CN116449331A - Dust particle number concentration estimation method based on W-band radar and meteorological satellite - Google Patents
Dust particle number concentration estimation method based on W-band radar and meteorological satellite Download PDFInfo
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- G—PHYSICS
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/06—Investigating concentration of particle suspensions
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/06—Investigating concentration of particle suspensions
- G01N15/0656—Investigating concentration of particle suspensions using electric, e.g. electrostatic methods or magnetic methods
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Abstract
The invention relates to a dust particle number concentration estimation method based on a W-band radar and a meteorological satellite, which belongs to the technical field of meteorological radar detection and comprises the steps of collecting earth surface temperature, atmospheric temperature and humidity profile, dust assimilation data and dust aerosol observation data; identifying outliers and culling by computing a Z-score to obtain a reliable dataset; matching the data sets to form a matching data set of surface temperature, atmospheric temperature, humidity profile and dust aerosol profile; calculating the reflectivity of a visible light channel, the brightness of an infrared channel and the backscattering coefficient of a laser radar by using RTTOV simulation; adjusting the particle number concentration, simulating, and selecting a sand-dust sensitive channel; and inverting the particle number concentration of the sand dust by utilizing the optimal estimation and Newton iteration method. The invention fully utilizes the data of the W-band radar and the satellite, can acquire sand and dust information from different electromagnetic bands and different angles, and improves the estimation precision and space-time coverage of the sand and dust particle number concentration.
Description
Technical Field
The invention relates to the technical field of meteorological radar detection, in particular to a dust particle number concentration estimation method based on a W-band radar and meteorological satellites.
Background
The sand and dust weather is a weather phenomenon that ground dust and sand is rolled into the air under the action of strong wind, so that the air becomes turbid, and the visibility is obviously reduced. Under such weather conditions, according to the difference of the horizontal visibility, the dust weather with the horizontal visibility of less than 10 km is generally called floating dust, the dust weather with the horizontal visibility of 1 to 10 km is called sand lifting, and the dust weather with the horizontal visibility of less than 1 km and 500 km is respectively called sand storm and strong sand storm. The visibility reduction caused by sand and dust seriously threatens traffic safety and healthy life of people, and causes huge economic loss. In addition, sand in the air can also affect the survival and natural environment of land and marine organisms.
The horizontal visibility is one of the most intuitive indicators of the sand weather, and the most important factor causing the reduction of the horizontal visibility is the number concentration of sand, namely, the larger the number concentration of sand particles is, the lower the horizontal visibility of the sand weather is. Currently, there are mainly several methods for studying particle number concentration of sand and dust: obtaining an empirical formula between the particle concentration and visibility by using observation data of the particle concentration and the visibility and adopting a fitting method; acquiring extinction coefficients under different numbers of concentration sand weather conditions by using a laser radar, acquiring a relationship between the two, and further estimating the sand particle number concentration by using the extinction coefficients; calculating the dust number concentration in different particle size ranges based on a theoretical model between the average particle size and the dust particle number concentration by utilizing the physical properties of the dust; directly measuring the particle number concentration by using an aerosol mass spectrometer; however, there are some drawbacks to the above method of calculating the dust particle number concentration, such as: the empirical relationship between the particle number concentration and the visibility is difficult to be applied to the condition of wide concentration distribution, and calculation errors are increased; the mode of adopting the laser radar has the problem of limited monitoring range; by utilizing the physical characteristics of sand, the calculation error is often larger because the quantity of particles in different particle size ranges in real sand weather is greatly different; the method of direct measurement by the instrument has low efficiency, limited particle size detection range and calculation error depending on the stability of the instrument. Therefore, by means of the single detection means, accurate calculation of the dust particle number concentration in a large-range area with wide particle size distribution cannot be realized, and in the current research, the information of the dust obtained by the foundation microwave radar and the satellite infrared and visible light is rarely fused, and the advantages of the foundation and the space-based remote sensing equipment are not fully utilized to improve the monitoring precision and the space-time coverage range of the dust particle number concentration.
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 particle number concentration estimation method based on a W-band radar and a meteorological satellite, and solves the defects of the existing dust particle number concentration estimation method.
The aim of the invention is achieved by the following technical scheme: a dust particle number concentration estimation method based on a W-band radar and a meteorological satellite, the estimation method comprising:
preprocessing collected European numerical forecasting center data, golbini atmosphere monitoring service sand and dust assimilation data and sand and dust aerosol observation data to obtain a data set, performing time and space matching on the data set to obtain a matched data set, and performing radiation parameter simulation through an atmosphere radiation transmission model to obtain weather satellite imager data and a backscattering coefficient of a W-band radar;
step two, adjusting the matched data set, carrying out simulation on weather satellite imager data and W-band radar backscattering coefficients by combining the set weather satellite imager and W-band radar parameters, selecting a channel according to the input data of the atmospheric radiation transmission model, the weather satellite imager data and the W-band radar backscattering coefficients, and analyzing by PCA to obtain a dust sensitive channel;
step three, acquiring actual observation data of a weather satellite imager and a backscattering coefficient of a W-band radar, combining the obtained sand-dust sensitive channels to form visible light, infrared and W-band observation vectors of the sand-dust sensitive channels, and simulating according to the preprocessed data set and an atmospheric radiation transmission model in the step one to obtain visible light, infrared and W-band simulation vectors of the sand-dust sensitive channels;
and fourthly, comparing and analyzing differences of visible light, infrared and W wave band simulation vectors and observation vectors of the sand and dust sensitive channel in the third step, and realizing estimation inversion of the sand and dust particle number concentration through an optimal estimation method and a Newton iteration method.
The first step specifically comprises the following steps:
a1, collecting earth surface temperature, atmospheric temperature and humidity profile of an ERA5 of a European numerical forecasting center, namely, dust and aerosol observation data of a Goinby atmosphere monitoring service dust and aerosol automatic observation network, identifying an abnormal value by calculating Z-score, namely, the distance between each observation value and an average value and dividing the distance by a standard deviation, judging the observation value of which the Z-score is larger than a or smaller than-a as the abnormal value, and eliminating the abnormal value to obtain a reliable data set;
a2, performing time and space matching on the data set collected in the step A1 to form a surface temperature, atmospheric temperature, humidity profile and dust aerosol profile matching data set, converting the matching data set into a standard input file of an atmospheric radiation transmission model RTTOV, setting the reflectivity of a visible light channel of a weather satellite imager, the brightness of an infrared channel and instrument parameters of a W-band radar, and running the atmospheric radiation transmission model RTTOV to simulate the radiation parameters to generate the reflectivity of the visible light channel of the weather satellite imager and the brightness of the infrared channel and the backscattering coefficient of the W-band radar.
The second step specifically comprises the following steps:
b1, adjusting the particle number concentration based on the matched data set in the step A2, forming a new adjusted data set in a mode of increasing and decreasing 5%, 10%, 15% and 20% on the basis of the particle number concentration of the original data set, and performing simulation on the reflectivity of the visible light channel of the weather satellite imager, the brightness of the infrared channel and the backscattering coefficient of the W-band radar according to the reflectivity of the visible light channel of the weather satellite imager, the brightness of the infrared channel and the same instrument parameter setting and the adjusted data set of the W-band radar;
b2, according to the reflectivity of an atmospheric radiation transmission model RTTOV input data and a visible light channel, the brightness of an infrared channel and the backscattering coefficient of a W-band radar, selecting the reflectivity of the visible light channel when the concentration of particles changes by 5%, the brightness of the infrared channel and the backscattering coefficient of the W-band radar change by more than 3%, collecting all relevant channel data, standardizing the data so that each channel has the same scale, performing PCA analysis to obtain the interpretation variance ratio of each main component, wherein the higher the interpretation variance ratio is, the more information the main component contains is indicated, the number of the main components which should be reserved is determined, only the main components with characteristic values larger than 1 are reserved by using Kaiser criterion, sand-dust sensitive channels are preliminarily determined, the correlation coefficient between each channel and the main components is calculated, and the channel with the absolute value of the correlation coefficient larger than 0.5 is selected as the sand-dust sensitive channel.
The third step specifically comprises the following steps:
acquiring the reflectivity of a visible light channel, the brightness of an infrared channel and the backscattering coefficient of a W-band radar actually observed by a meteorological satellite imager;
and B2, forming visible light, infrared and W-band observation vectors of the sand and dust sensitive channel according to the sand and dust sensitive channel selection result of the step B, acquiring ERA5 surface temperature, atmospheric temperature and humidity profile which are matched with the space in time and space of a weather satellite imager and a W-band radar, calculating reflectivity of the visible light channel, brightness of the infrared channel and backscattering coefficient of the W-band radar based on an atmospheric radiation transmission model RTTOV simulation, and forming visible light, infrared and W-band simulation vectors of the sand and dust sensitive channel.
The dust aerosol observation data of the aerosol automatic observation net comprises: optical thickness of the dust aerosol 500nm, 870nm and 1020nm, dust aerosol particle size distribution, single scattering albedo and dust aerosol phase function;
the gothic atmospheric monitoring service sand assimilation data comprises: the vertical integration mass of the dust aerosol, the particle number concentration, the optical thickness of the dust aerosol 550 nm, and the dust aerosol atmospheric mixing ratio.
The invention has the following advantages: a dust particle number concentration estimation method based on a W-band radar and a meteorological satellite can acquire dust information from different angles by utilizing the data of visible light and infrared images of the W-band radar and the meteorological satellite, and improves dust monitoring precision and space-time coverage range. The data of the W-band radar and the sunflower satellite are fully utilized, sand and dust information can be acquired from different electromagnetic bands and different angles, and the estimation accuracy and space-time coverage range of the sand and dust particle number concentration are improved. The visible light and infrared data of the meteorological satellite can be acquired and processed in real time, so that sand and dust monitoring has higher timeliness, and the method has important significance in the aspects of early warning, emergency and the like.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a flow chart of the channel selection by radiation transmission calculation according to the present invention.
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 particle number concentration estimation method based on a W-band radar and a meteorological satellite, which comprises the following steps: first, the European numerical forecasting center ERA5 surface temperature, atmospheric temperature and humidity profile, the Golbini atmospheric monitoring service (CAMS) dust assimilation data and the dust aerosol observation data of an aerosol automatic observation network (AERONE) are collected, and data integrity and accuracy check is performed. Second, outliers are identified by computing the Z-score and culled to obtain a reliable dataset. The data sets are matched temporally and spatially to form a surface temperature, atmospheric temperature, humidity profile and dust aerosol profile matched data set. Then, the reflectivity of the visible light channel, the brightness of the infrared channel and the backscattering coefficient of the laser radar are calculated by using an atmospheric radiation transmission model RTTOV simulation. Then, the particle concentration is adjusted and simulated, and a channel with a change of more than 3% is selected as a sand-dust sensitive channel. And finally, inverting the concentration of the dust particle number by utilizing an optimal estimation method and a Newton iteration method.
As shown in fig. 1 and 2, the following are specifically included:
step 1: the earth surface temperature, the atmospheric temperature and the humidity profile of the European numerical forecasting center ERA5 are collected, the CAMS (CAMS) dust assimilation data and the AES (automatic air line) dust aerosol observation data of the AES are collected, whether the data are complete and accurate or not is checked, and time sequence analysis, statistical analysis and the like are carried out. Outliers were identified by calculating the Z-score, i.e., the distance between each observation and the mean, and dividing it by the standard deviation. And judging the observed value of Z-score larger than 3 or smaller than-3 as an abnormal value, and eliminating the abnormal value, so as to further obtain a reliable data set.
Wherein, AERONET dust aerosol observation data includes: optical thickness of the dust aerosol 500nm, 870nm and 1020nm, dust aerosol particle size distribution, single scattering albedo and dust aerosol phase function.
Wherein, CAMS sand and dust assimilation data includes: the vertical integration mass of the dust aerosol, the particle number concentration, the optical thickness of the dust aerosol 550 nm, and the dust aerosol atmospheric mixing ratio.
Step 2: and (3) performing time and space matching on the data set collected in the step (S1) to form a surface temperature, atmospheric temperature, humidity profile and dust aerosol profile matching data set. And forming a standard input file of an atmospheric radiation transmission model RTTOV by the data set, setting instrument parameters of visible light, infrared channels and W-band radars of a Himaware-8 weather satellite imager, performing radiation transmission simulation, and calculating the reflectivity of the visible light channels of the satellite imager, the brightness of the infrared channels and the backscattering coefficients of the W-band radars.
Among the instrument parameters are channel frequency, instrument response function, instrument noise, antenna gain, scan time and angle.
Step 3: and (3) adjusting the particle number concentration based on the matched data set collected in the step (S2), and increasing and decreasing the particle number concentration by 5%, 10%, 15% and 20% on the basis of the original data set concentration to form a new adjusted data set. And (3) based on the same instrument parameter setting and the adjusted data set of the visible light, the infrared channel and the W-band radar of the imager in the step (S2), simulating the reflectivity of the visible light channel, the brightness of the infrared channel and the backscattering coefficient of the W-band radar of the Himaware-8 satellite imager.
Step 4: and (3) selecting a channel with the reflectivity of the visible light channel, the brightness of the infrared channel and the backscattering coefficient of the W-band radar changing more than 3% when the concentration of particles changes by 5% based on the reflectivity of the visible light channel, the brightness of the infrared channel and the backscattering coefficient of the W-band radar of the input data and the mode output of the atmospheric radiation transmission models in the steps S2 and S3. Further, collecting all channel data that may be relevant, normalizing the data so that each channel has the same dimensions; PCA (principal component) analysis is carried out to obtain an interpretation variance ratio of each principal component, wherein the higher the interpretation variance ratio is, the more information contained in the principal component is indicated, and the number of principal components to be reserved is determined; using Kaiser criterion to only reserve main component with characteristic value greater than 1, and preliminarily determining sand-dust sensitive channel; and for the reserved main components, calculating the correlation coefficient between each channel and the main components, and selecting the channel with the absolute value of the correlation coefficient larger than 0.5 as the sand-dust sensitive channel.
Step 5: and (3) acquiring the reflectivity of a visible light channel actually observed by the Himawai-8 weather satellite imager, the brightness of an infrared channel and the backscattering coefficient of a W-band radar, and forming visible light, infrared and W-band observation vectors of the sand-dust sensitive channel according to the sand-dust sensitive channel selection result in the step (S4). And (3) acquiring ERA5 earth surface temperature, atmospheric temperature and humidity profile matched with the time and space of the Himaware-8 satellite imager and the W-band radar, wherein CAMS sand and dust assimilation data are obtained by the Golbini atmosphere monitoring service, and the reflectivity of a visible light channel, the brightness of an infrared channel and the backscattering coefficient of the W-band radar are calculated based on an atmospheric radiation transmission model RTTOV simulation to form visible light, infrared and W-band simulation vectors of a sand and dust sensitive channel.
Step 6: and (3) comparing and analyzing the difference of visible light, infrared and W wave band simulation and observation vectors of the sand and dust sensitive channel in the step (S5), further adjusting the sand and dust particle number concentration by utilizing an optimal estimation method and utilizing a Newton iteration method, minimizing a cost function, and finally realizing inversion of the sand and dust particle number concentration.
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 (5)
1. A dust particle number concentration estimation method based on a W-band radar and a meteorological satellite is characterized by comprising the following steps of: the estimation method comprises the following steps:
preprocessing collected European numerical forecasting center data, golbini atmosphere monitoring service sand and dust assimilation data and sand and dust aerosol observation data to obtain a data set, performing time and space matching on the data set to obtain a matched data set, and performing radiation parameter simulation through an atmosphere radiation transmission model to obtain weather satellite imager data and a backscattering coefficient of a W-band radar;
step two, adjusting the matched data set, carrying out simulation on weather satellite imager data and W-band radar backscattering coefficients by combining the set weather satellite imager and W-band radar parameters, selecting a channel according to the input data of the atmospheric radiation transmission model, the weather satellite imager data and the W-band radar backscattering coefficients, and analyzing by PCA to obtain a dust sensitive channel;
step three, acquiring actual observation data of a weather satellite imager and a backscattering coefficient of a W-band radar, combining the obtained sand-dust sensitive channels to form visible light, infrared and W-band observation vectors of the sand-dust sensitive channels, and simulating according to the preprocessed data set and an atmospheric radiation transmission model in the step one to obtain visible light, infrared and W-band simulation vectors of the sand-dust sensitive channels;
and fourthly, comparing and analyzing differences of visible light, infrared and W wave band simulation vectors and observation vectors of the sand and dust sensitive channel in the third step, and realizing estimation inversion of the sand and dust particle number concentration through an optimal estimation method and a Newton iteration method.
2. The method for estimating the concentration of dust particles based on the W-band radar and the meteorological satellite according to claim 1, wherein the method comprises the following steps of: the first step specifically comprises the following steps:
a1, collecting earth surface temperature, atmospheric temperature and humidity profile of an ERA5 of a European numerical forecasting center, namely, dust and aerosol observation data of a Goinby atmosphere monitoring service dust and aerosol automatic observation network, identifying an abnormal value by calculating Z-score, namely, the distance between each observation value and an average value and dividing the distance by a standard deviation, judging the observation value of which the Z-score is larger than a or smaller than-a as the abnormal value, and eliminating the abnormal value to obtain a reliable data set;
a2, performing time and space matching on the data set collected in the step A1 to form a surface temperature, atmospheric temperature, humidity profile and dust aerosol profile matching data set, converting the matching data set into a standard input file of an atmospheric radiation transmission model RTTOV, setting the reflectivity of a visible light channel of a weather satellite imager, the brightness of an infrared channel and instrument parameters of a W-band radar, and running the atmospheric radiation transmission model RTTOV to simulate the radiation parameters to generate the reflectivity of the visible light channel of the weather satellite imager and the brightness of the infrared channel and the backscattering coefficient of the W-band radar.
3. The method for estimating the concentration of dust particles based on the W-band radar and the meteorological satellite according to claim 2, wherein the method comprises the following steps of: the second step specifically comprises the following steps:
b1, adjusting the particle number concentration based on the matched data set in the step A2, forming a new adjusted data set in a mode of increasing and decreasing 5%, 10%, 15% and 20% on the basis of the particle number concentration of the original data set, and performing simulation on the reflectivity of the visible light channel of the weather satellite imager, the brightness of the infrared channel and the backscattering coefficient of the W-band radar according to the reflectivity of the visible light channel of the weather satellite imager, the brightness of the infrared channel and the same instrument parameter setting and the adjusted data set of the W-band radar;
b2, according to the reflectivity of an atmospheric radiation transmission model RTTOV input data and a visible light channel, the brightness of an infrared channel and the backscattering coefficient of a W-band radar, selecting the reflectivity of the visible light channel when the concentration of particles changes by 5%, the brightness of the infrared channel and the backscattering coefficient of the W-band radar change by more than 3%, collecting all relevant channel data, standardizing the data so that each channel has the same scale, performing PCA analysis to obtain the interpretation variance ratio of each main component, wherein the higher the interpretation variance ratio is, the more information the main component contains is indicated, the number of the main components which should be reserved is determined, only the main components with characteristic values larger than 1 are reserved by using Kaiser criterion, sand-dust sensitive channels are preliminarily determined, the correlation coefficient between each channel and the main components is calculated, and the channel with the absolute value of the correlation coefficient larger than 0.5 is selected as the sand-dust sensitive channel.
4. A method for estimating the concentration of dust particles based on W-band radar and weather satellites according to claim 3, wherein: the third step specifically comprises the following steps:
acquiring the reflectivity of a visible light channel, the brightness of an infrared channel and the backscattering coefficient of a W-band radar actually observed by a meteorological satellite imager;
and B2, forming visible light, infrared and W-band observation vectors of the sand and dust sensitive channel according to the sand and dust sensitive channel selection result of the step B, acquiring ERA5 surface temperature, atmospheric temperature and humidity profile which are matched with the space in time and space of a weather satellite imager and a W-band radar, calculating reflectivity of the visible light channel, brightness of the infrared channel and backscattering coefficient of the W-band radar based on an atmospheric radiation transmission model RTTOV simulation, and forming visible light, infrared and W-band simulation vectors of the sand and dust sensitive channel.
5. A method for estimating concentration of dust particles based on W-band radar and weather satellite according to any one of claims 1 to 4, wherein: the dust aerosol observation data of the aerosol automatic observation net comprises: optical thickness of the dust aerosol 500nm, 870nm and 1020nm, dust aerosol particle size distribution, single scattering albedo and dust aerosol phase function;
the gothic atmospheric monitoring service sand assimilation data comprises: the vertical integration mass of the dust aerosol, the particle number concentration, the optical thickness of the dust aerosol 550 nm, and the dust aerosol atmospheric mixing ratio.
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| CN116819490A (en) * | 2023-08-31 | 2023-09-29 | 成都远望科技有限责任公司 | Cloud and aerosol classification method based on cloud radar and laser radar |
| CN117272866A (en) * | 2023-09-28 | 2023-12-22 | 中国科学技术大学 | Variable grid simulation method and equipment for mass concentration of particle size segmented sand dust |
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