Air-sea coupling strength determination method and device, storage medium and electronic device

Abstract

The invention discloses an air-sea coupling strength determination method and device, a storage medium and an electronic device, and the method comprises the steps: obtaining day-by-day meteorological data of a target space range above the sea in a target time interval, the day-by-day meteorological data comprises first data and second data, the first data is sea surface temperature abnormal data, and the second data is sea surface temperature abnormal data; the second data is outward long-wave radiation data; respectively constructing a first dynamic phase space and a second dynamic phase space through the first data and the second data; a second state corresponding to a first state of a first moment in a third dynamic phase space in a target time interval is determined, the second state is a historical similar state of the first state, the third dynamic phase space comprises a first dynamic phase space and a second dynamic phase space, and the second dynamic phase space is a historical similar state of the first state; the first moment is any moment in the target time interval; and determining the air-sea coupling strength of the target space range through the historical similar state.

Description

Technical Field

[0001] The present application relates to the technical field of sea-air coupling, and in particular to a method and device for determining the strength of sea-air coupling, a storage medium, and an electronic device. Background Art

[0002] Abnormal changes in the sea surface temperature in the tropical Pacific have a vital impact on the global climate, especially the most intense interannual variability, El Niño, which has attracted widespread attention. ) and La Nina ) events, which are also the most typical ocean-atmosphere coupling phenomena in the world. Together with the Southern Oscillation, they are called El Niño and Southern Oscillation (El El Oscillation, referred to as ENSO) and La As the main interannual variability of the global climate system, it can affect global precipitation, North American temperature, and even global warming through teleconnection mechanisms. In-depth research on it has very important scientific significance and practical application value.

[0003] However, how to accurately describe the atmospheric and oceanic coupling processes associated with ENSO remains a challenge. Recently, researchers have found that the warming of sea temperatures in the central and eastern tropical Pacific is not always accompanied by deep convection anomalies in the atmosphere, that is, the ocean and atmosphere are not always coupled, which is also the reason why El A special form of diversity. Some scholars therefore define "uncoupled El " and explored its different climate impacts. Most of the climate prediction centers currently in operation rely on some regional average sea temperature index (such as 3.4) as the basis for ENSO prediction and monitoring, but in reality, these simple sea surface temperature anomaly (SSTA) indices can only reflect the intensity of SST events, but not the coupling between the atmosphere and the ocean. In the context of increasing attention to the diversity of ENSO and the climate impacts of different SSTA modes, how to correctly quantify the air-sea coupling over the tropical Pacific is an important issue.

[0004] Regarding the problem in related technologies that there is a lack of solutions that can quantify the intensity of sea-air coupling over the ocean, no effective solution has yet been found. Summary of the invention

[0005] The embodiments of the present application provide a method and device for determining the sea-air coupling strength, a storage medium, and an electronic device to at least solve the problem in the prior art that there is a lack of a solution that can quantify the sea-air coupling strength over the ocean.

[0006] According to an embodiment of the embodiments of the present application, a method for determining the intensity of sea-air coupling is provided, including: obtaining daily meteorological data of a target spatial range over the ocean within a target time interval, wherein the daily meteorological data includes: first data and second data, the first data being sea surface temperature anomaly data, and the second data being outward long-wave radiation data; constructing a first dynamical phase space and a second dynamical phase space respectively through the first data and the second data; determining a second state corresponding to a first state in a third dynamical phase space at a first moment within the target time interval, wherein the second state is a historical similar state of the first state, wherein the third dynamical phase space includes: the first dynamical phase space and the second dynamical phase space, wherein the first moment is any moment within the target time interval; determining the intensity of sea-air coupling of the target spatial range through the historical similar state.

[0007] In an exemplary embodiment, a first kinetic phase space and a second kinetic phase space are constructed respectively by the first data and the second data, including: determining the first phase space trajectory by a distribution diagram of the first data within the target space range per unit time, and determining the second phase space trajectory by a distribution diagram of the second data within the target space range per unit time; determining the first kinetic phase space by the first phase space trajectory, and determining the second kinetic phase space by the second phase space trajectory.

[0008] In an exemplary embodiment, determining the second state corresponding to the first state in the third kinetic phase space at the first moment in the target time interval includes: acquiring the first state in the third kinetic phase space at the first moment through a third phase space trajectory, wherein the third phase space trajectory includes: the first phase space trajectory and the second phase space trajectory; determining the second state corresponding to the first state through a predefined observable function.

[0009] In an exemplary embodiment, the second state corresponding to the first state is determined by a predefined observable function, including: inputting the third state at the second moment in the third kinetic phase space and the first state into the observable function to obtain a function value, wherein the second moment is any moment in the target time interval except any moment; comparing the function value with a preset threshold; and when the function value is greater than the preset threshold, determining the third state as the second state.

[0010] In an exemplary embodiment, determining the air-sea coupling strength of the target spatial range through the historical similarity state includes: obtaining a first historical similarity state corresponding to the first state in the first dynamic phase space at the first moment, and obtaining a second historical similarity state corresponding to the first state in the second dynamic phase space at the first moment, wherein the historical similarity state includes: the first historical similarity state and the second historical similarity state; determining the synchronization occurrence rate of the first historical similarity state and the second historical similarity state; and determining the air-sea coupling strength through the synchronization occurrence rate.

[0011] In an exemplary embodiment, determining the synchronous occurrence rate of the first historical similarity state and the second historical similarity state includes: determining a ratio of simultaneous occurrence of the first historical similarity state and the second historical similarity state; and determining the ratio as the synchronous occurrence rate.

[0012] In an exemplary embodiment, determining the sea-air coupling strength through the synchronization occurrence rate includes: obtaining the synchronization occurrence rates corresponding to a plurality of the first moments in the target time interval; arranging the plurality of synchronization occurrence rates in a preset order to obtain the sea-air coupling strength.

[0013] According to another embodiment of the embodiment of the present application, a device for determining the intensity of sea-air coupling is also provided, including: an acquisition module, used to acquire daily meteorological data of a target spatial range over the ocean within a target time interval, wherein the daily meteorological data includes: first data and second data, the first data being sea surface temperature anomaly data, and the second data being outward long-wave radiation data; a construction module, used to construct a first dynamic phase space and a second dynamic phase space respectively through the first data and the second data; a first determination module, used to determine a second state corresponding to a first state in a third dynamic phase space at a first moment within the target time interval, wherein the second state is a historical similar state of the first state, wherein the third dynamic phase space includes: the first dynamic phase space and the second dynamic phase space, wherein the first moment is any moment within the target time interval; a second determination module, used to determine the intensity of sea-air coupling of the target spatial range through the historical similar state.

[0014] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above method when running.

[0015] According to another aspect of the embodiments of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method through the computer program.

[0016] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0017] In an embodiment of the present application, daily meteorological data of a target spatial range over the ocean within a target time interval is obtained, wherein the daily meteorological data includes: first data and second data, the first data is sea surface temperature anomaly data, and the second data is outward long-wave radiation data; a first dynamic phase space and a second dynamic phase space are constructed respectively through the first data and the second data; a second state corresponding to a first state in a third dynamic phase space at a first moment within the target time interval is determined, wherein the second state is a historical similarity state of the first state, wherein the third dynamic phase space includes: the first dynamic phase space and the second dynamic phase space, wherein the first moment is any moment within the target time interval; the sea-air coupling strength of the target spatial range is determined through the historical similarity state. Through the above embodiments, the problem of the lack of a solution that can quantify the sea-air coupling strength over the ocean in the prior art is solved, a solution for determining the sea-air coupling strength is provided, and the technical effect of quickly and accurately determining the sea-air coupling strength is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 It is a hardware structure block diagram of a computer terminal of a method for determining the sea-air coupling strength according to an embodiment of the present application;

[0020] Figure 2 is a flow chart of a method for determining the sea-air coupling strength according to an embodiment of the present application;

[0021] Figure 3 is a phase space schematic diagram of a method for determining the sea-air coupling strength according to an embodiment of the present application;

[0022] Figure 4 is another flow chart of a method for determining the sea-air coupling strength according to an embodiment of the present application;

[0023] Figure 5It is a structural block diagram of a device for determining the sea-air coupling strength according to an embodiment of the present application. DETAILED DESCRIPTION

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

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application 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 that the embodiments of the present application described herein can be implemented in an order other than those illustrated or 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 that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices; "plurality" means two or more.

[0026] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or similar computing device or cloud platform or independent physical server or software platform, wherein the above software platform is run through one or more servers. Taking running on a computer terminal as an example, Figure 1 1 is a hardware structure block diagram of a computer terminal of a method for determining the sea-air coupling strength in an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) processor 102 and memory 104 for storing data. In an exemplary embodiment, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108, wherein the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. A person skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Equivalent functions or comparisons shown Figure 1 A different configuration with more features is shown.

[0027] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as computer programs corresponding to the methods in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, that is, the above-mentioned methods are implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0028] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] In this embodiment, a method for determining the sea-air coupling strength is provided, which is applied to the above-mentioned computer terminal. Figure 2 : is a flow chart of a method for determining the sea-air coupling strength according to an embodiment of the present application, the process comprising the following steps:

[0030] Step S202, acquiring daily meteorological data of a target spatial range over the ocean within a target time interval, wherein the daily meteorological data includes: first data and second data, the first data being sea surface temperature anomaly data, and the second data being outward longwave radiation data;

[0031] Step S204, constructing a first dynamic phase space and a second dynamic phase space respectively by using the first data and the second data;

[0032] Step S206, determining a second state corresponding to a first state in a third kinetic phase space at a first moment within the target time interval, wherein the second state is a historically similar state of the first state, wherein the third kinetic phase space includes: the first kinetic phase space and the second kinetic phase space, wherein the first moment is any moment within the target time interval;

[0033] Step S208, determining the air-sea coupling intensity of the target spatial range through the historical similarity state.

[0034] Through the above steps, the daily meteorological data of the target spatial range over the ocean within the target time interval is obtained, wherein the daily meteorological data includes: first data and second data, the first data is the sea surface temperature anomaly data, and the second data is the outward long wave radiation data; the first dynamic phase space and the second dynamic phase space are constructed respectively through the first data and the second data; the second state corresponding to the first state in the third dynamic phase space at the first moment in the target time interval is determined, wherein the second state is the historical similarity state of the first state, wherein the third dynamic phase space includes: the first dynamic phase space and the second dynamic phase space, wherein the first moment is any moment in the target time interval; the sea-air coupling strength of the target spatial range is determined through the historical similarity state. Through the above embodiments, the problem of the lack of a solution that can quantify the sea-air coupling strength over the ocean in the prior art is solved, a solution for determining the sea-air coupling strength is provided, and the technical effect of quickly and accurately determining the sea-air coupling strength is achieved.

[0035] In an exemplary embodiment, a first kinetic phase space and a second kinetic phase space are constructed respectively by the first data and the second data, including: determining the first phase space trajectory by a distribution diagram of the first data within the target space range per unit time, and determining the second phase space trajectory by a distribution diagram of the second data within the target space range per unit time; determining the first kinetic phase space by the first phase space trajectory, and determining the second kinetic phase space by the second phase space trajectory.

[0036] It should be noted that the daily meteorological data includes the latitude and longitude of the target space range, the first data, and the second data. Within the target space range, the space-time field evolution of the first data and the second data can be regarded as two power systems respectively. The first phase space trajectory can approximately describe the time evolution of the state of the power system corresponding to the first data, and the second phase space trajectory can approximately describe the time evolution of the state of the power system corresponding to the second data.

[0037] Optionally, for daily meteorological data, "day" can be used as the unit time. For example, taking each day as the unit, the daily evolution of the state of the power system of the first data within the target time interval is regarded as the first phase space trajectory of the first data, and the daily evolution of the state of the power system of the second data within the target time interval is regarded as the second phase space trajectory of the second data.

[0038] In an exemplary embodiment, determining the second state corresponding to the first state in the third kinetic phase space at the first moment in the target time interval includes: acquiring the first state in the third kinetic phase space at the first moment through a third phase space trajectory, wherein the third phase space trajectory includes: the first phase space trajectory and the second phase space trajectory; determining the second state corresponding to the first state through a predefined observable function.

[0039] That is, the historical similarity state of the first state in the third dynamical phase space at the first moment can be calculated through the predefined observable function.

[0040] Furthermore, the second state corresponding to the first state is determined by a predefined observable function, including: inputting the third state at the second moment in the third kinetic phase space and the first state into the observable function to obtain a function value, wherein the second moment is any moment in the target time interval except any moment; comparing the function value with a preset threshold; and when the function value is greater than the preset threshold, determining the third state as the second state.

[0041] When the function value is less than the preset threshold, the third state is not a historically similar state of the first state. Optionally, the preset threshold can be pre-set, or it can be determined by the quantile of the function value after the function value is calculated, for example: w function values ​​are calculated, and the quantile at 98% of the w function values ​​is determined as the preset threshold. It should be noted that 98% is pre-selected, and it can also be 99%, etc. Therefore, the embodiment of the present application can efficiently and accurately determine the historically similar state of the first state through the observable function.

[0042] In an exemplary embodiment, determining the air-sea coupling strength of the target spatial range through the historical similarity state includes: obtaining a first historical similarity state corresponding to the first state in the first dynamic phase space at the first moment, and obtaining a second historical similarity state corresponding to the first state in the second dynamic phase space at the first moment, wherein the historical similarity state includes: the first historical similarity state and the second historical similarity state; determining the synchronization occurrence rate of the first historical similarity state and the second historical similarity state; and determining the air-sea coupling strength through the synchronization occurrence rate.

[0043] It can be understood that the first state corresponds to the first state in the first dynamic phase space and the second dynamic phase space respectively at the first moment; the first state corresponding to the first state in the first dynamic phase space at the first moment has a first historical similarity state in the first dynamic phase space, and the first state corresponding to the first state in the second dynamic phase space at the first moment also has a second historical similarity state in the second dynamic phase space. Then, by determining the synchronization rate between the first historical similarity state and the second historical similarity state, the sea-air coupling strength can be further determined by the synchronization rate.

[0044] Further, determining the synchronous occurrence rate of the first historical similarity state and the second historical similarity state includes: determining a ratio of the simultaneous occurrence of the first historical similarity state and the second historical similarity state; and determining the ratio as the synchronous occurrence rate.

[0045] In an exemplary embodiment, determining the sea-air coupling strength through the synchronization occurrence rate includes: obtaining the synchronization occurrence rates corresponding to a plurality of the first moments in the target time interval; arranging the plurality of synchronization occurrence rates in a preset order to obtain the sea-air coupling strength.

[0046] Optionally, the preset order is a time order, that is, a plurality of synchronization occurrence rates are sorted according to the time order within a target time interval to obtain the sea-air coupling intensity.

[0047] In order to better understand the process of the above-mentioned method for determining the sea-air coupling strength, the above-mentioned method for determining the sea-air coupling strength is described below in combination with an optional embodiment, but is not used to limit the technical solution of the embodiment of the present application.

[0048] In the related art, the sea temperature index defined by linear regional average or orthogonal decomposition can only characterize the intensity of sea temperature extreme events, but cannot quantify the sea-air coupling intensity over the tropical Pacific. The optional embodiment of the present application proposes a dynamic system method based on extreme value theory (equivalent to the method for determining the sea-air coupling intensity in the above embodiment), which regards the evolution of the sea temperature field and the atmospheric deep convection field in the tropical Pacific region over time as two dynamic systems, and calculates the dynamic system coupling coefficient α by measuring the synchronous occurrence rate of the spatial modes of the two meteorological fields at different times, so as to quantify the change of the sea-air coupling intensity over time over the tropical Pacific.

[0049] It should be noted that the dynamical system coupling coefficient α is based on the dynamical systems method (DSM) framework, which characterizes the instantaneous coupling intensity between the space-time fields of two meteorological variables by measuring the synchronous occurrence rate of the spatial modes of the two meteorological fields at different times.

[0050] Further, physically speaking, atmospheric deep convection in the central tropical Pacific is usually a representation of the atmosphere-ocean coupling associated with ENSO, and can also be used as a predictor of the impact of ENSO on the temperate zone, so atmospheric deep convection is selected as the meteorological element characterizing the atmospheric state; and the meteorological element characterizing the ocean state is still the sea surface temperature. Among them, atmospheric deep convection is represented by outgoing longwave radiation (OLR). It should be noted that the sea surface temperature anomaly in the embodiment of the present application is equivalent to the sea surface temperature anomaly data in the above embodiment, and the outgoing longwave radiation is equivalent to the outgoing longwave radiation data in the above embodiment.

[0051] It should be noted that the attractor of a dynamical system is a geometric set in the dynamical phase space that carries all the trajectories of the system and describes all the states experienced by the system. Reconstructing the dynamical attractor through data is an important method to characterize the evolution of a dynamical system. The dynamical system method is based on extreme value theory and Poincare recurrence theorem, and uses system trajectories to reconstruct the attractor of a dynamical system.

[0052] In an optional embodiment of the present application, the spatiotemporal field evolution of two meteorological variables (equivalent to the first data and the second data in the above embodiment) in a specific area (equivalent to the target spatial range in the above embodiment) is regarded as two dynamic systems, wherein the two dynamic systems can be represented as SSTA field and OLR field, respectively. After constructing the dynamic phase space of the two dynamic systems, two sets of phase space trajectories X(t) (equivalent to the first phase space trajectory in the above embodiment) and Y(t) (equivalent to the second phase space trajectory in the above embodiment) can be obtained. The phase space trajectories X(t) and Y(t) can approximately describe the time evolution of the state of the dynamic system. Figure 3 As shown, Figure 3 The schematic diagram of reconstructing the phase space using two meteorological space-time fields (SSTA field and OLR field) is given. Taking the SSTA field as an example, a set of continuously observed daily SSTA latitude and longitude distribution diagrams is used as the phase space trajectory X(t). Each point on the trajectory represents the latitude and longitude distribution mode of the SSTA on that day; similarly, each point on the Y(t) trajectory represents the latitude and longitude distribution mode of the OLR on that day. By measuring the synchronous occurrence rate of the spatial modes of the two meteorological fields at time t=ζ, the dynamic system coupling coefficient αSST,OLR of the two variable fields at time t=ζ can be calculated, reflecting the coupling strength of the ocean and atmosphere system at this time.

[0053] Based on the above content, in the optional embodiment of the present application, if Figure 4 As shown in Figure 2, the process of determining the intensity of sea-air coupling is as follows:

[0054] Step 41, input SSTA and OLR meteorological spatiotemporal field data;

[0055] Step 42, constructing the dynamic phase space based on the SSTA and OLR meteorological space-time field data, and defining the observable function at time t=ζ;

[0056] Selection of spatial range (equivalent to the target spatial range in the above embodiment) Region 3.4 is the calculation space domain of the SSTA field and the OLR field, and the time range (equivalent to the target time interval in the above embodiment) can select daily meteorological data from 1982 to 2020.

[0057] The SSTA field is regarded as a climate dynamic system, whose phase space trajectory is X(t), where X(t) is the continuous daily SSTA latitude-longitude space-time field; ζ x is a point in its phase space, such as Figure 3 As shown, it represents the latitude-longitude distribution mode of the SSTA field at time t = ζ. Similarly, the phase space trajectory of the external longwave radiation field is Y(t), Y(t) is the continuous daily OLR latitude-longitude space-time field, ζ y is a point in its phase space, representing the latitude-longitude distribution mode of the OLR field at time t=ζ.

[0058] Furthermore, it is necessary to determine the coupling state of the two dynamic systems at time t = ζ. The observable functions g of the SSTA field and the OLR field are defined logarithmically as shown in formula (1) and formula (2), respectively:

[0059] g(x(t),ζ x )=-log [dist(x(t),ζ x )] (1);

[0060] g(y(t),ζ y )=-log [dist(y(t),ζ y )] (2);

[0061] Where dist(m,n) is the distance function between two vectors. When m and n are close to each other, dist approaches zero. x ) represents the calculation of ζ in the SSTA field phase space x The phase space distance with all other moments is the observable function g(x(t),ζ y ) represents the ζ in the computational field phase space y The phase space distance to all other moments. For practical reasons, here dist uses the Euclidean distance, which is often used to calculate historical similarities.

[0062] Step 43, finding the historical similarity state at time t=ζ;

[0063] In formula (1), g(x(t),ζx ) defines a logarithmic time series when x(t) and ζ x When the distance between them is very small, formula (1) will return a larger value. x )(i.e. the value calculated by formula (1)), when a certain element (i.e. the value calculated by formula (1)) is a large value, it means that the state of the sea temperature field at the corresponding moment of this element is different from the concerned ζ x The state at each moment is similar (i.e., the moment is ζ x In the vector g(x(t),ζ x ) (i.e., the many values ​​calculated by formula (1)), we only need to pay attention to the x The historical states that are relatively similar at each moment, therefore, according to the vector g(x(t),ζ x ) in the data (i.e., numerous values), calculate its quantile q = 98%, as the threshold s for screening historical similar states x (q, ζ) (equivalent to the preset threshold in the above embodiment), the value greater than this threshold is the SSTA field in ζ x Similarly, for the OLR field, we also get g(y(t),ζ y )The 98% quantile of the sequence s y (q,ζ), the value greater than this threshold is the OLR field at ζ y Historical similarities of moments.

[0064] Step 44, calculating the synchronization occurrence rate;

[0065] The synchronization rate of the two sets of vector history similarities between the SSTA field and the OLR field can be calculated using formula (3):

[0066]

[0067] The symbol “v[]” in formula (3) means to count the events that meet the conditions in the brackets. Formula (3) means that at time ζ, X(t) and Y(t) are at ζ x and y The ratio of the two states with similar histories occurring simultaneously. x,y The value of (ζ) reflects ζ x and y Whether the two states always occur synchronously.

[0068] Step 45, traverse all the moments when t=ζ;

[0069] Step 46, output instantaneous coupling strength sequence α x,y (ζ).

[0070] For each moment in the data X(t) and Y(t), such measurements can be made, so a time series α describing the instantaneous coupling strength of the two meteorological space-time fields X(t) and Y(t) can be obtained. x,y (ζ).

[0071] α x,y (ζ) ranges from 0 to 1. x,y When (ζ) is close to 1, it means that the instantaneous coupling strength of the two space-time fields of interest is so strong that they always occur synchronously. x,y When (ζ) is equal to 0, it means that the dynamic evolution of the two space-time fields of interest at this moment is independent of each other. x,y (ζ) does not depend on the order of x and y, so x and y can be interchanged, which means that α x,y (ζ)Cannot be explained by causality.

[0072] In summary, the dynamic system coupling coefficient α SST，OLR It can accurately reflect the change of the instantaneous coupling strength between SST and OLR field and is an effective indicator for measuring the strength of sea-air coupling; (2) Dynamical system coupling coefficient α SST，OLR The measured changes in instantaneous coupling strength are closely related to extreme SST anomalies; (3) The dynamic system coupling coefficient α SST，OLR The ability to identify strongly coupled events is better than that based on area averaging. 3.4 Index.

[0073] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0074] The present application also provides a structural block diagram of a device for determining the sea-air coupling strength. Figure 5 is a structural block diagram of a device for determining the sea-air coupling strength according to an embodiment of the present application; Figure 5 As shown, including:

[0075] An acquisition module 52 is used to acquire daily meteorological data of a target spatial range over the ocean within a target time interval, wherein the daily meteorological data includes: first data and second data, the first data is sea surface temperature anomaly data, and the second data is outward long-wave radiation data;

[0076] A construction module 54, configured to construct a first dynamic phase space and a second dynamic phase space respectively using the first data and the second data;

[0077] A first determination module 56 is used to determine a second state corresponding to a first state in a third kinetic phase space at a first moment within the target time interval, wherein the second state is a historically similar state of the first state, wherein the third kinetic phase space includes: the first kinetic phase space and the second kinetic phase space, wherein the first moment is any moment within the target time interval;

[0078] The second determination module 58 is used to determine the air-sea coupling intensity of the target spatial range through the historical similarity state.

[0079] Through the above device, daily meteorological data of the target spatial range over the ocean within the target time interval is obtained, wherein the daily meteorological data includes: first data and second data, the first data is sea surface temperature anomaly data, and the second data is outward long wave radiation data; the first dynamic phase space and the second dynamic phase space are constructed respectively through the first data and the second data; the second state corresponding to the first state in the third dynamic phase space at the first moment in the target time interval is determined, wherein the second state is the historical similarity state of the first state, wherein the third dynamic phase space includes: the first dynamic phase space and the second dynamic phase space, wherein the first moment is any moment in the target time interval; the sea-air coupling strength of the target spatial range is determined through the historical similarity state. Through the above embodiment, the problem of the lack of a solution that can quantify the sea-air coupling strength over the ocean in the prior art is solved, a solution for determining the sea-air coupling strength is provided, and the technical effect of quickly and accurately determining the sea-air coupling strength is achieved.

[0080] In an exemplary embodiment, a construction module 54 is used to: determine a first phase space trajectory through a distribution diagram of the first data within the target space range per unit time, and determine a second phase space trajectory through a distribution diagram of the second data within the target space range per unit time; determine the first dynamic phase space through the first phase space trajectory, and determine the second dynamic phase space through the second phase space trajectory.

[0081] In an exemplary embodiment, the first determination module 56 is used to: obtain the first state in the third dynamic phase space at the first moment through a third phase space trajectory, wherein the third phase space trajectory includes: the first phase space trajectory, the second phase space trajectory; determine the second state corresponding to the first state through a predefined observable function.

[0082] In an exemplary embodiment, the first determination module 56 is used to: input the third state at a second moment in the third kinetic phase space and the first state into the observable function to obtain a function value, wherein the second moment is any moment in the target time interval except any moment; compare the function value with a preset threshold; and when the function value is greater than the preset threshold, determine the third state as the second state.

[0083] In an exemplary embodiment, the second determination module 58 is used to: obtain a first historical similarity state corresponding to the first state in the first dynamic phase space at the first moment, and obtain a second historical similarity state corresponding to the first state in the second dynamic phase space at the first moment, wherein the historical similarity state includes: the first historical similarity state and the second historical similarity state; determine the synchronization occurrence rate of the first historical similarity state and the second historical similarity state; determine the sea-air coupling intensity through the synchronization occurrence rate.

[0084] In an exemplary embodiment, the second determination module 58 is used to: determine a ratio of simultaneous occurrence of the first historical similarity state and the second historical similarity state; and determine the ratio as the synchronous occurrence rate.

[0085] In an exemplary embodiment, the second determination module 58 is used to: obtain the synchronization occurrence rates corresponding to a plurality of the first moments in the target time interval; and arrange the plurality of synchronization occurrence rates in a preset order to obtain the sea-air coupling intensity.

[0086] An embodiment of the present application further provides a storage medium, which includes a stored program, wherein the program executes any of the above methods when it is run.

[0087] Optionally, in this embodiment, the storage medium may be configured to store program codes for executing the following steps:

[0088] S1, acquiring daily meteorological data of a target spatial range over the ocean within a target time interval, wherein the daily meteorological data includes: first data and second data, the first data is sea surface temperature anomaly data, and the second data is outward long-wave radiation data;

[0089] S2, constructing a first dynamic phase space and a second dynamic phase space respectively by using the first data and the second data;

[0090] S3, determining a second state corresponding to a first state in a third kinetic phase space at a first moment within the target time interval, wherein the second state is a historically similar state of the first state, wherein the third kinetic phase space includes: the first kinetic phase space and the second kinetic phase space, wherein the first moment is any moment within the target time interval;

[0091] S4, determining the air-sea coupling intensity of the target spatial range through the historical similarity state.

[0092] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0093] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0094] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0095] S1, acquiring daily meteorological data of a target spatial range over the ocean within a target time interval, wherein the daily meteorological data includes: first data and second data, the first data is sea surface temperature anomaly data, and the second data is outward long-wave radiation data;

[0096] S2, constructing a first dynamic phase space and a second dynamic phase space respectively by using the first data and the second data;

[0097] S3, determining a second state corresponding to a first state in a third kinetic phase space at a first moment within the target time interval, wherein the second state is a historically similar state of the first state, wherein the third kinetic phase space includes: the first kinetic phase space and the second kinetic phase space, wherein the first moment is any moment within the target time interval;

[0098] S4, determining the air-sea coupling intensity of the target spatial range through the historical similarity state.

[0099] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0100] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0101] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0102] An embodiment of the present application also provides a computer program, which includes computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in any one of the above method embodiments.

[0103] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0104] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0105] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining the intensity of sea-air coupling, characterized in that: include: Acquire daily meteorological data of a target spatial range over the ocean within a target time interval, wherein the daily meteorological data includes: first data and second data, the first data is sea surface temperature anomaly data, and the second data is outward long-wave radiation data; Constructing a first kinetic phase space and a second kinetic phase space respectively by using the first data and the second data; Determine a second state corresponding to a first state in a third kinetic phase space at a first moment within the target time interval, wherein the second state is a historically similar state of the first state, wherein the third kinetic phase space includes: the first kinetic phase space and the second kinetic phase space, wherein the first moment is any moment within the target time interval; The air-sea coupling intensity of the target spatial range is determined through the historical similarity state.

2. The method for determining the sea-air coupling strength according to claim 1, characterized in that: Constructing a first kinetic phase space and a second kinetic phase space respectively by using the first data and the second data includes: Determine a first phase space trajectory through a distribution diagram of the first data within the target space range within a unit time, and determine a second phase space trajectory through a distribution diagram of the second data within the target space range within the unit time; The first kinetic phase space is determined by the first phase space trajectory, and the second kinetic phase space is determined by the second phase space trajectory.

3. The method for determining the sea-air coupling strength according to claim 2, characterized in that: Determining a second state corresponding to a first state in a third kinetic phase space at a first moment in the target time interval includes: Acquire the first state in the third dynamic phase space at the first moment through a third phase space trajectory, wherein the third phase space trajectory includes: the first phase space trajectory and the second phase space trajectory; The second state corresponding to the first state is determined by a predefined observable function.

4. The method for determining the sea-air coupling strength according to claim 3, characterized in that: Determining the second state corresponding to the first state by using a predefined observable function includes: Inputting a third state at a second moment in the third kinetic phase space and the first state into the observable function to obtain a function value, wherein the second moment is any moment other than the any moment within the target time interval; Comparing the function value with a preset threshold; When the function value is greater than the preset threshold, the third state is determined as the second state.

5. The method for determining the sea-air coupling strength according to claim 4, characterized in that: Determining the air-sea coupling intensity of the target spatial range through the historical similarity state includes: Acquire a first historical similarity state corresponding to a first state in the first dynamic phase space at the first moment, and acquire a second historical similarity state corresponding to a first state in the second dynamic phase space at the first moment, wherein the historical similarity states include: the first historical similarity state and the second historical similarity state; Determining a synchronization rate of the first historical similarity state and the second historical similarity state; The sea-air coupling strength is determined by the synchronization rate.

6. The method for determining the sea-air coupling strength according to claim 5, characterized in that: Determining a synchronization rate of the first historical similarity state and the second historical similarity state includes: determining a ratio of simultaneous occurrence of the first historical similarity and the second historical similarity; The ratio is determined as the synchronization incidence rate.

7. The method for determining the sea-air coupling strength according to claim 5, characterized in that: Determining the air-sea coupling intensity by the synchronization occurrence rate includes: Obtain synchronization occurrence rates corresponding to a plurality of first moments in the target time interval; Arrange the plurality of synchronization occurrence rates in a preset order to obtain the air-sea coupling intensity.

8. A device for determining the strength of sea-air coupling, characterized in that: include: An acquisition module is used to acquire daily meteorological data of a target spatial range over the ocean within a target time interval, wherein the daily meteorological data includes: first data and second data, the first data is sea surface temperature anomaly data, and the second data is outward long-wave radiation data; A construction module, used to construct a first kinetic phase space and a second kinetic phase space respectively through the first data and the second data; A first determination module is used to determine a second state corresponding to a first state in a third kinetic phase space at a first moment within the target time interval, wherein the second state is a historically similar state of the first state, wherein the third kinetic phase space includes: the first kinetic phase space and the second kinetic phase space, wherein the first moment is any moment within the target time interval; The second determination module is used to determine the air-sea coupling intensity of the target spatial range through the historical similarity state.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method described in any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 7 through the computer program.

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