All-weather temperature and humidity inversion method based on lidar

Abstract

The present invention relates to the field of atmospheric remote sensing technology, and in particular to a laser radar-based all-weather temperature and humidity inversion method, comprising the following steps: S1, obtaining laser radar data of relevant wavelengths required for water vapor and temperature inversion, and preprocessing the laser radar data; S2, performing noise filtering on the laser radar data preprocessed in S1; S3, using a temperature and humidity inversion formula and substituting calibration parameters to perform inversion processing on the laser radar data after noise filtering in S2, to obtain an atmospheric temperature profile #imgabs0# and an atmospheric humidity profile #imgabs1#. The present invention, through adaptive filtering, ensures signal smoothness while retaining rich high-altitude information, thereby increasing the detection distance of temperature and humidity, ensuring all-weather detection of temperature and humidity, and effectively improving the inversion accuracy of temperature and humidity. Furthermore, the present invention can adapt to complex atmospheric environments, has high data reliability, and further effectively supports all-weather, long-term temperature and humidity data inversion.

Description

Technical Field

[0001] The present invention relates to the field of atmospheric remote sensing technology, and in particular to an all-weather temperature and humidity inversion method based on laser radar. Background Art

[0002] Atmospheric temperature and humidity, as key meteorological elements, are not only core parameters for studying atmospheric dynamic and thermodynamic processes, but also crucial input variables for numerical weather forecasting, climate model assessment, and atmospheric pollution source tracking. Traditional contact measurement methods, such as radiosondes and ground-based meteorological stations, are limited by technical bottlenecks, including insufficient temporal and spatial coverage (typical radiosonde observation intervals ≥12 hours, spatial resolution >100 km) and high operation and maintenance costs (annual maintenance costs for a single station exceed 100,000 yuan), making them inadequate for modern high-resolution meteorological monitoring. In contrast, atmospheric lidar, with its high temporal and spatial resolution, can acquire real-time temperature and humidity information at various altitudes and locations, accurately capturing the dynamic changes in atmospheric parameters. Its high detection sensitivity enables it to detect weak signals, enabling accurate and effective data acquisition in complex environments. Its robust anti-interference capabilities enable stable operation even in inclement weather or high-interference scenarios. These advantages have made atmospheric lidar an irreplaceable and important tool in atmospheric monitoring, including environmental protection and meteorology, providing strong technical support for atmospheric science research and environmental monitoring.

[0003] Currently, the temperature and humidity inversion methods based on lidar include:

[0004] 1. Raman lidar-based atmospheric temperature and humidity detection method: Humidity is inverted using the nitrogen / water vapor Raman scattering signal ratio, combined with the pulled Raman signal to invert temperature. During the day, solar background noise causes the water vapor Raman signal-to-noise ratio (SNR) to decrease, resulting in a detection range of only 500m. Furthermore, the method relies on the assumption of uniform atmosphere, resulting in a humidity error of approximately 10%-15% (at 1km altitude).

[0005] 2. Differential Absorption Lidar (DIAL) Water Vapor Concentration Inversion Method: This method uses 935nm / 820nm dual-wavelength differential absorption technology to invert water vapor. Aerosol interference leads to deviations in the absorption cross-section calculation, resulting in humidity errors of 8%-12%. The system requires precise temperature control, resulting in poor operational stability.

[0006] 3. Temperature profile inversion method based on Mie-Raman lidar: Combined Mie scattering and rotational Raman signals are used to invert temperature. The temperature accuracy is ±1.5°C at night and deteriorates to ±3°C during the day. The problem of synchronous humidity inversion is not solved.

[0007] Existing technologies mainly use Raman scattering or differential absorption principles to detect temperature and humidity, but both have the following technical defects:

[0008] 1. Daytime detection performance is limited: Due to the extremely small Raman scattering cross section of water vapor (about 10 -30 cm 2 / sr), under the interference of strong solar background noise during the day, the signal-to-noise ratio drops sharply, resulting in an effective detection distance of less than 500 meters, which cannot meet the needs of all-weather monitoring;

[0009] 2. Insufficient inversion accuracy: Existing methods mostly rely on the assumption of atmospheric stratification and the ideal gas equation. Affected by factors such as uneven aerosol distribution and laser wavelength drift, humidity inversion errors generally exceed 15% and temperature errors exceed 2°C, far below the operational standards required by the World Meteorological Organization (WMO) (humidity error <5%, temperature error <1°C).

[0010] 3. Poor algorithm adaptability: Traditional inversion methods use empirical formulas with fixed parameters and cannot adapt to the complex atmospheric environment under different weather conditions. In particular, data reliability is significantly reduced in severe weather conditions such as clouds, fog, and haze. Summary of the Invention

[0011] The technical problem to be solved by the present invention is: in order to solve the technical problems of limited daytime detection performance and insufficient inversion accuracy of the existing temperature and humidity inversion method, the present invention provides an all-weather temperature and humidity inversion method based on lidar. By improving the temperature and humidity inversion method, all-weather detection of temperature and humidity can be achieved, and at the same time, the inversion accuracy of temperature and humidity can also be improved.

[0012] The technical solution adopted by the present invention to solve the technical problem is: an all-weather temperature and humidity inversion method based on laser radar, comprising the following steps:

[0013] S1. Obtain lidar data of relevant wavelengths required for water vapor and temperature inversion, and preprocess the lidar data;

[0014] S2, performing noise filtering on the lidar data pre-processed by S1;

[0015] S3, use the temperature and humidity inversion formula and substitute the calibration parameters to invert the lidar data after S2 noise filtering to obtain the atmospheric temperature profile , atmospheric humidity profile .

[0016] Therefore, through adaptive filtering processing, while ensuring signal smoothness, rich high-altitude information is retained, the detection distance of temperature and humidity is improved, all-weather detection of temperature and humidity is guaranteed, and the inversion accuracy of temperature and humidity can be effectively improved. In addition, it can adapt to complex atmospheric environments, with high data reliability, and further effectively support all-weather, long-term temperature and humidity data inversion.

[0017] Furthermore, in S1, preprocessing includes: background subtraction and electrical delay subtraction; after preprocessing, the effective signal, background baseline value, and signal-to-noise ratio of the lidar data are obtained.

[0018] Furthermore, the step S2 includes the following steps:

[0019] S2-1, the original signal Pass through a low-pass filter and downsample to obtain the approximate component ;

[0020] S2-2, the original signal Pass through a high-pass filter and downsample to obtain detail components ;

[0021] S2-3, for approximate components conduct decomposed to form Layer high frequency wavelet coefficients;

[0022] S2-4, for the first to the The high-frequency wavelet coefficients of the layer are quantized using a threshold to remove correlation noise;

[0023] S2-5. Approximate components after quantization conduct The reconstructed signal is obtained by up-sampling and passing the approximate component through a low-pass filter and the detail component through a high-pass filter.

[0024] Furthermore, in S2-3, the quantization process includes:

[0025] Hard threshold quantization processing and soft threshold quantization processing;

[0026] The expression for threshold processing is:

[0027] ;

[0028] in: represents the data after threshold processing, represents the symbolic function, represents the data before threshold processing, represents the threshold value, for Therefore, threshold processing can avoid the problem of poor continuity of hard threshold and local mutation of signal change, and can also avoid the problem of shrinking signal coefficient and causing signal distortion due to soft threshold, thereby effectively improving the inversion accuracy of temperature and humidity.

[0029] Furthermore, in S3, the high quantum number Raman scattering echo signal intensity The expression is:

[0030] ;

[0031] Low quantum number Raman scattering echo signal intensity The expression is:

[0032] ;

[0033] in: represents the quantum number, Indicates temperature, Indicates height, represents the system constant, express Quantum number at temperature The rotational Raman scattering cross section, express Quantum number at temperature The rotational Raman scattering cross section, represents the atmospheric extinction coefficient, Indicates altitude.

[0034] Furthermore, in S3, the signal strength is and temperature The expression between them is:

[0035] ;

[0036] in: 、 All represent calibration parameters;

[0037] In S3, height Atmospheric temperature profile The calculation formula is:

[0038] .

[0039] Furthermore, in S3, the signal strength is and temperature The expression between them is:

[0040] ;

[0041] in: Represents Adjacent spectral lines, 、 、 Indicates calibration parameters;

[0042] In S3, height Atmospheric temperature profile The calculation formula is:

[0043] .

[0044] Therefore, since a single Raman signal is too weak and difficult to extract, the Raman signals of multiple adjacent Raman channels are introduced to further improve the accuracy of temperature inversion.

[0045] Furthermore, in S3, the intensity of the Raman echo signal of nitrogen molecule vibration received by the lidar is The calculation formula is:

[0046] ;

[0047] The intensity of the Raman echo signal of water vapor molecule vibration received by the lidar The calculation formula is:

[0048] ;

[0049] in: represents the Raman scattering channel factor of nitrogen molecules, represents the output power of the laser radar, represents the Raman scattering area of ​​nitrogen molecules, Indicates that nitrogen molecules The molecular number density at height, represents the atmospheric transmittance of the lidar laser emission wavelength, Indicates the atmospheric transmittance of the Raman scattering wavelength of nitrogen molecules, represents the Raman scattering channel factor of water vapor molecules, represents the Raman scattering area of ​​water vapor molecules, Indicates that water vapor molecules The molecular number density at height, Indicates the atmospheric transmittance at the wavelength of Raman scattering by water vapor molecules.

[0050] Furthermore, in S3, the water vapor mixing ratio The expression is:

[0051] ;

[0052] System calibration constants The expression is:

[0053] ;

[0054] Atmospheric transmittance correction function The expression is:

[0055] ;

[0056] The water vapor mixing ratio The expression is:

[0057] ;

[0058] in: express The mass of water vapor in the same volume at the same height, express The mass of dry air in the same volume at the same height, the system calibration constant The atmospheric transmittance correction function is obtained through calibration. By calculating the atmospheric extinction, The laser radar signal collected by the water vapor molecules, LiDAR acquisition signal representing nitrogen molecules.

[0059] Furthermore, in S3, the atmospheric humidity profile The expression is:

[0060] ;

[0061] Saturated vapor pressure The calculation formula is:

[0062] ;

[0063] Water vapor mixing ratio The expression is:

[0064] ;

[0065] The atmospheric humidity profile Can be simplified to:

[0066] ;

[0067] in: It represents the water vapor pressure in units of , The unit is , is the temperature in degrees Celsius. , 0.622 represents the molar mass ratio of dry air to water vapor, Indicates atmospheric pressure in units of , Indicates atmospheric pressure.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] 1. The present invention uses adaptive filtering processing to ensure signal smoothness while retaining rich high-altitude information, thereby improving the detection distance of temperature and humidity, ensuring all-weather detection of temperature and humidity, and effectively improving the inversion accuracy of temperature and humidity. In addition, it can adapt to complex atmospheric environments, has high data reliability, and further effectively supports all-weather, long-term temperature and humidity data inversion.

[0070] 2. The present invention can avoid the problem of poor continuity of hard threshold and local mutation of signal change through threshold processing, and can also avoid the problem of shrinking signal coefficient of soft threshold and causing signal distortion, thereby effectively improving the inversion accuracy of temperature and humidity.

[0071] 3. The present invention introduces Raman signals from multiple adjacent Raman channels to solve the problem that a single Raman signal is too weak and difficult to extract, thereby further improving the accuracy of temperature inversion. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The present invention will be further described below with reference to the accompanying drawings and examples.

[0073] Figure 1 This is a flow chart of the all-weather temperature and humidity inversion method based on lidar of the present invention;

[0074] Figure 2 This is a flow chart of S2 of the present invention;

[0075] Figure 3 This is the effect diagram of the wavelet decomposition of the present invention;

[0076] Figure 4 This is an effect diagram of the original signal decomposition N times of the present invention;

[0077] Figure 5 This is the effect diagram of the wavelet reconstruction of the present invention;

[0078] Figure 6 This is a comparison diagram of the inversion and sounding before and after the invented wavelet change processing. DETAILED DESCRIPTION

[0079] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0080] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, features defined as "first" or "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0081] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0082] like Figures 1 to 6 As shown, a lidar-based all-weather temperature and humidity inversion method includes the following steps:

[0083] S1. Obtain lidar data of relevant wavelengths required for water vapor and temperature inversion, and preprocess the lidar data;

[0084] S2, performing noise filtering on the lidar data pre-processed by S1;

[0085] S3, use the temperature and humidity inversion formula and substitute the calibration parameters to invert the lidar data after S2 noise filtering to obtain the atmospheric temperature profile , atmospheric humidity profile Therefore, through adaptive filtering processing, while ensuring signal smoothness, rich high-altitude information is retained, the detection distance of temperature and humidity is improved, all-weather detection of temperature and humidity is guaranteed, and the inversion accuracy of temperature and humidity can be effectively improved. In addition, it can adapt to complex atmospheric environments, with high data reliability, further effectively supporting all-weather, long-term temperature and humidity data inversion.

[0086] Specifically, by combining the water vapor mixing ratio and temperature data, the relative humidity of the atmosphere can be directly calculated without relying on external sounding data or theoretical model data. This can reduce the errors of traditional assumptions and enhance environmental adaptability to achieve integrated temperature and humidity inversion.

[0087] Specifically, the time resolution of the lidar is one minute and the spatial resolution is fifteen meters, which can greatly improve the temporal and spatial resolution.

[0088] Specifically, lidar is a highly integrated optical transceiver module, signal modulation module, and processor, which can support long-term unmanned observation and has low operation and maintenance costs.

[0089] Specifically, the current inversion method requires temperature values ​​during extinction and backscattering, which are generally calculated through atmospheric theoretical models. This inversion method can improve the accuracy of temperature and humidity inversion by inverting temperature data.

[0090] In this embodiment, in S1, preprocessing includes: background subtraction and electrical delay subtraction; after preprocessing, the effective signal, background baseline value, and signal-to-noise ratio of the lidar data are obtained.

[0091] In this embodiment, S2 includes the following steps:

[0092] S2-1, the original signal Pass through a low-pass filter and downsample to obtain the approximate component ;

[0093] S2-2, the original signal Pass through a high-pass filter and downsample to obtain detail components ;

[0094] S2-3, for approximate components conduct decomposed to form Layer high frequency wavelet coefficients;

[0095] S2-4, for the first to the The high-frequency wavelet coefficients of the layer are quantized using a threshold to remove correlation noise;

[0096] S2-5. Approximate components after quantization conduct The reconstructed signal is then upsampled and the approximate component is passed through a low-pass filter and the detail component is passed through a high-pass filter to obtain the reconstructed signal.

[0097] In S2-3, the quantization process includes:

[0098] Hard threshold quantization processing and soft threshold quantization processing;

[0099] The expression for threshold processing is:

[0100] ;

[0101] in: represents the data after threshold processing, represents the symbolic function, represents the data before threshold processing, represents the threshold value, for Therefore, threshold processing can avoid the problem of poor continuity of hard threshold and local mutation of signal change, and can also avoid the problem of shrinking signal coefficient and causing signal distortion due to soft threshold, thereby effectively improving the inversion accuracy of temperature and humidity.

[0102] In this embodiment, in S3, the high quantum number Raman scattering echo signal intensity The expression is:

[0103] ;

[0104] Low quantum number Raman scattering echo signal intensity The expression is:

[0105] ;

[0106] in: represents the quantum number, Indicates temperature, Indicates height, represents the system constant, express Quantum number at temperature The rotational Raman scattering cross section, express Quantum number at temperature The rotational Raman scattering cross section, represents the atmospheric extinction coefficient, Indicates height;

[0107] Signal strength ratio and temperature The expression between them is:

[0108] ;

[0109] in: 、 All represent calibration parameters;

[0110] In S3, height Atmospheric temperature profile The calculation formula is:

[0111] .

[0112] In this embodiment, in S3, the intensity of the nitrogen molecule vibration Raman echo signal received by the laser radar is The calculation formula is:

[0113] ;

[0114] The intensity of the Raman echo signal of water vapor molecule vibration received by the lidar The calculation formula is:

[0115] ;

[0116] in: represents the Raman scattering channel factor of nitrogen molecules, represents the output power of the laser radar, represents the Raman scattering area of ​​nitrogen molecules, Indicates that nitrogen molecules The molecular number density at height, represents the atmospheric transmittance of the lidar laser emission wavelength, Indicates the atmospheric transmittance of the Raman scattering wavelength of nitrogen molecules, represents the Raman scattering channel factor of water vapor molecules, represents the Raman scattering area of ​​water vapor molecules, Indicates that water vapor molecules The molecular number density at height, Indicates the atmospheric transmittance of the Raman scattering wavelength of water vapor molecules;

[0117] Water vapor mixing ratio The expression is:

[0118] ;

[0119] System calibration constants The expression is:

[0120] ;

[0121] Atmospheric transmittance correction function The expression is:

[0122] ;

[0123] The water vapor mixing ratio The expression is:

[0124] ;

[0125] in: express The mass of water vapor in the same volume at the same height, express The mass of dry air in the same volume at the same height, the system calibration constant The atmospheric transmittance correction function is obtained through calibration. By calculating the atmospheric extinction, The laser radar signal collected by the water vapor molecules, LiDAR acquisition signal representing nitrogen molecules;

[0126] Atmospheric humidity profile The expression is:

[0127] ;

[0128] Saturated vapor pressure The calculation formula is:

[0129] ;

[0130] Water vapor mixing ratio The expression is:

[0131] ;

[0132] The atmospheric humidity profile Can be simplified to:

[0133] ;

[0134] in: It represents the water vapor pressure in units of , The unit is , is the temperature in degrees Celsius. , 0.622 represents the molar mass ratio of dry air to water vapor, Indicates atmospheric pressure in units of , Indicates atmospheric pressure.

[0135] It should be noted that: in this embodiment, Figure 6 As shown, the detection distance during the day can reach three kilometers. Through the sounding profile comparison chart, the sounding data has a time deviation (within one hour). The detection distance without wavelet denoising and inversion is about two kilometers, and the detection distance after wavelet denoising and inversion is more than four kilometers (four kilometers due to the presence of a water vapor mass at four kilometers on that day). Under normal circumstances, the detection distance is three kilometers.

[0136] Example 2:

[0137] The difference from Example 1 is that in S3, the signal intensity is and temperature The expression between them is:

[0138] ;

[0139] in: Represents Adjacent spectral lines, 、 、 Indicates calibration parameters;

[0140] In S3, height Atmospheric temperature profile The calculation formula is:

[0141] .

[0142] Therefore, since a single Raman signal is too weak and difficult to extract, the Raman signals of multiple adjacent Raman channels are introduced to further improve the accuracy of temperature inversion.

[0143] In summary, the present invention, through adaptive filtering processing, ensures signal smoothness while retaining rich high-altitude information, improves the detection distance of temperature and humidity, ensures all-weather detection of temperature and humidity, and can effectively improve the inversion accuracy of temperature and humidity. In addition, it can adapt to complex atmospheric environments, has high data reliability, and further effectively supports all-weather, long-term temperature and humidity data inversion; through threshold processing, it can avoid the problems of poor continuity of hard thresholds and local mutations in signal changes, and can also avoid the problem of soft threshold shrinkage of signal coefficients and signal distortion, thereby effectively improving the inversion accuracy of temperature and humidity; by introducing Raman signals of multiple adjacent Raman channels, it can solve the problem of a single Raman signal being too weak and difficult to extract, thereby further improving the accuracy of temperature inversion.

[0144] The above description is intended to serve as a guide for the preferred embodiments of the present invention. Based on the above description, relevant personnel are fully capable of making various changes and modifications without departing from the technical scope of the present invention. The technical scope of the present invention is not limited to the contents of the specification and must be determined according to the scope of the claims.

Claims

1. A laser radar-based all-weather temperature and humidity inversion method, characterized in that: The following steps are involved: S1. Obtain lidar data of relevant wavelengths required for water vapor and temperature inversion, and preprocess the lidar data; S2, performing noise filtering on the lidar data pre-processed by S1; S3, use the temperature and humidity inversion formula and substitute the calibration parameters to invert the lidar data after S2 noise filtering to obtain the atmospheric temperature profile , atmospheric humidity profile ; In S3, height Atmospheric temperature profile The calculation formula is: ； Atmospheric humidity profile The calculation formula is: ； in: 、 、 Indicates the calibration parameters, Indicates height The intensity ratio of the signal at represents atmospheric pressure, represents the water vapor mixing ratio, 0.622 represents the molar mass ratio of dry air to water vapor, It represents the saturated water vapor pressure in units of .

2. The all-weather temperature and humidity inversion method based on laser radar according to claim 1 is characterized in that: In S1, preprocessing includes: Background subtraction, electrical delay subtraction; After preprocessing, the effective signal, background baseline value, and signal-to-noise ratio of the lidar data are obtained.

3. The all-weather temperature and humidity inversion method based on lidar according to claim 1 is characterized in that: The S2 comprises the following steps: S2-1, the original signal Pass through a low-pass filter and downsample to obtain the approximate component ; S2-2, the original signal Pass through a high-pass filter and downsample to obtain detail components ; S2-3, for approximate components conduct decomposed to form Layer high frequency wavelet coefficients; S2-4, for the first to the The high-frequency wavelet coefficients of the layer are quantized using a threshold to remove correlation noise; S2-5. Approximate components after quantization conduct The reconstructed signal is obtained by up-sampling and passing the approximate component through a low-pass filter and the detail component through a high-pass filter.

4. The all-weather temperature and humidity inversion method based on lidar according to claim 3 is characterized in that: In S2-3, the quantization process includes: Hard threshold quantization processing and soft threshold quantization processing; The expression for threshold processing is: ； in: represents the data after threshold processing, represents the symbolic function, represents the data before threshold processing, represents the threshold value, for The maximum value in .

5. The all-weather temperature and humidity inversion method based on laser radar according to claim 1 is characterized in that: In S3, the high quantum number Raman scattering echo signal intensity The expression is: ； Low quantum number Raman scattering echo signal intensity The expression is: ； in: represents the quantum number, Indicates temperature, Indicates height, represents the system constant, express Quantum number at temperature The rotational Raman scattering cross section, express Quantum number at temperature The rotational Raman scattering cross section, represents the atmospheric extinction coefficient, Indicates altitude.

6. The all-weather temperature and humidity inversion method based on lidar according to claim 5, characterized in that: In S3, the signal strength is and temperature The expression between them is: ； in: 、 All represent calibration parameters; In S3, height Atmospheric temperature profile The calculation formula is: 。 7. The all-weather temperature and humidity inversion method based on laser radar according to claim 5 is characterized in that: In S3, the signal strength is and temperature The expression between them is: ； in: Represents Adjacent spectral lines.

8. The all-weather temperature and humidity inversion method based on lidar according to claim 1, characterized in that: In S3, the intensity of the nitrogen molecule vibration Raman echo signal received by the lidar The calculation formula is: ； The intensity of the Raman echo signal of water vapor molecule vibration received by the lidar The calculation formula is: ； in: represents the Raman scattering channel factor of nitrogen molecules, represents the output power of the laser radar, represents the Raman scattering area of ​​nitrogen molecules, Indicates that nitrogen molecules The molecular number density at height, represents the atmospheric transmittance of the lidar laser emission wavelength, Indicates the atmospheric transmittance of the Raman scattering wavelength of nitrogen molecules, represents the Raman scattering channel factor of water vapor molecules, represents the Raman scattering area of ​​water vapor molecules, Indicates that water vapor molecules The molecular number density at height, Indicates the atmospheric transmittance at the wavelength of Raman scattering by water vapor molecules.

9. The all-weather temperature and humidity inversion method based on laser radar according to claim 8, characterized in that: In S3, the water vapor mixing ratio The expression is: ； System calibration constants The expression is: ； Atmospheric transmittance correction function The expression is: ； The water vapor mixing ratio The expression is: ； in: express The mass of water vapor in the same volume at the same height, express The mass of dry air in the same volume at the same height, the system calibration constant The atmospheric transmittance correction function is obtained through calibration. By calculating the atmospheric extinction, The laser radar signal collected by the water vapor molecules is represented. LiDAR acquisition signal representing nitrogen molecules.

10. The all-weather temperature and humidity inversion method based on laser radar according to claim 9, characterized in that: In S3, atmospheric humidity profile The expression is: ； Saturated vapor pressure The calculation formula is: ； Water vapor mixing ratio The expression is: ； in: It represents the water vapor pressure in units of , is the temperature in degrees Celsius. , Indicates atmospheric pressure in units of .

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