Method, device and equipment for inverting cloud top height based on satellite-ground fusion

Abstract

The embodiment of the invention provides a method, a device and equipment for inverting the height of a cloud top based on satellite-ground fusion, and is applied to the technical field of atmospheric detection and remote sensing. The method comprises the following steps: firstly, acquiring cloud height real-time data observed by a foundation networking cloud radar, cloud top temperature real-time data and cloud detection real-time data observed by a static meteorological satellite, surface temperature real-time data and surface elevation data; performing space-time matching on the cloud height, cloud top temperature and surface temperature real-time data according to the networking cloud radar station metadata to obtain a satellite-ground matching data set; the vertical temperature average declining rate from the cloud top of the cloud radar station to the ground and the vertical temperature average declining rate of a cloud area in cloud detection are obtained, and finally the satellite-ground fusion cloud top height is obtained through inversion. According to the method, the characteristics of large observation scale of the stationary meteorological satellite and high observation precision of the ground-based cloud radar are combined, and the vertical temperature average declining rate is calculated in real time by using the satellite-ground temperature difference and the height from the ground, so that accurate inversion of the cloud top height on the surface is realized.

Description

Technical Field

[0001] The present disclosure relates to the field of atmospheric detection and remote sensing technology, and in particular to a method, device, and equipment for inverting cloud top height based on satellite-ground fusion. Background Art

[0002] Cloud top height, a key cloud parameter, plays a crucial role in meteorological research and weather forecasting. It not only influences the atmospheric radiation budget, the cloud life cycle, and precipitation formation, but is also closely related to the accuracy of climate model simulations. Accurate cloud top height information is crucial for understanding atmospheric physical processes, improving weather forecast accuracy, and studying climate change.

[0003] Among the existing observation methods, although ground-based cloud radar can measure cloud height in local areas relatively accurately, it is limited by its observation range and cannot obtain cloud information over a large area. While geostationary meteorological satellites can provide information such as cloud top temperature over a large area, they are not as accurate as ground-based cloud radar in inverting cloud top height.

[0004] Using either geostationary meteorological satellites or ground-based cloud radar alone to obtain cloud top height information has certain limitations. Therefore, existing technical solutions face the challenge of accurately retrieving cloud top height on a surface under complex meteorological conditions by fusing cloud top temperature from geostationary meteorological satellites with cloud top height from ground-based cloud radar. Summary of the Invention

[0005] The present disclosure provides a method, apparatus, device and storage medium for inverting cloud top height based on satellite-ground fusion.

[0006] According to a first aspect of the present disclosure, a method for inverting cloud top height based on satellite-ground fusion is provided, the method comprising:

[0007] Acquire real-time cloud height data from ground-based networked cloud radar observations, real-time cloud top temperature data and cloud detection data from geostationary meteorological satellites, real-time surface temperature data from live analysis sites, and surface elevation data from geographic information databases;

[0008] Based on the metadata of the ground-based network cloud radar, the real-time cloud height data, cloud top temperature data, and surface temperature data are temporally and spatially matched to obtain a satellite-ground matching data set;

[0009] Based on the satellite-ground matching data set, the average vertical temperature lapse rate from the cloud top to the ground at each site of the ground-based cloud radar network is calculated;

[0010] Based on the real-time cloud detection data, the vertical temperature average lapse rate of the ground-based network cloud radar stations in the cloud area is grid interpolated to obtain the vertical temperature average lapse rate grid data of the cloud area.

[0011] The cloud top height is determined based on the vertical average temperature lapse rate grid data of the cloud area, the real-time cloud top temperature data, the real-time surface temperature data and the surface elevation data.

[0012] In some implementations of the first aspect, performing spatiotemporal matching on the real-time cloud height data, the real-time cloud top temperature data, and the real-time ground temperature data to obtain the satellite-ground matching data set includes:

[0013] Find the closest position of the geostationary meteorological satellite and the live analysis field to the cloud radar installation site for spatial matching;

[0014] The real-time cloud top temperature data at the spatial matching position during the observation by the geostationary meteorological satellite is used as the cloud top temperature CTT in the satellite-ground matching data set. α ;

[0015] Based on the observation time of the geostationary meteorological satellite, M minutes are pushed forward as the cloud radar cloud height processing time window, and the cloud top height within the time window is averaged as the cloud radar cloud top height H in the satellite-ground matching data set. α , where M is a positive integer;

[0016] Based on the observation time of the geostationary meteorological satellite, the real-time surface temperature data of the live analysis field closest to the satellite observation time at the spatial matching position is used as the surface temperature T in the satellite-ground matching data set. α .

[0017] In some implementations of the first aspect, the calculation of the average vertical temperature lapse rate from the cloud top to the ground at each site of the ground-based networked cloud radar based on the satellite-ground matching data set satisfies the formula:

[0018]

[0019] in, is the average vertical temperature lapse rate.

[0020] In some implementations of the first aspect, performing grid interpolation on the average vertical temperature lapse rate of ground-based networked cloud radar sites in a cloud area based on real-time cloud detection data to obtain grid point data of the average vertical temperature lapse rate in the cloud area includes:

[0021] Calculate the vertical average temperature lapse rate of cloud radar sites that meet the satellite-ground matching conditions;

[0022] Using cloud detection real-time data as a constraint, the cloud area is divided into connected domains;

[0023] The vertical temperature average lapse rate of the cloud radar station in each connected domain coverage area is grid interpolated at the satellite spatial resolution to obtain the vertical temperature average lapse rate of each satellite pixel point in the cloud area. Among them, i and j are the row and column numbers of the satellite pixel point.

[0024] In some implementations of the first aspect, the method further includes:

[0025] If there is only one cloud radar station with an average vertical temperature lapse rate in the area covered by the connected domain, then the average vertical temperature lapse rate of each satellite pixel point in the corresponding connected domain coverage area is equal to the average vertical temperature lapse rate of the cloud radar station;

[0026] If there is no average vertical temperature lapse rate of the cloud radar site in the area covered by the connected domain, the subsequent satellite-ground fusion cloud top height calculation will not be performed in the corresponding area covered by the connected domain.

[0027] In some implementations of the first aspect, the cloud top height is determined based on the vertical average temperature lapse rate grid data, cloud top temperature real-time data, surface temperature real-time data, and surface elevation data in the cloud region, satisfying the formula:

[0028]

[0029] Among them, H ij CTT is the cloud top height of satellite-ground fusion, which represents the height of the pixel point in row i and column j of the geostationary meteorological satellite relative to the sea level; ij is the cloud top temperature corresponding to the pixel point in row i and column j of the geostationary meteorological satellite; is the average vertical temperature decrease rate corresponding to the pixel point in the i-th row and j-th column of the geostationary meteorological satellite; T ij is the surface temperature corresponding to the pixel point in row i and column j of the geostationary meteorological satellite; h ij is the surface elevation corresponding to the pixel point in the i-th row and j-th column of the geostationary meteorological satellite.

[0030] In some implementations of the first aspect, the real-time cloud height data includes layered cloud top height, cloud base height, and cloud thickness;

[0031] When the cloud radar observes multiple layers of clouds at the target observation time point, the cloud top height of the uppermost cloud layer whose cloud thickness is greater than the first preset threshold is taken to calculate the average cloud top height.

[0032] According to a second aspect of the present disclosure, a satellite-ground fusion cloud top height inversion system is provided, the system comprising:

[0033] The acquisition module is used to obtain real-time cloud height data observed by ground-based network cloud radar, real-time cloud top temperature data and cloud detection data observed by geostationary meteorological satellites, real-time surface temperature data in the live analysis field, and surface elevation data in the geographic information database;

[0034] The space-time matching module is used to perform space-time matching on the real-time cloud height data, cloud top temperature data, and ground surface temperature data based on the metadata of the ground-based network cloud radar to obtain a satellite-ground matching data set;

[0035] The vertical temperature average lapse rate calculation module is used to calculate the vertical temperature average lapse rate from the cloud top to the ground at each site of the ground-based network cloud radar based on the satellite-ground matching data set;

[0036] The interpolation module is used to perform grid interpolation on the vertical temperature average lapse rate of the ground-based network cloud radar stations in the cloud area based on the real-time cloud detection data to obtain the grid data of the vertical temperature average lapse rate in the cloud area;

[0037] The inversion module is used to determine the cloud top height based on the vertical temperature average lapse rate grid data of the cloud area, the real-time cloud top temperature data, the real-time surface temperature data and the surface elevation data.

[0038] According to a third aspect of the present disclosure, an electronic device is provided. The electronic device includes: a memory and a processor. The memory stores a computer program, and the processor implements the above method when executing the program.

[0039] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method according to the first aspect of the present disclosure is implemented.

[0040] This disclosure integrates data from ground-based cloud radar and geostationary meteorological satellites, leveraging the strengths of both to effectively improve the accuracy and reliability of cloud top height inversion. Furthermore, by combining information such as surface temperature and elevation, the impact of the atmospheric environment on cloud top height can be more comprehensively considered. Because different surface conditions and temperature distributions affect cloud formation and development, inversion based on these factors can accurately determine cloud top height under complex meteorological conditions.

[0041] It should be understood that the contents described in the Summary of the Invention section are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are provided for a better understanding of the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0043] Figure 1 A schematic flow chart of a method for inverting cloud top height based on satellite-ground fusion according to an embodiment of the present disclosure is shown;

[0044] Figure 2 A schematic flow chart of another method for inverting cloud top height based on satellite-ground fusion according to an embodiment of the present disclosure is shown;

[0045] Figure 3 A schematic diagram of inverting cloud top height based on ground-based networked cloud radar and geostationary meteorological satellite according to an embodiment of the present disclosure is shown;

[0046] Figure 4 A schematic diagram showing the calculation of the average vertical temperature lapse rate of a cloud radar site according to an embodiment of the present disclosure is shown;

[0047] Figure 5 A block diagram of a satellite-ground fusion cloud top height inversion system according to an embodiment of the present disclosure is shown;

[0048] Figure 6 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0049] To make the purpose, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0050] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0051] In order to solve the above-mentioned technical problems, the present disclosure provides a method, device, equipment and storage medium for inverting cloud top height based on satellite-ground fusion. By fusing the data of ground-based networked cloud radar and geostationary meteorological satellite, the advantages of both are fully utilized, which can effectively improve the accuracy and reliability of cloud top height inversion. In addition, by combining information such as surface temperature and surface elevation, the impact of the atmospheric environment on cloud top height can be more comprehensively considered. Therefore, by integrating different surface conditions and temperature distribution factors for inversion, the cloud top height can be determined more accurately under complex meteorological conditions.

[0052] Figure 1 A schematic diagram of a method for inverting cloud top height based on satellite-ground fusion according to an embodiment of the present disclosure is shown. Figure 2 A schematic diagram of another method for inverting cloud top height based on satellite-ground fusion according to an embodiment of the present disclosure is shown. Figure 2 It can be seen that the method of inverting cloud top height based on satellite-ground fusion is divided into data acquisition, time and space allocation, lapse rate calculation and cloud top height inversion process.

[0053] Figure 3 A schematic diagram of cloud top height inversion based on ground-based network cloud radar and geostationary meteorological satellite according to an embodiment of the present disclosure is shown, combined with Figure 1 、 Figure 2 as well as Figure 3 The method 100 for inverting cloud top height based on satellite-ground fusion may include:

[0054] S101, obtain real-time cloud height data observed by ground-based network cloud radar, real-time cloud top temperature data and cloud detection data observed by geostationary meteorological satellites, real-time surface temperature data in the live analysis field, and surface elevation data in the geographic information database.

[0055] S102 , performing spatiotemporal matching on the real-time cloud height data, the real-time cloud top temperature data, and the real-time surface temperature data based on the metadata of the ground-based networked cloud radar to obtain a satellite-ground matching data set.

[0056] In some embodiments, in order to accurately obtain a satellite-ground matching data set and thus accurately determine cloud top height under complex meteorological conditions, the real-time cloud height data, the real-time cloud top temperature data, and the real-time ground surface temperature data are temporally and spatially matched in S102 to obtain the satellite-ground matching data set, which may include:

[0057] Find the closest position of the geostationary meteorological satellite and the live analysis field to the cloud radar installation site for spatial matching;

[0058] The real-time cloud top temperature data at the spatial matching position during the observation of the geostationary meteorological satellite is used as the cloud top temperature CTT in the satellite-ground matching data set. α ;

[0059] Based on the observation time of the geostationary meteorological satellite, the cloud radar cloud height processing time window is pushed forward M minutes, and the cloud top height within the time window is averaged as the cloud radar cloud top height H in the satellite-ground matching data set. α , where M is a positive integer;

[0060] Based on the observation time of the geostationary meteorological satellite, the real-time surface temperature data at the spatial matching position of the live analysis field closest to the satellite observation time is used as the surface temperature T in the satellite-ground matching data set. α .

[0061] In some embodiments, the cloud height real-time data includes layered cloud top height, cloud base height, and cloud thickness;

[0062] In order to accurately calculate the average cloud top height, when the cloud radar observes multiple layers of clouds at the target observation time point, the cloud top height of the uppermost cloud with a cloud thickness greater than a first preset threshold is taken to calculate the average cloud top height.

[0063] In the above embodiment, when multiple layers of clouds are observed at the target observation time point, the cloud top heights are averaged by taking the cloud top heights of the uppermost clouds whose cloud thickness is greater than the first preset threshold, and the cloud top heights can be accurately averaged to obtain an accurate satellite-ground matching data set, so that subsequent inversion can be performed based on different surface conditions and temperature distribution factors to accurately determine the cloud top height under complex meteorological conditions.

[0064] S103, based on the satellite-ground matching data set, calculating the average vertical temperature lapse rate from the cloud top to the ground at each site of the ground-based network cloud radar.

[0065] Figure 4 This is a schematic diagram of calculating the average vertical temperature lapse rate of a cloud radar site provided by an embodiment of the present disclosure, combined with Figure 4 In some embodiments, the process of calculating the average vertical temperature lapse rate from the cloud top to the ground at each site of the ground-based network cloud radar based on the satellite-ground matching data set in S103 satisfies the formula:

[0066]

[0067] in, is the average vertical temperature lapse rate.

[0068] S104 , based on the real-time cloud detection data, grid interpolation is performed on the vertical temperature average lapse rate of the ground-based networked cloud radar stations in the cloud area to obtain grid point data of the vertical temperature average lapse rate in the cloud area.

[0069] In some embodiments, in S104, grid interpolation is performed on the vertical temperature average lapse rate of the ground-based networked cloud radar station in the cloud area based on the real-time cloud detection data to obtain grid point data of the vertical temperature average lapse rate in the cloud area, which may include:

[0070] Calculate the vertical average temperature lapse rate of cloud radar sites that meet the satellite-ground matching conditions;

[0071] Using cloud detection real-time data as a constraint, the cloud area is divided into connected domains;

[0072] The vertical temperature average lapse rate of the cloud radar station in each connected domain coverage area is grid interpolated at the satellite spatial resolution to obtain the vertical temperature average lapse rate of each satellite pixel point in the cloud area. Among them, i and j are the row and column numbers of the satellite pixel point.

[0073] In the above embodiment, ground-based networked cloud radars can provide accurate single-point cloud top height data, while geostationary meteorological satellites can obtain macroscopic cloud top temperature distribution information over a wide area. Therefore, by calculating the average vertical temperature lapse rate at the cloud radar site and applying it to surrounding satellite pixels, accurate cloud top height inversion can be achieved on a satellite-ground fusion interface.

[0074] Furthermore, using real-time cloud detection data as a constraint to partition the cloud region into connected domains helps to rationally segment the cloud region into subregions with similar meteorological characteristics. Grid interpolation within each connected domain ensures that the interpolation process accounts for the continuity of cloud distribution and the consistency of regional meteorological characteristics. Compared to direct interpolation without partitioning the connected domain, this reduces interpolation errors caused by large variations in cloud distribution, ensuring that the resulting gridded data for the average vertical temperature lapse rate more accurately reflect the actual conditions in the cloud region.

[0075] In summary, in the above embodiment, the vertical temperature average lapse rate of the cloud radar sites that meet the satellite-ground matching conditions is calculated, and the vertical temperature average lapse rate of the cloud radar sites in each connected domain coverage area is grid-interpolated at the satellite spatial resolution, thereby obtaining the vertical temperature average lapse rate of each satellite pixel point in the cloud area. This allows the inversion to be performed based on different surface conditions and temperature distribution factors in the subsequent calculation process, thereby accurately obtaining the cloud top height under complex meteorological conditions.

[0076] Specifically, in order to accurately obtain the average vertical temperature lapse rate of each satellite pixel point in the cloud area, in some embodiments, if the vertical temperature lapse rate of only one cloud radar station exists in the connected domain coverage area, then the average vertical temperature lapse rate of each satellite pixel point in the corresponding connected domain coverage area is equal to the average vertical temperature lapse rate of the cloud radar station;

[0077] If there is no average vertical temperature lapse rate of the cloud radar site in the area covered by the connected domain, the subsequent satellite-ground fusion cloud top height calculation will not be performed in the corresponding area covered by the connected domain.

[0078] In the above embodiment, by dividing the conditions of the cloud radar stations in the connected domain coverage area, the average vertical temperature decrease rate of each satellite pixel point in the cloud area can be accurately obtained, so that in the subsequent calculation process, different surface conditions and temperature distribution factors can be integrated for inversion, and the cloud top height can be accurately obtained under complex meteorological conditions.

[0079] S105 , determining the cloud top height based on the vertical average temperature lapse rate grid data of the cloud area, the cloud top temperature real-time data, the surface temperature real-time data, and the surface elevation data.

[0080] In some embodiments, in S105, the cloud top height is determined based on the vertical temperature average lapse rate grid data of the cloud area, the cloud top temperature real-time data, the surface temperature real-time data, and the surface elevation data, satisfying the formula:

[0081]

[0082] Among them, H ij CTT is the cloud top height of satellite-ground fusion, which represents the height of the pixel point in row i and column j of the geostationary meteorological satellite relative to the sea level; ij is the cloud top temperature corresponding to the pixel point in row i and column j of the geostationary meteorological satellite; is the average vertical temperature decrease rate corresponding to the pixel point in the i-th row and j-th column of the geostationary meteorological satellite; T ij is the surface temperature corresponding to the pixel point in row i and column j of the geostationary meteorological satellite; h ij is the surface elevation corresponding to the pixel point in the i-th row and j-th column of the geostationary meteorological satellite.

[0083] The cloud top height determined above is the satellite-ground fusion cloud top height.

[0084] In the above embodiment, the vertical temperature average lapse rate grid data, cloud top temperature real-time data, surface temperature real-time data and surface elevation data of the cloud area are used to accurately obtain the satellite-ground fusion cloud top height, and then the inversion can be performed by combining different surface conditions and temperature distribution factors to achieve accurate determination of the cloud top height under complex meteorological conditions.

[0085] During the S101-S105 process, by integrating data from ground-based cloud radar and geostationary meteorological satellites, leveraging the strengths of both, the accuracy and reliability of cloud top height retrieval can be effectively improved. Furthermore, by incorporating information such as surface temperature and elevation, the impact of the atmospheric environment on cloud top height can be more comprehensively considered. Because different surface conditions and temperature distributions affect cloud formation and development, inversion based on these factors can accurately determine cloud top height under complex meteorological conditions.

[0086] The above is an introduction to the method embodiment. The following further illustrates the disclosed solution through an apparatus embodiment.

[0087] Figure 5 A block diagram of a satellite-ground fusion cloud top height inversion system according to an embodiment of the present disclosure is shown.

[0088] like Figure 5 As shown, the satellite-ground fusion cloud top height inversion system 500 may include:

[0089] Acquisition module 501 is used to obtain real-time cloud height data observed by ground-based network cloud radar, real-time cloud top temperature data and real-time cloud detection data observed by geostationary meteorological satellites, real-time surface temperature data in the live analysis field, and surface elevation data in the geographic information database;

[0090] The spatiotemporal matching module 502 is used to perform spatiotemporal matching on the real-time cloud height data, the real-time cloud top temperature data, and the real-time ground surface temperature data according to the metadata of the ground-based network cloud radar to obtain a satellite-ground matching data set;

[0091] The vertical temperature average lapse rate calculation module 503 is used to calculate the vertical temperature average lapse rate from the cloud top to the ground of each site of the ground-based network cloud radar based on the satellite-ground matching data set;

[0092] Interpolation module 504 is used to perform grid point interpolation on the vertical temperature average lapse rate of the ground-based networked cloud radar stations in the cloud area based on the real-time cloud detection data to obtain grid point data of the vertical temperature average lapse rate in the cloud area;

[0093] The inversion module 505 is used to determine the cloud top height based on the vertical temperature average lapse rate grid data of the cloud area, the cloud top temperature real-time data, the surface temperature real-time data and the surface elevation data.

[0094] In some embodiments, spatiotemporal matching is performed on the real-time cloud height data, the real-time cloud top temperature data, and the real-time ground temperature data to obtain a satellite-ground matching data set, including:

[0095] Find the closest position of the geostationary meteorological satellite and the live analysis field to the cloud radar installation site for spatial matching;

[0096] The real-time cloud top temperature data at the spatial matching position during the observation of the geostationary meteorological satellite is used as the cloud top temperature CTT in the satellite-ground matching data set. α ;

[0097] Based on the observation time of the geostationary meteorological satellite, the cloud radar cloud height processing time window is pushed forward M minutes, and the cloud top height within the time window is averaged as the cloud radar cloud top height H in the satellite-ground matching data set. α , where M is a positive integer;

[0098] Based on the observation time of the geostationary meteorological satellite, the real-time surface temperature data at the spatial matching position of the live analysis field closest to the satellite observation time is used as the surface temperature T in the satellite-ground matching data set. α .

[0099] In some embodiments, based on the satellite-ground matching data set, the average vertical temperature lapse rate from the cloud top to the ground at each site of the ground-based network cloud radar is calculated to satisfy the formula:

[0100]

[0101] in, is the average vertical temperature lapse rate.

[0102] In some embodiments, based on real-time cloud detection data, grid point interpolation is performed on the vertical temperature average lapse rate of ground-based networked cloud radar stations in the cloud area to obtain grid point data of the vertical temperature average lapse rate in the cloud area, including:

[0103] Calculate the vertical average temperature lapse rate of cloud radar sites that meet the satellite-ground matching conditions;

[0104] Using cloud detection real-time data as a constraint, the cloud area is divided into connected domains;

[0105] The vertical temperature average lapse rate of the cloud radar station in each connected domain coverage area is grid interpolated at the satellite spatial resolution to obtain the vertical temperature average lapse rate of each satellite pixel point in the cloud area. Among them, i and j are the row and column numbers of the satellite pixel point.

[0106] In some embodiments, if there is only one cloud radar site with an average vertical temperature lapse rate in the connected domain coverage area, the average vertical temperature lapse rate of each satellite pixel point in the corresponding connected domain coverage area is equal to the average vertical temperature lapse rate of the cloud radar site;

[0107] If there is no average vertical temperature lapse rate of the cloud radar site in the area covered by the connected domain, the subsequent satellite-ground fusion cloud top height calculation will not be performed in the corresponding area covered by the connected domain.

[0108] In some embodiments, the cloud top height is determined based on the vertical average temperature lapse rate grid data, cloud top temperature real-time data, surface temperature real-time data, and surface elevation data in the cloud region, satisfying the formula:

[0109]

[0110] Among them, H ij CTT is the cloud top height of satellite-ground fusion, which represents the height of the pixel point in row i and column j of the geostationary meteorological satellite relative to the sea level; ij is the cloud top temperature corresponding to the pixel point in row i and column j of the geostationary meteorological satellite; is the average vertical temperature decrease rate corresponding to the pixel point in the i-th row and j-th column of the geostationary meteorological satellite; T ij is the surface temperature corresponding to the pixel point in row i and column j of the geostationary meteorological satellite; h ij is the surface elevation corresponding to the pixel point in the i-th row and j-th column of the geostationary meteorological satellite.

[0111] In some embodiments, the above-mentioned real-time cloud height data includes layered cloud top height, cloud base height and cloud thickness;

[0112] When the cloud radar observes multiple layers of clouds at the target observation time point, the cloud top height of the uppermost cloud layer whose cloud thickness is greater than the first preset threshold is taken to calculate the average cloud top height.

[0113] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0114] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0115] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0116] Figure 6A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0117] The device 600 includes a computing unit 601 that can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. Various programs and data required for the operation of the device 600 can also be stored in the RAM 603. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0118] Various components in device 600 are connected to I / O interface 605, including an input unit 606, such as a keyboard, mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a magnetic disk, optical disk, etc.; and a communication unit 609, such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows device 600 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0119] The computing unit 601 may be a variety of general and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the method 100 described above may be performed.

[0120] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0121] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0122] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0123] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0124] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0125] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0126] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions of this disclosure can be achieved, and this document is not limited here.

[0127] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for inverting cloud top height based on satellite-ground fusion, characterized in that: The method comprises: Acquire real-time cloud height data from ground-based networked cloud radar observations, real-time cloud top temperature data and cloud detection data from geostationary meteorological satellites, real-time surface temperature data from live analysis sites, and surface elevation data from geographic information databases; performing spatiotemporal matching on the cloud height real-time data, the cloud top temperature real-time data, and the ground surface temperature real-time data according to the metadata of the ground-based networked cloud radar to obtain a satellite-ground matching data group; Based on the satellite-ground matching data set, the average vertical temperature lapse rate from the cloud top to the ground at each site of the ground-based network cloud radar is calculated; Based on the real-time cloud detection data, grid interpolation is performed on the vertical temperature average lapse rate of the ground-based network cloud radar stations in the cloud area to obtain grid data of the vertical temperature average lapse rate in the cloud area; The cloud top height is determined based on the vertical temperature average lapse rate grid data of the cloud area, the cloud top temperature real-time data, the surface temperature real-time data and the surface elevation data.

2. The method according to claim 1, characterized in that The performing spatiotemporal matching on the cloud height real-time data, the cloud top temperature real-time data, and the ground surface temperature real-time data to obtain a satellite-ground matching data group includes: Find the closest position of the geostationary meteorological satellite and the live analysis field to the cloud radar installation site for spatial matching; The real-time cloud top temperature data at the spatial matching position during the observation by the geostationary meteorological satellite is used as the cloud top temperature CTT in the satellite-ground matching data set. α ; Based on the observation time of the geostationary meteorological satellite, M minutes are pushed forward as the cloud radar cloud height processing time window, and the cloud top height within the time window is averaged as the cloud radar cloud top height H in the satellite-ground matching data set. α , where M is a positive integer; Based on the observation time of the geostationary meteorological satellite, the real-time surface temperature data of the live analysis field closest to the satellite observation time at the spatial matching position is used as the surface temperature T in the satellite-ground matching data set. α .

3. The method according to claim 2, characterized in that Based on the satellite-ground matching data set, the vertical temperature average lapse rate from the cloud top to the ground of each ground-based network cloud radar station is calculated to satisfy the formula: in, is the average vertical temperature lapse rate.

4. The method according to claim 3, characterized in that The method of performing grid interpolation on the average vertical temperature lapse rate of the ground-based networked cloud radar stations in the cloud area according to the real-time cloud detection data to obtain grid point data of the average vertical temperature lapse rate in the cloud area includes: Calculate the vertical average temperature lapse rate of cloud radar sites that meet the satellite-ground matching conditions; Using cloud detection real-time data as a constraint, the cloud area is divided into connected domains; The vertical temperature average lapse rate of the cloud radar station in each connected domain coverage area is grid interpolated at the satellite spatial resolution to obtain the vertical temperature average lapse rate of each satellite pixel point in the cloud area. Among them, i and j are the row and column numbers of the satellite pixel point.

5. The method according to claim 4, characterized in that The method further comprises: If there is only one cloud radar station with an average vertical temperature lapse rate in the area covered by the connected domain, then the average vertical temperature lapse rate of each satellite pixel point in the corresponding connected domain coverage area is equal to the average vertical temperature lapse rate of the cloud radar station; If there is no average vertical temperature lapse rate of the cloud radar site in the area covered by the connected domain, the subsequent satellite-ground fusion cloud top height calculation will not be performed in the corresponding area covered by the connected domain.

6. The method according to claim 1, characterized in that The cloud top height is determined based on the vertical temperature average lapse rate grid data of the cloud area, the cloud top temperature real-time data, the surface temperature real-time data and the surface elevation data, satisfying the formula: Among them, H ij CTT is the cloud top height of satellite-ground fusion, which represents the height of the pixel point in row i and column j of the geostationary meteorological satellite relative to the sea level; ij is the cloud top temperature corresponding to the pixel point in row i and column j of the geostationary meteorological satellite; is the average vertical temperature decrease rate corresponding to the pixel point in the i-th row and j-th column of the geostationary meteorological satellite; T ij is the surface temperature corresponding to the pixel point in row i and column j of the geostationary meteorological satellite; h ij is the surface elevation corresponding to the pixel point in the i-th row and j-th column of the geostationary meteorological satellite.

7. The method according to claim 2, characterized in that The cloud height real-time data includes layered cloud top height, cloud base height and cloud thickness; When the cloud radar observes multiple layers of clouds at the target observation time point, the cloud top height of the uppermost cloud layer whose cloud thickness is greater than the first preset threshold is taken to calculate the average cloud top height.

8. A satellite-ground fusion cloud top height inversion system, characterized by: The system comprises: The acquisition module is used to obtain real-time cloud height data observed by ground-based network cloud radar, real-time cloud top temperature data and cloud detection data observed by geostationary meteorological satellites, real-time surface temperature data in the live analysis field, and surface elevation data in the geographic information database; a space-time matching module, configured to perform space-time matching on the cloud height real-time data, the cloud top temperature real-time data, and the ground surface temperature real-time data according to the metadata of the ground-based networked cloud radar, to obtain a satellite-ground matching data group; A vertical temperature average lapse rate calculation module is used to calculate the vertical temperature average lapse rate from the cloud top to the ground of each site of the ground-based network cloud radar based on the satellite-ground matching data group; An interpolation module is used to perform grid point interpolation on the vertical temperature average lapse rate of the ground-based networked cloud radar stations in the cloud area based on the real-time cloud detection data to obtain grid point data of the vertical temperature average lapse rate in the cloud area; The inversion module is used to determine the cloud top height based on the vertical temperature average lapse rate grid data of the cloud area, the cloud top temperature real-time data, the surface temperature real-time data and the surface elevation data.

9. An electronic device, characterized in that: include: at least one processor; and a memory communicatively coupled to the at least one processor; It is characterized in that the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.

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