CN114578456A

CN114578456A - Data processing method, device, equipment and medium applied to radar forecast strong convection weather

Info

Publication number: CN114578456A
Application number: CN202210115312.1A
Authority: CN
Inventors: 冉令坤; 李娜; 焦宝峰; 周括; 平凡; 周玉淑; 杨帅
Original assignee: Beijing Zhongke Fengyun Technology Co ltd; Institute of Atmospheric Physics of CAS
Current assignee: Beijing Zhongke Fengyun Technology Co ltd; Institute of Atmospheric Physics of CAS
Priority date: 2022-01-30
Filing date: 2022-01-30
Publication date: 2022-06-03
Anticipated expiration: 2042-01-30
Also published as: CN114578456B

Abstract

The disclosure provides a data processing method, device, equipment and medium applied to radar forecast of strong convection weather. The data processing method comprises the following steps: determining a historical forecast individual data set according to the historical forecast output data set and the historical radar observation base data set; acquiring the variance of similar physical quantities of each historical forecast case in the historical forecast case data set based on the current forecast output data set; and processing the data set of the variance of the similar physical quantity of each historical forecast case to screen a plurality of historical similar forecast cases in the data set of the historical forecast cases, wherein the plurality of historical similar forecast cases are used for realizing radar combined reflectivity objective correction forecast applied to radar forecast strong convection weather. Therefore, the method disclosed by the invention develops a radar combined reflectivity objective correction forecasting method with longer forecasting time based on numerical mode output data by adopting the similarity principle.

Description

Data processing method, device, equipment and medium applied to radar forecast strong convection weather

Technical Field

The present disclosure relates to the field of computer technologies and weather forecasting technologies, and in particular, to a data processing method, apparatus, device, medium, and computer program product for radar forecasting of strong convection weather.

Background

Strong convective weather such as thunderstorms has strong radar combined reflectivity (also referred to as radar echo), so people usually forecast the development and change of the strong convective weather by detecting the development and movement of the radar echo. The current core forecasting means is an approach forecasting technology, and the approach extrapolation forecasting is carried out by depending on a weather radar, but the forecasting time is only 2 hours, the effective forecasting time is about 30 minutes, the long-time forecasting of 0-72 hours cannot be provided, only the range of a radar reflectivity high-value area can be forecasted, and the reflectivity intensity change cannot be effectively forecasted. The weather service department also uses the thunderstorm weather potential forecasting technology at present. The forecasting technology can forecast the existence of the problem of strong convection weather such as thunderstorms in the future, but still cannot forecast the generation and the consumption evolution of the thunderstorms. Because the critical value of the convection index has locality, the critical value is effective in a certain region, and the effect of transplanting the critical value to other regions is obviously reduced or even fails. In addition, the different modes have obvious difference in the calculated convection index critical value due to different initial fields and physical parameterization schemes.

Disclosure of Invention

Technical problem to be solved

In order to solve at least one of the technical problems in the prior art in the forecasting process of the strong convection weather, the present disclosure provides a data processing method, apparatus, device, medium and computer program product for radar forecasting of the strong convection weather.

(II) technical scheme

A first aspect of the present disclosure provides a data processing method applied to radar forecast strong convection weather, where the method includes: determining a historical forecast individual data set according to the historical forecast output data set and the historical radar observation base data set; acquiring the variance of similar physical quantities of each historical forecast case in the historical forecast case data set based on the current forecast output data set; and processing the data set of the variance of the similar physical quantity of each historical forecast case to screen a plurality of historical similar forecast cases in the data set of the historical forecast cases, wherein the plurality of historical similar forecast cases are used for realizing radar combined reflectivity objective correction forecast applied to radar forecast strong convection weather.

According to an embodiment of the present disclosure, in determining a historical forecast personal dataset from a historical forecast output dataset and a historical radar observation base dataset, the method includes: acquiring a historical forecast similar physical quantity data set according to the historical forecast output data set; determining a grid point observation reflectivity jigsaw data set by the grid point historical radar observation base data set; determining a historical forecast individual case data set through a historical forecast similar physical quantity data set and a lattice point observation reflectivity jigsaw data set; and the data grids and the data time between the historical forecast similar physical quantity data set and the grid point observation reflectivity jigsaw data set are consistent.

According to an embodiment of the present disclosure, in acquiring a historical forecast similar physical quantity data set according to a historical forecast output data set, the method includes: determining a plurality of historical similar physical quantities in a historical forecast output data set; and acquiring a historical forecast similar physical quantity data set according to the plurality of historical similar physical quantities.

According to an embodiment of the present disclosure, determining a grid point observed reflectivity tile dataset in a grid-binned historical radar observation base dataset comprises: interpolating a historical radar observation base data set into a three-dimensional grid space; and carrying out reflectivity three-dimensional networking jigsaw on the grid data generated in the three-dimensional grid space through interpolation, and determining a grid point observation reflectivity jigsaw data set.

According to the embodiment of the disclosure, in determining the historical forecast individual case data set through the historical forecast similar physical quantity data set and the lattice point observation reflectivity jigsaw data set, the method comprises the following steps: determining historical forecast similar physical quantity at the same moment on a grid point in a historical forecast similar physical quantity data set and data pairs among radar combined reflectivity historical observation data at the same moment on the same grid point in a grid point observation reflectivity jigsaw data set; historical forecast individual data sets are determined from the data pairs.

According to an embodiment of the present disclosure, before obtaining the variance of the case similarity physical quantity of each of the historically-predicted case data sets based on the current-prediction output data set, the method further includes: extracting meteorological element data of the current forecast output data set; determining a plurality of current similar physical quantities of the current forecast output data set through meteorological element data of the current forecast output data set; wherein the plurality of current similar physical quantities comprises: weftwise wind, meridional wind, vertical velocity, potential altitude, ground air pressure, ground wind speed, ground temperature, vorticity, divergence, potential temperature, water reducible amount, convection effective potential energy, convection suppression energy, layer junction stability, uplift index, Sa's index, Q vector divergence, generalized potential temperature, vertical wind shear, and Rough-Risk number.

According to an embodiment of the present disclosure, in acquiring a variance of an individual case similarity physical quantity of each historical forecast individual case in a historical forecast individual case data set based on a current forecast output data set, the method includes: and determining the variance according to one current similar physical quantity in a plurality of current similar physical quantities of the current forecast output data set and the similar physical quantity of the corresponding historical forecast individual in the historical forecast individual data set.

According to an embodiment of the present disclosure, in processing a data set of variances of individual case similarity physical quantities of each historical forecast individual case to filter a plurality of historical similarity forecast individual cases in the data set of the historical forecast individual cases, the method includes: sorting the variances of the similar physical quantities of each case corresponding to each historical forecast case in the variance data set; and screening the data set of the variance after the sorting processing to obtain a plurality of history similar forecast cases.

According to an embodiment of the present disclosure, after processing the data set of the variance of the individual case similarity physical quantity of each historical forecast individual case to filter a plurality of historical forecast individual cases in the data set of the historical forecast individual cases, the method further includes: determining a weight coefficient of each historical similarity prediction case in a plurality of historical similarity prediction cases; and determining a plurality of average combined reflectivities corresponding to the current forecast output data set according to the weight coefficient of each historical similarity forecast case.

The second aspect of the present disclosure provides a data processing apparatus applied to radar forecast strong convection weather, wherein the apparatus includes a data determining module, a variance obtaining module and a data processing module. The data determining module is used for determining a historical forecast individual data set according to the historical forecast output data set and the historical radar observation base data set; the variance acquisition module is used for acquiring the variance of the similar physical quantity of each history forecast case in the history forecast case data set based on the current forecast output data set; and the data processing module is used for processing the data set of the variance of the similar physical quantity of each history forecast case so as to screen a plurality of history forecast cases in the data set of the history forecast cases, and the plurality of history forecast cases are used for realizing radar combined reflectivity objective correction forecast applied to radar forecast strong convection weather.

A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the above-described data processing method applied to radar-forecasted strongly convective weather.

A fourth aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the above-described data processing method applied to radar-forecasted strongly convective weather.

A fifth aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the above-mentioned data processing method applied to radar forecast strongly convective weather.

(III) advantageous effects

The present disclosure provides a data processing method, apparatus, device, medium, and computer program product for radar forecasting strong convection weather. The data processing method comprises the following steps: determining a historical forecast individual data set according to the historical forecast output data set and the historical radar observation base data set; acquiring the variance of similar physical quantities of each historical forecast case in the historical forecast case data set based on the current forecast output data set; and processing the data set of the variance of the similar physical quantity of each historical forecast case to screen a plurality of historical similar forecast cases in the data set of the historical forecast cases, wherein the plurality of historical similar forecast cases are used for realizing radar combined reflectivity objective correction forecast applied to radar forecast strong convection weather. Therefore, the method disclosed by the invention develops a radar combined reflectivity objective correction forecasting method with longer forecasting time based on numerical mode output data by adopting the similarity principle.

Drawings

FIG. 1 schematically shows a flow diagram of a data processing method applied to radar forecast strongly convective weather according to an embodiment of the present disclosure;

FIG. 2 schematically illustrates an application scenario flow diagram of a data processing method applied to radar forecast strong convection weather according to an embodiment of the present disclosure;

FIG. 3 schematically shows an architecture diagram of a data processing apparatus applied to radar forecast heavy convection weather according to an embodiment of the present disclosure; and

fig. 4 schematically shows an architecture diagram of an electronic device suitable for the above-described data processing method applied to radar forecast strong convection weather according to an embodiment of the present disclosure.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.

It is to be noted that, in the attached drawings or in the description, the implementation modes not shown or described are all the modes known by the ordinary skilled person in the field of technology, and are not described in detail. Further, the above definitions of the various elements and methods are not limited to the various specific structures, shapes or arrangements of parts mentioned in the examples, which may be easily modified or substituted by those of ordinary skill in the art.

It should also be noted that directional terms, such as "upper", "lower", "front", "rear", "left", "right", and the like, used in the embodiments are only directions referring to the drawings, and are not intended to limit the scope of the present disclosure. Throughout the drawings, like elements are represented by like or similar reference numerals. Conventional structures or constructions will be omitted when they may obscure the understanding of the present disclosure.

And the shapes and sizes of the respective components in the drawings do not reflect actual sizes and proportions, but merely illustrate the contents of the embodiments of the present disclosure. Furthermore, in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim.

Furthermore, the word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements.

The use of ordinal numbers such as "first," "second," "third," etc., in the specification and in the claims to modify a corresponding element does not by itself connote any ordinal number of the element or any ordering of one element from another or the order of manufacture, and the use of the ordinal numbers is only used to distinguish one element having a certain name from another element having a same name.

Those skilled in the art will appreciate that the modules in the device of an embodiment may be adaptively changed and placed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Furthermore, in the unit claims enumerating several means, several of these means can be embodied by one and the same item of hardware.

Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the disclosure, various features of the disclosure are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various disclosed aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that is, the claimed disclosure requires more features than are expressly recited in each claim. Rather, as the following claims reflect, disclosed aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this disclosure.

Aiming at forecasting of strong convection weather, the approach forecasting technology utilizes continuous radar reflectivity observation, calculates the moving direction and displacement of a radar reflectivity high-value area according to a mass center velocity vector, and linearly extrapolates a strong radar echo (the reflectivity is more than 35dBZ), so as to forecast the moving position of the thunderstorm system in 0-2 hours in the future. The linear extrapolation prediction technology generally adopts an optical flow method, and the core idea is to assume that the gray scale (representative intensity) of a high-value reflectivity area is unchanged in a short time. However, the radar reflectivity of strong convection weather changes rapidly, so that the optical flow method cannot be used for long-time extrapolation prediction, and the effective prediction time is very short, often within 30 minutes. Because the proximity prediction technology assumes that the intensity of the radar reflectivity is unchanged in a short time, only the range of a high-value area of the radar reflectivity can be predicted, and the intensity change of the reflectivity cannot be effectively predicted, so that the proximity prediction technology cannot predict the generative and destructive evolution of strong convection, which is a bottleneck problem to be solved urgently by the current proximity prediction.

The mesoscale numerical model provides reflectivity forecast for 72 hours and longer and also describes the development and movement of thunderstorm weather, but the numerical model reflectivity forecast is calculated by the precipitation particles (rain, snow and aragonite) of the numerical model, the reflectivity forecast error is larger for 72 hours, and the thunderstorm weather forecast is not accurate enough. In contrast, basic meteorological elements such as temperature, humidity, wind speed and air pressure of numerical mode prediction are much more accurate than the predicted precipitation particles, the effective prediction time is long and can reach 72 hours, and a foundation is provided for developing a radar combined reflectivity objective correction prediction method by utilizing numerical mode prediction data.

The current weather service department also uses the thunderstorm weather potential forecasting technology. The forecasting technology is characterized in that a relevant strong convection index is calculated by using mode forecasting data, and the existence of the problem of strong convection weather such as a thunderstorm in the future is forecasted according to a critical value of the index, but the generation and the consumption of the thunderstorm weather can not be forecasted. The key point of the forecasting technology is the selection of a critical value of the flow index, which has decisive influence on the forecasting result. Because the critical value of the convection index has locality, the critical value is effective in a certain region, and the effect of transplanting the critical value to other regions is obviously reduced or even fails. In addition, the different modes have obvious difference in the calculated convection index critical value due to different initial fields and physical parameterization schemes. If a universal critical value is adopted, the false alarm and the false alarm of strong convection weather are often caused.

As shown in fig. 1, a first aspect of the present disclosure provides a data processing method applied to radar forecast strong convection weather, which includes operations S101-S103.

Determining a historical forecast personal data set according to a historical forecast output data set and a historical radar observation base data set in operation S101;

acquiring a variance of similar physical quantities of each case in historical forecast individual case data sets based on a current forecast output data set in operation S102; and

in operation S103, the data set of the variance of the case similarity physical quantity of each historical prediction case is processed to screen a plurality of historical similarity prediction cases in the data set of the historical prediction cases, where the plurality of historical similarity prediction cases are used to realize radar combined reflectivity objective correction prediction applied to radar forecast weather with strong convection.

The history prediction output data set is a data set of history prediction data predicted in a long-term history numerical mode, such as a set of long-term numerical mode prediction history data acquired in a set fixed history period in summer and the like. The historical radar observation base data set is a data set of historical observation base data of long-term radars in different wave bands, such as a set of historical observation base data of radars in different wave bands performed in a set fixed historical period in summer and the like. And the historical forecast individual case data set is a data set of historical forecast individual cases corresponding to the historical forecast output data set and the historical radar observation base data set.

The current forecast output data set is a set of forecast output data in a current numerical mode for starting forecast at the current moment. A corresponding case-like physical quantity corresponding to each historical forecast case may be acquired from each current forecast output data in the current forecast output data set. Further, the variance may be obtained by using the similar physical quantities of the respective cases corresponding to the history forecast respective cases and the similar physical quantities corresponding to the respective current forecast output data. The plurality of historical forecast instances corresponding to the historical forecast instance data set may have a corresponding number of the plurality of instance-like physical quantities and their corresponding variances, the set of variances constituting the data set of variances.

And screening each historical similarity prediction case in the historical prediction case data set through each variance value in the variance data set, and taking the historical similarity prediction cases as the parameters of radar combined reflectivity objective correction prediction of radar forecast strong convection weather.

Strong convection weather refers to weather conditions accompanied by strong convection wind, hail, and short-term strong precipitation. The thunderstorm is strong convection weather with small space scale, short time and strong locality. The meteorological service numerical model forecasting system has low resolution and cannot effectively capture a thunderstorm system. Although the nowcasting system can forecast the movement of the combined reflectivity of the thunderstorm radar, the intensity change of the combined reflectivity of the radar cannot be forecasted, and therefore the generation and consumption evolution of the thunderstorm weather cannot be forecasted. The effective prediction time for short-lived predictions is 0-2 hours, and longer-lived combined reflectance predictions cannot be provided. While disaster prevention and reduction often require a longer time of forecast, for example, 48 hours, the current short forecast cannot meet the demand of disaster prevention and reduction. Meanwhile, no long-term thunderstorm forecasting method exists at present.

In order to solve the problems of growth and disappearance and long-term effect forecast of thunderstorm strong convection weather, the data processing method applied to radar forecast strong convection weather based on the embodiment of the disclosure can be based on a radar combined reflectivity objective correction forecast method of a numerical forecast product, a plurality of historical similar forecast examples are utilized to search historical similar forecast members from a historical forecast and reflectivity observation database, then the reflectivity observation and forecast errors corresponding to the similar forecast members are utilized to carry out error correction forecast, probability forecast and comprehensive similar forecast of radar combined reflectivity, and long-term effect forecast of thunderstorm strong convection weather are realized. Wherein, the above-mentioned example similar physical quantities may include at least 21 meteorological elements and convection parameters as follows.

The combined reflectance forecast for the patterns is derived from a cloud micro physical parameterization scheme. The method comprises the steps of firstly calculating the mixing ratio content of rainwater, snow and shot particles by a cloud physical parameterization scheme, and then reversely calculating the reflectivity by utilizing the empirical statistical relationship (also called Z-R relationship) between the reflectivity and the rainwater, snow, shot and other precipitation particles. Because the cloud micro physical parameterization scheme and the Z-R relation have artificial subjective experience, and have obvious difference in different regions and different weather environments, the reflectivity of the numerical mode forecast has great uncertainty. In contrast, the forecast of the numerical mode to the macroscopic meteorological elements such as temperature, humidity, pressure, wind and the like is much more accurate than the forecast of the mixing ratio content of the cloud micro physical precipitation particles.

Thunderstorm weather has typical dynamic and thermodynamic characteristics of strong atmospheric instability, large potential unstable energy, significant vertical wind shear, and the like. For these features of thunderstorm weather, the data processing method applied to radar forecast strong convection weather in the embodiments of the present disclosure may use 21 typical meteorological elements and convection parameters calculated based on these elements as example similar physical quantities, and search for similar forecast examples from a historical forecast example database. Since the macroscopic meteorological elements and convection parameters of the pattern forecast have long-term effectiveness, the development and evolution of thunderstorm weather can be described for a long time, and therefore the method can be used for screening similar cases of long-term forecast. Meanwhile, since history similar forecast instances occur at different times and are obviously different from each other, the corresponding radar reflectivity observation ranges and intensities are different, and the forecast errors also change along with time, the objective correction forecast based on the forecast deviation of the similar forecast members and the reflectivity observation has the capacity of reflecting strong convection digestion.

Therefore, it can be seen that the data processing method applied to radar forecast strong convection weather in the embodiment of the disclosure can fully utilize basic meteorological elements such as temperature, humidity, air pressure and wind speed and the like with more accurate numerical mode forecast, and calculate convection parameters representing dynamics and thermodynamic characteristics of thunderstorm weather; searching historical similar forecast examples from a historical forecast database by taking the macroscopic meteorological elements and the convection parameters as similar physical quantities; and further utilizing the reflectivity forecast error of a similar forecast member and radar reflectivity observation to correct the error according to the current forecast, and manufacturing a combined reflectivity correction forecast and probability forecast product to realize the long-time and life and consumption forecast of thunderstorm weather.

The data processing method applied to radar forecast strong convection weather in the embodiment of the disclosure is established on the basis of describing macroscopic meteorological elements and convection parameters of dynamic and thermodynamic characteristics of thunderstorm weather, combines dynamics with a similar theory organically, utilizes forecast error information of historical similar examples to carry out deviation correction on current forecast, and essentially comprises contribution of the historical forecast to the current forecast. The method is suitable for numerical modes of different resolutions and dynamic frames, and can be applied as long as 21 meteorological elements and convection parameters can be calculated and provided, so that the method has strong flexibility and adaptability.

In addition, the data processing method applied to radar forecast strong convection weather in the embodiment of the disclosure does not require numerical mode forecast to output reflectivity data, and combined reflectivity correction forecast can be realized only by providing basic meteorological element data such as temperature, humidity, pressure, wind and the like. If the mode forecast can provide reflectivity forecast data, the reflectivity forecasted by the method and the reflectivity forecasted by the mode forecast can be subjected to non-equal weight linear fusion, the advantages of the reflectivity and the reflectivity forecasted by the mode forecast are exerted, and the forecasting accuracy of long-term aging and life and disappearance of thunderstorms and other strong convection weather is improved together by strong and strong combination. Therefore, if the data processing method is implemented, due to the small calculation amount and the high calculation speed, a common computer can meet the requirements of a calculation processing environment, and the data processing method has a wider application scene.

Therefore, the data processing method disclosed by the invention adopts the similarity principle, and develops a set of radar combined reflectivity objective correction forecasting method with longer forecasting time efficiency based on numerical mode output data. Using a numerical mode to forecast relatively accurate macro-factors such as temperature, humidity, wind speed and air pressure and calculating strong convection parameters capable of representing dynamic and thermodynamic characteristics of thunderstorm weather; then, taking the parameters as similar physical quantities, and searching historical similar forecast examples from a historical database according to a similarity principle; and then deviation correction is carried out on the current mode forecast by utilizing the combined reflectivity forecast error and radar combined reflectivity observation corresponding to the combined reflectivity forecast error, an objective correction forecast product of the combined reflectivity is generated, and the forecast problems of long aging and life and extinction evolution of thunderstorm weather are solved.

As shown in fig. 1 and 2, according to an embodiment of the present disclosure, determining a historical forecast personal dataset from a historical forecast output dataset and a historical radar observation base dataset in operation S101 includes:

acquiring a historical forecast similar physical quantity data set according to the historical forecast output data set;

determining a grid point observation reflectivity jigsaw data set by the grid point historical radar observation base data set;

determining a historical forecast individual case data set through a historical forecast similar physical quantity data set and a lattice point observation reflectivity jigsaw data set;

and the data grids and the data time between the historical forecast similar physical quantity data set and the grid point observation reflectivity jigsaw data set are consistent.

As shown in fig. 1 and 2, according to an embodiment of the present disclosure, in acquiring a historical forecast similar physical quantity data set from a historical forecast output data set, the method includes:

determining a plurality of historical similar physical quantities in a historical forecast output data set;

and acquiring a historical forecast similar physical quantity data set according to the plurality of historical similar physical quantities.

As shown in fig. 2, collecting the historical data of the historical numerical pattern prediction as an integral part of the historical prediction output data set, as in operation S201, the summer long-term numerical pattern prediction historical data may be collected as the historical prediction output data. The collected historical forecast output data is used to calculate corresponding historical similar physical quantities, such as vorticity, divergence, convection parameters and other historical similar physical quantities of the historical forecast output data, and a historical data set of the numerical mode forecast similar physical quantities, that is, the historical forecast similar physical quantity data set is generated, as in operation S202. The historical forecast similar physical quantity data set is a historical forecast similar physical quantity data set of each example.

It should be noted that the grids of the historical forecast similar physical quantity data set are completely consistent with the grids of the historical radar observation base data set, and the time corresponds to the grids completely, so that the one-to-one correspondence of the historical forecast examples can be realized.

As shown in fig. 1 and 2, determining a lattice observation reflectivity tile dataset in a lattice historical radar observation base dataset according to an embodiment of the present disclosure includes:

interpolating a historical radar observation base data set into a three-dimensional grid space;

and carrying out reflectivity three-dimensional networking jigsaw on the grid data generated in the three-dimensional grid space through interpolation, and determining a grid point observation reflectivity jigsaw data set.

Collecting historical multiband radar base data as a historical radar observation base data set, for example, collecting historical observation base data of a plurality of different waveband radars in a long term in summer, that is, the historical observation base data of the multiband radar. Further, in operation S204, a networking mosaic is performed by using the collected historical radar observation base data sets to form a grid-based observation combined reflectivity historical data set, i.e., a grid-point observation reflectivity mosaic data set. Specifically, radar volume scanning base data is interpolated into a three-dimensional grid space by using a radar data quality control module 88d2ARPS of an Advanced numerical Prediction System (ARPS for short) and a radar data mosaic module rad-mosaic, and reflectivity three-dimensional networking mosaic is performed to generate a lattice radar combined reflectivity historical observation data set, namely a lattice point observation reflectivity mosaic data set.

As shown in fig. 1 and fig. 2, in determining historical forecast individual data sets by using historical forecast similar physical quantity data sets and lattice observation reflectivity mosaic data sets, according to an embodiment of the present disclosure, the method includes:

determining historical forecast similar physical quantity at the same moment on a grid point in a historical forecast similar physical quantity data set and data pairs among radar combined reflectivity historical observation data at the same moment on the same grid point in a grid point observation reflectivity jigsaw data set;

historical forecast individual data sets are determined from the data pairs.

In order to establish a historical forecast case library of data pairs of forecast similarity factors and reflectivity observations, in operation S205, historical forecast case data pairs corresponding to similar physical quantity forecasts and reflectivity observations one to one are formed by using the generated historical data sets of forecast similar physical quantities and reflectivity observations. Specifically, a data pair formed by similar physical quantity forecast at any time on any grid point in a historical forecast similar physical quantity data set and radar reflectivity observation at the same time of the same grid is established. For example: the 48 th predicted similar physical quantity data (the actual time of this prediction is 2021 year 1 month 3 day 00) starting at 1 month 1 day 00 (world time) in 2021 is a pair of data pairs with the radar reflectance observation at the same grid point as at 1 month 3 day 00 in 2021 year. Thus, each grid point has a data pair corresponding to the forecast similar physical quantity and observation, and the data pair can be expressed as:

wherein the content of the first and second substances,

the similar physical quantity is forecasted for the jth at the tth time of the ith history example,

is prepared by reacting with

And combining the radar combination reflectivity observed values of the same grid point at the same time.

And taking the data set of the plurality of data pairs determined as a historical forecast individual data set.

As shown in fig. 1 and fig. 2, according to the embodiment of the present disclosure, before acquiring, based on the current forecast output dataset, the variance of the similar physical quantity of each case in the historical forecast individual case data set in operation S102, the method further includes:

extracting meteorological element data of a current forecast output data set;

determining a plurality of current similar physical quantities of a current forecast output data set through meteorological element data;

wherein the plurality of current similar physical quantities comprises: at least two of a latitudinal wind, a longitudinal wind, a vertical velocity, a potential altitude, a ground air pressure, a ground air speed, a ground temperature, a vorticity, a divergence, a potential temperature, a reducible water quantity, a convection effective potential energy, a convection suppression energy, a layer junction stability, a lift index, a Save's index, a Q vector divergence, a generalized potential temperature, a vertical wind shear, and a Rough-Zeson number.

In operation S206, the current numerical prediction data is collected as a current prediction output data set, such as a numerical pattern prediction output data that starts prediction at the current time. Further, three-dimensional meteorological element forecast data, namely meteorological element data, such as temperature, humidity, air pressure, latitudinal wind, longitudinal wind components, radar reflectivity and the like, in the current forecast output data set are extracted. Further, in operation S207, a similar physical quantity of the current forecast is calculated using the meteorological element data.

Specifically, at least 20 similar physical quantities including 700hPa latitudinal wind, 700hPa transverse wind, 500hPa vertical velocity, 500hPa potential altitude, ground air pressure, ground 10 meter wind speed, ground 2 meter temperature, 700hPa vorticity, 700hPa divergence, 700hPa potential temperature, reducible water volume, convection effective potential energy, convection suppression energy, layer junction stability, lift index, sha index, Q vector divergence, 850hPa generalized potential temperature, 700hPa vertical wind shear, cricken number, and the like are calculated using the meteorological element data output by the current numerical model prediction.

Wherein the expressions and physical meanings of the above-mentioned respective similar physical quantities are shown in table 1:

TABLE 1

When the radar combined reflectivity objective correction forecasting method is used for screening historical similar forecasting cases, the selection of similar physical quantities is important, because the selection is related to the screening accuracy of the historical similar cases. The selection of similar physical quantities is not only initiated from the pattern forecast itself, but also takes into account the relevant situational field configuration and weather process evolution mechanisms. In order to accurately screen history similar forecast examples, the data processing method of the embodiment of the disclosure selects physical quantities and various convection indexes capable of representing occurrence and development dynamics and thermodynamic characteristics of a thunderstorm weather system as similar physical quantities, as shown in table 1. These similar physical quantities include 20 physical parameters such as 700hPa wefting wind, 700hPa passing wind, 500hPa vertical velocity, 500hPa potential altitude, ground pressure, ground 10 meter wind speed, ground 2 meter temperature, 700hPa vorticity, 700hPa divergence, 700hPa potential temperature, reducible water PW, convective effective potential, convective rejection energy, convective stability index, lift index, crazy charson number, modified K index MK, Q vector divergence, 850hPa generalized potential temperature, 700hPa vertical wind shear, as described in table 1 above.

As shown in fig. 1 and 2, according to an embodiment of the present disclosure, in acquiring a variance of an individual case similarity physical quantity of each historical forecast individual case in a historical forecast individual case data set based on a current forecast output data set in operation S102, the method includes:

and determining the variance according to one current similar physical quantity in a plurality of current similar physical quantities of the current forecast output data set and the similar physical quantity of the corresponding historical forecast individual in the historical forecast individual data set.

In operation S208, a variance between a current forecast similar physical quantity of each data of the current forecast output data set and a corresponding historical forecast individual case similar physical quantity in the historical forecast individual case data set is calculated, the variance satisfying the following formula (1):

wherein, F_t，jThe jth similar argument forecasted for time t,

the jth similar theorem, K, predicted at the same time t of the ith historical case in the historical prediction case library_i，tIs the variance of the two, S_jStandard deviation, w, of the jth similar physical quantity forecasted for the time t of all historical cases_jWeight of jth similar physical quantity predicted at time t, N_vNumber of similar physical quantities, N in the present invention_v＝20。

Based on the above equation (1), it can be seen that the variance K_i，tThe smaller the current forecast is, the higher the similarity between the current forecast and the ith historical forecast case in the historical case library is; k_i，tThe larger the difference between the current similar physical quantity and the similar physical quantity of the ith historical forecast case is, the lower the similarity between the current similar physical quantity and the similar physical quantity of the ith historical forecast case is.

As shown in fig. 1 and fig. 2, according to an embodiment of the present disclosure, processing a data set of variances of example similar physical quantities of each historical prediction example in operation S103 to filter a plurality of historical similar prediction examples in the historical prediction example data set includes:

sorting the variances of the similar physical quantities of each case corresponding to each historical forecast case in the variance data set;

and screening the data set of the variance after the sorting processing to obtain a plurality of history similar forecast cases.

In operation S209, according to the variance data set determined by the above formula (1), the similarity corresponding to each variance data is determined, and based on the similarity, 10 history similar forecast members can be screened from the history forecast case bases. Specifically, the variances of all similar physical quantities currently predicted and the similar physical quantities corresponding to all history prediction examples can be sorted from small to large, and the history prediction examples with the smallest variance of the top 10 can be selected as history similar prediction members to complete the screening of the history similar prediction examples.

For example, in M historical cases, the similarity prediction member K with the smallest variance_E1，tThe similarity forecast member K_E1，tSatisfies the following formula (2):

K_E1，t＝min(K_1，t，K_2，t，...，K_M，t) (2)

wherein M is the sample capacity of the history forecast individual case, and the corresponding radar combined reflectivity observation

By analogy, the variances of 10 similar forecast members are respectively (K)_E1，t，K_E2，t，......，K_E10，t) The corresponding reflectance observation can be expressed as

As shown in fig. 1 and fig. 2, according to the embodiment of the present disclosure, after processing the data set of the variance of the individual case similarity physical quantity of each historical forecast individual case to filter a plurality of historical forecast individual cases in the historical forecast individual case data set in operation S103, the method further includes:

determining a weight coefficient of each historical similarity prediction case in a plurality of historical similarity prediction cases;

and determining a plurality of average combined reflectivities corresponding to the current forecast output data set according to the weight coefficient of each historical similarity forecast case.

In operation S210, corresponding weight coefficients of historical similar forecast examples, for example, weight coefficients of the aforementioned 10 historical similar forecast examples, may be calculated, and using the obtained weight coefficients, various reflectivity forecast products may be calculated, including: a radar combined reflectivity error correction forecast product 211, a radar combined reflectivity weight set average forecast product 212, a radar combined reflectivity average forecast product 213, a radar combined reflectivity probability forecast product 214, a radar combined reflectivity fusion forecast product 215, and a radar combined reflectivity comprehensive similarity forecast product 216, as shown in fig. 2. And taking different reflectivity forecasting products as radar combined reflectivity objective correction forecasting products, and corresponding to the average combined reflectivity of different current forecast output data sets.

The radar combined reflectivity objective correction forecasting product is concretely described as follows:

(1) combined reflectivity error correction prediction

If the numerical model prediction itself provides radar combined reflectivity prediction data, then according to the theoretical assumption that similar cases have similar prediction errors, the prediction errors of the historical similar prediction members can be used to perform error correction on the current numerical model prediction to generate a combined reflectivity error correction prediction, i.e. a radar combined reflectivity error correction prediction product 211. The method comprises the following specific steps:

firstly, calculating radar reflectivity prediction errors of historical similarity prediction members; wherein, the radar reflectivity prediction error of the ith historical similarity prediction member meets the following formula (3):

wherein the content of the first and second substances,

the forecasted combined reflectance for the ith historical likeness forecast member,

for the combined reflectance observation group, Er, corresponding to the ith historical similarity prediction member_i，tAnd (4) forecasting the reflectivity error of the ith historical similarity forecasting member at the t moment.

Then, generating error correction forecast of the combined reflectivity; wherein the reflectivity of the radar can be predicted using the current numerical pattern and the method thereofPrediction error Er_i，tAnd correcting errors of the current forecast to obtain an error correction forecast of the combined reflectivity, wherein the error correction forecast satisfies the following formulas (4) and (5):

wherein R is_tCombined reflectance, G, predicted for numerical mode_iIs the weight coefficient of the ith history similar member, K_i，tVariance, Er, of similar members of the current forecast and the ith history_i，tPrediction error, Rc, for the ith historical likeness prediction member_tFor poor-corrected combined reflectivity mispredictions, N is the total number of similar members. Wherein, in the disclosed embodiments, N ═ 10.

(2) Combined reflectance weight ensemble averaging prediction

Taking the reciprocal of the variance of the historical similarity prediction members as the weight, and linearly superposing the observations corresponding to the N historical similarity prediction members to obtain the combined reflectivity weight set average prediction, namely a radar combined reflectivity weight set average prediction product 212, wherein the radar combined reflectivity weight set average prediction product 212 meets the following formula (6):

wherein R is_EThe combined reflectance averaged over the set of weights forecasted at time t.

(3) Combined reflectance mean prediction

And averaging the reflectivities corresponding to the N historical similarity prediction members by equal weight to obtain combined reflectivity average prediction, namely a radar combined reflectivity average prediction product 213, wherein the radar combined reflectivity average prediction product 213 meets the following formula (7):

wherein R is_MThe average combined reflectance forecasted at time t.

(4) Combined reflectivity probability prediction

Setting a certain threshold R of the combined reflectivity₀If there are N historical similarity forecast members₀Reflectance observations greater than R for individual semblance forecast members₀Then a threshold value R₀The combined reflectivity probability forecast (i.e., the radar combined reflectivity probability forecast product 214) satisfies the following formula (8):

wherein R is_pThe combined reflectance forecasted for the t-th moment is greater than R₀The probability of (c).

(5) Combined reflectance fusion prediction

In order to blend the observed reflectivity into the numerical model reflectivity forecast, the model forecast is corrected, the model reflectivity forecast and the average observed reflectivity forecast are linearly blended to realize the combined reflectivity fusion forecast, namely, the radar combined reflectivity fusion forecast product 215 meets the following formula (9):

R_b＝a·R_t+b·R_M (9)

wherein a is the combined reflectivity R of numerical mode prediction_tB is the average prediction R of the observed combined reflectivity_MThe weight coefficient of (2).

(6) Combined reflectivity integrated similarity prediction

In order to exert the advantages of the front multiple reflectivity forecast, the front forecast products are fused to realize the comprehensive similar forecast of the combined reflectivity, namely the obtained radar combined reflectivity comprehensive similar forecast product 216 meets the following formula (10):

wherein a is the combined reflectivity R of numerical mode prediction_tB is the combined reflectance mean prediction R_MWeight coefficient of (c), max (R)_p) The maximum probability forecast is carried out.

The radar combined reflectivity comprehensive similarity prediction product 216 can realize the nonlinear fusion of at least 4 reflectivity prediction products in front, give play to the advantages of the respective prediction products, optimize the combined reflectivity prediction and improve the prediction accuracy of the combined reflectivity. Therefore, the radar combined reflectivity objective correction forecasting method can utilize the similar physical quantity to calculate the similarity between the current forecast and the historical forecast, and screen out historical similar forecast cases; on the basis, deviation correction is carried out on the currently predicted combined reflectivity according to radar reflectivity observation and combined reflectivity prediction deviation corresponding to history similar cases, and finally a combined reflectivity objective correction prediction product is generated.

Therefore, the data processing method of the embodiment of the disclosure can be well applied to radar combined reflectivity objective correction and forecast, and not only can forecast the movement of the reflectivity high value area, but also can forecast the intensity change of the reflectivity high value area. In addition, another advantage is that the forecast time period can reach 72 hours, which far exceeds the forecast time period, and the long-time forecast of the reflectivity is realized. This means that the data processing method of the embodiment of the present disclosure can forecast long-term evolution and life-and-consumption changes of strong convection weather such as thunderstorms.

Therefore, the data processing method of the embodiment of the disclosure develops a radar combined reflectivity objective correction forecasting method based on a similar dynamic theory. The core of the similar dynamics theory is that when the numerical mode prediction fields at two different moments are similar, the prediction deviation of the two moments is also similar. According to the similar dynamic theory, the basic idea of the radar combined reflectivity objective correction forecasting method is that for similar numerical forecasting examples, the reflectivity forecasting deviation and the corresponding reflectivity observation are also similar. Therefore, historical forecast examples similar to the current numerical forecast are searched from the historical numerical forecast example database. Since the reflectivity prediction error of the historical forecast instances is known, the current forecast error can be inferred from the historical forecast errors similar to the forecast instances, and corrections can be made to the current reflectivity forecast.

In order to overcome the problems, the data processing method disclosed by the embodiment of the disclosure adopts a similarity principle, and develops a radar combination reflectivity objective correction forecasting method with longer forecasting time effect based on numerical mode output data. Forecasting relatively accurate macroscopic factors such as temperature, humidity, wind speed and air pressure by using a numerical mode, and calculating strong convection parameters capable of representing dynamics and thermodynamic characteristics of thunderstorm weather; then, taking the parameters as similar physical quantities, and searching historical similar forecast examples from a historical database according to a similarity principle; and then deviation correction is carried out on the current mode forecast by utilizing the combined reflectivity forecast error and radar combined reflectivity observation corresponding to the combined reflectivity forecast error, an objective correction forecast product of the combined reflectivity is generated, and the forecast problems of long aging and life and extinction evolution of thunderstorm weather are solved.

In addition, the data processing method of the embodiment of the disclosure utilizes meteorological elements and various convection parameters output by numerical mode prediction to establish a radar combined reflectivity objective correction and prediction method with time aging of 0-72 hours. The radar combined reflectivity refers to the reflectivity obtained by projecting the vertical maximum reflectivity on a plane Cartesian grid point in radar volume scanning. Because the high value (more than 35dBZ) of the radar combined reflectivity can represent strong convection activities such as thunderstorm strong wind, the method realizes the forecast of the thunderstorm weather by forecasting the radar combined reflectivity. The data processing method of the embodiment of the disclosure is expected to provide a long-term forecasting technical means for thunderstorm weather forecast of the meteorological service department, improve the forecast level of the strong convection weather service, and make contribution to national and social disaster prevention and reduction.

Therefore, the data processing method of the embodiment of the disclosure provides a method for objectively correcting and forecasting the radar combined reflectivity in 0-72 hours by using numerical forecast meteorological element data and convection parameter data. Therefore, the data processing method disclosed by the embodiment of the disclosure belongs to the category of thunderstorm and other strong convection weather forecast, can be applied to weather service forecast of thunderstorm and strong convection weather of a meteorological service department, and can also be used for guiding and forecasting thunderstorm and other aviation dangerous weather of a civil aviation meteorological department.

The data processing method of the embodiment of the disclosure has clear realization way, and input data comprises mode forecast data such as temperature, humidity, air pressure and wind speed and radar reflectivity observation data, and can be seamlessly coupled with any numerical mode. Moreover, such data is basically available from the internet. In addition, the data processing method of the embodiment of the disclosure implements flow modular design, has clear logical structure, is simple and efficient, has moderate calculation amount, small storage space occupied by data, high calculation efficiency, simple and easy system building, is convenient to transplant, and is easy to apply in meteorological services and various industry departments. Meanwhile, the historical forecast case database can be updated and expanded in real time, and new historical forecast cases are continuously brought into the historical forecast case database. The larger the sample capacity of the historical forecast case is, the more accurate the similar forecast member is to select, and the more ideal the forecast effect is.

Obviously, the data processing method of the embodiment of the disclosure has a wide application range, and the meteorological service department can adopt the method to make thunderstorm weather forecast 24 hours in advance. Thunderstorms are a convection weather phenomenon, and are often accompanied by disastrous weather such as storms, rainstorms, hailstones, tornadoes, lightning strikes, lightning and the like to cause lightning strike fire risks, strong wind blows down houses, fruits, vegetables and other crops are damaged by hailstones, and local rainstorms can also cause geological disasters such as mountain torrents, torrents and debris flows. The accurate long-term effective thunderstorm weather forecast can provide scientific support for lightning protection and disaster reduction, promotion of agricultural production and guarantee of life and property safety of people, and has huge application potential.

Meanwhile, the data processing method disclosed by the embodiment of the disclosure has a wide application prospect in civil aviation departments, and thunderstorms are the most important aviation dangerous weather affecting flight safety, and are accompanied by weather phenomena endangering flight safety, such as turbulence, bump, ice accumulation, lightning strikes (lightning strikes), downburst flows, low-altitude wind shear and the like. When the airplane mistakenly enters the thunderstorm activity area, the light person causes man-machine damage, and the heavy person causes machine damage and death. Thus, thunderstorms are natural enemies currently recognized by the world aviation world and meteorological departments as severely threatening aviation flight safety. The forecasting method can provide 72-hour thunderstorm early warning on airports and air routes, and provides reference for taking avoidance measures in advance and planning air routes.

In addition, for the meteorological service department, the data processing method of the embodiment of the disclosure can be seamlessly linked with the numerical mode forecasting system of the meteorological service station and fused with each other, so as to provide accurate thunderstorm weather forecast for the country and the society. For the agricultural department, thunderstorm strong wind and hail forecast can be provided for fruit tree and vegetable production, and service is provided for agricultural grain safety; for the traffic management department, the system can provide strong wind and thunder forecast, and makes contribution to the traffic travel safety of people. For an emergency disaster reduction department, thunderstorm forecast of disaster weather such as rainstorm, typhoon and the like can be provided, and scientific support is provided for taking emergency measures for disaster prevention and reduction; in conclusion, the method can meet the requirements of industry departments such as agriculture, traffic, disaster prevention and reduction and the like on long-term effective thunderstorm forecasting, and has wide application prospect and obvious application value.

Based on the data processing method applied to the radar forecast strong convection weather, the disclosure also provides a data processing device applied to the radar forecast strong convection weather. The apparatus 300 will be described in detail below with reference to fig. 3.

Fig. 3 schematically shows a block diagram of a data processing apparatus 300 applied to radar forecast strong convection weather according to an embodiment of the present disclosure.

As shown in fig. 3, the data processing apparatus 300 applied to radar forecast strong convection weather of this embodiment includes a data determination module 310, a variance acquisition module 320, and a data processing module 330.

The data determination module 310 is configured to determine historical forecast case data sets according to the historical forecast output data sets and the historical radar observation base data sets. In an embodiment, the data determining module 310 may be configured to perform the operation S101 described above, which is not described herein again.

And the variance of the similar physical quantity of each case in the historical forecast individual case data set is obtained based on the current forecast output data set. In an embodiment, the variance obtaining module 320 may be configured to perform the operation S102 described above, which is not described herein again.

The data processing module 330 is configured to process a data set of variances of similar physical quantities of each history forecast case to screen a plurality of history forecast cases in the data set of history forecast cases, where the plurality of history forecast cases are used to implement radar combined reflectance objective correction forecast applied to radar forecast weather with strong convection. In an embodiment, the data processing module 330 may be configured to perform the operation S103 described above, which is not described herein again.

According to an embodiment of the present disclosure, any plurality of the data determining module 310, the variance obtaining module 320, and the data processing module 330 may be combined and implemented in one module, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the data determining module 310, the variance obtaining module 320, and the data processing module 330 may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or in any one of three implementations of software, hardware, and firmware, or in any suitable combination of any of them. Alternatively, at least one of the data determination module 310, the variance acquisition module 320 and the data processing module 330 may be at least partially implemented as a computer program module, which when executed, may perform a corresponding function.

Fig. 4 schematically shows a block diagram of an electronic device 400 adapted to implement a data processing method applied to radar forecast strongly convective weather according to an embodiment of the present disclosure.

An embodiment of the present disclosure also provides an electronic device, including: one or more processors; a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the above-described data processing method for radar predicted strongly convective weather.

As shown in fig. 4, an electronic device 400 according to an embodiment of the present disclosure includes a processor 401 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. Processor 401 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 401 may also include onboard memory for caching purposes. Processor 401 may include a single processing unit or multiple processing units for performing the different actions of the method flows in accordance with embodiments of the present disclosure.

In the RAM 403, various programs and data necessary for the operation of the electronic apparatus 400 are stored. The processor 401, ROM 402 and RAM 403 are connected to each other by a bus 404. The processor 401 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 402 and/or the RAM 403. Note that the programs may also be stored in one or more memories other than the ROM 402 and RAM 403. The processor 401 may also perform various operations of the method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.

According to an embodiment of the present disclosure, electronic device 400 may also include an input/output (I/O) interface 405, input/output (I/O) interface 405 also being connected to bus 404. Electronic device 400 may also include one or more of the following components connected to I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. A driver 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.

Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the above-described data processing method applied to radar-forecasted strongly convective weather.

The computer-readable storage medium of the embodiments of the present disclosure may be embodied in the devices/apparatuses/systems described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include ROM 402 and/or RAM 403 and/or one or more memories other than ROM 402 and RAM 403 described above.

Embodiments of the present disclosure also provide a computer program product, which includes a computer program, and when the computer program is executed by a processor, the data processing method applied to radar forecast strong convection weather is implemented.

The computer program product of an embodiment of the disclosure comprises a computer program comprising program code for performing the method illustrated in the flow chart. The program code is for causing a computer system to carry out the above-mentioned methods of the embodiments of the disclosure when the computer program product is run on the computer system.

The computer program performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure when executed by the processor 401. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed in the form of a signal on a network medium, downloaded and installed through the communication section 409, and/or installed from the removable medium 411. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409, and/or installed from the removable medium 411. The computer program, when executed by the processor 401, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. 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 or flowchart illustration, and combinations of blocks in the block diagrams 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.

Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.

The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

So far, the embodiments of the present disclosure have been described in detail with reference to the accompanying drawings.

The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only examples of the present invention, and should not be construed as limiting the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A data processing method applied to radar forecast strong convection weather comprises the following steps:

determining a historical forecast individual data set according to the historical forecast output data set and the historical radar observation base data set;

acquiring the variance of similar physical quantities of each historical forecast case in the historical forecast case data set based on the current forecast output data set; and

and processing the data set of the variance of the similar physical quantity of each historical forecast case to screen a plurality of historical forecast cases in the historical forecast case data set, wherein the plurality of historical forecast cases are used for realizing radar combined reflectivity objective correction forecast applied to radar forecast strong convection weather.

2. The method of claim 1, wherein determining historical forecast personal datasets from the historical forecast output dataset and the historical radar observation base dataset comprises:

gridding the historical radar observation base data set to determine a grid point observation reflectivity jigsaw data set;

determining historical forecast individual case data sets through the historical forecast similar physical quantity data sets and the grid point observation reflectivity jigsaw data sets;

3. The method of claim 2, wherein in the obtaining historical forecast similar physical quantity data sets from the historical forecast output data sets comprises:

determining a plurality of historical similar physical quantities in the historical forecast output data set;

and acquiring the historical forecast similar physical quantity data set according to the plurality of historical similar physical quantities.

4. The method of claim 2, wherein determining a grid point observed reflectivity tile data set at the gridding the historical radar observation base data set comprises:

interpolating the historical radar observation base data set into a three-dimensional grid space;

and performing reflectivity three-dimensional networking jigsaw on the grid data generated in the three-dimensional grid space through interpolation, and determining the grid point observation reflectivity jigsaw data set.

5. The method of claim 2, wherein said determining said historically predicted case dataset from said historically predicted similar physical quantity dataset and said grid point observed reflectance puzzle dataset comprises:

determining historical forecast similar physical quantity at the same moment on a grid point in the historical forecast similar physical quantity data set and a data pair between radar combined reflectivity historical observation data at the same moment on the same grid point in the grid point observed reflectivity jigsaw data set;

and determining the historical forecast individual case data set according to the data pairs.

6. The method according to claim 1, wherein before said obtaining the variance of the case-like physical quantity of each of the historically forecast case data sets based on the current forecast output data set, further comprising:

extracting meteorological element data of the current forecast output data set;

determining a plurality of current similar physical quantities of the current forecast output dataset from the meteorological element data;

wherein a plurality of the current similar physical quantities include: weftwind, meridional wind, vertical velocity, potential altitude, ground air pressure, ground wind speed, ground temperature, vorticity, divergence, potential temperature, reducible water content, convection effective potential energy, convection suppression energy, layer junction stability, lift index, Save's index, Q vector divergence, generalized potential temperature, vertical wind shear, and Rough Charson number.

7. The method according to claim 6, wherein in the obtaining of the variance of the individual case similarity physical quantity of each historical forecast individual case in the historical forecast individual case data set based on the current forecast output data set, comprises:

8. The method according to claim 1, wherein in the processing the data set of the variance of the individual case similarity physical quantity of each historical forecast individual case to filter a plurality of historical forecast individual cases in the data set of the historical forecast individual cases, the method comprises:

sorting the variances of the similar physical quantities of the individual cases corresponding to the historical forecast individual cases in the variance data set;

9. The method according to claim 1, wherein after processing the data set of the variance of the individual case similarity physical quantity of each historical forecast individual case to filter a plurality of historical forecast individual cases in the data set of historical forecast individual cases, further comprising:

determining a weight coefficient of each historical similarity prediction example in a plurality of historical similarity prediction examples;

10. A data processing apparatus for radar application to forecast strongly convective weather, comprising:

the data determining module is used for determining a historical forecast individual data set according to the historical forecast output data set and the historical radar observation base data set;

the variance obtaining module is used for obtaining the variance of the similar physical quantity of each historical forecast individual case in the historical forecast individual case data set based on the current forecast output data set; and

and the data processing module is used for processing the data set of the variance of the similar physical quantity of each historical forecast case to screen a plurality of historical similar forecast cases in the data set of the historical forecast cases, and the plurality of historical similar forecast cases are used for realizing radar combined reflectivity objective correction forecast applied to radar forecast strong convection weather.

11. An electronic device, comprising:

one or more processors;

a storage device to store one or more programs,

wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-9.

12. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 9.