Sea fog ensemble forecasting system and method based on multi-physical process parameterization combination

Abstract

The invention relates to the technical field of meteorological information processing, and discloses a sea fog ensemble forecasting system and method based on multi-physical process parameterization combination, and the system comprises an ensemble forecasting initial field generation module which is used for interpolating forecasting field data to a preset numerical forecasting mode surface based on a horizontal and interpolation method, and generating a sea fog forecasting initial field; obtaining an initial field and side boundary data; the gathering module is used for providing the initial field and side boundary data to ensemble forecasting members to complete mode integration of the ensemble forecasting members, and the ensemble forecasting members are obtained based on a plurality of physical parameterization scheme sets influencing sea fog forecasting performance; wherein sea fog simulation of different combination schemes is carried out for different scheme sets, and the optimal scheme combination is determined based on comparison of different simulation results and sea fog live information. Ensemble forecasting of multi-physical process parameterization combination of sea fog is achieved, and the purpose of improving the sea fog forecasting capacity is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological information processing, and in particular to a sea fog ensemble forecasting system and method based on parameterized combination of multiple physical processes. Background Art

[0002] Sea fog is an important weather phenomenon that affects human production activities at sea. Low-visibility sea fog can easily cause ships and vessels to suffer maritime accidents such as deviation, grounding or running aground. Therefore, improving the accuracy of sea fog forecasts is of great significance to maritime economic activities, ocean navigation decisions, etc.

[0003] Numerical models are the "chips" and key components of today's meteorology, and their forecast products have become the most important reference for modern meteorological forecasts. Scientists have used numerical models to forecast sea fog and have carried out many scientific explorations and attempts. For example, many scholars have used numerical model simulation methods to analyze the influence of sea temperature, air temperature, wind and humidity on the formation of sea fog. Some scholars have used three-dimensional sea fog models to simulate the evolution of the formation and disappearance of sea fog in my country's sea areas and analyze the formation mechanism of sea fog. The accurate prediction of sea fog by numerical models depends not only on the quality of the initial field, but also on the physical process scheme of the model. The near-surface process calculates the latent heat flux and sensible heat flux between the sea and the air, and describes the cooling and humidifying effect of the ocean on the atmosphere during the formation of sea fog. The boundary layer process transmits this effect from the near-surface layer to the boundary layer through turbulent mixing, so that the fog area expands upward and the fog area thickens. The microphysical process determines the condensation process under wet and cold conditions and the distribution characteristics of hydrometeors, which is crucial for the accurate description of the internal structure of the fog area. The heating and cold start effects of the radiation process on the atmosphere have significant interactions with the formation and disappearance of fog. However, due to the complex causes of sea fog, the physical mechanism of its formation is not yet completely clear. Although a large number of studies on the sensitivity of sea fog forecasting have been conducted from the aspects of boundary layer, cloud microphysics, sea-air coupling, vertical stratification, and visibility parameterization, it is still quite difficult to accurately forecast sea fog using a single numerical model. How to further improve the sea fog forecasting capability is an urgent problem to be solved. Summary of the invention

[0004] The main purpose of the present invention is to provide a sea fog ensemble forecasting system and method based on multi-physical process parameterized combination to solve the deficiencies existing in the related art.

[0005] In order to achieve the above-mentioned purpose, according to a first aspect of the present invention, there is provided a sea fog ensemble forecasting system based on a combination of multi-physical process parameters, comprising an ensemble forecasting initial field generating module, for interpolating forecasting field data onto a preset numerical forecasting model surface based on a horizontal and interpolation method to obtain initial field and lateral boundary data; an ensemble module, for providing the initial field and lateral boundary data to ensemble forecasting members to complete the model integration of the integrated members, wherein the ensemble forecasting members are obtained based on a set of multiple physical parameterization schemes that affect the sea fog forecasting performance; wherein, when combining different schemes, sea fog simulations of different combination schemes are performed, and the optimal scheme combination is determined based on the comparison of different simulation results with the actual sea fog information.

[0006] Optionally, the ensemble forecast members are obtained based on a set of multiple physical parameterization schemes that affect the sea fog forecast performance, including: ensemble forecast members based on a boundary layer scheme, a cloud microphysics scheme that describes cloud and fog generation, and a visibility parameterization scheme set.

[0007] Optionally, the cloud microphysics schemes include LiuMa, WSM6, WSM6New, and Thompson; the boundary layer schemes include MRF and YSU; and the visibility parameterization schemes include Steolinga and Warner and NOAA Forecast Systems Laboratory.

[0008] Optionally, when determining the optimal combination of schemes, the cloud microphysics scheme and the boundary layer scheme are combined, and the regional historical sea fog process is simulated based on the combined scheme; during the simulation process, a scheme that is consistent with the regional historical sea fog process data is determined, wherein the boundary layer scheme is eliminated during the simulation process; the determined combination of the cloud microphysics scheme and the boundary layer scheme is combined with the visibility parameterization scheme to obtain the optimal combination of schemes.

[0009] Optionally, in the optimal scheme combination, the set members include cloud microphysics schemes LiuMa, WSM6, WSM6New, Thompson; boundary layer schemes MRF, YSU; visibility parameterization schemes Steolinga and Warner, NOAAForecast Systems Laboratory.

[0010] Optionally, the system also includes a sea fog ensemble forecast product generation module, which generates a sea fog ensemble forecast product based on the optimal combination scheme, and sends the sea fog ensemble forecast product to a designated business system via FTP.

[0011] Optionally, the system further comprises a sea fog ensemble forecast product verification and evaluation module for performing performance verification and evaluation on the sea fog ensemble forecast product.

[0012] According to a second aspect of the present invention, there is provided a sea fog ensemble forecasting method based on a parameterized combination of multiple physical processes, comprising interpolating forecast field data onto a preset numerical forecast model surface based on a horizontal and interpolation method to obtain initial field and lateral boundary data; providing the initial field and lateral boundary data to ensemble forecast members to complete the model integration of the integrated members, wherein the ensemble forecast members are obtained based on a plurality of sets of physical parameterized schemes that affect the sea fog forecasting performance; wherein, for different scheme sets, sea fog simulations of different combination schemes are performed, and based on the comparison of different simulation results with actual sea fog information, the optimal scheme combination is determined.

[0013] Optionally, the ensemble forecast members are obtained based on a set of multiple physical parameterization schemes that affect the sea fog forecast performance, including: ensemble forecast members based on a boundary layer scheme, a cloud microphysics scheme that describes cloud and fog generation, and a visibility parameterization scheme set.

[0014] Optionally, the cloud microphysics schemes include LiuMa, WSM6, WSM6New, and Thompson; the boundary layer schemes include MRF and YSU; and the visibility parameterization schemes include Steolinga and Warner and NOAA Forecast Systems Laboratory.

[0015] Optionally, when determining the optimal combination of schemes, the cloud microphysics scheme and the boundary layer scheme are combined, and the regional historical sea fog process is simulated based on the combined scheme; during the simulation process, a scheme that is consistent with the regional historical sea fog process data is determined, wherein the boundary layer scheme is eliminated during the simulation process; the determined combination of the cloud microphysics scheme and the boundary layer scheme is combined with the visibility parameterization scheme to obtain the optimal combination of schemes.

[0016] Optionally, in the optimal scheme combination, the set members include cloud microphysics schemes LiuMa, WSM6, WSM6New, Thompson; boundary layer schemes MRF, YSU; visibility parameterization schemes Steolinga and Warner, NOAAForecast Systems Laboratory.

[0017] Optionally, the system also includes a sea fog ensemble forecast product generation module, which generates a sea fog ensemble forecast product based on the optimal combination scheme, and sends the sea fog ensemble forecast product to a designated business system via FTP.

[0018] Optionally, the system further comprises a sea fog ensemble forecast product verification and evaluation module for performing performance verification and evaluation on the sea fog ensemble forecast product.

[0019] According to a third aspect of the present invention, there is provided a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, wherein the computer instructions are used to cause the computer to execute any one of the methods described in the first aspect.

[0020] According to a fourth aspect of the present invention, there is provided an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor executes the method described in any one implementation of the first aspect.

[0021] The present embodiment is a sea fog ensemble forecasting system and method based on a parameterized combination of multiple physical processes, wherein the system comprises an ensemble forecasting initial field generating module, which is used to interpolate the forecasting field data onto a preset numerical forecasting model surface based on a horizontal and interpolation method to obtain initial field and lateral boundary data; an ensemble module, which is used to provide the initial field and lateral boundary data to ensemble forecasting members to complete the model integration of the integrated members, wherein the ensemble forecasting members are obtained based on a set of multiple physical parameterized schemes that affect the sea fog forecasting performance; wherein, when combining different schemes, sea fog simulations of different combination schemes are performed, and the optimal scheme combination is determined based on the comparison of different simulation results with the actual sea fog information. Breaking through the traditional approach of only using deterministic forecast results of numerical models to forecast sea fog, the three types of processes that most significantly affect the uncertainty of sea fog forecast, namely boundary layer scheme, cloud microphysics scheme and sea fog visibility parameterization scheme, are used to combine different physical parameterization schemes as ensemble forecast members. Schemes for obviously unreasonable boundary layer processes are eliminated, and the optimal combination of physical parameterization schemes is retained to achieve ensemble forecast of multi-physical process parameterization combinations of sea fog, thereby achieving the goal of improving the sea fog forecasting capability. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 It is a sea fog ensemble forecasting system based on multi-physical process parameterization combination according to an embodiment of the present invention; Figure 2 It is a flow chart of a sea fog ensemble forecasting method based on parameterized combination of multiple physical processes according to an embodiment of the present invention; Figure 3is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so as to describe the embodiments of the present invention described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0027] According to an embodiment of the present invention, a sea fog ensemble forecasting system based on multi-physical process parameterization combination is provided. Figure 1 As shown, it includes an ensemble forecast initial field generation module, which is used to interpolate the forecast field data onto a preset numerical forecast model surface based on a horizontal and interpolation method to obtain initial field and lateral boundary data; an ensemble unit, which is used to provide the initial field and lateral boundary data to ensemble forecast members to complete the model integration of the integrated members, wherein the ensemble forecast members are obtained based on a set of multiple physical parameterization schemes that affect the sea fog forecast performance; wherein, when collecting different schemes, sea fog simulations of different combination schemes are performed, and based on the comparison of different simulation results with the actual sea fog information, the optimal combination of schemes is determined.

[0028] In this implementation, reference Figure 1Based on the high-resolution regional numerical prediction model system CMA-MESO V6.0 (China Meteorological Administration RegionalMesoscale Model Version 6.0) of the Earth System Numerical Prediction Center of the Meteorological Administration, a sea fog ensemble forecasting method using multi-physical process parameterization combination is developed. The method includes an ensemble forecast initial field generation module, a multi-physical parameterization combination ensemble module, namely the ensemble module, as well as a sea fog ensemble forecast product generation module and a sea fog ensemble forecast product inspection and evaluation module, a total of four components.

[0029] In the ensemble forecast initial field generation module, the initial field and lateral boundary data are provided for the multi-physical process parameterized ensemble forecast members. Based on the pre-processing module of the independently developed regional high-resolution business model CMA-MESO V6.0, the forecast field data of the China Meteorological Administration's global medium-term numerical forecast business model CMA-GFS or the US global medium-term numerical forecast model NCEP-GFS are interpolated onto the model surface using horizontal and vertical interpolation methods to form the initial field and lateral boundary data necessary for model integration, providing preparation for the model integration of ensemble members.

[0030] The multi-physics parameterization combination ensemble technology can be used for ensemble, and the ensemble module can construct ensemble members based on different combinations of boundary layer, cloud microphysics, and visibility parameterization schemes, and complete the model integration of the integrated members. This part is the key component of the multi-physics process parameterization combination sea fog ensemble forecast method, including the confirmation technology of the performance of different physical process parameterization schemes, the optimal combination matching technology, and the ensemble member model integration technology.

[0031] As an optional implementation of this embodiment, the ensemble forecast members are obtained based on a set of multiple physical parameterization schemes that affect the sea fog forecast performance, including: ensemble forecast members based on a boundary layer scheme, a cloud microphysics scheme that describes cloud and fog generation, and a visibility parameterization scheme set.

[0032] In this optional implementation, different from the current sea fog forecasting technology, this embodiment utilizes the planetary boundary layer parameterization scheme that has the most significant impact on the formation, development and disappearance of sea fog, the cloud microphysics scheme that describes the formation of cloud fog, and the visibility parameterization scheme with the greatest difference. Through the idea of ​​ensemble forecasting, these parameterization schemes are combined to construct a sea fog ensemble forecasting technology based on a combination of multi-physical parameterizations.

[0033] As an optional implementation method of this embodiment, the cloud microphysics scheme includes LiuMa, WSM6, WSM6New, and Thompson; the boundary layer scheme includes MRF and YSU; and the visibility parameterization scheme includes Steolingaand Warner and NOAA Forecast Systems Laboratory.

[0034] In this optional implementation, CMA-MESO V6.0 contains multiple physical parameterization schemes that affect the sea fog forecast performance. After investigation and screening, three types of parameterization processes, namely the boundary layer scheme, cloud microphysics scheme and visibility parameterization scheme, which have the most significant impact on the sea fog performance forecast, are selected. Table 1 shows the four microphysics schemes, four boundary layer schemes and two visibility parameterization schemes initially selected. They are combined and simulated individually, and compared with the actual observation of sea fog, and the preliminary results are screened.

[0035] Table 1

[0036] As an optional implementation of this embodiment, when determining the optimal combination of schemes, the method includes: combining a cloud microphysics scheme and a boundary layer scheme, and simulating the regional historical sea fog process based on the combined scheme; determining a scheme that is consistent with the regional historical sea fog process data during the simulation process, wherein the boundary layer scheme is eliminated during the simulation process; combining the determined combination of the cloud microphysics scheme and the boundary layer scheme with the visibility parameterization scheme to obtain the optimal combination of schemes.

[0037] In this optional implementation, a simulation analysis of a sea fog process is performed by combining four boundary layer schemes and four cloud microphysics schemes. Specifically, the 16 combinations of boundary layer and cloud microphysics schemes in Table 1 can be used to simulate a sea fog process on a certain historical day. The experimental results of matching the four cloud microphysics schemes with the four boundary layer schemes in Figure 1 show that the boundary layer has a significant impact on the sea fog simulation. Specifically, the two parameterization schemes NMRF and GFSEDMF have obvious underestimation, which is related to the unreasonable diffusion of low-level water vapor and temperature in the model by the two boundary layer schemes, while the MRF and YSU schemes are in good agreement with the sea fog observations. In addition, it can be seen that the four cloud microphysics schemes are not significantly different. Therefore, only the MRF and YSU schemes and the four cloud microphysics schemes are considered as physical process options in the ensemble forecast members of the subsequent physical process combination.

[0038] As an optional implementation method of this embodiment, in the optimal solution combination, the set members include cloud microphysics solutions LiuMa, WSM6, WSM6New, Thompson; boundary layer solutions MRF, YSU; visibility parameterization solutions Steolinga and Warner, NOAA Forecast Systems Laboratory.

[0039] In this optional implementation, two boundary layer schemes, four cloud microphysics schemes and two sea fog parameterization schemes are combined to simulate the sea fog process. After simulating the sea fog process on a certain day in history, the sea fog visibility parameterization scheme has a significant impact. The SW99 scheme has the characteristics of a small distribution range and low visibility; the FSL scheme has a large distribution range and high visibility. That is, both the SW99 visibility parameterization and the FSL scheme have good effects, but the low visibility calculated by the FSL scheme is more widely distributed, but the low visibility is not as strong as the SW99. Both can be used as members of the sea fog parameterization scheme ensemble forecast.

[0040] Through the sensitivity analysis of the above-mentioned individual simulations, it is not difficult to see that there are significant differences in the forecasting performance of sea fog among the existing boundary layer schemes of the CMA-MESO regional model. Both the NMRF and GFSEDMF boundary layer schemes have obvious underreporting of sea fog forecasts. In order to confirm this phenomenon more rigorously and quantitatively, we used these two schemes to simulate a total of 32 sea fog processes in the Yellow Sea and Bohai Sea in the winter and spring of 2022 and 2023. The results showed that 87.5% of the sea fog processes also had obvious underestimation problems, which further showed that the NMRF and GFSEDMF schemes were not suitable for simulating sea fog. Therefore, in this method, the NMRF and GFSEDMF schemes are no longer selected as the physical parameterization ensemble member options. Finally, two boundary layer schemes, four cloud microphysics schemes and two visibility parameterization schemes as shown in Table 2 are selected for combination, forming a sea fog ensemble forecasting method of multi-physical process parameterization combination of 16 members.

[0041] Table 2

[0042] As an optional implementation of this embodiment, the system also includes a sea fog ensemble forecast product generation module, which generates a sea fog ensemble forecast product based on the optimal combination solution, and sends the sea fog ensemble forecast product to a designated business system via FTP.

[0043] In this optional implementation, the sea fog ensemble forecast product generation module is used to realize the calculation and processing of sea fog ensemble forecast products of multi-physical process parameterization combination. Develop sea fog ensemble forecast products, including integrated member sea fog visibility products, ensemble average sea fog visibility products, low-level total hydrometeoric ensemble member products, and ensemble average forecast products. These products will be distributed to national meteorological business users such as the National Meteorological Center and the Atmospheric Detection Center of the China Meteorological Administration through FTP to realize the benefits of sea fog ensemble forecast products in business.

[0044] As an optional implementation of this embodiment, the system further includes a sea fog ensemble forecast product inspection and evaluation module, which is used to perform performance inspection and evaluation on the sea fog ensemble forecast product.

[0045] In this optional implementation, the sea fog ensemble product inspection and evaluation module performs performance inspection and evaluation on the sea fog ensemble forecast product of the multi-physical process parameterization combination. The forecast performance of the members of the ensemble forecast or the ensemble average visibility product, hydrometeor product and cloud cover product is evaluated by using TS score or ETS score, combined with conventional coastal sea fog observations, FY satellite data inversion sea fog products, etc., to provide a basis for further improvement of users or models.

[0046] The sea fog ensemble forecasting method of multi-physical process parameterized combination in this embodiment breaks through the traditional practice of only using the deterministic forecast results of numerical models to forecast sea fog. It uses the three types of processes that most significantly affect the uncertainty of sea fog forecast, namely the boundary layer scheme, cloud microphysics scheme and sea fog visibility parameterized scheme, to combine different physical parameterized schemes as ensemble forecast members, and eliminates the schemes of boundary layer processes that are obviously unreasonable, retains the optimal physical parameterized scheme combination, and establishes an ensemble forecasting method of multi-physical process parameterized combination for sea fog. This method has been implemented on the CMA-MESO V6.0 version of the operational numerical model of the Center for Numerical Prediction of the Earth System of the China Meteorological Administration, and has been running in real time in the operational environment for two months, providing product support for the sea fog forecasting business of the National Meteorological Center and the unmanned boat sea fog cruise observation scientific experiment of the China Meteorological Atmospheric Detection Center.

[0047] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0048] According to an embodiment of the present invention, a sea fog ensemble forecasting method based on multi-physical process parameterization combination is also provided. Figure 2As shown, it includes interpolating the forecast field data onto a preset numerical forecast model surface based on a horizontal and interpolation method to obtain initial field and lateral boundary data; providing the initial field and lateral boundary data to ensemble forecast members to complete the model integration of the integrated members, wherein the ensemble forecast members are obtained based on a set of multiple physical parameterization schemes that affect the sea fog forecast performance; wherein, when performing sea fog simulations of different combination schemes for different scheme sets, and determining the optimal scheme combination based on the comparison of different simulation results with the actual sea fog information.

[0049] As an optional implementation method of this embodiment, the ensemble forecast members are obtained based on a set of multiple physical parameterization schemes that affect the sea fog forecast performance, including: ensemble forecast members based on a boundary layer scheme, a cloud microphysics scheme that describes cloud and fog generation, and a visibility parameterization scheme set.

[0050] As an optional implementation method of this embodiment, the cloud microphysics scheme includes LiuMa, WSM6, WSM6New, and Thompson; the boundary layer scheme includes MRF and YSU; and the visibility parameterization scheme includes Steolingaand Warner and NOAA Forecast Systems Laboratory.

[0051] As an optional implementation method of this embodiment, when determining the optimal solution combination, the cloud microphysics solution and the boundary layer solution are combined, and the regional historical sea fog process is simulated based on the combined solution; during the simulation process, a solution that is consistent with the regional historical sea fog process data is determined, wherein the boundary layer solution is eliminated during the simulation process; the determined combination of the cloud microphysics solution and the boundary layer solution is combined with the visibility parameterization solution to obtain the optimal solution combination.

[0052] As an optional implementation method of this embodiment, in the optimal solution combination, the set members include cloud microphysics solutions LiuMa, WSM6, WSM6New, Thompson; boundary layer solutions MRF, YSU; visibility parameterization solutions Steolinga and Warner, NOAA Forecast Systems Laboratory.

[0053] As an optional implementation of this embodiment, the system also includes a sea fog ensemble forecast product generation module, which generates a sea fog ensemble forecast product based on the optimal combination solution, and sends the sea fog ensemble forecast product to a designated business system via FTP.

[0054] As an optional implementation of this embodiment, the system further includes a sea fog ensemble forecast product inspection and evaluation module, which is used to perform performance inspection and evaluation on the sea fog ensemble forecast product.

[0055] According to an embodiment of the present invention, the present invention also provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can implement the method described in any of the above embodiments when executing.

[0056] According to an embodiment of the present invention, the present invention further provides a readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to implement the method described in any of the above embodiments when executed.

[0057] According to an embodiment of the present invention, the present invention further provides a computer program product, which can implement the method described in any of the above embodiments when executed by a processor.

[0058] Figure 3 A schematic block diagram of an example electronic device 300 that can be used to implement an embodiment of the present invention 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 processing, cellular phones, smart phones, wearable devices, and other similar computing devices.

[0059] like Figure 3 As shown, the electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0060] A number of components in the electronic device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0061] The computing unit 301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 301 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 running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 301 performs the various methods and processes described above, such as the object matching method. For example, in some embodiments, the object matching method may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the method described above may be performed.

[0062] Various implementations of the systems and techniques described above 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), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including 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.

[0063] The program code for implementing the method of the present invention 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 the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0064] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may 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.

Claims

1. A sea fog ensemble forecasting system based on parameterized combination of multiple physical processes, characterized in that: include: The ensemble forecast initial field generation module is used to interpolate the forecast field data onto the preset numerical forecast model surface based on the horizontal and interpolation methods to obtain the initial field and lateral boundary data; An ensemble module, used for providing the initial field and lateral boundary data to ensemble forecast members to complete the model integration of the integrated members, wherein the ensemble forecast members are obtained based on a set of multiple physical parameterization schemes that affect sea fog forecast performance; Among them, when collecting different schemes, sea fog simulations of different combination schemes are carried out, and the optimal combination of schemes is determined based on the comparison of different simulation results with the actual sea fog information.

2. The sea fog ensemble forecasting system based on multi-physical process parameterization combination according to claim 1 is characterized in that: The ensemble forecast members are obtained based on a set of multiple physical parameterization schemes that affect the sea fog forecast performance, including: ensemble forecast members based on a boundary layer scheme, a cloud microphysics scheme that describes cloud and fog generation, and a visibility parameterization scheme set.

3. The sea fog ensemble forecasting system based on multi-physical process parameterization combination according to claim 2 is characterized in that: The cloud microphysics schemes include LiuMa, WSM6, WSM6New, and Thompson; the boundary layer schemes include MRF and YSU; the visibility parameterization schemes include Steolinga and Warner and NOAA Forecast Systems Laboratory.

4. The sea fog ensemble forecasting system based on multi-physical process parameterization combination according to claim 3 is characterized in that: When determining the optimal scheme combination, the cloud microphysics scheme and the boundary layer scheme are combined, and the regional historical sea fog process is simulated based on the combined scheme; in the simulation process, a scheme that is consistent with the regional historical sea fog process data is determined, wherein the boundary layer scheme is eliminated in the simulation process; The determined combination of cloud microphysics scheme and boundary layer scheme is combined with the visibility parameterization scheme to obtain the optimal scheme combination.

5. The sea fog ensemble forecasting system based on multi-physical process parameterization combination according to claim 4 is characterized in that: In the optimal scheme combination, the set members include cloud microphysics schemes LiuMa, WSM6, WSM6New, Thompson; boundary layer schemes MRF, YSU; visibility parameterization schemes Steolinga and Warner, NOAA Forecast Systems Laboratory.

6. The sea fog ensemble forecasting system based on multi-physical process parameterization combination according to claim 1 is characterized in that: The system also includes a sea fog ensemble forecast product generation module, which generates sea fog ensemble forecast products based on the optimal combination scheme and sends the sea fog ensemble forecast products to the designated business system via FTP.

7. The sea fog ensemble forecasting system based on multi-physical process parameterization combination according to claim 6 is characterized in that: The system also includes a sea fog ensemble forecast product inspection and evaluation module for performing performance inspection and evaluation on the sea fog ensemble forecast product.

8. A sea fog ensemble forecasting method based on multi-physical process parameterization combination, characterized in that: include: Based on the horizontal and interpolation method, the forecast field data is interpolated onto the preset numerical forecast model surface to obtain the initial field and lateral boundary data; Providing the initial field and lateral boundary data to ensemble forecast members to complete model integration of integrated members, wherein the ensemble forecast members are obtained based on a set of multiple physical parameterization schemes that affect sea fog forecast performance; Among them, when collecting different schemes, sea fog simulations of different combination schemes are carried out, and the optimal combination of schemes is determined based on the comparison of different simulation results with the actual sea fog information.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the method of claim 8.

10. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor executes the method of claim 8.

Images