Method for calculating continuity parameter of sea temperature mode, electronic equipment and storage medium

Abstract

The invention provides a method for calculating a sea temperature modal continuity parameter, electronic equipment and a storage medium. The method comprises the following steps: constructing a dynamic phase space based on meteorological space-time field data, wherein the phase space is used for representing the sea temperature change of a tropical pacific ocean region; establishing a similarity measurement function at the current moment in the phase space, wherein the similarity measurement function is used for measuring the similarity between the observation state at the current moment and the state at the historical moment; obtaining a historical state similar to the observation state at the current moment through the similarity function; calculating shape parameters of the historical state by fitting generalized Pareto distribution; and calculating a continuity parameter at the current moment through the shape parameter.

Description

Technical Field

[0001] The present invention relates to the field of meteorology and climate dynamics, and in particular to a method for calculating sea temperature modal persistence parameters, electronic equipment and storage medium. Background Art

[0002] Abnormal changes in the sea temperature state in the tropical Pacific region have a vital impact on the global climate, especially the most concerned El Niño and La Niña events with the strongest interannual variability. In addition, there are coastal warming events and marine heat wave events with smaller temporal and spatial scales, as well as the extension of the Pacific meridional mode in the tropical range. As the main interannual variability of the global climate system, El Niño and La Niña can affect global precipitation, North American temperature, and even global warming through teleconnection mechanisms. Even coastal warming events with smaller spatial ranges can trigger a variety of natural disasters. Since these multi-scale sea temperature anomaly events are the main driving factors of high-impact weather and climate, natural disasters, etc., in-depth research on them has very important scientific significance and practical application value.

[0003] Currently, some sea surface temperature indices are commonly used to quantitatively describe the intensity of different El Niño and La Niña events, all of which are based on the average sea surface temperature anomalies in specific regions.

[0004] However, due to the multi-scale spatiotemporal complexity of SST variations, the traditional SST index defined by linear regional average or orthogonal decomposition can only characterize the intensity of SST events, but cannot fully characterize other properties of the SST mode, such as the complexity / degrees of freedom of the SST mode.

[0005] In order to solve the above problems, a method for calculating sea temperature modal persistence parameters, an electronic device and a storage medium are proposed herein. Summary of the invention

[0006] The embodiments of the present disclosure aim to solve at least one of the technical problems existing in the prior art, and provide a method, electronic device and storage medium for calculating the persistence parameters of the sea temperature modal. The persistence parameter sequence calculated by the dynamic system method can effectively characterize the change of the persistence of the tropical Pacific sea temperature system over time, and provide a quantitative indicator for understanding the sea temperature modal changes from a dynamic perspective, and supplement the existing sea temperature index system defined by linear regional average or orthogonal decomposition.

[0007] A first aspect of the present disclosure provides a method for calculating a sea temperature modal persistence parameter, characterized in that the method comprises:

[0008] constructing a dynamic phase space based on meteorological spatiotemporal field data, wherein the phase space is used to represent the sea temperature change in the tropical Pacific region;

[0009] Establishing a similarity measurement function at the current moment in the phase space, wherein the similarity measurement function is used to measure the similarity between the observed state at the current moment and the state at the historical moment;

[0010] Acquire a historical state similar to the observed state at the current moment through the similarity function;

[0011] Calculating shape parameters of the historical state by fitting a generalized Pareto distribution;

[0012] The persistence parameter at the current moment is calculated using the shape parameter.

[0013] In combination with the first aspect, the meteorological spatiotemporal field data includes sea temperature anomaly data of the target area.

[0014] In combination with the first aspect, the similarity measurement function at the current moment is:

[0015] g(x(t),ζ)=-log[dist(x(t),ζ)]

[0016] Among them, x(t) is the daily sea temperature anomaly data, ζ is the current time, and dist is the distance function between two vectors. When x(t) and ζ are close to each other, dist tends to zero.

[0017] In combination with the first aspect, the acquiring, by using the similarity function, a historical state similar to the observed state at the current moment includes:

[0018] According to the data in the vector g(x(t),ζ), the 98th percentile of the data is calculated as the threshold s(q,ζ) for screening similar historical states;

[0019] The maximum value vector u(t,ζ) is screened according to the threshold value, and the elements less than zero are removed.

[0020] In combination with the first aspect, the screening of the maximum value vector u(t,ζ) according to the threshold value includes using the following formula:

[0021] u(t,ζ)=g(x(t),ζ)-s(q,ζ)

[0022] In combination with the first aspect, calculating the shape parameter of the historical state by fitting the generalized Pareto distribution includes:

[0023] Fitting the maximum value data u(t,ζ) to a generalized Pareto distribution;

[0024] The shape parameters are obtained using an estimator.

[0025] In combination with the first aspect, the probability density function of the generalized Pareto distribution is:

[0026]

[0027] Among them, θ(ζ) is the shape parameter, σ(ζ) is the scale parameter, and u(ζ) is the maximum value data at time ζ.

[0028] In conjunction with the first aspect, calculating the persistence parameter at the current moment by using the shape parameter includes using the following formula:

[0029] e(ζ)=θ(ζ)

[0030] Among them, e(ζ) is the persistence parameter at time ζ.

[0031] According to a second aspect of the present disclosure, there is provided an electronic device, comprising:

[0032] one or more processors;

[0033] A storage unit is used to store one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement any of the above-mentioned methods for calculating the sea temperature modal persistence parameter.

[0034] According to a third aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that when the computer program is executed by a processor, any of the above-mentioned methods for calculating the sea temperature modal persistence parameter can be implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of a flow chart of a method for calculating sea temperature modal persistence parameters according to an embodiment of the present disclosure;

[0036] Figure 2 It is a schematic diagram of reconstructing the phase space of the meteorological space-time field according to an embodiment of the present disclosure;

[0037] Figure 3 Schematic diagram of scale parameter d(ζ) and shape parameter θ(ζ) in phase space of a dynamic system according to an embodiment of the present disclosure;

[0038] Figure 4 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0039] Here, exemplary embodiments are described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the embodiments of the present disclosure.

[0040] The terms used in the disclosed embodiments are only for the purpose of describing specific embodiments and are not intended to limit the disclosed embodiments. The singular forms of "a", "said" and "the" used in the disclosed embodiments and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0041] It should be understood that although the terms first, second, third, etc. may be used to describe various information in the disclosed embodiments, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the disclosed embodiments, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0042] like Figure 1 FIG. 1 is a flow chart of a method for calculating sea temperature modal persistence parameters according to an embodiment of the present disclosure. The extraction method comprises the following steps:

[0043] S101: Construct a dynamic phase space based on meteorological spatiotemporal field data, which is used to represent the sea temperature changes in the tropical Pacific region;

[0044] S102: establishing a similarity measurement function at the current moment in the phase space, where the similarity measurement function is used to measure the similarity between the observed state at the current moment and the state at the historical moment;

[0045] S103: Obtaining a historical state similar to the observed state at the current moment through the similarity function;

[0046] S104: Calculate the shape parameters of the historical state by fitting the generalized Pareto distribution.

[0047] S105: Calculate the persistence parameter at the current moment using the shape parameter.

[0048] Refer to the following Figure 2-Figure 4 The method is described in detail.

[0049] like Figure 2 As shown, it is a schematic diagram of reconstructing the phase space using the meteorological space-time field according to an embodiment of the present disclosure.

[0050] 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.

[0051] Reconstructing dynamical attractors through data is an important method to characterize the evolution process of dynamical systems.

[0052] The dynamical system method is based on extreme value theory and Poincare recurrence theorem, and uses the system trajectory X(t) to reconstruct the attractor of the dynamical system.

[0053] The evolution of the meteorological space-time field in a specific area is regarded as a dynamic system, and its phase space trajectory X(t) can approximately describe the time evolution of the state of the dynamic system.

[0054] A dynamic phase space is constructed based on meteorological spatiotemporal field data, which is used to represent the sea surface temperature changes in the tropical Pacific region.

[0055] Among them, the sea surface temperature anomaly data in the tropical Pacific region indicate the degree to which the sea surface temperature at a specific time and space deviates from the long-term average.

[0056] A set of continuously observed daily longitude and latitude distribution diagrams of sea surface temperature anomalies is taken as the phase space trajectory X(t), and each point on the trajectory represents the longitude and latitude distribution diagram of the sea surface temperature anomaly on that day.

[0057] Exemplarily, daily sea surface temperature anomaly data in the tropical Pacific region are obtained. These data are represented in a grid format, with each grid point corresponding to a specific longitude and latitude. For each day t, the longitude and latitude distribution map of the sea surface temperature anomaly of that day is recorded. These distribution maps constitute a multi-persistent parameter data set, in which each data point X(t) represents the abnormal state of the sea surface temperature at a specific time t.

[0058] like Figure 2 , the phase space trajectory form near a certain day (such as t = ζ), that is, its historical similarity state, can reflect the dynamic state of the SST system at this time by quantifying the geometric properties of the nearby trajectories.

[0059] The black line represents the trajectory in phase space; the white circles along the trajectory represent discrete observational data points of the continuous evolution of the sea surface temperature anomaly, such as the observational data provided by the reanalysis data set, each of which represents the meteorological spatiotemporal field at that moment; t = ζ is the current moment, and the range circled in red is the historical similarity state at t = ζ. A partial enlargement of (a) is shown, indicating that for a specific moment in phase space, some geometric methods can be used to estimate the dynamic state of the device at that moment.

[0060] Establish a similarity measurement function for the current moment in the phase space, which is used to measure the similarity between the observed state at the current moment and the state at the historical moment

[0061] Specifically, in order to analyze the observed state at the current moment, it is necessary to find the state at the most similar historical moment.

[0062] Furthermore, the similarity measurement function is defined as:

[0063] g(x(t),ζ)=-log[dist(x(t),ζ)]

[0064] Among them, x(t) is the daily sea temperature anomaly data, ζ is the current time, and dist is the distance function between two vectors. When x(t) and ζ are close to each other, dist tends to zero.

[0065] Furthermore, dist selects the Euclidean distance:

[0066]

[0067] xi(t) represents the abnormal value of the sea surface temperature at the i-th grid point at the historical time t, and xi(ζ) represents the abnormal value of the sea surface temperature at the i-th grid point at the current time ζ.

[0068] The distance metric is converted to a similarity measure using a logarithmic function:

[0069]

[0070] When the distance between x(t) and ζ is small, g(x(t),ζ) takes a larger value, indicating that the similarity between the two is high. Conversely, when the distance is large, g(x(t),ζ) takes a smaller value, indicating that the similarity is low.

[0071] The similarity function is used to obtain the historical state similar to the observed state at the current moment.

[0072] According to the data in the vector g(x(t),ζ), the 98th percentile of the data is calculated as the threshold s(q,ζ) for screening similar historical states;

[0073] The maximum value vector u(t,ζ) is screened according to the threshold, and the elements less than zero are removed.

[0074] Among the many elements of the vector g(x(t),ζ), we only need to pay attention to the historical states that are relatively similar to the moment ζ. To this end, based on the data in the vector g(x(t),ζ), we calculate the 98th percentile of the data as the threshold s(q,ζ) for screening historical similar states.

[0075] Calculate a new vector

[0076] u(t,ζ)=g(x(t),ζ)-s(q,ζ)

[0077] , and directly remove the elements less than zero in the vector u(t,ζ). In this way, the vector u(t,ζ) only contains the distance information of the historical states that are similar to the time ζ.

[0078] When the value of quantile q is between 90% and 99%, it has no significant effect on the subsequent statistical quantitative values.

[0079] The obtained maximum vector u(t,ζ) is fitted.

[0080] Specifically, since the vector u(t,ζ) is the maximum value extracted from the vector g(x(t),ζ), the cumulative probability distribution F(u,ζ) of u(t,ζ) will converge to the generalized Pareto distribution of extreme values.

[0081]

[0082] Among them, θ(ζ) is the shape parameter, σ(ζ) is the scale parameter, and u(ζ) is the maximum value data at time ζ.

[0083] The cumulative distribution function F(u,ζ) represents the probability that the random variable u is less than or equal to a certain value. In extreme value theory, the cumulative distribution function of the extracted maximum value data u(t,ζ) can be approximated as a generalized Pareto distribution.

[0084] The generalized Pareto distribution is used to describe extreme events that exceed a certain high threshold. The cumulative distribution function of this distribution is:

[0085]

[0086] In a specific case (i.e., the location parameter μ = 0 and exponential transformation of F(u,ζ)), it can be simplified to the above approximate formula:

[0087]

[0088] The shape parameter θ(ζ) describes the shape characteristics of the distribution and is often related to the tail behavior of the extreme value distribution. Larger values ​​of the shape parameter indicate heavier tails.

[0089] The scale parameter σ(ζ) represents the scale or range of the data, and larger scale parameter values ​​mean greater dispersion.

[0090] The value range of θ(ζ) is 0<θ(ζ)<1. To estimate θ(ζ), the Süveges estimator is used.

[0091] The log-likelihood function of the Süveges estimator includes extreme values ​​and exceedance times.

[0092]

[0093] Where: u i is the extreme value distance data after screening, τ i is the time interval between adjacent extreme values.

[0094] The physical meaning of θ(ζ) is the dynamic system ( Figure 3 ) is the inverse of the duration that the system stays near point ζ, so the value of θ(ζ) is inversely correlated with the persistence of the system.

[0095] When the value of θ(ζ) is close to 1, it means that the SST system ( Figure 2 The evolution trajectory of (a)) quickly leaves point ζ, that is, its persistence at point ζ is weak; when the value of θ(ζ) is closer to 0, it means that the phase space trajectory stays longer in the area near point ζ, that is, its persistence at point ζ is strong.

[0096] Optionally, for each day in the observation data, the persistence parameter e(ζ) of the SST mode on that day can be quantified. Therefore, by taking all moments as the reference point ζ in turn, we can obtain the e(ζ) time series that describes the temporal changes of the SST complexity in the selected spatial domain.

[0097] Through the above steps, the construction of the dynamic phase space based on the daily sea surface temperature anomaly data in the tropical Pacific region was realized. This phase space uses the daily sea temperature anomaly distribution map as a trajectory to effectively depict the change of sea temperature over time. By defining the similarity measurement function, the similarity between the current sea temperature state and the state at the historical moment was successfully evaluated, and then the historical state similar to the current state was screened out. Using the extreme value theory method, the generalized Pareto distribution was fitted to the extracted historical state maximum data, and the shape parameters and scale parameters were obtained, thereby revealing the persistence parameters of the sea temperature system at different times, and deeply understanding the spatiotemporal structure and evolution law of the tropical Pacific sea temperature.

[0098] The embodiments of the present disclosure provide a method for calculating the persistence parameter of the sea temperature mode, an electronic device and a storage medium. The persistence parameter e(ζ) sequence calculated by the dynamic system method can effectively characterize the change of the persistence of the tropical Pacific sea temperature system over time, provide a quantitative index for understanding the change of the sea temperature mode from a dynamic perspective, and supplement the existing sea temperature index system defined by linear regional average or orthogonal decomposition. The persistence parameter e(ζ) has a good correlation with the forecast error of the climate model ensemble forecast, and can also be used to detect high-persistence extreme events. It is an effective statistic for measuring the potential predictability and extreme events in the climate subsystem observation data.

[0099] The electronic device 400 may be a desktop computer, a notebook, a PDA, a cloud server, or other electronic device. The electronic device 400 may include, but is not limited to, a processor 401 and a memory 402. Those skilled in the art will appreciate that Figure 4 It is only an example of the electronic device 400 and does not constitute a limitation of the electronic device 400. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0100] The processor 401 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0101] The memory 402 may be an internal storage unit of the electronic device 400, for example, a hard disk or memory of the electronic device 400. The memory 402 may also be an external storage device of the electronic device 400, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 400. Further, the memory 402 may also include both an internal storage unit of the electronic device 400 and an external storage device. The memory 402 is used to store computer programs and other programs and data required by the electronic device. The memory 402 may also be used to temporarily store data that has been output or is to be output.

[0102] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present disclosure. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0103] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0104] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0105] In the embodiments provided in the present disclosure, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. Multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0106] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0107] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0108] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present disclosure implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. The computer program may include computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signals and telecommunication signals.

[0109] The above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should all be included in the protection scope of the present disclosure.

Claims

1. A method for calculating sea temperature modal persistence parameters, characterized in that: The method comprises: constructing a dynamic phase space based on meteorological spatiotemporal field data, wherein the phase space is used to represent the sea temperature change in the tropical Pacific region; Establishing a similarity measurement function at the current moment in the phase space, wherein the similarity measurement function is used to measure the similarity between the observed state at the current moment and the state at the historical moment; Acquire a historical state similar to the observed state at the current moment through the similarity function; Calculating shape parameters of the historical state by fitting a generalized Pareto distribution; The persistence parameter at the current moment is calculated using the shape parameter.

2. The method according to claim 1, characterized in that The meteorological spatiotemporal field data include sea temperature anomaly data of the target area.

3. The method according to claim 1, characterized in that The similarity measurement function at the current moment is: g(x(t),ζ)=-log[dist(x(t),ζ)] Among them, x(t) is the daily sea temperature anomaly data, v is the current time, dist is the distance function between two vectors, and when x(t) and ζ are close to each other, dist tends to zero.

4. The method according to claim 1, characterized in that: The obtaining of a historical state similar to the observed state at the current moment by using the similarity function includes: According to the data in the vector g(x(t),ζ), the 98th percentile of the data is calculated as the threshold s(q,ζ) for screening similar historical states; The maximum value vector u(t,ζ) is screened according to the threshold value, and the elements less than zero are removed.

5. The method according to claim 4, characterized in that The screening of the maximum value vector u(t,ζ) according to the threshold value includes using the following formula: u(t,ζ)=g(x(t),ζ)-s(q,ζ).

6. The method according to claim 1, characterized in that The calculating of the shape parameters of the historical state by fitting the generalized Pareto distribution comprises: Fitting the maximum value data u(t,ζ) to a generalized Pareto distribution; The shape parameters are obtained using an estimator.

7. The method according to claim 6, characterized in that The probability density function of the generalized Pareto distribution is: Among them, θ(v) is the shape parameter, σ(ζ) is the scale parameter, and u(ζ) is the maximum value data at time ζ.

8. The method according to claim 1, characterized in that Calculating the persistence parameter at the current moment through the shape parameter includes using the following formula: e(ζ)=θ(v) Among them, e(ζ) is the persistence parameter at time v.

9. An electronic device, characterized in that: include: one or more processors; A storage unit, used to store one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the method for calculating the sea temperature modal persistence parameter according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for calculating the sea temperature modal persistence parameter according to any one of claims 1 to 8 can be implemented.

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