PM2.5 forecasting method based on weather circulation and stability index

Abstract

The invention discloses a PM2.5 forecasting method based on weather circulation and a stability index, and relates to the technical field of air pollution prevention and control, and the method comprises the steps: carrying out the calculation according to the monitoring data of a target region, obtaining a sea level air pressure distance flat field of the target region, carrying out the clustering, and obtaining a weather type; calculating the circulation index value of each meteorological station, and determining the circulation type of the target area; calculating an atmospheric stability index of the target area, and determining a K index interval of the target area; according to the PM2.5 concentration time sequence of each monitoring point position in the target area, establishing a PM2.5 concentration historical data set by matching weather typing, circulation typing and a K index interval of corresponding time; and according to meteorological observation data of a future time period, calculating a weather type, a circulation type and a K index interval of the future time period, and searching a PM2.5 concentration historical data set to obtain a PM2.5 concentration prediction result. The prediction time can be shortened, and the interpretability of the prediction result can be improved.

Description

Technical Field

[0001] The present application relates to the field of air pollution prevention and control technology, in particular to a PM based on weather circulation and stability index. 2.5 Forecasting method. Background Art

[0002] PM 2.5 Pollution forecasting has always been a focus of urban air pollution prevention and control. Traditional numerical forecasting methods require high computing power, and the temporal and spatial resolution of forecast results is limited. In recent years, machine learning methods have been increasingly used in the field of atmospheric science. Although the forecast results of machine learning have improved to a certain extent compared with traditional numerical forecasts, their results lack physical meaning and are difficult to apply in business applications. Therefore, how to take the theoretical basis of atmospheric science as a starting point, combine long-term meteorological and pollutant concentration data sets, and achieve more accurate and simple PM through modeling? 2.5 Pollution forecasting deserves further study. Summary of the invention

[0003] The purpose of this application is to provide a PM based on weather circulation and stability index. 2.5 The forecasting method can shorten the forecasting time and improve the interpretability of the forecasting results.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In the first aspect, the present application provides a PM based on weather circulation and stability index. 2.5 Forecast method, the PM based on weather circulation and stability index 2.5 Forecasting methods include:

[0006] Obtain monitoring data of the target area and meteorological observation data for future periods; the monitoring data of the target area includes: gridded sea level pressure data of the target area, ground wind speed data of the target area meteorological station, vertical temperature and dew point temperature data of the target area, and PM at each monitoring point in the target area. 2.5 concentration time series; the gridded sea level pressure data of the target area is a three-dimensional matrix including longitude, latitude and time dimensions; the ground wind speed data of the meteorological stations in the target area are the hourly ground wind speed observation data of each meteorological station; the meteorological observation data for the future period include: the gridded sea level pressure data for the future period, the ground wind speed data of the meteorological stations for the future period, and the vertical temperature and dew point temperature data for the future period.

[0007] Keeping the time dimension unchanged, the average sea level pressure of the target area is subtracted from the gridded sea level pressure data of the target area to obtain the sea level pressure anomaly field of the target area.

[0008] The sea level pressure anomaly field of the target area is clustered to obtain a clustering result having the same length as the time dimension; the clustering result is a weather classification.

[0009] According to the ground wind speed data of the meteorological stations in the target area, the circulation index value (Recirculation Factor, RF) of each meteorological station is calculated.

[0010] According to the circulation index value of each meteorological station, the atmospheric circulation characteristics of the target area are determined; the atmospheric circulation characteristics are circulation types.

[0011] The atmospheric stability index of the target area is calculated according to the vertical temperature and dew point temperature data of the target area.

[0012] According to the atmospheric stability index of the target area, the atmospheric stability characteristics of the target area are determined; the atmospheric stability characteristics are K index intervals.

[0013] According to the PM of each monitoring point in the target area 2.5 The concentration time series is constructed by matching the weather type, circulation type and K index interval at the corresponding time. 2.5 Concentration history dataset.

[0014] According to the meteorological observation data for the future period, the weather type, circulation type and K index range of the future period are calculated.

[0015] According to the weather type, circulation type and K index range of the future period, find the PM 2.5 Concentration historical data set, get PM 2.5 Concentration prediction results.

[0016] Optionally, keeping the time dimension unchanged, subtracting the average sea level pressure of the target area from the gridded sea level pressure data of the target area to obtain the sea level pressure anomaly field of the target area, specifically including:

[0017] The ERA5 sea level pressure data is divided by time to obtain the hourly sea level pressure data.

[0018] Subtract the average value of all elements from each element in the hourly sea level pressure data of the target area to obtain the hourly sea level pressure anomaly data.

[0019] The sea level pressure anomaly data at all times are grouped together every 24 hours to calculate the average value and obtain the sea level pressure anomaly field of the target area.

[0020] Optionally, clustering the sea level pressure anomaly field of the target area to obtain a clustering result having the same length as the time dimension specifically includes:

[0021] The sea level pressure anomaly field of the target area is input into a SOM (Self-Organizing Map) neural network to obtain an identification matrix; the SOM neural network is composed of a group of two-dimensional matrices called nodes or neurons, each neuron is connected to all neurons in the input layer and has a weight vector with the same dimension as the input data; the identification matrix includes the clustering results at each moment.

[0022] Optionally, the calculation formula of the circulation index value is:

[0023]

[0024] Among them, RF is the circulation index value, u j is the meridional wind speed at the jth moment, v j is the zonal wind speed at the jth moment, and τ is the total time.

[0025] Optionally, determining the atmospheric circulation characteristics of the target area according to the circulation index value of each meteorological station specifically includes:

[0026] For each time dimension, the mean and standard deviation of the circulation index values ​​at each meteorological station are calculated.

[0027] When the average value of the circulation index is less than or equal to 0.2 and the standard deviation of the circulation index is less than or equal to 0.1, the atmospheric circulation characteristics are persistent circulation.

[0028] When the average value of the circulation index is greater than or equal to 0.3 and the standard deviation of the circulation index is less than or equal to 0.15, the atmospheric circulation is characterized by a rotational circulation.

[0029] When the standard deviation of the circulation index value is greater than 0.15, the atmospheric circulation characteristics are discrete circulation.

[0030] When the average value of the circulation index is greater than 0.2 and less than 0.3, and the standard deviation of the circulation index is greater than 0.1 and less than 0.15, the atmospheric circulation characteristics are mixed circulation.

[0031] Optionally, the calculation formula of the atmospheric stability index is:

[0032] K=(T 1000 -T 850 )+T d1000 -(T 925 -T d925 );

[0033] Where K is the atmospheric stability index, T 1000 is the temperature of the 1000 geopotential layer, T 850 is the temperature of the 850° geopotential layer, T d1000is the dew point temperature at 1000 geopotential level, T 925 is the dew point temperature of the 925 geopotential layer, T d925 It is the dew point temperature at the 925 geopotential layer.

[0034] Optionally, determining the atmospheric stability characteristics of the target area according to the atmospheric stability index of the target area specifically includes:

[0035] For each time dimension, the average value of the K index of each meteorological station is calculated.

[0036] When the average value of the K index is greater than -40 and less than or equal to -30, the K index interval is 1.

[0037] When the average value of the K index is greater than -30 and less than or equal to -20, the K index interval is 2.

[0038] When the average value of the K index is greater than -20 and less than or equal to -10, the K index interval is 3.

[0039] When the average value of the K index is greater than -10 and less than or equal to 0, the K index interval is 4.

[0040] When the average value of the K index is greater than 0 and less than or equal to 10, the K index interval is 5.

[0041] When the average value of the K index is greater than 10 and less than or equal to 20, the K index interval is 6.

[0042] When the average value of the K index is greater than 20 and less than or equal to 30, the K index interval is 7.

[0043] When the average value of the K index is greater than 30 and less than or equal to 40, the K index interval is 8.

[0044] When the average value of the K index is greater than 40 or less than or equal to -40, the K index interval is 9.

[0045] Optionally, according to the PM of each monitoring point in the target area 2.5 The concentration time series is constructed by matching the weather type, circulation type and K index interval at the corresponding time. 2.5 Concentration history data set, including:

[0046] According to the weather type, circulation type and K index range, the PM at each monitoring point is 2.5 PM corresponding to the concentration time series 2.5 The concentrations are stored in the matrix pm.

[0047] Take the matrix pm as PM 2.5 Concentration history dataset.

[0048] Optionally, according to the meteorological observation data for the future period, the weather type, circulation type and K index interval for the future period are calculated, specifically including:

[0049] Keeping the time dimension unchanged, the gridded sea level pressure data of the future period is subtracted from the average sea level pressure of the future period to obtain the sea level pressure anomaly field of the future period.

[0050] According to the sea level pressure anomaly field in the future period, the distance to each weather type is calculated respectively.

[0051] The weather type with the smallest distance is used as the weather type for the future period.

[0052] The circulation index value for the future period is calculated based on the ground wind speed data of the meteorological station for the future period.

[0053] The circulation type of the future period is determined according to the circulation index value of the future period.

[0054] The atmospheric stability index of the future period is calculated according to the vertical temperature and dew point temperature data of the future period.

[0055] According to the atmospheric stability index of the future period, a K index range of the future period is determined.

[0056] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0057] This application provides a PM based on weather circulation and stability index. 2.5 The forecasting method comprises the following steps: firstly, obtaining monitoring data of the target area and meteorological observation data for the future period; keeping the time dimension unchanged, subtracting the average sea level pressure of the target area from the gridded sea level pressure data of the target area to obtain the sea level pressure anomaly field of the target area; secondly, clustering the sea level pressure anomaly field of the target area to obtain a clustering result of the same length as the time dimension; the clustering result is a weather classification; according to the ground wind speed data of the meteorological station in the target area, calculating the circulation index value of each meteorological station; according to the circulation index value of each meteorological station, determining the atmospheric circulation characteristics of the target area; the atmospheric circulation characteristics are circulation classification; according to the vertical temperature and dew point temperature data of the target area, calculating the atmospheric stability index of the target area; according to the atmospheric stability index of the target area, determining the atmospheric stability characteristics of the target area; the atmospheric stability characteristics are K index intervals; then, according to the PM values ​​of each monitoring point in the target area, determining the atmospheric stability characteristics of the target area; the atmospheric stability characteristics of the target area are K index intervals. 2.5 The concentration time series is constructed by matching the weather type, circulation type and K index interval at the corresponding time. 2.5concentration historical data set; finally, based on the meteorological observation data for the future period, calculate the weather type, circulation type and K index range for the future period; based on the weather type, circulation type and K index range for the future period, find the PM 2.5 Concentration historical data set, get PM 2.5 Compared with numerical prediction, this application establishes PM 2.5 Concentration historical data set, using the “lookup table method” based on historical real data sets to predict PM in future periods 2.5 concentration, shortening the forecast time and greatly saving time and labor costs; compared with the machine learning forecast of PM based on historical observation data 2.5 Compared with the concentration method, this application is based on the theoretical basis of atmospheric science and outputs PM 2.5 In addition to the concentration prediction results, diagnostic variables such as weather circulation and atmospheric stability are also retained, which improves the interpretability of the prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0059] Figure 1 A PM based on weather circulation and stability index in one embodiment of the present application 2.5 Diagram of the application environment of the forecast method.

[0060] Figure 2 A PM based on weather circulation and stability index provided in an embodiment of the present application 2.5 Schematic diagram of the overall process of the forecasting method.

[0061] Figure 3 A PM based on weather circulation and stability index provided in an embodiment of the present application 2.5 Schematic diagram of the forecasting method. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0063] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0064] PM based on weather circulation and stability index provided in the embodiment of the present application 2.5 The forecasting method can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the acquired monitoring data of the target area and the meteorological observation data for the future period to the server 104. The monitoring data of the target area includes: gridded sea level pressure data of the target area, ground wind speed data of the meteorological station in the target area, vertical temperature and dew point temperature data of the target area, and PM at each monitoring point in the target area. 2.5 concentration time series; the gridded sea level pressure data of the target area is a three-dimensional matrix including longitude, latitude and time dimensions; the ground wind speed data of the meteorological station in the target area is the hourly ground wind speed observation data of each meteorological station; the meteorological observation data for the future period includes: gridded sea level pressure data for the future period, ground wind speed data of the meteorological station for the future period, and vertical temperature and dew point temperature data for the future period; after the server 104 receives the monitoring data of the target area and the meteorological observation data for the future period, for the monitoring data and the meteorological observation data for the future period, the server 104 keeps the time dimension unchanged and subtracts the gridded sea level pressure data of the target area from the sea level pressure data of the target area The mean is used to obtain the sea level pressure anomaly field of the target area; the sea level pressure anomaly field of the target area is clustered to obtain a clustering result of the same length as the time dimension; the clustering result is a weather classification; the circulation index value of each meteorological station is calculated according to the ground wind speed data of the meteorological station in the target area; the atmospheric circulation characteristics of the target area are determined according to the circulation index value of each meteorological station; the atmospheric circulation characteristics are circulation classification; the atmospheric stability index of the target area is calculated according to the vertical temperature and dew point temperature data of the target area; the atmospheric stability characteristics of the target area are determined according to the atmospheric stability index of the target area; the atmospheric stability characteristics are K index intervals; according to the PM values ​​of each monitoring point in the target area 2.5 The concentration time series is constructed by matching the weather type, circulation type and K index interval at the corresponding time. 2.5 concentration historical data set; calculate the weather type, circulation type and K index range of the future period according to the meteorological observation data of the future period; find the PM 2.5Concentration historical data set, get PM 2.5 The server 104 can obtain the PM 2.5 The concentration prediction result is fed back to the terminal 102. In addition, in some embodiments, PM based on weather circulation and stability index 2.5 The forecasting method can also be implemented by the server 104 or the terminal 102 alone. For example, the terminal 102 can directly perform PM forecasting on the monitoring data of the target area and the meteorological observation data of the future period. 2.5 The server 104 may also obtain the monitoring data of the target area and the meteorological observation data of the future period from the data storage system, and perform PM on the monitoring data of the target area and the meteorological observation data of the future period. 2.5 forecast.

[0065] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, and tablet computers. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0066] In an exemplary embodiment, Figure 2 and Figure 3 As shown, a PM based on weather circulation and stability index is provided. 2.5 The forecast method is executed by a computer device, which can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps S1 to S10. Among them:

[0067] S1: Obtain the monitoring data of the target area and the meteorological observation data for the future period; the monitoring data of the target area includes: gridded sea level pressure data of the target area, ground wind speed data of the meteorological station in the target area, vertical temperature and dew point temperature data of the target area, and PM at each monitoring point in the target area. 2.5 concentration time series; the gridded sea level pressure data of the target area is a three-dimensional matrix including longitude, latitude and time dimensions; the ground wind speed data of the meteorological stations in the target area are the hourly ground wind speed observation data of each meteorological station; the meteorological observation data for the future period include: the gridded sea level pressure data for the future period, the ground wind speed data of the meteorological stations for the future period, and the vertical temperature and dew point temperature data for the future period.

[0068] S2: Keeping the time dimension unchanged, subtract the average sea level pressure of the target area from the gridded sea level pressure data of the target area to obtain the sea level pressure anomaly field of the target area.

[0069] S3: Clustering the sea level pressure anomaly field of the target area to obtain a clustering result of the same length as the time dimension; the clustering result is a weather classification.

[0070] S4: Calculate the circulation index value of each meteorological station according to the ground wind speed data of the meteorological station in the target area.

[0071] S5: Determine the atmospheric circulation characteristics of the target area according to the circulation index values ​​of each meteorological station; the atmospheric circulation characteristics are circulation types.

[0072] S6: Calculate the atmospheric stability index of the target area according to the vertical temperature and dew point temperature data of the target area.

[0073] S7: Determine the atmospheric stability characteristics of the target area according to the atmospheric stability index of the target area; the atmospheric stability characteristics are K index intervals.

[0074] S8: Based on the PM of each monitoring point in the target area 2.5 The concentration time series is constructed by matching the weather type, circulation type and K index interval at the corresponding time. 2.5 Concentration history dataset.

[0075] S9: Calculate the weather type, circulation type and K index range of the future period according to the meteorological observation data of the future period.

[0076] S10: Find the PM according to the weather type, circulation type and K index range of the future period 2.5 Concentration historical data set, get PM 2.5 Concentration prediction results.

[0077] Implementing the above steps S1 to S10, compared with numerical forecasting, this application establishes PM 2.5 Concentration historical data set, using the “lookup table method” based on historical real data sets to predict PM in future periods 2.5 concentration, shortening the forecast time and greatly saving time and labor costs; compared with the machine learning forecast of PM based on historical observation data 2.5 Compared with the concentration method, this application is based on the theoretical basis of atmospheric science and outputs PM 2.5 In addition to the concentration prediction results, diagnostic variables such as weather circulation and atmospheric stability are also retained, which improves the interpretability of the prediction results.

[0078] The following is a description of PM based on weather circulation and stability index. 2.5 The forecast method uses ERA5 reanalysis data, observation data of each meteorological station in the target area, and historical PM values ​​of each monitoring point. 2.5Data to predict future PM 2.5 Example of concentration.

[0079] In an exemplary embodiment, in step S2, it specifically includes:

[0080] S21: Divide the ERA5 sea level pressure data by time to obtain the sea level pressure data for each hour.

[0081] S22: Subtract the average value of all elements from each element in the sea level pressure data of each hour in the target area to obtain the sea level pressure anomaly data of each hour.

[0082] S23 calculates the average value of the sea level pressure anomaly data at all times every 24 hours to obtain the sea level pressure anomaly field of the target area.

[0083] Specifically, the ERA5 sea level pressure data is first divided by time, and the sea level pressure data P of the target area (M×N longitude and latitude grid) at the i-th hour is i Each element in (M×N matrix) is subtracted from the mean value p of all its elements i , get the sea level pressure anomaly data D for the ith hour i , calculate the average value of the sea level pressure anomaly data at all times every 24 hours, and reorganize it into an M×N×X sea level pressure anomaly field D1…D j …D X .

[0084] In an exemplary embodiment, in step S3, it specifically includes:

[0085] The sea level pressure anomaly field of the target area is input into the SOM neural network to obtain an identification matrix; the SOM neural network is composed of a group of two-dimensional matrices called nodes or neurons, each neuron is connected to all neurons in the input layer and has a weight vector with the same dimension as the input data; the identification matrix includes the clustering results at each moment.

[0086] Specifically, the SOM neural network is usually composed of a set of two-dimensional matrices called nodes or neurons. Each neuron is connected to all neurons in the input layer and has a weight vector with the same dimension as the input data. The training principle is to continuously adjust these weight vectors so that the characteristics of the input data can be mapped and characterized. The SOM neural network uses an unsupervised training method of competitive learning to map high-dimensional input data to a common two-dimensional topological structure, and in the process maintains the topological relationship of the original data. It is very suitable for clustering and visualization of high-dimensional data, and the sea level pressure anomaly field is a three-dimensional matrix of M×N×X.

[0087] In this embodiment, a 3×3 SOM neural network is used, the input is the M×N×X sea level pressure anomaly field, and the output is an X×1 identification matrix C, which represents the clustering result at each moment, with a total of 9 categories. The average value of the sea level pressure anomaly field belonging to the same category is calculated to obtain 9 M×N matrices NODE1, ..., NODE9, which are called weather types. For example, for moment e, there are:

[0088] NODE1: C(e)=1; NODE2: C(e)=2; NODE3: C(e)=3;

[0089] NODE4: C(e)=4; NODE5: C(e)=5; NODE6: C(e)=6;

[0090] NODE7: C(e)=7; NODE8: C(e)=8; NODE9: C(e)=9.

[0091] This embodiment classifies atmospheric circulations of different scales according to the local circulation index of different meteorological stations. Based on the ratio of air vector displacement and scalar displacement, the specific calculation formula of the circulation index value is as follows:

[0092]

[0093] Among them, RF is the circulation index value, u j is the meridional wind speed at the jth moment, v j is the zonal wind speed at the jth moment, and τ is the total time.

[0094] In an exemplary embodiment, in step S5, it specifically includes:

[0095] S51: For each time dimension, calculate the mean and standard deviation of the circulation index value of each meteorological station.

[0096] S52: When the average value of the circulation index is less than or equal to 0.2, and the standard deviation of the circulation index is less than or equal to 0.1, the atmospheric circulation characteristics are persistent circulation.

[0097] S53: When the average value of the circulation index is greater than or equal to 0.3 and the standard deviation of the circulation index is less than or equal to 0.15, the atmospheric circulation is characterized by a rotational circulation.

[0098] S54: When the standard deviation of the circulation index value is greater than 0.15, the atmospheric circulation characteristics are discrete circulation.

[0099] S55: When the average value of the circulation index is greater than 0.2 and less than 0.3, and the standard deviation of the circulation index is greater than 0.1 and less than 0.15, the atmospheric circulation characteristics are mixed circulation.

[0100] In this embodiment, τ is set to 24 hours. The calculation result RF represents the recirculation degree of the wind field at the meteorological station, and its value range is [0, 1]. A low RF value represents a lower recirculation degree, that is, a more ventilated condition; on the contrary, it represents an unfavorable ventilation condition. The wind speed data of I meteorological stations in the target area are calculated to obtain I time series RF1,..., RF of X×1. I For each time dimension, calculate the average value RF_avg and the standard deviation RF_std of RF of I meteorological stations, and classify them according to the following criteria:

[0101] RF_avg ≤ 0.2, RF_std ≤ 0.1: Continuous circulation;

[0102] RF_avg ≥ 0.3, RF_std ≤ 0.15: Rotating circulation;

[0103] RF_std > 0.15: Discrete circulation;

[0104] 0.2 < RF_avg < 0.3, 0.1 < RF_std < 0.15: Mixed circulation.

[0105] Thus, an identification matrix RF_type of X×1 is obtained, representing the classification result at each moment, which is called circulation classification, with a total of 4 categories.

[0106] The K index is an instability index considering the water vapor conditions in the air layer. As an empirical index, it reflects both the atmospheric stratification stability and the water vapor conditions in the middle and lower layers, with the unit of °C. In this embodiment, the expression of the atmospheric stability index is as follows:

[0107] K = (T 1000 - T 850 ) + T d1000 - (T 925 - T d925 ) (2);

[0108] Among them, K is the atmospheric stability index, T 1000 is the temperature at the 1000 geopotential height layer, T 850 is the temperature at the 850 geopotential height layer, T d1000 is the dew point temperature at the 1000 geopotential height layer, T 925 is the dew point temperature at the 925 geopotential height layer, T d925 is the dew point temperature at the 925 geopotential height layer.

[0109] It should be noted that the unit of the corresponding geopotential height layer is hPa.

[0110] In an exemplary embodiment, in step S7, it specifically includes:

[0111] S71: For each time dimension, calculate the average value of the K index of each meteorological station.

[0112] S72: When the average value of the K index is greater than -40 and less than or equal to -30, the K index interval is 1.

[0113] S73: When the average value of the K index is greater than -30 and less than or equal to -20, the K index interval is 2.

[0114] S74: When the average value of the K index is greater than -20 and less than or equal to -10, the K index interval is 3.

[0115] S75: When the average value of the K index is greater than -10 and less than or equal to 0, the K index interval is 4.

[0116] S76: When the average value of the K index is greater than 0 and less than or equal to 10, the K index interval is 5.

[0117] S77: When the average value of the K index is greater than 10 and less than or equal to 20, the K index interval is 6.

[0118] S78: When the average value of the K index is greater than 20 and less than or equal to 30, the K index interval is 7.

[0119] S79: When the average value of the K index is greater than 30 and less than or equal to 40, the K index interval is 8.

[0120] S710: When the average value of the K index is greater than 40 or less than or equal to -40, the K index interval is 9.

[0121] Specifically, the K index of I meteorological stations in the target area is calculated according to Formula 2, and I X×1 time series K1,…,K I For each time dimension, the K index average value K_avg (X×1 matrix) of I meteorological stations is calculated. This embodiment classifies according to the following standards:

[0122] -40 <K_avg≤-30,K_type=1;

[0123] -30 <K_avg≤-20,K_type=2;

[0124] -20 <K_avg≤-10,K_type=3;

[0125] -10 <K_avg≤0,K_type=4;

[0126] 0 <K_avg≤10,K_type=5;

[0127] 10 <K_avg≤20,K_type=6;

[0128] 20 <K_avg≤30,K_type=7;

[0129] 30 <K_avg≤40,K_type=8;

[0130] K_avg>40 or K_avg≤-40, K_type=9.

[0131] Thus, an X×1 identification matrix K_type is obtained, which represents the range of K index values ​​at each moment, called K index interval, with a total of 9 categories.

[0132] In an exemplary embodiment, in step S8, it specifically includes:

[0133] S81: According to the weather type, circulation type and K index range, the PM at each monitoring point is calculated. 2.5 PM corresponding to the concentration time series 2.5 The concentrations are stored in the matrix pm.

[0134] S82: Take the matrix pm as PM 2.5 Concentration history dataset.

[0135] Specifically, the PM2.5 concentration time series (X×R matrix) of the R monitoring points in the target area are averaged into the X×1 PM2.5 concentration time series CONC. According to the classification results of the identification matrix C, RF_type, and K_type, the PM2.5 concentration at the time corresponding to CONC is stored in the 9×4×9×X matrix pm. The matrix pm is called the PM2.5 concentration historical data set.

[0136] In an exemplary embodiment, in step S8, it specifically includes:

[0137] S81: Keeping the time dimension unchanged, subtracting the average sea level pressure of the future period from the gridded sea level pressure data of the future period to obtain the sea level pressure anomaly field of the future period.

[0138] S82: Calculate the distance to each weather type according to the sea level pressure anomaly field in the future period.

[0139] S83: The weather type with the smallest distance is used as the weather type for the future period.

[0140] S84: Calculate the circulation index value for the future period according to the ground wind speed data of the meteorological station for the future period.

[0141] S85: Determine the circulation type of the future time period according to the circulation index value of the future time period.

[0142] S86: Calculate the atmospheric stability index for the future period according to the vertical temperature and dew point temperature data for the future period.

[0143] S87: Determine a K index range for the future period according to the atmospheric stability index for the future period.

[0144] Specifically, for the future period of sea level pressure anomaly field SLP1,…,SLP t (M×N×t matrix, t is in days), calculate the sea level pressure anomaly field SLP on the kth day k Distance d from weather type NODE1, ..., NODE9 1k , …, d 9k , take the NODE type corresponding to the smallest distance as the weather type for day k. For example, for day k, min{d 1k , …, d 9k} = d 3k , then the weather type of the kth day is NODE3, which is stored in the matrix node and recorded as node(k) = 3. Thus, the weather type node (t×1 matrix) of the target area in the future period is obtained.

[0145] According to the above method of using the circulation index to extract the atmospheric circulation characteristics of the target area, the identification matrix rf_type (t×1 matrix) of the future period is calculated.

[0146] According to the above method of using the K index to extract the atmospheric stability characteristics of the target area, the identification matrix k_type (t×1 matrix) of the future period is calculated.

[0147] In step S10, it specifically includes:

[0148] According to the identification matrix node, rf_type, k_type of the future time period, the corresponding search is performed in the established PM2.5 concentration historical data set pm. For example, the weather type of the kth day is node(k), the circulation type is rf_type(k), and the K index interval is k_type(k). Then the PM2.5 concentration sequence corresponding to node(k), rf_type(k), and k_type(k) in the PM2.5 concentration historical data set pm matrix is ​​Y k , take Y k the median y k is the predicted PM2.5 concentration of the target area on the kth day. Thus, the predicted PM2.5 concentration results y1,…,y1 for the target area in the future period are obtained. t Returns the user.

[0149] The present application also provides an application scenario, which applies the PM based on weather circulation and stability index mentioned above.2.5 Forecast method. Specifically: PM based on weather circulation and stability index provided in this embodiment 2.5 Forecasting methods can be applied to PM 2.5 In the forecast scenario. PM 2.5 Forecast scenarios include: data acquisition, PM 2.5 The establishment and prediction of historical concentration data sets: First, obtain the monitoring data of the target area and the meteorological observation data for the future period; the monitoring data of the target area include: gridded sea level pressure data of the target area, ground wind speed data of the meteorological station in the target area, vertical temperature and dew point temperature data of the target area, and PM at each monitoring point in the target area. 2.5 concentration time series; the gridded sea level pressure data of the target area is a three-dimensional matrix containing longitude, latitude and time dimensions; the ground wind speed data of the meteorological station in the target area is the hourly ground wind speed observation data of each meteorological station; the meteorological observation data for the future period includes: the gridded sea level pressure data for the future period, the ground wind speed data of the meteorological station for the future period, and the vertical temperature and dew point temperature data for the future period; secondly, keeping the time dimension unchanged, the gridded sea level pressure data of the target area is subtracted from the average sea level pressure of the target area to obtain the sea level pressure anomaly field of the target area; the sea level of the target area is The surface pressure anomaly field is clustered to obtain a clustering result of the same length as the time dimension; the clustering result is a weather classification; according to the ground wind speed data of the meteorological station in the target area, the circulation index value of each meteorological station is calculated; according to the circulation index value of each meteorological station, the atmospheric circulation characteristics of the target area are determined; the atmospheric circulation characteristics are circulation classification; according to the vertical temperature and dew point temperature data of the target area, the atmospheric stability index of the target area is calculated; according to the atmospheric stability index of the target area, the atmospheric stability characteristics of the target area are determined; the atmospheric stability characteristics are K index intervals; according to the PM values ​​of each monitoring point in the target area 2.5 The concentration time series is constructed by matching the weather type, circulation type and K index interval at the corresponding time. 2.5 concentration historical data set; then, according to the meteorological observation data of the future period, calculate the weather type, circulation type and K index interval of the future period; according to the weather type, circulation type and K index interval of the future period, find the PM 2.5 Concentration historical data set, you can get PM 2.5 Concentration prediction results.

[0150] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0151] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A PM2.5 based on weather circulation and stability index 2.5 The forecasting method is characterized in that The PM based on the weather circulation and stability index 2.5 Forecasting methods include: Obtain monitoring data of the target area and meteorological observation data for future periods; the monitoring data of the target area includes: gridded sea level pressure data of the target area, ground wind speed data of the target area meteorological station, vertical temperature and dew point temperature data of the target area, and PM at each monitoring point in the target area. 2.5 concentration time series; the gridded sea level pressure data of the target area is a three-dimensional matrix including longitude, latitude and time dimensions; the ground wind speed data of the meteorological station in the target area is the hourly ground wind speed observation data of each meteorological station; the meteorological observation data for the future period includes: gridded sea level pressure data for the future period, ground wind speed data of the meteorological station for the future period, and vertical temperature and dew point temperature data for the future period; Keeping the time dimension unchanged, subtracting the average sea level pressure of the target area from the gridded sea level pressure data of the target area to obtain the sea level pressure anomaly field of the target area; Clustering the sea level pressure anomaly field of the target area to obtain a clustering result of the same length as the time dimension; the clustering result is a weather classification; Calculate the circulation index value of each meteorological station according to the ground wind speed data of the meteorological station in the target area; Determine the atmospheric circulation characteristics of the target area according to the circulation index values ​​of each meteorological station; the atmospheric circulation characteristics are circulation types; Calculating the atmospheric stability index of the target area according to the vertical temperature and dew point temperature data of the target area; Determining the atmospheric stability characteristics of the target area according to the atmospheric stability index of the target area; the atmospheric stability characteristics are K index intervals; According to the PM of each monitoring point in the target area 2.5 The concentration time series is constructed by matching the weather type, circulation type and K index interval at the corresponding time. 2.5 Concentration history dataset; Calculate the weather type, circulation type and K index range of the future period according to the meteorological observation data of the future period; According to the weather type, circulation type and K index range of the future period, find the PM 2.5 Concentration historical data set, get PM 2.5 Concentration prediction results.

2. PM based on weather circulation and stability index according to claim 1 2.5 The forecasting method is characterized in that Keeping the time dimension unchanged, the gridded sea level pressure data of the target area is subtracted from the average sea level pressure of the target area to obtain the sea level pressure anomaly field of the target area, specifically including: Divide the ERA5 sea level pressure data by time to obtain the hourly sea level pressure data; Subtract the average value of all elements from each element in the sea level pressure data of each hour in the target area to obtain the sea level pressure anomaly data of each hour; The sea level pressure anomaly data at all times are grouped together every 24 hours to calculate the average value and obtain the sea level pressure anomaly field of the target area.

3. PM based on weather circulation and stability index according to claim 1 2.5 The forecasting method is characterized in that Cluster the sea level pressure anomaly field of the target area to obtain a clustering result of the same length as the time dimension, including: The sea level pressure anomaly field of the target area is input into the SOM neural network to obtain an identification matrix; the SOM neural network is composed of a group of two-dimensional matrices called nodes or neurons, each neuron is connected to all neurons in the input layer and has a weight vector with the same dimension as the input data; the identification matrix includes the clustering results at each moment.

4. PM based on weather circulation and stability index according to claim 1 2.5 The forecasting method is characterized in that The calculation formula of the circulation index value is: Among them, RF is the circulation index value, u j is the meridional wind speed at the jth moment, v j is the zonal wind speed at the jth moment, and τ is the total time.

5. PM based on weather circulation and stability index according to claim 1 2.5 The forecasting method is characterized in that According to the circulation index values ​​of each meteorological station, the atmospheric circulation characteristics of the target area are determined, including: For each time dimension, the mean and standard deviation of the circulation index values ​​at each meteorological station are calculated; When the average value of the circulation index is less than or equal to 0.2, and the standard deviation of the circulation index is less than or equal to 0.1, the atmospheric circulation is characterized by a persistent circulation; When the average value of the circulation index is greater than or equal to 0.3, and the standard deviation of the circulation index is less than or equal to 0.15, the atmospheric circulation is characterized by a rotational circulation; When the standard deviation of the circulation index value is greater than 0.15, the atmospheric circulation is characterized by a discrete circulation; When the average value of the circulation index is greater than 0.2 and less than 0.3, and the standard deviation of the circulation index is greater than 0.1 and less than 0.15, the atmospheric circulation characteristics are mixed circulation.

6. PM based on weather circulation and stability index according to claim 1 2.5 The forecasting method is characterized in that The calculation formula of the atmospheric stability index is: K=(T 1000 -T 850 )+T d1000 -(T 925 -T d925 ); Where K is the atmospheric stability index, T 1000 is the temperature of the 1000 geopotential layer, T 850 is the temperature of the 850° geopotential layer, T d1000 is the dew point temperature at 1000 geopotential level, T 925 is the dew point temperature of the 925 geopotential layer, T d925 It is the dew point temperature at the 925 geopotential layer.

7. PM based on weather circulation and stability index according to claim 1 2.5 The forecasting method is characterized in that Determining the atmospheric stability characteristics of the target area according to the atmospheric stability index of the target area specifically includes: For each time dimension, the average value of the K index of each meteorological station is calculated; When the average value of the K index is greater than -40 and less than or equal to -30, the K index interval is 1; When the average value of K index is greater than -30 and less than or equal to -20, the K index interval is 2; When the average value of the K index is greater than -20 and less than or equal to -10, the K index interval is 3; When the average value of the K index is greater than -10 and less than or equal to 0, the K index interval is 4; When the average value of the K index is greater than 0 and less than or equal to 10, the K index interval is 5; When the average value of the K index is greater than 10 and less than or equal to 20, the K index interval is 6; When the average value of the K index is greater than 20 and less than or equal to 30, the K index interval is 7; When the average value of the K index is greater than 30 and less than or equal to 40, the K index interval is 8; When the average value of the K index is greater than 40 or less than or equal to -40, the K index interval is 9.

8. The PM based on weather circulation and stability index according to claim 1 2.5 The forecasting method is characterized in that According to the PM of each monitoring point in the target area 2.5 The concentration time series is constructed by matching the weather type, circulation type and K index interval at the corresponding time. 2.5 Concentration history data set, including: According to the weather type, circulation type and K index range, the PM at each monitoring point is 2.5 PM corresponding to the concentration time series 2.5 The concentration is stored in the matrix pm; Take the matrix pm as PM 2.5 Concentration history dataset.

9. The PM based on weather circulation and stability index according to claim 1 2.5 The forecasting method is characterized in that According to the meteorological observation data in the future period, the weather type, circulation type and K index range of the future period are calculated, including: Keeping the time dimension unchanged, subtracting the average sea level pressure of the future period from the gridded sea level pressure data of the future period to obtain the sea level pressure anomaly field of the future period; According to the sea level pressure anomaly field in the future period, the distance to each weather type is calculated respectively; The weather type with the smallest distance is used as the weather type for the future period; Calculating the circulation index value for the future period according to the ground wind speed data of the meteorological station for the future period; Determining the circulation type of the future period according to the circulation index value of the future period; Calculating the atmospheric stability index for the future period based on the vertical temperature and dew point temperature data for the future period; According to the atmospheric stability index of the future period, a K index range of the future period is determined.

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