CN112632792B - Shore-approaching wave reanalysis simulation system based on measured data and numerical simulation - Google Patents
Shore-approaching wave reanalysis simulation system based on measured data and numerical simulation Download PDFInfo
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Abstract
The invention relates to an offshore wave reanalysis simulation system based on measured data and numerical simulation, which comprises: the data management layer is used for carrying out standardization processing on the acquired data required by the model; the model operation layer comprises a meteorological model and a wave model which are mutually related; the data assimilation layer is used for adjusting parameters of each model of the model operation layer; and the data visualization layer is used for outputting the operation result of the model operation layer in a chart mode. The invention can utilize short-term and limited observation results to invert long-term wave data, relatively simplifies the complex numerical simulation process and provides reliable wave historical results; the method has the advantages that the method can fully utilize the source database, properly measure data and simplify the process of model assimilation, reduces the requirement of measured data and reduces the cost of simulation models overall; the method can provide relatively accurate reanalysis data support for offshore engineering decision.
Description
Technical Field
The invention relates to the field of near-shore wave reanalysis simulation, in particular to a near-shore wave reanalysis simulation system based on measured data and numerical simulation.
Background
At present, although the numerical simulation model technology of waves is relatively mature technology, open source software such as WW3, SWAN, FunWave and the like, and commercial software such as MIKE21 and the like are already available on the market for numerical simulation, no software is available for realizing reanalysis simulation of near-shore waves. The software listed above can only be used for simulating extreme ocean currents and wave conditions, such as typhoon, extreme high water level, extreme waves and the like, but the model does not support a series of functions required by long-time numerical simulation. These functions include data entry, model interactive operation, and data assimilation.
Domestic global comprehensive models such as ERA5 of European Meteorological center (ECMWF) and GFS of American Meteorological forecasting center (NOAA) can also provide reanalyzed wave simulation data in a wide sea area, but because the grid is too coarse and the time accuracy is relatively low, the method is not suitable for the requirement of high-accuracy wave simulation near shore.
At present, some open-source programs such as COAST and ROMS exist in the market for the exchange operation of a plurality of computing modes and the feedback assimilation of a single computing mode, but the program setting is too complicated to pay too much attention to certain specific physical processes. The assimilation part of the model is traditionally assimilating by adopting Kalman filtering assimilation or four-dimensional variation assimilation aiming at a numerical simulation result of a large and medium scale, the assimilation is generally used for correcting forecast simulation, a large number of actual measurement points are needed, a large number of parameters are calculated, attention is focused only on a wind storm surge process with reduced number, and long-time accurate simulation on the wave condition of an appointed area cannot be carried out.
Disclosure of Invention
The invention aims to solve the defects of the prior art and provides an offshore wave reanalysis simulation system based on measured data and numerical simulation.
In order to achieve the purpose, the invention adopts the following technical scheme:
an offshore wave reanalysis simulation system based on measured data and numerical simulation, comprising:
the data management layer is used for carrying out standardized processing on the data required by the acquired model, the data comprises large-scale reanalysis data, measured data and boundary data, and the large-scale reanalysis data comprises an open source or commercial database of meteorological hydrology; the measured data comprises measured meteorological hydrological data of important positions of the target area; the boundary data comprises terrain data and land boundary data;
the model operation layer comprises a meteorological model and a wave model which are mutually associated, the two models divide areas according to large, medium, small and micro spatial scales, the area with the larger scale provides boundary conditions for the area with the smaller scale, the meteorological model adopts a WRF model to calculate in the large and medium scale areas, a wind field of the small scale area is obtained according to the result interpolation of the medium scale area, and the micro scale area is an area uniform wind field; the wave model adopts a calculation result of a foreign open source database or a WaveWacth3 model in a medium-scale region, the small-scale region adopts a wave model based on a spectral analysis equation, and the micro-scale region adopts a wave model based on a Boussineq equation;
the data assimilation layer is used for comparing model data output by the model operation layer with actually measured data and adjusting parameters of each model of the model operation layer according to the error analysis structure until errors of each model in a certain historical duration meet the requirement of target precision;
and the data visualization layer is used for outputting the operation result of the model operation layer in a chart mode.
Further, the normalized processing mode in the data management layer is as follows: and processing the data into an input format of each model of the model running layer through the script.
Furthermore, the script comprises a data downloading script, a data error analysis script and a data format conversion script.
Furthermore, in the model operation layer, the unit area divided according to the large scale is 20 longitude x 20 latitude; the unit area size divided according to the mesoscale is 500km x 500 km; the unit area size divided according to small scale is 100km x 100 km; the unit area size divided according to the microscale is 30km by 30km, and the highest precision of the grid is 2 m.
Further, in the model operation layer, the wave model based on the spectrum analysis equation comprises a SWAN model and a MIKE21 SW model, and the wave model based on the Boussineq equation comprises a MIKE21 SW model and a FunWave model.
Further, the time step in the mesoscale model is 600s, and the algorithm parameters include: the wind energy input adopts Komen parameters, the white wave dissipation adopts Hasselman parameters, and the nonlinear waves and the linear waves adopt four-wave Hasselman DIA algorithm and three-wave Elderberky.
Further, the initial coefficient in the small-scale model is determined according to the terrain median diameter and historical wind conditions in the region, and the coefficient comprises a bottom friction coefficient, a wind influence coefficient, a wave boundary gain coefficient and a wind gain coefficient.
Furthermore, in the process of adjusting the parameters of each model by the data assimilation layer, when the actually measured data amount is small, the simulation precision of the wave model under small and micro scales is preferentially ensured.
Further, the error analysis in the data assimilation layer comprises two parts, wherein one part is used for calculating the accuracy of the whole sample and comprises the steps of calculating the average deviation value, the variance and the average error rate of the whole sample; and the other part is the accuracy of calculating partial extreme value time intervals, including calculating average deviation values, maximum deviation values, average error rates and maximum error rates.
Further, the graphs in the data visualization layer comprise effective wave height, wave direction and wave period time variation curves of the target point, a time variation graph of the effective wave height of the target point, a numerical simulation graph of the effective wave height of the target area changing with time, a comprehensive flow vector graph of the effective wave height and the wave direction, and a flow vector graph of wind speed and wind direction of the target area.
The invention has the beneficial effects that:
1. the invention can utilize short-term and limited observation results to invert long-term wave data, relatively simplifies the complex numerical simulation process and provides reliable wave historical results;
2. the method can fully utilize the source database, properly measure the data, simplify the assimilation process of the model, reduce the calculation amount of the model, reduce the requirement of the measured data and generally reduce the cost of the simulation model;
3. the method can provide more accurate reanalysis data support for offshore engineering decision, and is beneficial to the evaluation of potential risks and benefits of users.
Drawings
FIG. 1 is a schematic view of the present invention;
FIG. 2 is a comparison graph of wind speed data and measured data output by a meteorological model;
FIG. 3 is a comparison of small scale wave model results and actual measurement results;
wherein: in fig. 2, more sample points are actually measured data;
the following detailed description will be made in conjunction with embodiments of the present invention with reference to the accompanying drawings.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
as shown in the figure, an inshore wave reanalysis simulation system based on measured data and numerical simulation comprises:
the data management layer is used for carrying out standardized processing on the data required by the acquired model, the data comprises large-scale reanalysis data, measured data and boundary data, and the large-scale reanalysis data comprises an open source or commercial database of meteorological hydrology; the measured data comprises measured meteorological hydrological data of important positions of the target area; the boundary data comprises terrain data and land boundary data;
the model operation layer comprises a meteorological model and a wave model which are mutually associated, the two models divide areas according to large, medium, small and micro spatial scales, the area with the larger scale provides boundary conditions for the area with the smaller scale, the meteorological model adopts a WRF model to calculate in the large and medium scale areas, the calculated boundary data can adopt data of a US meteorological center CFSR or a European meteorological center ECMWF, a wind field of the small scale area is obtained by interpolation according to the result of the medium scale area, and the micro scale area is an area uniform wind field; the wave model adopts a calculation result of a foreign open source database or a WaveWacth3 model in a medium-scale region, the small-scale region adopts a wave model based on a spectral analysis equation, and the micro-scale region adopts a wave model based on a Boussineq equation;
the data assimilation layer is used for comparing model data output by the model operation layer with actually measured data and adjusting parameters of each model of the model operation layer according to the error analysis structure until errors of each model in a certain historical duration meet the requirement of target precision;
and the data visualization layer is used for outputting the operation result of the model operation layer in a chart mode.
Further, the normalized processing mode in the data management layer is as follows: and processing the data into an input format of each model of the model running layer through the script.
Furthermore, the script comprises a data downloading script, a data error analysis script and a data format conversion script.
Furthermore, in the model operation layer, the unit area divided according to the large scale is 20 longitude x 20 latitude; the unit area size divided according to the mesoscale is 500km x 500 km; the unit area size divided according to small scale is 100km x 100 km; the unit area size divided according to the microscale is 30km by 30km, and the highest precision of the grid is 2 m.
Furthermore, in the model operation layer, the wave models based on the spectral analysis equation comprise a SWAN model and a MIKE21 SW model, the wave models based on the Boussineq equation can effectively improve the operation efficiency and comprise a MIKE21 SW model and a FunWave model, the grid division is fine and smooth, the reflection and refraction conditions of different boundaries are fully considered, the spectral analysis equation is a wave propagation equation based on the simplification of the wave spectrum, the calculation speed is high, but the refraction and diffraction description of the building is not accurate enough and is not suitable for a micro-scale model, and therefore the model is reasonably described by adopting the Boussineq equation comprising mass conservation and momentum conservation. .
Further, the time step in the mesoscale model is 600s, and the algorithm parameters include: the wind energy input adopts Komen parameters, the white wave dissipation adopts Hasselman parameters, and the nonlinear waves and the linear waves adopt four-wave Hasselman DIA algorithm and three-wave Elderberky.
Further, the initial coefficient in the small-scale model is determined according to the terrain median diameter and historical wind conditions in the region, and the coefficient comprises a bottom friction coefficient, a wind influence coefficient, a wave boundary gain coefficient and a wind gain coefficient.
Furthermore, in the process of adjusting parameters of each model by the data assimilation layer, when the actually measured data amount is small, the simulation precision of the wave model under the small and micro scale is preferentially ensured, and when the parameters of the small and micro scale models are assimilated, the grid size (resolution ratio) can be adjusted according to actual needs.
Further, the error analysis in the data assimilation layer comprises two parts, wherein one part is used for calculating the accuracy of the whole sample and comprises the steps of calculating the average deviation value, the variance and the average error rate of the whole sample; and the other part is the accuracy of calculating partial extreme value time intervals, including calculating average deviation values, maximum deviation values, average error rates and maximum error rates. When the simulation result is used for judging the condition of a conventional threshold, such as calculating the number of operable days of a ship in a harbor area, calculating a construction window period and the like, the error condition of the whole sample is mainly considered at the moment; when the simulation result is used for avoiding extreme conditions, such as the condition of large wave in a judgment area, the condition of large wave height in a construction period, the condition of large wave directly influenced by historical non-typhoon at a certain time, and the like, the error condition of an extreme value period is mainly considered. Iterative tuning of models, particularly small scale models and micro-scale models, is generally required.
Further, the graphs in the data visualization layer comprise effective wave height, wave direction and wave period time variation curves of the target point, a time variation graph of the effective wave height of the target point, a numerical simulation graph of the effective wave height of the target area changing with time, a comprehensive flow vector graph of the effective wave height and the wave direction, and a flow vector graph of wind speed and wind direction of the target area.
According to the invention, different numerical simulation result sets are obtained through comparison of long-time numerical simulation and verification points and parametric scheme change, and an optimal parameter scheme is selected through comparison of errors of the simulation result sets and the verification points, so that model approximation with reality, namely a model assimilation process is realized, as shown in the following table, wherein the table 1 is a comparison table of simulation values and actual measurement values of daily effective wave heights of the micro-scale model.
TABLE 1 comparison table of analog value and measured value of daily effective wave height of micro-scale model
Different from the traditional large and medium scale assimilation, a large number of actual measuring points are needed, a large number of parameters are calculated, and the limited storm surge process is concerned. The simplified assimilation process of the invention mainly observes a target point or a target boundary, reduces errors by comparing a large number of conventional simulation samples, and realizes effective simplification of the data assimilation process.
The method can provide important background data and information for ocean science researchers, port and channel engineering builders and government decision makers, for example, wave conditions are needed when the operable days of ships in a certain area are researched, and the near-shore waves of the area for nearly 5-10 years can be reconstructed by using a reanalysis wave simulation system; before the offshore engineering is implemented, an engineer can use the system to reconstruct the wave condition of an engineering area within the construction period of 5-10 years. The method has good practical application value in the aspects of offshore wind power, beach protection, port construction and the like.
The invention has been described in connection with the accompanying drawings, it is to be understood that the invention is not limited to the specific embodiments disclosed, but is intended to cover various modifications, adaptations or uses of the invention, and all such modifications and variations are within the scope of the invention.
Claims (10)
1. The utility model provides an inshore wave reanalysis analog system based on measured data and numerical simulation which characterized in that includes:
the data management layer is used for carrying out standardized processing on the data required by the acquired model, the data comprises large-scale reanalysis data, measured data and boundary data, and the large-scale reanalysis data comprises an open source or commercial database of meteorological hydrology; the measured data comprises measured meteorological hydrological data of important positions of the target area; the boundary data comprises terrain data and land boundary data;
the model operation layer comprises a meteorological model and a wave model which are mutually associated, the two models divide areas according to large, medium, small and micro spatial scales, the area with the larger scale provides boundary conditions for the area with the smaller scale, the meteorological model adopts a WRF model to calculate in the large and medium scale areas, a wind field of the small scale area is obtained according to the result interpolation of the medium scale area, and the micro scale area is an area uniform wind field; the wave model adopts a calculation result of a foreign open source database or a WaveWacth3 model in a medium-scale region, the small-scale region adopts a wave model based on a spectral analysis equation, and the micro-scale region adopts a wave model based on a Boussineq equation;
the data assimilation layer is used for comparing model data output by the model operation layer with actually measured data and adjusting parameters of each model of the model operation layer according to the error analysis structure until errors of each model in a certain historical duration meet the requirement of target precision;
and the data visualization layer is used for outputting the operation result of the model operation layer in a chart mode.
2. The measured data and numerical simulation based near-shore wave reanalysis simulation system according to claim 1, wherein the normalized processing mode in the data management layer is: and processing the data into an input format of each model of the model running layer through the script.
3. The quayside wave reanalysis simulation system based on measured data and numerical simulation of claim 2, wherein the script comprises a data download script, a data error analysis script, and a data format conversion script.
4. The measured data and numerical simulation based near-shore wave reanalysis simulation system according to claim 1, wherein in the model operating layer, the unit area divided according to the large scale is 20 longitude x 20 latitude; the unit area size divided according to the mesoscale is 500km x 500 km; the unit area size divided according to small scale is 100km x 100 km; the unit area size divided according to the microscale is 30km by 30km, and the highest precision of the grid is 2 m.
5. The quayside wave reanalysis simulation system based on measured data and numerical simulation of claim 1, wherein in the model operation layer, the wave models based on the spectral analysis equation comprise a SWAN model and a MIKE21 SW model, and the wave models based on the Boussineq equation comprise a MIKE21 SW model and a FunWave model.
6. The measured data and numerical simulation based near-shore wave reanalysis simulation system of claim 5, wherein the time step in the mesoscale model is 600s, and the algorithm parameters comprise: the wind energy input adopts Komen parameters, the white wave dissipation adopts Hasselman parameters, and the nonlinear waves and the linear waves adopt four-wave Hasselman DIA algorithm and three-wave Elderberky.
7. The measured data and numerical simulation based near-shore wave reanalysis simulation system of claim 5, wherein the initial coefficients in the small-scale model are determined according to terrain median diameter in the area and historical wind conditions, and the coefficients include a bottom friction coefficient, a wind influence coefficient, a wave boundary gain coefficient and a wind gain coefficient.
8. The quayside wave reanalysis simulation system based on measured data and numerical simulation of claim 1, wherein in the process of adjusting parameters of each model by the data assimilation layer, when the measured data amount is small, the simulation accuracy of the wave model under small and micro scale is preferentially ensured.
9. The measured data and numerical simulation based near-shore wave reanalysis simulation system of claim 1, wherein the error analysis in the data assimilation layer comprises two parts, one part is to calculate the accuracy of the whole sample, and the other part comprises to calculate the average deviation value, variance and average error rate of the whole sample; and the other part is the accuracy of calculating partial extreme value time intervals, including calculating average deviation values, maximum deviation values, average error rates and maximum error rates.
10. The system of claim 1, wherein the graphs in the data visualization layer include curves of time variation of effective wave height, wave direction and wave period of the target point, a time variation graph of effective wave height of the target point, a numerical simulation graph of time variation of effective wave height of the target area, a comprehensive sagitta of effective wave height and wave direction, and a sagitta of wind speed and wind direction of the target area.
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