All-weather temperature and humidity inversion method based on laser radar

Abstract

The invention relates to the technical field of atmospheric remote sensing, in particular to an all-weather temperature and humidity inversion method based on a laser radar, which comprises the following steps: S1, acquiring laser radar data of related wavelengths required by water vapor and temperature inversion, and preprocessing the laser radar data; s2, carrying out noise filtering processing on the laser radar data preprocessed in the step S1; and S3, performing inversion processing on the laser radar data subjected to noise filtering processing in the step S2 by using a temperature and humidity inversion formula and substituting the calibration parameters so as to obtain an atmospheric temperature profile # imgabs0 # and an atmospheric humidity profile # imgabs1 #. Through self-adaptive filtering treatment, signal smoothness is ensured, abundant high-altitude information is reserved, the temperature and humidity detection distance is increased, all-weather detection of the temperature and humidity is ensured, the temperature and humidity inversion precision can be effectively improved, in addition, the method can adapt to the complex atmospheric environment, the data reliability is high, and the method is suitable for popularization and application. And all-weather and long-term temperature and humidity data inversion is further effectively supported.

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] As key meteorological elements, atmospheric temperature and humidity are not only the core parameters for studying atmospheric dynamic and thermal processes, but also important input variables for numerical weather forecasting, climate model assessment, and atmospheric pollution tracing. Traditional contact measurement methods such as radiosondes and ground meteorological stations are limited by technical bottlenecks such as insufficient temporal and spatial coverage (typical radiosonde observation time interval ≥ 12 hours, spatial resolution > 100km), high operation and maintenance costs (annual maintenance costs for a single station exceed 100,000 yuan), and are difficult to meet the needs of modern high-resolution meteorological monitoring. In contrast, atmospheric lidar, with its high temporal and spatial resolution, can obtain temperature and humidity information at different altitudes and regions in real time, and accurately capture the dynamic changes of atmospheric parameters; its high detection sensitivity enables it to have outstanding perception of weak signals, and can accurately obtain effective data in complex environments; coupled with its strong anti-interference ability, it can operate stably even in bad weather or in scenes with more interference. Because of these outstanding advantages, atmospheric lidar plays an irreplaceable and important role in atmospheric monitoring-related fields such as environmental protection and meteorology, and provides strong technical support for atmospheric science research and environmental monitoring.

[0003] At present, the temperature and humidity inversion methods based on lidar include: 1. Atmospheric temperature and humidity detection method based on Raman lidar: humidity is inverted using the nitrogen / water vapor Raman scattering signal ratio, combined with the pulled Raman signal to invert temperature. The solar background noise during the day causes the water vapor Raman signal-to-noise ratio (SNR) to decrease. The detection distance is only 500m, and it relies on the assumption of uniform atmosphere. The humidity error is about 10%-15% (1km altitude); 2. Differential absorption lidar (DIAL) water vapor concentration inversion method: 935nm / 820nm dual-wavelength differential absorption technology is used to invert water vapor. Aerosol interference leads to calculation deviation of absorption cross section, humidity error reaches 8%-12%, the system requires precise temperature control, and the operational stability is poor; 3. Temperature profile inversion method based on Mie-Raman lidar: The temperature is inverted by combining Mie scattering and rotational Raman signals. The temperature accuracy is ±1.5°C at night and deteriorates to ±3°C during the day. The problem of synchronous inversion of humidity is not solved. The existing technologies mainly use Raman scattering or differential absorption principles to detect temperature and humidity, but both have the following technical defects: 1. Daytime detection performance is limited: due to the extremely small Raman scattering cross section of water vapor (about 10 -30 cm 2 / sr), in the presence of strong solar background noise interference during the day, the signal-to-noise ratio drops sharply, resulting in an effective detection range that is usually less than 500 meters and unable to meet the all-weather monitoring requirements; 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, the humidity inversion error generally exceeds 15%, and the temperature error is greater than 2°C, far lower than the operational standards required by the World Meteorological Organization (WMO) (humidity error < 5%, temperature error < 1°C); 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. Especially in bad weather such as clouds, fog, and haze, the data reliability is significantly reduced. Summary of the Invention

[0004] 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 existing temperature and humidity inversion methods, 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. At the same time, the inversion accuracy of temperature and humidity can also be improved.

[0005] The technical solution adopted by the present invention to solve its technical problems is: An all-weather temperature and humidity inversion method based on lidar, comprising the following steps: S1. Obtain lidar data of relevant wavelengths required for water vapor and temperature inversion, and preprocess the lidar data; S2. Perform noise filtering on the lidar data preprocessed in S1; S3. Use the temperature and humidity inversion formula and substitute the calibration parameters to perform inversion processing on the lidar data after noise filtering in S2 to obtain the atmospheric temperature profile , atmospheric humidity profile .

[0006] Thus, through adaptive noise filtering, while ensuring signal smoothness, rich high-altitude information is retained, the detection range of temperature and humidity is improved, all-weather detection of temperature and humidity is ensured, and the inversion accuracy of temperature and humidity can be effectively improved. In addition, it can adapt to the complex atmospheric environment, with high data reliability, and further effectively support all-weather and long-term temperature and humidity data inversion.

[0007] Further, in S1, the 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.

[0008] Further, the S2 includes the following steps: S2-1. The original signal Pass through a low-pass filter and perform downsampling to obtain an approximation component ; S2-2. Pass the original signal through a high-pass filter and perform downsampling to obtain a detail component ; S2-3. Perform times of decomposition on the approximation component to form layers of high-frequency wavelet coefficients; S2-4. Quantize the high-frequency wavelet coefficients from the first layer to the th layer using a threshold to remove correlated noise; S2-5. Perform times of reconstruction on the quantized approximation component then perform upsampling, and make the approximation component pass through a low-pass filter and the detail component pass through a high-pass filter to obtain a reconstructed signal.

[0009] Furthermore, in S2-3, the quantization processing includes: hard threshold quantization processing and soft threshold quantization processing; The expression for the threshold processing is: ; Where: represents the data after threshold processing, represents the sign function, represents the data before threshold processing, represents the threshold, is the maximum value in. Thus, through threshold processing, it is possible to avoid the problem of poor continuity of the hard threshold and local mutations in signal changes, and also avoid the problem of signal distortion caused by soft threshold shrinking of signal coefficients, thereby effectively improving the inversion accuracy of temperature and humidity.

[0010] Furthermore, in S3, the expression for the high quantum number Raman scattering echo signal intensity is: ; The expression for the low quantum number Raman scattering echo signal intensity is: ; Where: represents the quantum number, represents the temperature, represents the height, represents the system constant, represents the quantum number at temperature The rotational Raman scattering cross-section, denotes the rotational Raman scattering cross-section at the quantum number at temperature denotes the atmospheric extinction coefficient, denotes the altitude.

[0011] Furthermore, in S3, the intensity ratio of the signal and the temperature has the following expression: ; where: , both denote calibration parameters; In S3, the calculation formula for the atmospheric temperature profile at altitude is: .

[0012] Furthermore, in S3, the intensity ratio of the signal and the temperature has the following expression: ; where: denotes the spectral line adjacent to , , , denote calibration parameters; In S3, the calculation formula for the atmospheric temperature profile at altitude is: .

[0013] Therefore, since the single Raman signal is too weak and difficult to extract, multiple Raman signals from adjacent Raman channels are introduced to further improve the accuracy of temperature inversion.

[0014] Furthermore, in S3, the calculation formula for the intensity of the vibrational Raman echo signal of nitrogen molecules received by the lidar is: ; The calculation formula for the intensity of the vibrational Raman echo signal of water vapor molecules received by the lidar is: ; where: denotes the Raman scattering channel factor of nitrogen molecules, denotes the outgoing power of the lidar, represents the Raman scattering area of nitrogen molecules, represents the number density of nitrogen molecules at height, represents the atmospheric transmittance of the lidar laser emission wavelength, represents 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, represents water vapor molecules at height of the number density of molecules, represents the atmospheric transmittance of the Raman scattering wavelength of water vapor molecules.

[0015] Furthermore, in S3, the water vapor mixing ratio expression is: ; system calibration constant expression is: ; atmospheric transmittance correction function expression is: ; then the water vapor mixing ratio expression is: ; Where: represents the mass of water vapor in the same volume at height, represents the mass of dry air in the same volume at height, system calibration constant obtained by calibration, atmospheric transmittance correction function obtained by calculating the atmospheric extinction, represents the lidar acquisition signal of water vapor molecules, represents the lidar acquisition signal of nitrogen molecules.

[0016] Furthermore, in S3, the atmospheric humidity profile expression is: ; saturation water vapor pressure calculation formula is: ; water vapor mixing ratio expression is: ; then the atmospheric humidity profile Can be simplified to: ; Where: represents the water vapor pressure, with the unit of , has the unit of , is the Celsius temperature, with the unit of , 0.622 represents the molar mass ratio of dry air to water vapor, represents the atmospheric pressure, with the unit of , represents the atmospheric pressure.

[0017] Compared with the prior art, the beneficial effects of the present invention are: 1. Through adaptive filtering processing, the present invention ensures signal smoothness while retaining rich high-altitude information, improves the detection distance of temperature and humidity, guarantees 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 and long-term inversion of temperature and humidity data.

[0018] 2. Through threshold processing, the present invention can not only avoid the problem of poor continuity of hard thresholds and local mutations in signal changes, but also avoid the problem of signal distortion caused by soft threshold shrinkage of signal coefficients, and thus can effectively improve the inversion accuracy of temperature and humidity.

[0019] 3. By introducing Raman signals from multiple adjacent Raman channels, the present invention solves the problem that a single Raman signal is too weak and difficult to extract, and can further improve the accuracy of temperature inversion. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention will be further described below with reference to the drawings and embodiments.

[0021] Figure 1 is the flowchart of the all-weather temperature and humidity inversion method based on lidar of the present invention; Figure 2 is the flowchart of S2 of the present invention; Figure 3 is the effect diagram of wavelet decomposition of the present invention; Figure 4 is the effect diagram of the N - th decomposition of the original signal of the present invention; Figure 5 is the effect diagram of wavelet reconstruction of the present invention; Figure 6 is the comparison diagram of inversion and sounding before and after wavelet transformation processing of the invention. DETAILED DESCRIPTION OF THE INVENTION

[0022] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are all simplified schematic diagrams, only illustrating the basic structure of the present invention in a schematic manner, so they only show the components related to the present invention.

[0023] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. are based on the orientation or positional relationships shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation on the present invention. In addition, features defined as "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise stated, the meaning of "a plurality" is two or more.

[0024] In the description of the present invention, it should be noted that unless otherwise clearly specified and defined, the terms "mounted", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0025] As Figures 1 to 6 shown, an all-weather temperature and humidity inversion method based on lidar includes the following steps: S1. Obtain lidar data of relevant wavelengths required for water vapor and temperature inversion, and preprocess the lidar data. S2. Perform noise filtering on the lidar data preprocessed in S1. S3. Use the temperature and humidity inversion formula and substitute the calibration parameters to perform inversion processing on the lidar data after noise filtering in S2 to obtain the atmospheric temperature profile and the atmospheric humidity profile . Thus, through adaptive filtering processing, while ensuring signal smoothness, rich high-altitude information is retained, the detection distance of temperature and humidity is increased, all-weather detection of temperature and humidity is ensured, 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 and long-term temperature and humidity data inversion.

[0026] Specifically, by combining water vapor mixing ratio and temperature data, the relative humidity of the atmosphere can be directly calculated, no longer relying on external sounding data or theoretical model data, which can reduce traditional assumption errors and enhance environmental adaptability to achieve integrated temperature and humidity inversion.

[0027] Specifically, the time resolution of the lidar is one minute and the spatial resolution is fifteen meters. Thus, the spatio-temporal resolution can be greatly improved.

[0028] Specifically, the lidar is a highly integrated optical transceiver module, signal modulation module, and processor, which can support long-term unattended observation and has low operation and maintenance costs.

[0029] Specifically, the current inversion method requires temperature values during extinction and backscattering, which are generally calculated through an atmospheric theoretical model. This inversion method can improve the accuracy of temperature and humidity inversion through the inverted temperature data.

[0030] In this embodiment, in S1, the 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.

[0031] In this embodiment, S2 includes the following steps: S2-1. Pass the original signal through a low-pass filter and perform downsampling to obtain an approximate component ; S2-2. Pass the original signal through a high-pass filter and perform downsampling to obtain a detail component ; S2-3. Perform times of decomposition on the approximate component to form layers of high-frequency wavelet coefficients; S2-4. Quantize the high-frequency wavelet coefficients from the first layer to the th layer using a threshold to remove correlated noise; S2-5. Perform times of reconstruction on the quantized approximate component , then perform upsampling, and make the approximate component pass through a low-pass filter and the detail component pass through a high-pass filter to obtain a reconstructed signal; In S2-3, the quantization processing includes: hard threshold quantization processing and soft threshold quantization processing; The expression for the threshold processing is: ; Where: represents the data after threshold processing, represents the sign function, represents the data before threshold processing, represents the threshold, is the maximum value in. Thus, through threshold processing, it is possible to avoid the problems of poor continuity of the hard threshold and local mutations in signal changes, and also avoid the problem of signal distortion caused by soft threshold shrinking of signal coefficients, thereby effectively improving the inversion accuracy of temperature and humidity.

[0032] In this embodiment, in S3, the intensity of the high quantum number Raman scattered echo signal has the expression: ; The intensity of the low quantum number Raman scattered echo signal has the expression: ; Where: represents the quantum number, represents the temperature, represents the height, represents the system constant, represents the rotational Raman scattering cross section of the quantum number at temperature represents the rotational Raman scattering cross section of the quantum number at temperature represents the atmospheric extinction coefficient, represents the height; The intensity ratio of the signal and the temperature has the expression: ; Where: , both represent calibration parameters; In S3, the calculation formula for the atmospheric temperature profile at height is: .

[0033] In this embodiment, in S3, the calculation formula for the intensity of the nitrogen molecular vibration Raman echo signal received by the lidar is: ; The calculation formula for the intensity of the water vapor molecular vibration Raman echo signal received by the lidar is: ; Among them: represents the Raman scattering channel factor of nitrogen molecules, represents the output power of the lidar, represents the Raman scattering area of nitrogen molecules, represents at the molecular number density at the height, represents the atmospheric transmittance of the lidar laser emission wavelength, represents 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, represents at the molecular number density at the height, represents the atmospheric transmittance of the Raman scattering wavelength of water vapor molecules; Water vapor mixing ratio The expression is: ; System calibration constant The expression is: ; Atmospheric transmittance correction function The expression is: ; Then the water vapor mixing ratio The expression is: ; Among them: represents the water vapor mass in the same volume at the height, represents the dry air mass in the same volume at the height, system calibration constant is obtained through calibration, and the atmospheric transmittance correction function is obtained by calculating the atmospheric extinction, represents the lidar acquisition signal of water vapor molecules, represents the lidar acquisition signal of nitrogen molecules; Atmospheric humidity profile The expression is: ; Saturation water vapor pressure The calculation formula is: ; Water vapor mixing ratio The expression is: ; Then the atmospheric humidity profile can be simplified to: ; Where: represents the water vapor pressure, with the unit of , The unit of is is the Celsius temperature, with the unit of , 0.622 represents the molar mass ratio of dry air to water vapor, represents the atmospheric pressure, with the unit of , represents the atmospheric pressure.

[0034] It should be noted that: in this embodiment, as Figure 6 shown, the detection distance during the day can reach three kilometers. Through the sounding profile comparison chart, there is a time deviation (within one hour) in the sounding data. The detection distance without wavelet denoising inversion is about two kilometers, and the detection distance after wavelet denoising inversion exceeds four kilometers (the four kilometers benefit from a water vapor mass at four kilometers on that day). Generally, the detection distance is three kilometers.

[0035] Embodiment 2: The difference from Embodiment 1 is that in S3, the intensity ratio of the signal and the temperature The expression between them is: ; Where: represents the adjacent spectral lines to , , represent calibration parameters; In S3, the calculation formula for the atmospheric temperature profile at the height is: .

[0036] Therefore, since a single Raman signal is too weak and difficult to extract, for this reason, Raman signals from multiple adjacent Raman channels will be introduced to further improve the accuracy of temperature inversion.

[0037] In summary, through adaptive filtering processing, the present invention retains rich high-altitude information while ensuring signal smoothness, improves the detection range of temperature and humidity, guarantees 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 and long-term inversion of temperature and humidity data. Through threshold processing, it can not only avoid the problem of poor continuity of hard thresholds and local mutations in signal changes, but also avoid the problem of signal distortion caused by soft threshold shrinking of signal coefficients, thereby effectively improving the inversion accuracy of temperature and humidity. By introducing Raman signals from multiple adjacent Raman channels, the problem of too weak single Raman signal and difficulty in extraction can be solved, and further improve the accuracy of temperature inversion.

[0038] Based on the ideal embodiments of the present invention as described above, through the above description, relevant staff can make various changes and modifications within the scope not deviating from the technical idea of the present invention. The technical scope of the present invention is not limited to the content in the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. An all-weather temperature and humidity inversion method based on lidar, characterized in that, Including the following steps: S1. Obtain lidar data of relevant wavelengths required for water vapor and temperature inversion, and preprocess the lidar data; S2. Perform noise filtering on the lidar data preprocessed in S1; S3. Use the temperature and humidity inversion formula and substitute the calibration parameters to perform inversion processing on the lidar data after S2 noise filtering to obtain the atmospheric temperature profile , the atmospheric humidity profile ; In S3, the atmospheric temperature profile at the altitude is calculated by the following formula: ​ ； Atmospheric humidity profile The calculation formula is as follows: ； Wherein: , , represent calibration parameters, represents the intensity ratio of the signal at the height , represents the atmospheric pressure, represents the water vapor mixing ratio, and 0.622 represents the molar mass ratio of dry air to water vapor, represents the saturated water vapor pressure, with the unit of .

2. The all-weather temperature and humidity inversion method based on lidar according to claim 1, wherein In S1, the 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.

3. The all-weather temperature and humidity inversion method based on lidar according to claim 1, characterized in that The said S2 includes the following steps: S2-1. Feed the original signal through a low-pass filter and downsample it to obtain an approximate component ; S2-2. Feed the original signal through a high-pass filter and perform downsampling to obtain the detail component ; S2-3. Perform decomposition on the approximate component for times to form layers of high-frequency wavelet coefficients; S2-4. Quantize the high-frequency wavelet coefficients from the first layer to the layer using a threshold to remove correlated noise; S2-5. For the approximated component after quantization processing perform reconstruction, then perform upsampling, and make the approximated component pass through a low-pass filter and the detailed component pass through a high-pass filter to obtain a reconstructed signal.

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

5. The all-weather temperature and humidity inversion method based on lidar according to claim 1, characterized in that In S3, the intensity of the Raman scattering echo signal with high quantum numbers is expressed as: ； Low quantum number Raman scattering echo signal intensity The expression is as follows: ； Wherein: represents the quantum number, represents the temperature, represents the altitude, represents the system constant, represents the rotational Raman scattering cross section of the quantum number at the temperature, represents the rotational Raman scattering cross section of the quantum number at the temperature, represents the atmospheric extinction coefficient, represents the altitude.

6. The all-weather temperature and humidity inversion method based on lidar according to claim 5, wherein In S3, the intensity ratio of the signals and the temperature The expression between them is: ； Wherein: and both represent calibration parameters; In S3, the atmospheric temperature profile at altitude is calculated by the following formula: ​ 。 7. The all-weather temperature and humidity inversion method based on lidar according to claim 5, characterized in that, In S3, the intensity ratio of the signals and the temperature The expression between them is: ； Wherein: represents the 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 vibrational Raman echo signal of nitrogen molecules received by the lidar is calculated by the formula: ； The intensity of the vibrational Raman echo signal of water vapor molecules received by the lidar The calculation formula is as follows: ； Wherein: represents the Raman scattering channel factor of nitrogen molecules, represents the output power of the lidar, represents the Raman scattering area of nitrogen molecules, represents at the molecular number density of nitrogen molecules at the height, represents the atmospheric transmittance of the lidar laser emission wavelength, represents 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, represents at the molecular number density of water vapor molecules at the height, represents the atmospheric transmittance of the Raman scattering wavelength of water vapor molecules.

9. The all-weather temperature and humidity inversion method based on lidar according to claim 8, wherein In S3, the water vapor mixing ratio is expressed as: ； System calibration constant The expression is as follows: ； Atmospheric transmittance correction function The expression is as follows: ； The water vapor mixing ratio is expressed as: ； Wherein: represents the water vapor mass in the same volume at the same altitude, represents the dry air mass in the same volume at the same altitude, system calibration constant obtained through calibration, atmospheric transmittance correction function obtained by calculating the atmospheric extinction, represents the lidar acquisition signal of water vapor molecules, represents the lidar acquisition signal of nitrogen molecules.

10. The all-weather temperature and humidity inversion method based on lidar according to claim 9, wherein In S3, the atmospheric humidity profile has the following expression: ； Saturation vapor pressure The calculation formula is as follows: ； Water vapor mixing ratio The expression is as follows: ； Wherein: represents the vapor pressure, with the unit of , is the Celsius temperature, with the unit of , represents the atmospheric pressure, with the unit of .

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