CN114859439B - Extreme high temperature event prediction method and apparatus - Google Patents
Extreme high temperature event prediction method and apparatus Download PDFInfo
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Abstract
The application provides a method and equipment for predicting an extreme high temperature event, which comprises the steps of carrying out empirical orthogonal function decomposition analysis and wavelet analysis on historical global sea surface temperature data to obtain a sea temperature abnormal sensitive area; analyzing and obtaining the correlation between the occurrence area of each historical extreme high-temperature event and each sea-temperature abnormal sensitive area; and calculating the lag time of the historical extreme high-temperature events in the occurrence areas associated with the sea-temperature abnormal sensitive areas, and constructing a prediction model according to the correlation between the occurrence areas of the historical extreme high-temperature events and the sea-temperature abnormal sensitive areas and the lag time of the historical extreme high-temperature events in the occurrence areas associated with the sea-temperature abnormal sensitive areas. Through the arrangement, the sea temperature abnormity of the target sea temperature abnormity sensitive area can be detected, and the prediction model is used for providing early warning for the land extreme high temperature event.
Description
Technical Field
The application relates to the field of weather forecasting, in particular to a method and equipment for predicting an extreme high temperature event.
Background
Most areas of a research area belong to the seasonal climate region, which is affected by warm and humid air flows from the oceans and dry and cool air flows from the land, and is typically characterized by a large variation in temperature and precipitation, so that extreme high temperature and drought weather disasters are common in the research area. In the climate change background, the frequent occurrence of these extreme climates poses great threat to human life and property and living environment, and seriously affects the development of society and economy. The ocean is used as a climate influence factor, has the characteristics of long duration and large spatial scale, plays an important role in the climate change process, and generally confirms that the abnormal sea temperature in a certain sea area is an important reason for the continuous abnormal atmospheric circulation.
The research area is adjacent to the Pacific ocean, the terrain is high in the west and low in the east, the tropical and subtropical monsoon climate is remarkable, and the land climate change is remarkably influenced by the whole world. Therefore, the influence rule of the sea temperature anomaly on the extreme meteorological disaster event in the research area is deeply researched, a new thought and scheme are provided for the advance prediction of the extreme meteorological disaster event in the monsoon area, beneficial thinking can be provided for effectively reducing the loss caused by the extreme meteorological disaster event, and great benefit is brought to social and economic benefits.
The prior art focuses on researching the influence of Elnino-Southern billow (El Ni ñ o-Southern climate, for short: ENSO) on the weather climate on the research region, and researches show that the influence of the Pacific ocean abnormal temperature type on the weather climate on the research region is not limited to monsoon, and the influence also influences the quantity and strength of tropical cyclones in the landing research region, land precipitation and the like. However, in the prior art, the research on the influence of the global sea temperature abnormal change on the research of the terrestrial extreme high temperature event on the upper land of the region is almost none, and a model for predicting the occurrence of the terrestrial extreme high temperature event on the research region based on the global sea surface temperature abnormality is not available.
Disclosure of Invention
The application provides an extreme high temperature event prediction method and equipment, which aim to construct a model for predicting occurrence of a land extreme high temperature event through abnormal sea temperature change and provide early warning for the land extreme high temperature event.
An embodiment of the present application provides a method for predicting an extreme high temperature event, where the method includes:
performing empirical orthogonal function decomposition analysis and wavelet analysis on historical global sea surface temperature data to obtain a sea temperature abnormal sensitive area;
carrying out correlation analysis on temperature data before the occurrence time of each historical extremely high temperature event occurring region and the temperature data of each sea temperature abnormal sensitive region in the corresponding time; obtaining the correlation between the occurrence area of each historical extreme high temperature event and each sea temperature abnormal sensitive area;
and calculating the lag time of the historical extreme high-temperature events in the occurrence areas associated with the sea-temperature abnormal sensitive areas, and constructing a prediction model according to the correlation between the occurrence areas of the historical extreme high-temperature events and the sea-temperature abnormal sensitive areas and the lag time of the historical extreme high-temperature events in the occurrence areas associated with the sea-temperature abnormal sensitive areas.
In an embodiment, the method further comprises:
detecting surface temperature data of the target sea temperature abnormity sensitive area, and judging whether the surface temperature data of the target sea temperature abnormity sensitive area is abnormal or not;
and if so, processing the surface temperature data of the target sea temperature abnormal sensitive area by using a prediction model to obtain the occurrence area and the occurrence time of the extremely high temperature event.
In one embodiment, the method for obtaining the sea temperature anomaly sensitive area by performing empirical orthogonal function decomposition analysis and wavelet analysis on historical global sea surface temperature data specifically comprises the following steps:
performing empirical orthogonal function decomposition analysis on the historical global sea surface temperature spatial distribution data to obtain a sea temperature anomaly sensitive candidate area;
and performing wavelet analysis on the historical global sea surface temperature time series data to obtain an analysis result, and obtaining a sea temperature anomaly sensitive area from the sea temperature anomaly sensitive candidate area by using the analysis result.
In one embodiment, the method for obtaining the sea temperature anomaly sensitive candidate area by performing empirical orthogonal function decomposition analysis on the historical global sea surface temperature spatial distribution data specifically comprises the following steps:
performing empirical orthogonal function decomposition analysis on the historical global sea surface temperature spatial distribution data to obtain variance contribution rates and time coefficients of different modes;
obtaining the average distribution state and the consistent change region of the global sea temperature according to the variance contribution rate, and obtaining seasonal change cycles according to time coefficients of different modes;
and obtaining the sea temperature abnormal sensitive alternative area from the area with consistent change according to the seasonal change period and the average distribution state of the sea temperature in the whole world.
In one embodiment, the wavelet analysis is performed on the historical global sea surface temperature time-series data to obtain an analysis result, and the sea temperature anomaly sensitive area is obtained from the sea temperature anomaly sensitive candidate area by using the analysis result, and the method specifically includes:
performing wavelet analysis on historical global sea surface temperature to obtain a periodic characteristic rule of sea temperature time series change;
and obtaining the sea temperature abnormal sensitive area from the sea temperature abnormal sensitive candidate area by using the time series cycle characteristic rule of the sea temperature change.
In one embodiment, calculating the lag time of the historical extreme high temperature event on the occurrence area associated with each sea temperature anomaly sensitive area specifically includes:
aiming at each historical extreme high-temperature event, obtaining first temperature data of the corresponding sea temperature abnormal sensitive area in the current year and the previous year of the occurrence of the historical extreme high-temperature event; calculating the month-by-month moment average of the first temperature data, and forming a left field by the month-by-month moment average of the first temperature data;
acquiring second temperature data of the current year of the historical extreme high-temperature event, calculating the monthly-oriented moment average of the second temperature data, and forming a right field by the monthly-oriented moment average of the second temperature data;
performing singular value decomposition analysis on the left field and the right field to obtain an analysis result; and determining a hysteresis period of the historical extreme high temperature event based on the analysis.
Another embodiment of the present application provides an extreme high temperature event prediction apparatus, including:
the processing module is used for performing empirical orthogonal function decomposition analysis and wavelet analysis on historical global sea surface temperature data to obtain a sea temperature abnormal sensitive area;
the processing module is used for carrying out correlation analysis on temperature data before the occurrence time of each historical extremely high temperature event occurring region and the temperature data of each sea temperature abnormal sensitive region in the corresponding time; obtaining the correlation between the occurrence area of each historical extreme high-temperature event and each sea temperature abnormal sensitive area;
and the construction module is used for calculating the lag time of the historical extreme high-temperature events on the occurrence areas associated with the sea temperature abnormal sensitive areas, and constructing a prediction model according to the correlation between the occurrence areas of the historical extreme high-temperature events and the sea temperature abnormal sensitive areas and the lag time of the historical extreme high-temperature events on the occurrence areas associated with the sea temperature abnormal sensitive areas.
In another embodiment, the processing module is further configured to:
detecting surface temperature data of the target sea temperature abnormity sensitive area, and judging whether the surface temperature data of the target sea temperature abnormity sensitive area is abnormal or not;
and if so, processing the surface temperature data of the target sea temperature abnormal sensitive area by using a prediction model to obtain the occurrence area and the occurrence time of the extremely high temperature event.
Another embodiment of the present application provides an electronic device, including: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes the computer-executable instructions stored in the memory to implement the flow computation monitoring method provided by the above-described embodiments.
Yet another embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the method for monitoring flow computation provided by the foregoing embodiment is implemented.
The application provides an extreme high temperature event prediction method and equipment, which are used for acquiring historical global sea surface temperature data and land extreme high temperature event temperature information and preprocessing the data; and (3) obtaining the sea temperature abnormal sensitive area from the global sea temperature range flat field by combining empirical orthogonal function decomposition analysis and wavelet analysis, and further determining the sea temperature abnormal sensitive area and the obvious influence time period by using a simple correlation method. Considering the hysteresis of the atmospheric circulation influence, performing time hysteresis analysis on the historical extreme high-temperature event and the sea temperature sensitive area by adopting a singular value decomposition method; according to the correlation coefficient and the lag time between the sea temperature anomaly sensitive area and the extreme high temperature of the land, a prediction model for the extreme high temperature climatic events of the land based on the sea surface temperature change of the sea temperature anomaly sensitive area is established, and the occurrence area and the occurrence time of the extreme high temperature events of the land are predicted in time by detecting the sea temperature anomaly of the target sea temperature anomaly sensitive area, so that early warning is provided for the extreme high temperature events of the land.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and, together with the description, serve to explain the principles of the application.
FIG. 1 is a flowchart illustrating a method for predicting an extreme high temperature event according to an embodiment of the present disclosure;
FIG. 2 is a flowchart illustrating a method for predicting an extreme high temperature event according to another embodiment of the present disclosure;
FIG. 3 is a flowchart of a method for predicting an extreme high temperature event according to another embodiment of the present disclosure;
FIG. 4 is a schematic structural diagram of an extreme high temperature event prediction apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to another embodiment of the present application;
with the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings in which the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The sea, as a climate influencing factor, has the characteristics of long duration and large spatial scale, plays an important role in the climate change process, and generally considers that the abnormal sea temperature in a certain sea area is an important reason for the continuous abnormal atmospheric circulation. The sea temperature refers to the temperature of water on the surface layer of seawater, and is usually expressed in centigrade, and is an important marine environmental parameter depending on the heat balance condition of the seawater.
Atmospheric circulation remote correlation determines global climate annual change, and various extreme meteorological disasters frequently occur under the background of climate change, thereby causing great threat to the safety of human life and property and living environment. Extreme high-temperature heat waves are one of the manifestations of extreme meteorological disasters, the standard of the high-temperature heat waves is mainly established according to the degree of influence or harm of high temperature on human beings, the definition of the high-temperature heat waves in different regions is greatly different, and a uniform and definite high-temperature heat wave standard does not exist at present. The highest daily temperature of 35 ℃ or higher is generally called high temperature, and the continuous high temperature weather process for more than 3 days is called high temperature heat wave.
The existing research focuses on the influence of the sea temperature anomaly on the number and strength of tropical cyclones landing on land and the influence of the sea temperature anomaly on land rainfall and drought, and the research on the influence of the global sea temperature anomaly change on the land extreme high temperature events is almost none.
In order to solve the technical problems, the application provides an extreme high temperature event prediction method and equipment, and aims to construct a model for predicting the occurrence of a land extreme high temperature event due to abnormal sea temperature change and provide early warning for the land extreme high temperature event. The technical idea of the application is as follows: the method comprises the steps of firstly, obtaining temperature information of historical extreme high-temperature events and global sea surface temperature information, identifying sea temperature abnormal sensitive areas in a global range, determining the sea temperature abnormal sensitive areas and influence time periods of the historical extreme high-temperature events, carrying out time lag analysis on the historical extreme high-temperature events and the sea temperature abnormal sensitive areas, establishing a prediction model for the extreme high-temperature climatic events of each area based on sea temperature changes according to the correlation coefficient and the lag time of the sea temperature abnormal sensitive areas and the extreme high temperature of land, and assisting the prediction of the extreme high-temperature events and the relevant emergency treatment work.
As shown in fig. 1, an embodiment of the present application provides a method for predicting an extreme high-end event, where the method specifically includes the following steps:
s101, performing empirical orthogonal function decomposition analysis and wavelet analysis on historical global sea surface temperature data to obtain a sea temperature abnormal sensitive area.
The Historical Global sea surface temperature data is extracted based on Global sea monitoring data such as height field and sea water surface temperature reanalysis data of a certain region data center and a certain region atmosphere research center, Hybrid Coordinate Ocean Model (HYCOM) simulation sea surface data of a certain region Ocean cooperation plan, a Global Historical Climatology Network-data (GHCND) monthly summary database provided by a certain region Ocean and atmosphere administration, a certain region meteorological office observation data set, certain region forecast weather center meteorological data, middle resolution Imaging spectrometer (MODIS) sea temperature data and the like.
Empirical Orthogonal Function (EOF) analysis is a multivariate statistical method, and the principle is to use the variance of data to concentrate useful information in the data on a few spatial distributions and time sequences, so as to reflect the spatial-temporal change of an element field, and a space sample, also called a spatial feature vector or a spatial mode, corresponds to a feature vector, so as to reflect the spatial distribution characteristics of the element field to a certain extent. The essence of the EOF analysis is to decompose the evolution of a physical quantity field into independent evolution processes of orthogonal modes, which reflect the influence and contribution of independent factors on the evolution of the physical quantity.
Wavelet analysis is to perform scale refinement on signals step by step through local transformation of time and frequency such as telescopic translation operation, and the like, finally achieves the effects of time subdivision at a high frequency and frequency subdivision at a low frequency, can automatically adapt to the requirements of time-frequency signal analysis, can focus on any details of the signals, and effectively extracts information.
And preprocessing the extracted historical global sea surface temperature data, performing EOF analysis, and finding out a sea temperature abnormal sensitive area by combining wavelet change.
S102, carrying out correlation analysis on temperature data before the occurrence time of each historical extremely high temperature event occurring region and the temperature data of each sea temperature abnormal sensitive region in the corresponding time; and obtaining correlation between the occurrence area of each historical extreme high temperature event and each sea temperature anomaly sensitive area.
The extreme high temperature event is influenced by the atmospheric circulation, and has certain hysteresis, so that temperature data before the occurrence time in the region where the historical extreme high temperature event occurs are selected, the temperature data also need to be preprocessed, and then correlation analysis is performed on the temperature data in the time corresponding to each sea temperature abnormal sensitive region.
The correlation analysis can adopt a simple correlation method, and simple correlation coefficients are generally usedrExpressed, its calculation formula is as follows:
in the formula,x、yare elements of two variable fields of sea surface temperature and historical extreme high temperature events respectively,
、
is the average value of each of the variable fields,r xy is a correlation coefficient in the range of [ -1,1]According tor xy To determine whether there is a correlation between the two,r>0 is a positive correlation, and 0 is a positive correlation,r<a 0 is then an indication of a negative correlation,rand =0 means that there is no relation between the two variables.
S103, calculating the lag time of the historical extreme high-temperature events on the occurrence areas associated with the sea-temperature abnormal sensitive areas, and constructing a prediction model according to the correlation between the occurrence areas of the historical extreme high-temperature events and the sea-temperature abnormal sensitive areas and the lag time of the historical extreme high-temperature events on the occurrence areas associated with the sea-temperature abnormal sensitive areas.
The correlation and time lag between the occurrence region of each historical extreme high temperature event and each sea temperature anomaly sensitive region need to be analyzed by using a Singular Value Decomposition (SVD) method. The SVD method can be used for analyzing the correlation relationship between two meteorological element field sequences, and can maximally separate a plurality of mutually independent coupling modes from the two meteorological element fields, thereby revealing the spatial relation of time domain correlation existing in the two meteorological element fields, and the coupled spatial distribution type can maximally explain the cross covariance of the two meteorological element fields.
According to the correlation between the occurrence region of each historical extreme high temperature event and each sea temperature abnormal sensitive region and the lag time of the occurrence of the extreme high temperature, a simple prediction model can be constructed.
In the scheme, the acquired historical global sea surface temperature data and the historical extreme high temperature event early-stage temperature data are subjected to data preprocessing, a spatial-temporal structure with a flat global sea temperature is determined through EOF analysis and wavelet transformation, temperature correlation analysis is carried out on the basis of the historical extreme high temperature events and the sea temperature anomaly sensitive areas on the land, the sea temperature anomaly sensitive areas and the sea temperature anomaly sensitive areas which affect the extreme high temperature events on the land are further determined, the hysteresis of atmospheric circulation influence is considered, the correlation and the time hysteresis between the occurrence areas of the historical extreme high temperature events and the sea temperature anomaly sensitive areas are analyzed, and a simple prediction model is constructed.
In some embodiments, after constructing the prediction model, the extreme high temperature event prediction method provided herein further comprises:
detecting surface temperature data of the target sea temperature abnormity sensitive area, and judging whether the surface temperature data of the target sea temperature abnormity sensitive area is abnormal or not; and if so, processing the surface temperature data of the target sea temperature abnormal sensitive area by using a prediction model to obtain the occurrence area and the occurrence time of the extremely high temperature event.
When the surface temperature data of the target sea temperature abnormal sensitive area is detected to be abnormal, the occurrence area and the delay time of the related land extreme high temperature event can be confirmed according to the established prediction model, and the prediction of the extreme high temperature event and the related emergency treatment work are assisted.
As shown in fig. 2, an embodiment of the present application provides another method for predicting an extreme high temperature event, to obtain a sea temperature anomaly sensitive area, where the method specifically includes the following steps:
s201, performing empirical orthogonal function decomposition analysis on the historical global sea surface temperature spatial distribution data to obtain variance contribution rates and time coefficients of different modes.
The variance contribution rate reflects the importance degree of the corresponding mode, the higher the variance contribution rate is, the more important the corresponding mode is, and the time coefficient of the mode can display the period of sea temperature change.
For example: calculating the daily sea surface temperature data of the global research area in the historical period to obtain a data matrixX m×n With a covariance matrix ofC m×m =X T XCalculating the eigenvalue of the covariance matrixλ=(λ 1 ,λ 2 ,…,λ m ) And a corresponding feature vector, i.e. an EOF modality. Arranging the characteristic values in a non-ascending order, and correspondingly changing the ordinal number of the characteristic vector:
λ 1 ≥λ 2 ≥…≥λ m ≥0
the variance contribution ratio of each feature vector is calculated by:
the feature vector constitutes a matrix ofV m×m Projecting EOF onto the raw data matrixX m×n In the above, the event coefficients corresponding to all the spatial feature vectors are obtained, i.e.
Wherein,PC m×n each row of data is a time coefficient corresponding to each feature vector, e.g., the first rowPCAnd (1) is the time coefficient of the first EOF mode.
The essence of the EOF analysis is to decompose the evolution of a physical quantity field into independent evolution processes of each orthogonal mode, the processes reflect the influence and contribution of each independent factor on the evolution of the physical quantity, and whether the decomposed independent factors are meaningless noise or not needs to be subjected to significance test. According to the North criterion, only the feature vectors with the feature root error ranges not overlapped can be subjected to significance test to obtain variance contribution rates and time coefficients of different modes.
S202, obtaining the average distribution state and the consistent change area of the global sea temperature according to the variance contribution rate, and obtaining seasonal change periods according to time coefficients of different modes.
In the step, the variance contribution rates of the first three modes after the EOF decomposition generally account for most of the total variance contribution rates, the distribution of the first three mode space field values can represent the average distribution state of the global sea temperature, when the values are the same positive value or the same negative value, it is indicated that the sea temperature change of the corresponding region is consistent, and the seasonal change period is obtained according to the time coefficients of the first three modes.
S203, obtaining a sea temperature abnormal sensitive alternative area from the area with consistent change according to the seasonal change period and the average distribution state of the sea temperature in the whole world.
For example: the numerical distribution of the spatial field in the first mode shows that the equator takes the north indian ocean, the pacific ocean, the atlantic ocean and the north icebound ocean as positive value areas, which shows that the sea temperature changes of the four sea areas are consistent and can be used as a candidate area sensitive to sea temperature abnormality.
And S204, performing wavelet analysis on the historical global sea surface temperature to obtain a periodic characteristic rule of sea temperature time series change.
In the step, wavelet analysis can clearly reveal multi-period features in the time series, fully reflect the development trends of the time series at different time scales, and can evaluate the future development trend of the system. The cycle time of the global sea surface temperature change can be analyzed using wavelet transform.
For example: historical global sea temperature data off(t) The wavelet function expression is:
wherein,ψ(t) Is a basic wavelet function, can form a cluster of function systems through translation and expansion transformation, and has specific historical global sea temperature dataf(t) The continuous wavelet transform is as follows:
in the formula:W f (a,b) Is a function of the wavelet coefficients and,adetermining the width of the wavelet for the scaling factor,bthe shift factor is a parameter reflecting the wavelet position shift,
is composed ofψ(t) Complex conjugate function ofW f (a,b) Has a square value ofbIntegration in the domain to obtain the wavelet variance Var (a)：
Wavelet variance with time scaleaThe transformation process is called as a wavelet variance map, the wavelet variance map can reflect the distribution condition of the change of annual global sea temperature on a time series along with the time scale, and can be used for obtaining the periodic characteristic rule of the change of the sea temperature time series.
S205, obtaining the sea temperature abnormal sensitive area from the sea temperature abnormal sensitive candidate area by using the periodic characteristic rule of sea temperature time series change.
In the step, according to the periodic characteristic rule of sea temperature time series change obtained by wavelet analysis, the sea temperature change period displayed by the time coefficient of the sea temperature anomaly sensitive alternative area is compared and verified, and the global sea temperature anomaly sensitive area is obtained more accurately.
In the scheme, historical global sea surface temperature data is processed by distance per month, the moment average of the global sea surface temperature is subjected to EOF analysis, and a sea temperature abnormal sensitive alternative area is determined according to space fields and time coefficients of the first three modes with high variance contribution rate. The periodic characteristic rule of sea temperature change is obtained through wavelet analysis, and the sea temperature change period displayed by the time coefficient of the sea temperature anomaly sensitive candidate area is combined for comparison and verification, so that the global sea temperature anomaly sensitive area and the global sea temperature change period can be obtained more accurately.
As shown in fig. 3, the present application provides a method for predicting an extreme high temperature event, which calculates a lag time of a historical extreme high temperature event on an occurrence area associated with each sea temperature anomaly sensitive area, and specifically includes the following steps:
s301, acquiring first temperature data of the corresponding sea temperature abnormal sensitive area in the current year and the previous year of the occurrence of the historical extreme high temperature events aiming at each historical extreme high temperature event; and calculating the monthly moment average of the first temperature data, and forming a left field by the monthly moment average of the first temperature data.
For example: the method comprises the steps that an extremely high temperature event occurs in a research area of a certain year, a sea temperature abnormal sensitive area corresponding to the area is a North Pacific ocean, first temperature data of the same year and the previous year of the North Pacific ocean are obtained, the month-by-month interval of the temperature data is calculated, and the interval sequence of the temperature data isX(t)=(x 1 (t)、x 2 (t)、…、x 24 (t) 1 to 12 represent months of the year before the extreme high temperature event, 13 to 24 represent months of the same year as the extreme high temperature event,tis year, the pitch sequence is taken as the left field.
S302, second temperature data of the current year of the historical extreme high-temperature event is obtained, month-by-month moment average of the second temperature data is calculated, and the month-by-month moment average of the second temperature data forms a right field.
For example: the year month-to-month temperature interval sequence of extreme high temperature events in the research area isY(t)=(y 1 (t)、y 2 (t)、…、y 12 (t) 1-12 represents the month of the year,tthe year of (a) is consistent with that of the left field.
S303, carrying out singular value decomposition analysis on the left field and the right field to obtain an analysis result; and determining a hysteresis period of the historical extreme high temperature event based on the analysis.
In this step, the cross covariance matrix of the left and right fields is:
wherein, the symbol < > represents the averaging, and the cross covariance matrix is subjected to SVD to obtain:
C=UΣV T
wherein,Ucorrespond toXThe spatial mode of (a) is a left singular vector,Vcorrespond toYThe space vector is a right singular vector, the sigma diagonal is a singular value gamma, and the original observation field is projected to the corresponding singular vector, so that the time coefficient matrix can be obtained.
The variance contribution ratio of the first mode of the two variable fields accounts for a large part of the total variance contribution ratio, and therefore the isotropic correlation coefficient and the anisotropic correlation coefficient of the first mode are analyzed. The sea temperature field uses the same-polarity correlation coefficient, and when the same-polarity correlation coefficient is larger, the sea temperature range modal time coefficient has good representativeness to the range sequence. The extreme high-temperature field uses an opposite sex correlation coefficient, the value can reflect the correlation degree of the time relation between the sea-temperature range flat field and the average temperature modal, the lag time period of the sea-temperature anomaly sensitive area influencing the correlated occurrence area can be analyzed according to the opposite sex correlation coefficient, and the credibility of the analysis result needs to be further checked by a Monte Carlo method.
For example: if SVD analysis shows that the correlation coefficient of the same sex is larger from 9 months in the previous year to 12 months in the current year in the first modality, it indicates that the time coefficient of the first modality mainly represents the change of the sea temperature from 9 months in the previous year to 12 months in the current year. If the heterosexual correlation coefficient of the first mode shows that the monthly temperature distance in 7 months in summer of the research region has obvious positive correlation with the northern Taiping sea temperature distance field from 12 months in the previous year to 2 months in the current year, the lag time of the sea temperature anomaly sensitive region influencing the associated occurrence region is about 5-7 months, and the significance test is carried out by the Monte Carlo method in the above analysis.
In the scheme, the time lag analysis is carried out on extreme high-temperature historical events and sea temperature abnormal sensitive areas by adopting an SVD (singular value decomposition) method, the representative range of the time coefficient to the sea temperature abnormal sensitive areas is determined according to the modal isotropy correlation coefficient, and the lag time of the historical extreme high-temperature events on the occurrence areas associated with the sea temperature abnormal sensitive areas is calculated according to the anisotropic correlation coefficient.
As shown in fig. 4, an embodiment of the present application provides an extreme high temperature event prediction apparatus 100, including:
the processing module 101 is used for performing empirical orthogonal function decomposition analysis and wavelet analysis on historical global sea surface temperature data to obtain a sea temperature abnormal sensitive area;
the processing module 101 is configured to perform correlation analysis on temperature data before occurrence time in an area where each historical extreme high-temperature event occurs and temperature data of each sea temperature anomaly sensitive area in corresponding time; obtaining the correlation between the occurrence area of each historical extreme high-temperature event and each sea temperature abnormal sensitive area;
the building module 102 calculates a lag time of the historical extreme high-temperature event on the occurrence area associated with each sea-temperature abnormal sensitive area, and builds a prediction model according to the correlation between the occurrence area of each historical extreme high-temperature event and each sea-temperature abnormal sensitive area and the lag time of the historical extreme high-temperature event on the occurrence area associated with each sea-temperature abnormal sensitive area.
In an embodiment, the processing module 101 is further configured to:
detecting surface temperature data of the target sea temperature abnormity sensitive area, and judging whether the surface temperature data of the target sea temperature abnormity sensitive area is abnormal or not;
and if so, processing the surface temperature data of the target sea temperature abnormal sensitive area by using a prediction model to obtain the occurrence area and the occurrence time of the extremely high temperature event.
In an embodiment, the processing module 101 is specifically configured to:
performing empirical orthogonal function decomposition analysis on the historical global sea surface temperature spatial distribution data to obtain a sea temperature anomaly sensitive candidate area;
and performing wavelet analysis on the historical global sea surface temperature time series data to obtain an analysis result, and obtaining a sea temperature anomaly sensitive area from the sea temperature anomaly sensitive candidate area by using the analysis result.
In an embodiment, the processing module 101 is specifically configured to:
performing empirical orthogonal function decomposition analysis on the historical global sea surface temperature spatial distribution data to obtain variance contribution rates and time coefficients of different modes;
obtaining the average distribution state and the consistent change region of the global sea temperature according to the variance contribution rate, and obtaining seasonal change cycles according to time coefficients of different modes;
and obtaining the sea temperature abnormal sensitive alternative area from the area with consistent change according to the seasonal change period and the average distribution state of the sea temperature in the whole world.
In an embodiment, the processing module 101 is specifically configured to:
performing wavelet analysis on historical global sea surface temperature to obtain a periodic characteristic rule of sea temperature time series change;
and obtaining the sea temperature abnormal sensitive area from the sea temperature abnormal sensitive candidate area by using the periodic characteristic rule of the sea temperature time series change.
In an embodiment, the processing module 101 is specifically configured to:
aiming at each historical extreme high-temperature event, obtaining first temperature data of the corresponding sea temperature abnormal sensitive area in the current year and the previous year of the occurrence of the historical extreme high-temperature event; calculating the month-by-month moment average of the first temperature data, and forming a left field by the month-by-month moment average of the first temperature data;
acquiring second temperature data of the current year of the historical extreme high-temperature event, calculating the monthly-oriented moment average of the second temperature data, and forming a right field by the monthly-oriented moment average of the second temperature data;
performing singular value decomposition analysis on the left field and the right field to obtain an analysis result; and determining a hysteresis period of the historical extreme high temperature event based on the analysis.
As shown in fig. 5, an embodiment of the present application provides an electronic device 200, where the electronic device 200 includes a memory 201 and a processor 202.
Wherein the memory 201 is used for storing computer instructions executable by the processor;
the processor 202, when executing the computer instructions, implements the steps of the method in the embodiments described above. Reference may be made in particular to the description relating to the method embodiments described above.
Alternatively, the memory 201 may be separate or integrated with the processor 202. When the memory 201 is separately provided, the electronic device further includes a bus for connecting the memory 201 and the processor 202.
The embodiment of the present application further provides a computer-readable storage medium, in which computer instructions are stored, and when the processor executes the computer instructions, the steps in the method in the foregoing embodiment are implemented.
Embodiments of the present application further provide a computer program product, which includes computer instructions, and when the computer instructions are executed by a processor, the computer instructions implement the steps of the method in the above embodiments.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A method of predicting an extreme high temperature event, comprising:
performing empirical orthogonal function decomposition analysis and wavelet analysis on historical global sea surface temperature data to obtain a sea temperature abnormal sensitive area;
carrying out correlation analysis on temperature data before the occurrence time of each historical extremely high temperature event occurring region and the temperature data of each sea temperature abnormal sensitive region in the corresponding time; obtaining a correlation between an occurrence region of each historical extreme high temperature event and each sea temperature anomaly sensitive region;
calculating the lag time of the historical extreme high-temperature events on the occurrence areas associated with the sea-temperature abnormal sensitive areas, and constructing a prediction model according to the correlation between the occurrence areas of the historical extreme high-temperature events and the sea-temperature abnormal sensitive areas and the lag time of the historical extreme high-temperature events on the occurrence areas associated with the sea-temperature abnormal sensitive areas.
2. The method of claim 1, further comprising:
detecting surface temperature data of a target sea temperature abnormity sensitive area, and judging whether the surface temperature data of the target sea temperature abnormity sensitive area is abnormal or not;
and if so, processing the surface temperature data of the target sea temperature abnormal sensitive area by using the prediction model to obtain the occurrence area and the occurrence time of the extremely high temperature event.
3. The method according to claim 1 or 2, wherein the empirical orthogonal function decomposition analysis and wavelet analysis are performed on the historical global sea surface temperature data to obtain the sea temperature anomaly sensitive zone, and specifically comprises:
performing empirical orthogonal function decomposition analysis on the historical global sea surface temperature spatial distribution data to obtain a sea temperature anomaly sensitive candidate area;
and performing wavelet analysis on the historical global sea surface temperature time-series data to obtain an analysis result, and obtaining the sea temperature anomaly sensitive area from the sea temperature anomaly sensitive candidate area by using the analysis result.
4. The method according to claim 3, wherein the performing an empirical orthogonal function decomposition analysis on the historical global sea surface temperature spatial distribution data to obtain the sea temperature anomaly sensitive candidate includes:
performing empirical orthogonal function decomposition analysis on the historical global sea surface temperature spatial distribution data to obtain variance contribution rates and time coefficients of different modes;
obtaining the average distribution state and the consistent change region of the global sea temperature according to the variance contribution rate, and obtaining seasonal change cycles according to time coefficients of different modes;
and obtaining the sea temperature abnormity sensitive alternative area from a change consistent area according to the seasonal change period and the average distribution state of the global sea temperature.
5. The method according to claim 3, wherein performing wavelet analysis on the historical global sea surface temperature time series data to obtain an analysis result, and using the analysis result to obtain the sea temperature anomaly sensitive zone from the sea temperature anomaly sensitive candidate zone comprises:
performing wavelet analysis on the historical global sea surface temperature to obtain a periodic characteristic rule of sea temperature time series change;
and obtaining the sea temperature abnormity sensitive area from the sea temperature abnormity sensitive alternative area by using the periodic characteristic rule of the sea temperature time series change.
6. The method of claim 3, wherein calculating the lag time of historical extreme high temperature events over the occurrence area associated with each sea temperature anomaly sensitive zone comprises:
aiming at each historical extreme high-temperature event, obtaining first temperature data of a corresponding sea temperature abnormal sensitive area in the current year and the previous year of the historical extreme high-temperature event; calculating the month-by-month moment average of the first temperature data, and forming a left field by the month-by-month moment average of the first temperature data;
acquiring second temperature data of the current year of the historical extreme high-temperature event, calculating the monthly-oriented moment average of the second temperature data, and forming a right field by the monthly-oriented moment average of the second temperature data;
carrying out singular value decomposition analysis on the left field and the right field to obtain an analysis result; and determining a hysteresis period of the historical extreme high temperature event according to the analysis result.
7. An extreme high temperature event prediction device, comprising:
the processing module is used for performing empirical orthogonal function decomposition analysis and wavelet analysis on historical global sea surface temperature data to obtain a sea temperature abnormal sensitive area;
the processing module is used for carrying out correlation analysis on temperature data before the occurrence time of each historical extremely-high-temperature event occurring region and the temperature data of each sea temperature abnormal sensitive region in the corresponding time; obtaining a correlation between an occurrence region of each historical extreme high temperature event and each sea temperature anomaly sensitive region;
the construction module is used for calculating the lag time of the historical extreme high-temperature events on the occurrence areas associated with the sea temperature abnormal sensitive areas, and constructing a prediction model according to the correlation between the occurrence areas of the historical extreme high-temperature events and the sea temperature abnormal sensitive areas and the lag time of the historical extreme high-temperature events on the occurrence areas associated with the sea temperature abnormal sensitive areas.
8. The apparatus of claim 7, wherein the processing module is further configured to:
detecting surface temperature data of a target sea temperature abnormity sensitive area, and judging whether the surface temperature data of the target sea temperature abnormity sensitive area is abnormal or not;
and if so, processing the surface temperature data of the target sea temperature abnormal sensitive area by using the prediction model to obtain the occurrence area and the occurrence time of the extremely high temperature event.
9. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored by the memory to implement the extreme high temperature event prediction method of any of claims 1 to 6.
10. A computer-readable storage medium having stored therein computer-executable instructions for implementing the method of predicting extreme high temperature events as claimed in any one of claims 1 to 6 when executed by a processor.
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