A typhoon monitoring method based on PWV and multi-parameter comprehensive weighting strategy

Abstract

The invention discloses a typhoon monitoring method based on PWV and multi-parameter comprehensive weighting strategy, comprising the following steps: step 1, respectively establishing a typhoon database and a meteorological parameter data set in the same area and the same time area; step 2, selecting and using RS observation data of any n stations and ERA5 meteorological parameter data of corresponding time and space to respectively calculate and verify PWV; step 3, according to the spatial distribution of PWV during each typhoon, then extracting the longitude and latitude corresponding to the peak value of each typhoon parameter, and finally calculating the discrete coefficient of the typhoon parameter during each typhoon; step 4, establishing a multi-parameter linear weighted model according to the discrete coefficient of the typhoon parameter; step 5, using the established multi-parameter linear weighted model, inputting PWV, air pressure, rainfall, wind speed and temperature within a certain time and space range; linearly transforming the output result into a [0,1] interval, extracting the longitude and latitude corresponding to the maximum value of the result as the predicted typhoon position.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological monitoring, and in particular to a typhoon monitoring method based on PWV and multi-parameter comprehensive weighting strategy. Background Art

[0002] Precipitable Water Vapor (PWV) refers to the amount of rainfall formed when all water vapor in the air column per unit cross-sectional area from the ground to the top of the atmosphere condenses into liquid water, which reflects the change in the water vapor content in the atmosphere. In extreme weather conditions such as typhoons, the spatiotemporal state of atmospheric water vapor changes dramatically. In recent years, with global warming, typhoon events have occurred frequently, seriously threatening people's lives and property. Therefore, timely and accurate grasp of the spatiotemporal distribution characteristics of atmospheric water vapor is of great significance to weather and climate research.

[0003] Traditional PWV acquisition technologies mainly include radiosonde (RS), microwave radiometer and lidar. Among them, RS can observe atmospheric parameters such as temperature and air pressure up to 30 km, and the uncertainty of inverted PWV is 0-1.5 mm, but it has defects such as high cost and low temporal and spatial resolution. Microwave radiometer can measure atmospheric radiation energy and provide real-time atmospheric conditions and vertical distribution information, but it is expensive, requires regular calibration, and the measurement is easily affected by precipitation. Lidar has high vertical resolution and observation accuracy, but the measurement cost is high, the measurement is easily affected by precipitation and dense clouds, and it is difficult to obtain long time series data sets that meet meteorological analysis. In recent years, the potential of using PWV to monitor typhoon movement has been confirmed. However, the current motion monitoring of typhoon algorithms mainly focuses on PWV, ignoring the influence of other meteorological parameters such as temperature and wind speed, which makes it difficult to describe the movement of typhoons in detail, resulting in insufficient accuracy.

[0004] At the same time, compared with the traditional PWV monitoring method, the PWV inverted using ERA5 (ECMWF Reanalysis v5) reanalysis data has the advantage of high temporal and spatial resolution (1 hour, 0.25°×0.25°). The ERA5 reanalysis dataset is a high-quality global meteorological dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Its data coverage is wide and the time span is long (from 1940 to the present). Therefore, based on the PWV and other meteorological parameters inverted from the ERA5 reanalysis data, a model for improving typhoon path monitoring is proposed, which not only solves the defects of the high cost and low temporal and spatial resolution of the traditional PWV acquisition method, but also effectively reduces the deviation of the single PWV in the typhoon path forecast. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention proposes a typhoon monitoring method based on PWV and a multi-parameter comprehensive weighted strategy to improve the accuracy and stability of typhoon monitoring. The method adopts a multi-parameter linear weighted model, incorporates multiple meteorological parameters into the analysis system, and targets the performance differences of different parameters in typhoon monitoring. By calculating the distribution center of PWV, air pressure, rainfall, wind speed, temperature and other parameters during each typhoon between 2018 and 2022 and the discrete coefficient of the typhoon position, the weight is calculated by taking the mean, thereby effectively improving the accuracy and stability of the monitoring results. The present invention can monitor typhoons in the eastern coastal areas of China, solving the problems of insufficient accuracy and poor stability caused by the existing methods due to ignoring the influence of multiple parameters and parameter differences.

[0006] In order to solve the above technical problems, the technical solution of the present invention is:

[0007] A typhoon monitoring method based on PWV and multi-parameter comprehensive weighting strategy, the specific steps are as follows:

[0008] Step 1: Establish a typhoon database. Through the real-time typhoon path release website of the Zhejiang Provincial Water Resources Department, collect typhoon data that landed on the eastern coast of China from 2018 to 2022, including typhoon occurrence time, center location, typhoon level and other information. These data are classified by year and typhoon name and saved in the local computer in Excel form.

[0009] Step 2: Establish a meteorological parameter data set. First, download hourly meteorological data for the eastern coast of China (105°E~135°E, 10°N~40°N) from the ERA5 (ECMWF Reanalysis v5) reanalysis data set from 2018 to 2022, including air pressure, rainfall, wind speed, temperature and specific humidity. Among them, specific humidity is pressure stratification data; wind speed data exists in the form of U-component (longitude direction) and V-component (latitude direction), and wind speed needs to be calculated by vector and formula; temperature data is in Kelvin (K) and needs to be converted to degrees Celsius (℃). All data are classified by year and variable name and saved in the local computer as nc files. Then, download meteorological parameter data for several RS stations in the eastern coast of China (105°E~135°E, 10°N~40°N) from the University of Wyoming RS data set from 2018 to 2022, including air pressure, specific humidity, etc. Classify by year and variable name. Save it in the local computer as an Excel spreadsheet.

[0010] Step 3: Extract and test ERA5-PWV. First, PWV is calculated using the RS meteorological parameter data of several stations downloaded in step 2 and the ERA5 meteorological parameter data of the corresponding time and space. Then, the accuracy and usability of ERA5-PWV are tested using RS-PWV as the standard.

[0011] Step 4: Calculate the distance difference between the parameter peak and the typhoon center. First, the spatial distribution of PWV during each typhoon was calculated using the ERA5 meteorological parameter data in the meteorological database. Then, the longitude and latitude corresponding to the hourly PWV, air pressure, rainfall, wind speed, and temperature peaks were extracted, and the distance between the peak center of each parameter and the typhoon center was calculated using the Haversine Formula. Finally, the mean and variance of each parameter distance difference sequence were calculated, and the discrete coefficient formula was used to calculate the discrete coefficient of each parameter during each typhoon.

[0012] Step 5: Establish a multi-parameter weighted model. Calculate the mean of the discrete coefficients of the distance difference between the PWV, air pressure, rainfall, wind speed, and temperature peak center and the typhoon center processed in the third step, and take the absolute value as the weight after subtracting 1. Take PWV as the key parameter and use the linear weighted formula to establish the weighted models of PWV two-parameter, three-parameter, and four-parameter. In addition, the weights of each parameter are normalized to ensure that the sum of the weights is 1.

[0013] Step 6: Predict the typhoon location. Using the weighted model established in step 4, input data such as PWV, air pressure, rainfall, wind speed, and temperature within a certain time and space range. Linearly transform the output result into the interval [0,1], and extract the longitude and latitude corresponding to the maximum value of the result as the predicted typhoon location.

[0014] Through the above algorithm, the present invention effectively solves the problems of low accuracy and poor stability caused by ignoring the influence of multiple parameters and the performance differences of different parameters in the existing methods, and provides a new idea for typhoon monitoring.

[0015] The present invention has the following characteristics and beneficial effects:

[0016] Compared with the existing typhoon monitoring methods based on radio detectors, lidar, etc., the above technical solution can not only solve the problems of high measurement cost and susceptibility to environmental influences, but also overcome the shortcomings of low measurement time and space resolution and inability to meet large-scale measurement needs. Compared with the existing single PWV typhoon monitoring method, this method not only improves the monitoring accuracy through multi-parameter weighted constraints, but also solves the problem of unstable monitoring caused by ignoring the influence of other meteorological parameters. In addition, considering the differences in the performance of different parameters in typhoon monitoring, the weighting coefficient is calculated through a long time series data sequence, which further ensures the accuracy and stability of the typhoon monitoring model. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] Figure 1 A flow chart for establishing a typhoon and meteorological parameter database in an embodiment of the present invention.

[0019] Figure 2 The flowchart of the typhoon monitoring method based on the PWV integral formula, discrete coefficient and weighting strategy in the embodiment of the present invention.

[0020] Figure 3 Schematic diagram for comparing the results of the two-parameter combination model based on PWV and any other parameter.

[0021] Figure 4 Schematic diagram for comparing the results of the three-parameter combination model based on PWV and any other two parameters.

[0022] Figure 5 Schematic diagram for comparing the results of the four-parameter combination model based on PWV and any other three parameters. DETAILED DESCRIPTION

[0023] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0024] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings.

[0025] On the contrary, the present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention as defined by the claims. Further, in order to make the public have a better understanding of the present invention, some specific details are described in detail in the detailed description of the present invention below. Those skilled in the art can fully understand the present invention without the description of these details.

[0026] Example 1

[0027] The present invention provides a typhoon monitoring method based on PWV and multi-parameter comprehensive weighting strategy, comprising the following steps:

[0028] Step 1: Establish a typhoon database and a meteorological parameter data set in the same area and the same time area respectively, wherein the typhoon database includes the typhoon occurrence time, center location and typhoon level, and the meteorological parameter data set includes air pressure, rainfall, wind speed, temperature and specific humidity.

[0029] Specifically, Figure 1 As shown, in this embodiment, the typhoon data that landed on the eastern coast of China from 2018 to 2022 are obtained through the real-time typhoon path release website of the Zhejiang Provincial Water Resources Department, including typhoon occurrence time, center location, typhoon level and other information. These data are classified by year and typhoon name and stored in the local computer in Excel form.

[0030] Furthermore, according to the established typhoon database, meteorological data such as air pressure, rainfall, temperature, wind speed, and specific humidity for the eastern coast of China (105°E–135°E, 10°N–40°N) from 2018 to 2022 were downloaded from the ERA5 reanalysis dataset and stored in the local computer in the form of nc files according to the year and variable name.

[0031] In a further configuration of this embodiment, a meteorological parameter data set is established, which extracts the meteorological parameter data set of the eastern coastal area of ​​China (105°E~135°E, 10°N~40°N) during each typhoon from 2018 to 2022, mainly including air pressure, rainfall, wind speed and temperature, and converts them into standard units (hPa, mm / h, m / s, ℃) respectively. The original wind speed observation value is the U component (longitude direction) and the V component (latitude direction) at a height of 10m, and the two need to be combined to be converted into the 10m wind speed. The conversion formula is as follows:

[0032]

[0033] Where v represents the v-direction component of wind speed, u represents the u-direction component of wind speed, and Wind represents the synthesized wind speed in m / s.

[0034] The original observed value of temperature is in Kelvin (K). In order to standardize the unit, it is converted as follows:

[0035] T=T K -273.15 (2)

[0036] Where, T K Indicates temperature data in Kelvin (K), and T indicates temperature data converted to Celsius (℃).

[0037] Step 2: Select the meteorological parameter data observed by RS at any n stations and the ERA5 meteorological parameter data of the corresponding time and space, calculate PWV respectively, and use the RS-PWV calculated by the meteorological parameter data observed by RS as the benchmark to verify the ERA5-PWV calculated by the ERA5 meteorological parameter data.

[0038] In this embodiment, RS meteorological parameter data for 2018-2022 at stations 45004 and 47102, including specific humidity, air pressure, etc., are downloaded from the University of Wyoming website and stored in a local computer in Excel spreadsheet format according to year and variable name.

[0039] It should be noted that PWV is defined as the total amount of water vapor contained in an air column per unit area. PWV is the most abundant greenhouse gas on Earth, accounting for 70% of the total atmospheric radiation absorption. It changes greatly in extreme weather such as typhoons and can be used as an important parameter for monitoring typhoon activity.

[0040] Specifically, the calculation process of PWV is divided into two steps. First, using the meteorological database established in step 2, extract the RS observation data of the two stations 45004 and 47102 within the research time range and the ERA5 meteorological parameter data of the corresponding time and space, including air pressure and specific humidity, and calculate PWV by the formula. The formula is as follows:

[0041]

[0042] In the formula, g represents the acceleration due to gravity, and its unit is m / s 2 ; Ps is the surface pressure, in hPa; q is the specific humidity of the specified pressure layer, in g / kg; dp represents the pressure difference; n represents the number of layers; p i and p i-1 is the surface pressure of layer i and layer i-1; q i and q i-1 Represent the specific humidity of layer i and layer i-1 respectively.

[0043] Furthermore, this embodiment also includes testing the accuracy and usability of ERA5-PWV.

[0044] Specifically, RS-PWV was used as a benchmark to test the usability of ERA5-PWV by using two criteria: root mean square error (RMSE) and mean absolute error (MAE). RMSE is used to measure the average difference between the statistically predicted value (ERA5-PWV) and the true value (RS-PWV). Mathematically, it is the standard deviation of the residual, which represents the distance between the regression line and the data point, and is calculated as follows:

[0045]

[0046] In the formula, m represents the total number of data; y i and represent the predicted value and the true value respectively.

[0047] In order to eliminate the influence of gross errors, MAE was also used to evaluate the performance of ERA5-PWV, which was defined as the mean absolute difference between the predicted value (ERA5-PWV) and the true value of the data (RS-PWV).

[0048]

[0049] In the formula, m represents the total number of data; y i and represent predicted value and true value respectively; |·| represents absolute value operation.

[0050] In addition, since the time resolution of RS data is 12 hours, ERA5-PWV uses the same time interval for comparison. In meteorological research, it is generally considered that the RMSE is between 0.5mm and 2mm and the MAE is between 0.3mm and 1.5mm. When the RMSE and MAE errors are within the acceptable range, it indicates that ERA5-PWV has good consistency with RS-PWV and can be used for further analysis and calculation.

[0051] If the test results do not meet the requirements, recalculate the RMSE and MAE year by year in different time periods to find out the abnormal years, and use the deviation correction formula to correct the error. The formula is as follows:

[0052]

[0053] ERA5corrected,k=ERA5 k -Bias;

[0054] Where Bias represents the average deviation, ERA5 k represents the value of the kth abnormal ERA5-PWV, RS k represents the value of RS-PWV at the corresponding moment, N represents the total number of abnormal data, ERA5corrected, k represents the corrected data, and RMSE and MAE are recalculated for the corrected ERA5-PWV. If the RMSE and MAE after deviation correction are within the acceptable range, continue the subsequent analysis, otherwise continue to repeat the correction

[0055] Step 3: Calculate the spatial distribution of PWV during each typhoon according to the ERA5 meteorological parameter data, then extract the longitude and latitude corresponding to the peak center of the typhoon parameter, and use the haversine formula to calculate the distance between the peak center of the typhoon parameter and the typhoon center. Finally, calculate the mean and variance of the typhoon parameter distance difference sequence, and use the discrete coefficient formula to calculate the discrete coefficient of the typhoon parameter during each typhoon.

[0056] The typhoon parameters include hourly PWV, air pressure, rainfall, wind speed, and temperature.

[0057] Specifically, in this embodiment, after verifying the accuracy and availability of ERA5-PWV within the study time range, the spatial distribution of PWV in the eastern coast of China (105°E~135°E, 10°N~40°N) during each typhoon from 2018 to 2022 was calculated according to the calculation formula of PWV.

[0058] Further, such as Figure 2 As shown, firstly, according to the meteorological parameter data and ERA5-PWV, the longitude and latitude corresponding to the peak center of hourly PWV, air pressure, rainfall, wind speed, and temperature during each typhoon period are extracted respectively. Combined with the typhoon center position at the corresponding epoch, the distance difference between the distribution center of each parameter and the typhoon center is calculated using the Haversine Formula. The Haversine Formula is a formula used to calculate the shortest distance (great circle distance) between two points on a sphere. It is often used in geographic information systems (GIS) and navigation to calculate the distance between two points on the earth. Its principle is based on spherical trigonometry. The calculation formula is as follows:

[0059]

[0060] In the formula, sin represents the sine function; cos represents the cosine function; a represents the difference between the latitudes of two places; b represents the difference between the longitudes of two places; Lat1 and Lat2 are the latitudes of the two places; R is the equatorial radius of the earth, which is 6371 kilometers in this embodiment.

[0061] Then calculate the coefficient of dispersion of the distance difference sequence between the peak distribution center of each parameter and the typhoon center. The coefficient of dispersion is the ratio of the standard deviation to the mean, a dimensionless unit that can directly compare data sets of different units or different orders of magnitude. At the same time, when comparing the degree of dispersion of different data sets, the variance can only reflect the absolute degree of dispersion, while the coefficient of dispersion can reflect the relative degree of dispersion, that is, the degree of dispersion of the data around the mean. In this way, even if the mean of the data set is different, an effective comparison can be made. The formula is as follows:

[0062]

[0063] In the formula, σ represents the standard deviation; represents the mean; M represents the number of data in the time series; cov represents the coefficient of dispersion. It can be understood that the smaller the value, the smaller the data fluctuation, that is, the better the performance of meteorological parameters in typhoon monitoring.

[0064] Step 4: Establish a multi-parameter linear weighted model based on the discrete coefficients of typhoon parameters.

[0065] Specifically, firstly, the weighting coefficient is extracted according to the calculated dispersion coefficient. Considering that the same parameter has different performances in different typhoon monitoring, the mean of the dispersion coefficient of each parameter is taken as the basis for calculating the weight. The smaller the dispersion coefficient, the better the performance of the parameter in monitoring typhoons, and the corresponding weight should be larger. Therefore, the weight of each parameter is determined according to the following formula:

[0066] Weight = 1-cov mean

[0067] In the formula, cov mean It represents the mean of the corresponding parameter dispersion coefficient; Weight represents the weight. The calculated Weight includes the weight coefficients corresponding to PWV, air pressure, rainfall, wind speed, and temperature.

[0068] Then, a linear weighted model was established with PWV as the key parameter, and the weighting coefficients were normalized to ensure that the sum of the weights of the parameters in the weighted model was 1. The model is as follows:

[0069]

[0070] Where yi represents the result of the combined model; φ, ɑ, β, and γ refer to the weight coefficients of the corresponding parameters. In this embodiment, PWV: 0.52, wind speed: 0.59, air pressure: 0.48, temperature: 0.47, and rainfall: 0.38; x i Represents variables: air pressure (hPa), rainfall (mm / h), wind speed (m / s) and temperature (℃).

[0071] Furthermore, for the convenience of comparison, the model output result range is transformed to [0,1] using the linear transformation formula. The linear transformation formula is as follows:

[0072]

[0073] In the formula, h represents the original output result of the model; min represents the minimum value of the original output result; max represents the maximum value of the original output result; and d represents the result after linear transformation. Finally, the peak position of the combined model result per hour is extracted, which is the predicted typhoon position.

[0074] In this embodiment, the average level (median) of the distance difference between the typhoon center predicted by the weighted model and the actual typhoon center is used as an indicator to reflect the reliability of the model. The median is robust and is not affected by extreme values. Therefore, it can more reliably represent the typical level of prediction error. The dispersion coefficient is the second indicator reflecting the overall fluctuation and stability of the prediction results. It takes into account the relative volatility of the prediction error and can better describe the consistency and stability of the model prediction results. A smaller dispersion coefficient indicates that the model prediction results fluctuate less and have better stability. The model input is the eight typhoons that landed on the eastern coast of China from 2018 to 2022 (Maria, Ampil, Mangkhut, Bailu, Lekima, Hagupit, In-Fa, Chaba).

[0075] The specific performance is as follows: Figure 3 It can be seen that the distribution of prediction deviation is represented by a box plot. The coefficient of dispersion is represented by an orange star line. It can be seen that the average level of the predicted distance deviation of the single PWV model is about 200km. After adding other meteorological parameters, both the distance deviation and the coefficient of dispersion have changed. For example, the combination of PWV and W is better than the single PWV model, the width of the box plot is significantly reduced, and from the results of the coefficient of dispersion, it can be seen that the coefficient of dispersion of the combined model of PWV and W has dropped from 0.53 to 0.47, all of which indicate that the overall prediction deviation of the combined model of PWV and W has decreased. Basically, except for the combined model of PWV and P2, the coefficients of dispersion of the other two-parameter models are better than the single PWV model.

[0076] pass Figure 4 It can be seen that, except for the combined model containing P2, the other combined models are better than the single PWV model. For example, the dispersion coefficient of PWV-P1-W is reduced to 0.47. This means that the performance of typhoon monitoring can be improved by combining the two meteorological parameters of temperature and air pressure compared with using only PWV.

[0077] pass Figure 5 It can be seen that the dispersion coefficient of the PWV-P1-WT combined model is reduced from 0.53 to 0.46, and the improvement rate is about 13.21% compared with the single PWV model. Among them, P1 represents air pressure, P2 represents rainfall, W represents wind speed, and T represents temperature.

[0078] Step 5: Using the established multi-parameter linear weighted model, input PWV, air pressure, rainfall, wind speed, and temperature within a certain time and space range; linearly transform the output result into the [0,1] interval, and extract the longitude and latitude corresponding to the maximum value of the result as the predicted typhoon position.

[0079] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions and variations of these embodiments including components are made without departing from the principles and spirit of the present invention, and still fall within the scope of protection of the present invention.

Claims

1. A typhoon monitoring method based on PWV and multi-parameter comprehensive weighting strategy, characterized in that: The steps include: Step 1, respectively establishing a typhoon database and a meteorological parameter data set in the same region and the same time region, wherein the typhoon database includes the typhoon occurrence time, center location and typhoon level, and the meteorological parameter data set includes air pressure, rainfall, wind speed, temperature and specific humidity; Step 2: Select the meteorological parameter data observed by RS at any n stations and the ERA5 meteorological parameter data of the corresponding time and space, calculate PWV respectively, and use the RS-PWV calculated by the meteorological parameter data observed by RS as the benchmark to verify the ERA5-PWV calculated by the ERA5 meteorological parameter data; Step 3: Calculate the spatial distribution of ERA5-PWV during each typhoon according to the ERA5 meteorological parameter data, then extract the longitude and latitude corresponding to the typhoon parameters, and use the haversine formula to calculate the distance between the peak center of the typhoon parameters and the typhoon center. Finally, calculate the mean and variance of the typhoon parameter distance difference sequence, and use the dispersion coefficient formula to calculate the dispersion coefficient of the typhoon parameters during each typhoon. The typhoon parameters include hourly ERA5-PWV, air pressure, rainfall, wind speed, and temperature; The distance between the peak center of the typhoon parameter and the typhoon center is calculated using the haversine formula as follows: In the formula, sin represents the sine function; cos represents the cosine function; a represents the difference between the latitudes of two places; b represents the difference between the longitudes of two places; Lat1 and Lat2 are the latitudes of the two places; R is the equatorial radius of the earth; The method for calculating the discrete coefficient of typhoon parameters during each typhoon period using the discrete coefficient formula is as follows: In the formula, σ represents the standard deviation; represents the mean; M represents the number of data in the time series; cov represents the coefficient of dispersion; Step 4: Establish a multi-parameter linear weighted model according to the discrete coefficient of the typhoon parameters. The multi-parameter linear weighted model is as follows: In the formula, y i represents the result of the combined model; φ, ɑ, β, γ refer to the weight coefficients of the corresponding parameters; x i Represents a variable; Step 5: Using the established multi-parameter linear weighted model, input ERA5-PWV, air pressure, rainfall, wind speed, and temperature within a certain time and space range; linearly transform the output result into the [0,1] interval, and extract the longitude and latitude corresponding to the maximum value of the result as the predicted typhoon position.

2. A typhoon monitoring method based on PWV and multi-parameter comprehensive weighting strategy according to claim 1, characterized in that: The data in the typhoon database are classified according to year and typhoon name, and saved in the local computer in the form of Excel tables.

3. A typhoon monitoring method based on PWV and multi-parameter comprehensive weighting strategy according to claim 1, characterized in that: The wind speed in the meteorological parameter data set exists in the form of a longitude component and a latitude component, and is calculated by a vector and a formula.

4. A typhoon monitoring method based on PWV and multi-parameter comprehensive weighting strategy according to claim 1, characterized in that: The temperature data in the meteorological parameter data set is in Kelvin and needs to be converted to Celsius.

5. The typhoon monitoring method based on PWV and multi-parameter comprehensive weighting strategy according to claim 1 is characterized in that: The ERA5 meteorological parameter data in the meteorological parameter data set is saved in the form of an nc file, and the RS observed meteorological parameter data is saved in the form of an Excel table.

6. The typhoon monitoring method based on PWV and multi-parameter comprehensive weighting strategy according to claim 1 is characterized in that: The PWV is calculated as follows: In the formula, g represents the acceleration due to gravity, and its unit is m / s 2 ; Ps represents the surface air pressure, in hPa; q is the specific humidity of the specified pressure layer, in g / kg; dp represents the pressure difference; n represents the number of layers; p i and p i-1 is the surface pressure of layer i and layer i-1; q i and q i-1 Represent the specific humidity of layer i and layer i-1 respectively.

7. A typhoon monitoring method based on PWV and multi-parameter comprehensive weighting strategy according to claim 6, characterized in that: The step 2 also includes a test of PWV, taking RS-PWV as a benchmark, and testing the obtained ERA5-PWV by using two criteria: root mean square error and mean absolute error.

8. A typhoon monitoring method based on PWV and multi-parameter comprehensive weighting strategy according to claim 7, characterized in that: If the ERA5-PWV test results do not meet the requirements, recalculate the RMSE and MAE year by year in different time periods to find out the abnormal years, and use the deviation correction formula to correct the error. The formula is as follows: ERA5 corrected,k =ERA5 k -Bias; Where Bias represents the average deviation, ERA5 k represents the value of the kth abnormal ERA5-PWV, RS k represents the value of RS-PWV at the corresponding moment, N represents the total number of abnormal data, ERA5 corrected,k Represents the corrected data. Recalculate RMSE and MAE for the corrected ERA5-PWV. If the RMSE and MAE after deviation correction are within the acceptable range, the subsequent analysis can be continued, otherwise the correction is repeated.

9. A typhoon monitoring method based on PWV and multi-parameter comprehensive weighting strategy according to claim 8, characterized in that: In step 5, the method of applying the multi-parameter linear weighted model to predict the typhoon path is: First, the weighting coefficient is extracted according to the discrete coefficient, and the mean of the discrete coefficient of each parameter is taken as the basis for calculating the weight: Weight=1-piece mean ; Then, the calculated weight coefficients and various parameter variables are input into the model, and the results are output; Finally, the linear transformation formula is used to transform the model output result range to [0,1]. The linear transformation formula is as follows: In the formula, h represents the original output result of the model; min represents the minimum value of the original output result; max represents the maximum value of the original output result; d represents the result after linear transformation. Finally, the peak position of the hourly combined model result is extracted, which is the predicted typhoon position.

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