MICAPS application localization processing method based on multi-source numerical forecasting data

Abstract

The invention discloses an MICAPS application localization processing method based on multi-source numerical forecasting data, and relates to the technical field of civil aviation data processing, and the method comprises the following steps: S100, collecting meteorological numerical forecasting historical data of different sources, and reading and decoding data matched with meteorological elements; s200, integrating accumulated rainfall data in the multi-source numerical forecasting data, and calculating rainfall predicted values in corresponding time periods; s300, carrying out secondary development on the decoded related data, and calculating a bumping index, an icing index and a convection index; and S400, analyzing the influence relationship between the bump index, the icing index and the convection index and the rainfall prediction value, and visually displaying weather forecast data. The method has the advantages that by collecting and decoding the multi-source numerical forecasting data, the weather forecasting data are analyzed in a multivariate mode and displayed in a visualized mode, more efficient data support and more multivariate data application are provided for aviation weather forecasting, and the accuracy and efficiency of forecasting service are improved.

Description

Technical Field

[0001] The invention relates to the technical field of civil aviation data processing, in particular to a MICAPS application localization processing method based on multi-source numerical forecast data. Background Art

[0002] Aviation meteorology is an important part of air traffic control business. Accurate and timely aviation meteorological service information can ensure the safety of air traffic control operations and improve the efficiency of air traffic control operations. According to the statistics of the Civil Aviation Administration, in recent years, flight delays caused by weather reasons account for more than 50% of all abnormal flights. Improving meteorological forecasting capabilities and meteorological service capabilities have become an important direction for improving air traffic control efficiency and service quality. Giving full play to the use value of various meteorological data is an important part of improving the quality of aviation meteorological forecast services. In the existing technology, the application form of numerical forecast data is relatively single, and most of them are only based on picture display, which cannot meet the needs of interactive and multivariate analysis; the application depth of numerical forecast data is insufficient, and there is a lack of secondary products with aviation meteorological characteristics; and there is currently no means to quickly visualize the historical data currently required, which brings difficulties to the replay analysis and competition data preparation. Therefore, it is necessary to design a MICAPS application localization processing method based on multi-source numerical forecast data that can be quickly displayed and convenient and practical. Summary of the invention

[0003] The purpose of this section is to provide a MICAPS application localization processing method based on multi-source numerical forecast data, which can solve the technical problem of the relatively single application form of numerical forecast data.

[0004] To solve the above technical problems, the present invention provides the following technical solutions: a MICAPS application localization processing method based on multi-source numerical forecast data, comprising the following steps: S100, collecting historical meteorological numerical forecast data from different sources, reading and decoding data matching meteorological elements; S200, integrating the accumulated precipitation data in the multi-source numerical forecast data, and calculating the precipitation forecast value for the corresponding time period; S300, performing secondary development on the decoded related data, and calculating the turbulence index, ice accumulation index and convection index; S400, analyzing the influence relationship between the turbulence index, ice accumulation index, convection index and the precipitation forecast value, respectively, and visually displaying the meteorological forecast data.

[0005] As a preferred solution of the MICAPS application localization processing method based on multi-source numerical forecast data described in the present invention, wherein: in S100, the data format of the numerical forecast data is identified and decoded, and the data formats include NetCDF, GRIB and GRIB2.

[0006] As a preferred solution of the MICAPS application localization processing method based on multi-source numerical forecast data described in the present invention, wherein: in S100, the data matching the meteorological elements include wind speed, wind direction, temperature, relative humidity and atmospheric pressure.

[0007] As a preferred solution of the MICAPS application localization processing method based on multi-source numerical forecast data described in the present invention, wherein: in S200, the accumulated precipitation data in the numerical forecast data is integrated, and piecewise calculations are performed at intervals of 3 hours, 6 hours, 12 hours and 24 hours to obtain the precipitation forecast value for the corresponding time period.

[0008] As a preferred solution of the MICAPS application localization processing method based on multi-source numerical forecast data described in the present invention, wherein: in S300, the horizontal wind shear SH and the vertical wind shear SV are calculated according to the wind speed and wind direction data, and the turbulence index E is further calculated, and the formula is: E=SH+SV. The ice accumulation index I is calculated according to the relative humidity RH and the temperature t, and the formula is:

[0009]

[0010] In the formula, the threshold range of the ice accumulation index I is [0-100], and the larger the value, the greater the ice accumulation intensity;

[0011] The convection index K is calculated according to the temperature corresponding to different altitudes. The formula is:

[0012] K=(T 850 -T 500 )+Td 850 -(T 700 -T 500 )

[0013] Where, T 850 , T 700 and T 500 Td represents the temperature at altitudes where the atmospheric pressure is 850 hPa, 700 hPa, and 500 hPa, respectively. 850 It indicates the dew point temperature under an atmospheric pressure of 850 hPa. The larger the value of the convection index K, the higher the instability of the atmospheric convection.

[0014] As a preferred solution of the MICAPS application localization processing method based on multi-source numerical forecast data described in the present invention, wherein: in S400, the specific steps are as follows: S401, obtain all turbulence indices in historical data and corresponding precipitation prediction values, set n turbulence index intervals, classify the turbulence indices according to whether they belong to the same interval, establish a linear relationship between the turbulence index and the precipitation prediction value in each category, and obtain n expressions: Y n=a n E+C n Where: Y n is the precipitation forecast value corresponding to the nth turbulence index interval, a n is the nth regression coefficient, C n is the nth constant; S402, calculate the difference between all precipitation prediction values ​​and the corresponding actual precipitation in the nth interval, divide each difference by the actual precipitation to obtain the deviation rate, and then take the average value to obtain the average deviation rate; S403, obtain the turbulence index in real time, substitute the turbulence index into the formula of the corresponding interval, calculate the precipitation prediction value, and display the average deviation rate of the interval where the turbulence index is located; S404, use the methods in S401, S402 and S403 by analogy to analyze the linear relationship between the precipitation prediction value Y and the ice accumulation index I and the convection index K, respectively, and display the precipitation prediction value and the average deviation rate in real time.

[0015] As a preferred solution of the MICAPS application localization processing system based on multi-source numerical forecast data described in the present invention, it includes: a data base and a visualization analysis layer, the data base is used to analyze and process the multi-source numerical forecast data to form a MICAPS format, the visualization analysis layer displays data elements based on the MICAPS platform, and the data base is electrically connected to the visualization analysis layer.

[0016] As a preferred solution of the MICAPS application localization processing system based on multi-source numerical forecast data described in the present invention, the data base includes a data monitoring module, a data decoding module, a data screening module, a data mining module and a data conversion module; the data monitoring module is used to monitor the data sequence of the multi-source numerical forecast in real time, the data decoding module is used to decode multiple data formats, and the data screening module is used to extract meteorological elements matching the user profile; the data mining module is used to recalculate the accumulated precipitation in the multi-source data, obtain 3-hour, 6-hour, 12-hour and 24-hour precipitation, and perform secondary development on the decoded data to calculate the turbulence index, ice accumulation index and convection index; the data conversion module is used to convert the data format into the MICAPS format and store it to form a local database.

[0017] As a preferred solution of the MICAPS application localization processing system based on multi-source numerical forecast data described in the present invention, the visualization analysis layer includes a localization configuration module and a display analysis module. The localization configuration module is used to intelligently identify and automatically adjust the data source path in the MICAPS platform configuration file to ensure that the system can accurately read and display the latest processed data. The display analysis module is based on the MICAPS platform to provide a rapid display function for processed forecast data, which is used for multivariate analysis processing.

[0018] Beneficial effects of the present invention:

[0019] By collecting and decoding multi-source numerical forecast data, conducting secondary development on the decoded data, calculating key indexes in civil aviation activities, and then achieving efficient data transmission and interaction through the electrical connection between the data base and the visualization analysis layer, the localized application of MICAPS is realized, and the meteorological forecast data is multi-dimensionally analyzed and visualized. It provides more efficient data support and more diverse data applications for aviation meteorological forecasts, improves the accuracy and efficiency of forecast services, and realizes the rapid display, interactivity and multi-dimensional analysis of meteorological data, assisting forecasters to carry out their work more efficiently, thereby improving the quality of forecast services. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. The drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creativity and labor. Among them:

[0021] Figure 1 Schematic diagram of the flow chart of the localized processing method for MICAPS application based on multi-source numerical forecast data. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below with reference to the accompanying drawings.

[0023] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from this description. The "embodiment" referred to here refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention.

[0024] Embodiment 1

[0025] Reference Figure 1 The present embodiment provides a MICAPS application localization processing method based on multi-source numerical forecast data, which specifically includes the following steps: S100, collecting historical meteorological numerical forecast data from different sources, reading and decoding data matching meteorological elements; S200, integrating the accumulated precipitation data in the multi-source numerical forecast data, and calculating the precipitation forecast value for the corresponding time period; S300, performing secondary development on the decoded related data, and calculating the turbulence index, ice accumulation index and convection index; S400, analyzing the influence relationship between the turbulence index, ice accumulation index, convection index and the precipitation forecast value, and visually displaying the meteorological forecast data.

[0026] In S100, historical meteorological numerical forecast data from three sources, namely the China Meteorological Administration's Global / Regional General Numerical Weather Forecast System (GRAPES-MESO), the European Centre for Medium-Range Numerical Weather Forecasts-THIN (ECMWF-THIN), and the East China Air Traffic Control Bureau's Rapid Update Cycle Forecast System (5M), are monitored in real time, and the monitored data are stored in the local database.

[0027] In S100, the data format of the numerical forecast data is identified and decoded. The data formats include NetCDF, GRIB and GRIB2.

[0028] NetCDF (Network Common Data Form) is a common network data format developed by project scientists of the Association of Universities for Research in the Atmosphere based on the characteristics of scientific data. It is used to store data in meteorological science.

[0029] GRIB (General Regularly Distributed Information in Binary) is a standard data format designed and maintained by the World Meteorological Organization (WMO) for storing and transmitting grid data. It is a computer-independent compressed binary code mainly used to represent product information for numerical weather forecasts.

[0030] GRIB2 can represent multidimensional data, has a modular structure, supports multiple compression methods, can store and transmit data more efficiently, and better meet the growing demand for meteorological data and complex application scenarios.

[0031] In S100 , the data matching the meteorological elements include wind speed, wind direction, temperature, relative humidity and atmospheric pressure.

[0032] In S200, the accumulated precipitation data in the numerical forecast data is integrated, and piecewise calculations are performed at intervals of 3 hours, 6 hours, 12 hours, and 24 hours to obtain precipitation forecast values ​​for the corresponding time periods.

[0033] In S300, the horizontal wind shear SH and the vertical wind shear SV are calculated according to the wind speed and wind direction data, and the turbulence index E is further calculated, and the formula is: E=SH+SV. The ice accumulation index I is calculated according to the relative humidity RH and the temperature t, and the formula is:

[0034]

[0035] In the formula, the threshold range of the ice accumulation index I is [0-100], and the larger the value, the greater the ice accumulation intensity;

[0036] The convection index K is calculated according to the temperature corresponding to different altitudes. The formula is:

[0037] K=(T 850 -T500 )+Td 850 -(T 700 -T 500 )

[0038] Where, T 850 , T 700 and T 500 Td represents the temperature at altitudes where the atmospheric pressure is 850 hPa, 700 hPa, and 500 hPa, respectively. 850 It indicates the dew point temperature under an atmospheric pressure of 850 hPa. The larger the value of the convection index K, the higher the instability of the atmospheric convection.

[0039] In S400, the specific steps are as follows: S401, obtain all turbulence indices in historical data and corresponding precipitation prediction values, set n turbulence index intervals, classify the turbulence indices according to whether they belong to the same interval, establish a linear relationship between the turbulence index and the precipitation prediction value in each category, and obtain n expressions: Y n =a n E+C n Where: Y n is the precipitation forecast value corresponding to the nth turbulence index interval, a n is the nth regression coefficient, C n is the nth constant; S402, calculate the difference between all precipitation prediction values ​​and the corresponding actual precipitation in the nth interval, divide each difference by the actual precipitation to obtain the deviation rate, and then take the average value to obtain the average deviation rate; S403, obtain the turbulence index in real time, substitute the turbulence index into the formula of the corresponding interval, calculate the precipitation prediction value, and display the average deviation rate of the interval where the turbulence index is located; S404, use the methods in S401, S402 and S403 by analogy to analyze the linear relationship between the precipitation prediction value Y and the ice accumulation index I and the convection index K, respectively, and display the precipitation prediction value and the average deviation rate in real time.

[0040] MICAPS refers to the comprehensive analysis and processing system for meteorological information. It is a business software system independently developed by the China Meteorological Administration. It is the basic software for meteorological business. It can realize weather map analysis, retrieval and display of multi-source data, and interactive forecast production based on computer human-computer interaction.

[0041] MICAPS (Meteorological Information Integrated Analysis and Processing System) format data is a meteorological data format used in meteorological information processing and weather forecast production. It contains multiple types, such as ground full-element mapping data, high-altitude full-element mapping data, general mapping and discrete point contour data, grid data, TLOGP and site profile data, etc.

[0042] Embodiment 2

[0043] The present embodiment provides a MICAPS application localization processing system based on multi-source numerical forecast data, specifically comprising: a data base and a visualization analysis layer, the data base is used to analyze and process the multi-source numerical forecast data to form a MICAPS format, the visualization analysis layer displays data elements based on the MICAPS platform, and the data base is electrically connected to the visualization analysis layer.

[0044] The data base includes a data monitoring module, a data decoding module, a data screening module, a data mining module and a data conversion module; the data monitoring module is used to monitor the data sequence of multi-source numerical forecasts in real time, the data decoding module is used to decode multiple data formats, and the data screening module is used to extract meteorological elements that match the user profile; the data mining module is used to recalculate the accumulated precipitation in multi-source data, obtain 3-hour, 6-hour, 12-hour and 24-hour precipitation, and perform secondary development on the decoded data to calculate the turbulence index, ice accumulation index and convection index; the data conversion module is used to convert the data format into the MICAPS format and store it to form a local database.

[0045] The visualization analysis layer includes a localization configuration module and a display analysis module. The localization configuration module is used to intelligently identify and automatically adjust the data source path in the MICAPS platform configuration file to ensure that the system can accurately read and display the latest processed data. The display analysis module is based on the MICAPS platform to provide a rapid display function for processed forecast data, which is used for multivariate analysis processing.

[0046] Importantly, although only a few embodiments are described in detail in this disclosure, it should be readily understood by those who refer to this disclosure that many modifications are possible without substantially departing from the subject matter described in this application, such as the size, structure, shape and proportion of the various elements, as well as temperature, pressure, mounting arrangements, use of materials, color, directional changes, etc.; for example, an element shown as integrally formed may be composed of multiple parts or elements, and the position of the element may be inverted or otherwise changed; therefore, all such modifications should be included within the scope of the present invention, and other substitutions, modifications, changes and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present invention.

Claims

1. A MICAPS application localization processing method based on multi-source numerical forecast data, characterized in that: The following steps are involved: S100, collecting historical data of meteorological numerical forecasts from different sources, and reading and decoding data matching meteorological elements; S200, integrating the accumulated precipitation data in the multi-source numerical forecast data, and calculating the precipitation forecast value for the corresponding time period; S300, perform secondary development on the decoded relevant data to calculate the turbulence index, ice accumulation index and convection index; S400: Analyze the influence relationship between turbulence index, ice accumulation index, convection index and precipitation forecast value, and visualize the weather forecast data.

2. The MICAPS application localization processing method based on multi-source numerical forecast data according to claim 1, characterized in that: In S100, the data format of the numerical forecast data is identified and decoded. The data formats include NetCDF, GRIB and GRIB2.

3. The MICAPS application localization processing method based on multi-source numerical forecast data according to claim 1, characterized in that: In S100 , the data matching the meteorological elements include wind speed, wind direction, temperature, relative humidity and atmospheric pressure.

4. The MICAPS application localization processing method based on multi-source numerical forecast data according to claim 1, characterized in that: In S200, the accumulated precipitation data in the numerical forecast data is integrated, and piecewise calculations are performed at intervals of 3 hours, 6 hours, 12 hours, and 24 hours to obtain precipitation forecast values ​​for the corresponding time periods.

5. The MICAPS application localization processing method based on multi-source numerical forecast data according to claim 1, characterized in that: In S300, the horizontal wind shear SH and the vertical wind shear SV are calculated according to the wind speed and wind direction data, and the turbulence index E is further calculated, and the formula is: E=SH+SV The ice accumulation index I is calculated based on the relative humidity RH and temperature t. The formula is: In the formula, the threshold range of the ice accumulation index I is [0-100], and the larger the value, the greater the ice accumulation intensity; The convection index K is calculated according to the temperature corresponding to different altitudes. The formula is: K=(T 850 -T 500 )+Td 850 -(T 700 -T 500 ) Where, T 850 , T 700 and T 500 Td represents the temperature at altitudes where the atmospheric pressure is 850 hPa, 700 hPa, and 500 hPa, respectively. 850 It indicates the dew point temperature under an atmospheric pressure of 850 hPa. The larger the value of the convection index K, the higher the instability of the atmospheric convection.

6. The MICAPS application localization processing method based on multi-source numerical forecast data according to claim 1, characterized in that: In S400, the specific steps are as follows: S401, obtaining all turbulence indices in historical data and corresponding precipitation prediction values, setting n turbulence index intervals, classifying the turbulence indices according to whether they belong to the same interval, establishing a linear relationship between the turbulence index and the precipitation prediction value in each category, and obtaining n expressions: Y n =a n E+C n Where: Y n is the precipitation forecast value corresponding to the nth turbulence index interval, a n is the nth regression coefficient, C n is the nth constant; S402, calculating the difference between all precipitation prediction values ​​and the corresponding actual precipitation in the nth interval, dividing each difference by the actual precipitation to obtain a deviation rate, and then taking the average to obtain an average deviation rate; S403, obtaining a turbulence index in real time, substituting the turbulence index into a formula of a corresponding interval, calculating a precipitation forecast value, and displaying an average deviation rate of the interval in which the turbulence index is located; S404: By analogy with the methods in S401, S402 and S403, the linear relationship between the precipitation prediction value Y and the ice accumulation index I and the convection index K is analyzed, and the precipitation prediction value and the average deviation rate are displayed in real time.

7. A MICAPS application localization processing system based on multi-source numerical forecast data, using the MICAPS application localization processing method based on multi-source numerical forecast data as claimed in claim 1, characterized in that: It includes a data base and a visualization analysis layer. The data base is used to analyze and process multi-source numerical forecast data to form a MICAPS format. The visualization analysis layer displays data elements based on a MICAPS platform. The data base is electrically connected to the visualization analysis layer.

8. The MICAPS application localization processing system based on multi-source numerical forecast data according to claim 7, characterized in that: The data base includes a data monitoring module, a data decoding module, a data screening module, a data mining module and a data conversion module; The data monitoring module is used to monitor the data sequence of multi-source numerical forecasts in real time, the data decoding module is used to decode multiple data formats, and the data screening module is used to extract meteorological elements that match the user profile; the data mining module is used to recalculate the accumulated precipitation in the multi-source data, obtain 3-hour, 6-hour, 12-hour and 24-hour precipitation, and perform secondary development on the decoded data to calculate the turbulence index, ice accumulation index and convection index; the data conversion module is used to convert the data format into the MICAPS format and store it to form a local database.

9. The MICAPS application localization processing system based on multi-source numerical forecast data according to claim 8, characterized in that: The visualization analysis layer includes a localization configuration module and a display analysis module. The localization configuration module is used to intelligently identify and automatically adjust the data source path in the MICAPS platform configuration file to ensure that the system can accurately read and display the latest processed data. The display analysis module is based on the MICAPS platform to provide a rapid display function for processed forecast data for multivariate analysis processing.

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