Meteorological ensemble forecast decision-making method for rainy day test flight

Abstract

The invention discloses a weather ensemble forecast decision-making method for rainy day test flight. The method comprises the following steps: setting a simulation area according to test flight airport site selection; obtaining one or more set members associated with the simulation area; generating meteorological data of an initial field and a boundary field of each of the one or more ensemble members; integrating a first specified number of hours forward based at least in part on the meteorological data to determine an initial trial flight window; cyclically integrating forward a second specified number of hours and cyclically updating the meteorological data of the background field using ensemble Kalman filtering based at least in part on the meteorological data, where the first specified number of hours is greater than the second specified number of hours; determining the number of the set members whose meteorological data meet one or more specified indexes to calculate a composite success rate of test flight in rainy days; and determining a final trial flight window based at least in part on the composite success rate.

Description

Technical Field

[0001] The invention relates to the field of flight testing, and in particular to a method for making meteorological ensemble forecast decisions for flight testing on rainy days. Background Art

[0002] In international aviation business, the overall trend of accident rate is gradually decreasing, but the number of flight accidents caused by meteorological reasons is increasing, and heavy rain and other weather conditions cause flight delays and cause huge losses. In rainy weather, raindrops hit the front windshield, seriously affecting the pilot's vision. Therefore, whether the aircraft can operate normally under precipitation conditions directly affects safety and economy. Therefore, it is necessary to implement special test flights of large passenger aircraft in rainy weather to verify that the windshield wiper system and rain drainage system function normally and that the rain removal performance meets the requirements. According to §25.773(b)(1) in CCAR-25 of the Airworthiness Standards for Transport Aircraft and the provisions of GJB 5190-2003, the prescribed test must be completed under heavy rain conditions and avoid adverse weather such as crosswinds, thunderstorms, and low clouds. The quantitative indicator is that the rainfall is greater than 8mm / h and the crosswind cannot exceed 20 knots (10.3m / s). The superposition of multiple requirements brings great difficulties to the capture of test flight windows, near-weather forecasts, and on-site meteorological decisions.

[0003] Traditional forecasting methods combine high-altitude and ground weather maps, as well as the results of numerical forecast models, and make judgments 3-7 days in advance based on the output of the comprehensive model, such as rainfall, convective effective potential, and movement path of the weather system. With the development of the system, forecasters pay attention to real-time ground observation station data and weather radar echo puzzles, organize consultations to determine the intensity of rainfall and crosswind size, and decide whether to organize rainy day test flights, which affects the progress and efficiency of test flights. With the development of computer performance and computing efficiency, numerical weather forecasting technology has become increasingly mature and can provide objective forecasts in business. However, as a typical nonlinear system, the atmosphere has significant differences in forecasts of different numerical models and initial conditions. In addition, this test uses a typhoon as the weather background, and heavy rain and crosswinds are often accompanied. The test flight window is limited, and the accuracy of the forecast is extremely high. Forecasters cannot make stable and reliable decisions based on limited single deterministic forecasts.

[0004] Therefore, it is urgent to propose a method for making meteorological ensemble forecast decisions for rainy day test flights, which can objectively estimate the composite success rate of flight tests to assist test flight decisions. Summary of the invention

[0005] A brief summary of one or more aspects is given below to provide a basic understanding of these aspects. This summary is not an exhaustive overview of all conceived aspects, and is neither intended to identify the key or critical elements of all aspects nor to define the scope of any or all aspects. Its only purpose is to give some concepts of one or more aspects in a simplified form as a prelude to a more detailed description given later.

[0006] In order to solve the above problems, various aspects of the present application propose a method for making weather ensemble forecast decisions for rainy day test flights.

[0007] On the one hand, the present application discloses a method for making meteorological ensemble forecast decisions for civil aircraft rainy day test flights, comprising: setting up a simulation area according to the site selection of the test flight airport; obtaining a meteorological ensemble associated with the simulation area; generating meteorological data of the initial field and the boundary field of each meteorological member in the meteorological ensemble; based at least in part on the meteorological data of the initial field and the boundary field, integrating forward for a first specified number of hours to determine an initial test flight window; based at least in part on the meteorological data of the initial field and the boundary field, cyclically integrating forward for a second specified number of hours and using a ensemble Kalman filter to cyclically update the meteorological data of the background field of each meteorological member in the meteorological ensemble, wherein the first specified number of hours is greater than the second specified number of hours; determining the number of meteorological members in the meteorological ensemble whose meteorological data meet one or more specified indicators to calculate the composite success rate of the civil aircraft rainy day test flight; and determining the final test flight window based at least in part on the composite success rate.

[0008] Preferably, the combination of parameterization schemes required to be configured for the forward integration of the model of each meteorological member includes: microphysical scheme WSM6; GCM rapid radiative transfer model; shortwave and longwave radiation schemes; Yonsei University planetary boundary layer PBL scheme; NOAH land surface model scheme; Kain-Fritsch convection scheme.

[0009] Preferably, generating the meteorological data of the initial field and the boundary field of each meteorological member in the meteorological set includes: pre-processing the meteorological data associated with each meteorological member in the meteorological set to generate meteorological variables of the meteorological member; and outputting the meteorological data of the initial field and the boundary field associated with each meteorological member in the meteorological set in combination with static terrain data.

[0010] Preferably, determining the initial test flight window comprises: calculating the meteorological data of the simulation area using bilinear interpolation; and determining the initial test flight window at least partially based on whether the meteorological data satisfies the one or more indicators.

[0011] Preferably, using the collective Kalman filter to cyclically update the meteorological data of the background field of each meteorological member in the meteorological set includes: using bilinear interpolation to calculate the meteorological data of the simulation area of ​​each meteorological member in the meteorological set; and using the collective Kalman filter to assimilate the collected multi-source observation data into the numerical model of each meteorological member.

[0012] Preferably, the algorithm of the ensemble Kalman filter is: where x a represents the analytical field, x b represents the background field, y 0 represents the multi-source observations within the assimilation window of the second specified number of hours, H represents the observation operator, and y 0 -Hx b Represents observation information, Represents the adjusted Kalman gain matrix.

[0013] Preferably, the multi-source observations include: meteorological department observations, field site observations and aircraft GPS observations or their combined satellite, radar, sounding and other open source meteorological observations.

[0014] Preferably, using an ensemble Kalman filter to assimilate the collected observation data of multi-source observations into the numerical model of a meteorological member includes: calculating observation priors based on the existing numerical model and observation data of the meteorological member by a grid statistical interpolation system (GSI); and assimilating the collected observation data into the numerical model of the meteorological member using an ensemble Kalman filter based at least in part on the observation priors.

[0015] Preferably, the multi-source observation includes open source meteorological observation, and the open source meteorological observation includes one or more of the following: satellite, radar, and sounding.

[0016] Preferably, the composite success rate R is defined as: Where k represents the number of the one or more specified indicators, N represents the number of members of the meteorological set, and n i It represents the number of meteorological members that meet the specified indicators, and i is a natural number.

[0017] Preferably, the one or more designated indicators include the first rain intensity level, the second rain intensity level, the crosswind intensity, or a combination thereof.

[0018] The present invention summary is provided to introduce some concepts in a simplified form, which will be further described in the following detailed description. The present invention summary is not intended to identify the key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Other aspects, features and / or advantages of each embodiment will be set forth in part in the following description, and will be apparent in part from the description, or can be learned through the practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to understand in detail the manner in which the above features of the present invention are used, the above briefly summarized contents may be described in more detail with reference to various embodiments, some of which are shown in the accompanying drawings. It should be noted, however, that the accompanying drawings only show certain typical aspects of the present invention and should not be considered to limit its scope, as the description may allow for other equally effective aspects. In the accompanying drawings, similar reference numerals are always similarly identified. It should be noted that the drawings described are only schematic and non-restrictive. In the drawings, the sizes of some components may be exaggerated and are not drawn to scale for illustrative purposes.

[0020] Figure 1 A method for making weather ensemble forecast decisions for a test flight in rainy weather according to an embodiment of the present invention is explained.

[0021] Figure 2 A diagram illustrating the path and intensity of Typhoon Fireworks in accordance with an embodiment of the present invention.

[0022] Figure 3 A diagram illustrating the changes in rainfall intensity and crosswind over time of a 3-day deterministic forecast, a 6-hour cycle forecast, and a 6-hour cycle ensemble forecast according to an embodiment of the present invention is illustrated.

[0023] Figure 4 A diagram illustrating the proportions that meet different rainfall intensities and crosswind flight test meteorological standards and the proportions that simultaneously meet different rainfall intensities and crosswind flight test meteorological standards according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. In the following detailed description, many specific details are set forth to provide a thorough understanding of the described exemplary embodiments. However, it is obvious to those skilled in the art that the described embodiments can be practiced without some or all of these specific details. In other exemplary embodiments, well-known structures or processing steps are not described in detail to avoid unnecessarily obscuring the concepts of the present disclosure.

[0025] In this specification, unless otherwise specified, the term "A or B" used throughout the specification refers to "A and B" and "A or B" rather than meaning that A and B are exclusive.

[0026] Figure 1 The method steps for making a weather ensemble forecast decision for a test flight in rainy weather according to an embodiment of the present invention are explained.

[0027] An improved method for decision-making on rainy day test flight tests based on numerical models, ensemble forecasts and cyclic assimilation techniques is disclosed in an embodiment of the present invention. The method uses meteorological elements such as rain intensity and crosswind size as judgment criteria, and uses a mesoscale model to calculate a suitable test flight window a specified number of days (e.g., 3 days) before the test. An ensemble Kalman filter (EnKF) is used to assimilate observation data at a specified frequency (e.g., 6 hours), and the rain intensity and crosswind forecasts are updated in a rolling manner to further improve the forecast accuracy and support test flight tests. Finally, by counting the number of members that meet the indicators in the ensemble forecast, the probability distribution of rain intensity and crosswind in the future is calculated, and then the composite success rate of the flight test is objectively estimated to assist the test flight decision.

[0028] In the embodiment of the present application, the following method is used to make a weather ensemble forecast decision for a rainy day test flight:

[0029] In step 110: a simulation area is set according to the site selection of the test flight airport.

[0030] In an embodiment of the present application, a high-precision meteorological simulation software Weather Research and Forecasting (WRF) v3.9 model can be selected as a power mode. For example, setting a simulation area includes using the WRF model to perform high-precision meteorological simulation, and the model area can include the test flight airport site and its surrounding area.

[0031] In the embodiment of the present application, the outermost layer of the mode region is as follows Figure 2 As shown, it covers most of China and the Northwest Pacific Ocean, with a horizontal resolution of 12 km and a grid number of 560*720. The model has 56 layers vertically, and the top of the model is 10 hPa.

[0032] In step 120: a meteorological set associated with the simulation area is obtained.

[0033] In an embodiment of the present application, obtaining a meteorological set associated with a simulation region includes configuring a parameterization scheme to obtain a model of each meteorological member in the meteorological set. The parameterization scheme is used to represent small-scale processes that cannot be directly resolved at the resolution of the mesoscale model, such as cloud physics, radiation transfer, boundary layer dynamics, and land surface processes. According to the requirements of the WRF model, the parameterization scheme combination required to be configured for the forward integration of the model of each meteorological member may include: WSM6 microphysics scheme, GCM rapid radiation transfer model (RRTM-G) and shortwave and longwave radiation schemes, Yonsei University planetary boundary layer (PBL) parameterization scheme, NOAH land surface model, and Kain Fritsch (KF) convection scheme. In an embodiment of the present application, the number of members in the set may be configurable. For example, 80 meteorological members may be set in total.

[0034] In an embodiment of the present application, the ensemble initial field obtained from the ensemble Kalman filter is integrated. Wherein "integration" refers to the integration of the atmospheric state over time by the numerical model through a specified step size to predict the meteorological elements at future moments. This method is called ensemble forecasting, and the reliability and range of the forecast can be increased by perturbing the initial field, the boundary field, or running multiple models with different parameterization schemes. Each member in the ensemble represents a possible response to the initial conditions and model uncertainty. By analyzing the forecast results of these members, the uncertainty and possible errors of the forecast can be better estimated.

[0035] In step 130: generate meteorological initial fields and boundary fields.

[0036] In an embodiment of the present application, meteorological data of the initial field and boundary field of each meteorological member in the meteorological set can be generated. For example, the GPS global model forecast data of the corresponding time can be downloaded, which usually includes the data of the global weather forecast system GFS (Global Forecast System) or other global weather forecast models, and combined with static terrain data, the WRF preprocessing system WPS is run to prepare the input data required for the WRF model. Horizontal interpolation generates meteorological data on the model area. Run real.exe in the WRF model, pre-process the meteorological data in the model area, generate the meteorological variables required for the model simulation, and accordingly output the initial field and boundary field of each meteorological member in the meteorological set.

[0037] In step 140 , an initial flight test window is determined by integrating forward a first designated number of hours based at least in part on meteorological data for the initial field and the boundary field.

[0038] In an embodiment of the present application, preferably, the first specified number of hours can be 3 days, i.e., 72 hours, so that the test flight window can be estimated 3 days in advance, assisting in making earlier decisions for the test flight. For example, the meteorological element data of the pattern area (e.g., the test flight airport area and its surrounding area) can be calculated by integrating forward for 3 days to preliminarily determine the test flight window. Preferably, the meteorological element data of the test flight airport area can be calculated using bilinear interpolation.

[0039] Due to the small scope of the airport, the simulation grid is often difficult to directly resolve to the airport runway, so it is necessary to horizontally interpolate the grid points under the Lambert projection, usually using bilinear interpolation. In an embodiment of the present application, for the Dongying Airport site, four grid points near the site can be taken for bilinear interpolation. Assuming that the interpolation point (x, y) is located within the four known grid points (x1, y1), (x1, y2), (x2, y1) and (x2, y2), then:

[0040]

[0041] Bilinear interpolation can be applied to the interpolation of meteorological elements, such as temperature, humidity, wind speed, etc., to obtain accurate meteorological information of the test flight airport area.

[0042] In an embodiment of the present application, determining the initial test flight window may include using the above-mentioned bilinear interpolation to calculate the meteorological data of the simulation area, and determining the initial test flight window based at least in part on the meteorological data satisfying one or more indicators. Preferably, the one or more specified indicators may include a first rain intensity level (greater than or equal to 4 mm / h), a second rain intensity level (greater than or equal to 8.1 mm / h), a crosswind intensity (not more than 20 knots, i.e., 10.3 m / s), or a combination thereof according to the requirements of the certification test flight.

[0043] In step 150: based at least in part on the meteorological data of the initial field and the boundary field, cyclically integrate forward a second specified number of hours and use a collective Kalman filter to cyclically update the meteorological data of the background field of each meteorological member in the meteorological set, wherein the first specified number of hours is greater than the second specified number of hours.

[0044] In an embodiment of the present application, an ensemble Kalman filter (EnKF) can be used to cyclically update the background field to generate an analysis field. For example, it can be achieved by iterative execution of prediction and update steps, in which the filter uses the dynamic model of the system to predict the background field of the next time step, and in the update step, the filter adjusts the prediction in combination with new observation data to generate an analysis field. This process is repeated with the arrival of each new observation data. For example, in the iteration, the second specified number of hours (for example, 6 hours) can be integrated forward each time in the cycle, and the meteorological element data of the mode area (for example, the test flight airport area and its surrounding area) can be calculated by bilinear interpolation. The second specified number of hours should be set to be less than the first specified number of hours to provide more accurate data for the test flight to make refined decisions. Preferably, the meteorological element data of the test flight airport area can be calculated using bilinear interpolation.

[0045] The algorithm of the ensemble Kalman filter is as follows:

[0046]

[0047] x a represents the analysis field, i.e. the variable after assimilation and update, x b represents the background field, i.e. the variable before assimilation update, y 0 represents the multi-source observations within the 6-hour assimilation window (for example, it can include open source observations such as satellites, radars, and soundings), H represents the observation operator, which transforms meteorological variables into the observation space, and y 0 -Hx b That is, the observation increment, which is the difference between the observation and the model. Represents the adjusted Kalman gain matrix.

[0048] In an embodiment of the present application, as described above, a total of 80 ensemble members can be set to minimize sampling errors under the premise of balancing the amount of calculation. Assimilation refers to the process of combining observation data with the numerical model of the ensemble member, with the purpose of generating an optimized initial state for more accurate weather forecasting. For example, observation data from ground stations, satellites, aircraft, etc. can be collected, and the collected observation data can be assimilated into the numerical model of the ensemble member. The observation prior used during assimilation update is calculated by a grid statistical difference system (GSI), and the observation prior can be calculated based on the numerical model and observation data of the ensemble member, and then, based at least in part on the observation prior, the collected observation data can be assimilated into the numerical model of the meteorological member using a ensemble Kalman filter. Preferably, the observation data that need to be assimilated include conventional field observation data and cloud guide wind and clear sky radiation observation data, etc. Preferably, the observational data to be assimilated include conventional observations and clear sky radiation observations of the Global Data Assimilation System (GDAS), such as the Advanced Microwave Sounder-A (AMSU-A), the Atmospheric Infrared Sounder (AIRS), the Microwave Humidity Sounder (MHS) and the High Resolution Infrared Radiation Sounder (HIRS / 4). Preferably, the minimum sea level pressure (SLP) and position of the tropical cyclone (TC) provided by the key characteristics (Vitals) of the tropical cyclone (TC) can be assimilated as traditional surface pressure observations. By using data assimilation technology to better predict weather patterns, especially in extreme weather events and complex meteorological conditions, the observational data can be more effectively utilized to improve the accuracy and reliability of numerical weather forecasts. In an embodiment of the present application, after the assimilation observations, a specified step size (e.g., 45 seconds or other step size) can be used to integrate forward to 6 hours to update the initial field, and then integrate forward for 6 hours to update the initial field until the updated initial field is obtained after integration to 3 days.

[0049] In step 160 : the number of meteorological members in the meteorological set whose meteorological data meets one or more specified indicators is determined to calculate the composite success rate of the rainy day test flight.

[0050] In an embodiment of the present application, for each member in the meteorological set, where each member is the result of a different meteorological forecast model or parameterization scheme, it is checked whether the meteorological data of each member meets these specified indicators.

[0051] According to the specified meteorological standards, count the number of set members n that meet a certain indicator i , where i = 1, 2, 3…, the composite success rate R is defined as:

[0052]

[0053] Where k represents the number of indicators and N represents the number of set members.

[0054] In step 170 : a final flight test window is determined based at least in part on the composite success rate.

[0055] In the embodiment of the present application, the composite success rate is objectively calculated by using an ensemble forecast method, providing an objective decision-making method for the test flight site. If the composite success rate reaches or exceeds a certain threshold, a test flight window can be determined, that is, a time period suitable for test flight.

[0056] Figure 2 This is a diagram showing the path and intensity of Typhoon "Firework" according to an embodiment of the present invention. The intensity refers to the regulations of the Regional Specialized Meteorological Center (RSMC).

[0057] In the embodiments of the present application, Figure 2 As shown in , it includes most of China and the northwest Pacific Ocean, with a horizontal resolution of 12km and a grid number of 560*720. The model has 56 layers vertically, and the top of the model is 10 hPa. The parameterization schemes used include: WSM6 microphysics scheme, GCM rapid radiation transfer model (RRTM-G) and shortwave and longwave radiation schemes, Yonsei University planetary boundary layer (PBL) parameterization scheme, NOAH land surface model and Kain Fritsch (KF) scheme. As shown in the figure, different colors represent different intensities of Typhoon "Fireworks". It should be understood that the parameterization schemes described in the embodiments of the present application are only exemplary and not restrictive, and those skilled in the art may also use other parameterization schemes without exceeding the scope of the present application.

[0058] Figure 3 It is a diagram showing the changes in rainfall intensity and crosswind (i.e., zonal wind speed) over time for the 3-day deterministic forecast (FCST), 6-hour cycle forecast (FCSTCYCLE) and 6-hour cycle ensemble forecast (FCST ENS) of the embodiment described in the present invention.

[0059] like Figure 3 As shown in the figure, the 3-day deterministic forecast FCST is represented by black lines, the 6-hour cycle forecast FCST CYCLE is represented by red lines, and the 6-hour cycle ensemble forecast FCST ENS is represented by blue lines. Figure 3 The horizontal axis is time, and the vertical axes are rainfall intensity and wind speed respectively.

[0060] It should be understood that the 3 days or 6 hours described in the embodiments of the present application are only exemplary and not limiting, and those skilled in the art may also use other numbers without exceeding the scope of the present application.

[0061] Figure 4It is the probability of (a) meeting the meteorological standards of different rainfall intensities and crosswind test flights in the embodiment of the present invention, and (b) meeting the meteorological standards of different rainfall intensities and crosswind test flights at the same time.

[0062] like Figure 4 As shown in the figure, under the probability of meeting different rainfall intensities and crosswind flight test meteorological standards (a), the probabilities of different rainfall intensities and crosswinds meeting the flight test conditions are shown separately, where the rainfall intensity is divided into level 1 (greater than or equal to 4 mm / h, and its success rate is represented by red) and level 2 (greater than or equal to 8.1 mm / h, and its success rate is represented by blue) according to the requirements of the certification flight test, and the success rate of the crosswind intensity (10.3 m / s) is represented by black.

[0063] In (b) the probability of simultaneously meeting the test flight meteorological standards for different rainfall intensities and crosswinds, the probabilities of meeting the test flight conditions for different rainfall intensities and crosswinds are combined, and the rainfall intensity is divided into level 1 (greater than or equal to 4 mm / h, and its success rate is represented by red) and level 2 (greater than or equal to 8.1 mm / h, and its success rate is represented by blue).

[0064] It should be understood that the specified indicators described in the embodiments of the present application, such as crosswind intensity, rain intensity level, etc., are only exemplary and not restrictive, and those skilled in the art may also use other indicators without exceeding the scope of the present application.

[0065] Now combine Figure 1-Figure 4 An embodiment of the present application is described in detail:

[0066] On July 27, 2021, the Central Meteorological Observatory predicted that Typhoon "Fireworks" will move northward along Anhui and Jiangsu to affect Dongying City, Shandong Province. The path and intensity of Typhoon "Fireworks" are as follows: Figure 2 As shown. There is a probability of precipitation in the outer rain belt of the typhoon, so the flight test center plans to conduct rainy day test flights at the Dongying base. The meteorological team made a subjective forecast on July 28, predicting that the heavy rain period will be mainly concentrated before 11:00 on July 29, with a high probability of precipitation and a low probability of thunderstorms. After the typhoon affected the central Shandong region in the afternoon, the crosswind continued to increase, and there was a possibility of exceeding the standard. The test was successful, but the excessive reliance on subjective judgment seriously affected the safety and efficiency of the flight test.

[0067] For the Dongying Airport site, four grid points near the site are selected for bilinear interpolation (see Figure 1 The rainfall intensity and crosswind speed of the three experimental forecasts (including the 3-day deterministic forecast, the 6-hour cycle forecast, and the 6-hour cycle ensemble forecast) vary with time as shown in FIG. Figure 3As shown. The black 3-day deterministic forecast FCST roughly estimates the test flight window, and the rainfall is mainly concentrated at 12:00 on July 29 (UTC). With the rolling update of the red 6-hour cycle forecast, the rainfall tends to be earlier, which further assists in adjusting the test flight window. The blue ensemble forecast FCST ENS can characterize the probability distribution of rainfall intensity. It can be found that the first rainfall peak is at 21:00 (UTC) on July 28. Many meteorological members in the meteorological ensemble accurately predicted this rainfall process. The second peak is at 12:00 on July 29, which is consistent with the other two groups of experiments, but the rainfall intensity of some ensemble members is greater and closer to observations.

[0068] Similar to the rainfall intensity, the black 3-day deterministic forecast FCST showed that the easterly wind speed was only 2m / s at 21:00 on July 28, indicating that the wind speed will not exceed the standard. The red 6-hour cyclic forecast FCST CYCLE predicts about 4.5m / s, which is closer to the observation than the 3-day forecast, indicating the risk of crosswind exceeding the standard. Some members of the blue ensemble forecast experiment FCST ENS are closer to the observed wind speed, and the ensemble average performance is also better than the other two groups of experiments. It can be found that as time develops to 12:00 on July 29, the wind speed gradually increases and approaches the critical point, indicating that the crosswind will directly affect the safe conduct of the experiment.

[0069] If the number of set members that meet the test flight meteorological standards is n, the success rate of the test flight test at different time nodes can be calculated according to formula (5): Figure 4 ). Compared with subjective forecasts and deterministic forecasts, ensemble forecasts can roll over the probability of successful flight tests. According to the results, the probability of success that meets the certification test flight requirements (level 1, red) is the highest at 18:00 on July 28, followed by 06:00 on July 29. The probability of success that meets the certification test flight requirements (level 2, blue) is the highest at 06:00 on July 29, followed by 18:00 on July 28.

[0070] The above results show that based on the numerical model, the test flight window that meets the rainfall and crosswind indicators can be captured 3 days in advance. Assimilating real-time observations every 6 hours can further improve the forecast accuracy, and the rainfall area and crosswind size are closer to the actual situation. Based on the cyclic assimilation, the ensemble forecasting technology can objectively calculate the test flight success rate for forecasters' reference. The results show that the test flight success rate is the highest between 18:00 on July 28 and 06:00 on July 29, which is consistent with the actual situation.

[0071] The above describes the method for making decisions based on the weather ensemble forecast for rainy day test flights according to the present invention. Compared with the prior art, the method of the present invention has at least the following advantages:

[0072] (1) The test flight window can be estimated in advance to assist in making earlier test flight decisions;

[0073] (2) Update the meteorological elements of the test flight airport at a specified frequency to provide more accurate data for test flights and make refined decisions;

[0074] (3) Use ensemble forecasting to objectively calculate the composite success rate and provide an objective decision-making method for the flight test site.

[0075] Reference has been made throughout the specification to "an embodiment" to mean that a particular described feature, structure, or characteristic is included in at least one embodiment. Thus, the use of these phrases may refer to more than just one embodiment. Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0076] The various steps and modules of the methods and devices described above can be implemented with hardware, software, or a combination thereof. If implemented in hardware, the various illustrative steps, modules, and circuits described in conjunction with the present disclosure can be implemented or executed with a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic components, hardware components, or any combination thereof. The general-purpose processor can be a processor, a microprocessor, a controller, a microcontroller, or a state machine, etc. If implemented in software, the various illustrative steps and modules described in conjunction with the present disclosure can be stored on a computer-readable medium or transmitted as one or more instructions or codes. The software modules that implement the various operations of the present disclosure can reside in a storage medium, such as RAM, flash memory, ROM, EPROM, EEPROM, registers, hard disks, removable disks, CD-ROMs, cloud storage, etc. The storage medium can be coupled to a processor so that the processor can read and write information from / to the storage medium, and execute corresponding program modules to implement the various steps of the present disclosure. Moreover, the software-based embodiments can be uploaded, downloaded, or remotely accessed by appropriate communication means. Such appropriate communications means include, for example, the Internet, the World Wide Web, an intranet, software applications, cable (including fiber optic cables), magnetic communications, electromagnetic communications (including RF microwave and infrared communications), electronic communications, or other such communications means.

[0077] The numerical values ​​given in each embodiment are only examples and are not intended to limit the scope of the present invention. In addition, as an overall technical solution, there are other components or steps that are not listed in the claims or description of the present invention. Moreover, a single name of a component does not exclude other names of the component.

[0078] It should also be noted that these embodiments may be described as a process depicted as a flow chart, flow diagram, structure diagram, or block diagram. Although the flow chart may describe the operations as sequential processes, many of these operations can be performed in parallel or concurrently. In addition, the order of these operations can be rearranged.

[0079] The disclosed methods, devices, and systems should not be limited in any way. On the contrary, the present disclosure covers all novel and non-obvious features and aspects of the various disclosed embodiments (alone and in various combinations and sub-combinations with each other). The disclosed methods, devices, and systems are not limited to any specific aspects or features or combinations thereof, nor do any disclosed embodiments require the existence of any one or more specific advantages or the resolution of specific or all technical problems.

[0080] The present invention is not limited to the above-mentioned specific embodiments, which are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can make many forms without departing from the scope of protection of the present invention and the claims, all of which belong to the protection scope of the present invention.

[0081] One skilled in the relevant art may recognize that the embodiments may be practiced without one or more of the specific details, or with other methods, resources, materials, etc. In other instances, well-known structures, resources, or operations are not shown or described in detail, merely to obscure aspects of the embodiments.

[0082] Although embodiments and applications have been illustrated and described, it should be understood that the embodiments are not limited to the precise configuration and resources described above. Various modifications, substitutions and improvements that are obvious to those skilled in the art may be made in the arrangement, operation and details of the methods and systems disclosed herein without departing from the scope of the claimed embodiments.

[0083] As used herein, the terms "and," "or," and "and / or" may include various meanings that are also intended to depend, at least in part, on the context in which such terms are used. In general, "or," if used in connection with a list, such as A, B, or C, is intended to mean A, B, and C (where used in an inclusive sense) as well as A, B, or C (where used in an exclusive sense). Additionally, the terms "one or more," as used herein, may be used to describe any feature, structure, or characteristic in the singular, or may be used to describe a plurality of features, structures, or characteristics, or some other combination thereof. It should be noted, however, that this is merely an illustrative example, and claimed subject matter is not limited to this example.

[0084] While what is presently considered to be example features has been illustrated and described, it will be appreciated by those skilled in the art that various other modifications may be made, and equivalents may be substituted, without departing from the claimed subject matter. Additionally, many modifications may be made to adapt a particular scenario to the teachings of the claimed subject matter without departing from the central concept described herein.

Claims

1. A method for making a weather ensemble forecast decision for a rainy day test flight, comprising: Set up a simulation area based on the site selection of the test flight airport; obtaining a meteorological set associated with the simulation area; Generate meteorological data of initial fields and boundary fields of each meteorological member in the meteorological set; integrating forward a first designated number of hours to determine an initial flight test window based at least in part on the meteorological data for the initial field and the boundary field; Based at least in part on the meteorological data of the initial field and the boundary field, cyclically integrating forward a second specified number of hours and cyclically updating the meteorological data of the background field of each meteorological member in the meteorological set using an ensemble Kalman filter, wherein the first specified number of hours is greater than the second specified number of hours; Determining the number of meteorological members in the meteorological set whose meteorological data meets one or more specified indicators to calculate the composite success rate of the rainy day test flight; and A final flight test window is determined based at least in part on the composite success rate.

2. The method according to claim 1, characterized in that The parameterization scheme combinations that need to be configured for the forward integration of the model of each meteorological member include: Microphysics scheme WSM6; GCM fast radiative transfer model; Short-wave and long-wave radiation schemes; Yonsei University Planetary Boundary Layer PBL Program; NOAH land surface model scheme; Kain-Fritsch convection scheme.

3. The method according to claim 1, characterized in that Generating meteorological data of the initial field and the boundary field of each meteorological member in the meteorological set includes: Pre-processing the meteorological data associated with each meteorological member in the meteorological set to generate a meteorological variable of the meteorological member; and Meteorological data of initial fields and boundary fields associated with each meteorological member in the meteorological set are output in combination with the static terrain data.

4. The method according to claim 1, characterized in that Determining the initial flight test window includes: Calculate the meteorological data of the simulation area using bilinear interpolation; The initial flight test window is determined based at least in part on the meteorological data satisfying the one or more criteria.

5. The method according to claim 1, characterized in that Using the ensemble Kalman filter to cyclically update the meteorological data of the background field of each meteorological member in the meteorological set includes: Calculate the meteorological data of the simulation area for each meteorological member in the meteorological set using bilinear interpolation; and The observational data collected from multiple sources are assimilated into the numerical model of each meteorological member using an ensemble Kalman filter.

6. The method according to claim 5, characterized in that The algorithm of the ensemble Kalman filter is: where x a represents the analytical field, x b represents the background field, y 0 represents the multi-source observations within the assimilation window of the second specified number of hours, H represents the observation operator, and y 0 -Hx b Represents observation information, Represents the adjusted Kalman gain matrix.

7. The method according to claim 6, characterized in that Assimilating the observational data collected from multiple sources into the numerical model of the meteorological member using an ensemble Kalman filter includes: The observation priors are calculated by the Grid Statistical Interpolation (GSI) system based on the numerical model and observation data of the meteorological members; The collected observational data is assimilated into a numerical model of the meteorological member using an ensemble Kalman filter based at least in part on the observation priors.

8. The method according to claim 5, characterized in that The multi-source observations include open source meteorological observations, which include one or more of the following: satellites, radars, and soundings.

9. The method according to claim 1, characterized in that The composite success rate R is defined as: Where k represents the number of the one or more specified indicators, N represents the number of members of the meteorological set, and n i It represents the number of meteorological members that meet the specified indicators, and i is a natural number.

10. The method according to claim 1, characterized in that The one or more designated indicators include a first rain intensity level, a second rain intensity level, a crosswind intensity, or a combination thereof.

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