CN108319772A - A kind of analysis method again of wave long term data - Google Patents
A kind of analysis method again of wave long term data Download PDFInfo
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Abstract
The present invention relates to a kind of analysis methods again of wave Long-Term Simulations data, include the following steps：Count off evidence after element of wave (significant wave height and average period) the long-term observation data of structure based on satellite altimeter and the element of wave Long-Term Simulations based on wave numerical model, and time-space relation is carried out to the two；Analysis correction is carried out to wave hindcasting data using space-time synchronous data, obtain element of wave analyzes data again.The present invention proposes a kind of novel analysis method again applied to count off evidence after element of wave Long-Term Simulations, compare common data assimilation method, the inventive method treatment effeciency is high, integrated observation data are flexible, it is particularly suitable for reporting element of wave afterwards for a long time the processing of analogue data, can be analyzed for the climatic variability of element of wave, Annual variations and ocean engineering Environmental Studies and strong support is provided.
Description
Technical field
The present invention relates to a kind of data analysis methods again, specifically, being a kind of analyzing again for wave Long-Term Simulations data
Method.
Background technology
Climate change has a major impact ocean dynamical environment and the generation of human lives' production safety, and Wave is ocean
It is (including significant wave height, flat to obtain high quality, high-resolution, continuous element of wave for one of major impetus process of dynamic environment
The equal period) long history data are to studying wave Secular Variation Tendency, Annual variations rule and for marine engineering design
Element of wave extreme SWH estimation is most important.It includes ocean weather station observation, satellite altitude to obtain the technological means of wave long term data at present
Meter observation and wave numerical model.Ocean weather station observation is based primarily upon buoy observation, and spatial distribution is extremely limited, does not have spatially
It is representative；Satellite altimeter observation method has had accumulated the global wave factor data more than 30 years (1985 so far), but
Since it uses the substar of SSO (Sun Synchronous Orbit) pattern to observe, data spatial and temporal resolution is very low, and in time for a certain spatial position
Discontinuously.Wave numerical model can obtain high-resolution, the element of wave Long-Term Simulations data of space and time continuous, but its data
Quality is affected by pattern physical mechanism reliability, wind field and landform field precision.
Analysis method is to utilize Objective Analysis Method again, and analysis correction is carried out using observation data logarithm analogue data；
Wherein, data assimilation method is the most commonly used, it is on the basis for considering data spatial and temporal distributions and observation field and background field error
On, the method for the new observation data of fusion in the dynamic running process of numerical model.Data assimilation is typically used in wave and wants
In element forecast, it can provide best initialization condition and mode parameter for the forecast of subsequent period.But it handles data and calculates
It is of high cost, it is unfavorable for obtaining the long-term rear count off evidence of element of wave.What is published at present has European weather forecast center
Element of wave analyzes data again, it is based primarily upon wave numerical model simulated waves element, and passes through four-dimensional variational Assimilation method
Logarithm mode data is counted with satellite altitude to be corrected, and has assimilated 12 satellite altimeters emitted since 1991
In 6 altimeter data products.Existing research report shows that the wave analyzes data and can describe element of wave well again
Climatic variability, but its wave significant wave height provided is integrally relatively low, especially high wind speed situation, this may be defended with what is used
It is related that elevation counts limited or assimilation method validity, and due to the technology restriction of assimilation method, determine that it can not be fast
The other altimeter observation data of use of speed are updated optimization.
A kind of for wave Long-Term Simulations data therefore, it is necessary to develop, analyzing processing is more efficient, to observing data set
At more flexible technical method, the sides such as climatic variability, Annual variations and ocean engineering environment to meet element of wave influence
The demand data in face.
Invention content
For prior art deficiency, the object of the present invention is to provide a kind of analysis methods again of wave Long-Term Simulations data.
The Simulation prediction that data assimilation method is mainly used for element of wave is compared, this method is mainly used in the Long-Term Simulations of element of wave
Count off evidence afterwards has analyzing processing efficient, integrates the flexible advantage of observation data.
For achieving the above object, the present invention is achieved using following technical proposals：A kind of wave Long-Term Simulations number
According to analysis method again, include the following steps：
1) element of wave observation data of the structure based on satellite altimeter and the element of wave mould based on wave numerical model
Quasi- data, and time-space relation is carried out to the two, obtain the space-time synchronous data of element of wave；
2) analysis correction is carried out to element of wave analogue data using space-time synchronous data, obtains analyzing again for element of wave
Data.
The step 1) includes the following steps：
Obtain the element of wave analogue data based on wave numerical model；
Each satellite altimeter element of wave observation data are corrected and gridding；
To element of wave analogue data and satellite altimeter element of wave observation data carry out time-space relation with it is synchronous, obtain
Space-time synchronous data.
It is described that each satellite altimeter element of wave observation data are corrected and gridding includes the following steps：
The element of wave observation data for choosing single satellite altimeter, the wave of a certain any one buoy of moment
Element observation data, are less than setting length with space interval and time interval is seen less than the altimeter element of wave of setting time
The average value of measured data is as space-time synchronous data；
Linear fit is carried out to the space-time synchronous data of single satellite；Different satellite altimeters obtains different Linear Quasis
Fitting between the element of wave observation data of conjunction relationship, i.e. buoy observation and the element of wave observation data of altimeter observation is closed
System；
Calibration one by one, the altimeter element of wave data after being corrected are carried out to multiple altimeters using fit correlation；
The analogue data of corresponding gridding, altimeter data is divided into different grids：To any one altimeter
Observation position, choose with the analogue data mesh point of its space length minimum, be divided under the mesh point；To same net
Lattice point, the altimeter element of wave observation data that time span is no more than setting value carry out algebraic mean, obtain inscribing height when this
The gridded data of degree meter element of wave.
It is described to element of wave analogue data and satellite altimeter element of wave observation data carry out time-space relation with it is synchronous,
Space-time synchronous data are obtained to include the following steps：
According to the gridded data of the analogue data of gridding and altimeter element of wave, time-space relation is carried out to the two：
The grid data of the altimeter element of wave at corresponding a certain moment, chooses data point closest therewith in analogue data, most
The element of wave space-time synchronous data group of the two is obtained eventually；Each altimeter grid data point obtains the simulation of corresponding synchronization
Data point；All data combine to form space-time synchronous data.
The step 2) includes the following steps：
Calibration model is built by statistical analysis technique based on space-time synchronous data：In space-time synchronous data, for single
Synchrodata subset B under year, a certain mesh pointjk, the j expressions of years, k is grid label, calculate element of wave analogue data and
Altimeter observes the related coefficient of data, only when the related coefficient of the two is more than threshold value, it is believed that the analogue data of element of wave
For subsequent correction, otherwise, it is not used in correction, is labeled as bad point data；It will be fitted to obtain B for the analogue data of correctionjkIn
Altimeter observes the correction relationship between element of wave and element of wave analogue data；
Analogue data is corrected according to correction relationship, wave is obtained and analyzes data again.
The invention has the advantages that and advantage：
1. the method for the present invention is applied to count off evidence after the Long-Term Simulations of element of wave, method is simple, quick, effective, can be
The researchs such as climatic variability, Annual variations and the influence of ocean engineering environment of element of wave provide necessary data and support.
2. the integrated observation data of the method for the present invention are flexible, convenient for be continuously improved element of wave analyze again data reliability and
Precision.
Description of the drawings
Fig. 1 is the implementing procedure figure of the analysis method again of count off evidence after the present invention is applied to wave for a long time；
Fig. 2 a are based on count off evidence after the forced wave significant wave height simulation of multi-platform fusion wind field (CCMP)；
Fig. 2 b are based on count off evidence after the forced wave significant wave height simulation in climatic prediction center wind field (CFSR)；
Fig. 2 c are to analyze data again based on what is reported after being simulated to CCMP wave significant wave heights；
Fig. 2 d are to analyze data again based on what is reported after being simulated to CFSR wave significant wave heights；
Fig. 2 e are that the wave significant wave height based on data assimilation of European forecasting centre's publication analyzes data again.
Specific implementation mode
Technical scheme of the present invention is described in further detail with reference to the accompanying drawings and detailed description.
A kind of analysis method again of wave Long-Term Simulations data includes the element of wave (significant wave based on satellite altimeter
High and average period) long-term observation data building process, count off evidence after the element of wave Long-Term Simulations based on wave numerical model
Building process, by time-space relation method obtain both element of wave space-time synchronous data procedures, using element of wave when
Calibration model process is analyzed and built to empty synchrodata, is corrected to count off evidence after the simulation of element of wave using calibration model
Optimization, obtain element of wave analyzes data procedures again.It is proposed the present invention be directed to element of wave Long-Term Simulations data, point
It is high to analyse treatment effeciency, integrates that element of wave observation data are flexible, be particularly suitable for the climatic variability of element of wave, Annual variations and
Research in terms of ocean engineering environment.
As shown in Figure 1, a kind of analysis method again of element of wave long term data, includes the following steps：
Build the element of wave long-term observation data based on satellite altimeter and the element of wave based on wave numerical model
Count off evidence after Long-Term Simulations, and time-space relation is carried out to the two, obtain the space-time synchronous data of element of wave；
Analyzing again for element of wave is obtained according to analysis correction is carried out to the rear count off of element of wave using space-time synchronous data
Data.
The element of wave long-term observation data of the structure based on satellite altimeter and the wave based on wave numerical model
Element for a long time after count off evidence, and time-space relation is carried out to the two, the space-time synchronous data for obtaining element of wave include the following steps：
It is reported after element of wave long term data simulation based on wave numerical model；
More satellite altimeter element of wave observation Data corrections and gridding；
The time-space relation and synchrodata of count off evidence and satellite altimeter observation data obtain after the numerical simulation of element of wave
It takes.
It is described using space-time synchronous data to the rear count off of element of wave according to analysis correction is carried out, obtain element of wave again
Data are analyzed, are included the following steps：
Space-time synchronous data based on element of wave build calibration model by analyzing approximating method；
Using calibration model to the rear count off of element of wave according to being corrected, obtain element of wave analyzes data again.
The present embodiment is as follows：
1. reporting simulation after the Long-Term Simulations of the element of wave (significant wave height and average period) based on wave numerical model.Wave
Pattern more mature at present, such as Wavewatch III patterns, SWAN patterns, WAM patterns etc. can be selected in unrestrained numerical model.
It is to publish, and also have preferably in operational use that the present embodiment, which selects Wavewatch III wave patterns, the pattern,
Performance.It is driven using wind field, the spatial and temporal resolution of wind field is more demanding, and the present embodiment selects the high-resolution wind published
：Multi-platform fusion wind field (CCMP) and U.S. climates forecasting centre wind field (CFSR), the temporal resolution of both wind fields is all
It it is 6 hours, spatial resolution is 0.25 °.This example selects both wind fields to respectively drive wave pattern, report simulation knot after acquisition
Fruit.Model topography ambient field selects the marine topography data published at U.S. geophysics center.The present embodiment is into traveling wave
The rear report of unrestrained element is simulated, and simulated time is on December 31,1 day to 2010 January in 1992, obtains 19 years elements of wave altogether
Count off evidence afterwards.Simulation output time interval is 1 hour, and spatial resolution is 1.5 °.
2. the element of wave of satellite altimeter more than observes Data correction and gridding.
(1) satellite altimeter element of wave (significant wave height and average period) the observation number published emitted is collected
According to satellite source includes the HY-2 satellites of China's satellite hub, ERS -1, ERS -2 and the Envisat satellite of European Space Agency, US Air
Between center TOPEX and Jason-1, Jason-2 satellites etc..Altimeter uses SSO (Sun Synchronous Orbit) observation mode, is wanted to the wave of substar
Element is observed, and the observation interval of two neighboring substar is about 1 second, and space interval is about 6.8 kilometers.
(2) although the element of wave inversion accuracy of satellite altimeter is higher, due to different in setting in instrument, data are seen
Certain system deviation can be had by surveying precision, this is very big for the climatic variability influence for studying element of wave, need to be wanted in conjunction with wave
The jellyfish observation data of element are corrected, and long-term buoy observation data existing at present are mainly derived from American National buoy
The buoy that center publishes observes data, and the selection standard of buoy is to choose offshore distance to be more than 50km, and meet condition has
44 buoy erect-positions.Using general bearing calibration, calibration one by one is carried out to multiple satellites.Time-space relation is carried out first：It chooses
The element of wave of single satellite altimeter observes data along rail, and carrying out space-time with the element of wave observation data of jellyfish matches
Standard, time-space relation window are selected as 50km and 30 minute, and time-space relation method is：For the wave of a certain any one buoy of moment
Element observes data, is less than 50km with space interval, and altimeter element of wave of the time interval less than 30 minutes observes number
According to average value as space-time synchronous data, the synchrodata at multiple moment can be summarized as synchrodata collection in this way.Here
In order to distinguish hereinafter, synchrodata collection is referred to as Ai, wherein subscript i indicates different satellite altimeter numbers.
(3) the space-time synchronous data set A based on single satellitei, linear fit is carried out by least square method：Y=gx+h,
Wherein y be buoy observation wave significant wave height or data average period, x be respective heights meter observation wave significant wave height or
Average period data, g and h are fitting coefficients undetermined.Satellite altimeters different in this way can obtain different linear fits and close
System, and wave significant wave height and the fit correlation of average period are also different.Multiple altimeters can be carried out using fit correlation
Calibration one by one：The wave significant wave height or data average period observed along rail with altimeter substitute into x, and obtained y is after correcting
Altimeter element of wave data.
(4) more satellite altimeter elements of wave observation data after correction are subjected to gridding processing：Corresponding wave numerical value
Altimeter data is divided into different grids by the gridding information of simulation, and method is the observation bit to any one altimeter
It sets, chooses the numerical model mesh point with its space length minimum, be divided under the mesh point.To same mesh point, when
Between span no more than 1 hour altimeter element of wave along rail observation data carry out algebraic mean, obtain inscribing altimeter when this
The gridded data of element of wave, it should be noted that data are observed for the altimeter element of wave of same mesh point,
It is discontinuous on time.
3. the time-space relation and synchrodata of count off evidence and satellite altimeter observation data after the numerical simulation of element of wave
It obtains.
The element of wave gridded data based on numerical model obtained according to step 1, and it is corrected based on step 2
Satellite altimeter gridded data carries out time-space relation to the two：As described in step 2, the mesh space information of the two is identical, because
Here need to only be registrated temporal information, and the temporal information of wherein altimeter grid data is discontinuous, and numerical hindcasting data
Temporal information be continuous (1 hour be spaced), therefore, the altimeter grid data at corresponding a certain moment chooses numerical hindcasting
Data point closest therewith in data finally obtains the element of wave space-time synchronous data group of the two；Each altimeter
Grid data point can all obtain the numerical simulation data point of corresponding synchronization, and all data combine to form data set, here should
Synchrodata collection is known as B.
4. based on the space-time synchronous data set of registration element of wave, calibration model is built by analyzing approximating method；
The synchrodata collection B obtained according to step 3, is analyzed and builds calibration model.In climate change and engineering ring
In the research of border, it is primarily upon the change in time and space process of annual or year extreme value, therefore, it is necessary to carry out year by year, by the analysis of grid
Correction.It tests analysis firstly the need of the reliability to pattern：In synchrodata collection B, under single year, a certain mesh point
Synchrodata subset Bjk(the subscript j expressions of years, k are grid labels) calculates significant wave height or the numerical simulation of average period
The related coefficient of data and altimeter observation data, only when the related coefficient of the two is more than 0.8, it is believed that the numerical value of element of wave
Analogue data is reliable, can be used for subsequent correction, otherwise, it is impossible to for correcting, be labeled as bad point data；Pass through correlation test
Numerical simulation data, fitting obtain BjkThe significant wave height or the model between average period of middle altimeter observation and simulation
Relationship：Due to not knowing the error character of mode data, a variety of curve-fitting methods, including fitting of a polynomial, index will be used
Fitting, logistic fit etc., that chooses error of fitting minimum is used as calibration model, here by taking linear fit as an example：y1=px1+ q,
Middle y1It is significant wave height or the average period of altimeter observation, x1It it is significant wave height or the average period of numerical simulation, p and q are to wait for
Cover half intends coefficient, can be obtained by least square method.
5. being optimized to element of wave numerical simulation data using calibration model, obtains wave and analyze data again.
The calibration model obtained by step 4 to wave hindcasting result correct year by year, by grid, it is assumed here that most
Excellent calibration model is linear relationship, the i.e. example of step 4：y1'=px1+ q, the wave significant wave height that step 1 is obtained or average
Period analogue data substitutes into x1, obtained y1' it is that wave analyzes data again.
The method of the present invention will be applied according to analysis correction again is carried out, it to be examined to calculate count off after wave Long-Term Simulations below
The actual effect of wave significant wave height climatic variability.Climatic variability is defined as the variation tendency of annual significant wave height.
Fig. 2 a- Fig. 2 e are the CHINESE OFFSHORE wave annual significant waves that different wave significant wave height long term datas are calculated
The representative of high climatic variability, wherein "+" passes through significance test.Fig. 2 a are based on the forced wave of multi-platform fusion wind field (CCMP)
Count off evidence after unrestrained significant wave height simulation；Fig. 2 b are based on the forced wave significant wave height simulation in climatic prediction center wind field (CFSR)
Count off evidence afterwards；Fig. 2 c are to analyze data again based on what is reported after being simulated to CCMP wave significant wave heights；Fig. 2 d are based on to CFSR waves
That is reported after unrestrained significant wave height simulation analyzes data again；Fig. 2 e are that the wave based on data assimilation of European forecasting centre's publication is effective
Wave height analyzes data again.
As shown in Fig. 2 a- Fig. 2 e, comparison diagram 2a and Fig. 2 b can see, count off after the wave simulation under different wind field drivings
According to significant wave height climatic variability there are apparent difference, the analysis method again proposed using this example carries out school respectively to them
After just, the result that comparison diagram 2c and Fig. 2 d can see them is very close, and they with European forecasting centre based on number
The result of calculation for analyzing data again according to the wave of assimilation method also has good consistency, long to wave to confirm the present invention
The validity of the analysis method again of phase analogue data.
In terms of computational efficiency, the single machine monokaryon CPU calculating treatmenting times of the step 1 of this paper are about 10-15 days, Qi Tabu
Rapid processing time is about 1-2 days, and data assimilation method then needs the time of several months that could complete analytic process again, it can be seen that
The method of the present invention greatly improves data analysis efficiency again, is particularly suitable for the research required wave of climatic effect and goes through for a long time
History data are built.
In data using upper, this example can flexibly use altimeter to observe data, such as：Pass through the step 1-6 of this example
After being analyzed data again, if new altimeter element of wave observation data can be obtained, can directly on the basis of step 1,
Only repeat step 2-5, you can the analytic process again for completing element of wave analogue data fast and effectively generates new wave again
Data are analyzed, the reliability of data is improved.
Claims (5)
1. a kind of analysis method again of wave Long-Term Simulations data, it is characterised in that include the following steps：
1) element of wave observation data of the structure based on satellite altimeter and the element of wave based on wave numerical model simulate number
According to, and time-space relation is carried out to the two, obtain the space-time synchronous data of element of wave；
2) analysis correction is carried out to element of wave analogue data using space-time synchronous data, obtain element of wave analyzes number again
According to.
2. a kind of analysis method again of wave Long-Term Simulations data according to claim 1, it is characterised in that the step
1) include the following steps：
Obtain the element of wave analogue data based on wave numerical model；
Each satellite altimeter element of wave observation data are corrected and gridding；
To element of wave analogue data and satellite altimeter element of wave observation data carry out time-space relation with it is synchronous, obtain space-time
Synchrodata.
3. a kind of analysis method again of wave Long-Term Simulations data according to claim 2, it is characterised in that described to each
Satellite altimeter element of wave observation data are corrected to be included the following steps with gridding：
The element of wave observation data for choosing single satellite altimeter, see the element of wave of a certain any one buoy of moment
Measured data is less than setting length with space interval and time interval is less than the altimeter element of wave observation number of setting time
According to average value as space-time synchronous data；
Linear fit is carried out to the space-time synchronous data of single satellite；Different satellite altimeters obtains different linear fits and closes
Fit correlation between the element of wave observation data of system, i.e. buoy observation and the element of wave observation data of altimeter observation；
Calibration one by one, the altimeter element of wave data after being corrected are carried out to multiple altimeters using fit correlation；
The analogue data of corresponding gridding, altimeter data is divided into different grids：Sight to any one altimeter
Location is set, and is chosen the analogue data mesh point with its space length minimum, is divided under the mesh point；To same grid
Point, the altimeter element of wave observation data that time span is no more than setting value carry out algebraic mean, obtain inscribing height when this
Count the gridded data of element of wave.
4. a kind of analysis method again of wave Long-Term Simulations data according to claim 2, it is characterised in that described to wave
Unrestrained element simulations data and satellite altimeter element of wave observation data carry out time-space relation with it is synchronous, obtain space-time synchronous data
Include the following steps：
According to the gridded data of the analogue data of gridding and altimeter element of wave, time-space relation is carried out to the two：It is corresponding
The grid data of the altimeter element of wave at a certain moment chooses data point closest therewith in analogue data, final to obtain
To the element of wave space-time synchronous data group of the two；Each altimeter grid data point obtains the analogue data of corresponding synchronization
Point；All data combine to form space-time synchronous data.
5. a kind of analysis method again of wave Long-Term Simulations data according to claim 1, it is characterised in that the step
2) include the following steps：
Calibration model is built by statistical analysis technique based on space-time synchronous data：In space-time synchronous data, for single year, certain
Synchrodata subset B under one mesh pointjk, the j expressions of years, k is grid label, calculates element of wave analogue data and altimeter
The related coefficient of data is observed, only when the related coefficient of the two is more than threshold value, it is believed that after the analogue data of element of wave is used for
Continuous correction, otherwise, is not used in correction, is labeled as bad point data；It will be fitted to obtain B for the analogue data of correctionjkMiddle altimeter
Observe the correction relationship between element of wave and element of wave analogue data；
Analogue data is corrected according to correction relationship, wave is obtained and analyzes data again.
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| CN108920877A (en) * | 2018-08-02 | 2018-11-30 | 中交第四航务工程勘察设计院有限公司 | A kind of global wave method for numerical simulation based on MIKE21-SW model |
| CN110222872A (en) * | 2019-05-12 | 2019-09-10 | 天津大学 | Long-term statistical prediction methods in the more elements in ocean based on empirical orthogonal function decomposition |
| CN111695250A (en) * | 2020-06-04 | 2020-09-22 | 哈尔滨工程大学 | Method for extracting internal tide features |
| CN112182759A (en) * | 2020-09-27 | 2021-01-05 | 中交第四航务工程勘察设计院有限公司 | Method for testing wave numerical simulation result based on satellite altimeter data |
| CN113128758A (en) * | 2021-04-16 | 2021-07-16 | 北京玖天气象科技有限公司 | Maximum wave height forecasting system constructed based on offshore buoy sea wave observation data |
| CN116976165A (en) * | 2023-07-10 | 2023-10-31 | 扬州大学 | Wave energy flow resource measuring and calculating method and system based on space-time combination |
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| CN110222872B (en) * | 2019-05-12 | 2023-04-18 | 天津大学 | Ocean multi-factor medium and long term statistical prediction method based on empirical orthogonal function decomposition |
| CN111695250A (en) * | 2020-06-04 | 2020-09-22 | 哈尔滨工程大学 | Method for extracting internal tide features |
| CN112182759A (en) * | 2020-09-27 | 2021-01-05 | 中交第四航务工程勘察设计院有限公司 | Method for testing wave numerical simulation result based on satellite altimeter data |
| CN113128758A (en) * | 2021-04-16 | 2021-07-16 | 北京玖天气象科技有限公司 | Maximum wave height forecasting system constructed based on offshore buoy sea wave observation data |
| CN113128758B (en) * | 2021-04-16 | 2023-10-24 | 北京玖天气象科技有限公司 | Maximum wave height forecasting system constructed based on offshore buoy wave observation data |
| CN116976165A (en) * | 2023-07-10 | 2023-10-31 | 扬州大学 | Wave energy flow resource measuring and calculating method and system based on space-time combination |
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