108] Calculate the average value aKk of Kk （1） ~Kk （mn） and the average value of H （1） ~H （mn）The average value aH;

[0109] According to ft （1,1） ~ ft （mn,24） and pt （1,1） ~ pt （mn,24） Calculate the state transition matrix A from 1 to 2 o'clock in the past month （1，1-2） ~ A （mn，1-2） ;

[0110] The state transition matrix A from 2 to 3 o'clock （1，2-3） ~ A （mn，2-3） ;

[0111] And so on, the state transition matrix A from 23 to 24 o'clock （1，23-24） ~ A （mn，23-24） ;

[0112] Calculate A （1，1-2） ~ A （mn，1-2） The mean matrix of A is used as the change matrix aA of the temperature from 1 to 2 o'clock （1-2） :

[0113] ;

[0114] Calculate A （1，2-3） ~ A （mn，2-3） The mean matrix of A is used as the change matrix aA of the temperature from 2 to 3 o'clock （2-3） :

[0115] ;

[0116] And so on, calculate A （1，23-24） ~ A （mn，23-24） The mean matrix of A is used as the change matrix aA of the temperature from 23 to 24 o'clock （23-24） :

[0117] ;

[0118] Let the actual temperature at time ti in the target area be t （ti） , and let the predicted temperatures at time ti and (ti + 1) predicted by the method to be tested be ft （ti） and ft （ti+1） ;

[0119] Let the change matrix from time ti to (ti + 1) be aA （ti-（ti+1）） , and the predicted temperature at (ti + 1) after correction be amt （ti+1） ;

[0120] Among them, the value range of ti is: 1 to 23, aA （ti-（ti+1）） ∈{aA （1-2） ~ aA （23-24）};

[0121] Structural relationship A1:

[0122] ;

[0123] Modify the method to be measured according to relationship A1;

[0124] (Among ft （1,1） ~ft （mn,24） and pt （1,1） ~pt （mn,24） ), extract the predicted temperature ft （1,1） ~ft （1,24） and temperature recorded as pt （1,1） ~pt （1,24） , calculate Kk （1） and H （1） as well as the state transition matrix A （1，1-2） ~A （1，23-24） ;

[0125] Step S2212: Calculate the Kalman gain Kk from 1 to 2 o'clock on the first day （1，1-2） , the observation mapping coefficient H （1，1-2） ; and the state transition matrix

[0126] Construct matrix Mf （1-2） : ;

[0127] Construct matrix Mp （1-2） : ;

[0128] where ft （1,2） represents the predicted temperature at 2 o'clock on the first day, and pt （1,2） represents the temperature at 2 o'clock on the first day;

[0129] Calculate the state transition matrix A （1，1-2） (Matrix A （1，1-2） is a (2×2) matrix):

[0130] ;

[0131] where T represents the transpose of the matrix, * represents matrix multiplication, and -1 represents the inverse of the matrix;

[0132] Calculate the process noise matrix wM （1-2） when matrix Mf （1-2） becomes matrix Mp （1-2） (Matrix wM （1-2） is a (1×2) matrix):

[0133] ;

[0134] Calculate the observation mapping coefficient H （1，1-2） :

[0135] ; where, - represents matrix subtraction;

[0136] Calculate the covariance RM of the observation noise （1-2） :

[0137] ;

[0138] Calculate the covariance QM of the process noise （1-2） :

[0139] ;

[0140] Calculate the prediction error covariance Pk （1-2） :

[0141] ;

[0142] Calculate Kk （1，1-2） :

[0143] ;

[0144] Step S2213: Repeat the calculation steps of Kk （1，1-2） and H （1，1-2） to calculate the Kalman gains Kk （1，2-3） , Kk （1，3-4） ~Kk （1，23-24） , the observation mapping coefficient H （1，2-3） , H （1，3-4） ~H （1，23-24） and the state transition matrix A （1，2-3） , A （1，3-4） ~A （1，23-24） ;

[0145] Calculate Kk （1，1-2） ~Kk （1，23-24） and take the average value as Kk （1） , and take the average value of H （1，1-2） ~H （1，23-24） as H （1） ;

[0146] Step S2214: Repeat the calculation steps of Kk （1） and H （1） and A （1，1-2） ~A （1，23-24） to calculate the corresponding Kalman gains Kk （2） ~Kk （mn） and the observation mapping coefficient H （2） ~H（mn） ;

[0147] Calculate the values from 1:00 to 2:00, 2:00 to 3:00, up to 23:00 to 24:00 corresponding to the 2nd to the mn-th day, and the state transition matrix A （2，1-2） 、A （2，2-3） ~A （mn，23-24） ;

[0148] Step S222: If the deviation factor is humidity (i.e., the numerical difference between the predicted humidity in the prediction information and the humidity in the meteorological information exceeds ε), then repeat the same steps for correcting the temperature error in the method to be tested to correct the humidity error in the method to be tested;

[0149] Step S223: Refer to Figure 3 , if the deviation factor is wind speed (i.e., the numerical difference between the predicted wind speed in the prediction information and the wind speed in the meteorological information exceeds ε), decompose the predicted wind speed in the prediction information vectorially with the wind direction in the meteorological information to obtain the lateral predicted wind speed hfv for the 1st, 2nd, up to the mn-th day of the last month （1） 、hfv （2） ~hfv （mn） and the longitudinal predicted wind speed zfv （1） 、zfv （2） ~zfv （mn） ;

[0150] Decompose the wind speed in the meteorological information vectorially with the wind direction in the meteorological information to obtain the lateral wind speed hv for the 1st, 2nd, up to the mn-th day of the last month （1） 、hv （2） ~hv （mn） and the longitudinal wind speed zv （1） 、zv （2） ~zv （mn） ;

[0151] According to hfv （1） ~hfv （mn） and hv （1） ~hv （mn） Correct the predicted lateral wind speed of the method to be tested;

[0152] Using hv （1） ~hv （mn） as the reference data, calculate the average value ahv of hv （1） ~hv （mn） ;

[0153] Calculate the white noise et （1） for the 1st day of the last month (i.e., hv （1） ): et （1） =hv （1） -ahv;

[0154] The second day of the last month (i.e., hv （2） ) of white noise et （2） : et （2） = hv （2） - ahv;

[0155] And so on, the white noise et （mn） ) of the mn-th day of the last month (i.e., hv （mn） ): et （mn） = hv （mn） - ahv;

[0156] Calculate the first-order differences corresponding to hfv （1） ~ hfv （mn） to obtain ▽hfv （2） , ▽hfv （3） ~ ▽hfv （mn） ;

[0157] Among them, the calculation formula of ▽hfv （2） is: ▽hfv （2） = hfv （2） - hfv （1） ;

[0158] The calculation formula of ▽hfv （3） is: ▽hfv （3） = hfv （3） - hfv （2） ;

[0159] And so on, the calculation formula of ▽hfv （mn） is: ▽hfv （mn） = hfv （mn） - hfv （mn-1） ;

[0160] Among them, hfv （3） represents the horizontal predicted wind speed on the third day of the last month in the prediction information; similarly, hfv （mn-1） represents the horizontal predicted wind speed on the (mn - 1)-th day of the last month in the prediction information;

[0161] Repeat the calculation steps of ▽hfv （2） ~ ▽hfv （mn） to calculate the first-order differences of hv （1） ~ hv （mn） to obtain ▽hv （2） ~ ▽hv （mn） ;

[0162] Let the date be da, and the first-order difference of the horizontal wind speed on the date da is ▽hv （da） ;

[0163] The first-order difference of the predicted crosswind speed with date (da + 1) is ▽hfv （da+1） The first-order difference of the crosswind speed is ▽hv （da+1） ;

[0164] The predicted crosswind speed hfv with date da （da） The crosswind speed hv （da） ;

[0165] The white noise on the da-th day is et （da） The white noise on the (da + 1)-th day is et （da+1） ; where the value range of da is: 2 to (mn - 1);

[0166] Construct relationship A2-1:

[0167] ;

[0168] where φ represents the autoregressive coefficient and θ represents the moving average coefficient;

[0169] Based on relationship A2-1, use the maximum likelihood estimation algorithm in the statsmodels library to calculate φ and θ;

[0170] Construct relationship A2-2:

[0171] ;

[0172] where α and β represent the relationship coefficients of "the predicted crosswind speed in the prediction information" with respect to "the crosswind speed in the meteorological information";

[0173] ηt （da+1） represents the residual term on the (da + 1)-th day, and the calculation formula of ηt （da+1） is:

[0174] ;

[0175] Based on relationship A2-2, use the maximum likelihood estimation algorithm in the statsmodels library to calculate α and β;

[0176] According to relationship A2-1 and relationship A2-2, correct the predicted crosswind speed in the method to be measured;

[0177] Repeat the same steps of correcting the predicted crosswind speed in the method to be measured, and according to zfv （1） ~zfv （mn） and zv （1） ~zv （mn） Correct the predicted longitudinal wind speed of the method to be measured;

[0178] Step S23: If the difference in the text information between the meteorological information and the prediction data is within ε, then analyze the predicted wind speed and predicted weather in the prediction information;

[0179] Step S231: If the predicted wind speed in the prediction information does not match the wind speed in the meteorological information, then

[0180] Determine whether there is a numerical difference between the wind speed in the prediction information and the wind speed in the meteorological information;

[0181] If there is no numerical difference, record the horizontal predicted wind speed as uv (positive to the west, negative to the east), and record the vertical predicted wind speed as vv (positive to the south, negative to the north);

[0182] Calculate the combined wind speed wv: ;

[0183] Calculate the wind direction angle jv: ;

[0184] If jv is between 0 o ~90 o then the wind direction is southwest; if jv is between 90 o ~180 o then the wind direction is southeast; if jv is between 180 o ~270 o then the wind direction is northeast; if jv is between 270 o ~360 o then the wind direction is northwest;

[0185] Step S232: If there is a numerical difference, then based on the horizontally predicted wind speed and vertically predicted wind speed corrected in step S223, repeat the processing steps of step S231 to correct the predicted wind direction in the prediction information;

[0186] Step S233: If the predicted weather in the prediction information does not match the weather in the meteorological information, then connect the multi-spectral fusion algorithm to the method to be tested, obtain the data of the infrared band, visible band, and short-wave infrared band of the corresponding cloud layer in the target area through the satellite, and use the GPM or DPR algorithm to correct the weather in the target area;

[0187] Step S3: Extract the temperature and humidity data of the target area in the past three years (in the target month) according to the historical information, and analyze the changing trends of the temperature and humidity in the target area (within the target month). Reconstruct a linear model based on the changing trends (using polynomial regression or time series) as the prediction part of the temperature and humidity of the method to be tested;

[0188] Perform vector splitting on the wind speed of the target area according to the historical information, and then predict the wind force change in the target area based on trigonometric function modeling;

[0189] The frequency of rainfall and snowfall in the target area (in the target month of the past three years) is counted, and a segmented probability model of precipitation or snowfall in the target area is established using temperature, humidity, air pressure and other meteorological conditions (such as air convection, air lift, vertical temperature distribution of the atmosphere, etc.) as judgment conditions to predict the weather in the target area;

[0190] It should be noted that the processing method of "Step S3" in the present invention is a complete reconstruction of the above-mentioned "method to be tested provided by the user or relevant technical personnel". It is to completely reconstruct the "method to be tested" under the premise that the "method to be tested" is completely unreliable and unavailable. Therefore, "Step S3" in the present invention only provides a reconstruction idea of the "method to be tested", and the specific reconstruction method is determined by the user or relevant technical personnel (such as the NWP digital weather forecast system).

[0191] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technical personnel in this field according to actual conditions. For example, if there are weight coefficients and proportional coefficients, their set sizes are to quantify each parameter to obtain a specific value, which is convenient for subsequent comparison. Regarding the size of the weight coefficient and the proportional coefficient, it is sufficient as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0192] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for evaluating the prediction error of regional meteorological elements based on multi-dimensional deviation, characterized in that, The method includes: Obtaining the meteorological information and satellite cloud images of the target area in the past month as the original information; Obtaining the prediction information corresponding to the original information; analyzing the original information and the prediction information to determine whether the error in the prediction information is within a reasonable range; if so, no processing is required; if not, analyzing the variation of meteorological factors in the prediction information over time, finding out the meteorological factors with errors, and correcting them; if they cannot be corrected, obtaining the meteorological information of the corresponding months in the past three years in the target area as historical information; Extracting the temperature and humidity data of the target area in the past three years based on the historical information, and analyzing the variation trends of the temperature and humidity in the target area. Based on the variation trends, reconstructing a linear model to predict the temperature and humidity in the target area; performing vector splitting on the wind speed in the target area according to the historical information, and then predicting the wind force variation in the target area based on trigonometric function modeling; counting the rainfall and snowfall frequencies in the target area, and establishing a piecewise probability model for precipitation or snowfall in the target area with temperature, humidity, air pressure and other meteorological conditions as judgment conditions to predict the weather in the target area; The specific steps for determining whether the error in the prediction information is within a reasonable range are as follows: Prediction data representation: predicted temperature, predicted humidity, predicted wind direction, predicted wind speed, and predicted weather; Judging whether the difference in numerical information in the meteorological information is within ε, and judging whether the difference in text information in the meteorological information is within ε; ε represents the error determination coefficient; If both the difference in numerical information and the difference in text information between the meteorological information and the prediction data are within ε, it indicates that the error in the prediction information is within a reasonable range, and the subsequent steps are skipped; If both the difference in numerical information and the difference in text information between the meteorological information and the prediction data are not within ε, it indicates that the prediction information is not credible; Taking the months in the past month as the target months, obtaining the meteorological information of the target area in the past three years during the target months as historical information, and not performing the subsequent correction steps; If the difference in numerical information between the meteorological information and the prediction data is within ε or the difference in text information is within ε, it indicates that part of the error in the prediction information is reasonable. Analyze the meteorological factors with errors in the prediction information and correct the prediction information; The specific steps for correcting the prediction information are as follows: If the difference in numerical information between the meteorological information and the prediction data is within ε, compare the predicted temperature, predicted humidity, and predicted wind speed in the prediction information with the temperature, humidity, and wind speed in the meteorological information, find out the meteorological factors with errors in the prediction information as the deviation factors; analyze the variation of the deviation factors over time and correct the prediction information; If the difference in text information between the meteorological information and the prediction data is within ε, analyze the predicted wind speed and predicted weather in the prediction information; The specific steps for analyzing the variation of the deviation factors over time and correcting the prediction information are as follows: If the deviation factor is temperature, taking the temperature in the meteorological information as the benchmark, analyze the variation of the predicted temperature in the prediction information with respect to temperature, and correct the temperature error in the prediction information; If the deviation factor is humidity, repeat the same steps for correcting the temperature error in the prediction information to correct the humidity error in the prediction information; If the deviation factor is the wind speed, the predicted wind speed in the prediction information is vectorially decomposed according to the wind direction in the meteorological information to obtain the lateral predicted wind speeds hfv （1） ~hfv （mn） and the longitudinal predicted wind speeds zfv （1） ~zfv （mn） ; Correct the predicted horizontal wind speed and predicted vertical wind speed in the prediction information.

2. The method for evaluating regional meteorological element forecast errors based on multi-dimensional deviation according to claim 1, wherein The specific steps for correcting the temperature error in the corrected prediction information are as follows: Denote the predicted temperature in the last month in the prediction information as ft （1,1） ~ft （mn,24） , and denote the temperature in the original information as pt （1,1） ~pt （mn,24） ; According to ft （1,1） ~ft （mn,24） and pt （1,1） ~pt （mn,24） Calculate the Kalman gain Kk （1） ~Kk （mn） and the observation mapping coefficient H （1） ~H （mn） ; Calculate Kk （1） ~Kk （mn） to obtain the average value aKk of H （1） ~H （mn） and the average value aH; According to ft （1,1） ~ft （mn,24） and pt （1,1） ~pt （mn,24） Calculate the state transition matrix A from 1 to 2 o'clock in the past month （1，1-2） ~A （mn，1-2） ; Similarly, the state transition matrix A from 23:00 to 24:00 （1，23-24） ~A （mn，23-24） ; Calculate A （1，1-2） ~A （mn，1-2） The mean matrix of ~A is used as the change matrix aA of the temperature from 1 to 2 o'clock （1-2） Similarly, calculate A （1，23-24） ~A （mn，23-24） The mean matrix of ~A is used as the change matrix aA of the temperature from 23 to 24 o'clock （23-24） ; Let the actual temperature in the target area at time \(t_i\) be \(t\). （ti） Let the predicted temperatures at time \(t_i\) and \((t_i + 1)\) be \(f_t\) （ti） and \(f_t\) （ti+1） ; Let the change matrix from time ti to time (ti + 1) be aA （ti-（ti+1）） , and the predicted temperature at time (ti + 1) after correction is amt （ti+1） ; Construct relationship A1: ； Correct the predicted temperature in the prediction information according to relationship A1.

3. The regional meteorological element forecast error evaluation method based on multi-dimensional deviation according to claim 2, characterized in that The Kk （1） and H （1） and the specific calculation steps of the state transition matrix A （1，1-2） ~A （1，23-24） are as follows: Construct matrix Mf （1-2） : ; Construct matrix Mp （1-2） : ; Calculate the state transition matrix A （1，1-2） : ； Calculate matrix Mf （1-2） transformed into matrix Mp （1-2） The process noise matrix wM （1-2） : ； Calculate the observation mapping coefficient H （1，1-2） : ； Calculate the covariance RM of the observation noise （1-2） : ； Calculate the covariance QM of the process noise （1-2） : ； Calculate the prediction error covariance Pk （1-2） : ； Calculate Kk （1，1-2） : 。 4. The regional meteorological element forecast error assessment method based on multi-dimensional deviation according to claim 1, characterized in that The specific steps for correcting the predicted crosswind speed in the prediction information are as follows: Calculate hv （1） ~hv （mn） and calculate the average value ahv of them; Calculate the white noise et from the 1st to the mn-th day of the last month （1） ~et （mn） ; Calculate hfv （1） ~hfv （mn） Take the first-order difference of, to obtain ▽hfv （2） ~▽hfv （mn） ; Calculate hv （1） ~hv （mn） Take the first-order difference of to obtain ▽hv （2） ~▽hv （mn） ; Let the date be da, and the first-order difference of the horizontal wind speed on date da be ▽hv （da） ; The first-order difference of the predicted lateral wind speed at date (da + 1) is ▽hfv （da+1） , the first-order difference of the lateral wind speed is ▽hv （da+1） ; Predicted crosswind speed hfv for date da （da） , crosswind speed hv （da） ; The white noise on the da-th day is et （da） , and the white noise on the (da + 1)-th day is et （da+1） ; Construct relationship A2-1: ； φ represents the autoregressive coefficient, and θ represents the moving average coefficient; Based on relationship A2-1, use the maximum likelihood estimation algorithm to calculate φ and θ; Construct relationship A2-2: ； α and β represent the relationship coefficients of the predicted crosswind speed with respect to the crosswind speed; ηt （da+1） Denotes the residual term on the (da + 1)-th day: ； Based on relationship A2-2, use the maximum likelihood estimation algorithm to calculate α and β; Correct the predicted crosswind speed in the prediction information according to relationship A2-1 and relationship A2-2.

5. The method for evaluating regional meteorological element forecast error based on multi-dimensional deviation according to claim 1, characterized in that The specific steps for analyzing the predicted wind speed and predicted weather in the prediction information: If the predicted wind speed in the prediction information does not match the wind speed in the meteorological information, record the predicted crosswind speed as uv and the predicted longitudinal wind speed as vv; Calculate the combined wind speed wv: ; Calculate the wind direction angle jv: ; If jv is between 0 o and 90 o , the wind direction is southwest; if jv is between 90 o and 180 o , the wind direction is southeast; if jv is between 180 o and 270 o , the wind direction is northeast; if jv is between 270 o and 360 o , the wind direction is northwest; If the predicted weather in the prediction information does not match the weather in the meteorological information, access the multi-spectral fusion algorithm, obtain data in the infrared band, visible band, and short-wave infrared band of the corresponding cloud layer in the target area through the satellite, and use the GPM algorithm to correct the weather in the target area.

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