Airborne weather radar self-adaptive thunderstorm identification method based on meteorological numerical simulation

Abstract

An airborne weather radar self-adaptive thunderstorm identification method based on meteorological numerical simulation, comprising: collecting various thunderstorm events and corresponding meteorological data (S100), and performing a preset number of simulations and calculating an ensemble mean result (S200, S300, S400); extracting, from the ensemble mean result, feature parameters of all types of thunderstorm and danger thresholds corresponding to the feature parameters (S500); storing the feature parameters and the danger thresholds of all types of thunderstorms into an airborne weather radar system (S600); on the basis of the position and the moment of a flight, determining a thunderstorm feature parameter in the region within the time period and the corresponding danger threshold (S700); and if a detection result of the airborne weather radar system exceeds the danger threshold, identifying the thunderstorm and issuing a warning (S800). According to the method, the credibility of statistical analysis results is improved, and feature parameters of thunderstorm events of different types and danger thresholds corresponding to the feature parameters can be objectively and accurately obtained.

Description

Adaptive thunderstorm identification method for airborne weather radar based on meteorological numerical simulation Technical Field

[0001] The present invention relates to the field of radar technology, and in particular to an airborne weather radar adaptive thunderstorm recognition method based on meteorological numerical simulation. Background Art

[0002] Thunderstorms are a serious threat to aviation safety, capable of generating severe weather events such as turbulence, wind shear, lightning, and hail. These events develop over time, progressing through different stages, with varying degrees of impact and threat to aircraft. Aircraft entering thunderstorm zones can encounter turbulence, icing, lightning strikes, and hail, potentially resulting in minor injuries to aircraft and passengers, or even fatalities.

[0003] Thunderstorms have extremely complex characteristics, and their various parameters, including morphology, intensity, and lifecycle, vary depending on terrain, season, weather conditions, and development stage. Typically, for thunderstorm detection and identification, airborne weather radar systems use statistical data from a large number of previous thunderstorm observations to determine a danger threshold. This threshold is applied uniformly across all terrains and flight ranges. However, this threshold only applies to thunderstorms in typical convective weather systems in general environments, but not necessarily across all terrains and time periods. For example, the morphology and reflectivity characteristics of continental versus oceanic thunderstorms, summer versus winter thunderstorms, and supercell thunderstorms versus squall lines vary significantly. Therefore, the use of a uniform threshold for thunderstorm detection and warning by current airborne weather radar systems presents significant limitations, making it difficult to identify thunderstorms across all terrains and flight ranges.

[0004] Summary of the Invention

[0005] In view of this, an embodiment of the present specification provides an airborne weather radar adaptive thunderstorm identification method based on meteorological numerical simulation to achieve the purpose of thunderstorm identification.

[0006] The embodiments of this specification provide the following technical solutions: an airborne meteorological radar adaptive thunderstorm identification method based on meteorological numerical simulation, comprising: collecting multiple thunderstorm events and corresponding meteorological data, simulating a set number of times, and calculating an ensemble average result; extracting characteristic parameters of each type of thunderstorm from the ensemble average result, as well as the danger threshold corresponding to each characteristic parameter; storing the characteristic parameters and danger threshold of each type of thunderstorm in an airborne meteorological radar system; determining the thunderstorm characteristic parameters and corresponding danger threshold for the area and time period based on the flight location and time; and identifying and alerting the thunderstorm when the detection result of the airborne meteorological radar system exceeds the danger threshold.

[0007] Furthermore, a variety of thunderstorm events and corresponding meteorological data are collected, and the results are calculated by simulating a set number of times and calculating the ensemble average. Specifically, the various thunderstorm events are classified according to the time and area of ​​thunderstorm occurrence; and the thunderstorm events under different classifications are simulated using meteorological numerical models.

[0008] Furthermore, collecting a variety of thunderstorm events and corresponding meteorological data, simulating a set number of times and calculating the ensemble average result also includes: for each type of thunderstorm event, changing its WRF simulation initial disturbance, performing no less than 10 simulations and calculating the ensemble average result.

[0009] Furthermore, the thunderstorm characteristic parameters include time parameters and location parameters.

[0010] Furthermore, the time parameters include seasonal parameters and diurnal variation parameters.

[0011] Furthermore, the location parameters include terrain classification parameters and terrain height parameters.

[0012] Furthermore, the danger threshold includes a radar reflectivity threshold, a speed threshold, and an index threshold.

[0013] Compared with the prior art, the beneficial effects that can be achieved by at least one of the above-mentioned technical solutions adopted in the embodiments of this specification include at least the following: the present invention utilizes a high-precision WRF meteorological numerical model to simulate a large number of historical thunderstorm events and calculate the ensemble average, thereby improving the credibility of the statistical analysis results and objectively and accurately obtaining the various characteristic parameters of thunderstorm events of different categories and their corresponding danger thresholds.

[0014] The present invention adaptively adjusts the thunderstorm danger threshold of the airborne weather radar system during actual flight according to various characteristic parameters of different thunderstorm events, which can more accurately detect and identify the existence and characteristics of thunderstorms at different locations and times.

[0015] The present invention can be applied to the fields of civil and military airborne weather radars, improving the detection, perception and warning capabilities of airborne weather radars for thunderstorm events, and further improving flight safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0017] FIG1 is a schematic diagram of a flow chart of an embodiment of the present invention;

[0018] FIG2 is a schematic diagram of an alarm process according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0020] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0021] FIG1 shows a flow chart of an airborne meteorological radar adaptive thunderstorm identification method based on meteorological numerical simulation according to an embodiment of the present invention. The airborne meteorological radar adaptive thunderstorm identification method based on meteorological numerical simulation according to an embodiment of the present invention begins at step S100, where a large amount of historical thunderstorm events and corresponding meteorological data are acquired. The meteorological data can be echo data, reflectivity data, and wind field data observed by airborne meteorological radar or ground-based radar, or can include meteorological data such as wind field, temperature, and air pressure from other observational data, or can include relevant data such as terrain and event occurrence time. Furthermore, the meteorological data file described in the present invention can be in any format, and the present embodiment does not impose any specific restrictions on this.

[0022] In step S200, historical thunderstorm observation data is classified. All thunderstorm events are classified according to the terrain, season, weather conditions, etc. at the time of the thunderstorm event, and the relevant data of each type of thunderstorm event is saved. The relevant data of each type of event is analyzed to obtain typical characteristics of each type of thunderstorm, including typical characteristics such as occurrence time, terrain characteristics, morphological characteristics, reflectivity intensity, wind field evolution, water content changes, microphysical characteristics, and life cycle. During flight, the characteristics of thunderstorm events in different terrains vary significantly. The characteristics of thunderstorms that aircraft may encounter in plains, mountains, and oceans, including the occurrence altitude, peak intensity, life cycle, and radar reflectivity of thunderstorms, are significantly different.

[0023] In step S300, the WRF model (Weather Research and Forecasting Model) is used to simulate thunderstorm events under different classifications. The basic idea of ​​meteorological numerical simulation is to give initial and boundary conditions, and use finite difference, spectral methods, and other methods to numerically solve the atmospheric motion equations. The calculations are performed on high-performance computers to obtain the computational solutions to the atmospheric motion equations, and the atmospheric motion process is quantitatively reproduced and predicted. The WRF model is a unified mesoscale weather forecast model developed by American research institutions such as the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). It uses a fully compressible non-hydrostatic equilibrium dynamic framework, Arakawa C grid points in the horizontal direction, and terrain-following mass coordinates in the vertical direction. The forecast equation variables include three-dimensional velocity wind components, air pressure, temperature, and water substances (water vapor, liquid cloud droplets, cloud ice, rain, snow, and hail or graupel particles). The model's physical processes encompass surface physics, planetary boundary layer physics, atmospheric radiation, and cloud microphysics, all described using physical parameterization schemes. Horizontal resolution can range from tens of meters to tens of kilometers, and a wide range of parameterization schemes are available. The model boasts numerous advantages, including portability, ease of maintenance, scalability, and high efficiency. It is widely used in various meteorological research areas, including numerical weather forecasting, idealized atmospheric simulation, data assimilation and development, and coupling with other numerical models.

[0024] Due to the strong stochastic characteristics of thunderstorms (randomness in occurrence time, location, life cycle, spatial scale, and intensity), conventional observational data rarely provide complete data on thunderstorms. Only a limited number of physical field quantities can be observed from a small number of thunderstorms within the monitoring area, far from meeting the data requirements for detailed studies of thunderstorm formation conditions, dynamic structures, and microphysical processes. Therefore, using meteorological numerical simulation methods to simulate typical thunderstorm events can better study the dynamic structure, microphysical processes, and the impact of different atmospheric environmental conditions on thunderstorms, thereby deriving typical characteristics of thunderstorms under different classifications. Numerous studies have demonstrated that the WRF model can accurately reproduce the convective development process during thunderstorms. Therefore, based on the typical characteristics of various thunderstorm events obtained in step S200, the terrain data and meteorological reanalysis data corresponding to each type of thunderstorm event are input, and an appropriate microphysical parameterization scheme, boundary layer parameterization method, and cumulus parameterization scheme are selected. A reasonable multi-layer nesting scheme is adopted to simulate each type of thunderstorm event and output high-resolution three-dimensional key physical parameters, including radar reflectivity, wind field, air pressure, temperature, humidity, mixing ratio and number concentration of various hydrometeors (water vapor, cloud water, rain water, ice water, snow water, graupel, hail), and other physical quantities.

[0025] In step S400, for each thunderstorm event type, the initial perturbation in the WRF simulation is varied, an ensemble simulation experiment is performed, and the ensemble average result is calculated. Due to the uncertainty of the numerical model, a single test result may have certain errors. Therefore, an ensemble simulation experiment is performed for each thunderstorm event type. For each thunderstorm event type, the initial perturbation in the initial field is varied. At least 10 ensemble experiments are performed, and the ensemble average result of all experiments is calculated as the final simulation result for each thunderstorm event type to improve the accuracy of the WRF simulation.

[0026] In step S500, characteristic parameters for each thunderstorm event type and corresponding danger thresholds are determined based on the ensemble average results of the simulation. Danger thresholds are determined based on the output of the WRF simulation and include thresholds for direct radar reflectivity, velocity, and any other indices that can characterize thunderstorms.

[0027] In step S600, characteristic parameters and danger thresholds of various thunderstorm events are stored in the airborne weather radar system, so that the corresponding characteristic parameters and danger thresholds can be retrieved according to flight parameters during flight, thereby adaptively adjusting the monitoring thresholds of the airborne weather radar system.

[0028] In step S700, the corresponding characteristic parameters and danger thresholds are retrieved based on the flight parameters of the aircraft, including the flight location and time, and the thunderstorm characteristic parameters and corresponding danger thresholds at the location and time are determined and updated.

[0029] In step S800, the airborne weather radar monitoring results are compared with the current thunderstorm danger threshold. When the airborne weather radar detection results exceed the danger threshold, a thunderstorm is identified and an alarm is issued. The alarm flow chart is shown in Figure 2. First, based on the aircraft's flight parameters, including the flight location and time, the corresponding characteristic parameters and danger threshold are retrieved. The thunderstorm characteristic parameters and the corresponding danger threshold at that location and time are determined and updated. Then, based on the airborne weather radar monitoring information, a judgment is made. If the danger threshold is not exceeded, the flight continues according to the expected flight path. If the intensity exceeds the danger threshold, the thunderstorm is identified and an alarm and guidance are issued.

[0030] The above description is merely a specific embodiment of the present invention and should not be construed as limiting the scope of the invention. Therefore, substitutions of equivalent components, or equivalent changes and modifications made within the scope of patent protection, should still fall within the scope of this patent. Furthermore, the technical features of the present invention may be freely combined with one another, with other technical solutions, and with other technical solutions.

Claims

1. An airborne weather radar adaptive thunderstorm identification method based on meteorological numerical simulation, characterized in that: include: Collect various thunderstorm events and corresponding meteorological data, simulate them a set number of times and calculate the ensemble average results; Extract the characteristic parameters of each type of thunderstorm from the ensemble average results, as well as the danger threshold corresponding to each characteristic parameter; The characteristic parameters and danger thresholds of each type of thunderstorm are stored in the airborne weather radar system; According to the flight location and time, determine the thunderstorm characteristic parameters and corresponding danger thresholds in the area and time period; When the detection results of the airborne weather radar system exceed the danger threshold, a thunderstorm identification and warning will be issued.

2. The method for adaptive thunderstorm identification using airborne meteorological radar based on meteorological numerical simulation according to claim 1 is characterized in that: The collection of multiple thunderstorm events and corresponding meteorological data, simulation by setting the number of times and calculating the ensemble average result is specifically: Classify various thunderstorm events according to the time and area of ​​thunderstorm occurrence; Meteorological numerical models are used to simulate thunderstorm events under different classifications.

3. The method for adaptive thunderstorm identification using airborne weather radar based on meteorological numerical simulation according to claim 2 is characterized in that: The collecting of multiple thunderstorm events and corresponding meteorological data, simulating by a set number of times and calculating the ensemble average result also includes: For each type of thunderstorm event, the initial perturbation of the WRF simulation is changed, no less than 10 simulations are performed, and the ensemble average results are calculated.

4. The method for adaptive thunderstorm identification using airborne weather radar based on meteorological numerical simulation according to claim 1 is characterized in that: The thunderstorm characteristic parameters include time parameters and location parameters.

5. The method for adaptive thunderstorm identification by airborne meteorological radar based on meteorological numerical simulation according to claim 4 is characterized in that: The time parameters include season parameters and diurnal variation parameters.

6. The method for adaptive thunderstorm identification by airborne weather radar based on meteorological numerical simulation according to claim 4 is characterized in that: The location parameters include terrain classification parameters and terrain height parameters.

7. The method for adaptive thunderstorm identification by airborne weather radar based on meteorological numerical simulation according to claim 1 is characterized in that: Danger thresholds include radar reflectivity threshold, speed threshold and index threshold.

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