CN108549961A - A method of wave significant wave height is estimated based on CMIP5 - Google Patents
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Abstract
The invention discloses a kind of methods for estimating wave significant wave height based on CMIP5, include the following steps：Initial data is obtained from ERA Interim and CMIP5 campaigns, carries out data prediction；Choose suitable sea level pressure field；Using 1,981 2000 years in ERA Interim Data correction prediction models；Preferred prediction model is assessed with the data of corresponding period in CMIP5；The following wave significant wave height is estimated using the prediction model.The present invention uses the data of CMIP5, and the ERA Interim steady in a long-term of the pre- measured center of European Study of Meso Scale Weather analyze data again, therefrom extract the data for estimating wave significant wave height, using the method for multivariate regression models, can to it is more when time wave significant wave height estimate.The method of the present invention has been firstly introduced earth system pattern on the basis of traditional air-ocean coupled mode, using the test data in CMIP5, solves period and the integrity problem of observational data；The present invention can effectively instruct the wave protected working of coastal region, have very strong operability.
Description
Technical field
The present invention relates to ocean wave parameter calculating field, especially a kind of based on CMIP5, (global coupled mode compares plan the
Five stages (Phase 5of Coupled Model Intercomparison Project, be abbreviated as CMIP5), which estimate wave, to be had
The method for imitating wave height.
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, wave power utilization etc. all have close association with wave.
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 than relatively limited.Global climate model is extensive as the important tool for estimating Future Climate Change
It applies in the research work of climate change related field, such as the hydrology, the multiple subjects in ocean are required for being based on climatic model pair
The estimation results of Future Climate Change study relevant climate change effect and adjustment.In recent years in order to be better achieved
Data sharing, model comparision and inspection, World Climate Research Program (WCRP) organized and implemented atmospheric model in succession from 1989
Compare plan, ocean model compares plan, surface process parameterization compares plan and coupled mode compares plan, but compared to coupling
Mode parameter scheme, flux processing scheme, resolution ratio and the coupler technologies of model comparision plan phase III (CMIP3)
All to be improved and improvement.
Invention content
Goal of the invention：A kind of method for estimating wave significant wave height based on CMIP5 is provided, it is deficient to solve prior art data
Weary defect improves the accuracy of sea wave height prediction.
Technical solution：A method of wave significant wave height is estimated based on CMIP5, is included the following steps：
S1：Initial data is obtained from ERA-Interim and CMIP5 campaigns, carries out data prediction；
S2：Choose suitable sea level pressure field；
S3：Using preselecting selected Data correction prediction model in ERA-Interim；
S4：The preferred prediction model is assessed with the data of corresponding period in CMIP5 campaigns；
S5：Following wave significant wave height is estimated using the prediction model.
The step S1 further comprises：
S11, the ERA-Interim reanalysis datasets for collecting the pre- measured center of European Study of Meso Scale Weather based on mesh point mode
Long duration it is each when time weather forecast data, and global coupled mode compares the 5th stage (Phase 5of of plan
Coupled Model Intercomparison Project, are abbreviated as CMIP5) in test data, including 6 hours it is primary
Sea-level pressure SLP, significant wave height Hs；
Collected by S12, acquisition when ERA-Interim and CMIP5 each time weather forecast data institute style point coordinate, with
The coordinate is foundation, extraction with it is described each when the corresponding sea-level pressure of the secondary coordinate of weather forecast data institute style point,
The sea-level pressure matrix of the ERA-Interim of middle extraction is E, significant wave height matrix H, the sea-level pressure of the CMIP5 of extraction
Matrix is C, wherein including m spatial point, each spatial point contains n times observation data：
Wherein, EmnSecondary sea-level pressure value, H when being the n-th of m-th of spatial point of ERA-InterimmnIt is m-th of space
Point n-th when time significant wave height, CmnSecondary sea-level pressure value when being the n-th of m-th of spatial point of CMIP5, m is spatial point
Number, it is secondary when n is observation.
The step S3 further comprises：
The mean value M of secondary sea-level pressure SLP when S31, ERA-Interim of the calculating based on mesh point mode are each；With original
Value E subtracts mean value M, the anomaly value P of SLP when obtaining each based on mesh point mode time；Calculate the standard deviation of SLP anomaly values P
Poor S：
Wherein,Secondary when n is observation, i representation space points are secondary when j is indicated.
S32, EOF analyses are done to SLP anomaly values P, the contribution rate of heterogeneity and each ingredient to population variance is obtained, before reservation
30 EOF and principal component；
Covariance calculating is carried out to P, obtains real symmetric matrix Lm×m：
The transposition of T representing matrixes.
Then covariance matrix L is soughtm×mFeature vector V and characteristic value Λ, meet LV=Λ V,
Matrix V is orthogonal matrix, and the jth column element of matrix V is exactly eigenvalue λjCorresponding feature vector；
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, bigger corresponding feature vector and the time coefficient of representing of variance contribution is in data
Middle development law is more notable, is ranked up to L according to the sequence of characteristic value from big to small, it is EOF to make number one1, with this
Analogize；
S33：To collected according to step S1 each based on lattice point when time original sea-level pressure SLP and significant wave height
Hs carries out Box-Cox transformation, the sea-level pressure trEt after being converted and significant wave height trHt；
S34：To the trEt and trHt after corresponding transformation on each lattice point, with k-th of principal component PCK, tIt is 4 small with lagging
When k-th of principal component PCK, t-428 PC when calculating its related coefficient, and taking related coefficient highestk,tOr PCk,t-4As having
Imitate the predictive factor of wave height；
S35：Calculate the standard deviation S of significant wave heightHtWith 30 predictive factor Xk,tStandard deviation SXk, save backup；
S36：It brings predictive factor into prediction model, the prediction of i-th of model and i+1 model is compared with F statistics
As a result, to select optimal predictive factor；Wherein model is as follows：
In formula, HtIt is the significant wave height by transformation on each mesh point, a is constant term, and P is relevant with predictand
The lag coefficient of parameter, Xk,tIt is k-th of predictor based on SLP, secondary when t is, bkCorrespond to Xk,tCoefficient, K is
The sum of predictor, 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.
The step S4 further comprises：
S41：Calculate the CMIP5 based on mesh point mode it is each when time sea-level pressure SLP mean value Mc, then with original value C
Mean value Mc is subtracted, the anomaly value Cp of SLP when obtaining each based on mesh point mode time；
Wherein,Secondary when n is observation, i representation space points are secondary when j is indicated.
S42：To according to step S41 obtain it is each when time the anomaly value Cp of sea-level pressure carry out Box-Cox transformation, obtain
Sea level pressure field trGt after to transformation；
S43：EOF analyses are done to trGt, obtain the contribution rate of heterogeneity and each ingredient to population variance, retain first 30
EOF and principal component PCk,t, k, t indicate secondary when ordinal sum；
S44：To corresponding trGt on each lattice point, with principal component PCK, tAnd PCK, t-4Its related coefficient is calculated, and takes correlation
28 PC when coefficient highestk,tOr PCk,t-4Predictive factor as significant wave height；
S45：It brings predictive factor into prediction model, the prediction of i-th of model and i+1 model is compared with F statistics
As a result, to select optimal predictive factor；Wherein shown in model such as formula (5).
The step S5 further comprises：
S51：The significant wave height that will be late by a step also brings model into, secondary for the moment under integrated forecasting as one of predictive factor
Each lattice point significant wave height, Optimized model parameter obtains final mask, wherein shown in model such as formula (5).
S52：All predictive factors are brought to the final mask of step S51 into, secondary significant wave when predicting each in target time period
The significant wave high level predicted is reverted to the value before Box-Cox transformation, saves as mesh point mode file by height；
S53：Using assessments such as RMSE (root-mean-square error, i.e. root-mean-square error, also known as standard error)
Index prediction level, is defined as：
In definite measured number, RMSE is indicated with following formula：
In formula, n is pendulous frequency；diFor the deviation of i-th group of measured value and average value.
Advantageous effect：The CMIP5 patterns that the present invention utilizes use more reasonably Parameterization Scheme, flux processing scheme and
Coupler technologies, and on the basis of traditional air-ocean coupled mode, it has been firstly introduced earth system pattern；In addition,
The horizontal resolution of CMIP5 patterns increases, and the vertical number of plies increased, and the description of physical process is more careful, coupled mode
Formula also no longer needs flux to adjust.The present invention compares plan the 5th stage (Phase 5of Coupled using global coupled mode
Model Intercomparison Project, are abbreviated as CMIP5) in test data, solve observational data period and
Integrity problem；The present invention using Box-Cox transformation initial data is modified, then according to revised sea-level pressure,
The meteorological datas such as wave significant wave height estimate the significant wave height of the following wave using the method for multiple regression analysis；The present invention can
Effectively instruct the wave protected working of coastal region, for safeguard the safety and stability of coastal area, mitigate wave disaster play to
Important and indispensable role is closed, there is very strong operability.
Description of the drawings
Fig. 1 is the flow diagram of the method for the present invention；
Fig. 2 is the algorithm flow chart of the embodiment of the present invention；
Fig. 3 is the schematic diagram that certain Chinese marine site annual wave height is estimated using the embodiment of the present invention.
Specific implementation mode
Fig. 2 is the algorithm flow chart of the embodiment of the present invention；In conjunction with Fig. 2 and by taking certain Chinese sea region as an example, the present invention is based on
The method of CMIP5 corrected parameters estimates wave significant wave height, and specific steps include as follows：
S11：Collect each of the long duration of the ERA-Interim reanalysis datasets in certain Chinese marine site based on mesh point mode
When time weather forecast data, and global coupled mode compares plan the 5th stage (Phase 5of Coupled Model
Intercomparison Project, are abbreviated as CMIP5) in test data,
Collected by S12, acquisition when ERA-Interim and CMIP5 each time weather forecast data institute style point coordinate, with
The coordinate is foundation, extraction with it is described each when the corresponding sea-level pressure of the secondary coordinate of weather forecast data institute style point,
The sea-level pressure matrix of the ERA-Interim of middle extraction is E, significant wave height matrix H, the sea-level pressure of the CMIP5 of extraction
Matrix is C, wherein including m spatial point, each spatial point contains n times observation data：
S2：Choose suitable sea level pressure field.The selection of different sea level pressure fields can cause model different
Prediction effect, so by comparing the suitable sea level pressure field of selection；
S3：With 6 hours primary data of the previous decades data of ERA-Interim come calibration model；Preferably choose
The data of ERA-Interim1981-2000.
Further comprise：
The mean value M of secondary sea-level pressure SLP when S31, ERA-Interim of the calculating based on mesh point mode are each；With original
Value E subtracts mean value M, the anomaly value P of SLP when obtaining each based on mesh point mode time；Calculate the standard deviation of SLP anomaly values P
Poor S：
Wherein,
S32, EOF analyses are done to SLP anomaly values P, the contribution rate of heterogeneity and each ingredient to population variance is obtained, before reservation
30 EOF and principal component；
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, obtains real symmetric matrix Lm×m：
The transposition of T representing matrixes.
Then covariance matrix L is soughtm×mFeature vector V and characteristic value Λ, meet LV=Λ V, wherein
Matrix V is orthogonal matrix, and the jth column element of matrix V is exactly eigenvalue λjCorresponding feature vector；
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, bigger corresponding feature vector and the time coefficient of representing of variance contribution is in data
Middle development law is more notable, is ranked up to L according to the sequence of characteristic value from big to small, it is EOF to make number one1, with this
Analogize；
S33：To collected according to step S1 each based on lattice point when time original sea-level pressure SLP and significant wave height
Hs carries out Box-Cox transformation, the sea-level pressure trEt after being converted and significant wave height trHt；
S34：To the trEt and trHt after corresponding transformation on each lattice point, with k-th of principal component PCK, tIt is 4 small with lagging
When k-th of principal component PCK, t-428 PC when calculating its related coefficient, and taking related coefficient highestk,tOr PCk,t-4As having
Imitate the predictive factor of wave height；
S35：Calculate the standard deviation S of significant wave heightHtWith 30 predictive factor Xk,tStandard deviation SXk, save backup；
S36：It brings predictive factor into prediction model, the prediction of i-th of model and i+1 model is compared with F statistics
As a result, to select optimal predictive factor.Wherein model is as follows：
In formula, HtIt is the significant wave height by transformation on each mesh point, a is constant term, and P is relevant with predictand
The lag coefficient of parameter, Xk,tIt is k-th of predictor based on SLP, secondary when t is, bkCorrespond to Xk,tCoefficient, K is
The sum of predictor, 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.
S4：It is commented with 6 hours primary data of corresponding period (such as 1980-1999) in CMIP5 campaigns
Estimate the preferred prediction model；
Further comprise：
S41：Calculate the CMIP5 based on mesh point mode it is each when time sea-level pressure SLP mean value Mc, then with original value C
Mean value Mc is subtracted, the anomaly value Cp of SLP when obtaining each based on mesh point mode time；
Wherein,Secondary when n is observation, i representation space points are secondary when j is indicated.
S42：To according to step S41 obtain it is each when time the anomaly value Cp of sea-level pressure carry out Box-Cox transformation, obtain
Sea level pressure field trGt after to transformation；
S43：EOF analyses are done to trGt, obtain the contribution rate of heterogeneity and each ingredient to population variance, retain first 30
EOF and principal component PCk,t, k, t indicate secondary when ordinal sum；
S44：To corresponding trGt on each lattice point, with principal component PCK, tAnd PCK, t-4Its related coefficient is calculated, and takes correlation
28 PC when coefficient highestk,tOr PCk,t-4Predictive factor as significant wave height；
S45：It brings predictive factor into prediction model, the prediction of i-th of model and i+1 model is compared with F statistics
As a result, to select optimal predictive factor；Wherein shown in model such as formula (5).
S5：Estimate the wave significant wave height in Chinese certain marine site future.
Specifically, in implementation process, step 5 further comprises：
S51：The significant wave height that will be late by a step also brings model into, the significant wave of each lattice point secondary for the moment under integrated forecasting
Height, Optimized model parameter, obtains final mask, as shown in formula (5).
S52：All predictive factors are brought to the final mask of step S51 into, secondary significant wave when predicting each in target time period
The significant wave high level predicted is reverted to the value before Box-Cox transformation, saves as mesh point mode file by height；
S53：Using assessments such as RMSE (root-mean-square error, i.e. root-mean-square error, also known as standard error)
Index prediction level, is defined as：
In definite measured number, RMSE is indicated with following formula：
In formula, n is pendulous frequency；diFor the deviation of i-th group of measured value and average value.
The present invention, being capable of estimating and preventing wave disaster and play well to wave significant wave height through validation trial
Directive function.
For current wave height forecasting research in the presence of that cannot obtain wave height observational data steady in a long-term, the present invention is using entirely
Ball coupled mode compares the 5th stage of plan (Phase 5of Coupled Model Intercomparison Project, letter
Be denoted as CMIP5) in test data, and the ERA-Interim of the European pre- measured center of Study of Meso Scale Weather analyzes data again, uses
Box-Cox transformation is modified initial data, then according to meteorological datas such as revised sea-level pressure, significant wave height, adopts
With the method for multiple regression, secondary wave significant wave height when can estimate more, operability is strong, accuracy rate is high.
All explanations not related to belong to techniques known in the specific implementation mode of the present invention, can refer to known skill
Art is implemented.
The preferred embodiment of the present invention has been described above in detail, still, during present invention is not limited to the embodiments described above
Detail can carry out a variety of equivalents to technical scheme of the present invention within the scope of the technical concept of the present invention, this
A little equivalents all belong to the scope of protection of the present invention.It is further to note that described in above-mentioned specific implementation mode
Each particular technique feature can be combined by any suitable means in the case of no contradiction.In order to avoid not
Necessary repetition, the present invention no longer separately illustrate various combinations of possible ways.
Claims (6)
1. a kind of method for estimating wave significant wave height based on CMIP5, which is characterized in that include the following steps：
S1：Initial data is obtained from ERA-Interim and CMIP5 campaigns, carries out data prediction；
S2：Choose suitable sea level pressure field；
S3：Using previously selected Data correction prediction model in ERA-Interim；
S4：The preferred prediction model is assessed with the data of corresponding period in CMIP5 campaigns；
S5：Following wave significant wave height is estimated using the prediction model after preferably.
2. the method according to claim 1 that wave significant wave height is estimated based on CMIP5,
It is characterized in that, the step S1 further comprises：
S11, collect the pre- measured center of European Study of Meso Scale Weather based on mesh point mode ERA-Interim reanalysis datasets length
Period it is each when time weather forecast data, and global coupled mode compares the test data in the 5th stage CMIP5 of plan, packet
Include 6 hours primary sea-level pressure SLP, significant wave height Hs；
Collected by S12, acquisition when ERA-Interim and CMIP5 each time weather forecast data institute style point coordinate, with the seat
Be designated as foundation, extraction with it is described each when the corresponding sea-level pressure of the coordinate of secondary weather forecast data institute style point, wherein carrying
The sea-level pressure matrix of the ERA-Interim taken is E, significant wave height matrix H, the sea-level pressure matrix of the CMIP5 of extraction
For C, wherein including m spatial point, each spatial point contains n times observation data：
Wherein, EmnSecondary sea-level pressure value, H when being the n-th of m-th of spatial point of ERA-InterimmnIt is m-th of spatial point
Secondary significant wave height, C when n-thmnSecondary sea-level pressure value when being the n-th of m-th of spatial point of CMIP5, m are of spatial point
Number, it is secondary when n is observation.
3. the method according to claim 2 for estimating wave significant wave height based on CMIP5, which is characterized in that the step
S3 further comprises：
The mean value M of secondary sea-level pressure SLP when S31, ERA-Interim of the calculating based on mesh point mode are each；Subtracted with original value E
Mean value M is removed, the anomaly value P of SLP when obtaining each based on mesh point mode time；Calculate the standard deviation S of SLP anomaly values P：
Wherein,Secondary when n is observation, i representation space points are secondary when j is indicated；
S32, EOF analyses are done to SLP anomaly values P, obtains the contribution rate of heterogeneity and each ingredient to population variance, retain first 30
EOF and principal component；
Covariance calculating is carried out to P, obtains real symmetric matrix Lm×m：
The transposition of T representing matrixes；
Then covariance matrix L is soughtm×mFeature vector V and characteristic value Λ, meet LV=Λ V,
Matrix V is orthogonal matrix, and the jth column element of matrix V is exactly eigenvalue λjCorresponding feature vector；
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, variance contribution is bigger to represent corresponding feature vector and time coefficient is drilled in data
It is more notable to become rule, L is ranked up according to the sequence of characteristic value from big to small, it is EOF to make number one1, and so on；
S33：To collected according to step S1 each based on lattice point when time original sea-level pressure SLP and significant wave height Hs, into
Row Box-Cox transformation, the sea-level pressure trEt after being converted and significant wave height trHt；
S34：To the trEt and trHt after corresponding transformation on each lattice point, with k-th of principal component PCK, tWith 4 hours of lag
K-th of principal component PCK, t-428 PC when calculating its related coefficient, and taking related coefficient highestk,tOr PCk,t-4As significant wave
High predictive factor；
S35：Calculate the standard deviation S of significant wave heightHtWith 30 predictive factor Xk,tStandard deviation SXk, save backup；
S36：It brings predictive factor into prediction model, the prediction result of i-th of model and i+1 model is compared with F statistics,
To select optimal predictive factor；Wherein model is as follows：
In formula, HtIt is the significant wave height by transformation on each mesh point, a is constant term, and P is become with the relevant ginseng of predictand
The lag coefficient of amount, Xk,tIt is k-th of predictor based on SLP, secondary when t is, bkCorrespond to Xk,tCoefficient, K be forecast
The sum of the 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.
4. the method according to claim 3 for estimating wave significant wave height based on CMIP5, which is characterized in that the step
S4 further comprises：
S41：The mean value Mc of secondary sea-level pressure SLP when CMIP5 of the calculating based on mesh point mode is each, then subtracted with original value C
Mean value Mc, the anomaly value Cp of SLP when obtaining each based on mesh point mode time；
Wherein,Secondary when n is observation, i representation space points are secondary when j is indicated；
S42：To according to step S41 obtain it is each when time the anomaly value Cp of sea-level pressure carry out Box-Cox transformation, become
Sea level pressure field trGt after changing；
S43：EOF analyses are done to trGt, obtain the contribution rate of heterogeneity and each ingredient to population variance, retain preceding 30 EOF with
Principal component PCk,t, k, t indicate secondary when ordinal sum；
S44：To corresponding trGt on each lattice point, with principal component PCK, tAnd PCK, t-4Its related coefficient is calculated, and takes related coefficient
28 PC when highestk,tOr PCk,t-4Predictive factor as significant wave height；
S45：It brings predictive factor into prediction model, the prediction result of i-th of model and i+1 model is compared with F statistics,
To select optimal predictive factor；Wherein shown in model such as formula (5).
5. the method according to claim 4 for estimating wave significant wave height based on CMIP5, which is characterized in that the step
S5 further comprises：
S51：The significant wave height that will be late by a step also brings model into, and as one of predictive factor, a period of time time is each under integrated forecasting
The significant wave height of lattice point, Optimized model parameter obtain final mask, wherein shown in model such as formula (5)；
S52：All predictive factors are brought to the final mask of step S51 into, secondary significant wave height when predicting each in target time period will
The significant wave high level predicted reverts to the value before Box-Cox transformation, saves as mesh point mode file；
S53：Using the evaluation indexes prediction level such as RMSE, it is defined as：
In definite measured number, RMSE is indicated with following formula：
In formula (7) and (8), n is pendulous frequency；diFor the deviation of i-th group of measured value and average value.
6. the method according to claim 1 for estimating wave significant wave height based on CMIP5, which is characterized in that the use
Previously selected data are the data of 1981-2000 in ERA-Interim.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112711915A (en) * | 2021-01-08 | 2021-04-27 | 自然资源部第一海洋研究所 | Sea wave effective wave height prediction method |
| CN113221385A (en) * | 2021-06-08 | 2021-08-06 | 上海交通大学 | Initialization method and system for dating forecast |
| CN113704693A (en) * | 2021-08-19 | 2021-11-26 | 华北电力大学 | High-precision effective wave height data estimation method |
| CN113701711A (en) * | 2021-09-02 | 2021-11-26 | 宁波九纵智能科技有限公司 | High-precision positioning method and system based on Beidou positioning and barometer |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104021434A (en) * | 2014-06-20 | 2014-09-03 | 河海大学 | Method for forecasting sea wave significant wave height based on ERA-Interim |
| CN104050514A (en) * | 2014-05-29 | 2014-09-17 | 河海大学 | Sea wave significant wave height long-term trend prediction method based on reanalysis data |
| WO2016079848A1 (en) * | 2014-11-20 | 2016-05-26 | 三菱電機株式会社 | State estimation device |
-
2018
- 2018-05-02 CN CN201810409041.4A patent/CN108549961B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104050514A (en) * | 2014-05-29 | 2014-09-17 | 河海大学 | Sea wave significant wave height long-term trend prediction method based on reanalysis data |
| CN104021434A (en) * | 2014-06-20 | 2014-09-03 | 河海大学 | Method for forecasting sea wave significant wave height based on ERA-Interim |
| WO2016079848A1 (en) * | 2014-11-20 | 2016-05-26 | 三菱電機株式会社 | State estimation device |
Non-Patent Citations (1)
| Title |
|---|
| XIAOLAN L.WANG ET AL.: "Changes in global ocean wave heights as projected using multimodel CMIP5 simulations", 《GEOPHYSICAL RESEARCH LETTERS》 * |
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| CN112711915A (en) * | 2021-01-08 | 2021-04-27 | 自然资源部第一海洋研究所 | Sea wave effective wave height prediction method |
| CN113221385A (en) * | 2021-06-08 | 2021-08-06 | 上海交通大学 | Initialization method and system for dating forecast |
| CN113221385B (en) * | 2021-06-08 | 2022-09-23 | 上海交通大学 | Initialization method and system for dating forecast |
| CN113704693A (en) * | 2021-08-19 | 2021-11-26 | 华北电力大学 | High-precision effective wave height data estimation method |
| CN113704693B (en) * | 2021-08-19 | 2024-02-02 | 华北电力大学 | High-precision effective wave height data estimation method |
| CN113701711A (en) * | 2021-09-02 | 2021-11-26 | 宁波九纵智能科技有限公司 | High-precision positioning method and system based on Beidou positioning and barometer |
| CN113701711B (en) * | 2021-09-02 | 2023-11-03 | 宁波九纵智能科技有限公司 | High-precision positioning method and system based on Beidou positioning and barometer |
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