CN117094254A - Method and system for improving ocean mode simulation precision based on wind field sensitivity parameters - Google Patents
Method and system for improving ocean mode simulation precision based on wind field sensitivity parameters Download PDFInfo
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Abstract
The invention belongs to the field of application research of a marine long-time sequence re-analysis wind field in a marine mode, and discloses a method and a system for improving the simulation precision of the marine mode based on wind field sensitivity parameters. The method is built in a general mode; selecting CFSV2 and ERA5 wind fields to analyze data, respectively driving the universal modes of the same grid, and obtaining two sets of driving mode results; respectively subtracting the wind speed and the wind direction of the CFSV2 wind field and the ERA5 wind field which are averaged in time, and calculating to obtain two-dimensional scalar fields of a wind speed difference value and a wind direction difference value; and then subtracting the time average fields of the results driven by the CFSV2 and ERA5 wind field driving modes to obtain a three-dimensional scalar field of flow velocity, flow direction and temperature difference between the two driving modes. The invention analyzes the circulation flow of the transition layer, thermocline, temperature frontal surface and medium and small scale vortex heat transport, and refines the process and rule of the circulation flow of a sea area and the structural change of the temperature field.
Description
Technical Field
The invention belongs to the field of application research of a marine long-time sequence re-analysis wind field in a marine mode, and particularly relates to a method and a system for improving the simulation precision of the marine mode based on wind field sensitivity parameters.
Background
In the ocean mode, wind field driving plays a crucial role in the accuracy of the mode result, and the limitation of the actual measurement wind field distribution in space-time aspect is far from meeting the requirement of high-precision ocean mode operation. It is common in the art to employ wind farm reanalytical products to provide sea surface drives for patterns. There are differences between the reanalyzed products of different wind farms, and there are differences in the applicability of different wind farms in ocean modes in different sea areas.
There are many factors that affect the accuracy of the ocean pattern, such as grid density, seafloor topography, atmospheric driving, etc. The prior art shows that the wind field directly drives the ocean flow field, especially coastal flow, so as to directly influence the ocean temperature field and the sound velocity field distribution.
The test area of a sea area is located in the continuation area of the tropical western Pacific heating pool, the wind field structure is complex, and the test area is mainly controlled by the monsoon and is often influenced by typhoons. The control of the ocean water body by the monsoon and typhoons is remarkable.
The seasonal variation law of the sea monsoon is obvious, the southwest monsoon is dominant in the half of summer (5 to 8 months), and the northeast monsoon is dominant in the half of winter (10 months to 5 months next year). The method is characterized in that: the occurrence frequency of northeast wind and north wind can reach about 50% at the beginning of 9 months, 10 months are increased to 70% to 80%,11 months northeast wind completely controls the area, and the time for controlling the area in one year can reach more than 8 months; in contrast, southwest monsoon has a prevalence time of only 3 months. The average speed of winter season wind is 7-10 m/s in wind field intensity, and is usually more than summer wind speed (less than 6 m/s); in the spring and autumn transition seasons, the wind direction is changeable and the wind speed is small.
Typhoons generally refer to strong warm low-pressure vortex formed in the atmosphere of tropical sea, and disaster phenomena such as strong wind, heavy rainfall, billow and the like are often accompanied when the typhoons pass. A northwest region of the sea is one of regions where the occurrence frequency of typhoons is highest worldwide, and may be affected by typhoons throughout the year, with the occurrence frequency of typhoons being highest between 7 months and 9 months. According to the statistics data of the prior art, in 2006 to 2015, a certain sea typhoon appears 45 times, wherein the sea typhoon comprises 16 times of strong typhoons and 13 times of super strong typhoons, the average value of the maximum wind speed of the typhoons is 46.71 m/s, and the maximum wind speed can reach 78 m/s. Typhoons are large in quantity and strength, and have a non-negligible effect on the ocean water body in a certain sea area.
When the wind field blows across the sea surface, energy transfer can be generated to drive the sea to form water body motion of various scales, and the driving of the wind field on the sea surface plays a key role no matter in small-scale waves or large-scale circulation. Thus, the accuracy of the wind field directly affects the reliability of the ocean pattern, while the selection of an appropriate sea surface wind field is critical to the ocean numerical pattern. However, since the condition for directly acquiring the observation data is severe, the cost is extremely high, and it is difficult to obtain complete large-scale, long-time series data, so that the atmospheric mode re-analysis data obtained based on limited observation data becomes an atmospheric driving wind field which is difficult to replace in the marine numerical mode. Currently, the prior art has proposed a number of analysis data sets for sea surface wind farms, and there are also scholars studying and analyzing the quality of different data sets, such as the difference in resolution of different data sets, the difference in wind vector farms, the influence on sea modes, etc. Of these, the NCEP CFSR/CFSV2 and ERA5 wind farm analysis data sets are widely used in marine modes.
The NCEP CFSR (National Centers for Environmental Prediction, NCEP for short, the climate forecast system re-analyzes (Climate Forecast System Reanalysis, CFSR for short) the data, provides the atmospheric wind field data from 1979 to 2010 with a horizontal resolution of 0.313 degrees by 0.312 degrees and a time resolution of 1h, while the CFSV2 (Climate Forecast System Version 2) data is an overall upgrade product of the CFSR, provides the wind field data from 2011 to date, and increases the horizontal resolution to 0.205 degrees by 0.204 degrees, while the time resolution remains 1h.ERA5, combines a large amount of observation data into the mode calculation by using advanced numerical mode and data assimilation techniques, providing the multi-parameter data product from 1950 to date including atmosphere, earth surface and ocean with a horizontal resolution of 0.25 degrees by 0.25 degrees and a time resolution of 1h.
For the accuracy of the two types of analysis data and the applicability in numerical mode, the prior art has been partially studied, and verification analysis is also performed in different sea areas. Sea surface wind fields and sea waves of ERA5 are more similar to observations in the indian ocean; in the atlantic, the re-analysis product of NECP CFSR/CFSV2 would produce a stronger cyclone than ERA 5; the prior art considers that ERA5 wind field as the driving wind field of ocean mode generally gives better simulation results, and that the simulation of severe weather, typhoons, is also dominant in this area. It follows that the accuracy of the re-analysis data for different wind farms and the applicability in marine modes are different, even in the same sea area.
On the marine power field structure, it is proved that the surface layer large-scale circulation of a certain sea area is mainly controlled by the winter-summer reverse monsoon. In winter, because the northeast monsoon presents a strong trend on the west side and the east side of a sea, a strong southerly boundary flow is generated in the sea, in the southerly of the sea, the flow is accumulated due to the blocking effect of a land frame and is forced to flow eastward and north along the northeast side of the sea, in the northward of the sea, the flow is forced to flow eastward and form an offshore flow due to the blocking of an island of the group, and after reaching the Lvsong island on the eastern side of the sea, the circulation structure forms circulation systems in two cyclone directions in the northward and the south of the sea respectively. In summer, the flow field at the west of a sea becomes a relatively wide northeast flow because the monsoon becomes southwest, and an eastern off-shore flow is still formed in a certain area to reach the vicinity of an island at the east of the sea, and two branches of north and south are generated, at the moment, the northwest of the sea is a relatively consistent northeast flow, and an anti-cyclone circulation of a sea basin scale and a secondary sea basin scale is formed at the south of the sea. The basic structure of the subsurface flow is substantially identical to the surface flow, but in the north part of the sea, coastal flow, which would otherwise move in the southwest direction in winter, enters the subsurface layer in summer, opposite to northeast flow at the surface layer. The study proves that the upper layer circulation form is basically consistent with the wind stress rotation structure of a certain sea.
In the research of the circulating structure of a transition layer and a deep circulating layer, the limitation of observation data always has a great dispute. Part of the researches consider that the circulation of a certain sea transition layer is similar to the circulation of an upper layer; it has also been studied to consider that the average state of the transition layer circulation is mainly occupied by the anti-cyclone circulation. The dispute of the research result is not only that the accuracy of the ocean mode is problematic, but also is related to the undefined definition of the depth of a certain sea transition layer to a certain extent.
For deep circulation in large areas of the sea, most observations and pattern results support the understanding that circulation in the cyclonic direction at the sea depth is dominant on a basin scale, despite the disputes in detail. With respect to seasonal changes in deep circulation, there is no more generally accepted conclusion in the academia.
In the temperature field structure, because the surface circulation of a large area of a sea is mainly controlled by the monsoon, the structure of the sea surface temperature is greatly controlled by the surface circulation, and a certain sea west boundary flow controlled by the northeast monsoon moves from the south to the south and the west in winter, flows through the southeast coast, and cold water from the north enters the northwest coast of a certain sea along with the flow field, so that the coast SST temperature is lower compared with other positions in the same latitude; in summer, although there is a southwest monsoon controlled coastal flow moving from southwest to northeast, an upward flow is generated in southeast and on the west side of an island due to the offshore flow generated by Ekman transport caused by the coastal wind field, resulting in a low temperature region at the sea surface.
Sea surface temperature SST is often used as a parameter for determining the position, strength, etc. of the ocean front, and research has demonstrated that wind stress at the east boundary flow can induce Ekman transport offshore, resulting in subsurface water entering the surface layer, resulting in an SST cold core in the upflow zone, and thus in the appearance of the temperature front. At sea, although the dominant flow field is western boundary flow, this theory applies here as well due to the variation in the direction of the summer monsoon and the flow field. In addition, the wind stress rotation also has an enhanced effect on the formation, strength and influence area of the updraft and frontal surface, and the SST data of a Moderate Resolution Imaging Spectroradiometer (MODIS) satellite is utilized to describe the frontal surface system of a sea more fully. The frontal probability (Frontal Probability, FP for short), i.e. the ratio of the number of times a frontal surface occurs to the total number of times the effective value is measured at that point in a certain period of time, is used to analyze the frontal surface of a sea area. Research shows that the frontal surface structure of a sea has strong seasonality like circulation: in winter, the low-temperature southward flow caused by the monsoon is concentrated in the land direction due to the Ekman transportation effect, so that the probability of occurrence of a frontal surface at the coastal border of a certain sea, especially the southern side of the Chinese continental is highest; in summer, the upflow and the offshore flow are brought by the quaternary wind at the west boundary, so that a low-temperature area is formed at the west boundary, and the probability of frontal surface occurrence is improved. It is noted that, although the probability of frontal occurrence at the western border is high in both winter and summer seasons, the generation mechanism of the two is different, and the intensity is stronger in winter than in summer.
Studies have shown that SST fronts play a very important role in the interaction of the sea, especially the temperature difference across the fronts directly affects the stability at the lower boundary of the atmosphere and thus the upper atmosphere. The accuracy of the pattern to SST front simulation will therefore be one of the very important criteria for measuring the pattern's ability.
In addition to the skin structure at ocean temperatures, structures with temperatures below the skin are also one of the focus. Firstly, the underwater temperature structure plays an important role in the aspects of ocean engineering, fishery, military and the like, and is a decisive parameter of an underwater sound field; and secondly, as the data below the sea surface cannot be observed through satellites, the research data are relatively less, and the difficulty is increased for the research of the underwater temperature field.
Thermocline is one of the important structural forms of the underwater temperature field. In the past, the characteristic analysis of a certain sea thermocline is relatively more, and after the prior art analyzes the observed data of the temperature of the certain sea for many years in 1907-1990, the thermocline of the certain sea is classified into a radiation type and a water mass superposition type; meanwhile, the prior art also clarifies the seasonality of a certain thermocline, namely, the thermocline is deepest in winter, thinnest in thickness and weakest in strength, is distributed in shallow north and south depths, is shallowest in spring, thickest in thickness and consistent in north and south depths, and has strongest strength in summer and autumn, wherein the thermocline is characterized in shallow north and south depths in summer, and is in a transition state from summer to winter in autumn; the prior art also demonstrates the seasonal nature of a change in a thermocline and further demonstrates that a monsoon is an important factor in controlling the seasonal change in a thermocline in a large area of the sea; SODA data is analyzed in the prior art, the influence of a wind field on a sea thermocline structure is further studied, and wind stress and net heat flux are considered to be dominant.
In addition, the marine sound velocity profile structure plays a crucial role in marine underwater activities. Research shows that sound velocity in the ocean is mainly a function of temperature, salinity and pressure, while in the upper ocean, the influence of ocean temperature on sound velocity is dominant. Part of the research also analyzed the marine sonic skip, where descriptions of the structure of the sonic skip and seasonal variations are closely related to the research of the sonic skip.
Through the above analysis, the problems and defects existing in the prior art are as follows: (1) In the mode analysis of a sea area by using the prior art, the accuracy and the applicability of analysis data products of various wind farms are poor. (2) The simulation precision of the transition layer in a certain sea area is not high, and the simulation precision of the upper circulation and the temperature field is affected. (3) The data accuracy of the analysis of the function of the middle-small scale ocean structure in ocean heat transport in the prior art is low.
Disclosure of Invention
In order to overcome the problems in the related art, the embodiment of the invention provides a method and a system for improving the simulation precision of a marine mode based on wind field sensitivity parameters.
The technical scheme is as follows: the method for improving the simulation precision of the ocean mode based on the wind field sensitivity parameter comprises the following steps:
S1, building a general mode: building a general mode under the windless condition of a certain sea area based on an area ocean model ROMS, and after the simulation result is verified to meet the requirements, using the model to simulate experiments under different wind field driving;
s2, analyzing data by selecting an international common CFSV2 wind field and an ERA5 wind field, respectively driving the common modes of the same grid, and obtaining two sets of driving mode simulation results; the wind speed and the wind direction of the CFSV2 wind field and the ERA5 wind field which are averaged in time are respectively subtracted to obtain a wind speed difference valueAnd wind direction difference->Two-dimensional scalar fields; then subtracting the time average fields of the simulation results of the driving modes of the CFSV2 wind field and the ERA5 wind field to obtain a flow velocity difference value between the two driving mode simulation results>Difference in flow direction->And temperature difference->Is a three-dimensional scalar field of (2);
s3, sensitivity analysis and test: based on the surface layer difference value result of the driving mode, dividing the distribution of extremely high, medium and low sensitive areas according to the sensitivity classification of the mode difference value result, and carrying out correlation analysis on the sensitive areas of the mode difference value result and the CFSV2 wind field and ERA5 wind field difference value result, and preferably obtaining the optimal wind field of each area;
and S4, based on the optimal wind field of the selected driving mode, performing key region simulation by using the high-precision ocean mode encrypted by the grid, and performing action analysis of the medium-small scale structure in ocean heat flux on a certain sea key region.
In step S1, the same grid of a sea area is used for a sensitivity experiment, and a dual nested key area encryption grid is used for optimizing a mode and analyzing a simulation result. In step S1, the method for building a universal mode includes:
a. partitioning: for a sea area, taking the whole sea area as a research area of a large area model, adopting orthogonal linear meshing, wherein an important area adopts orthogonal linear encryption meshing;
b. initial conditions: collecting investigation data and database data for revising water depth data on the basis of shared latest high-resolution water depth topographic data for different water depth topography;
for the shoreline, collecting satellite remote sensing images and extracting the average climax line of the climax as a general mode shoreline;
for warm salt, SODA climate state annual average warm salt data is adopted in a large-area mode initial field; the initial field of the key area mode adopts SODA climate state month average Wen Yanchang;
c. boundary conditions:
for the upper boundary conditions, the surface forcing field is driven by the climatic state month averaged CFSV2 heat flux data;
for the side boundary conditions, the tide wave adopts a high-resolution tide harmonic constant, and the actual measured tide level station data is adopted for verification; ocean currents and warm salts use AVISO data.
In step S2, CFSV2 wind field and ERA5 wind field analysis data driving general mode are selected to carry out simulation calculation, simulation results are firstly interpolated from sigma layers to a standard layer of z coordinates, and warp and weft components of a flow field and warp and weft components of the wind field are interpolated to a unified horizontal coordinate system by taking temperature variables as standards.
In step S2, for the driving mode of the nth layer, the sensitivity parameter is the ratio of the driving mode result difference value to the wind field difference value, and the calculation formula is as follows:
sensitivity parameter of temperature to wind speed Sensitivity parameter of temperature to wind direction-> Sensitivity parameter of flow velocity to wind speed-> Sensitivity parameter of flow velocity to wind direction-> Sensitivity parameter of flow direction to wind speed-> Sensitivity parameter of flow direction to wind direction-> 。
Further, sensitivity analysis is carried out on each sensitivity parameter of the obtained three-dimensional scalar field according to the requirement on any layer depth or vertical section, and the horizontal distribution state of sensitivity and the vertical influence depth are obtained. Further, sensitivity parameters are classified into an extremely high sensitivity interval, a medium sensitivity interval and a low sensitivity interval by using a classification method. And further, eliminating the threshold value of the area with the too small wind field difference value.
In step S3, the optimization selection of the two wind fields includes:
firstly, classifying collected wind field actual measurement data by combining observed longitude and latitude coordinates and regions defined by extremely high, medium and low sensitivity to respectively form A, B, C, D four data sets; for each observation data, carrying out interpolation on wind field data adopted by the two sets of modes, and then comparing the wind field data with the observation data;
then, respectively carrying out error statistics on the observed data and the interpolated mode data in the four sensitive areas of the high, medium and low to obtain the error sizes and distribution states of the two wind fields in the four sensitive areas;
finally, comparing and evaluating the quality of the wind field according to the error, and taking the ERA5 wind field as a wind field of a driving mode if the error of the ERA5 wind field in a high-sensitivity area is smaller than the error of the CFSV 2; if the errors of the two wind fields in the high-sensitivity area are consistent, and the CFSV2 is better than ERA5 in the medium-sensitivity area, the CFSV2 wind field is preferentially selected.
Another object of the present invention is to provide a system for improving accuracy of ocean mode simulation based on wind field sensitivity parameters, the system implementing the method for improving accuracy of ocean mode simulation based on wind field sensitivity parameters, the system comprising:
The general mode building module is used for building a general mode under the windless condition of a certain sea area based on the regional ocean model ROMS, and the simulation result is used for simulation experiments under different wind field driving after verification meets the requirements;
the three-dimensional scalar field acquisition module is used for selecting general CFSV2 wind field and ERA5 wind field analysis data, respectively driving general modes of the same grid, and acquiring two sets of driving mode simulation results; the wind speed and the wind direction of the CFSV2 wind field and the ERA5 wind field which are averaged in time are respectively subtracted to obtain a wind speed difference valueAnd wind direction difference->Two-dimensional scalar fields; then subtracting the time average fields of the simulation results of the driving modes of the CFSV2 wind field and the ERA5 wind field to obtain a flow velocity difference value between the two driving mode simulation results>Difference in flow direction->Temperature difference->Is a three-dimensional scalar field of (2);
the sensitivity analysis and detection module is used for dividing extremely high, medium and low sensitivity areas according to the sensitivity grading interval by taking the surface layer of the driving mode result as a reference, and selecting a CFSV2 wind field and an ERA5 wind field;
and the middle-small scale structure ocean heat flux analysis module is used for analyzing key areas by utilizing high-precision ocean mode results, and analyzing the effect of the middle-small scale structure in ocean heat flux in a certain sea large area based on a wind field of a selected driving mode.
By combining all the technical schemes, the invention has the advantages and positive effects that: the method for improving the accuracy of the ocean model simulation result is mainly obtained through analysis and evaluation of satellite remote sensing data, measured data, analysis data and the like and through the sensibility ocean numerical simulation experiments of different wind fields. The invention collects the data of initial conditions (deep water topography, coastline, warm salt) and open boundary conditions (harmonic constant, runoff, ocean current, warm salt, atmospheric radiation) required by the mode, carries out comprehensive analysis and optimization, and builds a ROMS universal model for a sea area for a sensitivity experiment. Acquiring analysis data of CFSV2 and ERA5 wind fields, comparing and verifying with the actually measured wind fields, and analyzing characteristics and differences of the wind fields, in particular analyzing the differences of the development process and the intensity of the strong wind; on the basis of a ROMS general model, a sensitivity experiment scheme taking two wind fields as unique variables is formulated. According to the sensitivity experimental scheme, the invention operates a certain sea mode, and the obtained circulation and temperature field results are respectively verified and comprehensively analyzed with measured data; and combining the difference of the wind fields, further analyzing the difference of simulation results of the circulation and the temperature fields, analyzing the generation reasons of errors, and providing a scheme for further optimizing the accuracy of the numerical mode. And establishing a double nested mode based on the optimized mode operation scheme, and improving the grid precision of a key area of a certain sea area. And analyzing the structure and change rule of the circulation and temperature field of a certain sea area based on the simulation result of the optimization mode, and mainly analyzing the circulation, thermocline, temperature frontal surface and medium-and-small-scale heat transport of the transition layer to further refine the process and rule of the structure change of the circulation and temperature field of the certain sea area. According to the invention, initial and open boundary conditions are accurately determined, and a reliable ROMS universal mode for a sensitivity experiment is established; establishing a scientific sensitivity experimental scheme based on wind field variables; the error of the process and the intensity difference of the strong wind of different wind fields to the ocean mode is obtained through comprehensive analysis of the sensitivity experiment result; based on the high-precision ocean mode result, the structural details of the circulation and the temperature field of a certain sea area and the influence on the ocean activity are clear. A new approach for improving the accuracy of the ocean mode based on the sensitivity experiment is obtained.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure;
FIG. 1 is a flowchart of a method for improving the simulation accuracy of a marine mode based on wind field sensitivity parameters provided by an embodiment of the invention;
FIG. 2 is a flow chart of error analysis between pattern data and observed data provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of a system for improving simulation accuracy of ocean modes based on different wind field sensitivity parameters according to an embodiment of the present invention;
in the figure: 1. building a module in a universal mode; 2. a three-dimensional scalar field acquisition module; 3. a sensitivity analysis and inspection module; 4. and the marine heat flux analysis module is of a medium-small scale structure.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The invention may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit or scope of the invention, which is therefore not limited to the specific embodiments disclosed below.
According to the invention, a sea mode precision of a sea area with complex environmental conditions is selected to conduct an experiment on the sensitivity of different wind fields, an optimization scheme is provided, a high-precision sea mode is obtained, the structure, evolution and mechanism of a sea area circulation and a temperature field are revealed through comprehensive analysis of observation data and mode results, and a medium-small scale structure in the sea area is further analyzed in more detail. A new way for optimizing and improving the precision of the ocean mode is explored, and the method has practical application value on the refined research result of a certain sea area.
Example 1 the object of the present invention is to reduce SST global pattern errors by various experiments, and finally reducing CMIP 5's 3 ℃ error to 1 ℃ to develop pattern accuracy evaluations around certain sea critical areas. According to the invention, two wind fields are preferably selected to drive a mode, two key parameters of circulation and temperature are selected as sensitivity evaluation indexes, three key interfaces of upward flow, frontal surface and thermocline are selected for comparison analysis, a set of large data set for sensitivity experiment is formed, and a sensitivity index evaluation system is constructed on the basis. At present, no detailed research report on the aspect is found in the field of physical ocean.
The invention provides a new scheme for optimizing ocean modes based on sensitivity experiments. And taking a certain sea as an experimental area and taking a certain sea area as an important point, performing systematic evaluation on the overall quality of the two preferable wind fields, selecting an optimal wind field, and simultaneously optimizing a sea mode to obtain a high-precision simulation result.
The device according to the invention comprises:
(1) Computing device high performance computing device 10 has a total computing power of about 50 trillion times. Mainly comprises 1 IBM x240 blade cluster (70 pieces, total 1120 cores to strong CPU), 1 SGI UV2000 (256 cores to strong CPU, SMP structure), 2 IBM Power 755 (32 cores Power 7 CPU, SMP structure each), 1 HP BL280c blade cluster system (128 pieces, total 1024 cores to strong CPU), 1 HP super dome (128 cores to Itanium CPU, SMP structure), 1 SGI Altix 4700 (168 cores to Itanium CPU, SMP structure) and the like. (2) The total storage devices were 12, all using a disk array of Hewlett-packard, with a total storage capacity of about 2.5PB. Mainly comprises 1 table 3PAR 10400, 1 table P1000 3PAR V400, 1 table EVA8400, 1 table EVA8100, 1 table EVA6400, 1 table EVA4400, 1 table EVA4100, 1 table EVA4000 and 4 tables MSA2000. All disk arrays constitute a fiber optic storage local area network.
The high-performance computing center has strong computing capacity and a large amount of storage space, and can meet the requirements of projects on computing and storage resources.
As shown in fig. 1, the method for improving the simulation precision of the ocean mode based on the wind field sensitivity parameter provided by the embodiment of the invention comprises the following steps:
s1, building a general mode: building a general mode of the whole sea area under the windless condition based on an area ocean model ROMS; after the simulation result is verified to meet the requirements, the simulation result is used for driving simulation experiments of different wind fields;
s2, selecting general CFSV2 wind fields and ERA5 wind fields to analyze data, respectively driving general modes of the same grid, and obtaining two sets of driving mode results; the wind speed and the wind direction of the CFSV2 wind field and the ERA5 wind field which are averaged in time are respectively subtracted to obtain a wind speed difference valueAnd wind direction difference->Two-dimensional scalar fields; then subtracting the time average fields of the simulation results of the driving modes of the CFSV2 wind field and the ERA5 wind field to obtain a flow velocity difference value between the two driving mode simulation results>Difference in flow directionAnd temperature difference->Is a three-dimensional scalar field of (2);
s3, sensitivity analysis and test: according to the sensitivity grading section, the surface layer of the driving mode result is used as the reference to divide the extremely high, medium and low sensitivity areas; selecting two wind fields;
And S4, based on the optimal wind field of the selected driving mode, performing key region simulation by using the high-precision ocean mode encrypted by the grid, and performing action analysis of the medium-small scale structure in ocean heat flux on a certain sea key region.
Illustratively, step S1 includes: and building a general mode of the whole sea area under the windless condition based on an advanced regional ocean model ROMS. In order to save computing resources, a single grid of a certain sea area is used for a sensitivity experiment, a double nested grid of a certain key area is used for optimizing a mode and performing simulation and analysis, and the following is a general mode configuration method:
a. partitioning: in a sea area, the whole sea area is used as a research area of an area model, orthogonal linear grids are adopted, and the resolution is 0.1 degree multiplied by 0.1 degree; vertically adopting Sigma grid, and dividing into 40 layers; the mode time is 2011-2020. An orthogonal linear grid is adopted in a certain key area, and the resolution of the grid is 0.025 degrees multiplied by 0.025 degrees; vertically adopting Sigma grid, and dividing into 40 layers; the mode time is 2011-2020.
b. Initial conditions: the water depth topography is revised by collecting survey data and database data on the basis of shared latest high-resolution water depth topography data (gemco). And (3) collecting the latest Landsat satellite remote sensing image and extracting the average climax line of the climax as a general-mode shoreline.
Salt warming: the initial field of the large area mode adopts SODA climate state annual average temperature salt data; the initial field of the key area mode adopts an SODA climate state month average temperature salt field.
c. Boundary conditions: upper boundary conditions, surface forcing field driven by CFSV2 heat flux data averaged over climatic states; because the wind field is a variable parameter of the sensitivity experiment, the wind field is not included in the construction of a general mode. Under the side boundary condition, the tide wave adopts shared high-resolution TPXO8 and NAO99 tide harmonic constants, and the actual measured tide level station data is adopted for further verification; ocean currents and warm salts use shared AVISO data.
Exemplary, step S2 sensitivity experiment designs include: selecting CFSV2 and ERA5 wind fields to analyze data, and respectively driving the universal modes of a single grid to obtain two sets of driving modes; the wind speed and the wind direction of the CFSV2 wind field and the ERA5 wind field which are averaged in time are respectively subtracted, and two-dimensional scalar fields of a wind speed difference value and a wind direction difference value are calculated and obtained, and are respectively expressed asAnd->The method comprises the steps of carrying out a first treatment on the surface of the Then the time average fields of the results driven by the CFSV2 and ERA5 wind field driving modes are subtracted to obtain two setsThree of flow velocity, flow direction and temperature difference between driving modes
A scalar field of dimensions, respectively denoted as The method comprises the steps of carrying out a first treatment on the surface of the For example, two wind fields CFSV2 and ERA5 are selected to analyze data, and the universal modes of the same grid are respectively driven to obtain two sets of driving mode results from 2011 to 2020. Because sigma layering adopted by the ROMS mode is inconvenient to directly carry out layering analysis according to depth, a mode result is needed to be firstly interpolated from sigma layering to a standard layer of a z coordinate, and warp and weft components of a flow field and warp and weft components of a wind field are interpolated to a unified horizontal coordinate system by taking temperature variables as standards.
The wind speed and the wind direction of the CFSV2 wind field and the ERA5 wind field which are averaged in time (such as month average, year average and the like) are subtracted respectively, and two-dimensional scalar fields of a wind speed difference value and a wind direction difference value can be calculated and are respectively expressed as DeltaV and DeltaD. Then the time average fields of the results (the flow taking field and the temperature field in the invention) driven by the CFSV2 and ERA5 wind fields are subtracted to obtain three-dimensional scalar fields of the flow velocity, the flow direction and the temperature difference between the two driving modes, which are respectively expressed as. For the mode of the nth layer, defining the sensitivity parameter as the ratio of the mode result difference value to the wind field difference value: sensitivity parameter of temperature to wind speed- > Sensitivity parameter of temperature to wind direction-> Sensitivity parameter of flow velocity to wind speed-> Sensitivity parameter of flow velocity to wind direction-> Sensitivity parameter of flow direction to wind speed-> Sensitivity parameters of flow direction to wind direction: />。
So far, each sensitivity parameter obtained is a three-dimensional scalar field. Sensitivity analysis can be performed on any layer depth or vertical section according to requirements, and the horizontal distribution state of sensitivity, the vertical influence depth and the like can be summarized. In order to analyze the sensitivity more intuitively, the sensitivity is classified into an extremely high sensitivity interval, a medium sensitivity interval and a low sensitivity interval by using a classification method; in addition, if the wind field difference value is too small, the calculated sensitivity parameter is amplified and loses the reference meaning, so that the region with the extremely small wind field difference value is eliminated. In the process of sensitivity analysis, the specifically adopted grading and rejecting threshold standards are formulated by combining actual mode conditions, historical research results and data comparison. Illustratively, step S3 of the sensitivity analysis test includes: according to the sensitivity grading section, the surface layer of the driving mode result is used as the reference to divide the extremely high, medium and low sensitivity areas; selecting two wind fields; for example, according to the sensitivity classification section, three sensitivity areas of extremely high, medium and low can be divided based on the surface layer of the driving mode result. It is considered that the ocean mode is most responsive to wind field changes in a highly sensitive region, and therefore the accuracy assessment of the wind field in this region should be considered most preferably; secondly, a middle sensitivity region with a priority lower than that of a high sensitivity region; finally, in the low sensitivity region, the influence of the change of the wind field on the ocean mode can be considered to be basically negligible, so that the priority is the lowest. In addition, in the region removed because the difference between the two wind fields is too small, since the two wind fields are substantially identical, it is considered that there is no choice of merits.
According to the method, more targeted evaluation can be performed on two wind fields. Firstly, the collected wind field actual measurement data are classified by combining the longitude and latitude coordinates of observation and regions defined by extremely high, medium and low sensitivity, and A, B, C, D data sets are respectively formed. For each observation data, the wind field data adopted by the two sets of modes are interpolated first and then compared with the observation data (the flow is shown in figure 2). And then, respectively carrying out error statistics on the observed data in the four areas of the high, medium and low and the interpolated mode data, so as to obtain the error magnitudes of the two sets of wind fields in the four sensitive areas. Finally, the quality of the wind field can be compared and evaluated according to the error, for example: if the error of the ERA5 wind field in the high sensitivity region is obviously smaller than the error of the CFSV2, the wind field which takes the ERA5 wind field as a driving mode can be limited; alternatively, if the errors of the two wind fields in the high sensitivity region are substantially identical, and CFSV2 performs significantly better than ERA5 in the medium sensitivity region, then CFSV2 wind fields are preferentially selected.
Illustratively, step S4 of utilizing the high-accuracy ocean pattern results for the emphasis analysis includes: and analyzing the middle-small scale structure in the ocean heat flux of a certain sea area based on the wind field operation mode of the selected driving mode. By way of example, with the above method, a wind park can be selected which is relatively more suitable for the drive mode. The wind field is brought into a dual nested general mode, and simulation of a heavy point area with higher grid precision is performed. By utilizing the set of high-precision driving modes, a certain sea area, particularly a certain sea area, is subjected to deeper analysis by combining the conventional research method, such as more detailed summarization and dissection of a multi-layer circulation structure, the morphology of a temperature frontal surface and thermocline and seasonal variation rules; and analyzing the contribution of the medium-small scale structure in ocean heat flux.
Embodiment 2, as shown in fig. 3, the embodiment of the invention provides a system for improving the simulation precision of ocean modes based on different wind field sensitivity parameters, comprising: the general mode building module 1 is used for building a general mode of the whole sea area under the windless condition based on the regional ocean model ROMS to obtain a simulation result; the three-dimensional scalar field acquisition module 2 is used for analyzing data by utilizing two wind fields of CFSV2 and ERA5, respectively driving the universal modes of the same grid, and acquiring the results of two sets of driving mode simulation; the wind speed and the wind direction of the CFSV2 wind field and the ERA5 wind field which are averaged in time are respectively subtracted, and two-dimensional scalar fields of a wind speed difference value and a wind direction difference value are calculated and obtained, and are respectively expressed asAnd->The method comprises the steps of carrying out a first treatment on the surface of the Then the time average fields of the results of the mode simulation driven by the CFSV2 and ERA5 wind fields are subtracted to obtain three-dimensional scalar fields of the flow velocity, the flow direction and the temperature difference between the two sets of simulation results, which are respectively expressed as +.>The method comprises the steps of carrying out a first treatment on the surface of the The sensitivity analysis and detection module 3 is used for dividing extremely high, medium and low sensitivity areas according to the sensitivity grading interval and based on the surface layer of the driving mode simulation result; and evaluating and optimally selecting the two wind fields; and the middle-small scale structure ocean heat flux analysis module 4 optimizes the wind field of the selected driving mode to perform high-precision simulation of the key region, performs the analysis of the key region by utilizing the high-precision ocean mode result, and performs the analysis of the effect of the middle-small scale structure in the ocean heat flux on a certain sea large region. In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
The content of the information interaction and the execution process between the devices/units and the like is based on the same conception as the method embodiment of the present application, and specific functions and technical effects brought by the content can be referred to in the method embodiment section, and will not be described herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. For specific working processes of the units and modules in the system, reference may be made to corresponding processes in the foregoing method embodiments. Based on the technical solutions described in the embodiments of the present application, the following application examples may be further proposed. According to an embodiment of the present application, there is also provided a computer apparatus including: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, which when executed by the processor performs the steps of any of the various method embodiments described above.
Embodiments of the present application also provide a computer readable storage medium storing a computer program which, when executed by a processor, performs the steps of the respective method embodiments described above. The embodiment of the application also provides an information data processing terminal, which is used for providing a user input interface to implement the steps in the method embodiments when being implemented on an electronic device, and the information data processing terminal is not limited to a mobile phone, a computer and a switch. The embodiment of the application also provides a server, which is used for realizing the steps in the method embodiments when being executed on the electronic device and providing a user input interface. Embodiments of the present application also provide a computer program product which, when run on an electronic device, causes the electronic device to perform the steps of the method embodiments described above.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing device/terminal apparatus, recording medium, computer memory, read-only memory (ROM), random access memory (Random Access Memory, RAM), electrical carrier signals, telecommunication signals, and software distribution medium. Such as a U-disk, removable hard disk, magnetic or optical disk, etc.
To further demonstrate the positive effects of the above embodiments, the present invention was based on the above technical solutions to perform the following experiments. Through the analysis of the driving mode results and the comparison analysis of other various wind fields, a relatively better wind field can be comprehensively evaluated, suggestions for optimizing the wind field and the mode are provided, and more accurate results can be predicted to be obtained by using the driving mode. And whether this wind field is truly as predicted requires further inspection and analysis. The first test is similar to the atmospheric test: firstly, acquiring A, B, C, D four data sets of flow field and temperature observation data in the ocean according to a coverage area of a sensitivity level; then, interpolating the two sets of mode data according to the observed data information, wherein the flow is also shown in fig. 2; and finally, carrying out error statistics on the three sensitivity areas, comparing the sizes of errors of the two sets of modes in the three areas, and analyzing whether a conclusion consistent with the error of the wind field can be obtained. And the second step of inspection is to perform error statistics on the whole sea area, namely integrating four sensitivity areas and all the observed data in the removed areas, and calculating the overall error after interpolating the mode result according to each observed data. After two-step verification, if the partition error distribution rule of the driving mode can be consistent with the wind field error, and the overall error of the mode calculated by using the better wind field is actually better than that of the other mode, the method for performing partition analysis on the wind field applicability by using the sensitivity is basically judged to be feasible.
Experiments show that: the ocean mode is one of the most important means of human cognition and via the slightly ocean, and can not be replaced in global climate change, ocean engineering, ocean pasture, ocean military and other activities. In the past, a great deal of work is done in improving the precision of the ocean mode, and the optimization of the sea surface driving wind field is relatively deficient, so that the invention fills the technical blank in the field. The simulation accuracy of the ocean mode is seriously affected by inaccurate wind fields, which is a difficult problem for the development of the mode for a long time. Because the prior analysis wind fields are not known enough, only one wind field can be selected in the same sea area, and in practice, different analysis wind fields have different wind condition precision on strong wind, constant wind and the like, and different analysis wind field data driving ocean modes can be selected in the same mode area. Therefore, the present invention overcomes the conventional wisdom and technical bias.
While the invention has been described with respect to what is presently considered to be the most practical and preferred embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications, equivalents, and alternatives falling within the spirit and scope of the invention.
Claims (10)
1. A method for improving the simulation precision of a marine mode based on wind field sensitivity parameters is characterized by comprising the following steps:
s1, building a general mode: building a general mode under the windless condition of a certain sea area based on an area ocean model ROMS, and after the simulation result is verified to meet the requirements, using the model to simulate experiments under different wind field driving;
s2, analyzing data by selecting an international common CFSV2 wind field and an ERA5 wind field, respectively driving the common modes of the same grid, and obtaining two sets of driving mode simulation results; the wind speed and the wind direction of the CFSV2 wind field and the ERA5 wind field which are averaged in time are respectively subtracted to obtain a wind speed difference valueAnd wind direction difference->Two-dimensional scalar fields; then subtracting the time average fields of the driving mode simulation results of the CFSV2 wind field and the ERA5 wind field to obtain a flow velocity difference value between the two driving mode simulation results>Difference in flow direction->And temperature difference->Is a three-dimensional scalar field of (2);
s3, sensitivity analysis and test: based on the surface layer difference value result of the driving mode, dividing the distribution of extremely high, medium and low sensitive areas according to the sensitivity classification of the mode difference value result, and carrying out correlation analysis on the sensitive areas of the mode difference value result and the CFSV2 wind field and ERA5 wind field difference value result, and preferably obtaining the optimal wind field of each area;
And S4, based on the optimal wind field of the selected driving mode, performing key region simulation by using the high-precision ocean mode encrypted by the grid, and performing action analysis of the medium-small scale structure in ocean heat flux on a certain sea key region.
2. The method for improving the simulation accuracy of the ocean mode based on the wind farm sensitivity parameters according to claim 1, wherein in the step S1, the same grid of a certain sea area is used for the sensitivity experiment, and the double nested key area encryption grid is used for optimizing the mode and analyzing the simulation result.
3. The method for improving the simulation accuracy of the ocean mode based on the wind farm sensitivity parameters according to claim 1, wherein in step S1, the method for constructing the universal mode comprises:
a. partitioning: for a sea area, taking the whole sea area as a research area of a large area model, adopting orthogonal linear meshing, wherein an important area adopts orthogonal linear encryption meshing;
b. initial conditions: collecting investigation data and database data for revising water depth data on the basis of shared latest high-resolution water depth topographic data for different water depth topography;
for the shoreline, collecting satellite remote sensing images and extracting the average climax line of the climax as a general mode shoreline;
For warm salt, SODA climate state annual average warm salt data is adopted in a large-area mode initial field; the initial field of the key area mode adopts SODA climate state month average Wen Yanchang;
c. boundary conditions:
for the upper boundary conditions, the surface forcing field is driven by the climatic state month averaged CFSV2 heat flux data;
for the side boundary conditions, the tide wave adopts a high-resolution tide harmonic constant, and the actual measured tide level station data is adopted for verification; ocean currents and warm salts use AVISO data.
4. The method for improving the simulation accuracy of the ocean mode based on the wind field sensitivity parameters according to claim 1, wherein in the step S2, the CFSV2 wind field and the ERA5 wind field analysis data are selected to drive a general mode to perform simulation calculation, simulation results are firstly interpolated from sigma layers to a standard layer of z coordinates, and warp and weft components of a flow field and warp and weft components of the wind field are interpolated to a unified horizontal coordinate system by taking temperature variables as standards.
5. The method for improving the simulation accuracy of the ocean mode based on the wind field sensitivity parameter according to claim 1, wherein in the step S2, for the driving mode of the nth layer, the sensitivity parameter is a ratio of a driving mode result difference value to a wind field difference value, and the calculation formula is as follows:
Sensitivity parameter of temperature to wind speed Sensitivity parameter of temperature to wind direction-> Sensitivity parameter of flow velocity to wind speed-> Sensitivity parameter of flow velocity to wind direction-> Sensitivity of flow direction to wind speedCount-> Sensitivity parameter of flow direction to wind direction-> 。
6. The method for improving the simulation precision of the ocean model based on the wind field sensitivity parameters according to claim 5, wherein sensitivity analysis is carried out on any layer depth or vertical section according to requirements on each sensitivity parameter of the obtained three-dimensional scalar field, and the horizontal distribution state of the sensitivity and the vertical influence depth are obtained.
7. The method for improving the simulation accuracy of the ocean mode based on the wind farm sensitivity parameters according to claim 5, wherein the sensitivity parameters are classified into an extremely high sensitivity interval, a medium sensitivity interval and a low sensitivity interval by using a classification method.
8. The method for improving the simulation accuracy of the ocean mode based on the wind field sensitivity parameters according to claim 5, wherein the region with the too small wind field difference value is subjected to threshold value elimination.
9. The method for improving accuracy of ocean mode simulation based on wind farm sensitivity parameters according to claim 5, wherein in step S3, the optimization selection of two wind farms comprises:
Firstly, classifying collected wind field actual measurement data by combining observed longitude and latitude coordinates and regions defined by extremely high, medium and low sensitivity to respectively form A, B, C, D four data sets; for each observation data, carrying out interpolation on wind field data adopted by the two sets of modes, and then comparing the wind field data with the observation data;
then, respectively carrying out error statistics on the observed data and the interpolated mode data in the four sensitive areas of the high, medium and low to obtain the error sizes and distribution states of the two wind fields in the four sensitive areas;
finally, comparing and evaluating the quality of the wind field according to the error, and taking the ERA5 wind field as a wind field of a driving mode if the error of the ERA5 wind field in a high-sensitivity area is smaller than the error of the CFSV 2; if the errors of the two wind fields in the high-sensitivity area are consistent, and the CFSV2 is better than ERA5 in the medium-sensitivity area, the CFSV2 wind field is preferentially selected.
10. A system for improving accuracy of ocean mode simulation based on wind farm sensitivity parameters, wherein the method for improving accuracy of ocean mode simulation based on wind farm sensitivity parameters according to any one of claims 1-9 is implemented, the system comprising:
The general mode building module (1) is used for building a general mode under the windless condition of a certain sea area based on the regional ocean model ROMS, and the simulation result is used for simulation experiments under different wind field driving after verification meets the requirements;
the three-dimensional scalar field acquisition module (2) is used for selecting general CFSV2 wind fields and ERA5 wind fields to analyze data, respectively driving general modes of the same grid, and acquiring two sets of driving mode simulation results; the wind speed and the wind direction of the CFSV2 wind field and the ERA5 wind field which are averaged in time are respectively subtracted to obtain a wind speed difference valueAnd wind direction difference->Two-dimensional scalar fields; then subtracting the time average fields of the simulation results of the driving modes of the CFSV2 wind field and the ERA5 wind field to obtain a flow velocity difference value between the two driving mode simulation results>Difference in flow direction->Temperature difference->Is a three-dimensional scalar field of (2); the sensitivity analysis and detection module (3) is used for dividing extremely high, medium and low sensitivity areas according to the sensitivity grading section by taking the surface layer of the driving mode result as a reference, and selecting a CFSV2 wind field and an ERA5 wind field; and the middle-small scale structure ocean heat flux analysis module (4) is used for analyzing key areas by utilizing high-precision ocean mode results, and analyzing the effect of the middle-small scale structure in ocean heat flux of a certain sea area based on a wind field of a selected driving mode. / >
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