A method for detecting the intensity of ground cold air

Abstract

The application relates to the technical field of weather forecasting, in particular to a detection method for the intensity of ground cold air, which comprises the following steps: S10, selecting a data type to be detected for the intensity of cold air; S20, extracting physical quantities of a region to be detected at a first moment and a second moment in the data type, and obtaining a difference value of the physical quantities between the first moment and the second moment, wherein the physical quantities comprise sea level pressure P, ground temperature T, ground dew point td and horizontal wind speed |V|; S30, taking the data obtained in S20 as variables, and calculating a density variation flux value DVF of the region to be detected between the first moment and the second moment through a set formula; and S40, determining the intensity of ground cold air based on the density variation flux value DVF. According to the positive and negative and specific numerical value of the density variation flux, whether the cold air invades a certain place and the invasion intensity can be quickly and quantitatively judged.

Description

Technical Field

[0001] This invention belongs to the field of weather forecasting technology, and in particular relates to a method for detecting the intensity of cold air at ground level. Background Technology

[0002] In meteorological forecasting, detecting (or diagnosing) the intensity of surface cold air is a crucial step in accurate weather forecasting. The traditional method uses 24-hour surface pressure changes to detect the strength of surface cold air; generally, the stronger the cold air, the greater the positive pressure change. However, pressure changes only reflect one characteristic of cold air and cannot reflect its comprehensive properties, such as temperature, humidity, and mobility. Furthermore, weather systems such as high-pressure circulation and tropical cyclones can also cause localized positive pressure changes, which can be easily confused with those caused by cold air, interfering with meteorological analysis. Summary of the Invention

[0003] The purpose of this invention is to provide a method for detecting the intensity of cold air on the ground, so as to improve the sensitivity of cold air intensity detection.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A method for detecting the intensity of cold air at ground level is provided, comprising the following steps:

[0006] S10, Select the data type for cold air intensity detection;

[0007] S20, extract the physical quantities of the area to be detected at the first and second times in the data type, and obtain the difference between the physical quantities at the first and second times. The physical quantities include sea level pressure P, surface temperature T, surface dew point td, and horizontal wind speed |V|.

[0008] S30, using the data obtained in S20 as variables, calculates the density change flux (DVF) value of the region to be detected between the first and second time points using a set formula;

[0009] S40, determine the intensity of cold air at ground level based on the density change flux (DVF) value.

[0010] The setting formula described in S30 is:

[0011] A=287.1 =P2-P1, =T2-T1, =td2-td1, where P2 is the sea level pressure at the second moment, P1 is the sea level pressure at the first moment, T2 is the surface temperature at the second moment, T1 is the surface temperature at the first moment, td2 is the surface dew point at the second moment, and td1 is the surface dew point at the first moment. In the formula, T, P, td, and |V| use the corresponding values ​​at the second moment, and |V| represents the magnitude of the horizontal wind speed vector V.

[0012] In step S20, missing data in the data type is represented by outliers that are not included in the calculation.

[0013] The collected data may contain missing variables. If these variables are included in the calculation, it may lead to errors or false results in the settlement. Therefore, in this solution, by representing these variables as outliers and excluding them from the calculation, the reliability of the calculation results can be improved.

[0014] In step S40, the process of determining the intensity of cold air at ground level based on the density change flux value (DVF) includes: visually representing the density change flux value as a contour map or a color-coded map. Further, in the contour map or color-coded map, the larger the density change flux value (DVF), the greater the color intensity.

[0015] In this scheme, by drawing a two-dimensional analysis diagram, the spatiotemporal distribution of cold air intensity can be displayed intuitively and clearly, making it highly demonstrative.

[0016] Compared with existing technologies, this invention can quantitatively determine the intensity of surface cold air invading a certain area and improve the sensitivity of surface cold air detection. This method is applicable to meteorological forecasting operations analysis and meteorological scientific research.

[0017] Taking 24-hour weather changes as an example, by utilizing physical quantities such as sea-level pressure, temperature, dew point, horizontal wind speed, pressure variation, and temperature variation from ground-based observations, and substituting them into a formula, the density change flux over the past 24 hours can be calculated. Based on the sign and magnitude of this density change flux, it is possible to quickly and quantitatively determine whether cold air has invaded a certain area and its intensity. Because the derived formula involves multiple independent variables such as air pressure, temperature, and horizontal wind speed, this method can not only characterize the intensity of cold air but also reflect its comprehensive properties.

[0018] Furthermore, in actual operational forecasting, forecasters use 24-hour surface pressure variations to determine whether a location is affected by cold air, with positive pressure variations indicating an impact and negative pressure variations indicating no impact. This method cannot eliminate pressure changes caused by certain weather systems, easily leading to false alarms and missed warnings about cold air activity. The method proposed in this invention can effectively avoid the above problems and has higher sensitivity in detecting cold air.

[0019] Other advantages of this invention are described in the embodiments section. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a method for detecting the intensity of cold air on the ground, as described in an embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram of flux due to density change.

[0023] Figure 3a , Figure 3b , Figure 3c These are, respectively, a 24-hour density change flux diagram, a 24-hour pressure change diagram, and a 24-hour temperature change diagram calculated using ground-based real-time data as of 08:00 on July 12, 2023. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0025] The specific implementation of the present invention will be described in detail below with reference to specific embodiments:

[0026] Please see Figure 1 This embodiment provides a method for detecting the intensity of cold air at ground level, including the following steps:

[0027] S10, Select the data type for cold air intensity detection.

[0028] The data can be actual ground observation data or numerical model forecast product data. Either data type can be selected, including but not limited to actual ground observation data (ground reports issued by the China Meteorological Administration), European Centre for Medium-Range Grid Model product data, and China Meteorological Administration CMA-GFS or CMA-MESO product data.

[0029] S20, extract the sea level pressure P, surface temperature T, surface dew point td, and horizontal wind speed |V| of the area to be detected at the first and second time points from the data type, and obtain the sea level pressure difference between the first and second time points. Surface temperature difference Ground dew point difference .

[0030] If the data type already contains the differences between the physical quantities at two different times, then the values ​​can be extracted and applied directly. Otherwise, the values ​​of the same physical quantity at the two different times need to be subtracted to obtain the corresponding difference.

[0031] Sometimes, ground message data or model forecast product data may contain missing values. In this case, missing values ​​can be represented by abnormally large or small values ​​such as 9999 or -9999. When processing, these values ​​should be set as missing_value (default value) and not calculated to avoid false results.

[0032] S30 uses the data obtained in S20 as a variable to calculate the density change flux (DVF) value of the region to be detected between the first and second time points using a set formula.

[0033] The data obtained in S20 specifically refers to the sea level pressure P, surface temperature T, surface dew point td, horizontal wind speed |V| at the first and second moments, as well as the corresponding differences.

[0034] In this step, the formula is set as follows: A=287.1 =P2-P1, =T2-T1, =td2-td1, where P2 is the sea level pressure at the second moment, P1 is the sea level pressure at the first moment, T2 is the surface air temperature at the second moment, T1 is the surface air temperature at the first moment, td2 is the surface dew point at the second moment, and td1 is the surface dew point at the first moment. In the formula, T, P, td, and V use the corresponding values ​​at the second moment, i.e., T=T2, P=P2, td=td2, V is the horizontal wind speed at the second moment, and |V| represents the magnitude of the horizontal wind speed vector V; the vector is converted to a scalar.

[0035] It is important to note that the unit for sea level pressure in the formula is Pa, the unit for surface temperature is K, the unit for surface dew point is °C, and the unit for horizontal wind speed is m / s. If the data obtained in step S2 is not in the corresponding units, unit conversion is required before substituting it into the formula.

[0036] Density change flux (DVF), specifically referring to horizontal density change flux, is defined as the amount of density change transported per unit cross-sectional area orthogonal to the airflow direction per unit time. Its direction is the same as the wind direction. Figure 2 As shown. The formula for calculating the flux value of density change in this embodiment is derived through the following process:

[0037] First, construct the formula based on the definition.

[0038] Figure 2 The ABCD plane is a plane orthogonal to the wind direction. The density change per unit time flowing through the ABCD plane... for

[0039] (1)

[0040] in This represents the change in air density. Indicates the magnitude of ground wind speed. The cross-sectional area is given.

[0041] Secondly, the independent variable of the formula is converted into a measurable variable.

[0042] Because the independent variable in equation (1) Since the independent variable of the density change formula is not directly measured by the weather station, it is necessary to derive it into a directly measurable physical quantity.

[0043] Assuming the atmosphere is an ideal gas, then we have the ideal gas law:

[0044] (2)

[0045] in (3)

[0046] For air pressure, The density of moist air, Let be the specific gas constant of moist air. Let be the specific gas constant of dry air. It is the Kelvin temperature.

[0047] According to equations (2) and (3), we can obtain (4)

[0048] Taking the total differential of equation (4), we have:

[0049] (5)

[0050] Since the values ​​of each variable are much larger than their changes, the differential equation can be approximated as follows (subsequent differential equations can be similarly transformed):

[0051] (6)

[0052] Comparison of wet calculation formula (constant (The ratio of the gas constants of dry air and water vapor) Taking the total differential, we have:

[0053] (7)

[0054] Right now (8)

[0055] in For wetness, It is the water vapor pressure.

[0056] From equations (6) and (8), we obtain the density change formula (9).

[0057] (9)

[0058] The density change formula shows that the density change of cold air is related to temperature, pressure, humidity, and their changes. Because... Much larger Therefore, 0.608 is omitted. ,have to

[0059] (10)

[0060] In the formula , recorded as Multiply both sides of equation (10) by And because ,have to

[0061] (11)

[0062] Tetens' empirical formula in atmospheric physics Differentiation yields:

[0063] (12)

[0064] in This is the dew point temperature, in °C.

[0065] Water vapor pressure Find the differential Combining equation (12), we get

[0066] (13)

[0067] From equations (11) and (13) and constants The formula for calculating density change is obtained.

[0068] (14)

[0069] Finally, the formula for flux due to density change was determined.

[0070] From equations (1) and (14), we get

[0071] (15)

[0072] Taking the cross-sectional area ΔxΔy as the unit area, the formula for the flux of density change is:

[0073] (16)

[0074] S40, the intensity of cold air at ground level is determined based on the density change flux (DVF) value.

[0075] The calculated density change flux (DVF) data is a two-dimensional physical quantity that varies with latitude and longitude. It can be visualized as contour maps or shaded maps, or the DVF values ​​of target stations can be directly analyzed. In contour maps or shaded maps, the larger the DVF value, the greater the chromaticity.

[0076] A positive density change flux (DVF) value, i.e., a DVF calculation result > 0, indicates that the region is affected by cold air, and the larger the DVF calculation result, the stronger the cold air. A negative DVF calculation result indicates that the region is not affected by cold air.

[0077] In the horizontal direction, if the latitude and longitude of the forecast station lie on a grid point in the density change flux calculation result, the value of this grid point is directly used as the station's density change flux forecast result. If it does not lie on a grid point, the calculation result is interpolated to the station's latitude and longitude using, but not limited to, bilinear interpolation or nearest neighbor interpolation. For example, if the input data is European Centre for Medium-Range Model (ECLM) product data, which is standard square grid data, the latitude and longitude of the forecast station that the user is interested in may lie exactly on the four points of the square grid, or it may lie on a point inside the square grid. If it lies on a point inside the square grid, the result is calculated based on the data at the grid point, and then interpolation is used to obtain the calculated value for the forecast station located inside the square grid.

[0078] To verify the effectiveness and reliability of the method of the present invention, relevant experiments were conducted.

[0079] Cold air activity is less frequent and less intense in summer than in other seasons. It is also easily mistaken for cold air due to false positive pressure changes at the surface caused by the swing of the subtropical high pressure in the western Pacific. Therefore, taking a cold air activity in summer as an example can better illustrate the application effect of this invention.

[0080] S1. Case study using MICAPS format surface message data issued by the National Meteorological Administration at 08:00 on July 11 and 08:00 on July 12:

[0081] S11. Extract the above data from Sichuan, Chongqing, Yunnan, and Guizhou provinces in Southwest China at 08:00 on the 11th and 08:00 on the 12th, including sea level pressure P, surface temperature T, surface dew point td, and horizontal wind speed. The dew point change Δtd was calculated from the pressure change ΔP and temperature change ΔT over the past 24 hours.

[0082] S12. Handling Default Data. During the data extraction and calculation process, the program file explicitly excludes the calculation of default values ​​(missing_value) in the ground message data, indicating that the corresponding grid point or station has no value, thus avoiding the calculation of false results.

[0083] S2. Calculate the density change flux values ​​for the four southwestern provinces during the 24 hours from 08:00 on July 11th to 08:00 on July 12th using the DVF formula:

[0084] Before inputting the data into the formula, ensure that the units for sea level pressure and pressure variation are Pa, the units for surface temperature and temperature variation are K, the units for surface dew point and dew point variation are ℃, and the units for horizontal wind speed are m / s.

[0085] S3. Visualize the density change flux data obtained from the formula calculation.

[0086] As shown in Figure 3(a), the calculated density change flux data are plotted as a two-dimensional contour map.

[0087] S4. Detect cold air intensity using density change flux data.

[0088] Figure 3a This paper presents the distribution of density flux (DVF) over the past 24 hours in the four southwestern provinces of Sichuan, Chongqing, Yunnan, and Guizhou as of 08:00 on July 12, 2023. Cool-toned areas represent positive DVF values, indicating a DVF calculation result > 0, suggesting the presence of cold air in these areas. A higher DVF value indicates a stronger cold air mass. Warm-toned areas represent negative DVF values, indicating no cold air influence in these areas.

[0089] To further illustrate the application effects of the present invention, Figure 3b and Figure 3c The distribution of 24-hour surface pressure and temperature variations in four provinces in Southwest China at 08:00 on July 12, 2023, is presented using MICAPS site-formatted ground message data. A comparison shows that at 08:00 on July 12, the area bordering southwestern Sichuan and Yunnan... Figure 3a (near Area A in Central China), the border area between northern Chongqing and Sichuan ( Figure 3a (near area B in central China) and southeastern Guizhou ( Figure 3a In areas near Central C region, the 24-hour pressure change is 0 or -1 hPa. Figure 3b Warm colors represent negative voltage transformation at stations, and cool colors represent positive voltage transformation at stations (specific values ​​are shown in the legend in the lower right corner). According to traditional methods, this could be considered as no influence from cold air; however, at the corresponding time... Figure 3cIt can be seen that the temperature in areas A, B, and C has dropped by about 1-2℃. Figure 3c Warm colors represent temperature increases at stations, and cool colors represent temperature decreases (see the legend in the lower right corner for specific numerical changes). In reality, there is the influence of cold air. This influence of cold air is reflected in the pressure variation diagram (…). Figure 3b This is not reflected in the data, making it easy for forecasters to miss cold air outbreaks. However, the density change flux proposed in this invention can be used to effectively detect it. Figure 3a The DVF values ​​in regions A, B, and C are greater than 0, indicating the influence of cold air.

[0090] The embodiments described above are merely specific implementations of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications, substitutions, and improvements within the technical scope disclosed in the present invention, and these modifications, substitutions, and improvements should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for detecting the intensity of cold air at ground level, characterized in that, Includes the following steps: S10, Select the data type for cold air intensity detection; S20, extract the physical quantities of the area to be detected at the first and second times in the data type, and obtain the difference between the physical quantities at the first and second times. The physical quantities include sea level pressure P, surface temperature T, surface dew point td, and horizontal wind speed |V|. S30, using the data obtained in S20 as variables, calculates the density change flux (DVF) value of the region to be detected between the first and second time points using a set formula; S40, determine the intensity of cold air at ground level based on the density change flux (DVF) value; The setting formula in S30 is: A=287.1 =P2-P1, =T2-T1, =td2-td1, where P2 is the sea level pressure at the second moment, P1 is the sea level pressure at the first moment, T2 is the surface temperature at the second moment, T1 is the surface temperature at the first moment, td2 is the surface dew point at the second moment, and td1 is the surface dew point at the first moment. In the formula, T, P, td, and |V| use the corresponding values ​​at the second moment, and |V| represents the magnitude of the horizontal wind speed vector V.

2. The method for detecting the intensity of cold air at ground level according to claim 1, characterized in that, In step S20, missing data in the data type is represented by outliers that are not included in the calculation.

3. The method for detecting the intensity of cold air at ground level according to claim 1, characterized in that, In step S40, the process of determining the intensity of cold air at ground level based on the density change flux (DVF) value includes: plotting the density change flux value as a contour map or a color map for visualization.

4. The method for detecting the intensity of cold air at ground level according to claim 3, characterized in that, In contour maps or color-filled maps, the larger the density variation flux (DVF) value, the greater the chromaticity.

5. The method for detecting the intensity of cold air at ground level according to claim 1, characterized in that, In step S40, if DVF > 0, cold air is detected, and the larger the value, the stronger the cold air intensity; if DVF < 0, no cold air is detected.

6. The method for detecting the intensity of cold air at ground level according to claim 1, characterized in that, The data type is one of the following: surface reports issued by the National Meteorological Administration, European Centre for Medium-Range Model (ECML) product data, and National Meteorological Administration's CMA-GFS and CMA-MESO product data.

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