CN106777949B - A kind of long-term trend prediction technique based on the wave wave direction for analyzing data again - Google Patents
A kind of long-term trend prediction technique based on the wave wave direction for analyzing data again Download PDFInfo
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Abstract
The present invention relates to a kind of long-term trend prediction techniques based on the wave wave direction for analyzing data again, it is characterised in that time weather forecast data when step includes: one, collection ERA-Interim each；Two, each lattice point coordinate is obtained；Three, sea-level pressure gradient G X, GY anomaly value and standard deviation are calculated；Four, GX, GY anomaly value principal component are analyzed；Five, Box-Cox transformation is carried out to sea area data；Six, the predictive factor of wave wave direction is calculated；Seven, the standard deviation of wave direction and predictive factor is calculated；Eight, predictive factor brings prediction model into；Nine, wave direction lagged value brings model into；Ten, GX, GY prediction on the basis of EOF；11, optimum choice predictive factor；12, model prediction wave wave direction；13, assessment prediction is horizontal；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 more, and accuracy rate is high.
Description
Technical field
The invention belongs to ocean wave parameter forecasting technique fields, more particularly to a kind of based on the wave wave direction for analyzing data again
Long-term trend prediction technique.
Background technique
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 an important parameter for reflecting wave feature, the folder of seashore dike line and wave wave direction
The trend that angle directly influences the feature of Coastal erosion and differentiation, therefore analyzes prediction wave wave direction can help to 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 wave in the variation of fixed point, and covering surface also has very much
Limit, the buoy for being difficult to obtain the continuous wave of the sea more than 20 years at present in China Seas observe data.With satellite remote sensing
The maturation of technology, satellite data are gradually applied, though the satellite data in relation to wave wave direction has wider coverage area, at most
Only nearly 20 years data, 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 urgent problem to be solved in current ocean wave parameter forecasting technique field.
Summary of the invention
Goal of the invention: it provides in order to overcome the deficiencies of the prior art a kind of based on the long-term of the wave wave direction for analyzing data again
Trend forecasting method, the present invention using the whole world it is advanced it is stable analyze data source again, using Box-Cox transformation to initial data into
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, has very strong operability.
Technical solution: a kind of long-term trend prediction technique based on the wave wave direction for analyzing data again of proposition, feature
It is to comprise the following specific steps that:
Step 1, the ERA-Interim for collecting the pre- measured center of European Study of Meso Scale Weather based on mesh point mode analyze number again
According to collection 20~30 years section it is each when time weather forecast data, wherein when each time weather forecast data refer to it is small including 4~8
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 is mentioned using the coordinate as foundation when obtaining collected each
Take with it is described each when the corresponding sea-level pressure gradient matrix GX and GY of the coordinate of time weather forecast data institute style point, 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 times sight
Measured data:
Wherein, GXmnValue of the secondary sea-level pressure gradient in longitude coordinate direction, GY when being the n-th of m-th of spatial pointmn
Value of the secondary sea-level pressure gradient in latitude coordinate direction, θ when being the n-th of m-th of spatial pointmnIt is the n-th of m-th of spatial point
When time wave direction, m is the number of spatial point, when n is observation time.
Step 3 calculates the sea-level pressure gradient matrix GX secondary when each based on the ERA-Interim based on mesh point mode
With the mean value M of GYXAnd MY, then with the original value of sea-level pressure gradient matrix GX and GY subtract mean value MXAnd 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 indicates secondary when observation, and i indicates empty
Between point, i=1 ... m, j indicate observation data, j=1 ... n；
Step 4, to sea-level pressure gradient matrix GX and GY anomaly value PXAnd PYDo EOF analysis respectively, obtain it is different at
Divide and each ingredient is to the contribution rate of population variance, retains preceding 30 EOF and principal component；Wherein:
To PXCovariance calculating is carried out, real symmetric matrix L is obtainedm×m, in which:
The transposition of T representing matrix；
Then covariance matrix L is soughtm×mFeature vector V and characteristic value Λ, as shown in (6) formula, to meet LV=Λ V,
In
Wherein, λ1≥λ2≥,...,≥λm(6),
Matrix V is orthogonal matrix, and the jth column element of matrix V is exactly eigenvalue λjCorresponding feature vector, wherein j value
From 1 to m；
According to real symmetric matrix Lm×mFeature vector V and characteristic value Λ, calculate each feature vector variance contribution ratio and
The accumulative variance contribution ratio of preceding several feature vectors；L is ranked up according to the sequence of characteristic value from big to small, is made number one
It is EOF1, and so on；
Step 5, to according to when each based on lattice point of step 1 and step 2 collection times wave wave direction matrix θ and sea
Plane barometric gradient matrix GX and GY carry out Box-Cox transformation, obtain transformed wave wave direction tr θtWith sea-level pressure ladder
Spend trGXt、trGYt；
Step 6, to tr θ corresponding on each lattice pointt, with k-th of principal component PCk,tWith k-th of master of 4 hours of lag
Ingredient PCk,t-428 PC when calculating its related coefficient, and taking related coefficient highestk,tOr PCk,t-4As the pre- of wave wave direction
The factor is surveyed, wherein PCk,tOr PCk,t-4Indicate principal component, k indicates ordinal number, when t is indicated time, t-4 indicate to lag 4 hours when
It is secondary；
Step 7 calculates the standard deviation S of wave wave directionθ1With 30 predictive factor Xk,tStandard deviation SXk, save standby
With；
Step 8, the predictive factor that step 6 is obtained bring prediction model into, compare i-th of model and i-th with F statistic
The prediction result of+1 model, to select optimal predictive factor；
Step 9, the wave wave direction that will be late by a step also bring 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 obtain final mask；Wherein model is as shown in (7) formula:
θ in above-mentioned (7) formulatIt is the wave wave direction by transformation on each mesh point, a is constant term, bkIt corresponds to
Xk,tCoefficient, θt-pIt is the wave wave direction for lagging p, cpCorrespond to θt-pCoefficient, p is relevant parameter with predictand
Lag coefficient, Xk,tIt is k-th of predictor based on SLP, utIt can be indicated with M rank autoregression model, if M=0, utJust
It is white noise；
Step 10, on the basis of preceding 30 EOF that step 4 obtains to it is each when time SLP gradient fields predict, obtain
To k-th of principal component PCk,t；
Step 11, the S saved backup with step 7XkMeasure 30 predictive factor X of selectionk,t；
The predictive factor that step 8 and step 11 obtain is brought into the final mask of step 9 by step 12, predicts mesh
The wave direction value predicted is reverted to the value before Box-Cox transformation, saves as lattice point mould by secondary wave wave direction when each in mark period
Formula file；
Step 13, it is horizontal using the evaluation indexes such as RMSE assessment prediction；
Step 14 calculates wave wave direction with trend calculation formula using the wave wave direction of step 12 prediction as foundation
Long-term trend finally obtain the long-term trend result of wave wave direction；
Step 15 draws out wave wave direction and becomes for a long time according to step 12 as a result, correspond to corresponding lattice point coordinate
Gesture figure.
The utility model has the advantages that first is that there is presently no the technical methods for calculating wave wave direction long-term trend.The present invention utilizes the whole world
It is advanced it is stable analyze data source again, method is established with decades even analyzing again across century-old wave wave direction data
On the basis of data, to solve the reliability of wave wave direction secular trend analysis；Second 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；Third is that the present invention can help to
How the coastal building of arrangement of science or the trend of sea wall, 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.
Detailed description of the invention
Fig. 1 is a kind of process of the long-term trend prediction technique based on the wave wave direction for analyzing data again proposed by the present invention
Block diagram.
Fig. 2 is that a kind of long-term trend prediction technique based on the wave wave direction for analyzing data again proposed by the present invention is applied to draw
The long-term trend result schematic diagram of the Pacific Ocean Partial Sea Area autumn wave wave direction of system.
Specific embodiment
Specific embodiments of the present invention will be 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 proposed by the present invention a kind of based on the wave wave direction for analyzing data again
Long-term trend prediction technique carrys out the long-term trend of Study on Predicting Wave wave direction, and in conjunction with Fig. 1, specific steps include the following:
Step 1 collects the 1981- of the ERA-Interim reanalysis datasets of certain Chinese sea region based on mesh point mode
Time sea-level pressure SLP and wave wave direction data when each during 2000, the data break are every 6 hours primary；
Step 2 obtains the coordinate of collected 6 hours primary data institute style points, using the coordinate as foundation, extract with
The coordinate corresponding sea-level pressure gradient matrix GX and GY of time weather forecast data institute style point 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 times 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 with original value GX and GY subtract mean value MXAnd MY, the anomaly of GX and GY when obtaining each based on mesh point mode time
Value PXAnd PY, and calculate GX and GY anomaly value PXAnd PYStandard deviation SXAnd SY, as shown in (4), (5) formula:
In above-mentioned (4), (5) formula:Wherein, n indicates secondary when observation, and i indicates i-th
A spatial point, i=1 ... m, j indicate j-th of observation data, j=1 ... n.
Step 4, to GX and GY anomaly value PXAnd PYEOF analysis is done respectively, obtains heterogeneity and each ingredient 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, in which:
The transposition of T representing matrix；
Then covariance matrix L is soughtm×mFeature vector V and characteristic value Λ, as shown in (6) formula, to meet LV=L Λ,
In:
Wherein, λ1≥λ2≥,...,≥λm(6),
Matrix V is orthogonal matrix, and the jth column element of matrix V is exactly eigenvalue λjCorresponding feature vector, wherein j value
From 1 to m；
According to real symmetric matrix Lm×mFeature vector V and characteristic value Λ, calculate each feature vector variance contribution ratio and
The accumulative variance contribution ratio of preceding several feature vectors, the bigger corresponding feature vector and time coefficient of representing of variance contribution is in data
Middle development law is more significant；L is ranked up according to the sequence of characteristic value from big to small, making number one is EOF1, with this
Analogize；
Step 5, to according to when each based on lattice point of step 1 and step 2 collection times wave wave direction matrix θ and sea
Plane barometric gradient GX and GY carry out Box-Cox transformation, obtain transformed wave wave direction tr θtWith sea-level pressure gradient
trGXt、trGYt；
Step 6, to tr θ corresponding on each lattice pointt, with k-th of principal component PCk,tWith k-th of master of 4 hours of lag
Ingredient PCk,t-428 k-th of principal component PC when calculating its related coefficient, and taking related coefficient highestk,tOr 4 hours of lag
K-th of principal component PCk,t-4Predictive factor as wave wave direction；
Step 7 calculates the standard deviation S θ of wave wave directionlWith 30 predictive factor Xk,tStandard deviation SXk, save standby
With；
Step 8, the predictive factor that step 6 is obtained bring prediction model into, compare i-th of model and i-th with F statistic
The prediction result of+1 model, to select optimal predictive factor；
Step 9, the wave wave direction that will be late by a step also bring 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 obtain final mask；Wherein model is as shown in (7) formula:
θ in above-mentioned (7) formulatIt is the wave wave direction by transformation on each mesh point, a is constant term, bkIt corresponds to
Xk,tCoefficient, θt-pIt is the wave wave direction for lagging p, cpCorrespond to θt-pCoefficient, p is relevant parameter with predictand
Lag coefficient, Xk,tIt is k-th of predictive factor based on SLP, utIt can be indicated with M rank autoregression model, if M=0, utJust
It is white noise；
Step 10, to 6 hours primary 2001- on the basis of preceding 30 EOF for the 1981-2000 that step 4 obtains
SLP gradient fields in 2010 are predicted, PC is obtainedk,t；
Step 11, the S saved backup with step 7XkMeasure 30 predictive factor X of selectionk,t；
The predictive factor that step 8 and step 11 obtain is brought into the final mask of step 9 by step 12, prediction
The wave direction value predicted is reverted to the value before Box-Cox transformation, saves as lattice by secondary wave wave direction when 2001-2010 is each
Dot pattern file；
Step 13, it is horizontal using the evaluation indexes such as RMSE assessment prediction；
RMSE (root-mean-square error) i.e. root-mean-square error, also known as standard error, is defined asI=1,2,3 ... n.In definite measured number, RMSE is indicated with following formula:In formula, n is to survey
Measure number；diFor the deviation of one group of measured value and average value.
Step 14, calculates wave wave direction long-term trend, and the wave wave direction predicted using step 12 uses trend as foundation
Calculation formula calculates, and finally obtains the long-term trend result of wave wave direction；
Step 15 draws out wave wave direction and becomes for a long time according to step 12 as a result, correspond to corresponding lattice point coordinate
Gesture figure.
Fig. 2 is that a kind of long-term trend prediction technique based on the wave wave direction for analyzing data again proposed by the present invention is applied to draw
The short-term trend result schematic diagram of the Pacific waters summer wave wave direction of system, wherein abscissa is (radian/year).Fig. 2 can have
Effect instructs the sea wall of coastal area to arrange, has important scientific value for maintenance shore stabilization, prevention Coastal erosion, can operate
Property is strong.
All explanations not related to belong to techniques known in a specific embodiment of the invention, can refer to known skill
Art is implemented.The present invention through validation trial, can prediction to the long-term trend of wave wave direction and prevention sea wall corrode
Play good directive function.The above specific embodiment and embodiment are to be based on analyzing data again to one kind proposed by the present invention
Wave wave direction long-term trend prediction technique technical idea specific support, this does not limit the scope of protection of the present invention,
All any equivalent variationss according to the technical idea provided by the invention, done on the basis of the technical program equivalent change
It is dynamic, still fall within the range of technical solution of the present invention protection.
Claims (2)
1. a kind of long-term trend prediction technique based on the wave wave direction for analyzing data again, which is characterized in that including in detail below
Step:
Step 1 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 including 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, using the coordinate as foundation, extract with
The coordinate corresponding sea-level pressure gradient matrix GX and GY of time weather forecast data institute style point 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 times observation number
According to:
Wherein, GXmnValue of the secondary sea-level pressure gradient in longitude coordinate direction, GY when being the n-th of m-th of spatial pointmnIt is m
A spatial point n-th when time value of the sea-level pressure gradient in latitude coordinate direction, θmnIt is secondary when being the n-th of m-th of spatial point
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 with the original value GX and GY of sea-level pressure gradient matrix subtract mean value MXAnd 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 value PXAnd PYStandard deviation SXAnd SY, as shown in (4), (5) formula:
In above-mentioned (4), (5) formula:Wherein, n indicates secondary when observation, i representation space point,
I=1 ... m, j indicate j-th of observation data, j=1 ... n；
Step 4, to sea-level pressure gradient matrix GX and GY anomaly value PXAnd PYDo EOF analysis respectively, obtain heterogeneity and
Each ingredient 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, in which:
The transposition of T representing matrix；
Then covariance matrix L is soughtm×mFeature vector V and characteristic value Λ, as shown in (6) formula, to meet LV=Λ V, wherein
Wherein, λ1≥λ2≥,...,≥λm(6),
Matrix V is orthogonal matrix, and the e column element of matrix V is exactly eigenvalue λeCorresponding feature vector, wherein e value from 1 to
m；
According to real symmetric matrix Lm×mFeature vector V and characteristic value Λ, calculate the variance contribution ratio of each feature vector and former
The accumulative variance contribution ratio of a feature vector；L is ranked up according to the sequence of characteristic value from big to small, making number one is
EOF1, and so on；
Step 5, to the wave wave direction data and sea level gas according to when each based on lattice point of step 1 and step 2 collection times
It presses gradient matrix GX and GY to carry out Box-Cox transformation, obtains transformed wave wave direction tr θtWith sea-level pressure gradient trGXt、
trGYt；
Step 6, to tr θ corresponding on each lattice pointt, with k-th of principal component PCK, tWith k-th of principal component of 4 hours of lag
PCK, t-428 PC when calculating its related coefficient, and taking related coefficient 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, the predictive factor that step 6 is obtained bring prediction model into, compare q-th of model and q+1 with F statistic
The prediction result of model, to select optimal predictive factor；
Step 9, the wave wave direction that will be late by a step also bring 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 obtain final mask；Wherein model is as shown in (7) formula:
θ in above-mentioned (7) formulatIt is the wave wave direction by transformation on each mesh point, a is constant term, bkCorrespond to Xk,t's
Coefficient, θt-pIt is the wave wave direction for lagging p, cpCorrespond to θt-pCoefficient, p is the lag system of the relevant parameter with predictand
Number, Xk,tIt is k-th of predictive factor based on SLP, utIt can be indicated with M rank autoregression model, if M=0, utIt is exactly white noise
Sound；
Step 10, on the basis of preceding 30 EOF that step 4 obtains to it is each when time SLP gradient fields predict, obtain
PCK, t；
Step 11, the S saved backup with step 7XkMeasure 30 predictive factor X of selectionk,t；
The predictive factor that step 8 and step 11 obtain is brought into the final mask of step 9 by step 12, when predicting target
The wave wave direction value predicted is reverted to the value before Box-Cox transformation, saves as lattice point mould by secondary wave wave direction when each in the phase
Formula file；
Step 13, it is horizontal using RMSE evaluation index assessment prediction；RMSE evaluation index refers to root-mean-square error, is defined asIn definite measured number, RMSE is indicated with following formula:In formula, n is measurement
Number；dzFor the deviation of one group of measured value and average value；
Step 14 calculates the long-term of wave wave direction with trend calculation formula using the wave wave direction of step 12 prediction as foundation
Trend finally obtains the long-term trend result of wave wave direction；
Step 15 draws out wave wave direction long-term trend according to step 12 as a result, correspond to corresponding lattice point coordinate
Figure.
2. a kind of long-term trend prediction technique based on the wave wave direction for analyzing data again according to claim 1, special
Sign is that time weather forecast data refer to including 6 hours primary sea-level pressure SLP and wave wave when each described in step 1
To.
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