CN104050514B - A kind of long-term trend Forecasting Methodology of the wave significant wave height based on analyze data again - Google Patents
A kind of long-term trend Forecasting Methodology of the wave significant wave height based on analyze data again Download PDFInfo
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Abstract
The present invention relates to a kind of long-term trend Forecasting Methodology of the wave significant wave height based on analyze data again, it is characterised in that step includes：First, time weather forecast data when collection ERA Interim are each；2nd, each lattice point coordinate is obtained；3rd, SLP anomaly value and standard deviation are calculated；4th, SLP anomaly value principal component analysis；5th, sea area data are carried out with Box Cox conversion；6th, the predictive factor of wave significant wave height is calculated；7th, the standard deviation of significant wave height and predictive factor is calculated；8th, predictive factor brings forecast model into；9th, significant wave height lagged value brings model into；Tenth, the SLP field predictions on the basis of EOF；11, optimum choice predictive factor；12, model prediction wave significant wave height；13, assessment prediction level；14, wave significant wave height long-term trend are calculated；15, significant wave height long-term trend figure is drawn.The long-term trend of secondary significant wave height when the present invention can forecast many, and accuracy rate is high.
Description
Technical field
The invention belongs to ocean wave parameter forecasting technique field, more particularly to a kind of wave based on analyze data again is effective
The long-term trend Forecasting Methodology of wave height.
Background technology
Wave has very important influence, such as sail, coastal port construction, navigation channel work to the production and living of people
Journey, fish production etc. all have substantial connection with wave.In addition, the safety of offshore oil platform is also closely bound up with wave.Wave
Significant wave height is exactly an important parameter for reflecting wave feature, thus analysis prediction wave significant wave height trend have it is important
Realistic meaning.Traditional observation method such as buoy etc., although can accurately obtain the change information of sea wave height, but they
Change of the wave in fixing point can only be obtained, and coverage rate is also very limited, is difficult to be obtained more than 20 in China Seas at present
The buoy observation data of the continuous wave of the sea in year.As the maturation of satellite remote sensing technology, satellite data are gradually employed, have
At most also it is the data of nearly 20 years though the satellite data for closing sea wave height has wider coverage, this is just seriously constrained
To the reliability of wave significant wave height long-term trend research.The deficiencies in the prior art how are overcome to turn into current ocean wave parameter pre-
One of emphasis problem urgently to be resolved hurrily in report technical field.
The content of the invention
The purpose of the present invention is to overcome the deficiencies in the prior art and to provide a kind of wave based on analyze data again effective
The long-term trend Forecasting Methodology of wave height, the present invention utilizes the analyze data source again of global advanced stabilization, and it is right to be converted using Box-Cox
Initial data is modified, then according to the meteorological data such as revised sea-level pressure, wave significant wave be high, using principal component point
Analysis method and long-term wave height trend formula, the long-term trend of secondary wave significant wave height when calculating and predicting each, with very strong
Operability.
According to a kind of long-term trend Forecasting Methodology of the wave significant wave height based on analyze data again proposed by the present invention, its
It is characterised by comprising the following specific steps that：
Step one, the ERA-Interim for collecting the pre- measured center of European Study of Meso Scale Weather based on mesh point mode analyzes number again
According to collection 20~30 years section it is each when time weather forecast data, wherein when each time weather forecast data to refer to 4~8 small
The sea-level pressure SLP and wave significant wave height Hs of Shi Yici；
Step 2, the coordinate of time weather forecast data institute style point, with the coordinate as foundation, carries when obtaining collected each
Take with it is described each when time weather forecast data institute style point the corresponding sea-level pressure matrix S of coordinate, as shown in (1) formula,
Wave significant wave height matrix H, as shown in (2) formula, including m spatial point, each spatial point contains n observation data：
Step 3, the average M of secondary sea-level pressure SLP when ERA-Interim of the calculating based on mesh point mode is each, then use
The original value of sea-level pressure matrix S subtracts average M, obtain based on mesh point mode it is each when time SLP anomaly value P, such as (3)
Shown in formula, and calculate the standard deviation of SLP anomaly values P：
In above-mentioned (3) formula：Wherein, n represents that each spatial point contains n observation data, SijRepresenting matrix S
(i, j) unit, i, j represent respectively positioned at sea-level pressure matrix S the i-th row jth arrange；
Step 4, EOF analyses are done to SLP anomaly values P, obtain the contribution rate of heterogeneity and each composition to population variance, are protected
Stay preceding 30 EOF and principal component；Wherein：
Covariance calculating is carried out to P, real symmetric matrix L is obtainedm×m, wherein：
The transposition of T representing matrixs；
Then covariance matrix L is soughtm×mCharacteristic vector V and characteristic value Λ, as shown in (4) formula, to meet LV=Λ V, its
In
Matrix V is orthogonal matrix, and the i-th column element of matrix V is exactly eigenvalue λiCorresponding characteristic vector, wherein i values
From 1 to m；
According to real symmetric matrix Lm×mCharacteristic vector V and characteristic value Λ, calculate each characteristic vector variance contribution ratio and
The accumulative variance contribution ratio of preceding several characteristic vectors；L is ranked up according to characteristic value order from big to small, is made number one
Be EOF1, by that analogy；
Step 5, to collected according to step one based on lattice point it is each when time original sea-level pressure SLP and wave have
Effect wave height Hs carries out Box-Cox conversion, sea-level pressure trGt and wave significant wave height trHt after being converted；
Step 6, to corresponding trHt on each lattice point, with k-th principal component PCK, tWith k-th master of delayed 4 hours
Composition PCK, t-428 PC when calculating its coefficient correlation, and taking coefficient correlation highestk,tOr PCk,t-4As wave significant wave height
Predictive factor, wherein PCk,tOr PCk,t-4Principal component is represented, secondary when k represents that ordinal number, t are represented, t-4 represents delayed 4 hours
When time；
Step 7, calculates the standard deviation S of wave significant wave heightHlWith 30 predictive factor X based on SLPk,tStandard deviation
Difference SXk, save backup；
Step 8, by the predictive factor PC of wave significant wave height in step 6k,tOr PCk,t-4And 30 bases in step 7
In the predictive factor X of SLPk,tBring forecast model into, predicting the outcome for i-th model and i+1 model compared with F statistics,
So as to select optimal predictive factor；
Step 9, the wave significant wave height that will be late by a step also brings model into, as one of predictive factor, under integrated forecasting
The wave significant wave height of each lattice point in a period of time time, Optimized model parameter obtains final mask；Wherein model is as shown in (5) formula：
A is constant term, b in above-mentioned (5) formulakCorrespond to Xk,tCoefficient, Xk,tIt is k-th predictive factor based on SLP,
cpCorrespond to Ht-pCoefficient, HtIt is the wave significant wave height by conversion on each mesh point, Ht-pIt is the wave of delayed p
Significant wave height, P is the lag coefficient with the related parameter of predictand, utCan be represented with M ranks autoregression model, if M
=0, utIt is exactly white noise；
Step 10, on the basis of preceding 30 EOF that step 4 is obtained to it is each when time SLP be predicted, obtain sea
The predictive factor PC of unrestrained significant wave heightk,t；
Step 11, the S saved backup with step 7XkWeigh 30 predictive factor X based on SLP of selectionk,t；
Step 12, by the predictive factor PC of the wave significant wave height in step 10k,tAnd in step 11 based on
The predictive factor X of SLPk,tBring the final mask of step 9 into, secondary wave significant wave height when each in prediction target time period will be pre-
The significant wave high level measured reverts to the value before Box-Cox conversion, saves as mesh point mode file；
Step 13, using the evaluation index assessment prediction level such as RMSE；
Step 14, with the wave significant wave height of step 12 prediction as foundation, calculating wave with trend computing formula has
The long-term trend of wave height are imitated, the long-term trend result of wave significant wave height is finally given；
Step 15, according to the result of step 12, corresponds to corresponding lattice point coordinate, draws out wave significant wave height long
Phase tendency chart.
The present invention and its remarkable advantage compared with prior art：One is that the present invention analyzes number again using global advanced stabilization
According to source, data are set up on the basis of the analyze data again of the wave significant wave height data with decades or even across a century,
So as to solve the reliability of wave significant wave height secular trend analysis；Two is that the present invention is converted to original number using Box-Cox
According to being modified, then according to the meteorological data such as revised sea-level pressure, wave significant wave be high, using principal component analytical method
With short-term wave height trend formula, the long-term trend of significant wave height when calculating and predicting each time, Study on Predicting Wave significant wave height is long-term
The accuracy rate of trend is high；Three is the wave protected working that the present invention can effectively instruct each coastal region, for safeguarding coastal area
Safety and stability, mitigate wave disaster play most important and indispensable effect, with very strong operability.
Brief description of the drawings
Fig. 1 is a kind of long-term trend Forecasting Methodology of wave significant wave height based on analyze data again proposed by the present invention
Process blocks schematic diagram.
Fig. 2 is using a kind of long-term trend prediction side of the wave significant wave height based on analyze data again proposed by the present invention
The long-term trend result schematic diagram of Chinese certain marine site winter maximum wave significant wave height that method is drawn.
Specific embodiment
Specific embodiment of the invention is described in further detail with reference to the accompanying drawings and examples.
Now by taking Chinese certain sea region as an example, using a kind of wave significant wave height based on analyze data again proposed by the present invention
Long-term trend Forecasting Methodology carry out the long-term trend of Study on Predicting Wave significant wave height, with reference to Fig. 1, its specific steps includes as follows：
Step one, collects the 1981- of the ERA-Interim reanalysis datasets of Chinese certain sea region based on mesh point mode
Time sea-level pressure SLP and significant wave height Hs data during each during 2000, the data break be every 6 hours once；But no
It is limited to this, the ERA-Interim reanalysis datasets of the pre- measured center of European Study of Meso Scale Weather based on mesh point mode can be collected
20~30 years section it is each when time weather forecast data, same effect is can reach, wherein time weather forecast data when each
Including 4~8 hours sea-level pressure SLP and wave significant wave height Hs once；
Step 2, obtains the coordinate of collected 6 hours data institute style points once, with the coordinate as foundation, extract with
The coordinate of time weather forecast data institute style point corresponding sea-level pressure matrix S when described each, as shown in (1) formula, wave
Significant wave height matrix H, as shown in (2) formula, including m spatial point, each spatial point contains n observation data：
Step 3, the average M of secondary sea-level pressure SLP when ERA-Interim of the calculating based on mesh point mode is each, then use
Original value S subtracts average M, obtain based on mesh point mode it is each when time SLP anomaly value P, and calculate SLP anomaly values P's
Standard deviation S, as shown in (3) formula：
In above-mentioned (3) formula：Wherein, n represents that each spatial point contains n observation data, SijRepresenting matrix S
(i, j) unit, i, j represent respectively positioned at matrix S the i-th row jth arrange；
Step 4, EOF analyses are done to SLP anomaly values P, obtain the contribution rate of heterogeneity and each composition to population variance, are protected
Stay preceding 30 EOF and principal component；Wherein：
EOF methods are a kind of Dimension Reduction Analysis methods, can be extracted according to maximum variance principle most typical in data
Spatial mode and temporal variation rule；
Covariance calculating is carried out to P, real symmetric matrix L is obtainedm×m, wherein：
The transposition of T representing matrixs.
Then covariance matrix L is soughtm×mCharacteristic vector V and characteristic value Λ, as shown in (4) formula, to meet LV=Λ V, its
In
Matrix V is orthogonal matrix, and the i-th column element of matrix V is exactly eigenvalue λiCorresponding characteristic vector, wherein i values
From 1 to m；
According to real symmetric matrix Lm×mCharacteristic vector V and characteristic value Λ, calculate each characteristic vector variance contribution ratio and
The accumulative variance contribution ratio of preceding several characteristic vectors, bigger corresponding characteristic vector and the time coefficient of representing of variance contribution is in data
Middle development law is more notable；L is ranked up according to characteristic value order from big to small, that make number one is EOF1, with this
Analogize；
Step 5, to collected according to step one based on lattice point it is each when time original sea-level pressure SLP and wave have
Effect wave height Hs carries out Box-Cox conversion, sea-level pressure trGt and wave significant wave height trHt after being converted；
Step 6, to corresponding trHt on each lattice point, with k-th principal component PCK, tWith k-th master of delayed 4 hours
Composition PCK, t-428 PC when calculating its coefficient correlation, and taking coefficient correlation highestk,tOr PCk,t-4It is effective as prediction wave
The predictive factor of wave height, wherein PCk,tOr PCk,t-4Represent principal component, when k represents that ordinal number, t are represented time, t-4 represent delayed 4 it is small
When time；
Step 7, calculates the standard deviation S of wave significant wave heightHlWith 30 predictive factor X based on SLPk,tStandard deviation
Difference SXk, save backup；
Step 8, by the predictive factor PC of wave significant wave height in step 6k,tOr PCk,t-4And 30 bases in step 7
In the predictive factor X of SLPk,tBring forecast model into, predicting the outcome for i-th model and i+1 model compared with F statistics,
So as to select optimal predictive factor；
Step 9, the significant wave height that will be late by a step also brings model into, as one of predictive factor, under integrated forecasting for the moment
The significant wave height of secondary each lattice point, Optimized model parameter obtains final mask；Wherein model is as shown in (5) formula：
A is constant term, b in above-mentioned (5) formulakCorrespond to Xk,tCoefficient, Xk,tIt is k-th predictive factor based on SLP,
cpCorrespond to Ht-pCoefficient, HtIt is the significant wave height by conversion on each mesh point, Ht-pIt is the significant wave height of delayed p,
P is the lag coefficient with the related parameter of predictand, utCan be represented with M ranks autoregression model, if M=0, utIt is exactly
White noise；
Step 10, to 6 hours 2001- once on the basis of preceding 30 EOF of the 1981-2000 that step 4 is obtained
SLP in 2010 is predicted, and obtains the predictive factor PC of wave significant wave heightk,t；
Step 11, the S saved backup with step 7XkWeigh 30 predictive factor X based on SLP of selectionk,t；
Step 12, by the predictive factor PC of the wave significant wave height in step 10k,tAnd in step 11 based on
The predictive factor X of SLPk,tBring the final mask of step 9 into, secondary wave significant wave height when prediction 2001-2010 is each will be pre-
The wave significant wave high level measured reverts to the value before Box-Cox conversion, saves as mesh point mode file；
Step 13, using the evaluation index assessment prediction level such as RMSE；
RMSE (root-mean-square error) is root-mean-square error, also known as standard error, and it is defined asIn definite measured number of times, RMSE is represented with following formula：In formula, n is survey
Amount number of times；diIt is one group of measured value and the deviation of average value.
Step 14, calculate wave significant wave height long-term trend, the wave significant wave height predicted with step (12) be according to
According to, calculated with trend computing formula, finally give the long-term trend result of wave significant wave height；
Step 15, according to the result of step 12, corresponds to corresponding lattice point coordinate, draws out wave significant wave height long
Phase tendency chart.
Fig. 2 is using a kind of long-term trend prediction side of the wave significant wave height based on analyze data again proposed by the present invention
The long-term trend result schematic diagram of 1981-2010 China certain marine site winter maximum wave significant wave height that method is drawn, wherein horizontal
Coordinate is cm/yr (centimetre/year), it is seen that the maximum amplification of Chinese certain marine site winter maximum wave significant wave height is annual 2 centimetres.
Fig. 2 rises to effectively instructing the wave protected working of coastal region for safeguarding the safety and stability of coastal area, mitigating wave disaster
To most important and indispensable effect, with very strong workable.
All explanations not related to belong to techniques known in specific embodiment of the invention, refer to known skill
Art is carried out.
The present invention, can be to the prediction of the long-term trend of wave significant wave height and prevention wave disaster through validation trial
Play good directive function.
Above specific embodiment and embodiment are effective to a kind of wave based on analyze data again proposed by the present invention
The specific support of the short-term trend Forecasting Methodology technological thought of wave height, it is impossible to which protection scope of the present invention is limited with this, it is every to press
According to technological thought proposed by the present invention, any equivalent variations done on the basis of the technical program or equivalent change, still
Belong to the scope of technical solution of the present invention protection.
Claims (1)
1. a kind of long-term trend Forecasting Methodology of the wave significant wave height based on analyze data again, it is characterised in that including following tool
Body step：
Step one, collects the ERA-Interim reanalysis datasets of the pre- measured center of European Study of Meso Scale Weather based on mesh point mode
20~30 years section it is each when time weather forecast data, wherein time weather forecast data refer to 6 hours once when each
Sea-level pressure SLP and wave significant wave height Hs；
Step 2, the coordinate of time weather forecast data institute style point when obtaining collected each, with the coordinate as foundation, extract with
The coordinate of time weather forecast data institute style point corresponding sea-level pressure matrix S when described each, as shown in (1) formula, wave
Significant wave height matrix H, as shown in (2) formula, including m spatial point, each spatial point contains n observation data：
Step 3, the average M of secondary sea-level pressure SLP when ERA-Interim of the calculating based on mesh point mode is each, then use Hai Ping
Face air pressure matrix S subtracts the average M of sea-level pressure SLP, obtain based on mesh point mode it is each when time SLP anomaly value P, such as
(3) shown in formula, and the standard deviation of SLP anomaly values P is calculated,
In above-mentioned (3) formula：Wherein, n represents that each spatial point contains n observation data, SijRepresenting matrix S's
(i, j) unit, i, j represent that the i-th row jth respectively positioned at sea-level pressure matrix S is arranged；
Step 4, EOF analyses are done to SLP anomaly values P, the contribution rate of heterogeneity and each composition to population variance are obtained, before reservation
30 EOF and principal component；Wherein：
EOF methods are a kind of Dimension Reduction Analysis methods, according to maximum variance principle extract in data most typical spatial mode and
Temporal variation rule；
Covariance calculating is carried out to P, real symmetric matrix L is obtainedm×m, wherein：
The transposition of T representing matrixs；
Then covariance matrix L is soughtm×mCharacteristic vector V and characteristic value Λ, as shown in (4) formula, to meet LV=Λ V, wherein,
Matrix V is orthogonal matrix, and the i-th column element of matrix V is exactly eigenvalue λiCorresponding characteristic vector, wherein i values from 1 to
m；
According to real symmetric matrix Lm×mCharacteristic vector V and characteristic value Λ, calculate the variance contribution ratio of each characteristic vector and former
The accumulative variance contribution ratio of individual characteristic vector；L is ranked up according to characteristic value order from big to small, what is made number one is
EOF1, by that analogy；
Step 5, to collected according to step one based on lattice point it is each when time original sea-level pressure SLP and wave significant wave
Hs high carries out Box-Cox conversion, sea-level pressure trGt and wave significant wave height trHt after being converted；
Step 6, to corresponding trHt on each lattice point, with k-th principal component PCK, tWith k-th principal component of delayed 4 hours
PCK, t-428 PC when calculating its coefficient correlation, and taking coefficient correlation highestk,tOr PCk,t-4As the pre- of wave significant wave height
Survey the factor, wherein PCk,tOr PCk,t-4Represent principal component, when k represents that ordinal number, t are represented time, t-4 represent delayed 4 hours when
It is secondary；
Step 7, calculates the standard deviation S of wave significant wave heightHlWith 30 predictive factor X based on SLPk,tStandard deviation
SXk, save backup；
Step 8, by the predictive factor PC of wave significant wave height in step 6k,tOr PCk,t-4And in step 7 30 be based on SLP
Predictive factor Xk,tBring forecast model into, predicting the outcome for i-th model and i+1 model is compared with F statistics, so that
Select optimal predictive factor；
Step 9, the wave significant wave height that will be late by a step also brings model into, as one of predictive factor, under integrated forecasting for the moment
The wave significant wave height of secondary each lattice point, Optimized model parameter obtains final mask；Wherein model is as shown in (5) formula：
A is constant term, b in above-mentioned (5) formulakCorrespond to Xk,tCoefficient, Xk,tIt is k-th predictive factor based on SLP, cpIt is
Corresponding to Ht-pCoefficient, HtIt is the wave significant wave height by conversion on each mesh point, Ht-pBe delayed p wave it is effective
Wave height, P is the lag coefficient with the related parameter of predictand, utCan be represented with M ranks autoregression model, if M=0, ut
It is exactly white noise；
Step 10, on the basis of preceding 30 EOF that step 4 is obtained to it is each when time SLP be predicted, obtaining wave has
Imitate the predictive factor PC of wave heightk,t；
Step 11, the S saved backup with step 7XkWeigh 30 predictive factor X based on SLP of selectionk,t；
Step 12, by the predictive factor PC of the wave significant wave height in step 10k,tAnd in step 11 based on SLP's
Predictive factor Xk,tBring the final mask of step 9 into, secondary wave significant wave height when each in prediction target time period, by what is predicted
Significant wave high level reverts to the value before Box-Cox conversion, saves as mesh point mode file；
Step 13, using the evaluation index assessment prediction level such as RMSE；
Step 14, with the wave significant wave height of step 12 prediction as foundation, wave significant wave is calculated with trend computing formula
Long-term trend high, finally give the long-term trend result of wave significant wave height；
Step 15, according to the result of step 12, corresponds to corresponding lattice point coordinate, draws out wave significant wave height and becomes for a long time
Gesture figure；
The evaluation indexes such as RMSE described in step 13 refer to root-mean-square error, also known as standard error, and it is defined asi
=1,2,3 ... n；In definite measured number of times, RMSE is represented with following formula：In formula, n is pendulous frequency；diIt is one
The deviation of group measured value and average value.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108763160A (en) * | 2018-05-28 | 2018-11-06 | 河海大学 | Method and its device based on 20CR data prediction wave significant wave heights |
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