CN115964593A - Method and device for generating meteorological satellite data set and terminal equipment thereof - Google Patents
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Abstract
The invention provides a meteorological satellite data set generation method, a meteorological satellite data set generation device and terminal equipment thereof, which can improve cloud block identification accuracy and help to simulate clear air low-temperature conditions after radiation cooling at night. A meteorological satellite data set generation method comprises the following steps: calculating the cloud coverage rate of each historical meteorological satellite data set through cloud grid points; the historical meteorological satellite data set is historical infrared light data, historical visible light data and historical vertical temperature distribution data in the same observation time period of the same observation region; averagely dividing the cloud coverage into a plurality of cloud coverage intervals, and obtaining cloud coverage distribution data according to the cloud coverage intervals; extracting the same number of data samples from each cloud coverage rate interval to generate a meteorological satellite data set; and (4) carrying out down-regulation on the brightness temperature of a sunny space area in the daytime in a meteorological satellite data set so as to simulate the low-temperature condition of the sunny space after radiation cooling at night.
Description
Technical Field
The invention relates to the field of meteorological satellite data processing, in particular to a meteorological satellite data set generation method and device and terminal equipment thereof.
Background
The meteorological satellite is an important tool for monitoring weather, can help weather forecasters and meteorologists to judge the evolution of different weather phenomena by analyzing cloud layer changes, and is also a cloud layer observation data source for data assimilation of a numerical weather forecast model. Accurately identifying the cloud layer position can improve the monitoring capability of disaster weather (such as strong convection, typhoon, haze, frontal surface and the like), reduce the error of the initial field of the weather forecast model and improve the accuracy of the numerical weather forecast model.
Meteorological satellites typically detect the intensity of visible and infrared bands from the earth's surface, thereby identifying clouds. The visible light wavelength is 0.4-0.7 micron, and is the electromagnetic frequency range visible to the naked human eye. When visible light (sunlight) from the sun irradiates the earth in the daytime, the meteorological satellite can generate a true color and black and white visible light satellite cloud picture which is most intuitive for human beings by detecting the reflectivity (reflection) of a visible light wave band, and the position of the atmospheric low-middle layer liquid cloud layer is effectively reflected. However, high-rise solid state clouds (ice clouds) are highly transparent, making them difficult to monitor on visible light satellite clouds. The infrared light has a wavelength of 0.9-13.4 microns and is an electromagnetic band invisible to the naked eye, and the intensity of the infrared light reflects the surface of an object. The meteorological satellite can invert the detected infrared light intensity into cloud layer Brightness temperature (Brightness temperature) all day and night, and is helpful for identifying the position of high-rise ice cloud which is opaque to infrared light.
Under the complementation of visible light and infrared light, the algorithm for identifying the cloud layer in daytime is relatively simple and direct. However, after night the visible reflectance is lost, leaving only infrared data. When the ground is cooled to have inverse temperature at night, the ground surface temperature can be the same as the temperature of low cloud/fog, and the low cloud position cannot be directly judged. To solve this problem, the industry has developed a deep learning model that can invert the night infrared satellite data into the visible satellite reflectivity for cloud patch identification. However, the data set used in training the deep learning model only contains day satellite data, the land specific heat ratio is low, and the land cooling effect is obvious in clear air after entering the night, so that the model has the tendency of wrongly inverting the infrared light terrain brightness and temperature texture in the night clear and empty area into clouds, and the accuracy of cloud block identification is seriously influenced.
Disclosure of Invention
The invention aims to provide a meteorological satellite data set generation method, a meteorological satellite data set generation device and terminal equipment thereof, which can improve cloud block identification accuracy and help to simulate clear air low-temperature conditions after radiation cooling at night.
The embodiment of the invention is realized by the following steps:
a meteorological satellite data set generation method comprises the following steps:
calculating the cloud coverage rate of each historical meteorological satellite data set through cloud grid points; the historical meteorological satellite data set is historical infrared light data, historical visible light data and historical vertical temperature distribution data in the same observation time period of the same observation region;
averagely dividing the cloud coverage into a plurality of cloud coverage intervals, and obtaining cloud coverage distribution data according to the cloud coverage intervals;
and extracting the same number of data samples from each cloud coverage rate interval to generate a meteorological satellite data set.
In a preferred embodiment of the present invention, the specific method for acquiring the historical satellite data set includes the following steps:
acquiring infrared light brightness and temperature data and visible light reflectivity data in a specified time period in the daytime, and projecting the acquired data to a map coordinate system; combining the infrared light data and the visible light data of the same observation time into a satellite data set;
and acquiring vertical temperature distribution corresponding to the region and time according to the observation region and time of the satellite data set, and projecting the vertical temperature distribution to a map coordinate system to obtain a historical meteorological satellite data set.
In a preferred embodiment of the present invention, the method for calculating the cloud coverage includes:
acquiring cloud layer grid points corresponding to observation regions and time;
and calculating the proportion of the cloud layer grid points to all the grid points in the observation field, taking the proportion as the cloud layer coverage rate of the corresponding observation time and region, and storing the positions of the cloud layer grid points and the cloud layer coverage rate as new dimensions into a meteorological satellite data set.
In a preferred embodiment of the present invention, the cloud layer lattice point obtaining method includes:
taking the data lattice points with the visible light red waveband reflectivity larger than 0.15 in the historical meteorological satellite data set as cloud layer lattice points;
taking the data lattice point with the bright temperature difference value Tb (8.6 mu m) -Tb (10.8 mu m) being more than or equal to 2K as a cloud layer lattice point;
reading a temperature T2m of nearly 2 meters from atmospheric environment data, and taking the data lattice points of T2m-Tb (10.4 mu m) >20K as cloud layer lattice points.
In a preferred embodiment of the present invention, the cloud coverage intervals are continuous and have the same length.
In a preferred embodiment of the present invention, the method for extracting the data sample includes: and calculating the number of data samples in the cloud coverage rate interval, taking the minimum number S of the samples as an extraction number, storing the S extracted data samples in each cloud coverage rate interval into a historical meteorological satellite data set, and generating a meteorological satellite data set.
In a preferred embodiment of the present invention, the weather satellite data set is used for simulating clear sky low-temperature conditions after cooling by surface radiation in a clear sky area at night, and the simulation method includes:
acquiring cloud layer lattice points, and taking non-cloud layer lattice points as clear sky lattice points;
and (4) reducing the brightness temperature value of the infrared light channel on each clear space lattice point on each land, and storing the regulated data in a night clear space low-temperature data set.
A weather satellite dataset generation apparatus, the apparatus comprising:
the cloud coverage rate calculation module is used for calculating the cloud coverage rate of each historical meteorological satellite data set through cloud layer lattice points;
the cloud coverage balancing module is used for averagely dividing the cloud coverage into a plurality of cloud coverage intervals and obtaining cloud coverage distribution data according to the cloud coverage intervals;
and the meteorological satellite data set generating module is used for generating a meteorological satellite data set from the data obtained by the cloud coverage rate balancing module.
A terminal device comprises a memory and a processor, wherein the memory stores a computer program, and the computer program executes the meteorological satellite data set generation method when running on the processor.
A readable storage medium storing a computer program which runs the above-mentioned meteorological satellite dataset generation method on a processor.
The embodiment of the invention has the beneficial effects that: according to the cloud layer coverage rate balancing method, cloud layer coverage rate data are balanced, so that the condition that a machine learning or deep learning model is emphasized in training is avoided; meanwhile, the processed meteorological satellite data set can be used for simulating the clear sky low-temperature condition after radiation cooling at night, the brightness temperature of the day clear space area of the meteorological satellite data set is subjected to down-regulation processing and is added into the night clear space low-temperature data set as artificial data, a machine learning or deep learning model is enabled to simultaneously master data samples of the day clear space area and the night clear space area, the probability that the model judges the earth surface low-brightness temperature signal of the night clear space area as a cloud layer by mistake is reduced, and the accuracy of the model is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a schematic flow chart diagram of a meteorological satellite data set generation method according to a first embodiment of the present invention;
fig. 2 is a schematic view of a brightness and temperature correction process of infrared light satellite data in clear sky according to a second embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined or explained in subsequent figures.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the present invention belong. The terms (such as those defined in commonly used dictionaries) should be interpreted as having a meaning that is consistent with their contextual meaning in the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein in various embodiments of the present invention.
First embodiment
The embodiment aims at the existing satellite data combination method, only the geographic position and the solar time are used as screening standards to collect data such as infrared light, visible light and the like, and data balance is not considered, so that the machine learning or deep learning model is emphasized on cloud coverage rate during training.
Based on this, the embodiment provides a method for generating a meteorological satellite data set, and the practical application fields of the method include: the method comprises the following steps of weather monitoring, weather forecast, aviation weather forecast, marine weather forecast and numerical weather forecast data assimilation, wherein the weather satellite data set generation comprises the following steps:
1. infrared and visible light data acquisition and processing
The historical meteorological satellite data file is decoded according to the data format by a conventional method to obtain infrared light, light and temperature data and visible light reflectivity data in a specified time period in the daytime, wherein the infrared light data selects an electromagnetic wave band with the wavelength larger than 0.86 micrometer, the visible light data selects an electromagnetic wave band with the wavelength between 0.4 and 0.86 micrometer, and in the actual use process, a general meteorological satellite detects near-infrared data with the wavelength of 0.86 micrometer and also uses the near-infrared data as visible light, so that the visible light data in the embodiment actually comprises the near-infrared light data.
Projecting the acquired infrared light data and the acquired visible light data onto a specified map coordinate system, wherein the projection mode adopts a geometric equation and an interpolation method for projection;
and combining the infrared light data and the visible light data of the same observation time into a satellite data set.
2. Acquisition and processing of vertical temperature distribution in observation region and time
According to the observation region and time of each satellite data set, reading the historical three-dimensional atmospheric environment field data file at the corresponding time by a conventional method and decoding according to the data format;
acquiring vertical temperature distribution corresponding to an observation region and time;
projecting the data to a map coordinate system through a conventional geometric equation and an interpolation method;
and obtaining a historical meteorological satellite data set, wherein the historical meteorological satellite data set comprises historical infrared light data, historical visible light data and historical vertical temperature distribution data in the same observation time period of the same observation region.
3. Calculating the cloud coverage rate of each historical meteorological satellite data set:
and marking the data grid points, which are closest to the visible light red waveband reflectivity of which is greater than the specified value X%, in the gas satellite data set in the historical observation region as cloud layer grid points, wherein X in the embodiment is 0.15. The wavelength of the visible red band is about 0.64 microns;
then, calculating a bright temperature Tb (8.6 μm) with the wavelength of about 8.6 micrometers and a bright temperature Tb (10.8 μm) with the wavelength of about 10.8 micrometers of the meteorological satellite data set in the historical observation region, and marking data lattice points with the difference Tb (8.6 μm) -Tb (10.8 μm) being more than or equal to 2K as cloud layer lattice points;
then, reading a temperature T2m of nearly 2 meters from atmospheric environment data, and taking the data lattice points of T2m-Tb (10.4 mu m) >20K as cloud layer lattice points;
the ratio of the cloud layer grid points to all grid points of the observation region is used as the cloud layer coverage rate of the observation time and region, and the positions of the cloud layer grid points are used as new data dimensions to be stored in a meteorological satellite data set.
4. Cloud coverage data balancing
And averagely dividing the cloud coverage into a plurality of cloud coverage intervals, and obtaining cloud coverage distribution data according to the cloud coverage intervals. Specifically, 0% to 100% of cloud coverage is averagely divided into a plurality of cloud coverage intervals, each interval accounts for a specified percentage ratio Y, Y in the embodiment is 10%, that is, 10 data intervals (0-10%, 11-20%, 21-30%, 91-100%) are total;
and making data distribution of the cloud layer coverage rate of the data according to the cloud layer coverage rate interval, calculating the number of data samples in the cloud layer coverage rate interval, and taking the minimum number S of the samples as the extraction number of each cloud layer coverage rate interval.
5. And extracting the same number of data samples from each cloud coverage rate interval to generate a meteorological satellite data set.
Taking an integer as a label for the observation data group of the cloud layer coverage rate interval;
and generating S integers by using a random integer generator, and extracting a corresponding observation data set to a final meteorological satellite data set.
Second embodiment
Under the complementation of visible light and infrared light, the algorithm for identifying the cloud layer in daytime is relatively simple and direct. However, after night the visible reflectance is lost, leaving only infrared data. When the ground is cooled to have inverse temperature at night, the ground surface temperature can be the same as the temperature of low cloud/fog, and the low cloud position cannot be directly judged.
At present, the industry can invert night infrared light satellite data into a deep learning model of visible light satellite reflectivity so as to identify cloud blocks. However, the data set used in training the deep learning model only contains daytime satellite data, the land specific heat ratio is low, and the land cooling effect in clear sky after night is obvious, so that the model has a tendency of wrongly inverting infrared light terrain brightness and temperature textures in a clear sky at night into clouds, and the accuracy of cloud block identification is seriously influenced.
Based on this, the final meteorological satellite data set obtained in the first embodiment is used to simulate an interplanetary minimum brightness temperature value after cooling of the earth surface radiation in a clear sky area at night, and the specific operation method is as follows:
reading the positions of the cloud layer lattice points calculated in the step 3 from the meteorological satellite data set, and marking the positions of the non-cloud layer lattice points as the positions of the clear sky lattice points;
decoding the earth surface type data according to a related data format by a conventional method, reading earth surface type distribution in the observation region, and projecting the data to a specified map coordinate system by a conventional geometric equation and an interpolation method;
according to the surface type data, marking out the positions of clear sky lattice points positioned above the non-water body as the land clear sky lattice points of the observation time and the region;
the brightness temperature value of each infrared light channel on each land clear sky lattice point is adjusted downwards, and the specific adjustment amplitude can be obtained by freely selecting and utilizing a boundary layer temperature change model in atmospheric boundary layer meteorology or comparing the brightness temperature distribution before the sunrise closest to the observation time, for example, a reasonable number is selected, the calculation can be carried out through a boundary layer theory, or the comparison of the data immediately before the sunrise is taken as a down-regulation amplitude value. And then, storing the adjusted brightness temperature value as new data (without overwriting the original data).
Third embodiment
The embodiment provides a meteorological satellite data set generating device, which comprises:
the cloud coverage rate calculation module is used for calculating the cloud coverage rate of each historical meteorological satellite data set through cloud grid points;
the cloud coverage balancing module is used for averagely dividing the cloud coverage into a plurality of cloud coverage intervals and obtaining cloud coverage distribution data according to the cloud coverage intervals;
and the meteorological satellite data set generating module is used for generating a meteorological satellite data set from the data obtained by the cloud coverage rate balancing module.
The meteorological satellite data set generating device disclosed in this embodiment is used in cooperation with the cloud coverage computing module, the cloud coverage balancing module, and the meteorological satellite data set generating module, to execute the meteorological satellite data set generating method described in the above embodiment, and the implementation and beneficial effects related to the above embodiment are also applicable to this embodiment, and are not described herein again.
It is to be understood that the present application relates to a terminal device comprising a memory and a processor, the memory storing a computer program which, when run on the processor, performs said meteorological satellite dataset generation method.
It is to be understood that the present application relates to a readable storage medium storing a computer program which, when run on a processor, performs the meteorological satellite dataset generation method described herein.
In summary, the data set generation method provided in this embodiment is different from the existing meteorological satellite observation data set composition method, and the method of the present invention achieves the balance of clear sky/cloud coverage in the data sample, and avoids the situation that the machine learning or deep learning model is more focused on the cloud coverage during training. Meanwhile, the invention also carries out down-regulation processing on the brightness temperature of the daytime clear space in the data sample to simulate the low-temperature condition of the clear space after radiation cooling at night, and the low-temperature condition is added into the final data set as artificial data, so that the machine learning or deep learning model can simultaneously master the data sample of the daytime clear space and the night clear space, the chance that the model judges the earth surface low-brightness temperature signal of the night clear space as a cloud layer by mistake is reduced, and the accuracy of the model is improved.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are merely illustrative and, for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, each functional module or unit in each embodiment of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the present invention or a part of the technical solution that contributes to the prior art in essence can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a smart phone, a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention.
Claims (10)
1. A method for generating a meteorological satellite dataset, the method comprising the steps of: calculating the cloud coverage rate of each historical meteorological satellite data set through cloud grid points; the historical meteorological satellite data set is historical infrared light data, historical visible light data and historical vertical temperature distribution data in the same observation time period of the same observation region;
evenly dividing the cloud coverage into a plurality of cloud coverage intervals, and obtaining cloud coverage distribution data according to the cloud coverage intervals;
and extracting the same number of data samples from each cloud layer coverage rate interval to generate a meteorological satellite data set.
2. The weather satellite data set generation method as claimed in claim 1, wherein the specific acquisition method of the historical satellite data set comprises the following steps:
acquiring infrared light and temperature data and visible light reflectivity data in a specified time period in the daytime, and projecting the acquired data to a map coordinate system; combining the infrared light data and the visible light data of the same observation time into a satellite data set;
and acquiring vertical temperature distribution corresponding to the region and the time according to the observed region and the time of the satellite data set, and projecting the vertical temperature distribution to the map coordinate system to obtain a historical meteorological satellite data set.
3. The weather satellite dataset generation method of claim 1, wherein the cloud coverage calculation method comprises:
acquiring cloud layer grid points corresponding to observation regions and time;
and calculating the proportion of the cloud layer grid points to all grid points in the observation field as the cloud layer coverage rate of the corresponding observation time and region, and storing the positions of the cloud layer grid points and the cloud layer coverage rate as new dimensions into a meteorological satellite data set.
4. The meteorological satellite dataset generation method of claim 1, wherein the cloud grid point acquisition method comprises:
taking the data grid points with the visible light red waveband reflectivity larger than 0.15 in the historical meteorological satellite data set as cloud layer grid points;
taking the data lattice point with the bright temperature difference value Tb (8.6 mu m) -Tb (10.8 mu m) being more than or equal to 2K as a cloud layer lattice point;
reading a temperature T2m of nearly 2 meters from atmospheric environment data, and taking data lattice points with T2m-Tb (10.4 mu m) >20K as cloud layer lattice points.
5. The weather satellite dataset generation method of claim 1, wherein the cloud coverage intervals are consecutive and have the same interval length.
6. The weather satellite dataset generation method of claim 1, wherein the data sample extraction method comprises: and calculating the number of data samples in the cloud coverage rate interval, taking the minimum number S of samples as an extraction number, and storing the S data samples extracted in each cloud coverage rate interval into a historical meteorological satellite data set to generate a meteorological satellite data set.
7. The weather satellite dataset generation method of claim 1, wherein the weather satellite dataset is used for simulating clear sky low temperature conditions after cooling of surface radiation in a clear sky area at night, and the simulation method comprises:
acquiring the cloud layer lattice points, and taking the non-cloud layer lattice points as clear sky lattice points;
and adjusting the brightness temperature value of the infrared light channel on the clear space lattice point on each land downwards, and storing the adjusted data in a night clear space low-temperature data set.
8. A weather satellite dataset generation apparatus, said apparatus comprising:
the cloud coverage rate calculation module is used for calculating the cloud coverage rate of each historical meteorological satellite data set through cloud layer lattice points;
the cloud coverage balancing module is used for averagely dividing the cloud coverage into a plurality of cloud coverage intervals and obtaining cloud coverage distribution data according to the cloud coverage intervals;
and the meteorological satellite data set generating module is used for generating a meteorological satellite data set from the data obtained by the cloud coverage rate balancing module.
9. A terminal device comprising a memory and a processor, the memory storing a computer program which, when run on the processor, performs the weather satellite dataset generation method of any one of claims 1 to 7.
10. A readable storage medium, characterized in that it stores a computer program which, when run on a processor, the meteorological satellite dataset generating method of any one of claims 1 to 7.
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| PCT/CN2024/074496 WO2024160175A1 (en) | 2023-01-31 | 2024-01-29 | Meteorological satellite data set generation method and apparatus, and terminal device thereof |
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| CN118153732A (en) * | 2024-01-11 | 2024-06-07 | 二十一世纪空间技术应用股份有限公司 | Remote sensing satellite photographable cloud coverage prediction method based on multisource meteorological model |
| WO2024160175A1 (en) * | 2023-01-31 | 2024-08-08 | 知天(珠海横琴)气象科技有限公司 | Meteorological satellite data set generation method and apparatus, and terminal device thereof |
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| US11674843B2 (en) * | 2015-10-06 | 2023-06-13 | View, Inc. | Infrared cloud detector systems and methods |
| CN104502999B (en) * | 2014-12-10 | 2019-07-19 | 中国科学院遥感与数字地球研究所 | A kind of cloud detection method of optic round the clock and device based on fixed statellite multi-channel data |
| CN108761484A (en) * | 2018-04-26 | 2018-11-06 | 江苏省气象台 | A kind of sea fog monitoring method based on Multi-sensor satellite remote sensing |
| CN108627879A (en) * | 2018-05-10 | 2018-10-09 | 中南林业科技大学 | A kind of multi-source meteorological satellite cloud detection method of optic |
| KR102000630B1 (en) * | 2018-12-20 | 2019-07-16 | 한국해양과학기술원 | Apparatus for detecting cloud based on satellite image and method thereof |
| CN115267941B (en) * | 2022-07-29 | 2023-05-23 | 知天(珠海横琴)气象科技有限公司 | High-resolution true color visible light model generation and inversion method and system |
| CN115964593A (en) * | 2023-01-31 | 2023-04-14 | 知天(珠海横琴)气象科技有限公司 | Method and device for generating meteorological satellite data set and terminal equipment thereof |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2024160175A1 (en) * | 2023-01-31 | 2024-08-08 | 知天(珠海横琴)气象科技有限公司 | Meteorological satellite data set generation method and apparatus, and terminal device thereof |
| CN118153732A (en) * | 2024-01-11 | 2024-06-07 | 二十一世纪空间技术应用股份有限公司 | Remote sensing satellite photographable cloud coverage prediction method based on multisource meteorological model |
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