CN109709558B - Physical inversion method of space-borne microwave remote sensing land overhead PWV - Google Patents
Physical inversion method of space-borne microwave remote sensing land overhead PWV Download PDFInfo
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Abstract
The invention provides a physical inversion method of space-borne microwave remote sensing land overhead PWV, which starts from a disturbance form of an atmospheric radiation transmission equation and synchronously calculates an initial atmospheric humidity profile and the offset of the surface temperature by solving a linear equation set so as to realize the synchronous inversion of the surface brightness and the PWV of a window channel. The method provided by the invention is basically not influenced by the surface type except the extremely dry atmosphere. The algorithm is verified by using the measured data of ATMS in the United states, and the correlation coefficient, RMSE and bias of the inversion result and SuomiNet GPS PWV are respectively as follows: 0.95,0.05cm and 0.05cm. In addition, a linear correction model of the PWV result based on water vapor channel simulation and observation of the bright temperature difference is provided, the corrected PWV has better inosculation degree with the GPS PWV, and the correlation coefficient, RMSE and bias are respectively as follows: 0.98,0.02cm and 0.01cm.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a physical inversion method of space-borne microwave remote sensing terrestrial aerial PWV.
Background
Water vapor plays an important role in the researches of climate change, water circulation, energy balance and the like (Solomon et al 2007; zveryaev andAlllan 2005). In addition, it is also the most abundant greenhouse gas in the earth's atmosphere, and water vapor and its changes are the main driving force for weather and climate changes, which have important influence on the prediction of rainfall and medium and small scale severe weather (Dessler et al 2008; raval and Ramanathan 1989). The atmospheric water reducible (hereinafter referred to as PWV) refers to the total water vapor content integrated in the whole layer of atmosphere per unit area. It is a very important parameter in climate energy balance analysis, water circulation and numerical weather forecast applications (Nakamura et al 2004; smith et al 2000; trenberth et al 2009). In addition, PWV is also an important parameter affecting earth surface satellite remote sensing applications, such as auxiliary parameters of PWV needed for earth surface temperature inversion, atmosphere correction and the like (Julie et al 2015; li et al 2013; sobrino and Romaguera 2008.
Currently, there are several technical approaches to obtain PWV information, such as sounding, GPS, microwave radiometers, ground-based sun photometers, and satellite telemetry observations (Alshawafet al.2015; czajkowski et al 2002; firsov et al 2013; li et al 2003; liu et al 2017; seemann et al 2003; wang et al 2015). The satellite observation can effectively provide PWV information in a region or even a global range by virtue of unique characteristics of time and space resolution. Satellite remote sensing PWVs can be divided by band into Near Infrared (NIR), thermal Infrared (TIR) and microwave algorithms (Deeter 2007, gao and Kaufman 2003. NIR remote sensing is generally used to obtain PWV information with higher accuracy, such as NIR water vapor products of MODIS (Gao and kaufman 2003). However, the NIR algorithm is susceptible to cloud, aerosol, and it can only acquire PWV information in daytime situations. The TIR algorithm can acquire PWV information at day and night under clear sky conditions, but also cannot acquire PWV information of cloud regions because infrared radiation cannot penetrate cloud layers (Julie et al 2015; liu et al 2015; ren et al 2015).
The passive microwave remote sensing has small atmospheric interference, can penetrate through cloud layers and even rain areas to a certain degree, and can make up the defects of NIR and TIR remote sensing PWV (Bobylev et al 2010; grody et al 1980). The method has good development potential in all-weather PWV detection. Water vapor has weak absorption lines and strong absorption lines at 22.235GHz and 183.31GHz respectively, and at present, microwave water vapor remote sensing is mainly developed around the two wave bands (Bobylev et al 2010; grody 1976.
PWV inversion algorithms for passive microwave data can be roughly classified into 4 categories: statistical algorithms, semi-statistical algorithms, neural network algorithms and physical inversion algorithms (Aires et al.2001; alishouse et al.1990; bobylev et al.2010; boukabara et al.2010; deeter 2007; gridy et al.2017. In addition, passive microwave data is also often used in conjunction with infrared hyperspectral data to invert the atmospheric profile. The statistical and semi-statistical algorithm mainly constructs an empirical relationship between the microwave Brightness Temperature (BT) and the PWV to realize the inversion of the PWV. The neural network constructs the nonlinear relation between the PWV and the input parameters through training data so as to realize the inversion of the PWV. The physical model method is to consider the radiation transmission process of atmospheric microwaves, develop forward calculation on the basis of the given atmospheric and surface parameter initial fields, and finally realize the inversion of the PWV by realizing the minimization of a cost function, such as optimal estimation and a 1-dimensional variational algorithm. Generally, the above algorithm mostly needs the initial value information of the emissivity. The ocean surface is uniform, the emissivity is easy to estimate, and the inversion effect of the PWV over the ocean of the algorithms is good.
In contrast, the inversion of the above-ground PWV is a very challenging task due to the large uncertainty of the surface emissivity of the land (Boukabara et al 2010; wang et al 2015), which is currently still under investigation. The channel penetration near 22.235GHz is good, good effect is achieved in the inversion of the above-sea-surface empty PWV, but the spatial resolution is low. The channel space resolution near 183.31GHz is higher, and the channel space resolution is widely applied to PWV detection in recent years. At present, a plurality of instruments have 183.311GHz water vapor channels, such as AMSU/NOAA, AMSU/Metop, MHS/NOAA, MHS/FY-3, ATMS/NPP and the like. ATMS is one of five observation instruments carried on Suomi NPP, is a subsequent microwave detector of AMSU-A and MHS instruments, integrates temperature and humidity observation, and has higher spatial resolution, larger width and higher observation precision. The system is combined with a Cross-track infrared detector CrIs (Cross-track Infrared detectors) carried by the NPP to generate a global temperature and humidity profile data set with high resolution and serve weather forecast, and the ATMS also provides good opportunity for acquiring high-precision PWV.
NOAA NESDIS (National Environmental software, data, and Information Service) developed two inversion systems for Microwave detectors (Advanced Technology Microwave Sounder, hereinafter ATMS): miRS (Microwave Integrated regenerative System) and NUCAPS (NoAAunique combined automated processing System) are both operated in a business. The MiRS is a method based on one-dimensional variational (1D-Var), the microwave emissivity of different earth surfaces (e.g. land, sea ice and snow) can be inverted by using multi-channel brightness temperature data of a satellite, and the MiRS has all-weather inversion capability of atmospheric, cloud and earth surface parameters. NUCAPS is an inversion algorithm of succeeding AIRS, and is used for processing CrIS/ATMS data to obtain cloud-free radiation data and products such as atmospheric temperature, humidity profile and atmospheric trace gas. Both MiRS and NUCAPS use a plurality of channel light temperatures (e.g. water vapor and oxygen absorption channels) to perform inversion, and need prior information such as surface reflectivity and atmospheric profile, and the theory and the business process are complex.
Disclosure of Invention
The invention aims to provide a PWV physical inversion method based on water vapor absorption of ATMS 165.5GHz and 183.311GHz and window area channels.
A land-above-ground PWV physical inversion method comprises the following steps:
step 1: acquiring the earth surface temperature, the atmospheric temperature and the humidity profile forecast field of the ECMWF, then carrying out bilinear interpolation on the ECMWF forecast field according to the longitude and latitude and the time information of a satellite observation pixel, and taking the atmospheric profile and the earth surface temperature after interpolation as initial guess values of the atmospheric field and the earth surface temperature;
step 2: calculating the brightness temperature T of each channel of a 165GHz window area channel and two water vapor absorption channels near 183GHz of the satellite-borne microwave radiometer by using the observation angle of the satellite-borne microwave radiometer, the response function of the instrument channel, the pixel altitude and the initial guessed values of the atmospheric field and the surface temperature obtained in the step (1) f1 Atmospheric transmittance tau f1 To the atmosphereUpper radiation
And 3, step 3: disturbing the initial guess value of the atmospheric humidity profile w (p) obtained in the step (1), adjusting the initial guess value of the humidity profile to 1.10 w (p), and calculating the brightness temperature T of each channel of the microwave radiometer 165GHz window area channel and the two water vapor absorption channels near 183GHz after the atmospheric humidity profile is adjusted by utilizing the observation angle of the satellite-borne microwave radiometer, the response function of the instrument channel and the pixel altitude f2 Atmospheric transmittance tau f2 Atmospheric upward radiation
And 4, step 4: calculating the atmospheric transmittance tau f Atmospheric upward radiation
The partial derivatives of the humidity profile change, the atmospheric transmittance and the upward atmospheric radiation have the calculation formulas:
and 5: initial guess value T by using surface temperature gf Atmospheric transmittance τ f And calculated in step (4)
And
calculating coefficient C λ And D λ ；
And 6: calculating actual observation of 165GHz and two 183GHz nearby channels of satellite-borne microwave radiometerDifference delta T between brightness temperature and RTTOV simulated brightness temperature fn Then combining the coefficient C calculated in the step (5) λ And D λ Solving a linear equation set of the following formula based on a least square method to obtain correction quantities delta r and delta T of the surface temperature and humidity profile gf Finally, the inversion of the PWV is realized;
where n is the total number of channels used, δ T λn Is the calculation and observation of the bright temperature difference, C, of the different channels (f 1, f 2.., fn) under specific initial field conditions λn And D λn Can be calculated based on the earth surface, atmosphere prior information and radiation transmission mode.
Has the beneficial effects that:
the method provided by the invention is mainly developed based on light temperature data of a window area near 165GHz and a water vapor channel at 183.31GHz, and the offset of the initial guess value of the light temperature and the water vapor profile on the surface of the window area is synchronously inverted, so that the PWV inversion is realized. The method does not need prior information of the surface emissivity, and the required auxiliary data mainly comprises the atmospheric temperature and humidity profile. Because higher frequency channel observations are used in the inversion process, the spatial resolution of the PWV obtained is also relatively high.
Drawings
FIG. 1 shows ATMS data selecting research area and SuomiNet site location information;
FIG.2 is a flow chart of a land-above-ground PWV physical inversion method of the present invention;
FIG. 3 (a) is a plot of the inverse of PWV versus GPS PWV;
FIG. 3 (b) is a two-dimensional histogram of the error of the PWV inversion and VIIRS COD.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention are described below clearly and completely, and it is obvious that the described embodiments are some, not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
The data utilized by the invention mainly comprises the L1b microwave brightness temperature of ATMS, the L2 cloud optical thickness of VIIRS, the ECMWF internal atmospheric profile and the GPS PWV data of SuomiNet. The ECMWF data are mainly used for numerical simulation and serve as an initial field of the method, and are used for verifying the feasibility of the method in the simulation inversion process; GPS PWV is reference data for the authentication method; and the satellite observation data and the cloud observation data are used for actual inversion.
The ATMS detector is a new-generation microwave vertical detector which inherits AMSU-A/MHS and is developed, and provides atmospheric temperature and humidity information for meteorological service and climate application. Currently, ATMS is carried on NPP (NPOESS preliminary Programme) satellite and will become one of the main detecting instruments of JPSS-1/JPSS-2. ATMS has 22 detection channels, the first 15 channels are mainly used for temperature detection, and the last 7 channels are used for humidity detection, wherein the channels 1-7 and 16-17 are window area channels. Compared with AMSU-a and MHS, ATMS has a large difference in the number of channels, probe frequency, and polarization properties. ATMS 4, 19 and 21 are added detection channels, and the added detection channels can provide richer observation information for an atmosphere and surface parameter inversion and data assimilation system. The AMTS track width is 2300km, the point resolution of the channels 1-2 under the satellite is 75km, the channels 3-16 are 32km, the channels 17-22 are 169m, and the ATMS can provide more observation data than the traditional microwave radiometer.
The ATMS has five water vapor absorption channels (see table 1) near 183.31GHz, which is 183.31 +/-1.8 GHz and 183.31 +/-4.5 GHz more than AMSU, which is beneficial to improving the inversion accuracy of water vapor. The invention is to develop PWV inversion based on the window regions of ATMS channels 17-22 and the water vapor absorption channel.
TABLE 1 ATMS channel characteristics
The invention selects PWV data of hourly GPS of SuomiNet as reference data (http:// www. Suominet. Ucar. Edu/data. Html) to verify the PWV inversion result of North American area. A typical PWV precision for SuomiNet is 1-2mm.
The SuomiNet sites used in the present invention range primarily between 30.5-48.8N,68.0-124.5W (within the dashed box of FIG. 1), and the elevation ranges of these sites range between 0.006-2.92 km. Wherein most sites are located on land, a few sites are located in sea-land junction areas, the variation range of GPS PWV of the sites is 0.2-6.87cm between 2016, 8, 6 and 22, and the method has good representativeness.
In addition, the present invention uses the monthly and 3-hour averaged re-analytical data of ECMWF, including atmospheric temperature, humidity, pressure and potential height profiles, etc., which are used to perform radiation delivery simulation calculations and as the initial field of the process.
The PWV inversion method provided by the invention is based on a mathematical small disturbance theory, converts a radiation transmission equation into a linear equation, calculates the offset of the initial surface temperature and the atmospheric profile by solving a multi-element linear equation system, and finally realizes the inversion of PWV and LST (or surface BT).
Under the condition that atmospheric scattering effect is not considered in a microwave band, atmospheric top upward radiation mainly comprises ground surface radiation attenuated by atmosphere, atmospheric uplink radiation and atmospheric downlink radiation reflected by the ground surface and attenuated by atmosphere, and a Planck function meets Rayleigh-Jeans approximation in a microwave band interval, so that satellite observation brightness temperature can be expressed as follows:
in the formula, τ f And ε f Respectively transmittance and emissivity at frequency f, T s Is the temperature of the earth's surface,
is the equivalent brightness temperature of the downward radiation of the atmosphere, T sky Is the cosmic background radiation temperature (-2.7K),
is the upward radiation of the atmosphereEquivalent light temperature.
Surface emitted and reflected atmospheric down and cosmic background radiation may be treated with surface BT T gf To express, at this time, equation (1) can be further written as:
in the channel of the window area, the satellite observation bright temperature is closely related to the surface BT, and in the water vapor strong absorption channel (e.g. 183GHz), the observation bright temperature is mainly influenced by the atmospheric parameters (e.g. temperature and humidity profile) and hardly influenced by the surface BT.
We assume that the first-stress temperature profile is identical to the "true" profile, and that the vertical structural distribution of the humidity profile is identical to the "true" humidity profile, but requires a scaling factor γ for correction, which is defined as:
wherein w (p), w '(p) are the first-gusss and "true" water mixing profiles, respectively, and PW' and PW are the corresponding first-gusss and "true" PWV, respectively. At this time, γ and T gf Of the disturbances δ γ and δ T gf The resulting change in observed light temperature can be expressed as:
suppose that
Equation (4) then becomes linear (δ T) f ＝δrC f +δT gf D f ) Wherein T is gf And δ r are unknowns. At this time, applying it to two or more channels may constitute the following system of equations:
where n is the total number of channels used, δ T λn Is the calculated and observed Bright Temperature Difference (BTD), C, for the different channels (f 1, f 2.., fn) under certain initial field conditions λn And D λn Can be calculated using equation (5) based on the earth's surface, atmospheric prior information, and the radiation transmission mode (i.e., MODTRAN, RTTOV or CRTM).
Equation (6) is underdetermined, and n equations always have n +1 unknowns (n channel surfaces BT and 1 γ), which is mathematically a solution to the ill-conditioned equation. We can choose channels reasonably to reduce the uncertainty of equation solution (or reduce the unknown number of equations), for example, one window channel and several water vapor absorption channels around 183GHz can be chosen. As mentioned previously, the strong moisture absorption channel observation is mainly affected by the atmosphere, and hardly affected by the surfacent. Therefore, the surface BT of the strong absorption channel can be set as the surface BT of the window channel, so that obvious errors can not be caused, and the number of unknown numbers can be effectively reduced.
So configured, equation set (6) has only two unknowns: (1) a window area channel surface BT; and (2) a water vapor correction factor gamma. In general, γ may be set to 1,T gf The initial value is set as the window channel (Ch 17) observation bright temperature. With respect to equation (6), δ r and δ T can be found based on the least squares method gf The final inversion value of PWV is PW x (1 + delta gamma), and the inversion value of the window area channel surfacenT is T gf +δT gf . The method can synchronously invert the surface brightness and the PWV, does not need surface emissivity information, and avoids inversion errors caused by uncertainty of prior emissivity.
Referring to fig.2, fig.2 is a flowchart of a land-above-ground PWV physical inversion method according to the present invention, which includes the following steps:
step 1: downloading a surface temperature and atmospheric temperature and humidity profile forecast field of an European middle-term Weather forecast center (ECMWF), performing bilinear interpolation on the ECMWF forecast field according to longitude and latitude and time information of a satellite observation pixel, and taking the interpolated atmospheric profile and surface temperature as initial guesses of an atmospheric field and surface temperature;
step 2: inputting the observation angle, the instrument channel response function, the pixel altitude and the initial guess values of the atmospheric field and the earth surface temperature obtained in the step (1) into an atmospheric radiation transmission rapid calculation mode RTTOV 11.2) to calculate the brightness temperature (T) of each channel of a 165GHz window area channel and two 183GHz accessory water vapor absorption channels of a satellite-borne microwave radiometer (such as ATMS) f1 ) Atmospheric transmittance (tau) f1 ) Atmospheric upward radiation
And step 3: disturbing the initial guess value of the atmospheric humidity profile w (p) obtained in the step (1), adjusting the initial guess value of the humidity profile to 1.10 w (p), simultaneously inputting the observation angle of the satellite-borne microwave radiometer, the instrument channel response function and the pixel altitude into an atmospheric radiation transmission rapid calculation mode RTTOV11.2, and calculating the brightness temperature (T) of each channel of a microwave radiometer (such as an ATMS) 165GHz window area channel and two 183GHz accessory water vapor absorption channels after the atmospheric humidity profile is adjusted f2 ) Atmospheric transmittance (tau) f2 ) Atmospheric upward radiation
And 4, step 4: calculating the atmospheric transmittance (tau) f ) Atmospheric upward radiation
The partial derivatives of the humidity profile change, the atmospheric transmittance and the upward atmospheric radiation have the calculation formulas:
and 5: using the initial guess value T of the surface temperature gf Atmospheric transmittance τ f And calculated in step (4)
And
calculating the coefficient C of equation (5) λ And D λ ；
And 6: calculating the difference delta T between the actually observed bright temperature and the RTTOV simulated bright temperature of the channels around 165GHz and two 183GHz of the satellite-borne microwave radiometer fn Then combining the coefficient C calculated in the step (5) λ And D λ The linear equation set of equation (6) is solved based on the least square method. Obtaining correction delta r and delta T of surface temperature and humidity profile gf And finally realizing the inversion of the PWV, wherein the inversion value of the PWV is PW x (1 + delta gamma).
Fig.2 shows a flow chart of the proposed PWV inversion method, and after an initial field (e.g. atmospheric profile and surface BT) and observation geometry information are given, a forward model (e.g. rttov) can be used to calculate atmospheric transmittance, atmospheric upward and downward radiation and brightness temperature of each channel, compare simulation and observation BTD of each channel, and solve an equation set (eq. (6)) to realize synchronous inversion of water vapor and surface BT.
To further evaluate the PWV inversion method, we validated the method using the observations of ATMS. The selected study area was the united states area (fig. 2) and the PWV reference data was GPS PWV data from SuomiNet. The study used data between 12 days 8/2015 and 10 days 11/2015. Inversion is only performed if the ATMS observation points are located at points within 0.15 x 0.15 deg. around the GPS site.
The ATMS channel emittance was set to 1, the initial surface temperature was set to the light temperature of Ch17, and the initial atmospheric profile was taken from the 6h forecast data (section 2.2) for the GFS mentioned earlier. Furthermore, the scattering effect of the cloud is not taken into account in the forward calculation process. We evaluated the sensitivity of the method to clouds using the VIIRS/NPP Cloud Optical Depth (COD) product.
The matching degree of the inversion result of the PWV and the GPS PWV is better, and the inversion value of the PWV and R2, RMSE and bias of the GPS PWV are respectively as follows: 0.895,0.43cm and-0.02 cm (FIG. 3 (a)). Wherein 78.9% of PWV inversion error is less than 0.5cm, and 96.2% of pixel point PWV error is less than 1.0cm. When the PWV is larger than 3.0cm, the matching degree of the inversion value of the PWV and the GPS is better, and the inversion value of the PWV is closer to a 1. When the PWV is larger than 5.0cm, the error of the PWV is larger, and a certain degree of overestimation exists.
We also analyzed the PWV error as a function of VIIRS COD (fig. 3 (b)), and it can be seen that the PWV inversion error did not increase with increasing COD, and when the average VIIRS COD of ATMS pixel is less than 30, the PWV error decreased with increasing COD. When COD is more than 20, the error of PWV is mostly less than 1.00cm. This means that the presence of the cloud does not significantly affect the PWV inversion results.
The invention develops forward simulation calculation, sensitivity analysis and simulation inversion test aiming at the instrument characteristics of ATMS. The results show that except for extremely dry atmosphere (e.g. PWV <0.25 cm), the PWV physical inversion method based on the ATMS window region of 165.5GHz and absorption channel = near 183.31GHz is less affected by the surface type, which means that the new method effectively reduces the effect of emissivity uncertainty on the PWV inversion results.
The method is verified by ATMS data in the United states, the inversion result and the GPS PWV goodness of fit are good, and the inversion error is relatively large under the condition of high water vapor. After analyzing the relation between the ATMS water vapor channel observation and the simulated BTD and PWV errors, the ATMS water vapor channel observation and the simulated BTD and PWV errors are found to have a better linear relation. Based on the relation, a simple linear correction model is provided, and the result shows that the steam accuracy after correction is obviously improved, especially for the condition of high steam. In addition, the MiRS L2 PWV product of ATMS is evaluated, and the result shows that the inversion accuracy of the PWV based on the method provided by the invention is equivalent to that of the MiRS L2 PWV.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (1)
1. A land-above-ground PWV physical inversion method is characterized by comprising the following steps:
step 1: acquiring the earth surface temperature, the atmospheric temperature and the humidity profile forecast field of the ECMWF, then carrying out bilinear interpolation on the ECMWF forecast field according to the longitude and latitude and the time information of a satellite observation pixel, and taking the atmospheric profile and the earth surface temperature after interpolation as initial guess values of the atmospheric field and the earth surface temperature;
step 2: calculating the brightness temperature T of each channel of a 165GHz window area channel and two water vapor absorption channels near 183GHz of the satellite-borne microwave radiometer by using the observation angle, the instrument channel response function, the pixel altitude and the initial guess values of the atmospheric field and the surface temperature obtained in the step (1) f1 Atmospheric transmittance tau f1 Atmospheric upward radiation
And 3, step 3: disturbing the initial guess value of the atmospheric humidity profile w (p) obtained in the step (1), adjusting the initial guess value of the humidity profile to 1.10 w (p), and calculating the brightness temperature T of each channel of the microwave radiometer 165GHz window area channel and the two water vapor absorption channels near 183GHz after the atmospheric humidity profile is adjusted by utilizing the observation angle of the satellite-borne microwave radiometer, the response function of the instrument channel and the pixel altitude f2 Atmospheric transmittance tau f2 Atmospheric upward radiation
And 4, step 4: calculating the atmospheric transmittance tau f Atmospheric upward radiation
The partial derivatives of the humidity profile change, the atmospheric transmittance and the upward atmospheric radiation have the calculation formulas:
and 5: using the initial guess value T of the surface temperature gf Atmospheric transmittance τ f And calculated in step (4)
And
calculating coefficient C λ And D λ ；
Step 6: calculating the difference delta T between the actual observed bright temperature and the RTTOV simulated bright temperature of the channels around 165GHz and two 183GHz of the satellite-borne microwave radiometer fn Then combining the coefficient C calculated in the step (5) λ And D λ Solving a linear equation set of the following formula based on a least square method to obtain correction quantities delta r and delta T of the surface temperature and humidity profile gf And finally realizing the inversion of PWV:
where n is the total number of channels used, δ T λn Is the calculation and observation of the bright temperature difference, C, of the different channels under the condition of a specific initial field λn And D λn Can be calculated based on the earth surface, the atmosphere prior information and the radiation transmission mode.
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