Drought data monitoring and processing methods

Abstract

The embodiment of the present invention relates to a drought data monitoring and processing method, comprising: obtaining drought information of a plurality of monitoring points within a preset time period and a preset area; the drought information comprises temperature, precipitation and location information of the monitoring points; determining a relative humidity index according to the temperature, precipitation and location information; determining a probability density function of a random variable; and calculating the probability that the random variable is less than the precipitation within a preset second time period; calculating an approximate estimated value of the event probability; calculating the event probability when the precipitation is 0; performing normal standardization processing on the probability to obtain a standardized normal distribution function; performing an approximate solution on the standardized normal distribution function to determine a standardized precipitation index; calculating a comprehensive meteorological drought index according to the relative humidity index and the standardized precipitation index; and performing drought warning according to the comprehensive meteorological drought index of each monitoring point.

Description

Technical Field

[0001] The invention relates to the field of meteorological technology, and in particular to a drought data monitoring and processing method. Background Art

[0002] Drought is a common meteorological phenomenon. Drought is characterized by different drought indicators. In the existing technology, drought information of monitoring points cannot generally be comprehensively expressed by combining multiple indexes, and drought information cannot be used to automatically evaluate rainmaking operations. Summary of the invention

[0003] The purpose of the present invention is to provide a drought data monitoring and processing method to solve the problems existing in the prior art.

[0004] To achieve the above object, the present invention provides a drought data monitoring and processing method, comprising:

[0005] Obtain drought information of multiple monitoring points in a preset time period and a preset area; the drought information includes temperature, precipitation and location information of the monitoring points;

[0006] Determining a relative humidity index based on the temperature, precipitation and location information;

[0007] according to Determine the probability density function of the random variable; where f(x) is the probability density function, β is, and γ is

[0008] according to Calculate the probability that the random variable is less than the precipitation in the preset second time period; where F(x<x 0 ) is the probability that the random variable is less than the precipitation in the preset second time period, x is the random variable, x 0 is the precipitation in the second period;

[0009] Will Substitution Calculate approximate estimates of event probabilities;

[0010] The probability of an event when the precipitation is 0 is calculated according to F(x=0)=m / n; where m is the number of samples with precipitation of 0, and n is the total number of samples;

[0011] The probability is normalized to obtain a standardized normal distribution function.

[0012] right Perform an approximate solution to determine the Standardized Precipitation Index Among them, c 0 、c 1 and c2 The threshold of the standardized precipitation index is calculated using the z-score method;

[0013] Calculating a comprehensive meteorological drought index based on the relative humidity index and the standardized precipitation index;

[0014] Drought warning is issued based on the comprehensive meteorological drought index of each monitoring point.

[0015] In a possible implementation, determining the relative humidity index according to the potential evapotranspiration and precipitation specifically includes:

[0016] According to the formula Determine the relative humidity index; where PET is the potential evapotranspiration at the monitoring point and P is the average monthly precipitation at the monitoring point.

[0017] In a possible implementation, the calculating of the comprehensive meteorological drought index according to the relative humidity index and the standardized precipitation index specifically includes:

[0018] According to the formula CI = aZ 30 +bZ 90 +cM 30 Calculate the comprehensive meteorological drought index;

[0019] Among them, Z 30 is the 30-day standardized precipitation index, Z 90 90-day Standardized Precipitation Index;

[0020] M 30 is the 30-day relative humidity index; a is the 30-day standardized precipitation coefficient, b is the 90-day standardized precipitation coefficient, and c is the 30-day relative humidity coefficient.

[0021] In a possible implementation, the method further includes:

[0022] Determine an impact area during a rainfall enhancement operation and a plurality of initial comparison areas of the impact area;

[0023] Obtaining an initial first rainfall data set of an initial comparison area and an initial second rainfall data set of an impact area during a historical period within a preset time period; the historical period is a period without rainfall enhancement operations;

[0024] Processing the initial first rainfall data set and the initial second rainfall data set respectively to determine a first rainfall data set and a second rainfall data set;

[0025] According to the first rainfall data set and the second rainfall data set, the formula Calculate the correlation coefficient between the first rainfall data set of each initial comparison area and the second rainfall data set of the impact area respectively; where r is the correlation coefficient, x i is the first rainfall data, y i is the second rainfall data, is the average value of the first rainfall data, is the average value of the second rainfall data; n is the number of historical samples;

[0026] Performing a significance test on each of the correlation coefficients, and determining the initial comparison area corresponding to the correlation coefficient that passes the test as the target comparison area;

[0027] According to the first rainfall data set of the target comparison area and the second rainfall data set of the impact area, a relationship between the impact area and each target comparison area in the historical period is established;

[0028] Obtain the actual precipitation in the target comparison area during the rainfall enhancement operation period;

[0029] Determining the estimated precipitation in the affected area during the rainfall enhancement operation period according to the precipitation and the relationship;

[0030] Obtain the actual precipitation in the affected area during the rainfall enhancement operation period;

[0031] Determine the absolute rainfall increase and relative rainfall increase rate in the affected area based on the estimated rainfall and the actual rainfall;

[0032] Performing a significance test on the absolute rainfall increase and the relative rainfall increase rate according to the correlation coefficient, the first rainfall data set, the second rainfall data set, the actual rainfall and the estimated rainfall in the affected area during the rainfall enhancement operation period;

[0033] Based on the test results, determine whether the rainfall during the rain-making operation period meets the requirements.

[0034] In a possible implementation, determining the influence area of ​​the rainfall enhancement operation period and the multiple initial comparison areas of the influence area specifically includes:

[0035] Calculate the diffusion time based on the catalyst type and bomb amount;

[0036] Determine the operation height according to the type of rainfall enhancement operation;

[0037] Calculate the horizontal transport distance of the catalyst along the wind direction based on the diffusion time, working height, wind speed and wind direction;

[0038] Determine the impact area based on the horizontal conveying distance and operation trajectory;

[0039] Selecting a plurality of first areas having radar echoes and having shapes similar to the influence area according to the first wind direction and the second wind direction of the influence area;

[0040] Obtaining a first variation law of a radar detection physical quantity and a second variation law of a satellite cloud parameter in each first area;

[0041] Obtaining a first variation law of a radar detection physical quantity in the impact area and a second variation law of a satellite cloud parameter;

[0042] According to the first change law and the second change law of the impact area, and the first change law and the second change law of each of the first areas, a second area is determined from the plurality of first areas; the second area is an initial comparison area.

[0043] In a possible implementation, the processing the initial first rainfall data set and the initial second rainfall data set respectively to determine the first rainfall data set and the second rainfall data set specifically includes:

[0044] Taking each first rainfall data in the initial first rainfall data set as an element and sorting them in a preset order to obtain an array;

[0045] Calculating the mean value and standard deviation of the initial first rainfall data in the initial first rainfall data set;

[0046] Calculate the empirical distribution function value of each element in the array based on the ratio of the sequence number of the element minus 1 to the number of elements;

[0047] The theoretical distribution function value of each element is calculated based on the difference between the initial first rainfall data of each element and the average value, divided by the standard deviation;

[0048] Calculate the absolute value of the difference between the empirical distribution function value and the theoretical distribution function value of each element;

[0049] Determine the maximum of the absolute values ​​of each element;

[0050] Determine a judgment value according to the square root of the number of the elements and the product of the maximum value;

[0051] Comparing the judgment value with a preset threshold, and when the judgment value is less than the preset threshold, determining that the array obeys a normal distribution;

[0052] When the judgment value is not less than the preset threshold, the array is screened until the array obeys the normal distribution, and it is determined that the elements in the array obeying the normal distribution constitute the first rainfall data set.

[0053] In a possible implementation, the step of establishing a relationship between the impact area and each target comparison area in the historical period further includes:

[0054] Performing a regression test on the relationship;

[0055] Get n sets of data x from the first rainfall data set and the second rainfall data set respectively 1i ,x 2i.。。。。。 x ki ,y i ; where i = 1, 2, ... n;

[0056] according to Determine the regression equation;

[0057] according to Determine the mean of the regression equation;

[0058] according to Calculate the total variation sum of squares; where SST is the total variation sum of squares;

[0059] according to Calculate the regression sum of squares; where SSR is the regression sum of squares;

[0060] according to Calculate the residual sum of squares; where SSE is the residual sum of squares;

[0061] according to Determine the coefficient of determination; where R is the coefficient of determination;

[0062] The original hypothesis is that the linear relationship is not significant;

[0063] according to Calculate a first statistic; wherein F is the first statistic;

[0064] According to the significance level, query the preset F distribution table to obtain the second statistic;

[0065] When the first statistic is greater than the second statistic, the null hypothesis is rejected and the linear relationship is determined to be significant.

[0066] In a possible implementation, performing a significance test on each of the correlation coefficients and determining the initial comparison area corresponding to the correlation coefficient that passes the test as the target comparison area specifically includes:

[0067] Based on each correlation coefficient, calculate the overall correlation coefficient;

[0068] When ρ=0, assuming H0 is ρ=0, H1 is ρ≠0, when When t>(t-0.5) / 2, H0 is rejected and r is determined to be significant; if t<(t-0.5) / 2, r is determined to be insignificant; where, ρ is the overall correlation coefficient; r is the correlation coefficient, and n is the number of historical samples;

[0069] When ρ≠0, the correlation coefficient and the overall correlation coefficient are subjected to Fisher transformation to obtain the Fisher correlation coefficient and the Fisher overall correlation coefficient in turn;

[0070] According to the formula Determine the Z value; where Zr is the Fisher correlation coefficient and Zρ is the Fisher overall correlation coefficient;

[0071] When r is significant, the corresponding initial contrast area is determined as the target contrast area.

[0072] In a possible implementation, establishing a relationship between the impact area and each target comparison area in the historical period based on the first rainfall data set of the target comparison area and the second rainfall data set of the impact area specifically includes:

[0073] Establish the relationship y=a+bx; where, x i is the first rainfall data, y i is the second rainfall data, is the average value of the first rainfall data, is the average value of the second rainfall data; n is the number of historical samples.

[0074] In a possible implementation, determining the absolute rainfall increase and the relative rainfall increase rate in the affected area according to the estimated rainfall and the actual rainfall specifically includes:

[0075] According to the formula HR =Y 2 -Y 2 'Calculate the absolute rainfall increase;

[0076] According to the formula Calculate the relative rainfall increase rate; where O HR is the absolute rainfall increase, Y 2 ′ is the estimated precipitation in the affected area during the rainfall enhancement operation period, Y 2 is the actual precipitation in the affected area during the rainfall enhancement operation period, R HR is the relative rainfall increase rate.

[0077] The drought data monitoring and processing method provided in the embodiment of the present invention can comprehensively evaluate drought information in combination with multiple indexes, thereby greatly improving the evaluation accuracy of drought information, and can further use drought information to automatically evaluate rain-enhancing operations, thereby realizing integrated processing from drought information processing to rain-enhancing operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 A flowchart of a drought data monitoring and processing method provided by an embodiment of the present invention;

[0079] Figure 2A schematic diagram of the process flow of the artificial rainfall enhancement method in the experimental area provided by an embodiment of the present invention;

[0080] Figure 3 for Figure 2 Schematic diagram of the process of step 201;

[0081] Figure 4 for Figure 2 Schematic diagram of the process of step 203;

[0082] Figure 5 This is the relationship diagram between ρ and r. DETAILED DESCRIPTION

[0083] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0084] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments.

[0085] Figure 1 The flowchart of the drought data monitoring and processing method provided by the embodiment of the present invention is as follows. Figure 1 , the technical solution of the present invention is described with specific embodiments.

[0086] Step 100, obtaining drought information of multiple monitoring points in a preset time period and a preset area; the drought information includes temperature, precipitation and location information of the monitoring points;

[0087] Specifically, the preset duration is a duration set according to needs, the temperature in the drought information may be the daily temperature at the monitoring point, the precipitation is the daily precipitation at the monitoring point, and the location information may be longitude and latitude information.

[0088] Step 110, determining a relative humidity index according to the temperature, precipitation and location information;

[0089] Wherein, the potential evapotranspiration is calculated by the Thornthwaite method according to the formula PET = a*Nb / A*I+c, and according to the potential evapotranspiration and precipitation, the Determine the relative humidity index;

[0090] Among them, PET is potential evapotranspiration; N represents the average solar radiation in a year, I represents the average temperature in a year, and the annual average temperature can be calculated based on the daily temperature. a, b, and c are coefficients determined according to different regional climate conditions. M is the relative humidity index, and P is precipitation.

[0091] Step 120, according to Determine the probability density function of the random variable; wherein; f(x) is the probability density function, β and γ are standard parameters in the probability density function of the Γ distribution;

[0092] Specifically, assuming that the precipitation in a certain period is a random variable x, the probability density function of its Γ distribution is β>0, γ>0 are scale and shape parameters respectively, which can be obtained by maximum likelihood estimation method. in, x i is the precipitation, The average precipitation amount.

[0093] Step 130, according to Calculate the probability that the random variable is less than the precipitation in the preset second time period; where F(x<x 0 ) is the probability that the random variable is less than the precipitation in the preset second time period, x is the random variable, x 0 is the precipitation in the second period;

[0094] Specifically, after determining the parameters in the probability density function, for the precipitation x in a certain year 0 , we can find that the random variable x is less than x 0 The probability of an event.

[0095] Step 140: Substitution Calculate approximate estimates of event probabilities;

[0096] Specifically, numerical integration can be used to calculate the approximate estimated value of the event probability after substituting equation (3) into equation (4).

[0097] Step 150, calculating the event probability when the precipitation is 0 according to F(x=0)=m / n; wherein m is the number of samples with precipitation being 0, and n is the total number of samples;

[0098] Specifically, the event probability when the precipitation is 0 is estimated by F(x=0)=m / n.

[0099] Step 160, normalize the probability to obtain a normal distribution function

[0100] Specifically, the probability of the Γ distribution is subjected to normal standardization, and the obtained probability value is substituted into the standardized normal distribution function.

[0101] Step 170, Perform an approximate solution to determine the Standardized Precipitation Index

[0102] Among them, c 0 、c 1 、c 2 The threshold of the standardized precipitation index is calculated by the z-score method; d 1 d 2 and d 3 It is also the threshold for calculating the standardized precipitation index using the z-score method.

[0103] Specifically, the Z value can be obtained through the formula in step 170, and the Z value is the standardized precipitation index.

[0104] Step 180, calculating a comprehensive meteorological drought index according to the relative humidity index and the standardized precipitation index;

[0105] Specifically, according to the formula CI = aZ 30 +bZ 90 +cM 30 Calculate the comprehensive meteorological drought index;

[0106] Among them, Z 30 is the 30-day standardized precipitation index, Z 90 90-day Standardized Precipitation Index;

[0107] M 30 is the 30-day relative humidity index; a is the 30-day standardized precipitation coefficient, ranging from Z to above the mild drought level 30 The average value divided by the minimum Z in history 30 The value can be obtained, for example, the average can be 0.4; b is the 90-day standardized precipitation coefficient, from the light drought level Z 90 The average value divided by the historical minimum Z 90 The value can be obtained, for example, the average value can be 0.4; c is the 30-day relative humidity coefficient, from the level of mild drought or above M 30 The average value, divided by the minimum M in history 30 The value can be obtained, for example, 0.8 can be taken on average.

[0108] Therefore, the standardized precipitation index of the precipitation in the past 30 days (equivalent to the monthly scale) and the past 90 days (equivalent to the seasonal scale) and the relative humidity index in the past 30 days are used to synthesize the index. This index reflects both the short-term (monthly) and long-term (seasonal) precipitation climate anomalies and the short-term (affecting crops) water deficit. This index is suitable for real-time meteorological drought monitoring and historical meteorological drought assessment in the same period.

[0109] Step 190, providing drought warning according to the comprehensive meteorological drought index of each monitoring point.

[0110] Specifically, through the CI calculation formula, the previous average temperature and precipitation can be used to roll out the daily comprehensive drought index CI for drought monitoring.

[0111] In one example, based on special drought monitoring data, the CI drought index can be automatically calculated to conduct real-time monitoring and diagnosis of meteorological drought, intuitively demonstrate the spatial distribution map of drought, analyze the temporal evolution of drought, compare historical data for the same period, and estimate the drought area of ​​each grade standard; automatically draw charts, display lists or save them as files.

[0112] Further, after step 190, if a drought warning has been issued, and if the drought warning has met the requirements for artificial rainfall, artificial rainfall can be carried out in the area where the drought warning has been issued. During the artificial rainfall, the following method can be used to evaluate it.

[0113] Figure 2 A schematic diagram of the process flow of the artificial rainfall enhancement method in the experimental area provided by the embodiment of the present invention is shown in FIG. Figure 2 shown.

[0114] Step 201, determining an impact area during a rainfall enhancement operation period and a plurality of initial comparison areas of the impact area;

[0115] The rain enhancement operation period is the period during which rain enhancement operations were carried out, and is for comparison with the historical period, which is the period when rain enhancement operations were not carried out. The impact area is the area that can be affected by rain enhancement operations, that is, the area for drought warning in this application. The comparison area is the area that cannot be affected by rain enhancement operations. The initial comparison area is the comparison area that has not been verified for significance, and the target comparison area is the initial comparison area that has passed the significance verification.

[0116] Specifically, Figure 3 As shown, the defect impact area and the initial comparison area in step 201 can be performed according to the following steps.

[0117] Step 301, calculating the diffusion time according to the catalyst type and the amount of bomb used;

[0118] Among them, the catalyst type is a commonly used catalyst for rain-making operations, including but not limited to silver iodide, calcium carbonate, and sodium chloride.

[0119] Step 302, determining the operation height according to the type of rainfall enhancement operation;

[0120] Among them, the rain enhancement operation types include the first type and the second type. The first type is aircraft operation, and the second type is ground operation. When the rain enhancement operation is the first type, the operation altitude is determined according to the flight altitude; when the rain enhancement operation is the second type, the operation altitude is calculated according to the azimuth and elevation angle of the rocket launched from the ground operation point.

[0121] Step 303, calculating the horizontal transport distance of the catalyst along the wind direction according to the diffusion time, working height, wind speed and wind direction;

[0122] Specifically, when the aircraft is operating, the wind speed and direction data at the operating altitude are read, the diffusion time is calculated according to the catalyst type and the amount of bomb used, and the horizontal transportation distance of the catalyst along the wind direction is calculated.

[0123] When working on the ground, the working height is calculated based on the azimuth and elevation of the rocket (shell) launched from the ground working point, the wind speed and direction data at the working height are read, the diffusion time is calculated based on the catalyst type and the amount of shells used, and the horizontal transport distance of the catalyst along the wind direction is calculated.

[0124] Step 304, determining the impact area according to the horizontal conveying distance and the operation trajectory;

[0125] Specifically, when the operation is performed by aircraft, the operation impact range is calculated in combination with the flight trajectory of the aircraft. When the operation is performed on the ground, the horizontal transport distance of the catalyst along the wind direction is calculated, thereby calculating the operation impact range. The operation impact range is the operation impact area.

[0126] Step 305, selecting a plurality of first areas having radar echoes and having shapes similar to the influence area according to the first wind direction and the second wind direction of the influence area;

[0127] The first wind direction is the upwind direction, and the second wind direction is the crosswind direction. According to the scope of the operation impact area and the wind direction and speed at the time of operation, multiple areas with radar echoes in the upwind and crosswind directions of the operation impact area are selected as comparison areas, that is, the first area.

[0128] Step 306, obtaining a first variation law of a radar detection physical quantity and a second variation law of a satellite cloud parameter in each first area;

[0129] Among them, the first change law is the change law of the physical quantity detected by radar, such as the law of change of radar basic reflectivity over time; the second change law is the change law of satellite cloud parameters over time, such as the law of change of satellite cloud top temperature over time.

[0130] Step 307, obtaining a first variation law of a radar detection physical quantity in the impact area and a second variation law of a satellite cloud parameter;

[0131] Step 308: determine a second area from the plurality of first areas according to the first change rule and the second change rule of the impact area, and the first change rule and the second change rule of each of the first areas; the second area is an initial comparison area.

[0132] Specifically, the variation patterns of radar detection physical quantities and satellite cloud parameter products over time in each comparison area and impact area are calculated, and the area with the most similar changes in physical quantities before the operation and in the impact area is found in the first area as the second area, which is the original comparison area. In this way, multiple original comparison areas can be determined.

[0133] Step 202, obtaining an initial first rainfall data set of an initial comparison area and an initial second rainfall data set of an impact area during a historical period within a preset time period; the historical period is a period without rainfall enhancement operation;

[0134] Specifically, according to the preset time interval, multiple first rainfall data of each initial comparison area within the preset time length are collected to form the first rainfall data set of each initial comparison area, and multiple initial second rainfall data of the impact area are collected to form the initial second rainfall data set.

[0135] Among them, the preset time length can be a longer time length such as 20 or 30 years, and the preset time interval can be 24 hours. For example, the initial first rainfall data of each original comparison area from 1971 to 2000 can be collected to form an initial first rainfall data set, and the initial first rainfall data can be daily rainfall data, that is, rainfall data for one day. When there are multiple rainfall monitoring stations in the original comparison area, the daily rainfall data can take the average of the daily rainfall data of multiple rainfall monitoring stations. Correspondingly, in the historical period, the initial second rainfall data set of the impact area is similar to the first rainfall data set of the initial comparison area, which will not be repeated here.

[0136] Step 203: Process the initial first rainfall data set and the initial second rainfall data set respectively to determine a first rainfall data set and a second rainfall data set.

[0137] The initial first rainfall data set and the initial second rainfall data set are screened to determine the first rainfall data set and the second rainfall data set.

[0138] Specifically, Figure 4 As shown, step 203 includes the following steps:

[0139] Step 401, taking each first rainfall data in the initial first rainfall data set as an element and sorting them in a preset order to obtain an array;

[0140] Specifically, the Kolmogorov distribution function fit test method can be used to perform normal distribution fit test and normal transformation on the rainfall data of the initial comparison area and the impact area in the historical period. Assume that the daily precipitation data of each station is a one-dimensional array, set as a, and sort the array a in ascending order to obtain a new array b.

[0141] Step 402, calculating the mean value and standard deviation of the initial first rainfall data in the initial first rainfall data set;

[0142] Specifically, find the mean x and standard deviation S of array b.

[0143] Step 403, calculating the empirical distribution function value of each element in the array according to the ratio of the sequence number of the element minus 1 to the number of elements;

[0144] Specifically, the empirical distribution function value is equal to the ratio of (i-1) to n Where i represents the serial number corresponding to the data in array b, taking values ​​of 1,…,n, and n represents the total number of samples.

[0145] Step 404, calculating the theoretical distribution function value of each element according to the difference between the initial first rainfall data of each element and the average value divided by the standard deviation;

[0146] Specifically, first find the difference between each data value in array b and the mean of array b and then divide it by the standard deviation of array b. (in ), look up the standard normal distribution function table to obtain the theoretical distribution function value F(x)=Φ(x) corresponding to the data in array b.

[0147] Step 405, calculating the absolute value of the difference between the empirical distribution function value and the theoretical distribution function value of each element;

[0148] Specifically, find the absolute value of the difference between the empirical distribution function value of each data in array b and the corresponding theoretical distribution function value: d(x i )=|F n (x i )-F(x i )|.

[0149] Step 406, determining the maximum value among the absolute values ​​of each element;

[0150] Specifically, find the maximum value of the n differences obtained: D n =sup|F n (x)-F(x)|.

[0151] Step 407, determining a judgment value according to the square root of the number of the elements and the product of the maximum value;

[0152] Specifically, multiply the maximum value by Get a value, called the judgment value, set as m,

[0153] Step 408, comparing the judgment value with a preset threshold, and when the judgment value is less than the preset threshold, determining that the array obeys a normal distribution;

[0154] Specifically, the preset threshold may be 1.36, and m is compared with 1.36. If m<1.36, it is considered that the statistical variable approximately obeys the normal distribution; if m>1.36, it is considered that the statistical variable does not satisfy the normal distribution.

[0155] Step 409: when the judgment value is not less than the preset threshold, the array is screened until the array obeys the normal distribution, and it is determined that the elements in the array obeying the normal distribution constitute the first rainfall data set.

[0156] Specifically, according to the continuous iteration of steps 401 to 409, through continuous testing and continuous substitution, the substitution includes logarithmic transformation, square root transformation, and cube root transformation, and finally the substitution variable that obeys (or approximately obeys) the normal distribution is selected as a new statistical variable for regional historical regression statistical test, so as to determine the first rainfall data set from the initial first rainfall data set. Correspondingly, a similar method can be used for verification, so as to screen and obtain the second rainfall data from the initial second rainfall data set. No further details are given here.

[0157] Step 204: According to the first rainfall data set and the second rainfall data set, the formula Calculate the correlation coefficient between the first rainfall data set of each initial comparison area and the second rainfall data set of the impact area respectively; where r is the correlation coefficient, x i is the first rainfall data, y i is the second rainfall data, is the average value of the first rainfall data, is the average value of the second rainfall data; n is the number of historical samples;

[0158] Specifically, the correlation coefficient between each initial comparison area and the impact area during the historical period was calculated. The size of the correlation coefficient reflects the degree of correlation between the average precipitation in the two areas. The larger the correlation coefficient, the higher the correlation.

[0159] The numerator in the formula is the covariance The denominator is the standard deviation and The product of .

[0160] Step 205, performing a significance test on each of the correlation coefficients, and determining the initial comparison area corresponding to the correlation coefficient that passes the test as the target comparison area;

[0161] Among them, the significance test of the correlation coefficient is the difference test between the sample correlation coefficient and the overall correlation coefficient. Since the sample distribution of the correlation coefficient r is relatively complex and is greatly affected by the overall correlation coefficient ρ, it is generally divided into two cases: ρ=0 and ρ≠0.

[0162] The overall correlation coefficient can be calculated using the following formula:

[0163]

[0164] Among them, ρ represents the overall correlation coefficient, X i and Y i They represent the i-th sample observation value, and Sequentially represents X i and Y i The mean value, σ X and σ Y Sequentially represents X i and Y i The standard deviation of n is the number of historical samples. In this application, X i represents the i-th first rainfall data in the first rainfall data set, Y i represents the i-th second rainfall data in the second rainfall data set.

[0165] The significance test of the correlation coefficient also includes two cases: one is the comparison of the correlation coefficient r with the overall correlation coefficient ρ; the other is to infer whether there is a difference between the respective populations ρ1 and ρ2 by comparing the difference between the two samples r (r1-r2).

[0166] Specifically, based on each correlation coefficient, the overall correlation coefficient is calculated;

[0167] When ρ=0, assuming H0 is ρ=0, H1 is ρ≠0, when When t>the preset first threshold, H0 is rejected and r is determined to be significant; if t<the preset first threshold, r is determined to be insignificant; where ρ is the overall correlation coefficient; r is the correlation coefficient, and n is the number of historical samples;

[0168] When ρ≠0, the correlation coefficient and the overall correlation coefficient are subjected to Fisher transformation to obtain the Fisher correlation coefficient and the Fisher overall correlation coefficient in turn;

[0169] According to the formula Determine the Z value; where Zr is the Fisher correlation coefficient and Zρ is the Fisher overall correlation coefficient;

[0170] When r is significant, the corresponding initial contrast area is determined as the target contrast area.

[0171] The following is a more specific description of the above two situations:

[0172] (I) When ρ≠0

[0173] from Figure 5 It can be seen that the distribution of the sample r is sampled from two populations (n=8) with ρ=0 and ρ=0.8. It can be seen that when ρ=0, the distribution of r is symmetrical on the left and right, and when ρ=0.8, the distribution of r is more biased. This is not difficult to understand. The value range of ρ is -1~+1, and the value range of r is also -1~+1. When ρ=0, the distribution of should be symmetrical around 0. When ρ=0.8, the range of r is still -1~+1, but the value of r is definitely affected by ρ, so the distribution shape cannot be symmetrical. Therefore, it is generally believed that when ρ=0, the distribution of r is approximately normal; when ρ≠0, the distribution of r is not normal.

[0174] When we get r = 0.30 (or any other value) in actual research, we will naturally think of two situations: ① Since r = 0.30, it means that the two columns of variables are correlated in general (ρ≠0). ② Although r = 0.30, this may be a coincidence and there may be no correlation in general (ρ = 0). Therefore, we need to conduct a significance test on r = 0.30. At this time, we can still use the t test method.

[0175] H0: ρ=0

[0176] H1: ρ≠0

[0177]

[0178] If t>the preset first threshold, H0 is rejected, indicating that the obtained r is not from the population with ρ=0, or r is significant.

[0179] If t < the preset first threshold, it means that the obtained r value is accidental, and it cannot be concluded from the r value that the population has a correlation. In other words, r is not significant.

[0180] (ii) When ρ≠0

[0181] ρ = 0 only solves the problem of whether the two populations are related, or in other words, it can only explain whether r comes from the population with ρ = 0. Therefore, it is also necessary to determine whether r comes from a population with a specific value of ρ, that is, the significance test of r when ρ ≠ 0.

[0182] exist Figure 4 As analyzed in the previous section, when ρ≠0, the sample distribution of r is not normal, so the formula ρ=0 cannot be used for t-test. At this time, both r and ρ need to be converted into Fisher Zr. After r is converted into Zr, the distribution of Zr can be considered normal, and its mean Zρ and standard error This allows the Z test to be performed.

[0183]

[0184] Step 206, establishing a relationship between the impact area and each target comparison area in the historical period based on the first rainfall data set of the target comparison area and the second rainfall data set of the impact area;

[0185] Specifically, establish the relationship y=a+bx; where, x i is the first rainfall data, y i is the second rainfall data, is the average value of the first rainfall data, is the average value of the second rainfall data; n is the number of historical samples.

[0186] Specifically, after step 106, the method further includes: performing a significance test on the relationship expression.

[0187] Get n sets of data x from the first rainfall data set and the second rainfall data set respectively 1i ,x 2i ……x ki ,y i ; where i = 1, 2, ... n;

[0188] according to Determine the regression equation; where a 1 -a k is the preset coefficient threshold;

[0189] according to Determine the mean of the regression equation;

[0190] according to Calculate the total variation sum of squares; where SST is the total variation sum of squares;

[0191] according to Calculate the regression sum of squares; where SSR is the regression sum of squares;

[0192] according to Calculate the residual sum of squares; where SSE is the residual sum of squares;

[0193] according to Determine the coefficient of determination; where R is the coefficient of determination;

[0194] Propose the null hypothesis H 0 The linear relationship is not significant;

[0195] according to Calculate a first statistic; wherein F is the first statistic;

[0196] According to the significance level, the preset F distribution table is queried to obtain the second statistic; wherein, F is obtained from the significance level a.a (k, nk-1).

[0197] When the first statistic is greater than the second statistic, the null hypothesis is rejected and the linear relationship is determined to be significant.

[0198] Among them, if F>F a , reject H 0 , that is, the linear relationship is considered significant, otherwise H 0 , that is, the linear relationship is considered significant. After the relationship passes the significance test, the relationship is retained. If the relationship does not pass the significance test, the sample is re-obtained and the significance test of the relationship is performed again.

[0199] Step 207, obtaining the actual precipitation of the target comparison area during the rainfall enhancement operation period;

[0200] Specifically, during the rain-making operation period, the actual precipitation in the target comparison area can be collected.

[0201] Step 208, determining the estimated precipitation in the affected area during the rainfall enhancement operation period according to the precipitation and the relationship;

[0202] Specifically, the actual precipitation in the target comparison area can be substituted into the relational expression in step 104 to obtain the estimated precipitation in the affected area during the rainfall enhancement operation period.

[0203] Step 209, obtaining the actual precipitation in the affected area during the rainfall enhancement operation period;

[0204] Specifically, the actual precipitation in the affected area during the rain-making operation period can be collected.

[0205] Step 210, determining the absolute rainfall increase and relative rainfall increase rate of the affected area according to the estimated rainfall and the actual rainfall;

[0206] Specifically, according to formula O HR =Y 2 -Y 2 'Calculate the absolute rainfall increase;

[0207] According to the formula Calculate the relative rainfall increase rate; where O HR is the absolute rainfall increase, Y 2 ′ is the estimated precipitation in the affected area during the rainfall enhancement operation period, Y 2 is the actual precipitation in the affected area during the rainfall enhancement operation period, R HR is the relative rainfall increase rate.

[0208] Step 211, performing a significance test on the absolute rainfall increase amount and the relative rainfall increase rate according to the correlation coefficient, the first rainfall data set, the second rainfall data set, the actual rainfall and the estimated rainfall in the affected area during the rainfall increase operation period;

[0209] Specifically, the t-test method is used to conduct a statistical significance test on the absolute rainfall increase and relative rainfall increase rate of the regional historical regression statistical test. The formula of the test statistic t is as follows:

[0210] According to the formula Conduct a significance test; where x i is the first rainfall data, y i is the second rainfall data, n is the number of historical samples, is the average value of the first rainfall data set in the historical target comparison area, is the average value of the second rainfall data set in the historical period, It represents the average value of actual precipitation in the target comparison area during the rainfall enhancement operation period. It is the average value of actual precipitation in the affected area during the rainfall enhancement operation period; is the average estimated precipitation in the affected area during the rainfall enhancement operation period, r is the correlation coefficient, and k is the number of samples during the rainfall enhancement operation period.

[0211] According to the t value obtained above, the numerical table of t-distribution is checked, where the degree of freedom ν = n-2, and the obtained α value is the significance level of the rainfall enhancement effect in the operation influence area estimated by the regional historical regression method. The smaller the α value, the higher the credibility of the statistical test results.

[0212] Step 212, based on the test results, determine whether the rainfall during the rain enhancement operation period meets the requirements.

[0213] Specifically, the artificial rainfall enhancement effect tested by statistical test methods is a test result in a statistical sense. Therefore, when the statistical test results of the artificial rainfall enhancement effect are given, the significance level of the statistical test results must be given at the same time.

[0214] By applying the drought data monitoring and processing method provided by the embodiment of the present invention, the drought data monitoring and processing method provided by the embodiment of the present invention can comprehensively evaluate drought information in combination with multiple indexes, thereby greatly improving the evaluation accuracy of drought information, and further can use drought information to automatically evaluate rain-enhancing operations, thereby realizing integrated processing from drought information processing to rain-enhancing operations.

[0215] The professionals should further realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0216] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0217] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A drought data monitoring and processing method, characterized in that: The method comprises: Obtain drought information of multiple monitoring points in a preset time period and a preset area; the drought information includes temperature, precipitation and location information of the monitoring points; Determining a relative humidity index based on the temperature, precipitation and location information; according to Determine the probability density function when the precipitation is a random variable; wherein; is the probability density function of precipitation, and for The standard parameters in the probability density function of the distribution, x is the precipitation in any period; according to Calculate the probability that the random variable is less than the precipitation in the preset second time period; where, is the probability that the random variable is less than the precipitation in the preset second time period, is the precipitation in the second period; Will Substitution , calculate the approximate estimate of the probability of the event; according to Calculate the probability of an event when the precipitation is 0; where m is the number of samples with 0 precipitation, is the total sample number; The probability is normalized to obtain a standardized normal distribution function. ; right Perform an approximate solution to determine the Standardized Precipitation Index ,in, , and is the threshold of the standardized precipitation index calculated by the z-score method; d1, d2 and d3 are the thresholds of the standardized precipitation index calculated by the z-score method; Calculating a comprehensive meteorological drought index based on the relative humidity index and the standardized precipitation index; Drought warning is carried out according to the comprehensive meteorological drought index of each monitoring point; The method then further comprises: Determine an impact area during a rainfall enhancement operation and a plurality of initial comparison areas of the impact area; Obtaining an initial first rainfall data set of an initial comparison area and an initial second rainfall data set of an impact area during a historical period within a preset time period; the historical period is a period without rainfall enhancement operations; Processing the initial first rainfall data set and the initial second rainfall data set respectively to determine a first rainfall data set and a second rainfall data set; According to the first rainfall data set and the second rainfall data set, the formula The correlation coefficient between the first rainfall data set of each initial comparison area and the second rainfall data set of the impact area is calculated respectively; where r is the correlation coefficient, is the first rainfall data, is the second rainfall data, is the average value of the first rainfall data, is the average value of the second rainfall data; n is the number of historical samples; Performing a significance test on each of the correlation coefficients, and determining the initial comparison area corresponding to the correlation coefficient that passes the test as the target comparison area; According to the first rainfall data set of the target comparison area and the second rainfall data set of the impact area, a relationship between the impact area and each target comparison area in the historical period is established; Obtain the actual precipitation in the target comparison area during the rainfall enhancement operation period; Determining the estimated precipitation in the affected area during the rainfall enhancement operation period according to the precipitation and the relationship; Obtain the actual precipitation in the affected area during the rainfall enhancement operation period; Determine the absolute rainfall increase and relative rainfall increase rate in the affected area based on the estimated rainfall and the actual rainfall; Performing a significance test on the absolute rainfall increase and the relative rainfall increase rate according to the correlation coefficient, the first rainfall data set, the second rainfall data set, the actual rainfall and the estimated rainfall in the affected area during the rainfall enhancement operation period; According to the test results, determine whether the rainfall during the rain enhancement operation period meets the requirements; Wherein, determining the absolute rainfall increase and the relative rainfall increase rate of the affected area according to the estimated rainfall and the actual rainfall specifically includes: According to the formula Calculate the absolute rainfall increase; where, is the absolute rainfall increase, is the estimated precipitation in the affected area during the rainfall enhancement operation period, The actual precipitation in the affected area during the rain enhancement operation period; According to the formula Calculate the relative rainfall increase rate; where, is the relative rainfall increase rate.

2. The method according to claim 1, characterized in that Determining the relative humidity index according to the temperature, precipitation and location information specifically includes: According to the formula Determine the relative humidity index; where PET is the potential evapotranspiration at the monitoring point and P is the average monthly precipitation at the monitoring point.

3. The method according to claim 1, characterized in that The calculating of the comprehensive meteorological drought index according to the relative humidity index and the standardized precipitation index specifically includes: According to the formula Calculate the comprehensive meteorological drought index; in, is the 30-day Standardized Precipitation Index, is the 90-day Standardized Precipitation Index; is the 30-day relative humidity index; a is the 30-day standardized precipitation coefficient, b is the 90-day standardized precipitation coefficient, and c is the 30-day relative humidity coefficient.

4. The method according to claim 1, characterized in that: The determination of the influence area during the rainfall enhancement operation period and the multiple initial comparison areas of the influence area specifically include: Calculate the diffusion time based on the catalyst type and bomb amount; Determine the operation height according to the type of rainfall enhancement operation; Calculate the horizontal transport distance of the catalyst along the wind direction based on the diffusion time, working height, wind speed and wind direction; Determine the impact area based on the horizontal conveying distance and operation trajectory; Selecting a plurality of first areas having radar echoes and having shapes similar to the influence area according to the first wind direction and the second wind direction of the influence area; Obtaining a first variation law of a radar detection physical quantity and a second variation law of a satellite cloud parameter in each first area; Obtaining a first variation law of a radar detection physical quantity in the impact area and a second variation law of a satellite cloud parameter; According to the first change law and the second change law of the impact area, and the first change law and the second change law of each of the first areas, a second area is determined from the plurality of first areas; the second area is an initial comparison area.

5. The method according to claim 1, characterized in that The processing of the initial first rainfall data set and the initial second rainfall data set respectively to determine the first rainfall data set and the second rainfall data set specifically includes: Taking each first rainfall data in the initial first rainfall data set as an element and sorting them in a preset order to obtain an array; Calculating the mean value and standard deviation of the initial first rainfall data in the initial first rainfall data set; Calculate the empirical distribution function value of each element in the array based on the ratio of the sequence number of the element minus 1 to the number of elements; The theoretical distribution function value of each element is calculated based on the difference between the initial first rainfall data of each element and the average value, divided by the standard deviation; Calculate the absolute value of the difference between the empirical distribution function value and the theoretical distribution function value of each element; Determine the maximum of the absolute values ​​of each element; Determine a judgment value according to the square root of the number of the elements and the product of the maximum value; Comparing the judgment value with a preset threshold, and when the judgment value is less than the preset threshold, determining that the array obeys a normal distribution; When the judgment value is not less than the preset threshold, the array is screened until the array obeys the normal distribution, and it is determined that the elements in the array obeying the normal distribution constitute the first rainfall data set.

6. The method according to claim 1, characterized in that The establishment of the relationship between the impact area and each target comparison area in the historical period also includes: Performing a regression test on the relationship; Get n sets of data x from the first rainfall data set and the second rainfall data set respectively 1i , x 2i...... x ki ,y i ; where i=1,2,…n; according to Determine the regression equation; where a1-a k is the preset coefficient threshold; according to , Determine the mean of the regression equation; according to Calculate the total variation sum of squares; where SST is the total variation sum of squares; according to Calculate the regression sum of squares; where SSR is the regression sum of squares; according to Calculate the residual sum of squares; where SSE is the residual sum of squares; according to Determine the coefficient of determination; where R is the coefficient of determination; The original hypothesis is that the linear relationship is not significant; according to Calculate a first statistic; wherein F is the first statistic; According to the significance level, query the preset F distribution table to obtain the second statistic; When the first statistic is greater than the second statistic, the null hypothesis is rejected and the linear relationship is determined to be significant.

7. The method according to claim 1, characterized in that The performing of a significance test on each of the correlation coefficients to determine the initial comparison area corresponding to the correlation coefficient that passes the test as the target comparison area specifically includes: Based on each correlation coefficient, calculate the overall correlation coefficient; When ρ=0, assuming H0 is ρ=0, H1 is ρ≠0, when If t1>(t1-0.5) / 2, then H0 is rejected and r is determined to be significant; if t1<(t1-0.5) / 2, then r is determined to be insignificant; where ρ is the overall correlation coefficient; r is the correlation coefficient, n is the number of historical samples; t1 is the t statistic in the t test; When ρ≠0, the correlation coefficient and the overall correlation coefficient are subjected to Fisher transformation to obtain the Fisher correlation coefficient and the Fisher overall correlation coefficient in turn; According to the formula Determine the Z1 value; where Zr is the Fisher correlation coefficient, Zρ is the average of Zr, and Z1 is the Z statistic in the Z test; When r is significant, the corresponding initial contrast area is determined as the target contrast area.

8. The method according to claim 1, characterized in that The establishing of the relationship between the impact area and each target comparison area in the historical period based on the first rainfall data set of the target comparison area and the second rainfall data set of the impact area specifically includes: Establish The relationship between , , is the first rainfall data, is the second rainfall data, is the average value of the first rainfall data, is the average value of the second rainfall data; n is the number of historical samples, S xy is the covariance, , S x is the standard deviation, .

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