CN113704693A - High-precision effective wave height data estimation method - Google Patents
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Abstract
The invention provides a high-precision effective wave height data estimation method which comprises the steps of obtaining effective wave height data and geographic and atmospheric parameter data of each offshore land site and ocean buoy in a target area, screening parameters with high correlation, dividing sample data in different historical periods into training data and verification data, obtaining a multivariate regression relation of the effective wave height data and the geographic and atmospheric parameter data based on a step-by-step clustering method, and obtaining an accumulated probability distribution function and a transfer function between an analog value and an observed value of the effective wave height data based on the verification data by using a quantile mapping method. And predicting the future effective wave height data by using the geographic and atmospheric parameter data under the future situation in the global climate model as the input quantity of the multiple regression statistical relationship, and correcting the future effective wave height data by using a transfer function. The method establishes the relation between the effective wave height and the climate factor, can analyze the influence of the ocean indexes under the climate change condition, and improves the accuracy of effective wave height data prediction.
Description
Technical Field
The invention relates to the technical field of meteorological information, in particular to a high-precision effective wave height data estimation method.
Background
As one of the manifestations of ocean power, waves perform regular periodic rolling movements, which are associated with many climatic factors such as sea winds, air pressure changes, etc. At the same time, the motion of the waves directly affects the temporal and spatial evolution law of the region and even of the global sea. Studying the spatial and temporal distribution of waves and the future trend of changes is an important part of studying the mechanisms of interaction of weather and oceans.
The nature of waves is an irregular combination of waves of different wave heights, periods, peaks. Wave height is one of the wave elements, and its study is an important aspect in analyzing wave properties and its effects. One of the main parameters of the study wave is the effective wave height. The effective wave height is an actual wave height value obtained by analyzing the probability density of a plurality of single wave heights by a statistical method. Compared with the parameters of other waves, the method has the following advantages: (1) the effective wave height represents the visual average level of wave fluctuation and is close to the visual wave height value; (2) compared with the obvious wave height, the method has general universality; (3) can be obtained by the shape of the radar echo.
When the wave information is integrated into the quantification of the climate change influence, the wave information cannot be directly used for the climate change influence evaluation due to low data accuracy and the particularity of parameters; in addition, when using migration changes of beach sediments, wave fluctuation and rising components of sea level, etc., the analysis process relies on high-precision and high-resolution sea wave information (effective wave height data); these two points require that the wave data be down-scaled. In addition, the uncertainty in the downscaling operation on the significant wave height data also needs to be quantified. The precision, accuracy and quantitative representation of uncertainty of the effective wave height data obtained by the existing statistical downscaling method are all required to be further improved.
Disclosure of Invention
In order to overcome the defects that the data precision is low, the parameters are special, the wave information cannot be directly used for evaluating the influence of the climate change and the like in the process of integrating the wave information into the evaluation of the influence of the climate change, the method utilizes a step-by-step clustering method to perform the scale reduction operation on the effective wave height data of the selected area, and therefore the effective wave height data with high precision and high accuracy are obtained.
The invention provides a high-precision effective wave height data estimation method, which comprises the following steps:
step 1: obtaining effective wave height data and geographic and atmospheric parameter data of each offshore land site and ocean buoy in a target area, and taking the effective wave height data and the geographic and atmospheric parameter data as sample data;
step 2: in a selected time range, dividing the sample data in the step 1 into training data and verification data;
and step 3: selecting the effective wave height data as dependent variables and corresponding geographic and atmospheric parameters as independent variables, calculating correlation coefficients between the dependent variables, and screening geographic and atmospheric parameter data indexes with higher correlation coefficients with the effective wave height data;
and 4, step 4: establishing a multivariate regression statistical relationship between the screened training data of the geographic and atmospheric parameters with higher correlation and the corresponding training data of the effective wave height based on a stepwise clustering method;
and 5: substituting the verification data of the geographic and atmospheric parameters in the sample data into the multivariate regression statistical relationship established in the step 4 to obtain corresponding effective wave height data simulation values;
step 6: calculating verification data of the effective wave height corresponding to the verification data of the geographic and atmospheric parameters in the step 5 and an accumulative probability distribution function of the analog value of the effective wave height data obtained in the step 5 based on a quantile mapping method, and constructing a transfer function between the verification data and the analog value;
and 7: for each offshore land site and ocean buoy in the target area, calculating to obtain an effective wave height data analog value, namely an effective wave height data estimation value, of a corresponding period in the future by using the multivariate regression statistical relationship established in the step (4) and taking geographic and atmospheric parameter data in a global climate model under the future situation as input quantities;
and 8: and (4) carrying out deviation correction on the effective wave height data analog value calculated in the step (7) in the corresponding time period in the future by using the transfer function obtained in the step (6) to obtain an effective wave height data estimation value.
Preferably, the significant wave height data in the sample data acquired in step 1 is hourly scale data.
More preferably, the same spatial resolution is adopted, and the significant wave height data in the sample data is processed into data of different time step lengths by an averaging or maximum value method.
Preferably, the training data time span in the sample data is longer than the validation data.
Preferably, the correlation coefficient in step 3 is calculated using a pearson correlation coefficient calculation method.
More preferably, a parameter with a correlation coefficient greater than 0.7 is selected as the final argument.
Preferably, the transfer function is obtained by using a non-parametric transform QUANT based on the cumulative probability distribution function.
Preferably, the cumulative probability distribution function in step 6 is divided into a shallow water region and a deep water region, and the cumulative probability distribution function of the shallow water region is:
wherein H*The water content is the coefficient of shallow water,
when H is present*When the wave height is 0, the wave height is an empirical cumulative probability distribution function of a deepwater area, and H is all wave heights in one-time sea wave continuous recording;
for all in one continuous recording of sea wavesAverage value of wave height; d is the depth of the water.
Preferably, in step 1, the effective wave height data of the remote sensing satellite of the longitude and latitude corresponding to each station and ocean buoy is obtained, and the data of each station and ocean buoy and the satellite remote sensing data of the longitude and latitude corresponding to each station and ocean buoy are subjected to spatial interpolation processing to obtain final sample data.
Preferably, at least three types of global climate model data with the same spatial resolution and the same geographic and meteorological parameters are selected as future prediction data, and the estimated values of the effective wave height are obtained through the steps 7 and 8 respectively, and are subjected to ensemble prediction.
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FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a flow chart of one embodiment of the method of the present invention;
FIG. 3 is a flow chart of another embodiment of the method of the present invention;
fig. 4 is a flow chart of yet another embodiment of the method of the present invention.
Detailed Description
In order to make the features of the method of the present invention clearer and more comprehensible, embodiments of the present invention are described in detail below with reference to the accompanying drawings.
The effective wave height is a term in the field of marine science. Sea surface waves are actually single waves formed by the vibration of sea water, and are combined into a complex through irregularity. Since these single waves have different wave heights, periods, and directions of travel, it is not practical nor representative to separately study the parameters of a wave. The effective wave height is that the wave heights in a given wave train are arranged from big to small, and the largest front 1/3 part is selected, which is also called as 1/3 big wave average wave height. The wave complex parameters after certain statistical processing can represent the visual average level of wave motion, is similar to the visual wave height value, and is widely used for describing wave characteristics in a specific area or space.
The ocean buoy is one of the most main carriers for observing and acquiring the marine hydrology, water quality and meteorological data, and particularly can collect severe astronomical and multi-year continuous observation samples which are difficult to collect by a marine scientific investigation survey ship. The ocean buoy is anchored on the sea for a long time, and is an automatic observation station which mainly comprises an observation buoy. Generally, an ocean buoy is composed of a floating body, a mast, an anchor system and a counterweight, and is provided with various meteorological sensors, hydrological sensors and ecological environment sensors to obtain hydrological (wave, temperature, salt, depth and the like) parameters, water quality (chlorophyll, COD and the like) parameters and meteorological (sea level air pressure, ocean surface wind speed, wind direction and the like) parameters. In addition, the action positions of the waves are not only in the ocean, but also in coastal zones and intertidal zones at sea-land boundaries, and are also subject to the erosion and scouring action of the waves, so that relevant marine hydrology water quality meteorological data can be acquired from observation stations at the offshore edges. With the development of statistics and geographic information technology, the existing scientific research process usually performs spatial interpolation on the original data of known sites and buoys, thereby realizing point-to-surface conversion and forming a spatial distribution data network on a certain spatial resolution.
Sea-related factors such as sea level, waves, storm surge and rainfall caused by climate change are all changing rapidly. By finding existing relations between climate parameters and sea parameters, it is considered feasible to apply such relations to the sea itself and to sea-land interaction system variations in the context of future climate change. As a key component of the study of climate change, global climate models including geographic and atmospheric parameter data have studied the trend of future climate change in the context of typical concentration paths. However, due to the defects of poor performance and low precision of the regional scale simulation result, when the regional scale is scientifically researched by using the global climate model, the global climate model needs to be downscaled to obtain high-precision simulation data.
In the downscaling process, a deviation correction is necessary in order to ensure that the error between the obtained simulated value and the actual value is minimal, the obtained simulated value is closest to the actual value. Deviation correction methods for climate model simulation or downscaling results are roughly divided into two categories, including a delta disturbance method based on mean correction and a quantile mapping method based on probability distribution correction. The perturbation method is relatively simple to operate, but errors in the aspect of probability distribution cannot be corrected, and the perturbation method has a large defect. The quantile mapping method is to obtain the cumulative distribution function of the observed value and the analog value, to build the transfer function by using the two, to correct the cumulative probability function of other analog values by using the transfer function, to finally achieve the purpose of correcting the error from the probability distribution.
Based on the background, in order to obtain a high-precision result of the ocean parameter change under the condition of the climate change in the regional scale range, the method carries out deviation correction on the effective wave height data by using a quantile mapping method after reducing the scale through a stepwise clustering method, and obtains the required final data.
Fig. 1 is a flowchart illustrating a method for estimating a significant wave height according to an embodiment of the present invention. As shown in fig. 1, the method specifically includes:
according to certain spatial geographic distribution characteristics, sample data of each offshore land site and ocean buoy in the target area and corresponding various types of global climate model data containing geographic and atmospheric parameters are obtained. Typically, a region within 50 km of the coastline is selected as the target region, and the geographic distribution characteristics of the region are those of a typical coastline.
Optionally, the geographic and atmospheric parameters include longitude, latitude, elevation, altitude, transverse wind, latitudinal wind, and the like, and the parameters selected by each offshore land site and ocean buoy are the same.
Specifically, effective wave height data including a land source and a sea source in a target area are obtained through an existing station and an existing ocean buoy, wherein the effective wave height data are hourly scale data. It is to be understood that the significant wave height data in the present invention is the hour scale data without specific description.
And taking the obtained two kinds of data as sample data, sorting the sample data according to time sequence, wherein the time period in the front is taken as training data, and the time period in the back is taken as verification data.
Specifically, the data of the effective wave height and the corresponding geographic and atmospheric parameter data can be selected from 15 to 20 years of data as training data and 10 to 15 years of data as verification data according to experience. The training data is used for establishing a model relationship, and the verification data is used for verifying whether the established model relationship is reliable. For example, a total of 30 years of time is selected to build the model, then the previous 20 years are used as training data for modeling, i.e., "previous time period", the training data being 20 years; the following time period is the remaining 10 years of data as the verification data, i.e. the verification data is selected for 10 years.
Alternatively, the time length of the sample data for classification may be determined according to specific situations, and the span of the general training data is longer than that of the verification data.
And taking the sample data of the effective wave height and the corresponding geographic and atmospheric parameter data as input values, and calculating a correlation coefficient between the effective wave height and the corresponding parameter. Calculating the correlation coefficient by using a Pearson correlation coefficient calculation mode, wherein a specific formula is as follows:
the method comprises the steps of selecting as many geographical and atmospheric parameter training data with high correlation coefficients as possible as independent variables to a certain extent, using corresponding effective wave height sample training data as dependent variables, and establishing a relation between the geographical and atmospheric parameter training data and the dependent variables by utilizing a step-by-step clustering method.
The step-by-Step Clustering Approach (SCA) is a multivariate statistical approach for dealing with complex non-linear relationships between independent and dependent variables. The process adopted by the method can ensure that the samples in the final so-called sub-sample set have higher homogeneity, namely, the correlation between the geographic and atmospheric parameters and the effective wave height is higher and does not present a linear relation in one sub-sample set aiming at the invention.
The step-by-step clustering method adopts corresponding standards to carry out cutting and merging cyclic operation on the samples, and finally the samples are divided into sub-sample sets which are highly correlated in each set and relatively independent among the sets on a certain confidence level. Specifically, on a certain given standard, the sample set is divided into two parts, and after division, whether the current sub-sample set meets the corresponding standard or not is judged, namely whether the division can be continued or not is judged until the division cannot be continued; judging whether the current subsample set meets the corresponding standard, if so, executing the combination of the two into one, and then dividing; and repeating the processes until all the segmentation and combination processes are rejected, ending the processes and deriving results. Specifically, when the dispersion ratio of any two variables calculated based on the Wilks criterion is the minimum, the two parameters with the weakest correlation are represented, if the dispersion ratio of the two parameters is larger than an F test standard on a certain confidence level, the result obtained based on the Wilks criterion is statistically significant, the two variables have statistically significant difference, and the current sample can be segmented; conversely, if the F-test criterion is less than a certain confidence level, it indicates that the results obtained based on the Wilks criterion are not statistically significant, i.e., the two variables are not statistically significantly different, and a merge operation is taken.
And aiming at each offshore land site and ocean buoy in the selected target area, combining the geographic and atmospheric parameters and the effective wave height data included by the site or buoy into a sample, wherein the geographic and atmospheric parameters are independent variables, and the effective wave height is a dependent variable. And then based on the segmentation and combination process of the step-by-step clustering method, the sample data of the site or the buoy is divided, finally a data group which can not be divided and combined is obtained, and a corresponding multiple regression statistical relationship between the selected parameters and the effective wave height is obtained.
Specifically, parameters having a relatively high correlation coefficient may be selected as much as possible, and a parameter having a correlation coefficient greater than 0.7 is preferable as the final argument in the embodiment of the present invention.
In practical application, each offshore land site and ocean buoy have different influence factors, so that parameters with high possibility of being obtained are different, and the selected parameters need to be obtained by integrating the conditions of all the sites according to actual conditions.
And substituting the geographic and atmospheric parameter verification data into the multivariate regression statistical relationship established in the step to obtain an analog value, namely the effective wave height data obtained according to the statistical relationship in the verification stage. And comparing the obtained analog value with an original observation value corresponding to the verification data, namely the effective wave height data in the verification data, so as to judge the quality of the analog result.
Optionally, to verify the reliability of the resulting multivariate statistical relationship, a plurality of multivariate regression correlation coefficients are calculated (R, R)2RMSE, etc.) and the reliability of the statistical relationship is obtained by comprehensively considering a plurality of criteria.
And calculating the cumulative probability distribution function of the observed value of the effective wave height data in the verification data and the simulated value of the effective wave height data obtained by subjecting the verification data to a multivariate statistical relationship by using a quantile mapping method, and constructing a transfer function between the observed value and the simulated value.
Quantile-Mapping (QM) is a probability distribution-based correction method. Specifically, in a selected time scale, cumulative probability distribution functions of an observed value of the significant wave height data and a simulated value obtained through a model in the verification data are calculated respectively, and a transfer function is constructed by using the observed value and the simulated value. When the quantile mapping method is applied, two methods are used for establishing the transfer function: one is a hypothesis mode, where the simulated and observed values already fit a known probability distribution function, and the other is to establish a transfer function based on an empirical probability distribution. From another classification point of view, there are two methods of parametric transformation, in which the transfer function is represented by a linear or non-linear model, and non-parametric transformation, in which no precondition assumption is required for the original data.
Specifically, for a selected specific near-shore land site or ocean buoy, an effective wave height data observation value in verification data and an accumulated probability distribution function of a simulation value obtained through a model are respectively calculated, and a transfer function is obtained by using a non-parametric transformation QUANT, namely:
Fcdf1(x1)＝Fcdf0(x0)
the established transfer function is then applied to the future prediction phase, namely:
xbc＝Fcdf0[Fcdf2(x2)]
wherein: x is the number of1、x2、x0、Fcdf1、Fcdf2、Fcdf0Corresponding to the simulation data, future simulation value, observation data, cumulative probability distribution function and transfer function of the simulation data in the validation stage, xbcIs the correction data obtained by verification.
Specifically, the empirical cumulative probability distribution function corresponding to the effective wave height data is divided according to a shallow water region and a deep water region, and the empirical cumulative probability distribution function of the shallow water region is as follows:
wherein: h*The water content is the coefficient of shallow water,
when H is present*When the value is 0, the probability distribution function is empirically accumulated in the deepwater area; h is all wave heights in one-time wave continuous recording;
the average value of all wave heights in one-time sea wave continuous recording is obtained; d is the depth of the water.
And (3) taking future geographic and atmospheric parameters corresponding to corresponding sites or buoys of the selected area as input values, and inputting the input values into the relation obtained by the step-by-step clustering method to obtain a predicted future effective wave height analog value.
Specifically, a time period needing to predict future changes is selected, global climate model geography and atmospheric parameter data of a selected area in the time period are used as input values, namely, dependent variables are input into a multivariate statistical relationship obtained through a step-by-step clustering method, and effective wave height simulation values in a corresponding time period in the future are predicted.
Alternatively, different global climate model data can be selected as dependent variables according to different regions and performances, and different performances of different model data can be checked.
The change of the data of the effective wave height in any time step and any time period in the future can be predicted.
The predicted analog value of the effective wave height is further modified by a transfer function.
Specifically, the transfer function obtained by accumulating probability distribution functions by observed and simulated values is used for post-processing correction for predicting future effective wave height results. And substituting the numerical value obtained by simulation into the transfer function to obtain an estimated value of the effective wave height after deviation correction.
According to the method for estimating the effective wave height data, disclosed by the embodiment of the invention, downscaling processing is carried out through a step-by-step clustering method, and in the post-processing process, deviation correction is carried out on the analog value obtained by downscaling through the step-by-step clustering method by using a quantile mapping method so as to obtain a high-precision and high-accuracy predicted analog value. The method for estimating the effective wave height can accurately represent the geographic and meteorological parameters with the maximum correlation with the effective wave height data, obtain a reliable prediction analog value by utilizing a multiple regression statistical relationship established by the parameters with the large correlation, and perform post-processing by utilizing nonparametric conversion in a quantile mapping method. The statistical relationship obtained through the process is reliable, the precision and the accuracy of the effective wave height data of the future target area are improved, the method is particularly suitable for coastal zones and intertidal zones with frequent coastal sea-land interaction, and reliable data are provided for evaluation of wave changes and corresponding influences of the zone caused by climate changes.
Fig. 2 is a flowchart illustrating a method for estimating the effective wave height data according to another embodiment of the present invention. This embodiment explains the simulation operation based on different time scales in detail based on the embodiment described in fig. 1. As shown in fig. 2, an average method and a maximum method are used to process effective wave height data of offshore land sites and ocean buoys in a target area, and data of different time scales including hours, days, months, years and the like are obtained, including:
typically, the effective wave height data obtained from offshore land sites and ocean buoys is hourly scale data. And processing the acquired effective wave height data by adopting an average value method or a maximum value method based on the same spatial resolution to obtain a plurality of time scale effective wave height data of the target area. Similarly, the data of the geographic and atmospheric parameters corresponding to the significant wave height data are also hourly scale data. In the following, the processing of the significant wave height data is taken as an example only, and data of geographic and atmospheric parameters are processed accordingly.
Specifically, taking the hour scale data processing as the day scale data as an example, the average value of all hour data points on the day needs to be calculated as the effective wave height data on the day. The daily effective wave height data obtained by the average value method represents the average level of the daily effective wave height, can reflect the general rule under daily scale, but is slightly deficient in the aspect of obtaining the extreme value of the day.
Alternatively, when the hourly scale data is processed as daily scale data, the maximum value of all the hourly scale data on the day is extracted as the daily significant wave height data. The daily effective wave height data obtained by the maximum value method represents the maximum effective wave height of all time points of the day and can reflect extreme conditions under daily scale. In the research process of sea wave parameters and corresponding influence evaluation, the research of extreme values has certain practical significance compared with the research of mean values.
Alternatively, the effective wave height data obtained from the target area offshore land sites and ocean buoys can be 3, 6, 8 hour scale data. The basic principle of processing raw data of different hour scales is consistent, and a mean value solving method needs to be paid attention to during processing.
Optionally, when the effective wave height hour data obtained by the offshore land site and the ocean buoy in the target area is processed into a month scale, a year scale and the like, the basic principle of the processing is consistent with the processing of the hour scale into a daily scale. Average value data under the scales of the month and the year can be respectively obtained by adopting an average value method, and the average value data can be used for analyzing to obtain general rules of the scales of the month and the year; maximum value data under the scales of months and years can be respectively obtained by adopting a maximum value method, and the maximum value data can be analyzed to obtain extreme conditions under corresponding scales.
The remaining steps in this embodiment are the same as those in the embodiment of fig. 1, and are not described herein again.
The embodiment provides a method for processing raw data under different time scales. The data obtained is typically hourly scale data for each offshore land site and ocean buoy. The average value data and the maximum value data of the effective wave height can be obtained by utilizing an average value method and a maximum value method respectively and are used for representing corresponding effective wave height data under a time scale. The data of the two time scales are expected to be used for general rule and extreme case research under each time scale. The embodiment provides a method for processing the original data into other multiple time scales on the basis of the method, and provides more detailed and reliable data for researching the change and corresponding influence evaluation under different time scale conditions.
Fig. 3 is a flowchart illustrating a method for estimating the effective wave height data according to another embodiment of the present invention. In this embodiment, on the basis of the embodiment described in fig. 1, the source of the sample data is further extended, and ocean buoy data of a near-shore land site and satellite remote sensing data of a corresponding longitude are selected as original observation values. And performing interpolation processing on the two data at corresponding longitude and latitude positions by adopting a spatial interpolation algorithm. The observation data obtained after the interpolation processing adopted by the embodiment is more accurate compared with the original data.
In the data acquisition and pretreatment processes, a large amount of incomplete, deviated and non-objective data exist in the original observation data. These abnormal or missing data will have a large impact on the downscaling and correction processes, so that the calculation process cannot be performed, and even the reality and integrity of the result cannot be guaranteed. The data cannot be processed by simply deleting a certain piece of position data, and especially under the condition that the sample size is not large, the objectivity and the accuracy of an analysis result are directly influenced by deleting the data. In general, in preprocessing of data, spatial interpolation processing is better performed in a process of repairing such abnormal data.
With the rapid development of satellite remote sensing technology, remote sensing technology is widely applied in the fields of ocean monitoring and data acquisition. The satellite remote sensing data has the advantages of wide coverage space range, no influence of the surrounding environment of the site or the ocean buoy and the like, the obtained data is more comprehensive, the defects of the observation data of the site and the ocean buoy can be made up, and the satellite remote sensing data can be singly used as the observation data to carry out scientific research.
Specifically, the embodiment interpolates the obtained near-shore land site, ocean buoy data and remote sensing data of the corresponding longitude and latitude of the site, and the selection can improve the accuracy of the original data by interpolating the part lacking the abnormal value. Firstly, two sets of corresponding data are respectively obtained, and a spatial interpolation algorithm is adopted to perform an interpolation process at each longitude and latitude coordinate point of a target area, so that original data with higher accuracy after interpolation is obtained. The data after interpolation can be divided into training data and verification data for establishing the subsequent stepwise clustering method relationship.
Optionally, the spatial interpolation algorithm may be any one of the following: inverse Distance Weight (IDW), Kriging (Kriging), spline, and the like. Preferably, a cubic spline interpolation method is selected, and further description of other methods is omitted. The cubic spline function interpolation method not only keeps various advantages of the segmented low-order interpolation polynomial, but also improves the smoothness of the interpolation function and has higher precision.
The remaining steps in this embodiment are the same as those in the embodiment of fig. 1, and are not described herein again.
This embodiment provides a method of data preprocessing. And acquiring satellite remote sensing data of longitude and latitude corresponding to each offshore land site and each ocean buoy, and performing interpolation processing on the satellite remote sensing data and the satellite remote sensing data to obtain more accurate and more accurate original effective wave height data. Alternatively, cubic spline interpolation can be used to obtain better interpolation results. The embodiment provides a method for processing missing values and abnormal values on the basis of the method, and the process can improve the accuracy of original data and lay a foundation for obtaining accurate multivariate statistical relationship and prediction results subsequently.
Fig. 4 is a flowchart illustrating a method for estimating the effective wave height data according to still another embodiment of the present invention. In this embodiment, based on the embodiment described in fig. 1, at least three (and more) global climate models including the selected parameter data are selected, future data is predicted, and ensemble prediction of different global climate models is performed. The simulation result after ensemble prediction adopted by the embodiment is more real and credible.
Global Climate Model (GCM) is a main tool for researching future Climate change development, covers almost Global future Climate prediction, and is wide in variety. Meanwhile, the performance of each model in a specific area is different, and a plurality of sets of global climate models need to be screened to see the simulation accuracy of the model in the target area.
The climate model has errors and uncertainties from various aspects in the process of simulating predictions. The nonlinear instability of the atmosphere, the ocean and the coupled system thereof causes larger errors of observed data, the expression of the statistical relationship on the real physical process cannot completely reflect the real situation, and the like, and the results of poor fitting effect, inaccurate prediction and the like can be caused. Ensemble prediction is often used to estimate the prediction error, resulting in more accurate simulation values. The ensemble forecasting system can forecast through ensemble dispersion, and can forecast probability information of time occurrence.
Specifically, in the present embodiment, the HadCM3 developed by Hadley center of the british weather service, the national oceanic and atmospheric administration GFDL-CM2.1, and the north american multi-model ensemble prediction dataset CanCM4 are taken as examples. Geographic and meteorological parameter data of target areas of historical periods (1980) -2020) and future periods (2021-2099) of each model are extracted, and the data are used as input values of multivariate statistical relationships obtained by a step-by-step clustering method, namely independent variables, so as to obtain three sets of output values, namely effective wave height simulation value data. And transversely comparing the data obtained by the three sets of global climate models, and performing ensemble prediction at the same time.
Alternatively, the method of ensemble forecasting may be selected from, but is not limited to, any of the following methods: monte Carlo Method (MCF), Singular vector methods (SVs), Conditional nonlinear optimal perturbation method (CNOP), mean value method, etc. Preferably, the present invention is not based on the development of ensemble forecasting method, and because it is simple and fast, and can obtain better results, the present embodiment uses the mean value method for ensemble forecasting, and further description of other methods is omitted.
The remaining steps in this embodiment are the same as those in the embodiment of fig. 1, and are not described herein again.
The present embodiment provides a method of data post-processing. At least three sets of global climate models which are good in performance and contain corresponding geographic and meteorological parameter data are selected as input values aiming at different target areas, and three sets of future effective wave height predicted values are obtained. The results are compared and ensemble-predicted laterally, resulting in a more reliable prediction than a single result. On the basis of the method, the prediction accuracy is improved, the uncertainty is reduced, and meanwhile, the data with high precision and good accuracy can be obtained for a thought of data post-processing.
The present invention is not limited to the above embodiments, and any changes or substitutions that can be easily made by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A high-precision effective wave height data estimation method comprises the following steps:
step 1: obtaining effective wave height data and geographic and atmospheric parameter data of each offshore land site and ocean buoy in a target area, and taking the effective wave height data and the geographic and atmospheric parameter data as sample data;
step 2: in a selected time range, dividing the sample data in the step 1 into training data and verification data;
and step 3: selecting the effective wave height data as a dependent variable and the corresponding geographic and atmospheric parameters as independent variables for the sample data obtained in the step 1, calculating the correlation coefficient between the dependent variables and the geographic and atmospheric parameters, and screening out geographic and atmospheric parameter data indexes with higher correlation coefficient with the effective wave height data;
and 4, step 4: establishing a multivariate regression statistical relationship between the screened training data of the geographic and atmospheric parameters with higher correlation and the corresponding training data of the effective wave height based on a stepwise clustering method;
and 5: substituting the verification data of the geographic and atmospheric parameters in the sample data into the multivariate regression statistical relationship established in the step 4 to obtain corresponding effective wave height data simulation values;
step 6: calculating verification data of the effective wave height corresponding to the verification data of the geographic and atmospheric parameters in the step 5 and an accumulative probability distribution function of the analog value of the effective wave height data obtained in the step 5 based on a quantile mapping method, and constructing a transfer function between the verification data and the analog value;
and 7: for each offshore land site and ocean buoy in the target area, calculating to obtain an effective wave height data analog value, namely an effective wave height data estimation value, of a corresponding period in the future by using the multivariate regression statistical relationship established in the step (4) and taking geographic and atmospheric parameter data in a global climate model under the future situation as input quantities;
and 8: and (4) carrying out deviation correction on the effective wave height data analog value of the corresponding time period in the future calculated in the step (7) by using the transfer function obtained in the step (6) to obtain an effective wave height data estimation value.
2. The method according to claim 1, wherein the method comprises: and (3) the effective wave height data in the sample data acquired in the step (1) is hourly scale data.
3. The method according to claim 2, wherein the method comprises: and processing the effective wave height data in the sample data into data with different time step lengths by an averaging or maximum value method by adopting the same spatial resolution.
4. The method according to claim 1, wherein the method comprises: the training data time span in the sample data is longer than the validation data.
5. The method according to claim 1, wherein the method comprises: and calculating the correlation coefficient in the step 3 by using a Pearson correlation coefficient calculation method.
6. The method according to claim 1 or 5, wherein the method comprises: selecting the parameter with the correlation coefficient larger than 0.7 as the final independent variable.
7. The method according to claim 1, wherein the method comprises: in the step 6, the transfer function is obtained by using a non-parametric transform QUANT based on the cumulative probability distribution function.
8. The method according to claim 1, wherein the method comprises: the cumulative probability distribution function in the step 6 is divided according to a shallow water area and a deep water area, and the cumulative probability distribution function of the shallow water area is as follows:
wherein H*The water content is the coefficient of shallow water,
when H is present*When the value is 0, the probability distribution function is empirically accumulated in the deepwater area; h is all wave heights in one-time wave continuous recording;
the average value of all wave heights in one-time sea wave continuous recording is obtained; d is the depth of the water.
9. The method according to claim 1, wherein the method comprises: in the step 1, the effective wave height data of the remote sensing satellite of the longitude and latitude corresponding to each station and the ocean buoy is obtained, and the spatial interpolation processing is carried out on the data of each station and the ocean buoy and the satellite remote sensing data of the corresponding longitude and latitude to obtain final sample data.
10. The method according to claim 1, wherein the method comprises: and selecting at least three types of global climate model data adopting the same spatial resolution and the same geographic and atmospheric parameters as future prediction data, respectively obtaining estimated values of the effective wave height through the steps 7 and 8, and performing ensemble prediction.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114564629A (en) * | 2022-04-22 | 2022-05-31 | 国家海洋技术中心 | Abnormal data processing method and device, computer equipment and storage medium |
| CN114814779A (en) * | 2022-06-29 | 2022-07-29 | 国家海洋技术中心 | Buoy surge wave height observation data error evaluation method, system, equipment and medium |
| CN116822253A (en) * | 2023-08-29 | 2023-09-29 | 山东省计算中心（国家超级计算济南中心） | Hybrid precision implementation method and system suitable for MANUM sea wave mode |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2000020893A2 (en) * | 1998-08-04 | 2000-04-13 | Rowe-Deines Instruments | System and method for measuring wave directional spectrum and wave height |
| WO2015148887A1 (en) * | 2014-03-28 | 2015-10-01 | Northeastern University | System for multivariate climate change forecasting with uncertainty quantification |
| CN106990404A (en) * | 2017-03-30 | 2017-07-28 | 南京信息工程大学 | A kind of autoscale algorithm using X-band radar inverting sea wave height of navigating |
| CN108549961A (en) * | 2018-05-02 | 2018-09-18 | 河海大学 | A method of wave significant wave height is estimated based on CMIP5 |
-
2021
- 2021-08-19 CN CN202110952850.1A patent/CN113704693B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2000020893A2 (en) * | 1998-08-04 | 2000-04-13 | Rowe-Deines Instruments | System and method for measuring wave directional spectrum and wave height |
| WO2015148887A1 (en) * | 2014-03-28 | 2015-10-01 | Northeastern University | System for multivariate climate change forecasting with uncertainty quantification |
| CN106990404A (en) * | 2017-03-30 | 2017-07-28 | 南京信息工程大学 | A kind of autoscale algorithm using X-band radar inverting sea wave height of navigating |
| CN108549961A (en) * | 2018-05-02 | 2018-09-18 | 河海大学 | A method of wave significant wave height is estimated based on CMIP5 |
Non-Patent Citations (2)
| Title |
|---|
| ALAIN ULAZIA等: "Reduction of the capture width of wave energy converters due to long-term seasonal wave energy trends", 《RENEWABLE AND SUSTAINABLE ENERGY REVIEWS》, pages 1 - 13 * |
| 韩振宇等: "分位数映射法在RegCM4 中国气温模拟订正中的应用", 《气候变化研究进展》, vol. 14, no. 4, pages 331 - 340 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114564629A (en) * | 2022-04-22 | 2022-05-31 | 国家海洋技术中心 | Abnormal data processing method and device, computer equipment and storage medium |
| CN114814779A (en) * | 2022-06-29 | 2022-07-29 | 国家海洋技术中心 | Buoy surge wave height observation data error evaluation method, system, equipment and medium |
| CN116822253A (en) * | 2023-08-29 | 2023-09-29 | 山东省计算中心（国家超级计算济南中心） | Hybrid precision implementation method and system suitable for MANUM sea wave mode |
| CN116822253B (en) * | 2023-08-29 | 2023-12-08 | 山东省计算中心（国家超级计算济南中心） | Hybrid precision implementation method and system suitable for MANUM sea wave mode |
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