CN114510875A - Circulation-current-situation red tide forecasting system and method based on multi-source meteorological data - Google Patents
Circulation-current-situation red tide forecasting system and method based on multi-source meteorological data Download PDFInfo
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Abstract
The application provides a circulation situation red tide forecasting system and method based on multi-source meteorological data, and the method comprises the following steps: acquiring a weather reanalysis data set of an area to be forecasted, and calculating typical field data of the atmospheric circulation situation according to an Euclidean distance algorithm, a contour coefficient and a similar algorithm; automatically acquiring and downloading meteorological field forecast data and meteorological field data; calculating daily variation atmospheric circulation situation distance flat field data; calculating the red tide outbreak probability according to a similar algorithm; and drawing a visual product diagram of the red tide outbreak probability trend and the meteorological element field time sequence. Therefore, the method can solve the use problem of the previous monitoring data by utilizing the real-time weather forecast data which is easy to obtain and stable; the visual forecast can be automatically provided, so that the red tide occurrence probability and the information conditions of the elements of the ecological and meteorological environments can be conveniently and visually known; in addition, the method can be used for scientifically and accurately carrying out objective typing on the atmospheric circulation situation, so that the accuracy of red tide forecasting is improved.
Description
Technical Field
The application relates to the fields of atmospheric science and marine ecology, in particular to a circulation situation red tide forecasting system and method based on multi-source meteorological data.
Background
The red tide is one of the main disasters of ocean disasters, which not only can seriously threaten the balance of an ocean ecosystem, but also can cause huge economic loss to coastal areas. Therefore, there is a need for a forecast warning for red tide.
However, the existing red tide forecasting and early warning method usually requires the experiential person in the field to perform the proximity early warning according to the real-time change of the key monitoring elements of the ocean water quality buoy (whether the chlorophyll a, the dissolved oxygen saturation and the pH value exceed the critical values).
However, in practice, the problems of distortion, missing, small quantity and difficult capture of monitoring data always occur in this way, and meanwhile, the red tide forecasting by artificial experience is inevitably affected by human subjectivity, so that the forecasting limitation is caused.
Disclosure of Invention
The embodiment of the application aims to provide a circulation trend red tide forecasting system and method based on multi-source meteorological data, which can solve various problems caused by previous monitoring data by utilizing more easily-obtained stable real-time meteorological forecasting data, so that the system is more suitable for actual forecasting; meanwhile, the system can also automatically provide a visual forecast product, so that the occurrence probability of the red tide and the information condition of the elements of the ecological and meteorological environments can be conveniently and visually known; in addition, the system can be used for scientifically and accurately carrying out objective typing on the atmospheric circulation situation, so that the accuracy of red tide forecasting is improved.
In a first aspect, the embodiments of the present application provide a system for forecasting a red tide around an circumfluent situation based on multi-source meteorological data, where the system for forecasting a red tide around an circumfluent situation includes an objective atmospheric circumfluent situation typing subsystem, a data processing subsystem, a system for forecasting a red tide around an circumfluent situation, and a product information subsystem, where,
the atmospheric circulation situation objective typing subsystem is used for automatically acquiring a weather reanalysis data set of an area to be forecasted, classifying the weather reanalysis data set and calculating a synthetic field according to an Euclidean distance algorithm, a contour coefficient and a similar algorithm to obtain typical field data of the atmospheric circulation situation;
the data processing subsystem is used for downloading meteorological field forecast data of the area to be forecasted based on a data crawler technology and acquiring meteorological field data of the area to be forecasted;
the circulation situation red tide forecasting subsystem is used for calculating the difference value according to the meteorological field forecasting data and the meteorological field data to obtain day-to-day variation atmospheric circulation situation distance flat field data; according to the similarity algorithm, carrying out correlation operation on the daily variation atmospheric circulation situation distance level field data and the atmospheric circulation situation typical field data to obtain correlation percentage, and determining the correlation percentage as red tide outbreak probability;
the product information subsystem is used for acquiring a plurality of red tide outbreak probabilities and drawing a visible product diagram of the red tide outbreak probability trend according to the plurality of red tide outbreak probabilities; and the meteorological field forecasting data are used for generating a meteorological element field time sequence visualization product diagram.
A second aspect of the embodiments of the present application provides a method for forecasting a red tide in an circulation situation based on multi-source meteorological data, where the method includes:
acquiring a weather reanalysis data set of an area to be forecasted, and classifying and synthesizing the weather reanalysis data set according to an Euclidean distance algorithm, a contour coefficient and a similar algorithm to obtain typical field data of the atmospheric circulation situation;
downloading meteorological field forecast data of the area to be forecasted based on a data crawler technology, and acquiring meteorological field data of the area to be forecasted;
performing difference value calculation according to the meteorological field forecast data and the meteorological field data to obtain daily change atmospheric circulation situation distance flat field data; according to the similarity algorithm, carrying out correlation operation on the daily variation atmospheric circulation situation distance level field data and the atmospheric circulation situation typical field data to obtain correlation percentage, and determining the correlation percentage as red tide outbreak probability;
acquiring a plurality of red tide outbreak probabilities, and drawing a visible product diagram of the red tide outbreak probability trend according to the red tide outbreak probabilities; and generating a meteorological element field time sequence visualization product diagram according to the meteorological field forecast data.
A third aspect of the embodiments of the present application provides an electronic device, including a memory and a processor, where the memory is used to store a computer program, and the processor runs the computer program to make the electronic device execute the method for forecasting the red tide around the circulation situation based on the multi-source meteorological data according to the second aspect of the embodiments of the present application.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, which stores computer program instructions, and when the computer program instructions are read and executed by a processor, the method for forecasting the red tide of the circulation situation based on the multi-source meteorological data according to the second aspect of the embodiments of the present application is executed.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic structural diagram of a system for forecasting a red tide around a circulation situation based on multi-source meteorological data according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a method for forecasting a red tide in an circumfluence situation based on multi-source meteorological data according to an embodiment of the present application;
fig. 3 is a functional structure diagram of a system for forecasting a red tide around a circulation situation based on multi-source meteorological data according to an embodiment of the present application;
fig. 4 is a functional structure diagram of an atmospheric circulation situation objective typing subsystem according to an embodiment of the present application;
fig. 5 is a functional structure diagram of a data processing subsystem according to an embodiment of the present application;
fig. 6 is a functional structure diagram of a circular current trend red tide forecasting subsystem according to an embodiment of the present application;
fig. 7 is a functional structure diagram of a product information subsystem according to an embodiment of the present disclosure;
fig. 8 is a trend chart of red tide outbreak probability provided in the embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Example 1
Referring to fig. 1, fig. 1 is a schematic structural diagram of a system for forecasting red tide in circulation situation based on multi-source meteorological data in this embodiment. Wherein, the circulation situation red tide forecasting system based on the multi-source meteorological data comprises an atmospheric circulation situation objective typing subsystem 100, a data processing subsystem 200, a circulation situation red tide forecasting subsystem 300 and a product information subsystem 400, wherein,
the atmospheric circulation situation objective typing subsystem 100 is used for acquiring a weather reanalysis data set of an area to be forecasted, classifying the weather reanalysis data set according to an Euclidean distance algorithm, a contour coefficient and a similar algorithm, and calculating a synthesis field to obtain typical field data of the atmospheric circulation situation;
the data processing subsystem 200 is used for downloading meteorological field forecast data of an area to be forecasted based on the wget data crawler and acquiring meteorological field data of the area to be forecasted;
the circulation situation red tide forecasting subsystem 300 is used for calculating the difference value according to the meteorological field forecasting data and the meteorological field data to obtain day-to-day variation atmospheric circulation situation distance flat field data; according to a similar algorithm, carrying out correlation operation on the daily change atmospheric circulation situation distance flat field data and the atmospheric circulation situation typical field data to obtain a correlation percentage, and determining the correlation percentage as the red tide outbreak probability;
the product information subsystem 400 is used for acquiring a plurality of red tide outbreak probabilities and drawing a visual product diagram of the red tide outbreak probability trend according to the plurality of red tide outbreak probabilities; and the system is also used for generating a meteorological element field time sequence visualization product diagram according to meteorological field forecast data.
Referring to fig. 3, fig. 3 is a functional structure diagram of a system for forecasting a red tide around a circulation situation based on multi-source meteorological data. Wherein, this system can realize following function:
(1) the network resource automatic acquisition technology based on the open source crawler wget realizes daily automatic batch downloading of meteorological field forecast data, and can overcome the defects of low efficiency and discontinuous data of meteorological data acquisition in the current red tide forecast;
(2) the method can realize objective classification of the atmospheric circulation situation at the first day of red tide outbreak and calculation and acquisition of a typical field of the synthesized atmospheric circulation situation according to the European distance algorithm and the contour coefficient operation, and can solve the limitation of acquiring the atmospheric circulation situation type by means of subjective experience analysis of a forecaster;
(3) according to the established circulation-situation red tide forecasting model, the red tide outbreak probability in the future 240 hours is calculated through a pearson similarity algorithm, and the defects that the current forecasting time efficiency is short and the forecasting conclusion is fuzzy in description are overcome;
(4) aiming at the red tide outbreak probability prediction, the meteorological element field situation, the red tide historical information, the ecological environment monitoring element real-time change and the like, the visualized product is output by the visualized manufacturing module to more intuitively know the red tide occurrence probability and the ecological and meteorological environment element information.
As an alternative embodiment, the atmospheric circulation situation objective typing subsystem 100 includes:
the data control module 110 is configured to automatically acquire a weather reanalysis data set and historical red tide data of an area to be forecasted, and perform calculation according to the weather reanalysis data set and the historical red tide data to obtain historical distance flat field data;
the cluster analysis module 120 is configured to obtain a plurality of historical distance flat field data, and perform classification and optimization on the plurality of historical distance flat field data according to an euclidean distance algorithm, a contour coefficient and a similarity algorithm to obtain an optimal classification scheme;
a composite field calculation module 130, configured to perform calculation according to a similarity algorithm and an optimal classification scheme to obtain multiple correlation distance flat field data; synthesizing according to the plurality of correlation distance flat fields to obtain atmospheric circulation situation synthetic field data;
and the typical field calculation module 140 is configured to calculate according to a similar algorithm, the multiple correlation distance flat field data, and the atmospheric circulation situation synthesized field data to obtain the atmospheric circulation situation typical field data.
As an alternative embodiment, the data control module 110 includes:
the parameter initialization submodule 111 is used for setting the layer direction parameter, the horizontal precision parameter and the red tide occurrence time parameter of the atmospheric circulation situation field;
the original data processing submodule 112 is used for acquiring a weather reanalysis data set of the area to be forecasted according to the atmospheric circulation situation field orientation parameter, the horizontal precision parameter and the red tide occurrence time parameter;
the original data processing submodule 112 is further configured to perform data fusion on the weather reanalysis data set to obtain weather fusion data;
the original data processing submodule 112 is further configured to perform calculation based on the meteorological fusion data to obtain historical weather field data;
the original data processing submodule 112 is further configured to extract atmospheric circulation situation field data of the first day of a past red tide outbreak based on the historical red tide data and the meteorological fusion data;
and the distance flat field calculation submodule 113 is configured to perform difference calculation on the atmospheric annular flow potential field data and the historical atmospheric weather field data of the first day of the past red tide outbreak to obtain historical distance flat field data.
As an optional implementation manner, the cluster analysis module 120 is specifically configured to obtain a plurality of historical distance flat field data;
the cluster analysis module 120 is further configured to divide the plurality of historical distance flat data into a plurality of different class clusters according to a euclidean distance algorithm;
the cluster analysis module 120 is further configured to perform scheme division calculation on the multiple clusters according to the number of the multiple historical distance flat field data and the number of the multiple clusters to obtain multiple classification schemes; wherein the plurality of classification schemes are different;
the cluster analysis module 120 is further configured to calculate a contour coefficient of each classification scheme;
the cluster analysis module 120 is further configured to use the classification scheme with the largest contour coefficient as the optimal classification scheme.
As an optional implementation manner, the composite field calculation module 130 is specifically configured to extract a plurality of class clusters in the optimal classification scheme;
the synthesized field calculating module 130 is further configured to calculate a cross-correlation coefficient from horizontal field data of each of the plurality of class clusters according to a pearson similarity algorithm;
the synthesized field calculation module 130 is further configured to extract a plurality of correlation distance flat field data whose cross correlation coefficient from the flat field data is greater than or equal to a preset strong correlation coefficient;
the synthetic field calculation module 130 is further configured to synthesize the multiple correlation distance flat field data to obtain atmospheric circulation situation synthetic field data.
As an alternative implementation, the representative field calculation module 140 is specifically configured to calculate a composite field correlation coefficient between the multiple correlation distance flat field data and the atmospheric circulation situation composite field data according to a pearson similarity algorithm;
the typical field calculation module 140 is specifically configured to determine whether the correlation coefficient of the synthesized field is greater than or equal to a preset extremely strong correlation coefficient;
the typical field calculation module 140 is further specifically configured to determine that the atmospheric circulation situation synthetic field data is the atmospheric circulation situation typical field data when the synthetic field correlation coefficient is greater than or equal to a preset extremely strong correlation coefficient.
Referring to fig. 4, fig. 4 is a functional structure diagram of an atmospheric circulation situation objective typing subsystem. Specifically, the atmospheric circulation situation objective typing subsystem includes 4 modules, which are respectively:
the data control module 110, which is composed of 3 sub-modules (M1, M2, and M3):
m1: the key area parameter initialization sub-module 111: the method is characterized in that information such as atmospheric circulation potential field (potential height field) layer direction parameters, horizontal precision parameters (latitude and longitude range and resolution), time parameters corresponding to the sea area red tide high-rise time period and the like are initially set;
m2: raw data processing sub-module 112: carrying out meteorological data fusion based on re-analysis data of the NCEP, the ECMWF and the NMEFC-WRF in the netcdf format in the last 30 years, calculating an area to be forecasted (a key area) weather field, and processing weather field data into a data file with a readable suffix of dat of a subsystem; extracting an atmospheric annular flow potential field of a region to be forecasted corresponding to the first day of the outbreak in the red tide process of the past based on the red tide historical data and the meteorological fusion data, and processing the atmospheric annular flow potential field into a dat data file;
m3: range flat field calculation submodule 113: calculating a distance flat field of the atmospheric annular flow potential field of the area to be forecasted corresponding to the first day of the outbreak in the past red tide process (the difference between the atmospheric annular flow potential field and the weather field corresponding to the first day of the outbreak of the red tide of the area to be forecasted); and respectively outputting the weather field data and the range data obtained by calculation to a dat data file which can be read by the subsystem.
A cluster analysis module 120, which divides the range flat field of the area to be forecasted at the first day of the red tide outbreak into different clusters by adopting an Euclidean distance algorithm; calculating the contour coefficients of different classification schemes, comparing the quality degrees of different classification schemes based on a superiority comparison method, and selecting a group of classifications with the largest contour coefficients as the optimal classification (i.e., if m red tide cases are provided and divided into k classes, the optimal classification is the one with the largest contour coefficients
A partition method that selects one having the largest contour coefficient as the optimal classification).
And thirdly, the synthetic field module 130 calculates the cross correlation coefficient of the first-day distance field of each red tide event in each class cluster in the optimal classification scheme by adopting a pearson similarity algorithm, takes the distance field with the result showing strong correlation (r is more than or equal to 0.6) as a sample of the synthetic field, and calculates the initial atmospheric circulation situation synthetic field (the synthetic field for short).
And fourthly, the typical field calculation module 140 performs a synthetic field sensitivity test, calculates the correlation between each range flat field in the class and the initial synthetic field pearson, and counts the range flat field into a sample of a new synthetic field if the result shows extremely strong correlation (r is more than or equal to 0.8). And repeating the test until the sample set of the synthetic field is not changed any more, wherein the synthetic field is the typical field of the atmospheric circulation situation which is most prone to red tide, and the typical field data is written into a data file with the suffix of dat.
As an alternative embodiment, the data processing subsystem 200 includes:
a GFS data FTP downloading module 210, which is used for generating a batch wget downloading file of meteorological field forecast data of 240 hours in the future every day based on Fortran programming language and system time; downloading meteorological field forecast initial data of an area to be forecasted based on the wget data crawler and the batch processing wget download file; acquiring weather field data of an area to be forecasted;
the data format processing module 220 is used for calling an executable program wgrib2 to convert the grb2 data format of the meteorological field initial forecast data into an out format by decoding a batch processing file;
the data interpolation integration module 230 is configured to extract and interpolate the out-format gas field initial prediction data based on a linear interpolation method to obtain gas field prediction data corresponding to the typical field data of the atmospheric circulation situation;
the first data archiving and storing module 240 is used for storing weather field data and weather field forecast data into a folder named by the date of the day based on Fortran programming language;
and the script control module 250 is used for controlling the running of the GFS data FTP downloading module, the data format processing module, the data interpolation integration module and the data archiving and storing module based on the shell script file.
Referring to fig. 5, fig. 5 is a functional structure diagram of a data processing subsystem. Specifically, the data processing subsystem includes 5 modules, which are respectively:
GFS data FTP download module 210: calling system time based on a Fortran programming language, generating a batch processing wget download file of meteorological field forecast data of 0-240 hours in the future every day, and downloading data in batches based on a wget data crawler technology;
data format processing module 220: processing GFS original grb2 format data into an out format data file readable by a subsystem through a decoding batch processing file calling executable program wgrib 2;
data interpolation integration module 230: extracting and interpolating the processed GFS meteorological forecast data file into a data format unified with the horizontal resolution of the typical field of the atmospheric circulation situation of the area to be forecasted on the basis of a linear interpolation method;
data archiving and storing module 240: automatically storing the downloaded grb 2-format data, the processed out-format data and the interpolated weather forecast field data into a folder named by the date of the day based on Fortran programming language;
the script control module 250: and controlling the operation of the data subsystem based on the shell script file, and carrying out batch processing files such as downloading, processing, archiving, storing, inquiring and the like on the data.
As an alternative embodiment, the circulation-situation red tide forecasting subsystem 300 includes:
the data calling and processing module 310 is used for calling meteorological field forecast data and meteorological field data based on Fortran programming language and calculating day-to-day variation atmospheric circulation situation distance flat field data of 240 hours in the future;
the red tide outbreak probability calculating module 320 is used for performing correlation operation on the daily change atmospheric circulation situation distance flat field data and the atmospheric circulation situation typical field data according to a Pearson similarity algorithm to obtain a correlation percentage, and determining the correlation percentage as the red tide outbreak probability;
the second data archiving and storing module 330 is configured to store the red tide occurrence probability as red tide probability forecast data in a txt format; and storing the meteorological field forecast data and the red tide probability forecast data into a folder named by the date of the day based on the Fortran programming language.
Referring to fig. 6, fig. 6 is a functional structure diagram of a circulation-type red tide forecasting subsystem. Specifically, the circulation-current-situation red tide forecasting subsystem comprises 3 modules which are respectively:
data call processing module 310: calling meteorological field forecast data and meteorological field data based on Fortran programming language, and calculating a daily change atmospheric circulation situation distance flat field of an area to be forecasted for 0-240 hours in the future;
② the red tide outbreak probability calculating module 320: and (3) performing correlation operation by using daily variation atmospheric circulation form-distance flat field data and each typical field data by adopting a Pearson similarity algorithm, and converting the calculation result into a percentage format to be used as a red tide outbreak probability index.
Data archiving storage module 330: storing the data calculated by the red tide outbreak probability calculation module into a probability forecast data file with the format txt; and automatically filing the downloaded meteorological field forecast data and red tide probability forecast data into a file named on the current date based on Fortran.
As an alternative embodiment, the product information subsystem 400 includes:
the prediction information display module 410 is used for acquiring a plurality of red tide outbreak probabilities and drawing a visible product diagram of the red tide outbreak probability trend of 240 hours in the future according to the plurality of red tide outbreak probabilities;
the meteorological element field data visualization drawing module 420 is used for performing visualization manufacturing on meteorological field forecast data based on Grads drawing software and generating a meteorological element field time sequence visualization product diagram comprising potential height, wind, temperature, air pressure and relative humidity;
and the information query module 430 is used for querying the historical red tide event, the historical red tide outbreak probability trend visualization product graph and the historical meteorological element field time sequence visualization product graph according to the user input conditions.
Referring to fig. 7, fig. 7 is a functional structure diagram of a product information subsystem. Specifically, the product information subsystem includes 3 modules, which are respectively:
predictive information display module 410: the module is mainly used for calling a red tide outbreak probability forecast data file and drawing a time sequence chart of 0-240 hours in the future;
a meteorological element field visual drawing module 420: and (4) performing visual production on meteorological field forecast data based on Grads drawing software to generate a potential height, a wind field, temperature, air pressure, relative humidity and other time sequence diagrams.
Information query module 430: the method mainly comprises the steps of inquiring historical red tide events and historical atmospheric circulation situation field weather maps, calling a water quality buoy related monitoring element information database through interface links to perform visual display and the like.
In this embodiment, the execution subject of the method may be a computing device such as a computer and a server, and is not limited in this embodiment.
It can be seen that, by implementing the circulation situation red tide forecasting system based on the multi-source meteorological data described in the embodiment, the red tide outbreak probability forecasting products can be issued in real time every day, and the forecasting timeliness can be extended to 240 hours in the future; then, the system can also adopt an objective method of an Euclidean distance algorithm and a contour coefficient for atmospheric circulation situation typing, so that the typing result is more scientific and accurate; secondly, the system can also express the red tide outbreak probability more clearly and clearly so as to have a quantified index value; in addition, the data information utilized by the system is easy to obtain stable real-time weather forecast data, meets the actual demand of coastal red tide service forecast, and can be applied to actual forecast service; finally, the system can automatically provide visual forecast products, so that the person in the field can intuitively know the red tide occurrence probability, the ecological and meteorological environment element information conditions.
Example 2
Referring to fig. 2, fig. 2 is a schematic structural diagram of a method for forecasting a red tide in an circumfluence situation based on multi-source meteorological data according to this embodiment. As shown in fig. 2, the method for forecasting the red tide of the circulation situation based on the multi-source meteorological data comprises the following steps:
s501, acquiring a weather reanalysis data set of an area to be forecasted, classifying the weather reanalysis data set and calculating a synthetic field according to an Euclidean distance algorithm, a contour coefficient and a similar algorithm to obtain typical field data of the atmospheric circulation situation.
As an optional implementation manner, the step of obtaining a weather reanalysis data set of an area to be forecasted, classifying the weather reanalysis data set according to an euclidean distance algorithm, a contour coefficient and a similar algorithm, and calculating a synthetic field to obtain typical field data of the atmospheric circulation situation comprises:
automatically acquiring a weather reanalysis data set and historical red tide data of an area to be forecasted, and calculating according to the weather reanalysis data set and the historical red tide data to obtain historical distance flat field data;
acquiring a plurality of historical distance flat field data, and classifying and optimizing the plurality of historical distance flat field data according to an Euclidean distance algorithm, a contour coefficient and a similar algorithm to obtain an optimal classification scheme;
calculating according to a similar algorithm and an optimal classification scheme to obtain a plurality of correlation distance flat field data; synthesizing according to the plurality of correlation distance flat fields to obtain atmospheric circulation situation synthetic field data;
and calculating according to a similar algorithm, the plurality of correlation distance flat field data and the atmospheric circulation situation synthetic field data to obtain the typical field data of the atmospheric circulation situation.
As a further optional implementation manner, the step of obtaining the weather reanalysis data set and the historical red tide data of the area to be forecasted automatically, and calculating according to the weather reanalysis data set and the historical red tide data to obtain the historical distance flat field data includes:
setting atmospheric circulation situation field layer direction parameters, horizontal precision parameters and red tide occurrence time parameters;
acquiring a weather reanalysis data set of an area to be forecasted according to atmospheric circulation situation field layer direction parameters, horizontal precision parameters and red tide occurrence time parameters; the meteorological reanalysis data set comprises NCEP reanalysis data in netcdf format, ECMWF reanalysis data and NMEFC-WRF reanalysis data;
performing data fusion on the meteorological reanalysis data set to obtain meteorological fusion data;
calculating based on the meteorological fusion data to obtain historical weather field data;
extracting atmospheric circulation situation field data of the first day of the red tide outbreak of each past time based on the historical red tide data and meteorological fusion data;
calculating difference values of atmospheric annular flow potential field data and historical atmospheric weather field data of the first day of the past red tide outbreak to obtain historical distance flat field data;
and converting the data formats of the weather field data, the atmospheric circulation potential field data of the first day of the past red tide outbreak and the historical distance flat field data into a dat format.
As a further optional implementation manner, the step of obtaining a plurality of historical distance flat field data, and performing classification and preference on the plurality of historical distance flat field data according to the euclidean distance algorithm, the contour coefficient and the similarity algorithm to obtain an optimal classification scheme includes:
acquiring a plurality of historical distance flat field data;
dividing a plurality of historical distance horizon data into a plurality of class clusters according to a Euclidean distance algorithm; the plurality of clusters are distinct;
according to the number of the plurality of historical distance flat field data and the number of the plurality of class clusters, carrying out scheme division calculation on the plurality of class clusters to obtain a plurality of classification schemes; wherein the plurality of classification schemes are different;
calculating a contour coefficient of each classification scheme;
and taking the classification scheme with the maximum contour coefficient as the optimal classification scheme.
As a further optional implementation, calculating according to a similar algorithm and an optimal classification scheme to obtain a plurality of correlation distance flat field data; and synthesizing according to a plurality of correlation distance flat fields to obtain data of the atmospheric circulation situation synthetic field, wherein the step of obtaining the data of the atmospheric circulation situation synthetic field comprises the following steps:
extracting a plurality of class clusters in the optimal classification scheme;
calculating a cross-correlation coefficient of range-horizontal data of each of the plurality of class clusters according to a pearson similarity algorithm;
extracting a plurality of correlation distance flat field data of which the cross correlation coefficient is greater than or equal to a preset strong correlation coefficient from the flat field data;
and synthesizing the plurality of correlation distance flat field data to obtain atmospheric circulation situation synthetic field data.
As a further alternative implementation, the step of calculating according to the similarity algorithm, the plurality of correlated distance flat field data and the atmospheric circulation situation synthesized field data to obtain the typical field data of the atmospheric circulation situation includes:
calculating a synthetic field correlation coefficient between the plurality of correlation distance flat field data and the atmospheric circulation situation synthetic field data according to a pearson similarity algorithm;
judging whether the correlation coefficient of the synthesized field is greater than or equal to a preset strong correlation coefficient or not;
and when the correlation coefficient of the synthetic field is greater than or equal to the preset strong correlation coefficient, determining the synthetic field data of the atmospheric circulation situation as the typical field data of the atmospheric circulation situation.
As a further optional embodiment, the method further comprises:
and converting data format data of the typical field data of the atmospheric circulation situation into a dat format.
S502, downloading meteorological field forecast data of the area to be forecasted based on the data crawler technology, and acquiring meteorological field data of the area to be forecasted.
As an optional implementation manner, the step of downloading weather field forecast data of an area to be forecasted based on a data crawler technology and acquiring the weather field data of the area to be forecasted includes:
generating a batch wget download file of meteorological field forecast data of 240 hours in the future every day based on Fortran programming language and system time; downloading the meteorological field initial forecast data of the area to be forecasted based on the wget data crawler and the batch processing wget download file; acquiring weather field data of an area to be forecasted;
calling an executable program wgrib2 by decoding a batch file to convert the grb2 data format of the meteorological field initial forecast data into an out format;
extracting and interpolating the initial prediction data of the meteorological field in the out format based on a linear interpolation method to obtain meteorological field prediction data corresponding to typical field data of the atmospheric circulation situation;
storing weather field data and weather field forecast data into a folder named by the date of the day based on Fortran programming language;
and controlling the operation of the GFS data FTP downloading module, the data format processing module, the data interpolation integration module and the data archiving and storing module based on the shell script file.
S503, calculating a difference value according to the weather field data and the weather field forecast data to obtain daily change atmospheric circulation situation distance flat field data; and according to a Pearson similarity algorithm, performing correlation operation on the daily change atmospheric circulation situation distance flat field data and the atmospheric circulation situation typical field data to obtain a correlation percentage, and determining the correlation percentage as the red tide outbreak probability.
As an optional implementation mode, performing difference calculation according to the weather field data and the weather field forecast data to obtain daily change atmospheric circulation situation distance flat field data; according to the Pearson similarity algorithm, carrying out correlation operation on the daily change atmospheric circulation situation distance flat field data and the atmospheric circulation situation typical field data to obtain the correlation percentage, and determining the correlation percentage as the red tide outbreak probability comprises the following steps:
calling meteorological field forecast data and meteorological field data based on Fortran programming language, and calculating daily change atmospheric circulation situation distance flat field data of 240 hours in the future;
according to a Pearson similarity algorithm, carrying out correlation operation on the daily change atmospheric circulation situation distance flat field data and the atmospheric circulation situation typical field data to obtain a correlation percentage, and determining the correlation percentage as the red tide outbreak probability;
storing the red tide outbreak probability as red tide probability forecast data in a txt format; and storing the meteorological field forecast data and the red tide probability forecast data into a folder named by the date of the day based on the Fortran programming language.
S504, obtaining a plurality of red tide outbreak probabilities, and drawing a visual product diagram of the red tide outbreak probability trend according to the plurality of red tide outbreak probabilities; and generating a meteorological element field time series visualization product diagram according to meteorological field forecast data.
As an optional implementation manner, obtaining a plurality of red tide emergence probabilities, and drawing a visual product diagram of red tide emergence probability trend according to the plurality of red tide emergence probabilities; and the step of generating the meteorological element field time series visualization product diagram according to the meteorological field forecast data comprises the following steps:
acquiring a plurality of red tide emergence probabilities, and drawing a visible product diagram of the red tide emergence probability trend of 240 hours in the future according to the plurality of red tide emergence probabilities;
visualizing meteorological field forecast data based on Grads drawing software to generate a meteorological element field time sequence visualization product diagram comprising potential height, wind, temperature, air pressure and relative humidity;
and inquiring the historical red tide event, the historical red tide outbreak probability trend visualization product graph and the historical meteorological element field time sequence visualization product graph according to the input conditions of the user.
Referring to fig. 8, fig. 8 shows a trend chart of red tide outbreak probability.
In the embodiment of the present application, for the explanation of the method for forecasting the red tide in the circulation situation based on the multi-source meteorological data, reference may be made to the description in embodiment 1, and details are not repeated in this embodiment.
It can be seen that by implementing the circulation-current-situation red tide forecasting method based on the multi-source meteorological data described in the embodiment, the red tide outbreak probability forecasting product can be released in real time every day, and the forecasting timeliness can be extended to 240 hours in the future; then, the system can also adopt an objective method of an Euclidean distance algorithm and a contour coefficient for atmospheric circulation situation typing, so that the typing result is more scientific and accurate; secondly, the system can also express the red tide outbreak probability more clearly and clearly so as to have a quantified index value; in addition, the data information utilized by the system is easy to obtain stable real-time weather forecast data, meets the actual demand of coastal red tide service forecast, and can be applied to actual forecast service; finally, the system can automatically provide a visual forecast product, so that the person in the field can intuitively know the red tide occurrence probability and the information condition of the elements of the ecological and meteorological environments.
The embodiment of the application provides an electronic device, which comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor runs the computer program to enable the electronic device to execute the method for forecasting the red tide of the circulation situation based on the multi-source meteorological data in embodiment 2 of the application.
The embodiment of the present application provides a computer-readable storage medium, which stores computer program instructions, and when the computer program instructions are read and executed by a processor, the method for forecasting the red tide of the circulation situation based on the multi-source meteorological data in embodiment 2 of the present application is executed.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A circulation situation red tide forecasting system based on multi-source meteorological data is characterized in that the circulation situation red tide forecasting system comprises an atmospheric circulation situation objective typing subsystem, a data processing subsystem, a circulation situation red tide forecasting subsystem and a product information subsystem, wherein,
the atmospheric circulation situation objective typing subsystem is used for automatically acquiring a weather reanalysis data set of an area to be forecasted, classifying the weather reanalysis data set and calculating a synthetic field according to an Euclidean distance algorithm, a contour coefficient and a similar algorithm to obtain typical field data of the atmospheric circulation situation;
the data processing subsystem is used for downloading meteorological field forecast data of the area to be forecasted based on a data crawler technology and acquiring meteorological field data of the area to be forecasted;
the circulation situation red tide forecasting subsystem is used for calculating the difference value according to the meteorological field forecasting data and the meteorological field data to obtain day-to-day variation atmospheric circulation situation distance flat field data; according to the similar algorithm, carrying out correlation operation on the daily variation atmospheric circulation situation distance flat field data and the atmospheric circulation situation typical field data to obtain correlation percentage, and determining the correlation percentage as the red tide outbreak probability;
the product information subsystem is used for acquiring a plurality of red tide outbreak probabilities and drawing a visible product diagram of the red tide outbreak probability trend according to the plurality of red tide outbreak probabilities; and the meteorological field forecasting data are used for generating a meteorological element field time sequence visualization product diagram.
2. The system for forecasting the red tide of the circulation situation based on the multi-source meteorological data as claimed in claim 1, wherein the subsystem for objectively classifying the circulation situation of the atmosphere comprises:
the data control module is used for automatically acquiring a weather reanalysis data set and historical red tide data of an area to be forecasted, and calculating according to the weather reanalysis data set and the historical red tide data to obtain historical distance flat field data;
the cluster analysis module is used for acquiring a plurality of historical distance flat field data, and carrying out classification and optimization on the plurality of historical distance flat field data according to an Euclidean distance algorithm, a contour coefficient and a similar algorithm to obtain an optimal classification scheme;
the synthetic field calculation module is used for calculating according to the similarity algorithm and the optimal classification scheme to obtain a plurality of correlation distance flat field data; synthesizing according to the plurality of correlation distance flat fields to obtain atmospheric circulation situation synthetic field data;
and the typical field calculation module is used for calculating according to the similarity algorithm, the plurality of correlation distance flat field data and the atmospheric circulation situation synthetic field data to obtain the atmospheric circulation situation typical field data.
3. The system of claim 2, wherein the data control module comprises:
the parameter initialization submodule is used for setting atmospheric circulation situation field layer direction parameters, horizontal precision parameters and red tide occurrence time parameters;
the original data processing submodule is used for acquiring a weather reanalysis data set of an area to be forecasted according to the atmospheric circulation situation field layer direction parameter, the horizontal precision parameter and the red tide occurrence time parameter;
the original data processing submodule is also used for carrying out data fusion on the meteorological reanalysis data set to obtain meteorological fusion data;
the original data processing submodule is also used for calculating based on the meteorological fusion data to obtain historical weather field data;
the original data processing submodule is also used for extracting the atmospheric circulation situation field data of the first day of the red tide outbreak of each time based on the historical red tide data and the meteorological fusion data;
and the distance flat field calculation submodule is used for calculating the difference value of the atmospheric annular flow potential field data of the first day of the past red tide outbreak and the historical atmospheric weather field data to obtain historical distance flat field data.
4. The system for forecasting the red tide of the circulation situation based on the multi-source meteorological data as claimed in claim 2, wherein the cluster analysis module is specifically configured to obtain a plurality of historical distance flat field data;
the cluster analysis module is specifically used for dividing the plurality of historical distance flat data into a plurality of different class clusters according to a Euclidean distance algorithm;
the cluster analysis module is specifically configured to perform scheme division calculation on the multiple classes according to the number of the multiple historical distance flat field data and the number of the multiple classes to obtain multiple classification schemes;
the cluster analysis module is specifically used for calculating the contour coefficient of each classification scheme;
the cluster analysis module is specifically configured to use the classification scheme with the largest contour coefficient as an optimal classification scheme.
5. The system according to claim 2, wherein the synthetic field calculation module is specifically configured to extract a plurality of clusters in the optimal classification scheme;
the composite field calculation module is specifically configured to calculate a cross-correlation coefficient from horizontal field data of each of the plurality of class clusters according to a pearson similarity algorithm;
the synthetic field calculation module is specifically further configured to extract a plurality of correlation range flat field data of which the cross-correlation coefficient is greater than or equal to a preset strong correlation coefficient;
the synthetic field calculation module is specifically configured to synthesize the multiple correlation distance flat field data to obtain atmospheric circulation situation synthetic field data.
6. The system according to claim 2, wherein the typical field calculation module is specifically configured to calculate a composite field correlation coefficient between the multiple correlation distance level field data and the atmospheric circulation situation composite field data according to a pearson similarity algorithm;
the typical field calculation module is specifically further configured to determine whether the correlation coefficient of the synthesized field is greater than or equal to a preset extremely strong correlation coefficient;
the typical field calculation module is specifically configured to determine that the atmospheric circulation situation typical field data is the atmospheric circulation situation typical field data when the synthetic field correlation coefficient is greater than or equal to the preset extremely strong correlation coefficient.
7. The system for forecasting the red tide of the circulation terrain based on the multi-source meteorological data as claimed in claim 1, wherein the data processing subsystem comprises:
the GFS data FTP downloading module is used for generating a batch wget downloading file of meteorological field forecast data of 240 hours in the future every day based on Fortran programming language and system time; downloading the meteorological field initial forecast data of the area to be forecasted based on the wget data crawler and the batch processing wget download file; acquiring weather field data of the area to be forecasted;
the data format processing module is used for calling an executable program wgrib2 to convert the grb2 data format of the meteorological field initial forecast data into an out format by decoding a batch processing file;
the data interpolation integration module is used for extracting and interpolating the weather field initial forecast data in the out format based on a linear interpolation method to obtain weather field forecast data corresponding to the atmospheric circulation situation typical field data;
the first data archiving and storing module is used for storing the weather field data and the weather field forecast data into a folder named according to the date of the day based on Fortran programming language;
and the script control module is used for controlling the running of the GFS data FTP downloading module, the data format processing module, the data interpolation integration module and the data archiving and storing module based on the shell script file.
8. The system for forecasting the red tide of the circulation situational weather based on the multi-source meteorological data of claim 1, wherein the subsystem for forecasting the red tide of the circulation situational weather comprises:
the data calling and processing module is used for calling meteorological field forecast data and meteorological field data based on Fortran programming language and calculating day-to-day variation atmospheric circulation situation distance flat field data of 240 hours in the future;
the red tide outbreak probability calculation module is used for performing correlation operation on the daily change atmospheric circulation situation distance flat field data and the atmospheric circulation situation typical field data according to a Pearson similarity algorithm to obtain a correlation percentage, and determining the correlation percentage as the red tide outbreak probability;
the second data archiving and storing module is used for storing the red tide outbreak probability as red tide probability forecast data in a txt format; and storing the meteorological field forecast data and the red tide probability forecast data into a folder named by the date of the day based on Fortran programming language.
9. The system of claim 1, wherein the product information subsystem comprises:
the prediction information display module is used for acquiring a plurality of red tide outbreak probabilities and drawing a visible product diagram of the red tide outbreak probability trend within 240 hours in the future according to the plurality of red tide outbreak probabilities;
the meteorological element field data visualization drawing module is used for performing visualization manufacturing on meteorological field forecast data based on Grads drawing software and generating a meteorological element field time sequence visualization product diagram comprising potential height, wind, temperature, air pressure and relative humidity;
and the information query module is used for querying the historical red tide event, the historical red tide outbreak probability trend visualization product graph and the historical meteorological element field time sequence visualization product graph according to the input conditions of the user.
10. A circulation topography red tide forecasting method based on multi-source meteorological data is characterized by comprising the following steps:
acquiring a weather reanalysis data set of an area to be forecasted, and classifying and synthesizing the weather reanalysis data set according to an Euclidean distance algorithm, a contour coefficient and a similar algorithm to obtain typical field data of the atmospheric circulation situation;
downloading meteorological field forecast data of the area to be forecasted based on a data crawler technology, and acquiring meteorological field data of the area to be forecasted;
performing difference value calculation according to the meteorological field forecast data and the meteorological field data to obtain daily change atmospheric circulation situation distance flat field data; according to the similarity algorithm, carrying out correlation operation on the daily variation atmospheric circulation situation distance level field data and the atmospheric circulation situation typical field data to obtain correlation percentage, and determining the correlation percentage as red tide outbreak probability;
acquiring a plurality of red tide emergence probabilities, and drawing a visible product diagram of the red tide emergence probability trend according to the red tide emergence probabilities; and generating a meteorological element field time sequence visualization product diagram according to the meteorological field forecast data.
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