CN117610303A - Fine simulation method and device for meteorological marine environment - Google Patents
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Abstract
The invention discloses a method and a device for fine simulation of a meteorological marine environment, wherein the method comprises the following steps: acquiring a meteorological ocean data set; the meteorological marine data set comprises an atmospheric marine observation data subset, a meteorological monitoring point observation data subset and a meteorological marine numerical mode forecast data subset; performing data preprocessing on the meteorological ocean data set to obtain a standard meteorological ocean data set; constructing and obtaining a meteorological marine environment simulation model by using a standard meteorological marine data set; and processing the meteorological marine environment simulation range information by using the meteorological marine environment simulation model to obtain meteorological marine environment simulation result information. The method can simulate the complex meteorological marine environment, and simultaneously meets the requirements that the complexity of the environment meets the action plan difficulty, the construction method is simple, convenient and flexible, and the simulation environment is dynamic and real.
Description
Technical Field
The invention relates to the technical fields of meteorological statistics analysis and computers, in particular to a method and a device for fine simulation of a meteorological marine environment.
Background
Currently, with the wide application of modern ocean technologies, particularly informatization technologies, in modern ocean technologies and ocean engineering equipment, the problems that offshore operation actions and equipment performances are affected by meteorological ocean environment factors and are restricted are increasingly prominent. In the field of simulation of the meteorological marine environment, how to construct a simulation environment approaching to the actual meteorological marine environment directly relates to the guarantee performance of offshore operation, and is a problem that environmental simulation personnel must pay high attention and focus to grasp in the simulation process.
The meteorological marine environment is complex and changeable, has great influence on equipment efficiency, offshore actions and decision making, and is an indispensable important component in the marine environment. The grid point data of the meteorological marine environment form a field model of the meteorological marine environment, and follow a certain physical distribution and change rule, so that the environment is accurately simulated. How to simulate a complex meteorological marine environment and meet the requirements that the complexity of the environment meets the action plan difficulty, the construction method is simple, convenient and flexible, and the simulation environment is dynamic and real is a current urgent problem to be solved.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method and a device for the fine simulation of the meteorological marine environment, so as to simulate the complex meteorological marine environment, and simultaneously meet the requirements that the environment complexity meets the action plan difficulty, the construction method is simple, convenient and flexible, and the simulation environment is dynamic and real.
In order to solve the technical problems, a first aspect of the embodiment of the invention discloses a method for fine simulation of a meteorological marine environment, which comprises the following steps:
s1, acquiring a meteorological ocean data set; the meteorological marine data set comprises an atmospheric marine observation data subset, a meteorological monitoring point observation data subset and a meteorological marine numerical mode forecast data subset; each data subset comprising a number of observation data; the observation data comprises data attributes, data values and data acquisition information; the data acquisition information comprises data acquisition time and data acquisition places;
s2, carrying out data preprocessing on the meteorological ocean data set to obtain a standard meteorological ocean data set;
s3, constructing a meteorological marine environment simulation model by using a standard meteorological marine data set;
s4, acquiring meteorological marine simulation range information; processing the meteorological marine simulation range information by using the meteorological marine environment simulation model to obtain meteorological marine environment simulation result information; the simulation result information of the meteorological marine environment is used for representing simulation results of the meteorological marine environment parameters.
The data preprocessing is performed on the meteorological ocean data set to obtain a standard meteorological ocean data set, and the method comprises the following steps:
s21, performing data cleaning processing on the meteorological ocean data set to obtain a cleaning data set;
s22, carrying out data protocol processing on the cleaning data set to obtain a protocol data set;
s23, carrying out unified processing on the acquired information of the protocol data set to obtain a standard data set;
s24, performing boundary check and category consistency check processing on the standard data set to obtain a consistency data set;
s25, based on each data attribute, combining the observation data with the same data attribute in the consistency data set to obtain a basic simulation database of the data attribute;
s26, combining the basic simulation databases of all the data attributes to obtain a standard meteorological ocean data set.
The step of performing data protocol processing on the cleaning data set to obtain a protocol data set comprises the following steps:
s221, determining a data attribute range of an atmospheric marine observation data subset in the cleaning data set;
s222, judging whether the data attribute of the observed data is within the data attribute range of the atmospheric marine observed data subset or not for each observed data of the atmospheric marine observed data subset in the cleaning data set to obtain a first judging result; deleting observation data with the first judging result being no from the atmospheric ocean observation data subset;
S223, determining a data attribute range of a meteorological monitoring point observation data subset in the cleaning data set;
s224, judging whether the data attribute of the observed data is in the data attribute range of the meteorological monitoring point observed data subset or not for each observed data of the meteorological monitoring point observed data subset in the cleaning data set to obtain a second judging result; deleting observation data with the second judging result being no from the meteorological monitoring point observation data subset;
s225, determining a data attribute range of a meteorological marine numerical mode forecast data subset in the cleaning data set;
s226, judging whether the data attribute of the observed data is in the data attribute range of the meteorological marine numerical value mode forecast data subset or not for each observed data of the meteorological marine numerical value mode forecast data subset in the cleaning data set to obtain a third judging result; deleting observation data with the third judging result being no from the meteorological ocean numerical model forecast data subset;
and S227, combining the atmospheric ocean observation data subset, the meteorological monitoring point observation data subset and the meteorological ocean numerical mode forecast data subset which are subjected to discrimination to obtain a protocol data set.
The step of carrying out unified processing on the acquired information of the protocol data set to obtain a standard data set comprises the following steps:
s231, setting unified data acquisition time and unified data acquisition place for the observation data with the same data attribute contained in the same data subset of the protocol data set;
s232, carrying out alignment processing on the acquisition time and the acquisition place of the observation data according to the unified data acquisition time and the unified data acquisition place on the observation data with the same data attribute contained in each data subset to obtain standard observation data;
s233, executing S232 on the observation data of each data attribute of each data subset to obtain a specification data subset corresponding to the data subset; the normative data subsets comprise an atmospheric ocean observation normative data subset, a meteorological monitoring point observation normative data subset and a meteorological ocean numerical mode forecast normative data subset;
s234, combining all the canonical data subsets to obtain a canonical data set.
Performing boundary check and category consistency check processing on the standard data set to obtain a consistency data set, wherein the method comprises the following steps:
s241, presetting a corresponding value range for each data attribute in each canonical data subset of the canonical data set; the boundary value of the value range comprises an upper boundary value of the value range and a lower boundary value of the value range;
S242, judging whether the value of each observation data of each canonical data subset of the canonical data set is in the value range according to the value range of the data attribute of each observation data; when the observation data is within the value range, the observation data is not processed; when the observed data is not in the value range, setting the observed data to be the boundary value of the value range closest to the observed data;
s243, after finishing S242 on all the observation data of each canonical data subset, obtaining a boundary canonical data subset corresponding to the canonical data subset;
s244, carrying out autoregressive-moving average modeling on the observed data of each type of data attribute of the boundary specification data subset corresponding to the atmospheric ocean observation specification data subset, taking the data acquisition information of the observed data as an independent variable and the data value of the observed data as a dependent variable to respectively obtain regression models of the type of data attribute; calculating the independent variable by using the regression model to obtain regression data values; judging whether the absolute value of the difference between the regression data value and the corresponding factor variable value is larger than a set first regression judging threshold value or not; if the observed data is larger than the first regression discrimination threshold, deleting the observed data from the boundary specification data subset corresponding to the atmospheric ocean observation specification data subset, and if the observed data is smaller than the first regression discrimination threshold, not processing the observed data;
S245, executing S244 on all the observation data of the boundary specification data subset corresponding to the atmospheric marine observation specification data subset to obtain the atmospheric marine observation consistency data subset;
s246, carrying out cluster analysis processing on the observed data of each type of data attribute of the boundary specification data subset corresponding to the meteorological monitoring point observation specification data subset, taking the data acquisition information of the observed data as an independent variable and the data value of the observed data as a dependent variable to respectively obtain the cluster result information of the type of data attribute; the clustering result information comprises clustering categories to which all the observation data of the class data attribute belong;
s247, determining the number of the observation data included in each cluster category; setting a data quantity threshold; deleting the observed data included in the clustering category with the number of the observed data smaller than the data quantity threshold value from the boundary specification data subset;
s248, executing S246 and S247 on all the observation data of the boundary specification data subset corresponding to the meteorological monitoring point observation specification data subset to obtain the meteorological monitoring point observation consistency data subset;
s249, regarding the observed data of each type of data attribute of the boundary specification data subset corresponding to the meteorological ocean numerical model forecast specification data subset, taking the data acquisition information of the observed data as a known independent variable, taking the data value of the observed data as a known dependent variable, constructing a curve to be approximated by using the known independent variable and the known dependent variable, and performing curve fitting on the curve to be approximated by using a function approximation method to obtain an optimal consistent approximation polynomial f (Ix) of the type of data attribute; calculating the known independent variable by using the optimal consistent approximation polynomial f (Ix) to obtain an approximate dependent variable; judging whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is larger than a set second regression judging threshold value; if the observed data is larger than the second regression discrimination threshold, deleting the observed data from the boundary specification data subset corresponding to the meteorological ocean numerical model forecast specification data subset, and if the observed data is smaller than the second regression discrimination threshold, not processing the observed data;
S2410, executing S249 on all the observed data of the boundary specification data subset corresponding to the meteorological marine numerical mode prediction specification data subset to obtain a meteorological marine numerical mode prediction consistency data subset;
s2411, carrying out combined processing on the atmospheric ocean observation consistency data subset, the weather monitoring point observation consistency data subset and the weather ocean numerical mode forecast consistency data subset to obtain a consistency data set.
The method for constructing the simulation model of the meteorological marine environment by using the standard meteorological marine data set comprises the following steps:
s31, constructing and obtaining an initialized meteorological marine environment simulation model; the initialized meteorological marine environment simulation model comprises unknown parameters and a prediction equation set;
s32, solving unknown parameters in the initialized weather marine environment simulation model by using the standard weather marine data set to obtain an unknown parameter solving value;
and S33, substituting the unknown parameter solving value into the prediction equation set to obtain the meteorological marine environment simulation model.
The expression of the prediction equation set of the initialized meteorological marine environment simulation model is as follows:
Wherein [ x, y, z]The position coordinate of the predicted point under the geodetic coordinate system is t, the predicted time is t, u and v are the speeds of the x axis and the y axis of the sea wave in the horizontal direction, and w is the vertical speed of the sea wave;representing differential operator +_>[i,j,k]Unit amounts in the x, y, and z axes; f is a coriolis force parameter; phi is the dynamic pressure of the ocean, phi=p/P o ，ρ o Is the sea water reference density; />And v θ Respectively a viscosity coefficient and a particle diffusion coefficient, g is a gravity constant; ρ is the field density of seawater, and P is the pressure of the weather marine environment; c represents calculating the concentration of particles; f (F) u 、F v 、F C D for external forcing terms in the x-axis, y-axis and z-axis directions, respectively u 、D v 、D C Dissipation terms in the x-axis, y-axis, and z-axis directions; /> And->Turbulence terms, K, representing u, v and w, respectively M And K C The vertical vortex viscosity and the turbulence diffusion coefficient of the sea surface are respectively; wherein (1)>v θ 、C、F u 、F v 、F C 、D u 、D v 、D C 、K M 、K C Is an unknown parameter.
The second aspect of the embodiment of the invention discloses a fine simulation device for a meteorological marine environment, which comprises:
a memory storing executable program code;
a processor coupled to the memory;
and the processor calls the executable program codes stored in the memory to execute the fine simulation method of the meteorological marine environment.
In a third aspect, an embodiment of the present invention discloses a computer-readable storage medium storing computer instructions that, when invoked, are used to perform a method of performing a refined simulation of a weather marine environment.
The fourth aspect of the embodiment of the invention discloses an information data processing terminal which is used for realizing the fine simulation method of the meteorological marine environment.
The beneficial effects of the invention are as follows:
1. the invention provides a method and a device for fine simulation of a meteorological marine environment, which are based on effective meteorological marine detection data, real-time observation detection data and a meteorological marine numerical prediction model product, and utilize a meteorological marine statistics method and a meteorological marine numerical prediction technology to construct an engine for meteorological marine environment simulation, so as to form a marine environment fine simulation and simulation system, realize the simulation of a target area typical three-dimensional flow field, a boundary layer structure, temperature salt distribution, marine processes such as a marine mesoscale vortex, a marine front, a marine skip layer and the like, so as to research the climate characteristics of the target area, the space-time distribution characteristics and the change rule of meteorological marine elements, and provide the simulation product of the meteorological marine environment for the practical operation demands on the sea.
2. In the method, a multi-dimensional data screening method is provided in the actual data preprocessing process, data are calibrated from multiple dimensions such as data attributes, numerical distribution, data sampling information and the like, an observation model is extracted from the same type of observation data, the observation model is utilized to calibrate the data again, reliability of the observation data is guaranteed, and accuracy and high efficiency of the constructed simulation model are further guaranteed.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
For a better understanding of the present disclosure, an embodiment is presented herein.
FIG. 1 is a flow chart of the method of the present invention.
In order to construct a complex weather marine environment suitable for simulating an actual environment, namely, the environment complexity accords with the action plan difficulty, the construction method is simple, convenient and flexible, the simulation environment is dynamic and real, and the invention provides a construction method system of the complex weather marine simulation environment by combining weather marine statistics and numerical modes. The meteorological marine simulation environment with proper complexity is built for the target area, so that the meteorological marine simulation environment is more in line with space-time distribution characteristics and change rules of regional climate and meteorological marine elements, and the reality of an environment model is improved.
The invention constructs an engine for simulating a meteorological marine environment based on effective meteorological marine detection data, training field real-time observation detection data and meteorological marine numerical prediction model products by utilizing a meteorological marine statistics method, a marine mesoscale process diagnosis analysis technology and a meteorological marine numerical prediction technology to form a western Pacific marine environment refined simulation and simulation system, and realizes the simulation of a target area typical three-dimensional flow field, a boundary layer structure and temperature salt distribution and marine processes such as marine mesoscale vortex, marine front, marine jump layer and the like so as to research the climate characteristics of the target area and the space-time distribution characteristics and change rules of meteorological marine elements.
In order to solve the technical problems, a first aspect of the embodiment of the invention discloses a method for fine simulation of a meteorological marine environment, which comprises the following steps:
s1, acquiring a meteorological ocean data set; the meteorological marine data set comprises an atmospheric marine observation data subset, a meteorological monitoring point observation data subset and a meteorological marine numerical mode forecast data subset; each data subset comprising a number of observation data; the observation data comprises data attributes, data values and data acquisition information; the data acquisition information comprises data acquisition time and data acquisition places;
S2, carrying out data preprocessing on the meteorological ocean data set to obtain a standard meteorological ocean data set;
s3, constructing a meteorological marine environment simulation model by using a standard meteorological marine data set;
s4, acquiring meteorological marine simulation range information; and processing the meteorological marine environment simulation range information by using the meteorological marine environment simulation model to obtain meteorological marine environment simulation result information. The simulation result information of the meteorological marine environment is used for representing simulation results of the meteorological marine environment parameters.
The acquiring of the meteorological ocean data set comprises the following steps:
s11, collecting and obtaining an atmospheric ocean observation data subset; the subset of atmospheric marine observations includes both regular observations and irregular observations. The conventional observation data comprises exploratory observation data and ground observation data of a fixed site, marine station observation data, ship observation data, buoy monitoring data and the like. The non-conventional observation data comprises radar observation data, satellite remote sensing data, aircraft report observation data and the like.
The various observation, remote sensing and monitoring data in the step S11 are obtained by taking the atmospheric marine environment as an observation target. The spatio-temporal distribution of aircraft report observations depends on the aircraft flight and its course (AIRCFT). The satellite remote sensing data comprise directly observed radiation brightness temperature, inverted acquired GPS temperature and humidity profile, SSMI atmospheric precipitation, AIRS atmospheric questionnaire profile, quik SCAT, sea surface wind QCAT and other data. Radar observation data includes radar radial wind, reflectivity, etc. The spatial resolution in time of radar observation data is high, but the spatial range of data is small.
S12, collecting and obtaining a meteorological monitoring point observation data subset; the meteorological monitoring point observation data subset comprises recorded data of various monitoring instruments of the meteorological monitoring points, wherein the monitoring instruments comprise meteorological monitoring aircrafts, wind profile radars, visibility meters and the like.
S13, collecting and obtaining a meteorological ocean numerical mode forecast data subset; the weather ocean numerical model forecast data subset is obtained from weather ocean numerical model forecast products, and comprises a high-precision Nemo ocean numerical model forecast product which can provide more than ten ocean numerical forecast products such as three-dimensional time-by-time ocean temperature, salinity, ocean surface dynamic height, ocean current, sea ice, sea water concentration and the like.
The data preprocessing is performed on the meteorological ocean data set to obtain a standard meteorological ocean data set, and the method comprises the following steps:
s21, performing data cleaning processing on the meteorological ocean data set to obtain a cleaning data set;
s22, carrying out data protocol processing on the cleaning data set to obtain a protocol data set;
s23, carrying out unified processing on the acquired information of the protocol data set to obtain a standard data set;
s24, performing boundary check and category consistency check processing on the standard data set to obtain a consistency data set;
S25, based on each data attribute, combining the observation data with the same data attribute in the consistency data set to obtain a basic simulation database of the data attribute;
s26, combining the basic simulation databases of all the data attributes to obtain a standard meteorological ocean data set.
The data cleaning processing comprises filling in missing values, smoothing noise data and smoothing or deleting wild value points; the smooth noise data is obtained by firstly judging the noise data, and then carrying out smoothing treatment on the noise data according to the front and rear data of the noise data; the noise data is a value whose value is smaller than the detection sensitivity of the sensor of the observation data or larger than the measurement upper limit of the sensor of the observation data. The discrimination of the outlier point can adopt a Kalman filtering method. And for the determination of the filling value of the missing value, the measured value in a certain sampling interval before and after the missing value can be averaged.
The step of performing data protocol processing on the cleaning data set to obtain a protocol data set comprises the following steps:
s221, determining a data attribute range of an atmospheric marine observation data subset in the cleaning data set;
s222, judging whether the data attribute of the observed data is within the data attribute range of the atmospheric marine observed data subset or not for each observed data of the atmospheric marine observed data subset in the cleaning data set to obtain a first judging result; deleting observation data with the first judging result being no from the atmospheric ocean observation data subset;
S223, determining a data attribute range of a meteorological monitoring point observation data subset in the cleaning data set;
s224, judging whether the data attribute of the observed data is in the data attribute range of the meteorological monitoring point observed data subset or not for each observed data of the meteorological monitoring point observed data subset in the cleaning data set to obtain a second judging result; deleting observation data with the second judging result being no from the meteorological monitoring point observation data subset;
s225, determining a data attribute range of a meteorological marine numerical mode forecast data subset in the cleaning data set;
s226, judging whether the data attribute of the observed data is in the data attribute range of the meteorological marine numerical value mode forecast data subset or not for each observed data of the meteorological marine numerical value mode forecast data subset in the cleaning data set to obtain a third judging result; deleting observation data with the third judging result being no from the meteorological ocean numerical model forecast data subset;
and S227, combining the atmospheric ocean observation data subset, the meteorological monitoring point observation data subset and the meteorological ocean numerical mode forecast data subset which are subjected to discrimination to obtain a protocol data set.
The data attribute range of the atmospheric ocean observation data subset comprises wave height, wave speed, ocean current, radiation brightness temperature, sea surface wind and the like;
the data attribute range of the meteorological ocean numerical model forecast data subset comprises three-dimensional time-by-time ocean temperature, salinity, ocean surface dynamic height, ocean current, sea ice, ocean dynamic pressure, meteorological ocean environment pressure, sea water concentration and the like;
the data attribute range of the meteorological monitoring point observation data subset comprises sea surface meteorological parameter attributes, visibility and the like;
the step of carrying out unified processing on the acquired information of the protocol data set to obtain a standard data set comprises the following steps:
s231, setting unified data acquisition time and unified data acquisition place for the observation data with the same data attribute contained in the same data subset of the protocol data set;
s232, carrying out alignment processing on the acquisition time and the acquisition place of the observation data according to the unified data acquisition time and the unified data acquisition place on the observation data with the same data attribute contained in each data subset to obtain standard observation data;
the processing of aligning the acquisition time and the acquisition place of the observed data comprises the following steps:
When the acquisition time interval of the observed data is larger than the time interval of the unified data acquisition time, interpolation processing is carried out on the adjacent observed data to obtain an observed data value at the unified data acquisition time, and the observed data value is used as standard observed data;
when the acquisition time interval of the observed data is smaller than the time interval of the unified data acquisition time, sampling the observed data to obtain the observed data consistent with the unified data acquisition time, and taking the observed data as standard observed data;
when the acquisition space interval of the observed data is larger than the space interval of the unified data acquisition place, interpolation processing is carried out on the adjacent observed data to obtain an observed data value at the unified data acquisition place, and the observed data value is used as standard observed data;
and when the acquisition space interval of the observed data is smaller than the space interval of the unified data acquisition place, sampling the observed data to obtain the observed data consistent with the unified data acquisition place, and taking the observed data as standard observed data.
S233, executing S232 on the observation data of each data attribute of each data subset to obtain a specification data subset corresponding to the data subset; the normative data subsets comprise an atmospheric ocean observation normative data subset, a meteorological monitoring point observation normative data subset and a meteorological ocean numerical mode forecast normative data subset;
S234, combining all the canonical data subsets to obtain a canonical data set;
performing boundary check and category consistency check processing on the standard data set to obtain a consistency data set, wherein the method comprises the following steps:
s241, presetting a corresponding value range for each data attribute in each canonical data subset of the canonical data set; the value range comprises an upper limit value of the value range and a lower limit value of the value range;
s242, judging whether the value of each observation data of each canonical data subset of the canonical data set is in the value range according to the value range of the data attribute of each observation data; when the observation data is within the value range, the observation data is not processed; when the observed data is not in the value range, setting the observed data to be the upper limit value or the lower limit value of the value range closest to the observed data;
s243, after finishing S242 on all the observation data of each canonical data subset, obtaining a boundary canonical data subset corresponding to the canonical data subset;
s244, carrying out autoregressive-moving average modeling on the observed data of each type of data attribute of the boundary specification data subset corresponding to the atmospheric ocean observation specification data subset, taking the data acquisition information of the observed data as an independent variable and the data value of the observed data as a dependent variable to respectively obtain regression models of the type of data attribute; calculating the independent variable by using the regression model to obtain regression data values; judging whether the absolute value of the difference between the regression data value and the corresponding factor variable value is larger than a set first regression judging threshold value or not; if the observed data is larger than the first regression discrimination threshold, deleting the observed data from the boundary specification data subset corresponding to the atmospheric ocean observation specification data subset, and if the observed data is smaller than the first regression discrimination threshold, not processing the observed data;
S245, executing S244 on all the observation data of the boundary specification data subset corresponding to the atmospheric marine observation specification data subset to obtain the atmospheric marine observation consistency data subset;
s246, carrying out cluster analysis processing on the observed data of each type of data attribute of the boundary specification data subset corresponding to the meteorological monitoring point observation specification data subset, taking the data acquisition information of the observed data as an independent variable and the data value of the observed data as a dependent variable to respectively obtain the cluster result information of the type of data attribute; the clustering result information comprises clustering categories to which all the observation data of the class data attribute belong;
s247, determining the number of the observation data included in each cluster category; setting a data quantity threshold; deleting the observed data included in the clustering category with the number of the observed data smaller than the data quantity threshold value from the boundary specification data subset;
s248, executing S246 and S247 on all the observation data of the boundary specification data subset corresponding to the meteorological monitoring point observation specification data subset to obtain the meteorological monitoring point observation consistency data subset;
s249, regarding the observed data of each type of data attribute of the boundary specification data subset corresponding to the meteorological ocean numerical model forecast specification data subset, taking the data acquisition information of the observed data as a known independent variable, taking the data value of the observed data as a known dependent variable, constructing a curve to be approximated by using the known independent variable and the known dependent variable, and performing curve fitting on the curve to be approximated by using a function approximation method to obtain an optimal consistent approximation polynomial f (Ix) of the type of data attribute; calculating the known independent variable by using the optimal consistent approximation polynomial f (Ix) to obtain an approximate dependent variable; judging whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is larger than a set second regression judging threshold value; if the observed data is larger than the second regression discrimination threshold, deleting the observed data from the boundary specification data subset corresponding to the meteorological ocean numerical model forecast specification data subset, and if the observed data is smaller than the second regression discrimination threshold, not processing the observed data;
S2410, executing S249 on all the observed data of the boundary specification data subset corresponding to the meteorological marine numerical mode prediction specification data subset to obtain a meteorological marine numerical mode prediction consistency data subset;
s2411, carrying out combined processing on the atmospheric ocean observation consistency data subset, the weather monitoring point observation consistency data subset and the weather ocean numerical mode forecast consistency data subset to obtain a consistency data set;
and performing curve fitting on the curve to be approximated by using a function approximation method, and adopting an optimal consistent linear approximation method. The best consistent approximation polynomial f (Ix) has the expression:
f(Ix)＝α P1 (Ix) P1 +α P1-1 (Ix) P1-1 +…+α 2 (Ix) 2 +α 1 (Ix)+α 0 ，
wherein P1 is the order of the best consistent approximation polynomial f (Ix), α0, α1, α2, …, α P1 Coefficients of the polynomial f (Ix) are approximated for the best agreement;
the method for constructing the simulation model of the meteorological marine environment by using the standard meteorological marine data set comprises the following steps:
s31, constructing and obtaining an initialized meteorological marine environment simulation model; the initialized meteorological marine environment simulation model comprises unknown parameters and a prediction equation set;
s32, solving unknown parameters in the initialized weather marine environment simulation model by using the standard weather marine data set to obtain an unknown parameter solving value;
And S33, substituting the unknown parameter solving value into the prediction equation set to obtain the meteorological marine environment simulation model.
The expression of the prediction equation set of the initialized meteorological marine environment simulation model is as follows:
wherein [ x, y, z]The position coordinate of the predicted point under the geodetic coordinate system is t, the predicted time is t, u and v are the speeds of the x axis and the y axis of the sea wave in the horizontal direction, and w is the vertical speed of the sea wave;representing differential operator +_>[i,j,k]Unit amounts in the x, y, and z axes; f is a coriolis force parameter; phi is the dynamic pressure of the ocean, phi=p/P o ，ρ o Is the sea water reference density; />And v θ Respectively a viscosity coefficient and a particle diffusion coefficient, g is a gravity constant; ρ is the field density of seawater, and P is the pressure of the weather marine environment; t, S, P are the temperature, salinity and pressure of the weather marine environment respectively; c represents calculating the concentration of particles; f (F) u 、F v 、F C D for external forcing terms in the x-axis, y-axis and z-axis directions, respectively u 、D v 、D C Is a dissipative term in the x-axis, y-axis, and z-axis directions. />And->Turbulence terms, K, representing u, v and w, respectively M And K C The vertical vortex viscosity and the turbulence diffusion coefficient of the sea surface are respectively; wherein (1)>v θ 、C、F u 、F v 、F C 、D u 、D v 、D C 、K M 、K C Is an unknown parameter.
The step of solving the unknown parameters in the initialized weather marine environment simulation model by using the standard weather marine data set to obtain an unknown parameter solving value comprises the following steps:
And substituting the known quantity into the prediction equation set by using the measurement data of the sea wave speed, the sea dynamic pressure and the sea density in the standard meteorological ocean data set as the known quantity, and solving the unknown parameters in the prediction equation set by using a discretization method to obtain an unknown parameter solving value.
The sea water concentration comprises sea water reference density and sea water site density;
the meteorological marine simulation range information is the position coordinates and the prediction time of a prediction point of meteorological marine prediction under a geodetic coordinate system;
processing the meteorological marine simulation range information by using the meteorological marine environment simulation model to obtain meteorological marine environment simulation result information, wherein the method comprises the following steps of:
inputting the position coordinates and the predicted time into the meteorological marine environment simulation model to obtain simulation results of sea wave speed, ocean dynamic pressure and sea water concentration;
the invention discloses a fine simulation device of a meteorological marine environment, which comprises:
a memory storing executable program code;
a processor coupled to the memory;
and the processor calls the executable program codes stored in the memory to execute the fine simulation method of the meteorological marine environment.
The fine simulation device of the meteorological marine environment utilizes a meteorological marine simulation model engine to process a basic database so as to obtain meteorological basic simulation data; the meteorological base simulation data comprises: three-dimensional ocean current velocity field, temperature distribution data and salinity data of the ocean area; data of temperature, air pressure, relative humidity, and wind speed of the atmospheric boundary layer;
before simulation is carried out by utilizing a fine simulation device of a meteorological marine environment, firstly configuring a meteorological marine environment simulation model, taking a statistical analysis database as a data source, configuring the model by simulation through a target area, month, weather phenomenon and the like, and providing an editing function of a basic model; taking a basic model library as a data source, carrying out simulated model configuration through a target area, month, weather phenomenon and the like, and providing an editing function of a basic model; based on the unused statistical analysis model, key technology and the meteorological ocean numerical model, a simulation environment frame is generated after 'splicing and combining', and an interface debugging function of the meteorological ocean numerical model is provided.
When the environment simulation device for the fine simulation of the meteorological marine environment is used for simulating the environment, the environment simulation device mainly comprises the processes of meteorological marine environment simulation product selection, system operation, record monitoring, product output and the like, and comprises the following specific steps:
The simulation method comprises the steps of selecting a simulation product of the meteorological marine environment, and specifically comprises a three-dimensional flow field, a boundary layer structure, temperature and salt distribution of a typical area, a marine typical process and the like, wherein a specific product or all products can be selected for simulation.
The meteorological marine environment simulation system is operated, and after the basic database and the parameter configuration are imported, the simulation system is operated.
The meteorological marine environment simulation system records and monitors, and records key nodes and debugging information in the simulation running process so as to inquire the running progress and accuracy.
And outputting a simulation result of the meteorological marine environment, and outputting a specific simulation result of the target area for researching a time-space change rule of the simulation result.
The invention discloses a computer storage medium which stores computer instructions for executing the fine simulation method of the meteorological marine environment when the computer instructions are called.
The invention discloses an information data processing terminal which is used for realizing the fine simulation method of the meteorological marine environment.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. A fine simulation method for a meteorological marine environment is characterized by comprising the following steps:
s1, acquiring a meteorological ocean data set; the meteorological marine data set comprises an atmospheric marine observation data subset, a meteorological monitoring point observation data subset and a meteorological marine numerical mode forecast data subset; each data subset comprising a number of observation data; the observation data comprises data attributes, data values and data acquisition information; the data acquisition information comprises data acquisition time and data acquisition places;
s2, carrying out data preprocessing on the meteorological ocean data set to obtain a standard meteorological ocean data set;
s3, constructing a meteorological marine environment simulation model by using a standard meteorological marine data set;
s4, acquiring meteorological marine simulation range information; processing the meteorological marine simulation range information by using the meteorological marine environment simulation model to obtain meteorological marine environment simulation result information; the simulation result information of the meteorological marine environment is used for representing simulation results of the meteorological marine environment parameters.
2. The method for fine simulation of a meteorological marine environment of claim 1, wherein the performing data preprocessing on the meteorological marine data set to obtain a standard meteorological marine data set comprises:
S21, performing data cleaning processing on the meteorological ocean data set to obtain a cleaning data set;
s22, carrying out data protocol processing on the cleaning data set to obtain a protocol data set;
s23, carrying out unified processing on the acquired information of the protocol data set to obtain a standard data set;
s24, performing boundary check and category consistency check processing on the standard data set to obtain a consistency data set;
s25, based on each data attribute, combining the observation data with the same data attribute in the consistency data set to obtain a basic simulation database of the data attribute;
s26, combining the basic simulation databases of all the data attributes to obtain a standard meteorological ocean data set.
3. The method for fine simulation of a meteorological marine environment according to claim 2, wherein the performing data reduction processing on the cleaning dataset to obtain a reduced dataset comprises:
s221, determining a data attribute range of an atmospheric marine observation data subset in the cleaning data set;
s222, judging whether the data attribute of the observed data is within the data attribute range of the atmospheric marine observed data subset or not for each observed data of the atmospheric marine observed data subset in the cleaning data set to obtain a first judging result; deleting observation data with the first judging result being no from the atmospheric ocean observation data subset;
S223, determining a data attribute range of a meteorological monitoring point observation data subset in the cleaning data set;
s224, judging whether the data attribute of the observed data is in the data attribute range of the meteorological monitoring point observed data subset or not for each observed data of the meteorological monitoring point observed data subset in the cleaning data set to obtain a second judging result; deleting observation data with the second judging result being no from the meteorological monitoring point observation data subset;
s225, determining a data attribute range of a meteorological marine numerical mode forecast data subset in the cleaning data set;
s226, judging whether the data attribute of the observed data is in the data attribute range of the meteorological marine numerical value mode forecast data subset or not for each observed data of the meteorological marine numerical value mode forecast data subset in the cleaning data set to obtain a third judging result; deleting observation data with the third judging result being no from the meteorological ocean numerical model forecast data subset;
and S227, combining the atmospheric ocean observation data subset, the meteorological monitoring point observation data subset and the meteorological ocean numerical mode forecast data subset which are subjected to discrimination to obtain a protocol data set.
4. The method for fine simulation of a meteorological marine environment according to claim 2, wherein the step of uniformly processing the acquired information of the protocol data set to obtain a specification data set comprises the steps of:
s231, setting unified data acquisition time and unified data acquisition place for the observation data with the same data attribute contained in the same data subset of the protocol data set;
s232, carrying out alignment processing on the acquisition time and the acquisition place of the observation data according to the unified data acquisition time and the unified data acquisition place on the observation data with the same data attribute contained in each data subset to obtain standard observation data;
s233, executing S232 on the observation data of each data attribute of each data subset to obtain a specification data subset corresponding to the data subset; the normative data subsets comprise an atmospheric ocean observation normative data subset, a meteorological monitoring point observation normative data subset and a meteorological ocean numerical mode forecast normative data subset;
s234, combining all the canonical data subsets to obtain a canonical data set.
5. The method for fine simulation of a meteorological marine environment according to claim 2, wherein said performing a boundary check and a class consistency check process on said canonical dataset results in a consistency dataset, comprising:
S241, presetting a corresponding value range for each data attribute in each canonical data subset of the canonical data set; the boundary value of the value range comprises an upper boundary value of the value range and a lower boundary value of the value range;
s242, judging whether the value of each observation data of each canonical data subset of the canonical data set is in the value range according to the value range of the data attribute of each observation data; when the observation data is within the value range, the observation data is not processed; when the observed data is not in the value range, setting the observed data to be the boundary value of the value range closest to the observed data;
s243, after finishing S242 on all the observation data of each canonical data subset, obtaining a boundary canonical data subset corresponding to the canonical data subset;
s244, carrying out autoregressive-moving average modeling on the observed data of each type of data attribute of the boundary specification data subset corresponding to the atmospheric ocean observation specification data subset, taking the data acquisition information of the observed data as an independent variable and the data value of the observed data as a dependent variable to respectively obtain regression models of the type of data attribute; calculating the independent variable by using the regression model to obtain regression data values; judging whether the absolute value of the difference between the regression data value and the corresponding factor variable value is larger than a set first regression judging threshold value or not; if the observed data is larger than the first regression discrimination threshold, deleting the observed data from the boundary specification data subset corresponding to the atmospheric ocean observation specification data subset, and if the observed data is smaller than the first regression discrimination threshold, not processing the observed data;
S245, executing S244 on all the observation data of the boundary specification data subset corresponding to the atmospheric marine observation specification data subset to obtain the atmospheric marine observation consistency data subset;
s246, carrying out cluster analysis processing on the observed data of each type of data attribute of the boundary specification data subset corresponding to the meteorological monitoring point observation specification data subset, taking the data acquisition information of the observed data as an independent variable and the data value of the observed data as a dependent variable to respectively obtain the cluster result information of the type of data attribute; the clustering result information comprises clustering categories to which all the observation data of the class data attribute belong;
s247, determining the number of the observation data included in each cluster category; setting a data quantity threshold; deleting the observed data included in the clustering category with the number of the observed data smaller than the data quantity threshold value from the boundary specification data subset;
s248, executing S246 and S247 on all the observation data of the boundary specification data subset corresponding to the meteorological monitoring point observation specification data subset to obtain the meteorological monitoring point observation consistency data subset;
s249, regarding the observed data of each type of data attribute of the boundary specification data subset corresponding to the meteorological ocean numerical model forecast specification data subset, taking the data acquisition information of the observed data as a known independent variable, taking the data value of the observed data as a known dependent variable, constructing a curve to be approximated by using the known independent variable and the known dependent variable, and performing curve fitting on the curve to be approximated by using a function approximation method to obtain an optimal consistent approximation polynomial f (Ix) of the type of data attribute; calculating the known independent variable by using the optimal consistent approximation polynomial f (Ix) to obtain an approximate dependent variable; judging whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is larger than a set second regression judging threshold value; if the observed data is larger than the second regression discrimination threshold, deleting the observed data from the boundary specification data subset corresponding to the meteorological ocean numerical model forecast specification data subset, and if the observed data is smaller than the second regression discrimination threshold, not processing the observed data;
S2410, executing S249 on all the observed data of the boundary specification data subset corresponding to the meteorological marine numerical mode prediction specification data subset to obtain a meteorological marine numerical mode prediction consistency data subset;
s2411, carrying out combined processing on the atmospheric ocean observation consistency data subset, the weather monitoring point observation consistency data subset and the weather ocean numerical mode forecast consistency data subset to obtain a consistency data set.
6. The method for fine simulation of a meteorological marine environment according to claim 1, wherein the constructing a meteorological marine environment simulation model using a standard meteorological marine data set comprises:
s31, constructing and obtaining an initialized meteorological marine environment simulation model; the initialized meteorological marine environment simulation model comprises unknown parameters and a prediction equation set;
s32, solving unknown parameters in the initialized weather marine environment simulation model by using the standard weather marine data set to obtain an unknown parameter solving value;
and S33, substituting the unknown parameter solving value into the prediction equation set to obtain the meteorological marine environment simulation model.
7. The method for the refined simulation of the meteorological marine environment according to claim 6, wherein the expression of the prediction equation set of the initialized meteorological marine environment simulation model is:
wherein [ x, y, z]The position coordinate of the predicted point under the geodetic coordinate system is t, the predicted time is t, u and v are the speeds of the x axis and the y axis of the sea wave in the horizontal direction, and w is the vertical speed of the sea wave;representing differential operator +_>[i,j,k]Unit amounts in the x, y, and z axes; f is a coriolis force parameter; phi is the dynamic pressure of the ocean, phi=p/P o ，ρ o Is the sea water reference density; />And v θ Respectively a viscosity coefficient and a particle diffusion coefficient, g is a gravity constant; ρ is the field density of seawater, and P is the pressure of the weather marine environment; c represents calculating the concentration of particles; f (F) u 、F v 、F C D for external forcing terms in the x-axis, y-axis and z-axis directions, respectively u 、D v 、D C Dissipation terms in the x-axis, y-axis, and z-axis directions; /> And->Turbulence terms, K, representing u, v and w, respectively M And K C The vertical vortex viscosity and the turbulence diffusion coefficient of the sea surface are respectively; wherein (1)>v θ 、C、F u 、F v 、F C 、D u 、D v 、D C 、K M 、K C Is an unknown parameter.
8. A device for the fine simulation of a meteorological marine environment, said device comprising:
a memory storing executable program code;
A processor coupled to the memory;
the processor invokes the executable program code stored in the memory to perform the refined simulated simulation method of the weather marine environment of any of claims 1-7.
9. A computer storage medium storing computer instructions which, when invoked, are operable to perform a refined simulated simulation method of a weather marine environment as claimed in any of claims 1-7.
10. An information data processing terminal, characterized in that the information data processing terminal is used for realizing the fine simulation method of the meteorological marine environment according to any one of claims 1 to 7.
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