CN108763160A - Method and its device based on 20CR data prediction wave significant wave heights - Google Patents

Method and its device based on 20CR data prediction wave significant wave heights Download PDF

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CN108763160A
CN108763160A CN201810536110.8A CN201810536110A CN108763160A CN 108763160 A CN108763160 A CN 108763160A CN 201810536110 A CN201810536110 A CN 201810536110A CN 108763160 A CN108763160 A CN 108763160A
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wave height
significant wave
level pressure
sea
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吴玲莉
秦杰
吴腾
梁桂兰
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Hohai University HHU
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Abstract

The invention discloses methods and its device based on 20CR data prediction wave significant wave heights, including obtain the initial data in 20CR data, and data prediction is carried out to initial data；Choose the sea level pressure field for meeting preset condition；According to First Year in 20CR data in the data of section with the relevant Data correction prediction model of sea level pressure field；Using being later than prediction model described in data assessment of the second year for section of the First Year for section in 20CR data；After assessment passes through, wave significant wave height is predicted using the prediction model.Present invention employs the 20CR data of U.S. Department of Energy and the joint publication of American National Atmosphere and Ocean office, time span is more than a century, after selection meets the sea level pressure field of preset condition, according to First Year in 20CR data in the data of section with the relevant Data correction prediction model of the sea level pressure field of selection, Optimized model parameter improves the accuracy of wave significant wave height prediction.

Description

Method and its device based on 20CR data prediction wave significant wave heights
Technical field
The present invention relates to ocean wave parameter calculating field, in particular to based on twentieth century analysis project (The again Twentieth Century Reanalysis Project, 20CR) data prediction wave significant wave height method.
Background technology
Wave be it is a kind of with human relation most directly, most close oceanographic phenomena, having to the production and living of people can not Influence of ignorance, such as sail, fish production, offshore oil platform, coastal waters harbor approach etc. all have close pass with wave Connection.
Significant wave height is exactly to reflect an important parameter of wave feature, therefore the forecasting research of wave height has important show Sincere justice.The wave height for wanting prediction wave will first obtain wave observation data steady in a long-term.But traditional observation method Such as buoy, although the change information of sea wave height can be obtained accurately, they can only obtain wave in fixed point Variation, and covering surface is also very limited, it is difficult to which the buoy for obtaining the continuous wave of the sea more than 20 years in China Seas is seen Measured data so that the significant wave height of the wave predicted is not accurate enough.
Invention content
The purpose of the present invention is to provide method and its device based on 20CR data prediction wave significant wave heights, Neng Gouti The accuracy of high wave significant wave height prediction.
First aspect present invention provides the method based on 20CR data prediction wave significant wave heights, including：
The initial data in 20CR data is obtained, data prediction is carried out to the initial data；
Choose the sea level pressure field for meeting preset condition；
According to First Year in the 20CR data in the data of section with the relevant Data correction of the sea level pressure field Prediction model；
Using being later than prediction model described in data assessment of the second year for section of the First Year for section in the 20CR data；
After assessment passes through, wave significant wave height is predicted using the prediction model.
Optionally, the initial data obtained in 20CR data carries out data prediction, packet to the initial data It includes：
Collect the long duration of the 20CR data based on mesh point mode it is each when time weather forecast data, including sea-level pressure SLP, significant wave height Hs；
The coordinate of time weather forecast data institute style point when obtaining collected each, based on coordinate extraction and the seat Corresponding sea-level pressure matrix S, significant wave height matrix H are marked, wherein including m spatial point, each spatial point contains n times sight Measured data：
Wherein, SmnSecondary sea-level pressure value, H when being the n-th of m-th of spatial pointmnIt is secondary when being the n-th of m-th of spatial point Significant wave height, m is the number of spatial point, when n is observation time.
Optionally, it is described according to First Year in the 20CR data for related to the sea level pressure field in the data of section Data correction prediction model, including：
The mean value M for calculating the sea-level pressure SLP of the sea level pressure field obtains being based on lattice point according to the mean value M Pattern it is each when time the sea-level pressure SLP anomaly value P：
Wherein,Secondary when n is observation, i representation space points are secondary when j is indicated；
EOF Dimension Reduction Analysis is done to the anomaly value P, obtains the contribution rate of heterogeneity and each ingredient to population variance, is retained Preceding 28 EOF and principal component；
Covariance calculating is carried out to the anomaly value P, obtains covariance matrix Lm×n,Wherein, T The covariance matrix L is calculated in the transposition of representing matrixm×nFeature vector V and characteristic value Λ, meet LV=Λ V, Λ Formula it is as follows：
Wherein, described eigenvector V is just Matrix is handed over, the jth column element of described eigenvector V is exactly eigenvalue λjCorresponding feature vector；
According to described eigenvector V and the characteristic value Λ, the variance contribution ratio of each described eigenvector V is calculated with before The accumulative variance contribution ratio of several described eigenvector V, according to the sequences of the characteristic value Λ from big to small to the covariance Matrix is ranked up, and it is EOF to make number one1；
The sea-level pressure SLP and the significant wave height Hs when to each based on mesh point mode time carry out Box-Cox Transformation, the sea-level pressure trGt after being converted and significant wave height trHt；
Based on k-th of principal component PCk,tWith 4 hours k-th of principal component PC of lagk,t-4To corresponding institute on each lattice point 28 PC when stating significant wave height trHt and calculate its related coefficient, and taking the related coefficient highestk,tOr the PCk,t-4 Predictive factor as the significant wave height trHt；
Calculate the standard deviation S of the significant wave height HsH1With the standard deviation S of 28 predictive factorsXkAnd it preserves；
It brings the predictive factor into prediction model, the prediction result of i-th and i+1 model is compared with F statistics, select The predictive factor of overall really degree can be represented by going out；
It will be late by the significant wave height H of pt-pBring the prediction model into, the prediction model is：
Wherein, HtIt is by the significant wave height of Box-Cox transformation on each lattice point, a is constant term, and P is with premeasuring phase The lag coefficient of the parameter of pass, Xk,tIt is k-th of predictive factor based on the sea-level pressure SLP, secondary when t is, bkIt is pair It should be in Xk,tCoefficient, K is the sum of the predictive factor, Ht-pIt is the significant wave height for lagging p, cpCorrespond to Ht-pCoefficient, utIt can be indicated with M ranks autoregression model, if M=0, utFor white noise.
Optionally, described using being later than in the 20CR data described in data assessment of the second year for section of the First Year for section Prediction model, including：
On the basis of 28 EOF to be later than First Year for section second year for section the sea level pressure field into Row prediction, obtains PCk,t；
According to the SXkSelect 28 predictive factors.
Optionally, described after assessment passes through, wave significant wave height is predicted using the prediction model, including：
Bring the predictive factor into the prediction model, secondary significant wave height, will predict when predicting each in target time period The significant wave high level gone out reverts to the value before Box-Cox transformation, using the value after the reduction as final significant wave height.
Optionally, the method further includes：
Horizontal using Pierre Si evaluation index PSS assessment predictions, the PSS is：
Wherein, piTo observe relative frequency, qiTo predict relative frequency, pijTo combine relative frequency.
Second aspect of the present invention provides the device based on 20CR data prediction wave significant wave heights, including：
Acquisition module, for obtaining the initial data in 20CR data, and the original to acquisition module acquisition Beginning data carry out data prediction；
Module is chosen, for choosing the sea level pressure field for meeting preset condition；
Correction module is used for according to First Year in the 20CR data for the sea level gas with selection in the data of section It has a meeting, an audience, etc. well under one's control relevant Data correction prediction model；
Evaluation module, for according to be later than in the 20CR data First Year for section second year for section data assessment institute State the prediction model after correction module correction；
Prediction module, for after evaluation module assessment passes through, wave significant wave to be predicted according to the prediction model It is high.
Optionally, the acquisition module is specifically used for：
Collect the long duration of the 20CR data based on mesh point mode it is each when time weather forecast data, including sea-level pressure SLP, significant wave height Hs；
The coordinate of time weather forecast data institute style point when obtaining collected each, based on coordinate extraction and the seat Corresponding sea-level pressure matrix S, significant wave height matrix H are marked, wherein including m spatial point, each spatial point contains n times sight Measured data：
Wherein, SmnSecondary sea-level pressure value, H when being the n-th of m-th of spatial pointmnIt is secondary when being the n-th of m-th of spatial point Significant wave height, m is the number of spatial point, when n is observation time.
Optionally, the correction module is specifically used for：
The mean value M for calculating the SLP of the sea level pressure field, when obtaining each based on mesh point mode according to the M time The anomaly value P of the SLP：
Wherein,Secondary when n is observation, i representation space points are secondary when j is indicated；
EOF Dimension Reduction Analysis is done to the anomaly value P, obtains the contribution rate of heterogeneity and each ingredient to population variance, is retained Preceding 28 EOF and principal component；
Covariance calculating is carried out to the anomaly value P, obtains covariance matrix Lm×n,Wherein, T The covariance matrix L is calculated in the transposition of representing matrixm×nFeature vector V and characteristic value Λ, meet LV=Λ V, Λ Formula it is as follows：
Wherein, described eigenvector V is just Matrix is handed over, the jth column element of described eigenvector V is exactly eigenvalue λjCorresponding feature vector；
According to described eigenvector V and the characteristic value Λ, the variance contribution ratio of each described eigenvector V is calculated with before The accumulative variance contribution ratio of several described eigenvector V, according to the sequences of the characteristic value Λ from big to small to the covariance Matrix is ranked up, and it is EOF to make number one1；
The sea-level pressure SLP and the significant wave height Hs when to each based on mesh point mode time carry out Box-Cox Transformation, the sea-level pressure trGt after being converted and significant wave height trHt；
Based on k-th of principal component PCk,tWith 4 hours k-th of principal component PC of lagk,t-4To corresponding institute on each lattice point 28 PC when stating significant wave height trHt and calculate its related coefficient, and taking the related coefficient highestk,tOr the PCk,t-4 Predictive factor as the significant wave height trHt；
Calculate the standard deviation S of the significant wave height HsH1With the standard deviation S of 28 predictive factorsxkAnd it preserves；
It brings the predictive factor into prediction model, the prediction result of i-th and i+1 model is compared with F statistics, select The predictive factor of overall really degree can be represented by going out；
It will be late by the significant wave height H of pt-pBring the prediction model into, the prediction model is：
Wherein, HtIt is by the significant wave height of Box-Cox transformation on each lattice point, a is constant term, and P is with premeasuring phase The lag coefficient of the parameter of pass, Xk,tIt is k-th of predictive factor based on the sea-level pressure SLP, secondary when t is, bkIt is pair It should be in Xk,tCoefficient, K is the sum of the predictive factor, Ht-pIt is the significant wave height for lagging p, cpCorrespond to Ht-pCoefficient, utIt can be indicated with M ranks autoregression model, if M=0, utFor white noise.
Optionally, the evaluation module is specifically used for：
On the basis of 28 EOF to be later than First Year for section second year for section the sea level pressure field into Row prediction, obtains PCk,t；
According to the SXkSelect 28 predictive factors.
Optionally, the prediction module is specifically used for：
Bring the predictive factor into the prediction model, secondary significant wave height, will predict when predicting each in target time period The significant wave high level gone out reverts to the value before Box-Cox transformation, using the value after the reduction as final significant wave height.
Optionally, the prediction module is additionally operable to：
Horizontal using Pierre Si evaluation index PSS assessment predictions after obtaining final significant wave height, the PSS is：
Wherein, piTo observe relative frequency, qiTo predict relative frequency, pijTo combine relative frequency.
Third aspect present invention provides a kind of electronic equipment, including：Processor, memory and bus, the memory are deposited The executable machine readable instructions of the processor are contained, when the electronic equipment is run, the processor and the storage The method described in first aspect is executed by bus communication between device, when the machine readable instructions are executed by the processor.
Fourth aspect present invention provides a kind of computer readable storage medium, is stored on the computer readable storage medium Computer program executes the method described in first aspect when the computer program is run by processor.
Fifth aspect present invention provides a kind of computer program product, and the computer program product is run on computers When so that computer executes the method described in first aspect.
The method for predicting wave significant wave height based on 20CR provided in the present invention, uses U.S. Department of Energy and the U.S. The 20CR data of national Atmosphere and Ocean office joint publication, time span are more than a century, the original number in obtaining 20CR data The sea level pressure field for meeting preset condition is chosen after being pre-processed according to and to initial data, according to First Year in 20CR data For in the data of section sea is improved with the relevant Data correction prediction model of the sea level pressure field of selection, Optimized model parameter The accuracy of unrestrained significant wave height prediction.
To enable the above objects, features and advantages of the present invention to be clearer and more comprehensible, alternative embodiment cited below particularly, and coordinate Appended attached drawing, is described in detail below.
Description of the drawings
It, below will be to required use in embodiment in order to illustrate more clearly of the technical solution of embodiment of the present invention Attached drawing be briefly described, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not to be seen as It is the restriction to range, it for those of ordinary skill in the art, without creative efforts, can be with root Other relevant attached drawings are obtained according to these attached drawings.
Fig. 1 shows the method flow diagram provided by the present invention based on 20CR data prediction wave significant wave heights；
Fig. 2 further illustrates the flow chart of method provided by the invention；
Fig. 3 further illustrates the flow chart of method provided by the invention；
Fig. 4 further illustrates the flow chart of method provided by the invention；
Fig. 5 shows the schematic diagram of the device provided by the invention based on 20CR data prediction wave significant wave heights.
Icon：
Acquisition module -100；Choose module -101；Correction module -102；Evaluation module -103；Prediction module -104.
Specific implementation mode
Below in conjunction with attached drawing in the embodiment of the present invention, technical solution in the embodiment of the present invention carries out clear, complete Ground describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Usually exist The component of the embodiment of the present invention described and illustrated in attached drawing can be arranged and be designed with a variety of different configurations herein.Cause This, the detailed description of the embodiment of the present invention to providing in the accompanying drawings is not intended to limit claimed invention below Range, but it is merely representative of the selected embodiment of the present invention.Based on the embodiment of the present invention, those skilled in the art are not doing The every other embodiment obtained under the premise of going out creative work, shall fall within the protection scope of the present invention.
Below in conjunction with the accompanying drawings, it elaborates to some embodiments of the present invention.In the absence of conflict, following Feature in embodiment and embodiment can be combined with each other.
First aspect present invention provides the method based on 20CR data prediction wave significant wave heights, refering to fig. 1, including：
S1：The initial data in 20CR data is obtained, data prediction is carried out to initial data；
S2：Choose the sea level pressure field for meeting preset condition；
S3：According to First Year in 20CR data mould is predicted with the relevant Data correction of sea level pressure field in the data of section Type；
S4：Using be later than in 20CR data First Year for section second year for section data assessment prediction model；
S5：After assessment passes through, wave significant wave height is predicted using prediction model.
Optionally, referring to Fig.2, step S1 further comprises：
S11：Collect the long duration of the 20CR data based on mesh point mode it is each when time weather forecast data, including sea level Air pressure SLP, significant wave height Hs；
S12：The coordinate of time weather forecast data institute style point when obtaining collected each, based on coordinate extraction and coordinate phase Corresponding sea-level pressure matrix S, significant wave height matrix H, wherein including m spatial point, each spatial point contains n times observation number According to：
Wherein, SmnSecondary sea-level pressure value, H when being the n-th of m-th of spatial pointmnIt is secondary when being the n-th of m-th of spatial point Significant wave height, m is the number of spatial point, when n is observation time.
Optionally, further comprise refering to Fig. 3, step S3：
S31：The mean value M for calculating the sea-level pressure SLP of sea level pressure field obtains being based on mesh point mode according to mean value M It is each when time sea-level pressure SLP anomaly value P：
Wherein,Secondary when n is observation, i representation space points are secondary when j is indicated；
S32：EOF Dimension Reduction Analysis is done to anomaly value P, obtains the contribution rate of heterogeneity and each ingredient to population variance, is retained Preceding 28 EOF and principal component；
S33：Covariance calculating is carried out to anomaly value P, obtains covariance matrix Lm×n,Wherein, T Covariance matrix L is calculated in the transposition of representing matrixm×nFeature vector V and characteristic value Λ, meet the public affairs of LV=Λ V, Λ Formula is as follows：
Wherein, feature vector V is orthogonal moment Battle array, the jth column element of feature vector V is exactly eigenvalue λjCorresponding feature vector；
S34：According to feature vector V and characteristic value Λ, the variance contribution ratio of each feature vector V and preceding several features are calculated The accumulative variance contribution ratio of vectorial V is ranked up covariance matrix according to the sequences of characteristic value Λ from big to small, comes first Position is EOF1；
S35：Sea-level pressure SLP and significant wave height Hs when to each based on mesh point mode time carry out Box-Cox transformation, Sea-level pressure trGt after being converted and significant wave height trHt；
S36：Based on k-th of principal component PCk,tWith 4 hours k-th of principal component PC of lagk,t-4To corresponding on each lattice point Significant wave height trHt calculate its related coefficient, and 28 PC when taking related coefficient highestk,tOr PCk,t-4As significant wave height The predictive factor of trHt；
S37：Calculate the standard deviation S of significant wave height HsH1With the standard deviation S of 28 predictive factorsXkAnd it preserves；
S38：It brings predictive factor into prediction model, the prediction result of i-th and i+1 model is compared with F statistics, select The predictive factor of overall really degree can be represented by going out；
S39：It will be late by the significant wave height H of pt-pBring prediction model into, prediction model is：
Wherein, HtIt is by the significant wave height of Box-Cox transformation on each lattice point, a is constant term, and P is with premeasuring phase The lag coefficient of the parameter of pass, Xk,tIt is k-th of predictive factor based on sea-level pressure SLP, secondary when t is, bkIt corresponds to Xk,tCoefficient, K is the sum of predictive factor, Ht-pIt is the significant wave height for lagging p, cpCorrespond to Ht-pCoefficient, utM can be used Rank autoregression model indicates, if M=0, utFor white noise.
Optionally, further comprise refering to Fig. 4, step S4：
S41：It is carried out in advance for the sea level pressure field of section for the second year of section on the basis of 28 EOF to being later than First Year It surveys, obtains PCk,t；
S42：According to SXkSelect 28 predictive factors.
Optionally, step S5 is specifically included：
Bring predictive factor into prediction model, secondary significant wave height, effective by what is predicted when predicting each in target time period Wave height value reverts to the value before Box-Cox transformation, using the value after reduction as final significant wave height.
Optionally, horizontal using Pierre Si evaluation index PSS assessment predictions, Pierre's Si evaluation index PSS is：
Wherein, piTo observe relative frequency, qiTo predict relative frequency, pijTo combine relative frequency.
Second aspect of the present invention provides the device based on 20CR data prediction wave significant wave heights, refering to Fig. 5, including：
Acquisition module 100, for obtaining the initial data in 20CR data, and the acquisition module 100 is obtained The initial data carries out data prediction；
Module 101 is chosen, for choosing the sea level pressure field for meeting preset condition；
Correction module 102 is used for according to First Year in the 20CR data for the Hai Ping with selection in the data of section The relevant Data correction prediction model of face field of pressure；
Evaluation module 103, for being commented for the data of section for the second year of section according to being later than First Year in the 20CR data Estimate the prediction model after the correction module 102 corrects；
Prediction module 104, for after the assessment of the evaluation module 103 passes through, wave to be predicted according to the prediction model Significant wave height.
Optionally, the acquisition module 100 is specifically used for：
Collect the long duration of the 20CR data based on mesh point mode it is each when time weather forecast data, including sea-level pressure SLP, significant wave height Hs；
The coordinate of time weather forecast data institute style point when obtaining collected each, based on coordinate extraction and the seat Corresponding sea-level pressure matrix S, significant wave height matrix H are marked, wherein including m spatial point, each spatial point contains n times sight Measured data：
Wherein, SmnSecondary sea-level pressure value, H when being the n-th of m-th of spatial pointmnIt is secondary when being the n-th of m-th of spatial point Significant wave height, m is the number of spatial point, when n is observation time.
Optionally, the correction module 102 is specifically used for：
The mean value M for calculating the sea-level pressure SLP of the sea level pressure field obtains being based on lattice point according to the mean value M Pattern it is each when time the sea-level pressure SLP anomaly value P：
Wherein,Secondary when n is observation, i representation space points are secondary when j is indicated；
EOF Dimension Reduction Analysis is done to the anomaly value P, obtains the contribution rate of heterogeneity and each ingredient to population variance, is retained Preceding 28 EOF and principal component；
Covariance calculating is carried out to the anomaly value P, obtains covariance matrix Lm×n,Wherein, T The covariance matrix L is calculated in the transposition of representing matrixm×nFeature vector V and characteristic value Λ, meet LV=Λ V, Λ Formula it is as follows：
Wherein, described eigenvector V is just Matrix is handed over, the jth column element of described eigenvector V is exactly eigenvalue λjCorresponding feature vector；
According to described eigenvector V and the characteristic value Λ, the variance contribution ratio of each described eigenvector V is calculated with before The accumulative variance contribution ratio of several described eigenvector V, according to the sequences of the characteristic value Λ from big to small to the covariance Matrix is ranked up, and it is EOF to make number one1；
The sea-level pressure SLP and the significant wave height Hs when to each based on mesh point mode time carry out Box-Cox Transformation, the sea-level pressure trGt after being converted and significant wave height trHt；
Based on k-th of principal component PCk,tWith 4 hours k-th of principal component PC of lagk,t-4To corresponding institute on each lattice point 28 PC when stating significant wave height trHt and calculate its related coefficient, and taking the related coefficient highestk,tOr the PCk,t-4 Predictive factor as the significant wave height trHt；
Calculate the standard deviation S of the significant wave height HsH1With the standard deviation S of 28 predictive factorsXkAnd it preserves；
It brings the predictive factor into prediction model, the prediction result of i-th and i+1 model is compared with F statistics, select The predictive factor of overall really degree can be represented by going out；
It will be late by the significant wave height H of pt-pBring the prediction model into, the prediction model is：
Wherein, HtIt is by the significant wave height of Box-Cox transformation on each lattice point, a is constant term, and P is with premeasuring phase The lag coefficient of the parameter of pass, Xk,tIt is k-th of predictive factor based on the sea-level pressure SLP, secondary when t is, bkIt is pair It should be in Xk,tCoefficient, K is the sum of the predictive factor, Ht-pIt is the significant wave height for lagging p, cpCorrespond to Ht-pCoefficient, utIt can be indicated with M ranks autoregression model, if M=0, utFor white noise.
Optionally, the evaluation module 103 is specifically used for：
On the basis of 28 EOF to be later than First Year for section second year for section the sea level pressure field into Row prediction, obtains PCk,t；
According to the SXkSelect 28 predictive factors.
Optionally, the prediction module 104 is specifically used for：
Bring the predictive factor into the prediction model, secondary significant wave height, will predict when predicting each in target time period The significant wave high level gone out reverts to the value before Box-Cox transformation, using the value after the reduction as final significant wave height.
Optionally, the prediction module 104 is additionally operable to：
Horizontal using Pierre Si evaluation index PSS assessment predictions after obtaining final significant wave height, the PSS is：
Wherein, piTo observe relative frequency, qiTo predict relative frequency, pijTo combine relative frequency.
Third aspect present invention provides a kind of electronic equipment, including：Processor, memory and bus, the memory are deposited The executable machine readable instructions of the processor are contained, when the electronic equipment is run, the processor and the storage The method described in first aspect is executed by bus communication between device, when the machine readable instructions are executed by the processor.
Fourth aspect present invention provides a kind of computer readable storage medium, is stored on the computer readable storage medium Computer program executes the method described in first aspect when the computer program is run by processor.
Fifth aspect present invention provides a kind of computer program product, and the computer program product is run on computers When so that computer executes the method described in first aspect.
Compared with prior art, beneficial effects of the present invention are：Use U.S. Department of Energy and American National Atmosphere and Ocean office Combine the 20CR data of publication, time span is more than a century, initial data in obtaining 20CR data and to initial data Chosen after being pre-processed and meet the sea level pressure field of preset condition, according to First Year in 20CR data in the data of section with The relevant Data correction prediction model of sea level pressure field of selection, Optimized model parameter improve the prediction of wave significant wave height Accuracy.
The foregoing is merely the alternative embodiments of the present invention, are not intended to restrict the invention, for the skill of this field For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, any made by repair Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.

Claims (10)

1. a kind of method based on 20CR data prediction wave significant wave heights, which is characterized in that including：
Initial data of the twentieth century again in analysis project 20CR data is obtained, data prediction is carried out to the initial data；
Choose the sea level pressure field for meeting preset condition；
It is predicted with the relevant Data correction of the sea level pressure field in the data of section according to First Year in the 20CR data Model；
Using being later than prediction model described in data assessment of the second year for section of the First Year for section in the 20CR data；
After assessment passes through, wave significant wave height is predicted using the prediction model.
2. according to the method described in claim 1, it is characterized in that, it is described obtain 20CR data in initial data, to described Initial data carries out data prediction, including：
Collect the long duration of the 20CR data based on mesh point mode it is each when time weather forecast data, including sea-level pressure SLP, Significant wave height Hs；
The coordinate of time weather forecast data institute style point when obtaining collected each, based on coordinate extraction and the coordinate phase Corresponding sea-level pressure matrix S, significant wave height matrix H, wherein including m spatial point, each spatial point contains n times observation number According to：
Wherein, SmnSecondary sea-level pressure value, H when being the n-th of m-th of spatial pointmnIt is secondary when being the n-th of m-th of spatial point to have Wave height is imitated, m is the number of spatial point, secondary when n is observation.
3. according to the method described in claim 2, it is characterized in that, it is described according to First Year in the 20CR data for the number of section In with the relevant Data correction prediction model of the sea level pressure field, including：
The mean value M for calculating the SLP of the sea level pressure field, when obtaining each based on mesh point mode according to the mean value M time The anomaly value P of the SLP：
Wherein,Secondary when n is observation, i representation space points are secondary when j is indicated；
EOF Dimension Reduction Analysis is done to the anomaly value P, obtains the contribution rate of heterogeneity and each ingredient to population variance, retains preceding 28 A EOF and principal component；
Covariance calculating is carried out to the anomaly value P, obtains covariance matrix Lm×n,Wherein, T is indicated The covariance matrix L is calculated in the transposition of matrixm×nFeature vector V and characteristic value Λ, meet the public affairs of LV=Λ V, Λ Formula is as follows：
λ1≥λ2≥…≥λm, wherein described eigenvector V is orthogonal matrix, described The jth column element of feature vector V is exactly eigenvalue λjCorresponding feature vector；
According to described eigenvector V and the characteristic value Λ, the variance contribution ratio of each described eigenvector V and preceding several is calculated The accumulative variance contribution ratio of described eigenvector V, according to the sequences of the characteristic value Λ from big to small to the covariance matrix It is ranked up, it is EOF to make number one1；
The sea-level pressure SLP and the significant wave height Hs when to each based on mesh point mode time carry out Box-Cox transformation, Sea-level pressure trGt after being converted and significant wave height trHt；
Based on k-th of principal component PCk,tWith 4 hours k-th of principal component PC of lagk,t-4Described have to corresponding on each lattice point Effect wave height trHt calculates its related coefficient, and 28 PC when taking the related coefficient highestk,tOr the PCk,t-4As The predictive factor of the significant wave height trHt；
Calculate the standard deviation S of the significant wave height HsH1With the standard deviation S of 28 predictive factorsXkAnd it preserves；
It brings the predictive factor into prediction model, the prediction result of i-th and i+1 model is compared with F statistics, selects energy Enough predictive factors for representing overall really degree；
It will be late by the significant wave height H of pt-pBring the prediction model into, the prediction model is：
Wherein, HtIt is by the significant wave height of Box-Cox transformation on each lattice point, a is constant term, and P is with the relevant ginseng of premeasuring The lag coefficient of variable, Xk,tIt is k-th of predictive factor based on the sea-level pressure SLP, secondary when t is, bkIt corresponds to Xk,tCoefficient, K is the sum of the predictive factor, Ht-pIt is the significant wave height for lagging p, cpCorrespond to Ht-pCoefficient, utIt can To be indicated with M ranks autoregression model, if M=0, utFor white noise.
4. according to the method described in claim 3, it is characterized in that, described use in the 20CR data is later than First Year for section Data assessment of the second year for section described in prediction model, including：
It is carried out in advance for the sea level pressure field of section for the second year of section on the basis of 28 EOF to being later than First Year It surveys, obtains PCk,t；
According to the SXkSelect 28 predictive factors.
5. pre- using the prediction model according to the method described in claim 4, it is characterized in that, described after assessment passes through Wave significant wave height is surveyed, including：
Bring the predictive factor into the prediction model, secondary significant wave height when predicting each in target time period, by what is predicted Significant wave high level reverts to the value before Box-Cox transformation, using the value after the reduction as final significant wave height.
6. according to the method described in claim 5, it is characterized in that, the method further includes：
Horizontal using Pierre Si evaluation index PSS assessment predictions, the PSS is：
Wherein, piTo observe relative frequency, qiTo predict relative frequency, pijTo combine relative frequency.
7. a kind of device of prediction wave significant wave height, which is characterized in that including：
Acquisition module, for obtaining the initial data in 20CR data, and the original number to acquisition module acquisition According to progress data prediction；
Module is chosen, for choosing the sea level pressure field for meeting preset condition；
Correction module is used for according to First Year in the 20CR data for the sea level pressure field with selection in the data of section Relevant Data correction prediction model；
Evaluation module, for according to school described in data assessment of the second year for section for being later than First Year in the 20CR data for section The prediction model after holotype block correction；
Prediction module, for after evaluation module assessment passes through, wave significant wave height to be predicted according to the prediction model.
8. device according to claim 7, which is characterized in that the acquisition module is specifically used for：
Collect the long duration of the 20CR data based on mesh point mode it is each when time weather forecast data, including sea-level pressure SLP, Significant wave height Hs；
The coordinate of time weather forecast data institute style point when obtaining collected each, based on coordinate extraction and the coordinate phase Corresponding sea-level pressure matrix S, significant wave height matrix H, wherein including m spatial point, each spatial point contains n times observation number According to：
Wherein, SmnSecondary sea-level pressure value, H when being the n-th of m-th of spatial pointmnIt is secondary when being the n-th of m-th of spatial point to have Wave height is imitated, m is the number of spatial point, secondary when n is observation.
9. device according to claim 8, which is characterized in that the correction module is specifically used for：
The mean value M for calculating the sea-level pressure SLP of the sea level pressure field obtains being based on mesh point mode according to the mean value M It is each when time the SLP anomaly value P：
Wherein,Secondary when n is observation, i representation space points are secondary when j is indicated；
EOF Dimension Reduction Analysis is done to the anomaly value P, obtains the contribution rate of heterogeneity and each ingredient to population variance, retains preceding 28 A EOF and principal component；
Covariance calculating is carried out to the anomaly value P, obtains covariance matrix Lm×n,Wherein, T is indicated The covariance matrix L is calculated in the transposition of matrixm×nFeature vector V and characteristic value Λ, meet the public affairs of LV=Λ V, Λ Formula is as follows：
λ1≥λ2≥…≥λm, wherein described eigenvector V is orthogonal matrix, described The jth column element of feature vector V is exactly eigenvalue λjCorresponding feature vector；
According to described eigenvector V and the characteristic value Λ, the variance contribution ratio of each described eigenvector V and preceding several is calculated The accumulative variance contribution ratio of described eigenvector V, according to the sequences of the characteristic value Λ from big to small to the covariance matrix It is ranked up, it is EOF to make number one1；
The SLP and the Hs when to each based on mesh point mode time carry out Box-Cox transformation, the sea level after being converted Air pressure trGt and significant wave height trHt；
Based on k-th of principal component PCk,tWith 4 hours k-th of principal component PC of lagk,t-4Described have to corresponding on each lattice point Effect wave height trHt calculates its related coefficient, and 28 PC when taking the related coefficient highestk,tOr the PCk,t-4As The predictive factor of the significant wave height trHt；
Calculate the standard deviation S of the significant wave height HsH1With the standard deviation S of 28 predictive factorsXkAnd it preserves；
It brings the predictive factor into prediction model, the prediction result of i-th and i+1 model is compared with F statistics, selects energy Enough predictive factors for representing overall really degree；
It will be late by the significant wave height H of pt-pBring the prediction model into, the prediction model is：
Wherein, HtIt is by the significant wave height of Box-Cox transformation on each lattice point, a is constant term, and P is with the relevant ginseng of premeasuring The lag coefficient of variable, Xk,tIt is k-th of predictive factor based on the SLP, secondary when t is, bkCorrespond to Xk,tCoefficient, K It is the sum of the predictive factor, Ht-pIt is the significant wave height for lagging p, cpCorrespond to Ht-pCoefficient, utM ranks can be used to return certainly Return model to indicate, if M=0, utFor white noise.
10. device according to claim 9, which is characterized in that the prediction module is specifically used for：
Bring the predictive factor into the prediction model, secondary significant wave height when predicting each in target time period, by what is predicted Significant wave high level reverts to the value before Box-Cox transformation, using the value after the reduction as final significant wave height.
CN201810536110.8A 2018-05-28 2018-05-28 Method and its device based on 20CR data prediction wave significant wave heights Pending CN108763160A (en)

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