CN108319772B - Wave long-term data reanalysis method - Google Patents
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Abstract
The invention relates to a re-analysis method of wave long-term simulation data, which comprises the following steps: constructing wave element (effective wave height and average period) long-term observation data based on a satellite altimeter and wave element long-term simulation report data based on a wave numerical mode, and performing space-time registration on the wave element long-term observation data and the wave element long-term simulation report data; and analyzing and correcting the post-wave report data by utilizing the space-time synchronous data to obtain the re-analysis data of the wave elements. Compared with a common data assimilation method, the method is high in processing efficiency, flexible in integrated observation data, particularly suitable for processing simulation data of the wave elements after a long period, and capable of providing powerful support for research and analysis of climate variability, annual change and ocean engineering environment of the wave elements.
Description
Technical Field
The invention relates to a data reanalysis method, in particular to a reanalysis method of wave long-term simulation data.
Background
The climate change has important influence on the ocean power environment and the safety of human life and production, wherein waves are one of main power processes of the ocean power environment, and the acquisition of high-quality, high-resolution and continuous long-term historical data of wave elements (including effective wave height and average period) is important for researching the long-term change trend of the waves, the annual change rule and the extreme value calculation of the wave elements for ocean engineering design. The current technical means for acquiring the long-term wave data comprise fixed-point observation, satellite altimeter observation and wave numerical mode. The fixed-point observation is mainly based on buoy observation, the spatial distribution of the fixed-point observation is extremely limited, and the fixed-point observation is not representative in space; satellite altimeter observations have accumulated over 30 years (1985 to date) of global wave element data, but due to their sub-satellite point observations in polar orbit mode, the data have low spatial and temporal resolution and are temporally discontinuous for a certain spatial location. The wave numerical mode can obtain high-resolution and space-time continuous wave element long-term simulation data, but the data quality of the wave numerical mode is greatly influenced by the reliability of a mode physical mechanism and the precision of a wind field and a terrain field.
The re-analysis method is to analyze and correct the numerical simulation data by using an objective analysis method and observation data; among them, the data assimilation method is the most common method, which is a method for fusing new observation data in the dynamic operation process of a numerical model on the basis of considering data space-time distribution and errors of an observation field and a background field. Data assimilation is typically applied on wave element forecasts, which can provide optimal initial conditions and mode parameters for forecasting at the next time period. However, the calculation cost for processing data is high, and the long-term post-report data of the wave elements are not easy to acquire. Wave element reanalysis data of a European weather forecast center is published at present, which is mainly based on wave numerical mode simulation wave elements and is corrected by satellite altimeter data through a four-dimensional variation assimilation method, and 6 altimeter data products of 12 satellite altimeters launched since 1991 are assimilated. Research reports have shown that this wave re-analysis data can well describe the climate variability of wave elements, but it gives a wave effective wave height that is overall low, especially in high wind speed situations, which may be related to the limited use of satellite altimeter data or the effectiveness of the assimilation method, which due to its technical limitations determines that it cannot be updated quickly using other altimeter observations for optimization.
Therefore, a technical method which aims at the long-term wave simulation data, has higher analysis and processing efficiency and more flexible observation data integration is needed to be developed so as to meet the data requirements of the wave elements in the aspects of climate variability, annual change, ocean engineering environmental influence and the like.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a re-analysis method of wave long-term simulation data. Compared with a data assimilation method which is mainly used for the simulation forecasting of the wave elements, the method is mainly used for forecasting the data after the long-term simulation of the wave elements and has the advantages of high analysis and processing efficiency and flexible integrated observation data.
In order to realize the purpose of the invention, the invention is realized by adopting the following technical scheme: a method for re-analyzing wave long-term simulation data, comprising the steps of:
1) wave element observation data based on a satellite altimeter and wave element simulation data based on a wave numerical mode are constructed, and the wave element observation data and the wave element simulation data are subjected to space-time registration to obtain space-time synchronous data of the wave elements;
2) and analyzing and correcting the wave element simulation data by utilizing the space-time synchronization data to obtain the re-analysis data of the wave elements.
The step 1) comprises the following steps:
acquiring wave element simulation data based on a wave numerical mode;
correcting and gridding wave element observation data of each satellite altimeter;
and performing space-time registration and synchronization on the wave element simulation data and the satellite altimeter wave element observation data to obtain space-time synchronization data.
The step of correcting and gridding the wave element observation data of each satellite altimeter comprises the following steps:
selecting wave element observation data of a single satellite altimeter, and regarding the wave element observation data of any buoy at a certain moment, taking the average value of the wave element observation data of the altimeter, the space interval of which is less than a set length and the time interval of which is less than a set time, as space-time synchronous data;
performing linear fitting on the space-time synchronous data of a single satellite; different satellite altimeters obtain different linear fitting relations, namely the fitting relation between wave element observation data observed by the buoy and wave element observation data observed by the altimeter;
correcting the altimeters one by applying the fitting relation to obtain corrected wave element data of the altimeters;
and dividing altimeter data into different grids corresponding to the gridded simulation data: selecting a simulated data grid point with the minimum spatial distance from the observation position of any altimeter, and dividing the simulated data grid point under the grid point; and carrying out algebraic averaging on the observation data of the wave elements of the altimeters with the time span not exceeding the set value at the same grid point to obtain the gridding data of the wave elements of the altimeters at the moment.
The time-space registration and synchronization of the wave element simulation data and the satellite altimeter wave element observation data to obtain the time-space synchronization data comprises the following steps:
and according to the gridded simulation data and the gridded data of the wave element of the altimeter, performing space-time registration on the gridded simulation data and the gridded data of the wave element of the altimeter: selecting the data point which is closest to the altimeter wave element in the simulation data corresponding to the grid data of the altimeter wave element at a certain moment, and finally obtaining a wave element space-time synchronous data set of the two; each altimeter grid data point obtains a corresponding synchronous analog data point; all the data are combined to form the space-time synchronization data.
The step 2) comprises the following steps:
constructing a correction model based on the time-space synchronous data through a statistical analysis method: in the space-time synchronous data, a synchronous data subset B under a certain grid point for a single yearjkJ represents the year, k is a grid mark number, correlation coefficients of the wave element simulation data and altimeter observation data are calculated, and only when the correlation coefficients of the wave element simulation data and the altimeter observation data are larger than a threshold value, the simulation data of the wave element is considered to be used for subsequent correction, otherwise, the simulation data are not used for correction and are marked as dead point data; fitting the simulated data for correction to obtain BjkThe medium-high altitude meter observes a correction relation between the wave elements and the wave element simulation data;
and correcting the simulation data according to the correction relation to obtain wave reanalysis data.
The invention has the following beneficial effects and advantages:
1. the method is applied to reporting data after long-term simulation of the wave elements, is simple, quick and effective, and can provide necessary data support for researches on climate variability, annual change, ocean engineering environmental influence and the like of the wave elements.
2. The method of the invention integrates and observes data flexibly, and is convenient for continuously improving the reliability and the precision of wave element re-analysis data.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention applied to a method of re-analyzing long-term wave afterreport data;
FIG. 2a is a report data based on multi-platform fusion wind farm (CCMP) forced wave effective wave height simulation;
FIG. 2b is a report data based on a climate forecast central wind field (CFSR) forced wave effective wave height simulation;
FIG. 2c is a diagram based on reanalysis data reported after simulating the effective wave height of a CCMP wave;
FIG. 2d is a re-analysis data reported after a CFSR wave effective wave height simulation;
fig. 2e is wave effective wave height reanalysis data based on data assimilation issued by european forecasting center.
Detailed Description
The technical solution of the present invention will be described in further detail with reference to the accompanying drawings and the detailed description.
A re-analysis method of wave long-term simulation data comprises a wave element (effective wave height and average period) long-term observation data construction process based on a satellite altimeter, a wave element long-term simulation post-reporting data construction process based on a wave numerical mode, a wave element space-time synchronous data process of the wave element and the wave element space-time synchronous data process obtained by a space-time registration method, a correction model process of analyzing and constructing the space-time synchronous data of the wave element, and a re-analysis data process of the wave element by correcting and optimizing the post-simulation reporting data of the wave element by using a correction model. The invention is provided for the long-term simulation data of the wave elements, has high analysis and processing efficiency, integrates the wave element observation data flexibly, and is particularly suitable for the research on the climate variability, the annual change and the ocean engineering environment of the wave elements.
As shown in fig. 1, a method for re-analyzing long-term data of wave elements includes the following steps:
constructing wave element long-term observation data based on a satellite altimeter and wave element long-term simulation report data based on a wave numerical mode, and performing space-time registration on the wave element long-term observation data and the wave element long-term simulation report data to obtain space-time synchronous data of the wave element;
and analyzing and correcting the post-report data of the wave elements by utilizing the space-time synchronization data to obtain the re-analysis data of the wave elements.
The method comprises the following steps of constructing wave element long-term observation data based on a satellite altimeter and wave element long-term post-report data based on a wave numerical mode, and performing space-time registration on the wave element long-term observation data and the wave element long-term post-report data to obtain space-time synchronous data of the wave element:
reporting the wave element long-term data after simulation based on the wave numerical mode;
correcting and gridding wave element observation data of the multi-satellite altimeter;
and performing time-space registration and synchronous data acquisition on the reported data after the numerical simulation of the wave elements and the observation data of the satellite altimeter.
The method for analyzing and correcting the post-report data of the wave elements by utilizing the space-time synchronization data to obtain the re-analysis data of the wave elements comprises the following steps:
constructing a correction model by an analysis fitting method based on the time-space synchronous data of the wave elements;
and correcting the post-report data of the wave elements by using the correction model to obtain the re-analysis data of the wave elements.
The specific steps of the embodiment are as follows:
1. long-term simulation of wave elements (effective wave height and average period) based on wave numerical patterns. The wave number mode can be selected from the current mature mode, such as Wavewatch III mode, SWAN mode, WAM mode, etc. The wave III wave mode is selected in the embodiment, is published publicly, and has better performance in business applications. The wind field is used for driving, the space-time resolution of the wind field is high in requirement, and the high-resolution wind field published in the embodiment is selected: the multi-platform fusion wind field (CCMP) and the American climate forecast center wind field (CFSR) have the time resolution of 6 hours and the spatial resolution of 0.25 degrees. In the embodiment, the two wind fields are selected to respectively drive the wave modes, and the post-reporting simulation result is obtained. The mode terrain background field adopts publicly published water depth terrain data of the American geophysical center. In the embodiment, the wave element post-report simulation is carried out, the simulation time is 1 month and 1 day in 1992 to 12 months and 31 days in 2010, and the wave element post-report data of 19 years is obtained. The analog output time interval was 1 hour and the spatial resolution was 1.5 °.
2. And correcting and gridding wave element observation data of the multi-satellite altimeter.
(1) Collecting wave element (effective wave height and average period) observation data of a launched publicly released satellite altimeter, wherein satellite sources comprise HY-2 satellites in the satellite center of China, ERS-1, ERS-2 and Envisat satellites in the European space, TOPEX and Jason-1, Jason-2 satellites in the space center of America and the like. The altimeter adopts a polar orbit observation mode to observe wave elements of the subsatellite points, the observation time interval of two adjacent subsatellite points is about 1 second, and the space interval is about 6.8 kilometers.
(2) Although the inversion accuracy of the wave element of the satellite altimeter is high, due to different internal settings of the instrument, certain system deviation exists in the data observation accuracy, the influence on the climate variability of the research wave element is large, the correction needs to be carried out by combining the sea surface buoy observation data of the wave element, the existing long-term buoy observation data mainly come from the buoy observation data published by the American national buoy center, and the selection standard of the buoy is that the offshore distance is selected to be more than 50km, and 44 buoy station positions meet the conditions. And correcting the plurality of satellites one by adopting a universal correction method. First, spatio-temporal registration is performed: selecting wave elements of a single satellite altimeter along the observation data, and performing space-time registration with the wave element observation data of the sea surface buoy, wherein a space-time registration window is selected to be 50km and 30 minutes, and the space-time registration method comprises the following steps: the average value of the wave element observation data of the altimeter, which is separated from the space by less than 50km and separated from the space by less than 30 minutes, of the wave element observation data of any buoy at a certain moment is taken as space-time synchronous data, so that the synchronous data at a plurality of moments can be collected into a synchronous data set. For the purpose of distinguishing from the following, the synchronous data set is referred to as AiWhere the subscript i represents the different satellite altimeter numbers.
(3) Single satellite based spatio-temporal synchronization dataset AiLinear fitting was performed by the least squares method: y-gx + h, where y is the effective wave observed by the buoyWave height or average period data, x is wave effective wave height or average period data observed by a corresponding altimeter, and g and h are undetermined fitting coefficients. Therefore, different satellite altimeters can obtain different linear fitting relations, and the fitting relations between the wave effective wave height and the average period are different. The fitting relationship is applied to correct the altimeters one by one: and substituting the effective wave height or average period data of the waves observed along the altimeter into x to obtain y, namely the corrected wave element data of the altimeter.
(4) And gridding the corrected wave element observation data of the multi-satellite altimeter: and dividing the altimeter data into different grids corresponding to the grid information of the wave numerical simulation, wherein the method comprises the steps of selecting a numerical mode grid point with the minimum space distance from the observation position of any one altimeter, and dividing the numerical mode grid point into the grid points. For the same grid point, the altimeter wave elements with the time span not more than 1 hour are subjected to algebraic averaging along the observation data to obtain the gridding data of the altimeter wave elements at the moment, and it should be noted that the observation data of the altimeter wave elements at the same grid point are discontinuous in time.
3. And performing time-space registration and synchronous data acquisition on the reported data after the numerical simulation of the wave elements and the observation data of the satellite altimeter.
And (3) performing space-time registration on the wave element gridding data based on the numerical mode obtained in the step (1) and the satellite altimeter gridding data corrected in the step (2): as shown in step 2, the grid space information of the two is the same, so that only time information needs to be registered, wherein the time information of the altimeter grid data is discontinuous, and the time information of the numerical post-reporting data is continuous (1 hour interval), so that corresponding to the altimeter grid data at a certain moment, a data point which is closest to the numerical post-reporting data is selected, and finally, a wave element space-time synchronization data set of the two is obtained; each altimeter grid data point will result in a corresponding synchronized numerical analog data point, all of which are combined to form a data set, referred to herein as B.
4. Establishing a correction model by an analysis fitting method based on a time-space synchronous data set of the registration wave elements;
and (4) analyzing and constructing a correction model according to the synchronous data set B obtained in the step (3). In the study of climate change and engineering environment, the time-space change process of annual average or annual extreme value is mainly concerned, so that the analysis and correction are needed to be carried out year by year and grid by grid. Firstly, the reliability of the mode needs to be checked and analyzed: in the synchronized data set B, a synchronized data subset B at a certain grid point for a single yearjk(subscript j represents year, k is grid mark number), calculating the correlation coefficient of the numerical simulation data of the effective wave height or average period and the altimeter observation data, and only when the correlation coefficient of the numerical simulation data and the altimeter observation data is more than 0.8, considering that the numerical simulation data of the wave element is reliable and can be used for subsequent correction, otherwise, the numerical simulation data cannot be used for correction and is marked as dead point data; fitting to obtain B through numerical simulation data of correlation testjkModel relationship between the effective wave height or average period of medium-high altimeter observation and numerical simulation: because of the error characteristics of the uncertain mode data, a plurality of curve fitting methods including polynomial fitting, exponential fitting, logarithmic fitting and the like are adopted, and the fitting error is the minimum to be used as a correction model, wherein linear fitting is taken as an example: y is1＝px1+ q, wherein y1Is the effective wave height or average period, x, observed by the altimeter1The effective wave height or average period of numerical simulation, and p and q are undetermined simulation coefficients which can be obtained by a least square method.
5. And optimizing the wave element numerical simulation data by using the correction model to obtain wave reanalysis data.
And (4) carrying out year-by-year and grid-by-grid correction on the wave postreporting result through the correction model obtained in the step (4), wherein the optimal correction model is assumed to be in a linear relation, namely the example of the step (4): y is1’＝px1+ q, substituting the simulation data of the wave effective wave height or average period obtained in the step 1 into x1Obtained y1' this is the wave reanalysis data.
The method of the invention is applied to the reanalysis correction of the report data after the long-term simulation of the wave, and the actual effect of the method in the calculation of the climate variability of the effective wave height of the wave is tested. Climate variability is defined as the trend of change in the mean annual effective wave height.
FIGS. 2 a-2 e are the calculated climate variability of the annual average effective wave height of the offshore waves in China from the long-term data of the effective wave heights of different waves, wherein "+" represents passing the significance test. FIG. 2a is a report data based on multi-platform fusion wind farm (CCMP) forced wave effective wave height simulation; FIG. 2b is a report data based on a climate forecast central wind field (CFSR) forced wave effective wave height simulation; FIG. 2c is a diagram based on reanalysis data reported after simulating the effective wave height of a CCMP wave; FIG. 2d is a re-analysis data reported after a CFSR wave effective wave height simulation; fig. 2e is wave effective wave height reanalysis data based on data assimilation issued by european forecasting center.
As shown in fig. 2 a-2 e, comparing fig. 2a and 2b, it can be seen that there is a significant difference in the effective wave height climate variability of the wave simulation report data under different wind field driving, and after they are respectively corrected by the re-analysis method proposed in this example, comparing fig. 2c and 2d, it can be seen that their results are very similar, and they also have good consistency with the calculation result of the wave re-analysis data based on the data assimilation method of the european forecasting center, thereby proving the effectiveness of the re-analysis method of the invention for the wave long-term simulation data.
In the aspect of computational efficiency, the single-machine single-core CPU in the step 1 has the computation processing time of about 10-15 days, the processing time of other steps is about 1-2 days, and the data assimilation method can finish the reanalysis process within months.
In terms of data usage, the present example may flexibly use altimeter observation data, such as: after reanalysis data is obtained through the steps 1-6 of the embodiment, if new wave element observation data of the altimeter can be obtained, the reanalysis process of wave element simulation data can be completed directly on the basis of the step 1 by only repeating the steps 2-5, new wave reanalysis data can be generated quickly and effectively, and the reliability of the data is improved.
Claims (3)
1. A re-analysis method of wave long-term simulation data is characterized by comprising the following steps:
1) wave element observation data based on a satellite altimeter and wave element simulation data based on a wave numerical mode are constructed, and the wave element observation data and the wave element simulation data are subjected to space-time registration to obtain space-time synchronous data of the wave elements;
the step 1) comprises the following steps:
acquiring wave element simulation data based on a wave numerical mode;
correcting and gridding wave element observation data of each satellite altimeter;
performing space-time registration and synchronization on the wave element simulation data and the satellite altimeter wave element observation data to obtain space-time synchronization data;
the step of correcting and gridding the wave element observation data of each satellite altimeter comprises the following steps:
selecting wave element observation data of a single satellite altimeter, and regarding the wave element observation data of any buoy at a certain moment, taking the average value of the wave element observation data of the altimeter, the space interval of which is less than a set length and the time interval of which is less than a set time, as space-time synchronous data;
performing linear fitting on the space-time synchronous data of a single satellite; different satellite altimeters obtain different linear fitting relations, namely the fitting relation between wave element observation data observed by the buoy and wave element observation data observed by the altimeter;
correcting the altimeters one by applying the fitting relation to obtain corrected wave element data of the altimeters;
and dividing altimeter data into different grids corresponding to the gridded simulation data: selecting a simulated data grid point with the minimum spatial distance from the observation position of any altimeter, and dividing the simulated data grid point under the grid point; algebraic averaging is carried out on altimeter wave element observation data with the time span not exceeding a set value at the same grid point to obtain gridding data of the altimeter wave element at the moment;
2) and analyzing and correcting the wave element simulation data by utilizing the space-time synchronization data to obtain the re-analysis data of the wave elements.
2. A method of re-analyzing long-term wave simulation data as claimed in claim 1, wherein said time-space registration and synchronization of wave element simulation data and satellite altimeter wave element observation data to obtain time-space synchronization data comprises the steps of:
and according to the gridded simulation data and the gridded data of the wave element of the altimeter, performing space-time registration on the gridded simulation data and the gridded data of the wave element of the altimeter: selecting the data point which is closest to the altimeter wave element in the simulation data corresponding to the grid data of the altimeter wave element at a certain moment, and finally obtaining a wave element space-time synchronous data set of the two; each altimeter grid data point obtains a corresponding synchronous analog data point; all the data are combined to form the space-time synchronization data.
3. A method of re-analysing wave long-term simulation data according to claim 1, wherein said step 2) comprises the steps of:
constructing a correction model based on the time-space synchronous data through a statistical analysis method: in the space-time synchronous data, a synchronous data subset B under a certain grid point for a single yearjkJ represents the year, k is a grid mark number, correlation coefficients of the wave element simulation data and altimeter observation data are calculated, and only when the correlation coefficients of the wave element simulation data and the altimeter observation data are larger than a threshold value, the simulation data of the wave element is considered to be used for subsequent correction, otherwise, the simulation data are not used for correction and are marked as dead point data; fitting the simulated data for correction to obtain BjkThe medium-high altitude meter observes a correction relation between the wave elements and the wave element simulation data;
and correcting the simulation data according to the correction relation to obtain wave reanalysis data.
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