CN106777949A - A kind of long-term trend Forecasting Methodology of the wave wave direction based on analyze data again - Google Patents
A kind of long-term trend Forecasting Methodology of the wave wave direction 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 wave direction 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, sea-level pressure gradient G X, GY anomaly value and standard deviation are calculated；4th, GX, GY anomaly value principal component are analyzed；5th, sea area data are carried out with Box Cox conversion；6th, the predictive factor of wave wave direction is calculated；7th, the standard deviation of wave direction and predictive factor is calculated；8th, predictive factor brings forecast model into；9th, wave direction lagged value brings model into；Tenth, GX, GY prediction on the basis of EOF；11, optimum choice predictive factor；12, model prediction wave wave direction；13, assessment prediction level；14, wave wave direction long-term trend are calculated；15, wave direction long-term trend figure is drawn.The long-term trend of secondary wave direction 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 wave direction based on analyze data again
Long-term trend Forecasting Methodology.
Background technology
Wave has very important influence to the production and living of people, such as coastal port construction, waterway engineering all with
Wave has substantial connection.Wave wave direction is exactly the folder of an important parameter for reflecting wave feature, seashore dike line and wave wave direction
Angle, directly influence Coastal erosion and develop feature, therefore analysis prediction wave wave direction trend can help to it is more scientific
Harbour and coast protection works are built, is had important practical significance.Traditional observation method such as buoy etc., although can be accurate
Acquisition wave wave direction change information, but they can only obtain change of the wave in fixing point, and coverage rate also has very much
Limit, the buoy for being difficult to obtain the continuous wave of the sea more than 20 years in China Seas at present observes data.With satellite remote sensing
The maturation of technology, satellite data is gradually employed, though there is wider coverage about the satellite data of wave wave direction, at most
The simply data of nearly 20 years, this just seriously constrains the reliability to wave wave direction long-term trend research.How existing skill is overcome
The deficiency of art is one of problem demanding prompt solution in current ocean wave parameter forecasting technique field.
The content of the invention
Goal of the invention：A kind of the long-term of wave wave direction based on analyze data again is provided to overcome the deficiencies in the prior art
Trend forecasting method, the present invention is entered using Box-Cox conversion using the analyze data source again of global advanced stabilization to initial data
Row amendment, then according to meteorological datas such as revised sea-level pressure gradient, wave wave direction, using principal component analytical method and length
Phase wave direction trend formula, the long-term trend of secondary wave wave direction when calculating and predicting each, with very strong operability.
Technical scheme：A kind of long-term trend Forecasting Methodology of the wave wave direction based on analyze data again for proposing, its feature
It is to comprise 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 wave direction data 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 corresponding sea-level pressure the gradient matrix GX and GY of coordinate, such as
(1), shown in (2) formula, wave wave direction matrix θ, as shown in (3) formula, including m spatial point, each spatial point contains n sight
Survey data：
Wherein, GXmnSea-level pressure gradient when being the n-th of m-th spatial point time longitude coordinate direction value, GYmn
Sea-level pressure gradient when being the n-th of m-th spatial point time latitude coordinate direction value, θmnIt is m-th the n-th of spatial point
When time wave direction, m is the number of spatial point, when n is observation time.
Step 3, calculating be based on mesh point mode ERA-Interim it is each when time sea-level pressure gradient matrix GX
With the average M of GYXAnd MY, then subtract average M with the original value of sea-level pressure gradient matrix GX and GYXAnd MY, obtain based on lattice
Dot pattern it is each when time sea-level pressure gradient matrix GX and GY anomaly value PXAnd PY, and calculate sea-level pressure gradient
Matrix GX and GY anomaly value PXAnd PYStandard deviation SXAnd SY, as shown in (4), (5) formula：
In above-mentioned (4), (5) formula：Wherein, n represents secondary during observation, and i represents empty
Between point, i=1 ... m, j represent observation data, j=1 ... n；
Step 4, to sea-level pressure gradient matrix GX and GY anomaly value PXAnd PYBe respectively EOF analysis, obtain it is different into
Divide and each composition is to the contribution rate of population variance, retain preceding 30 EOF and principal component；Wherein：
To PXCovariance calculating is carried out, 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 (6) formula, to meet LV=Λ V, its
In
Wherein, λ1≥λ2≥,...,≥λm(6),
Matrix V is orthogonal matrix, and the jth column element of matrix V is exactly eigenvalue λjCorresponding characteristic vector, wherein j 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 and step 2 based on lattice point it is each when time wave wave direction matrix θ and sea
Plane barometric gradient matrix GX and GY carry out Box-Cox conversion, the wave wave direction tr θ after being convertedtWith sea-level pressure ladder
Degree trGXt、trGYt；
Step 6, to corresponding tr θ on each lattice pointt, 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 the pre- of wave wave direction
Survey the factor, wherein PCk,tOr PCk,t-4Represent principal component, k represents ordinal number, when t is represented time, t-4 represent delayed 4 hours when
It is secondary；
Step 7, calculates the standard deviation S of wave wave directionθ1With 30 predictive factor Xk,tStandard deviation SXk, preserve standby
With；
Step 8, brings the predictive factor that step 6 is obtained into forecast model, and i-th model and i-th are compared with F statistics
+ 1 model predicts the outcome, so as to select optimal predictive factor；
Step 9, the wave wave direction that will be late by a step also brings model into, as one of predictive factor, under integrated forecasting for the moment
The wave wave direction of secondary each lattice point, Optimized model parameter obtains final mask；Wherein model is as shown in (7) formula：
θ in above-mentioned (7) formulatIt is the wave wave direction by conversion on each mesh point, a is constant term, bkCorrespond to
Xk,tCoefficient, θt-pIt is the wave wave direction of delayed p, cpCorrespond to θt-pCoefficient, p is with the related parameter of predictand
Lag coefficient, Xk,tIt is k-th predictor based on SLP, utCan be represented with M ranks autoregression model, if M=0, utJust
It is white noise；
Step 10, on the basis of preceding 30 EOF that step 4 is obtained to it is each when time SLP gradient fields be predicted, obtain
To k-th principal component PCk,t；
Step 11, the S saved backup with step 7XkWeigh 30 predictive factor X of selectionk,t；
Step 12, the predictive factor that step 8 and step 11 are obtained is brought into the final mask of step 9, predicts mesh
Secondary wave wave direction when each in the timestamp phase, the wave direction value that will be predicted reverts to the value before Box-Cox conversion, saves as lattice point mould
Formula file；
Step 13, using the evaluation index assessment prediction level such as RMSE；
Step 14, with the wave wave direction of step 12 prediction as foundation, wave wave direction is calculated with trend computing formula
Long-term trend, finally give the long-term trend result of wave wave direction；
Step 15, according to the result of step 12, corresponds to corresponding lattice point coordinate, draws out wave wave direction and becomes for a long time
Gesture figure.
Beneficial effect：One is there is presently no the technical method for calculating wave wave direction long-term trend.The present invention is using the whole world
The analyze data source again of advanced stabilization, method is set up with decades even analyzing again across century-old wave wave direction data
On the basis of data, so as to solve the reliability of wave wave direction secular trend analysis；Two is that the present invention is converted using Box-Cox
Initial data is modified, then according to meteorological datas such as revised sea-level pressure gradient, wave wave direction, using EOF points
Analysis method and long-term wave direction trend formula, the long-term trend of secondary wave direction when calculating and predicting each；Three is that the present invention can help to
How the coastal building of arrangement or the trend of sea wall of science, and research mitigate erosion of the wave to seashore and waterfront structure
There is important scientific value, also can be used as the important evidence of the long term evolution rule of research littoral zone.
Brief description of the drawings
Fig. 1 is a kind of flow of the long-term trend Forecasting Methodology of wave wave direction based on analyze data again proposed by the present invention
Block diagram.
Fig. 2 is to be painted using a kind of long-term trend Forecasting Methodology of the wave wave direction based on analyze data again proposed by the present invention
The long-term trend result schematic diagram of the Pacific Ocean Partial Sea Area autumn wave wave direction of system.
Specific embodiment
Specific embodiment of the invention is described in further detail with reference to the accompanying drawings and examples.
Now by taking the Partial Sea Area of the Pacific Ocean as an example, using a kind of wave wave direction based on analyze data again proposed by the present invention
Long-term trend Forecasting Methodology carrys out the long-term trend of Study on Predicting Wave wave direction, and 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 wave wave direction data during each during 2000, the data break be every 6 hours 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 gradient matrix GX and GY when described each, such as (1),
(2) shown in formula, wave wave direction matrix θ, as shown in (3) formula, including m spatial point, each spatial point contains n observation number
According to：
Step 3, secondary sea-level pressure gradient G X's and GY is equal when ERA-Interim of the calculating based on mesh point mode is each
Value MXAnd MY, then subtract average M with original value GX and GYXAnd MY, obtain based on mesh point mode it is each when time GX and GY anomaly
Value PXAnd PY, and calculate GX and GY anomaly values PXAnd PYStandard deviation SXAnd SY, as shown in (4), (5) formula：
In above-mentioned (4), (5) formula：Wherein, n represents secondary during observation, and i represents i-th
Individual spatial point, i=1 ... m, j represent j-th observation data, j=1 ... n.
Step 4, to GX and GY anomaly values PXAnd PYEOF analyses are done respectively, obtain heterogeneity and each composition to population variance
Contribution rate, retain preceding 30 EOF and principal component；Wherein：
To PXCovariance calculating is carried out, 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 (6) formula, to meet LV=L Λ, its
In：
Wherein, λ1≥λ2≥,...,≥λm(6),
Matrix V is orthogonal matrix, and the jth column element of matrix V is exactly eigenvalue λjCorresponding characteristic vector, wherein j 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 and step 2 based on lattice point it is each when time wave wave direction matrix θ and sea
Plane barometric gradient GX and GY carry out Box-Cox conversion, the wave wave direction tr θ after being convertedtWith sea-level pressure gradient
trGXt、trGYt；
Step 6, to corresponding tr θ on each lattice pointt, with k-th principal component PCk,tWith k-th master of delayed 4 hours
Composition PCk,t-428 k-th principal component PC when calculating its coefficient correlation, and taking coefficient correlation highestk,tOr delayed 4 hours
K-th principal component PCk,t-4As the predictive factor of wave wave direction；
Step 7, calculates the standard deviation S θ of wave wave directionlWith 30 predictive factor Xk,tStandard deviation SXk, preserve standby
With；
Step 8, brings the predictive factor that step 6 is obtained into forecast model, and i-th model and i-th are compared with F statistics
+ 1 model predicts the outcome, so as to select optimal predictive factor；
Step 9, the wave wave direction that will be late by a step also brings model into, as one of predictive factor, under integrated forecasting for the moment
The wave wave direction of secondary each lattice point, Optimized model parameter obtains final mask；Wherein model is as shown in (7) formula：
θ in above-mentioned (7) formulatIt is the wave wave direction by conversion on each mesh point, a is constant term, bkCorrespond to
Xk,tCoefficient, θt-pIt is the wave wave direction of delayed p, cpCorrespond to θt-pCoefficient, p is with the related parameter of predictand
Lag coefficient, Xk,tIt is k-th predictive factor based on SLP, utCan be represented with M ranks autoregression model, if M=0, utJust
It is 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
The SLP gradient fields of 2010 are predicted, and obtain PCk,t；
Step 11, the S saved backup with step 7XkWeigh 30 predictive factor X of selectionk,t；
Step 12, the predictive factor that step 8 and step 11 are obtained is brought into the final mask of step 9, prediction
Secondary wave wave direction when 2001-2010 is each, the wave direction value that will be predicted reverts to the value before Box-Cox conversion, saves as lattice
Dot pattern 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 asI=1,2,3 ... n.In definite measured number of times, RMSE is represented with following formula：In formula, n is measurement
Number of times；diIt is one group of measured value and the deviation of average value.
Step 14, calculates wave wave direction long-term trend, and the wave wave direction predicted with step 12 uses trend as foundation
Computing formula is calculated, and finally gives the long-term trend result of wave wave direction；
Step 15, according to the result of step 12, corresponds to corresponding lattice point coordinate, draws out wave wave direction and becomes for a long time
Gesture figure.
Fig. 2 is to be painted using a kind of long-term trend Forecasting Methodology of the wave wave direction based on analyze data again proposed by the present invention
The short-term trend result schematic diagram of the Pacific waters summer wave wave direction of system, wherein abscissa are (radian/year).Fig. 2 can have
Effect instructs the sea wall of coastal area to arrange, operable for safeguarding that shore stabilization, prevention Coastal erosion have important scientific value
Property is strong.
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 corroded the prediction of the long-term trend of wave wave direction and prevention sea wall through validation trial
Play good directive function.Above specific embodiment and embodiment are to proposed by the present invention a kind of based on analyze data again
Wave wave direction long-term trend Forecasting Methodology technological thought specific support, it is impossible to protection scope of the present invention is limited with this,
It is every according to technological thought proposed by the present invention, any equivalent variations done on the basis of the technical program or equivalent change
It is dynamic, still fall within the scope of technical solution of the present invention protection.
Claims (3)
1. a kind of long-term trend Forecasting Methodology of the wave wave direction based on analyze data again, it is characterised in that including in detail below
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 are referred to 4~8 hours one when each
Secondary sea-level pressure SLP and wave wave direction data；
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 gradient matrix GX and GY when described each, such as (1),
(2) shown in formula, wave wave direction matrix θ, as shown in (3) formula, including m spatial point, each spatial point contains n observation number
According to：
Wherein, GXmnSea-level pressure gradient when being the n-th of m-th spatial point time longitude coordinate direction value, GYmnIt is m
Individual spatial point n-th when time sea-level pressure gradient latitude coordinate direction value, θmnWhen being the n-th of m-th spatial point time
Wave direction, m is the number of spatial point, when n is observation time；
Step 3, secondary sea-level pressure gradient matrix GX's and GY is equal when ERA-Interim of the calculating based on mesh point mode is each
Value MXAnd MY, then subtract average M with the original value GX and GY of sea-level pressure gradient matrixXAnd MY, obtain based on mesh point mode
The anomaly value P of secondary sea-level pressure gradient matrix GX and GY when eachXAnd PY, and calculate sea-level pressure gradient matrix GX and
GY anomaly values PXAnd PYStandard deviation SXAnd SY, as shown in (4), (5) formula：
In above-mentioned (4), (5) formula：Wherein, n represents secondary during observation, i representation spaces point,
I=1 ... m, j represent j-th observation data, j=1 ... n；
Step 4, to sea-level pressure gradient matrix GX and GY anomaly value PXAnd PYBe respectively EOF analysis, obtain heterogeneity and
Each composition retains preceding 30 EOF and principal component to the contribution rate of population variance；Wherein：
To PXCovariance calculating is carried out, 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 (6) formula, to meet LV=Λ V, wherein,
Matrix V is orthogonal matrix, and the jth column element of matrix V is exactly eigenvalue λjCorresponding characteristic vector, wherein j 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 and step 2 based on lattice point it is each when time wave wave direction data and sea level gas
Pressure gradient matrix GX and GY carry out Box-Cox conversion, the wave wave direction tr θ after being convertedtWith sea-level pressure gradient trGXt、
trGYt；
Step 6, to corresponding tr θ on each lattice pointt, 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 wave wave direction prediction because
Son；
Step 7, calculates the standard deviation of wave wave directionWith 30 predictive factor Xk,tStandard deviation SXk, save backup；
Step 8, brings the predictive factor that step 6 is obtained into forecast model, and i-th model and i+1 are compared with F statistics
Model predicts the outcome, so as to selecting optimal predictive factor；
Step 9, the wave wave direction that will be late by a step also brings model into, as one of predictive factor, a period of time time under integrated forecasting
The wave wave direction of each lattice point, Optimized model parameter obtains final mask；Wherein model is as shown in (7) formula：
θ in above-mentioned (7) formulatIt is the wave wave direction by conversion on each mesh point, a is constant term, bkCorrespond to Xk,t's
Coefficient, θt-pIt is the wave wave direction of delayed p, cpCorrespond to θt-pCoefficient, p is the delayed system with the related parameter of predictand
Number, Xk,tIt is k-th predictive factor based on SLP, utCan be represented with M ranks autoregression model, if M=0, utIt is exactly white noise
Sound；
Step 10, on the basis of preceding 30 EOF that step 4 is obtained to it is each when time SLP gradient fields be predicted, obtain
PCK, t；
Step 11, the S saved backup with step 7XkWeigh 30 predictive factor X of selectionk,t；
Step 12, the predictive factor that step 8 and step 11 are obtained is brought the final mask of step 9 into, during prediction target
Secondary wave wave direction when each in phase, the wave wave direction value that will be predicted reverts to the value before Box-Cox conversion, saves as lattice point mould
Formula file；
Step 13, using the evaluation index assessment prediction level such as RMSE；
Step 14, with the wave wave direction of step 12 prediction as foundation, the long-term of wave wave direction is calculated with trend computing formula
Trend, finally gives the long-term trend result of wave wave direction；
Step 15, according to the result of step 12, corresponds to corresponding lattice point coordinate, draws out wave wave direction long-term trend
Figure.
2. the long-term trend Forecasting Methodology of a kind of wave wave direction based on analyze data again according to claim 1, it is special
Levy and be, time weather forecast data refer to 6 hours sea-level pressure SLP and wave ripple once when each described in step one
To.
3. the long-term trend Forecasting Methodology of a kind of wave wave direction based on analyze data again according to claim 1 and 2, its
It is characterised by, the evaluation index such as RMSE described in step 13 refers to root-mean-square 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 group of measurement
The deviation of value and average value.
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| CN114819053A (en) * | 2022-03-04 | 2022-07-29 | 中国人民解放军国防科技大学 | Average wave direction forecast deviation correction method based on space-time convolution LSTM |
| CN116453310A (en) * | 2023-06-14 | 2023-07-18 | 昆山工业大数据创新中心有限公司 | Method and device for alarming temperature abnormality of copper bar |
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