CN104050514A - Sea wave significant wave height long-term trend prediction method based on reanalysis data - Google Patents

Sea wave significant wave height long-term trend prediction method based on reanalysis data Download PDF

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CN104050514A
CN104050514A CN201410235103.6A CN201410235103A CN104050514A CN 104050514 A CN104050514 A CN 104050514A CN 201410235103 A CN201410235103 A CN 201410235103A CN 104050514 A CN104050514 A CN 104050514A
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wave height
significant wave
wave
sea
slp
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CN104050514B (en
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吴玲莉
张玮
吴腾
焦楚杰
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Hohai University HHU
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Abstract

The invention relates to a sea wave significant wave height long-term trend prediction method based on reanalysis data. The sea wave significant wave height long-term trend prediction method is characterized by comprising the steps that (1) weather forecast data of an ERA-Interim reanalysis data set at each time frequency are collected, (2) coordinates of all lattice points are obtained, (3) SLP anomaly and standard deviation are calculated, (4) principal component analysis of the SLP anomaly is conducted, (5) Box-Cox transformation is conducted on sea area data, (6) a predictive factor of sea wave significant wave height is calculated, (7) the standard deviation of the significant wave height and the predictive factor is calculated, (8) the predictive factor is applied into a prediction model, (9) a significant wave height lagged value is applied into the model, (10) SLP field prediction on the basis of EOF is carried out, (11) predictive factor optimization selection is conducted, (12) the sea wave significant wave height is predicted through the model, (13) the prediction level is evaluated, (14) the sea wave significant wave height long-term trend is calculated, and (15) a significant wave height long-term trend chart is drawn. According to the sea wave significant wave height long-term trend prediction method based on the reanalysis data, the significant wave height long-term trend of multiple time frequencies can be predicted, and accuracy is high.

Description

A kind of secular trend Forecasting Methodology of wave significant wave height based on analyzing again data
Technical field
The invention belongs to ocean wave parameter forecasting technique field, particularly relate to a kind of secular trend Forecasting Methodology of wave significant wave height based on analyzing again data.
Background technology
Wave has very important impact to people's productive life, as sail, coastal port construction, waterway engineering, 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 of reflection wave feature, and therefore the trend of analyses and prediction wave significant wave height has important practical significance.Traditional observation method is as buoy etc., although can obtain accurately the change information of sea wave height, but they can only obtain the variation of wave at point of fixity, and coverage rate is also very limited, is difficult at present obtain at China Seas the buoy observation data of the continuous wave of the sea that exceedes 20 years.Along with the maturation of satellite remote sensing technology, satellite data is employed gradually, though have wider coverage about the satellite data of sea wave height, is also at most the data of nearly 20 years, and this has just seriously restricted the reliability to wave significant wave height long-term trend research.How to overcome the deficiencies in the prior art and become one of emphasis difficult problem urgently to be resolved hurrily in current ocean wave parameter forecasting technique field.
Summary of the invention
The object of the invention is provides a kind of secular trend Forecasting Methodology of wave significant wave height based on analyzing again data for overcoming the deficiencies in the prior art, the present invention utilizes the advanced stable data source of analyzing again in the whole world, adopt Box-Cox transfer pair raw data to revise, again according to weather datas such as revised sea-level pressure, wave significant wave height, adopt principal component analytical method and long-term wave height trend formula, the secular trend of the wave significant wave height calculating and predict when each time, have very strong operability.
A kind of secular trend Forecasting Methodology of wave significant wave height based on analyzing again data proposing according to the present invention, is characterized in that comprising following concrete steps:
Step 1, collect the ERA-Interim reanalysis datasets of the pre-measured center of European Study of Meso Scale Weather based on lattice point pattern 20～30 year sections each time time weather forecast data, when wherein each, time weather forecast data refer to and comprise 4～8 hours sea-level pressure SLP and wave significant wave height Hs once;
Step 2, obtain the collected coordinate of time weather forecast data institute style point when each, taking this coordinate as foundation, extract and the described corresponding sea-level pressure matrix S of the coordinate of inferior weather forecast data institute style point when each, as shown in (1) formula, wave significant wave height matrix H, as shown in (2) formula, comprising m spatial point, each spatial point contains observation data n time:
Step 3, calculate the average M of ERA-Interim based on the lattice point pattern sea-level pressure SLP when each time, then deduct average M with original value S, the anomaly value P of inferior SLP while obtaining based on lattice point pattern each, and calculate the standard deviation S of SLP anomaly value P, as shown in (3) formula:
In above-mentioned (3) formula: m i = Σ j - 1 n S ij n ;
Step 4, is EOF to SLP anomaly value P and analyzes, and obtains heterogeneity and the contribution rate of each composition to population variance, retains front 30 EOF and major component; Wherein:
P is carried out to covariance calculating, obtain real symmetric matrix L m × m, wherein:
L m × m = 1 n PP T , The transposition of T representing matrix;
Then ask covariance matrix L m × mproper vector V and eigenwert Λ, as shown in (4) formula, to meet LV=Λ V, wherein
Matrix V is orthogonal matrix, and the j column element of matrix V is exactly eigenvalue λ jcharacteristic of correspondence vector;
According to real symmetric matrix L m × mproper vector V and eigenwert FL calculate the variance contribution ratio of each proper vector and the accumulative total variance contribution ratio of front several proper vectors; According to eigenwert order from big to small, L is sorted, that make number one is EOF 1, by that analogy;
Step 5, to collect according to step 1 based on lattice point each time time original sea-level pressure SLP and wave significant wave height Hs carry out Box-Cox conversion, obtain sea-level pressure trGt and wave significant wave height trHt after conversion;
Step 6, to trHt corresponding on each lattice point, uses PC k, tand PC k, t-4calculate its related coefficient, and get 28 PCs of related coefficient when the highest k,tor PC k, t-4as the predictor of wave significant wave height;
Step 7, the standard deviation S of calculating wave significant wave height hlwith 30 predictor X k,tstandard deviation S xk, save backup;
Step 8, brings predictor into forecast model, with predicting the outcome of i model of F statistic comparison and i+1 model, thereby selects optimum predictor;
Step 9, also brings the wave significant wave height of a step of lagging behind into model, as one of predictor, and the wave significant wave height of each lattice point for the moment time under integrated forecasting, Optimized model parameter, obtains final mask; Wherein model is as shown in (5) formula:
H t = a + Σ k - 1 K b k X k , t + Σ p - 1 P c p H t - p + u t - - - ( 5 ) ,
H in above-mentioned (5) formula tthe wave significant wave height through conversion on each net point, H t-pbe the wave significant wave height of hysteresis p, P is the lag coefficient of following the parameter that predictand is relevant, X k, tk the predictor based on SLP, u tcan represent with M rank autoregressive model, if M=0, u tit is exactly white noise;
Step 10, predict the SLP field on the basis of front 30 EOF that obtain in step 4 when each time, obtains PC k,t;
Step 11, the S saving backup by step 7 xkweigh and select 30 predictor X k,t;
Step 12, brings all predictor into the final mask of step 9, and the wave significant wave height when each in target of prediction period time, reverts to the value before Box-Cox conversion by the significant wave height value doping, and saves as lattice point schema file;
Step 13, adopts the evaluation index evaluation prediction levels such as RMSE;
Step 14, the wave significant wave height of predicting taking step 12, as foundation, calculates the secular trend of wave significant wave height by trend computing formula, finally obtain the secular 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 secular trend figure.
The present invention and its remarkable advantage compared with prior art: the one, the present invention utilizes the advanced stable data source of analyzing again in the whole world, data are based upon and have decades even on the basis of analyzing again data across century-old wave significant wave height data, thereby solved the reliability of wave significant wave height secular trend analysis; The 2nd, the present invention adopts Box-Cox transfer pair raw data to revise, again according to weather datas such as revised sea-level pressure, wave significant wave height, adopt principal component analytical method and short-term wave height trend formula, the secular trend of the significant wave height calculating and predict when each time, the accuracy rate of Study on Predicting Wave significant wave height secular trend is high; The 3rd, the present invention can effectively instruct the wave protected working of each maritime province, for safeguard coastal area safety and stability, alleviate wave disaster and play most important and indispensable effect, there is very strong operability.
Brief description of the drawings
Fig. 1 is a kind of process blocks schematic diagram of secular trend Forecasting Methodology of wave significant wave height based on analyzing again data that the present invention proposes.
Fig. 2 be a kind of secular trend Forecasting Methodology of wave significant wave height based on analyzing again data that application the present invention proposes draw China marine site winter maximum wave significant wave height secular trend result schematic diagram.
Embodiment
Below in conjunction with drawings and Examples, the specific embodiment of the present invention is described in further detail.
Now, taking certain sea region of China as example, a kind of secular trend Forecasting Methodology of wave significant wave height based on analyzing again data that application the present invention proposes is carried out the secular trend of Study on Predicting Wave significant wave height, and in conjunction with Fig. 1, its concrete steps comprise as follows:
Step 1, time sea-level pressure SLP and significant wave height Hs data while collecting each during the 1981-2000 of ERA-Interim reanalysis datasets of China's sea region based on lattice point pattern, this data break be every 6 hours once; But be not limited to this, can collect the ERA-Interim reanalysis datasets of the pre-measured center of European Study of Meso Scale Weather based on lattice point pattern 20～30 year sections each time time weather forecast data, all can reach same effect, when wherein each, time weather forecast data comprise 4～8 hours sea-level pressure SLP and wave significant wave height Hs once;
Step 2, obtain the coordinate of collected 6 hours data institute style points once, taking this coordinate as foundation, extract and the described corresponding sea-level pressure matrix S of the coordinate of inferior weather forecast data institute style point when each, as shown in (1) formula, wave significant wave height matrix H, as shown in (2) formula, comprising m spatial point, each spatial point contains observation data n time:
Step 3, calculate the average M of ERA-Interim based on the lattice point pattern sea-level pressure SLP when each time, then deduct average M with original value S, the anomaly value P of inferior SLP while obtaining based on lattice point pattern each, and calculate the standard deviation S of SLP anomaly value P, as shown in (3) formula:
In above-mentioned (3) formula: m i = Σ j - 1 n S ij n ;
Step 4, is EOF to SLP anomaly value P and analyzes, and obtains heterogeneity and the contribution rate of each composition to population variance, retains front 30 EOF and major component; Wherein:
EOF method is a kind of Dimension Reduction Analysis method, can maximize principle according to variance and extract most typical spatial mode and temporal variation rule in data;
P is carried out to covariance calculating, obtain real symmetric matrix L m × mwherein:
L m × m = 1 n PP T , The transposition of T representing matrix.
Then ask covariance matrix L m × mproper vector V and eigenwert Λ, as shown in (4) formula, to meet LV=Λ V, wherein
Matrix V is orthogonal matrix, and the j column element of matrix V is exactly eigenvalue λ jcharacteristic of correspondence vector;
According to real symmetric matrix L m × mproper vector V and eigenwert Λ, calculate the variance contribution ratio of each proper vector and the accumulative total variance contribution ratio of front several proper vectors, variance contribution is larger represents that characteristic of correspondence vector sum time coefficient development law in data is more remarkable; According to eigenwert order from big to small, L is sorted, that make number one is EOF 1, by that analogy;
Step 5, to collect according to step 1 based on lattice point each time time original sea-level pressure SLP and wave significant wave height Hs carry out Box-Cox conversion, obtain sea-level pressure trGt and wave significant wave height trHt after conversion;
Step 6, to trHt corresponding on each lattice point, uses PC k, tand PC k, t-4calculate its related coefficient, and get 28 PCs of related coefficient when the highest k,tor PC k, t-4as the predictor of prediction wave significant wave height;
Step 7, the standard deviation S of calculating wave significant wave height hlwith 30 predictor X k,tstandard deviation S xk, save backup;
Step 8, brings predictor into forecast model, with predicting the outcome of i model of F statistic comparison and i+1 model, thereby selects optimum predictor;
Step 9, also brings the significant wave height of a step of lagging behind into model, as one of predictor, and the significant wave height of each lattice point for the moment time under integrated forecasting, Optimized model parameter, obtains final mask; Wherein model is as shown in (5) formula:
H t = a + Σ k - 1 K b k X k , t + Σ p - 1 P c p H t - p + u t - - - ( 5 ) ,
H in above-mentioned (5) formula tthe significant wave height through conversion on each net point, H t-pbe the significant wave height of hysteresis p, P is the lag coefficient of following the parameter that predictand is relevant, X k, tk the predictor based on SLP, u tcan represent with M rank autoregressive model, if M=0, u tit is exactly white noise;
Step 10, to the SLP field of 6 hour 2001-2010 once predicts on the basis of front 30 EOF of the 1981-2000 obtaining in step 4, obtains PC k,t;
Step 11, the S saving backup by step 7 xkweigh and select 30 predictor X k,t;
Step 12, brings all predictor into the final mask of step 9, predicts wave significant wave height inferior when 2001-2010 is each, and the wave significant wave height value doping is reverted to the value before Box-Cox conversion, saves as lattice point schema file;
Step 13, adopts the evaluation index evaluation prediction levels such as RMSE;
RMSE (root-mean-squareerror) is root-mean-square error, also claims standard error, and it is defined as i=1,2,3 ... n.In definite measured number of times, RMSE represents with following formula: in formula, n is for measuring number of times; Di is the deviation of one group of measured value and mean value.
Step 14, calculates wave significant wave height secular trend, and the wave significant wave height doping taking step (12), as foundation, calculates by trend computing formula, finally obtains the secular 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 secular trend figure.
Fig. 2 is the secular trend result schematic diagram of certain marine site maximum wave significant wave height in winter of 1981-2010 China of drawing of a kind of secular trend Forecasting Methodology of wave significant wave height based on analyzing again data that application the present invention proposes, wherein horizontal ordinate is cm/yr (centimetre/year), and the maximum amplification of certain marine site maximum wave significant wave height in winter of visible China is annual 2 centimetres.Fig. 2 is to effectively instructing the wave protected working of maritime province, for safeguard coastal area safety and stability, alleviate wave disaster and play most important and indispensable effect, have very strong workable.
In the specific embodiment of the present invention, all explanations not relating to belong to the known technology of this area, can be implemented with reference to known technology.
The present invention, through validation trial, can play good directive function to the prediction of the secular trend of wave significant wave height and prevention wave disaster.
Above embodiment and embodiment are a kind of concrete supports of short-term trend Forecasting Methodology technological thought of wave significant wave height based on analyzing again data that the present invention is proposed; can not limit protection scope of the present invention with this; every technological thought proposing according to the present invention; the change of any equivalent variations of doing on the technical program basis or equivalence, all still belongs to the scope that technical solution of the present invention is protected.

Claims (3)

1. the secular trend Forecasting Methodology of wave significant wave height based on analyzing again data, is characterized in that comprising following concrete steps:
Step 1, collect the ERA-Interim reanalysis datasets of the pre-measured center of European Study of Meso Scale Weather based on lattice point pattern 20～30 year sections each time time weather forecast data, when wherein each, time weather forecast data refer to and comprise 4～8 hours sea-level pressure SLP and wave significant wave height Hs once;
Step 2, obtain the collected coordinate of time weather forecast data institute style point when each, taking this coordinate as foundation, extract and the described corresponding sea-level pressure matrix S of the coordinate of inferior weather forecast data institute style point when each, as shown in (1) formula, wave significant wave height matrix H, as shown in (2) formula, comprising m spatial point, each spatial point contains observation data n time:
Step 3, calculate the average M of ERA-Interim based on the lattice point pattern sea-level pressure SLP when each time, then deduct average M with original value S, the anomaly value P of inferior SLP while obtaining based on lattice point pattern each, and calculate the standard deviation S of SLP anomaly value P, as shown in (3) formula:
In above-mentioned (3) formula: m i = Σ j - 1 n S ij n ;
Step 4, is EOF to SLP anomaly value P and analyzes, and obtains heterogeneity and the contribution rate of each composition to population variance, retains front 30 EOF and major component; Wherein:
P is carried out to covariance calculating, obtain real symmetric matrix L m × mwherein:
L m × m = 1 n PP T , The transposition of T representing matrix;
Then ask covariance matrix L m × mproper vector V and eigenwert Λ, as shown in (4) formula, to meet LV=Λ V, wherein
Matrix V is orthogonal matrix, and the j column element of matrix V is exactly eigenvalue λ jcharacteristic of correspondence vector;
According to real symmetric matrix L m × mproper vector V and eigenwert -i, calculates the variance contribution ratio of each proper vector and the accumulative total variance contribution ratio of front several proper vectors; According to eigenwert order from big to small, L is sorted, that make number one is EOF 1, by that analogy;
Step 5, to collect according to step 1 based on lattice point each time time original sea-level pressure SLP and wave significant wave height Hs carry out Box-Cox conversion, obtain sea-level pressure trGt and wave significant wave height trHt after conversion;
Step 6, to trHt corresponding on each lattice point, uses PC k, tand PC k, t-4calculate its related coefficient, and get 28 PCs of related coefficient when the highest k,tor PC k, t-4as the predictor of wave significant wave height;
Step 7, the standard deviation S of calculating wave significant wave height hlwith 30 predictor X k,tstandard deviation S xk, save backup;
Step 8, brings predictor into forecast model, with predicting the outcome of i model of F statistic comparison and i+1 model, thereby selects optimum predictor;
Step 9, also brings the wave significant wave height of a step of lagging behind into model, as one of predictor, and the wave significant wave height of each lattice point for the moment time under integrated forecasting, Optimized model parameter, obtains final mask; Wherein model is as shown in (5) formula:
H t = a + Σ k - 1 K b k X k , t + Σ p - 1 P c p H t - p + u t - - - ( 5 ) ,
H in above-mentioned (5) formula tthe wave significant wave height through conversion on each net point, H t-pbe the wave significant wave height of hysteresis p, P is the lag coefficient of following the parameter that predictand is relevant, X k, tk the predictor based on SLP, u tcan represent with M rank autoregressive model, if M=0, u tit is exactly white noise;
Step 10, predict the SLP field on the basis of front 30 EOF that obtain in step 4 when each time, obtains PC k,t;
Step 11, the S saving backup by step 7 xkweigh and select 30 predictor X k,t;
Step 12, brings all predictor into the final mask of step 9, and the wave significant wave height when each in target of prediction period time, reverts to the value before Box-Cox conversion by the significant wave height value doping, and saves as lattice point schema file;
Step 13, adopts the evaluation index evaluation prediction levels such as RMSE;
Step 14, the wave significant wave height of predicting taking step 12, as foundation, calculates the secular trend of wave significant wave height by trend computing formula, finally obtain the secular 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 secular trend figure.
2. a kind of secular trend Forecasting Methodology of wave significant wave height based on analyzing again data according to claim 1, is characterized in that when each described in step 1 that time weather forecast data refer to and comprises 6 hours sea-level pressure SLP and wave significant wave height Hs once.
3. a kind of secular trend Forecasting Methodology of wave significant wave height based on analyzing again data according to claim 1 and 2, is characterized in that the evaluation index such as RMSE refers to root-mean-square error described in step 13, also claims standard error, and it is defined as i=1,2,3 ... n; In definite measured number of times, RMSE represents with following formula: in formula, n is for measuring number of times; d iit is the deviation of one group of measured value and mean value.
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Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104794304A (en) * 2015-05-06 2015-07-22 国家海洋信息中心 Fast calculation method for engineering sea area sea-level rise
CN104977583A (en) * 2015-07-08 2015-10-14 中国船舶重工集团公司第七一九研究所 Method for X-band radar wave retrieval based on empirical orthogonal decomposition
CN105158823A (en) * 2015-08-15 2015-12-16 大连天岛海洋科技有限公司 Hanging waveguide monitoring-forecasting system and method
CN106156874A (en) * 2015-03-31 2016-11-23 清华大学 Tsunami Forecasting Methodology and device, tsunami early warning method and device
CN106707367A (en) * 2015-11-13 2017-05-24 国家海洋局秦皇岛海洋环境监测中心站 Coastal tourism resort hydrometeorological monitoring method and system
CN106777949A (en) * 2016-12-08 2017-05-31 河海大学 A kind of long-term trend Forecasting Methodology of the wave wave direction based on analyze data again
CN106990402A (en) * 2017-03-30 2017-07-28 南京信息工程大学 A kind of navigation X-band radar wave group detection method based on Wave Theory
CN108549961A (en) * 2018-05-02 2018-09-18 河海大学 A method of wave significant wave height is estimated based on CMIP5
CN110399634A (en) * 2019-06-10 2019-11-01 中国电力科学研究院有限公司 It is a kind of based on weather system influence forecast area determine method and system
CN110717631A (en) * 2019-10-10 2020-01-21 国家海洋局北海预报中心 Sea wave prediction cyclic regression time-by-time correction method and device
CN110851790A (en) * 2019-10-29 2020-02-28 王金虎 Ocean current dynamic optimization forecasting model based on deep learning algorithm
CN115169439A (en) * 2022-06-16 2022-10-11 中国人民解放军国防科技大学 Method and system for predicting effective wave height based on sequence-to-sequence network
CN117407660A (en) * 2023-12-13 2024-01-16 中国海洋大学三亚海洋研究院 Regional sea wave forecasting method based on deep learning

Families Citing this family (1)

* Cited by examiner, † Cited by third party
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

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007007728A1 (en) * 2005-07-08 2007-01-18 Port And Airport Research Institute Tunami information providing method and tunami information providing system
CN102063564A (en) * 2010-12-20 2011-05-18 中国海洋大学 New method for calculating typhoon-influenced sea area designed wave height based on maximum entropy principle
CN102799770A (en) * 2012-06-29 2012-11-28 哈尔滨工程大学 Method for modeling sea wave significant wave height inversion model based on particle swarm optimization (PSO) self-adaptive piecewise linear fitting

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007007728A1 (en) * 2005-07-08 2007-01-18 Port And Airport Research Institute Tunami information providing method and tunami information providing system
CN102063564A (en) * 2010-12-20 2011-05-18 中国海洋大学 New method for calculating typhoon-influenced sea area designed wave height based on maximum entropy principle
CN102799770A (en) * 2012-06-29 2012-11-28 哈尔滨工程大学 Method for modeling sea wave significant wave height inversion model based on particle swarm optimization (PSO) self-adaptive piecewise linear fitting

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
G.FERRETTI,ET AL: "On Microseisms recorded near the Ligurian coast(Italy) and their relationship with sea wave height", 《GERPYSICAL JOURNAL INTERNATIONAL》 *
X.L.WANG ET AL.: "Historical and possible future changes of wave heights in northern hemisphere oceans", 《ATMOSPHERE OCEAN INTERACTIONS》 *
XIAOLAN L.WANG ET AL: "North Atlantic wave height trends as reconstructed from the 20th century reanalysis", 《GEOPHYSICAL RESEARCH LETTERS》 *
李杰: "风浪要素数值模拟", 《中国优秀博硕士学位论文全文数据库 (硕士) 工程科技Ⅱ辑》 *
赵喜喜: "中国海散射计风、浪算法研究及海面风场、有效波高的时空特征分析", 《中国优秀博硕士学位论文全文数据库 (博士) 基础科技辑》 *

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CN108549961B (en) * 2018-05-02 2021-10-15 河海大学 Method for predicting sea wave effective wave height based on CMIP5
CN110399634B (en) * 2019-06-10 2021-08-27 中国电力科学研究院有限公司 Forecast area determination method and system based on weather system influence
CN110399634A (en) * 2019-06-10 2019-11-01 中国电力科学研究院有限公司 It is a kind of based on weather system influence forecast area determine method and system
CN110717631B (en) * 2019-10-10 2022-02-08 国家海洋局北海预报中心 Sea wave prediction cyclic regression time-by-time correction method and device
CN110717631A (en) * 2019-10-10 2020-01-21 国家海洋局北海预报中心 Sea wave prediction cyclic regression time-by-time correction method and device
CN110851790A (en) * 2019-10-29 2020-02-28 王金虎 Ocean current dynamic optimization forecasting model based on deep learning algorithm
CN110851790B (en) * 2019-10-29 2023-09-22 王金虎 Ocean current force optimization forecasting method based on deep learning algorithm
CN115169439A (en) * 2022-06-16 2022-10-11 中国人民解放军国防科技大学 Method and system for predicting effective wave height based on sequence-to-sequence network
CN115169439B (en) * 2022-06-16 2023-07-07 中国人民解放军国防科技大学 Effective wave height prediction method and system based on sequence-to-sequence network
CN117407660A (en) * 2023-12-13 2024-01-16 中国海洋大学三亚海洋研究院 Regional sea wave forecasting method based on deep learning

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