CN116466368A - Dust extinction coefficient profile estimation method based on laser radar and satellite data - Google Patents

Dust extinction coefficient profile estimation method based on laser radar and satellite data Download PDF

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CN116466368A
CN116466368A CN202310717700.1A CN202310717700A CN116466368A CN 116466368 A CN116466368 A CN 116466368A CN 202310717700 A CN202310717700 A CN 202310717700A CN 116466368 A CN116466368 A CN 116466368A
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CN116466368B (en
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范鑫
丁虹鑫
王新宇
周炳旭
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CHENGDU YUANWANG TECHNOLOGY CO LTD
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Abstract

The invention relates to a dust extinction coefficient profile estimation method based on laser radar and satellite data, which belongs to the technical field of meteorological radar detection, and comprises the steps of collecting dust profile data, processing to obtain a space-time matching data set, and calculating the reflectivity of a visible light channel and the brightness temperature value of an infrared channel in a simulation manner; disturbing the sand and dust profile data, calculating to obtain the reflectivity of a visible light channel and the brightness temperature value of an infrared channel under the disturbance condition of different sand and dust parameters, and selecting a sand and dust sensitive channel; extracting a dust extinction coefficient profile of the synchronous laser radar to form a visible light/infrared observation and dust extinction coefficient data set; and inputting the data into a recurrent neural network for training, and estimating the data through a trained model. The invention considers the number concentration, effective particle radius and shape information of visible light observable sand and dust, and the infrared channel can observe the height and optical thickness information of sand and dust and combine the advantages of high space-time resolution observation, thus being capable of carrying out large-scale and high-efficiency sand and dust extinction coefficient profile estimation.

Description

Dust extinction coefficient profile estimation method based on laser radar and satellite data
Technical Field
The invention relates to the technical field of meteorological radar detection, in particular to a dust extinction coefficient profile estimation method based on laser radar and satellite data.
Background
The sand and dust weather refers to the phenomenon that large granular sand and dust generated by the soil in dry and arid areas under the action of wind erosion is involved in the atmosphere, so that disastrous weather with the horizontal visibility of less than 1 km is caused. The physical and chemical properties of the dust particles in the air change during the movement of the air stream. The sand weather can cause serious degradation of visibility and obvious degradation of air quality, and has serious threat to the transportation, production and life and physical health of people.
The extinction coefficient of sand is related to the size, concentration, etc. of the sand particles, and is directly related to the visibility of the sand weather. Thus, studies of the extinction coefficient of sand are beneficial for accurate measurement of the visibility of sand weather. At present, the direct observation of the extinction coefficient of sand and dust adopts a ground meteorological observation and pollution monitor, the observation means is time-consuming and labor-consuming and limited by the number of instruments, and the observation range is very limited. With the maturation of satellite passive remote sensing technology, the method can be used for realizing real-time dynamic and large-scale monitoring of the sand and dust process by utilizing the difference of scattering and absorption characteristics of sand and dust in different observation channels of the satellite. However, this observation is not sufficient to provide highly accurate information of the dust microphysics parameters, limited by the way of passive remote sensing and the satellite mode of operation. In addition, existing atmosphere models do not describe well the varying characteristics of sand on a vertical profile. The laser radar is used as active remote sensing equipment, can accurately detect the optical characteristics and the space-time distribution characteristics of sand and dust, can directly acquire accurate sand and dust extinction coefficient profile information, and has a too small monitoring range. By means of the single detection means, accurate measurement of the extinction coefficient profile of the sand dust in a large range cannot be achieved, influence of the type of the aerosol of the sand dust on observation data is less considered in current research, and an efficient and accurate estimation method capable of estimating the extinction coefficient profile of the sand dust in various types is lacking.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a dust extinction coefficient profile estimation method based on laser radar and satellite data, and solves the defects of the existing dust extinction coefficient profile estimation method.
The aim of the invention is achieved by the following technical scheme: the method for estimating the sand extinction coefficient profile based on the laser radar and satellite data comprises the following steps:
step one, collecting sand and dust profile data, forming a space-time matching data set according to time and position information of the sand and dust profile data, setting aerosol types of the sand and dust, inputting the aerosol types and the space-time matching data set into a radiation transmission model to be solved, and simulating to calculate the reflectivity of a visible light channel and the brightness temperature value of an infrared channel under the condition of the sand and dust;
step two, the sand and dust profile data are disturbed to form a new sand and dust data set, the new sand and dust data set is input into an atmospheric radiation transmission model to calculate the reflectivity of a visible light channel and the brightness temperature value of an infrared channel under the condition of different sand and dust parameter disturbance, the brightness temperature difference variable quantity between any two infrared channels and the sand and dust estimation degree of freedom data of each channel are calculated, and a sand and dust sensitive channel is obtained;
step three, extracting synchronous laser radar observation data to screen sand and dust data, combining a sand and dust sensitive channel to form a visible light/infrared observation and sand and dust extinction coefficient data set, inputting the data set into a recurrent neural network for training, and generating a recurrent neural network estimation model of a sand and dust extinction coefficient profile;
and step four, obtaining the visible light reflectivity and the infrared channel bright temperature observation of a real satellite, extracting the visible light reflectivity and the infrared bright temperature of the sand-dust sensitive channel by combining the sand-dust sensitive channel, and inputting the visible light reflectivity and the infrared bright temperature into a recurrent neural network estimation model to realize the estimation of the sand extinction coefficient profile.
The first step specifically comprises the following steps:
a1, collecting MERRA sand and dust profile data, and extracting MERRA atmospheric temperature, humidity profile and cloud parameter data according to time and position information of the MERRA sand and dust profile data to form an atmospheric temperature, humidity profile, cloud parameter and sand and dust profile space-time matching data set under the condition of sand and dust weather;
a2, setting aerosol types of sand and dust, extracting single scattering albedo, extinction coefficient and phase function of the set sand and dust types according to a sand and dust optical database, inputting the single scattering albedo, the extinction coefficient and the phase function and a space-time matching data set collected in the step A1 into an atmospheric radiation transmission model, setting simulation conditions and instrument channel parameters of a visible light and infrared imager, and running the atmospheric radiation transmission model to simulate and calculate visible light channel reflectivity and bright temperature values of an infrared channel under the sand and dust condition.
The second step specifically comprises the following steps:
b1, disturbing MERRA sand and dust profile data to form a new sand and dust data set, and re-inputting the new sand and dust data set into an atmospheric radiation transmission model to calculate and obtain the reflectivity of a visible light channel and the brightness temperature value of an infrared channel under the condition of different sand and dust parameter disturbance;
b2, analyzing the visible light reflectivity and the infrared brightness temperature variation of each channel under the condition of sand parameter variation, calculating the brightness temperature variation between any two infrared channels, calculating sand estimation degree of freedom data of each channel, sorting the reflectivity variation, the infrared brightness temperature variation, the brightness temperature variation among the infrared channels and the sand estimation degree of freedom of each channel according to the absolute value of the numerical values from large to small, and selecting the visible light channel and the infrared channel of the first half of the sorting as a sand sensitive channel.
The third step specifically comprises the following steps:
c1, extracting synchronous laser radar observation data to screen sand data according to time and position information when sand and dust provided by MERRA sand and dust profile data occur, taking a laser radar extinction coefficient reserved after screening as a sand and dust extinction coefficient profile, and extracting visible light reflectivity and infrared brightness temperature of a sand and dust sensitive channel according to the selected sand and dust sensitive channel to form a visible light/infrared observation and sand and dust extinction coefficient data set;
and C2, taking the visible light reflectivity and the infrared brightness temperature of the sand and dust sensitive channel obtained in the step C1 as input, taking the sand and dust extinction coefficient profile as output, inputting the input and output data sets into a recurrent neural network together, and training, verifying and evaluating the network model to generate a recurrent neural network estimation model of the sand and dust extinction coefficient profile.
The MERRA dust profile data includes: the mass concentration of the sand near the ground, the mass density of the sand, the 550nm optical thickness of the sand aerosol and the sand mixing ratio profile; perturbation of the MERRA dust profile data included increasing the dust near ground mass concentration, dust mass density, dust aerosol 550nm optical thickness, and dust mix ratio profile by ± 5%, ±10%, ±15%, ±20%, ±25% and ± 30%, respectively, on the basis of the original values.
The calculation formula of the degree of freedom is as follows:
，
wherein lambda is i Representing normalized jacobian matrix K for each channel n Is used to determine the singular value of (c),where K is the weight function of each channel, S ε Observing covariance for channel S a Is a priori covariance.
The invention has the following advantages: the dust extinction coefficient profile estimation method based on the laser radar and satellite data fully considers the number concentration, effective particle radius and shape information of visible light observable dust, the height and optical thickness information of visible light observable dust of an infrared channel, and the advantages of wide-range and high space-time resolution observation of visible light and infrared channels of a meteorological satellite, and can perform wide-range and high space-time resolution three-dimensional dust extinction coefficient profile estimation, and sensitivity analysis and selection are performed on the channels, so that the algorithm calculation speed is high, the accuracy is high, and the applicability is good.
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FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a flow chart of the present invention for channel selection by radiation delivery calculation.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Accordingly, the following detailed description of the embodiments of the present application, provided in connection with the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application. The invention is further described below with reference to the accompanying drawings.
The invention particularly relates to a dust extinction coefficient profile estimation method based on laser radar and satellite data, which comprises the following steps: and collecting MERRA sand and dust profile data, extracting MERRA atmospheric temperature, humidity profile and cloud parameter data, forming an atmospheric temperature, humidity profile, cloud parameter and sand and dust profile space-time matching data set, and simulating and calculating the reflectivity of a visible light channel and the brightness temperature value of an infrared channel under the sand and dust condition. And disturbing the mass concentration of the sand near the ground, the mass density of the sand, the 550nm optical thickness of the sand aerosol and the sand mixing ratio profile, and calculating to obtain the reflectivity of the visible light channel and the brightness temperature value of the infrared channel under the condition of different sand parameters disturbance. And (5) performing sand sensitivity analysis and freedom degree calculation, and selecting a sand sensitivity channel. And extracting the sand extinction coefficient profile of the synchronous laser radar according to the time and position information provided by the MERRA sand profile when the sand occurs, and forming a visible light/infrared observation and sand extinction coefficient data set. The visible light reflectivity and the infrared brightness temperature of the sand and dust sensitive channel are used as input, the sand and dust extinction coefficient profile is used as output, the data set is input into a recurrent neural network, training, verification and evaluation are carried out on the model, a recurrent neural network estimation model of the sand and dust extinction coefficient profile is generated, and the acquired real data is estimated through the recurrent neural network estimation model.
As shown in fig. 1 and 2, the following are specifically included:
step 1: and (3) collecting MERRA (latest atmospheric real-time re-analysis data in modern satellite age) sand and dust profile data, extracting MERRA atmospheric temperature, humidity profile and cloud parameter data according to time and position information of the MERRA sand and dust profile data, and forming an atmospheric temperature, humidity profile, cloud parameter and sand and dust profile space-time matching data set under the sand and dust weather condition.
The sand and dust profile data mainly comprise: near ground mass concentration of sand, mass density of sand, 550nm optical thickness of sand aerosol, and sand mixing ratio profile.
The cloud parameter data mainly comprises: cloud amount, cloud type, yun Yetai water mass fraction, water cloud optical thickness, and ice cloud optical thickness.
Step 2: setting the aerosol type of the sand, and extracting the single scattering albedo, the extinction coefficient and the phase function of the set sand type according to the OPAC sand optical database. Setting visible light and infrared channel parameters of a medium resolution imaging spectrometer (MODIS), and simultaneously inputting the space-time matching data set collected in the step 1 and single scattering albedo, extinction coefficient and phase function of the dust aerosol into an atmospheric radiation transmission model MODTRA. Meanwhile, simulation conditions are defined, including an atmosphere model, ground surface characteristics, zenith angles, solar altitude angles and the like, instrument channel parameters of the visible light and infrared imaging instrument are set, MODTRA model software is operated after the parameters are set, and the reflectivity of an MODIS visible light channel and the brightness temperature value of an MODIS infrared channel under the condition of sand and dust can be calculated in a simulation mode.
The instrument channel parameters of a medium resolution imaging spectrometer (MODIS) visible light and infrared imaging instrument are the center wavelength of the channel, the response function of the instrument, the spectral resolution and the equivalent noise temperature difference.
Step 3: and (3) disturbing the mass concentration of the sand and dust near the ground, the mass density of the sand and dust, the 550nm optical thickness of the sand and dust aerosol and the sand and dust mixing ratio profile obtained in the step (1) to form a new sand and dust data set, re-inputting the new sand and dust data set into an atmosphere radiation transmission model MODTRAN, and calculating to obtain the reflectivity of the visible light channel and the brightness temperature value of the infrared channel under different sand and dust parameter disturbance conditions.
Wherein, the near-ground mass concentration of the sand, the mass density of the sand, the 550nm optical thickness of the aerosol of the sand and the profile of the mixing ratio of the sand are disturbed, which means that the sand is respectively increased by plus or minus 5 percent, plus or minus 10 percent, plus or minus 15 percent, plus or minus 20 percent, plus or minus 25 percent and plus or minus 30 percent on the basis of the original values.
Step 4: and (3) analyzing the change amounts of visible light reflectivity and infrared brightness temperature of each channel of the MODIS and the change amount of brightness temperature difference between the infrared channels under the condition of changing the sand and dust parameters based on the calculated data in the step (3), and sequencing the change amounts of the reflectivity and the infrared brightness temperature of each channel of the MODIS and the change amount of brightness temperature difference between the infrared channels according to the absolute value of the numerical values from large to small. Based on the data of the steps 2 and 3, calculating the sand and dust estimation freedom degree data of each channel, and sequencing the sand and dust estimation freedom degrees according to the numerical value from large to small. And selecting the first half channels of reflectivity variation, infrared brightness temperature variation, brightness temperature difference variation among infrared channels and degree of freedom sequencing as sand and dust sensitive channels.
Wherein the degree of freedom is calculated by the following formula:
degree of freedom (d):，
wherein lambda is i Is a MODIS normalized jacobian matrix (K) n ) Is used to determine the singular value of (c),wherein K is the weight function of each channel of MODIS, S ε Observing covariance for channel S a Is a priori covariance.
Step 5: and extracting synchronous 532nm laser radar observation data according to time and position information of the occurrence of sand provided by the MERRA sand profile, screening the sand data, and taking the extinction coefficient of the 532nm laser radar reserved after screening as the sand extinction coefficient profile. And (3) extracting the visible light reflectivity and the infrared brightness temperature of the sand and dust sensitive channel according to the sand and dust sensitive channel selected in the step (4) to form a visible light/infrared observation and sand and dust extinction coefficient data set.
Step 6: taking the MODIS visible light reflectivity and the infrared brightness temperature of the sand and dust sensitive channel obtained in the step 5 as input data sets, taking the 532nm laser radar sand and dust extinction coefficient profile as output data sets, inputting the input and output data sets into a recurrent neural network together, training, verifying and evaluating the model, and generating a recurrent neural network estimation model of the sand and dust extinction coefficient profile.
Step 7: and (3) acquiring real MODIS satellite visible light reflectivity and infrared channel bright temperature observation, extracting the MODIS visible light reflectivity and infrared bright temperature of the sand and dust sensitive channel by combining the sand and dust sensitive channel acquired in the step (5), inputting the MODIS visible light reflectivity and infrared bright temperature to the recursive neural network estimation model constructed in the step (6), and realizing the estimation of the sand and dust extinction coefficient profile.
The foregoing is merely a preferred embodiment of the invention, and it is to be understood that the invention is not limited to the form disclosed herein but is not to be construed as excluding other embodiments, but is capable of numerous other combinations, modifications and environments and is capable of modifications within the scope of the inventive concept, either as taught or as a matter of routine skill or knowledge in the relevant art. And that modifications and variations which do not depart from the spirit and scope of the invention are intended to be within the scope of the appended claims.

Claims (6)

1. The dust extinction coefficient profile estimation method based on the laser radar and satellite data is characterized by comprising the following steps of: the estimation method comprises the following steps:
step one, collecting sand and dust profile data, forming a space-time matching data set according to time and position information of the sand and dust profile data, setting aerosol types of the sand and dust, inputting the aerosol types and the space-time matching data set into a radiation transmission model to be solved, and simulating to calculate the reflectivity of a visible light channel and the brightness temperature value of an infrared channel under the condition of the sand and dust;
step two, the sand and dust profile data are disturbed to form a new sand and dust data set, the new sand and dust data set is input into an atmospheric radiation transmission model to calculate the reflectivity of a visible light channel and the brightness temperature value of an infrared channel under the condition of different sand and dust parameter disturbance, the brightness temperature difference variable quantity between any two infrared channels and the sand and dust estimation degree of freedom data of each channel are calculated, and a sand and dust sensitive channel is obtained;
step three, extracting synchronous laser radar observation data to screen sand and dust data, combining a sand and dust sensitive channel to form a visible light/infrared observation and sand and dust extinction coefficient data set, inputting the data set into a recurrent neural network for training, and generating a recurrent neural network estimation model of a sand and dust extinction coefficient profile;
and step four, obtaining the visible light reflectivity and the infrared channel bright temperature observation of a real satellite, extracting the visible light reflectivity and the infrared bright temperature of the sand-dust sensitive channel by combining the sand-dust sensitive channel, and inputting the visible light reflectivity and the infrared bright temperature into a recurrent neural network estimation model to realize the estimation of the sand extinction coefficient profile.
2. The method for estimating a sand extinction coefficient profile based on lidar and satellite data of claim 1, wherein: the first step specifically comprises the following steps:
a1, collecting MERRA sand and dust profile data, and extracting MERRA atmospheric temperature, humidity profile and cloud parameter data according to time and position information of the MERRA sand and dust profile data to form an atmospheric temperature, humidity profile, cloud parameter and sand and dust profile space-time matching data set under the condition of sand and dust weather;
a2, setting aerosol types of sand and dust, extracting single scattering albedo, extinction coefficient and phase function of the set sand and dust types according to a sand and dust optical database, inputting the single scattering albedo, the extinction coefficient and the phase function and a space-time matching data set collected in the step A1 into an atmospheric radiation transmission model, setting simulation conditions and instrument channel parameters of a visible light and infrared imager, and running the atmospheric radiation transmission model to simulate and calculate visible light channel reflectivity and bright temperature values of an infrared channel under the sand and dust condition.
3. The method for estimating a sand extinction coefficient profile based on lidar and satellite data of claim 2, wherein: the second step specifically comprises the following steps:
b1, disturbing MERRA sand and dust profile data to form a new sand and dust data set, and re-inputting the new sand and dust data set into an atmospheric radiation transmission model to calculate and obtain the reflectivity of a visible light channel and the brightness temperature value of an infrared channel under the condition of different sand and dust parameter disturbance;
b2, analyzing the visible light reflectivity and the infrared brightness temperature variation of each channel under the condition of sand parameter variation, calculating the brightness temperature variation between any two infrared channels, calculating sand estimation degree of freedom data of each channel, sorting the reflectivity variation, the infrared brightness temperature variation, the brightness temperature variation among the infrared channels and the sand estimation degree of freedom of each channel according to the absolute value of the numerical values from large to small, and selecting the visible light channel and the infrared channel of the first half of the sorting as a sand sensitive channel.
4. A method of estimating a sand extinction coefficient profile based on lidar and satellite data as claimed in claim 3, wherein: the third step specifically comprises the following steps:
c1, extracting synchronous laser radar observation data to screen sand data according to time and position information when sand and dust provided by MERRA sand and dust profile data occur, taking a laser radar extinction coefficient reserved after screening as a sand and dust extinction coefficient profile, and extracting visible light reflectivity and infrared brightness temperature of a sand and dust sensitive channel according to the selected sand and dust sensitive channel to form a visible light/infrared observation and sand and dust extinction coefficient data set;
and C2, taking the visible light reflectivity and the infrared brightness temperature of the sand and dust sensitive channel obtained in the step C1 as input, taking the sand and dust extinction coefficient profile as output, inputting the input and output data sets into a recurrent neural network together, and training, verifying and evaluating the network model to generate a recurrent neural network estimation model of the sand and dust extinction coefficient profile.
5. A method of estimating a sand extinction coefficient profile based on lidar and satellite data as claimed in claim 3, wherein: the MERRA dust profile data includes: the mass concentration of the sand near the ground, the mass density of the sand, the 550nm optical thickness of the sand aerosol and the sand mixing ratio profile; perturbation of the MERRA dust profile data included increasing the dust near ground mass concentration, dust mass density, dust aerosol 550nm optical thickness, and dust mix ratio profile by ± 5%, ±10%, ±15%, ±20%, ±25% and ± 30%, respectively, on the basis of the original values.
6. A method of estimating a sand extinction coefficient profile based on lidar and satellite data as claimed in claim 3, wherein: the calculation formula of the degree of freedom is as follows:
，
wherein lambda is i Representing normalized jacobian matrix K for each channel n Is used to determine the singular value of (c),where K is the weight function of each channel, S ε Observing covariance for channel S a Is a priori covariance.
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