An evaluation method for near-space atmospheric environment data

Abstract

This invention discloses a method for evaluating near-space atmospheric environment data, comprising: acquiring raw near-space atmospheric environment data; preprocessing the raw data to obtain preprocessed data; establishing a near-space atmospheric variation range based on the preprocessed data; filtering and judging the preprocessed data based on the near-space atmospheric variation range to obtain valid data; gridding the valid data to obtain a verification data source; inputting an evaluation object; matching the evaluation object and the verification data source based on the principle of proximity matching to obtain a matching result; and verifying and evaluating the matching result. This invention rationally establishes threshold ranges for near-space temperature, density, air pressure, zonal wind, meridional wind, wind speed, and wind direction based on statistical results from a large amount of observational data. This invention is applicable to evaluating near-space atmospheric environment data such as reanalysis data, radiosonde data, rocket sounding data, and lidar data.

Description

Technical Field

[0001] This invention relates to the field of near-space environmental assessment technology, and in particular to a method for assessing near-space atmospheric environmental data. Background Technology

[0002] Near space typically refers to the region 20–100 kilometers above the Earth's surface, including the stratosphere, mesosphere, and thermosphere. Atmospheric environmental data for near space is scarce, making the validated and evaluated data crucial for atmospheric modeling and environmental support for aircraft. It also holds broad application prospects in both civilian and military fields.

[0003] Existing methods for assessing atmospheric environmental data, such as the literature "Applicability Assessment of CLDAS Temperature Data in China" (Authors: Liu Ying et al., Journal: Chinese Journal of Atmospheric Sciences, Vol. 44, No. 4, pp. 540-548) and "Evaluation of the reanalysis products from GSFS, NCEP, and ECMWF using fluxtower observation" (Authors: Mark Decker et al., Journal of Climate, Vol. 25, No. 6, pp. 1916-1944), use evaluation indicators such as mean bias and root mean square error to compare and analyze atmospheric environmental data such as CLDAS temperature data and reanalysis data. This method is a quantitative assessment method for atmospheric environmental data and requires further qualitative assessment, such as correlation analysis.

[0004] In the preparation of atmospheric environmental data, the invention patent "Method for Constructing Virtual Atmospheric Environmental Resources in Near Space" acquires historical data of near-space atmospheric environmental resources and processes outliers to ultimately obtain a three-dimensional cubic grid of temperature, density, and pressure. However, this method does not perform quality control on the physical characteristics of the near-space atmospheric environmental data and does not include meteorological parameters such as wind speed, wind direction, meridional wind, and zonal wind.

[0005] Currently, there are few patents related to data assessment in the near-space domain; most focus on hardware design and lack a complete and systematic method for atmospheric environmental data assessment. Summary of the Invention

[0006] To address the problems existing in the prior art, the present invention provides a method for evaluating near-space atmospheric environment data.

[0007] To achieve the above technical objectives, the present invention provides the following technical solution: a method for evaluating near-space atmospheric environment data, comprising the following steps:

[0008] Acquire raw data of the near-space atmospheric environment, including historical and real-time data;

[0009] The original data is preprocessed to obtain preprocessed data;

[0010] Based on the preprocessed data, a range of atmospheric variations in the near space is established. The preprocessed data is then filtered and judged based on this range to obtain valid data. The valid data is then processed into a grid to obtain a verification data source.

[0011] Input the evaluation object, and match the evaluation object with the test data source based on the principle of proximity to obtain the matching result. Then, test and evaluate the matching result.

[0012] Optionally, the meteorological parameters of the raw data include temperature, density, air pressure, zonal wind, meridional wind, wind speed, and wind direction;

[0013] The original data is divided into four categories based on different meteorological parameters: Category 1 data stratified by air pressure, Category 2 data containing only temperature and air pressure, Category 3 data containing only wind direction and wind speed, and Category 4 data containing only zonal and meridional winds.

[0014] Optionally, the preprocessing of each type of data in the original data includes:

[0015] For the first type of data, the geometric height is calculated to obtain the meteorological parameter values ​​for the first type of data; for the second type of data, the density is calculated based on the ideal gas law to obtain the meteorological parameter values ​​for the second type of data; for the third type of data, the zonal wind and meridional wind are calculated by horizontal decomposition to obtain the meteorological parameter values ​​for the third type of data; for the fourth type of data, the wind speed and wind direction are calculated to obtain the meteorological parameter values ​​for the fourth type of data.

[0016] Optionally, the ideal gas law is:

[0017] P = ρRT

[0018] Where P is pressure, in Pa; ρ is density, in kg / m³. 3 T is temperature, in Kelvin; R is the dry air gas constant, which is 287.04 J·J. -1 ·kg -1 .

[0019] Optionally, the process of filtering and determining the preprocessed data to obtain valid data based on the range of atmospheric changes in the near space includes:

[0020] The near-space atmospheric variation range is constructed based on the preprocessed data. The near-space atmospheric variation range includes the threshold of each meteorological parameter. The preprocessed data includes meteorological parameter values ​​of type I, type II, type III and type IV.

[0021] Data whose meteorological parameter values ​​fall within the threshold are selected as first data. Based on the ideal gas equation of state, the ideal gas constant of the first data is calculated. Data with ideal gas constants between 286 and 288 are selected as second data. Outliers in the second data are identified using a dual-weighting method and removed to obtain third data. Physical consistency checks are performed on the third data to obtain valid data.

[0022] Optionally, the means of performing the effective data gridding process include:

[0023] The altitude layer interval is 500 meters; the temperature and wind field are calculated using Lagrange interpolation; the density and air pressure are calculated using logarithmic interpolation.

[0024] The wind field includes wind direction, wind speed, meridional wind, and zonal wind.

[0025] Optionally, the means of verifying and evaluating the matching results include: calculating the average error and root mean square error of the verification data source and the evaluation object, and performing correlation analysis.

[0026] The present invention has the following technical effects:

[0027] Compared with existing technologies, this invention establishes maximum and minimum thresholds for near-space temperature, density, air pressure, zonal wind, meridional wind, wind speed, and wind direction based on observation data during the data quality control process, making the evaluation process of near-space atmospheric environment data more targeted.

[0028] This invention is applicable to the evaluation of near-space atmospheric environment data such as reanalysis data, radiosonde data, rocket sounding data, and lidar data. The evaluated data can be used for atmospheric modeling, spacecraft simulation, etc. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments 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.

[0030] Figure 1 This is a flowchart of the near-space atmospheric environment data evaluation method in an embodiment of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0033] Example 1

[0034] like Figure 1 As shown, this embodiment provides a method for evaluating near-space atmospheric environment data, including:

[0035] Step 1: Acquire historical data on the near-space atmospheric environment, including reanalysis data, radiosonde data, rocket sounding data, and lidar data. Meteorological parameters include temperature, density, air pressure, zonal wind, meridional wind, wind speed, and wind direction.

[0036] Step 2: Acquire real-time data of the near-space atmospheric environment, including numerical weather prediction data, radiosonde data, and rocket sounding data, and obtain meteorological parameters, including temperature, density, air pressure, zonal wind, meridional wind, wind speed, and wind direction.

[0037] Step 3: Preprocess each type of data. For data stratified by air pressure and altitude, calculate the geometric altitude. For data that only provides temperature and air pressure, calculate the density according to the ideal gas law. For data that only provides wind direction and wind speed, decompose it horizontally to obtain zonal and meridional winds. For data that only provides zonal and meridional winds, calculate the wind speed and wind direction.

[0038] (1) Atmospheric density

[0039] Temperature, density, and pressure satisfy the ideal gas law:

[0040] P = ρRT

[0041] Where P is pressure, in Pa; ρ is density, in kg / m³. 3 T is temperature, in K; R is the dry air gas constant, which is 287.04 J·K. -1 ·kg -1Given atmospheric temperature and atmospheric pressure, atmospheric density can be obtained using the ideal gas law.

[0042] (2) Wind speed

[0043] The wind speed here refers to the horizontal wind speed. Given the zonal and meridional winds, the wind speed can be calculated using the following formula:

[0044]

[0045] Where U represents zonal wind and V represents meridional wind.

[0046] (3) Wind direction

[0047] Wind direction refers to the direction from which the wind is coming, measured clockwise from due north. Due north is 0°, due east is 90°, due south is 180°, and due west is 270°. Given the zonal and meridional winds, it can be calculated using the following formula:

[0048] θ′=arctan(U / V)

[0049] Here, U represents zonal wind and V represents meridional wind. Then, θ′ needs to be adjusted according to the directions of the zonal and meridional winds to finally obtain the wind direction θ.

[0050] (4) Zonal wind

[0051] Zonal winds, with westerly winds as the primary characteristic, are calculated using the following formula:

[0052] U = W × sin(θ + π)

[0053] Where W is the wind speed and θ is the wind direction.

[0054] (5) Meridional wind

[0055] Meridional winds are taken as southerly winds and are calculated using the following formula:

[0056] U = W × cos(θ + π)

[0057] Where W is the wind speed and θ is the wind direction.

[0058] Step 4: Perform an information range check on the data. Specify the range of change for each meteorological element based on the observation data, as shown in Table 1. Any meteorological element that is not within the range of change is invalid. At the same time, the profiles of other parameters at that time and altitude are also invalidated.

[0059] Table 1

[0060] meteorological elements Range of variation Temperature (K) 160～360 <![CDATA[Density (kg / m 3 )]]> 0～2 Atmospheric pressure (hPa) 0～1100 Zonal wind (m / s) -200～200 Meridional wind (m / s) -200～200 Wind speed (m / s) 0～200 Wind direction (°) 0～360

[0061] Step 5: Check the ideal gas law. Calculate the ideal gas constant R based on temperature, pressure, and density. R must satisfy 286 < R < 288. Data with values ​​outside this range are considered invalid. At the same time, other meteorological parameters at this altitude are also considered invalid.

[0062] Step 6: Outlier check, use the double-weighted method to identify outliers and determine invalidity.

[0063] The two-weighted method reduces the impact of outliers on the overall mean by using a two-weighted average and a two-weighted bias. The principle behind the two-weighted method for identifying outliers is as follows:

[0064] For n observation samples X i (i = 1, 2, 3...n-1, n), the median of the sample is defined as M. i Arrange the data in order, and let M be the data in the middle of the sequence. If the total number of samples is odd, take the middle number; if the total number of samples is even, take the average of the two middle numbers.

[0065] The median absolute deviation is defined as the median: the absolute value of the deviation between the sample value and the median.

[0066] Weight function W i Defined as:

[0067]

[0068] Double-weighted average Defined as:

[0069]

[0070] The two-weighted standard deviation is defined as:

[0071]

[0072] Among them, X i Here is the sample size, M is the median, C represents the "check" value and is defined as 7.5, and MAD is the median absolute deviation. For the weighting function W... i If there is any |W i |>1.0, let W i =1.0. The calculated double-weighted mean and double-weighted standard deviation (BSD) are then used to define Z for any observed sample:

[0073]

[0074] A threshold for Z is determined, and data points in Z that do not fall within this threshold range are considered outliers and removed, thereby performing data quality control.

[0075] Step 7: Utilize the continuous vertical distribution of meteorological elements to conduct a physical consistency check, mainly targeting air pressure and density.

[0076] (1) Air pressure

[0077] a) Decreases with altitude; air pressure must increase monotonically with decreasing altitude. Therefore, data above the required altitude are deemed invalid. At the same time, other meteorological parameters above this altitude are also deemed invalid.

[0078] b) Exponential Decline: Set a reference pressure profile P L =1000×e -z / H (Unit: hPa), where z is the altitude, H is the elevation 7km, and the control is max{abs[log 10 (P)-log 10 (P L Since the value is less than or equal to 1, data above this altitude that are not within this range are deemed invalid. At the same time, other meteorological elements above this altitude are also deemed invalid.

[0079] (2) Density

[0080] a) Decreasing with height: The density must increase monotonically as the height decreases. Therefore, data above the required height are deemed invalid. At the same time, other parameters are also deemed invalid at the height.

[0081] b) Exponential decrease: Set a reference density profile ρ L =1.2×e -z / H (Unit: kg / m³) 3 ), where z is the altitude, H is the elevation 7km, and the control is max{abs[log 10 (ρ)-log 10 (ρ L Since the value is less than or equal to 1, the data above the height that is not within this range is considered invalid. At the same time, other parameters are also considered invalid in terms of height.

[0082] Step 8: Grid the data, with the height divided into layers at 500m intervals. Use Lagrange interpolation for temperature and wind field, and logarithmic interpolation for density and air pressure.

[0083] Step Nine: Use the "nearest matching" principle to match the data source and the evaluation object in terms of time and space;

[0084] Step 10: Calculate the average error and root mean square error of the data source and the evaluation object, and perform correlation analysis.

[0085] (1) Average error

[0086] The mean error is the arithmetic mean of all deviations of the evaluated data relative to the test data source. The formula is:

[0087]

[0088] Where F represents the data being evaluated, O represents the test data, and i represents the data of the i-th grid point.

[0089] 2) Root mean square error

[0090] Root mean square error (RMSE) is the square root of the ratio of the deviation between the evaluated data and the test data to the number of data points. It measures the deviation between the evaluated data and the test data. The formula is:

[0091]

[0092] Where F represents the data being evaluated, O represents the test data, and i represents the data of the i-th grid point.

[0093] (3) Correlation analysis

[0094] The correlation coefficient can be used to characterize the degree of correlation between two sets of data. The correlation coefficient is calculated as follows, where σ A σ B Let A and B be the standard deviations of the data source A and the evaluation object B, respectively, and let cov(A, B) be the covariance of the two sets of data.

[0095]

[0096] The closer the calculated correlation coefficient is to 1, the better the consistency between the two data.

[0097] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating near space atmospheric environment data, characterized by, The method comprises the following steps: Obtaining original data of the atmosphere in the near space, wherein the original data comprises historical data and real-time data; Meteorological parameters of the original data comprise temperature, density, air pressure, zonal wind, meridional wind, wind speed and wind direction; The original data is divided based on different meteorological parameters, and the original data comprises: one type of data divided by air pressure, two types of data comprising only temperature and air pressure, three types of data comprising only wind direction and wind speed, and four types of data comprising only zonal wind and meridional wind; The original data is preprocessed to obtain preprocessed data, and the preprocessing process for each type of data in the original data comprises: for the one type of data, calculating the geometric height to obtain meteorological parameter values of the one type of data; for the two types of data, calculating the density based on the ideal gas state equation to obtain meteorological parameter values of the two types of data; for the three types of data, calculating the zonal wind and the meridional wind by horizontal decomposition to obtain meteorological parameter values of the three types of data; and for the four types of data, calculating the wind speed and the wind direction to obtain meteorological parameter values of the four types of data; Based on the preprocessed data, a change range of the atmosphere in the near space is established, the preprocessed data is screened and judged based on the change range of the atmosphere in the near space to obtain effective data, and the effective data is grid processed to obtain a test data source; The process of screening and judging the preprocessed data comprises: constructing a change range of the atmosphere in the near space based on the preprocessed data, wherein the change range of the atmosphere in the near space comprises threshold values of each meteorological parameter, and the preprocessed data comprises meteorological parameter values of the one type of data, the two types of data, the three types of data and the four types of data; screening data whose meteorological parameter values belong to the threshold values as first data; calculating ideal gas constants of the first data based on the ideal gas state equation; screening the ideal gas constants between 286 and 288 as second data; judging outliers in the second data based on a double-weight method, and removing the outliers to obtain third data; and performing physical consistency inspection on the third data to obtain effective data; The means of grid processing the effective data comprises: an interval of height layers is 500 meters; the temperature and the wind field adopt a Lagrange interpolation method; and the density and the air pressure adopt a logarithmic interpolation method; wherein the wind field comprises wind direction, wind speed, meridional wind and zonal wind; An evaluation object is input, the evaluation object and the test data source are matched based on a nearest matching principle to obtain a matching result, and the matching result is evaluated.

2. The method of assessing near space atmospheric environment data according to claim 1, wherein, The ideal gas state equation is: P = ρRT Wherein, P is pressure, unit Pa; p is density, unit kg / m 3 ; T is temperature, unit K; R is the dry air specific gas constant, 287.04 J·K -1 ·kg -1 .

3. The method of assessing near space atmospheric environment data according to claim 1, wherein, The means of evaluating the matching result comprises: calculating average errors and root mean square errors of the test data source and the evaluation object, and performing correlation analysis.

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