METHOD AND DEVICE FOR MONITORING METEOROLOGICAL RISKS AROUND AN AIRCRAFT TRAJECTORY.

Abstract

A weather risk monitoring system is implemented within a predefined perimeter around an aircraft's flight path. This monitoring system collects and aggregates a set of heterogeneous meteorological data from various information sources. From this heterogeneous meteorological data, the system selects only those meteorological data representative of a risk of interest potentially present within the predefined perimeter around the aircraft's flight path, at a specific geographic location and time of interest. After assigning a preliminary relevance level to each selected meteorological data point, the monitoring system determines a final relevance level by applying spatial and / or temporal variation rules to the preliminary relevance level.The monitoring system then determines the probability of the relevant weather risk and generates an associated message. This facilitates decision-making regarding actions to take in response to the identification of a weather risk along the aircraft's flight path. Figure to be published with the abbreviation: Fig. 3.

Description

Title of the invention: METHOD AND DEVICE FOR MONITORING METEOROLOGICAL RISKS AROUND AN AIRCRAFT TRAJECTORY. Technical field

[0001] The present invention relates to the field of meteorological risks applied to aviation. In particular, the invention relates to a method and a device for monitoring meteorological risks in the vicinity of an aircraft flying along a trajectory as previously defined in a flight plan. STATE OF PRIOR ART

[0002] For an aircraft, flying in severe weather conditions can reduce the safety margins of certain maneuvers or require specific maintenance actions on certain parts. Thus, these weather conditions must be anticipated as best as possible, whether during the preparation of a flight plan, or during the flight.

[0003] Nowadays, numerous means are made available to the crew of an aircraft or ground personnel to provide meteorological information on meteorological phenomena likely to have an impact on the flight along a planned trajectory. In particular, it is possible to acquire a large number of meteorological data from different times, locations and information sources. These meteorological data include weather radar data on board the aircraft, ground weather radar data, data from sensors on board the aircraft, satellite images, data extracted from SIGMET messages (Significant Meteorological Information), METAR reports (Meteorological Aerodrome Report) or data obtained from full-fledged meteorological models.

[0004] However, these meteorological data are very often heterogeneous and users are obliged to synthesize themselves all these meteorological data which are provided to them. In particular, there is currently no means of presenting to the user a single and reliable piece of information representative of each meteorological risk encountered along the trajectory of the aircraft, or of a general meteorological risk for the integrity of a flight of the aircraft along its trajectory.

[0005] It is then desirable to overcome this drawback of the state of the art.

[0006] It is particularly desirable to provide a solution which makes it possible to obtain a ren single information representative of the probabilities of meteorological risks, or even of the probabilities of intensity levels of these meteorological risks, based on a plurality of heterogeneous meteorological data from different information sources. Statement of the invention

[0007] A method is proposed here for monitoring meteorological risks in a predefined perimeter around an aircraft trajectory. This method is implemented by a monitoring system in the form of electronic circuitry. The method comprises: - collect a plurality of heterogeneous meteorological data from a plurality of information sources, - aggregating said heterogeneous meteorological data collected by placing them all in at least one common reference frame, then by selecting from among the heterogeneous meteorological data collected and placed in at least one common reference frame, a set of meteorological data representative of a risk of interest potentially present in the predefined perimeter around the trajectory of the aircraft, in a geographical location of interest and at a time of interest, corresponding to a point among a set of points in the predefined perimeter around the trajectory of the aircraft, - assign a preliminary level of relevance to each meteorological data representative of the meteorological risk of interest, - determine a final level of relevance for each of the meteorological data representative of the meteorological risk of interest by applying at least one rule of variation of the preliminary level of relevance which is representative of a predetermined variation in space and / or time of the preliminary level of relevance, and which depends on: - a distance between a location of application of each of the meteorological data representative of the meteorological risk of interest and the geographical location of interest, and / or - a duration between a time of application of each of the meteorological data representative of the meteorological risk of interest and the time of interest, - determine, for each point of said set of points in the predefined perimeter around the trajectory of the aircraft, from the final level of relevance of each of the meteorological data representative of the meteorological risk of interest, a probability of said meteorological risk of interest, - transmit to a user system, information corresponding to the probability of said meteorological risk of interest for each point of at least one subset of points of said set of points.

[0008] Advantageously, it is possible to combine heterogeneous meteorological data from a plurality of information sources to generate a single piece of information, relevant for operational use by users (for example: aircraft crew and / or ground personnel). This single piece of information is representative of the probabilities of meteorological risks or of intensity levels of these meteorological risks. This single piece of information can then be associated with a flight plan and be made available to users over predefined geographical areas (e.g., for a given location (latitude x, longitude y, altitude z), or for a particular geographical area, along the trajectory of the aircraft) and at different times along the trajectory of the aircraft (i.e., a meteorological risk at a time t (e.g., in flight time) and at a time t+At).Thus, decision-making regarding the actions to be taken in response to the identification of a meteorological risk around the aircraft trajectory (i.e., within a predefined perimeter around the aircraft's planned trajectory) is facilitated.

[0009] According to a particular embodiment, the predetermined variation in space and / or time of the preliminary relevance level further depends on: - the presence of physical phenomena and / or obstacles in a predefined area around the location of application of each of the meteorological data representative of the meteorological risk of interest, and / or - a model of the evolution of each of the meteorological data representative of the meteorological risk of interest over time, and / or - a type of information source and / or a location of the information sources and / or a location with which each of the meteorological data representative of the meteorological risk of interest is associated, regardless of the geographical location of interest, and / or - a time of applicability of each of the meteorological data representative of the meteorological risk of interest independently of the time of interest.

[0010] According to a particular embodiment, the method further comprises: determining an inconsistency between the meteorological data representative of the meteorological risk of interest and the probability of the meteorological risk of interest when the meteorological data representative of the meteorological risk of interest indicate - “presence of the risk of interest” while the probability of the meteorological risk of interest is lower than a first predefined SI threshold, and vice versa, - “non-presence of the risk of interest” while the probability of the meteorological risk of interest is higher than the first predefined SI threshold, - “presence of the risk of interest” while at least one meteorological data among the meteorological data representative of the meteorological risk of interest indicates “non-presence of the risk” with the final level of relevance higher than a second predefined threshold S2, and vice versa, - “non-presence of the risk of interest” while at least one meteorological data among the meteorological data representative of the meteorological risk of interest indicates “presence of the risk of interest” with the final level of relevance higher than the second predefined threshold S2.

[0011] According to a particular embodiment, aggregating the collected heterogeneous meteorological data further comprises: propagating each of the heterogeneous meteorological data according to at least one spatial propagation rule and / or one temporal propagation rule.

[0012] According to a particular embodiment, the set of points in the predefined perimeter around the trajectory of the aircraft corresponds to a set of points of a mesh of the space, each of said points of said mesh being representative of a location of interest and of an instant of interest in the predefined perimeter around the trajectory of the aircraft.

[0013] According to a particular embodiment, collecting the plurality of heterogeneous meteorological data from the plurality of information sources comprises: processing the heterogeneous meteorological data.

[0014] Also proposed here is a system for monitoring meteorological risks within a predefined perimeter around an aircraft trajectory. This monitoring system comprises electronic circuitry configured to implement: - collect a plurality of heterogeneous meteorological data from a plurality of information sources, - aggregating said heterogeneous meteorological data collected by placing them all in at least one common reference frame, then by selecting from among the heterogeneous meteorological data collected and placed in at least one common reference frame, a set of meteorological data representative of a risk of interest potentially present in the predefined perimeter around the trajectory of the aircraft, in a geographical location of interest and at a time of interest, corresponding to a point among a set of points in the predefined perimeter around the trajectory of the aircraft, - assign a preliminary level of relevance to each meteorological data representative of the meteorological risk of interest, - determine a final level of relevance for each of the meteorological data representative of the meteorological risk of interest by applying at least one rule for variation of the preliminary level of relevance which is representative of a predetermined variation in space and / or time of the pre- preliminary, and which depends on: - a distance between a location of application of each of the meteorological data representative of the meteorological risk of interest and the geographical location of interest, and / or - a duration between a time of application of each of the meteorological data representative of the meteorological risk of interest and the time of interest, - determine, for each point of said set of points in the predefined perimeter around the trajectory of the aircraft, from the final level of relevance of each of the meteorological data representative of the meteorological risk of interest, a probability of said meteorological risk of interest, - transmit to a user system, information corresponding to the probability of said meteorological risk of interest for each point of at least a subset of points of said set of points.

[0015] Also proposed here is an aircraft comprising a surveillance system as described above according to one embodiment.

[0016] Also provided is a computer program product comprising instructions causing a processor to execute the method mentioned above according to any one of its embodiments, when said instructions are executed by the processor. Also provided is a storage medium storing such instructions. Brief description of the drawings

[0017] The characteristics of the invention mentioned above, as well as others, will appear more clearly on reading the following description of at least one exemplary embodiment, said description being made in relation to the attached drawings, among which:

[0018] [Fig-1] schematically illustrates, in side view, an aircraft equipped with a system of monitoring meteorological risks in a predefined perimeter around a flight path of the aircraft, according to one embodiment;

[0019] [Fig.2] schematically illustrates an example of a hardware platform allowing to implement, in the form of electronic circuitry, the monitoring system according to one embodiment;

[0020] [Fig.3] illustrates in diagram form the steps of the monitoring process of the meteorological risks within a predefined perimeter around a flight path of the aircraft, according to one embodiment;

[0021] [Fig.4A] schematically illustrates an example of application of a spatial variation rule of a preliminary level of relevance of meteorological data representative of a meteorological risk;

[0022] [Fig.4B] schematically illustrates another example of application of a rule of spatial variation of the preliminary level of relevance of meteorological data representative of a meteorological risk;

[0023] [Fig.5] schematically illustrates another example of application of a rule of spatial variation of the preliminary level of relevance of meteorological data representative of a meteorological risk, in the presence of an obstacle;

[0024] [Fig.6] schematically illustrates an example of application of a temporal variation rule of a preliminary level of relevance of meteorological data representative of a meteorological risk;

[0025] [Fig.7] schematically illustrates an example of application of a combination of spatial and temporal variation rules of a preliminary level of relevance of meteorological data representative of a meteorological risk;

[0026] [Fig.8A] schematically illustrates a first example of the result of a determination determination of a final level of relevance for two meteorological data respectively representative of the presence or absence of a meteorological risk, and of an application of a propagation rule;

[0027] [Fig.8B] schematically illustrates a second example of the result of a determination of a final level of relevance for two meteorological data respectively representative of the presence or absence of a meteorological risk;

[0028] [Fig.8C] schematically illustrates a third example of the result of a determination of a final level of relevance for two meteorological data respectively representative of the presence or absence of a meteorological risk

[0029] [Fig.9] illustrates an example of graphical representations of the probabilities of a meteorological risk before and after post-processing of these probabilities by the monitoring system; and

[0030] [Fig. 10] illustrates an example of a graphical representation of the probabilities of a meteorological risk for a location of interest and for a time of interest, displayed on a screen of a human-machine interface.

[0031] DETAILED DESCRIPTION OF EMBODIMENTS

[0032] The general principle of the following disclosure concerns obtaining a set of heterogeneous meteorological data from different information sources and aggregating them together in order to obtain a single and reliable piece of information concerning each meteorological risk, and / or a general meteorological risk, in the vicinity of an aircraft during all or part of its flight, i.e. within a predefined perimeter and at different times, around its flight path. This single piece of information is representative of one or more probabilities of a meteorological risk (for example: probability of hail at point P, at time t = 25%) or of one or more probabilities of meteorological risks of certain intensity levels (for example: probability of light hail = 20%; probability of moderate hail = 40%; probability of heavy hail = 30%). "Probability of event" or "probability of a meteorological risk" means the probability of encountering, at a time t, or during a predetermined period, and at a predetermined location (e.g., predefined perimeter around the aircraft's flight path), an associated meteorological risk (for example: probability of medium intensity hail: 20%, at a particular point in the predefined perimeter around the aircraft's flight path at time t of the aircraft's flight).

[0033] Hereinafter, we mean by:

[0034] - “meteorological risk” or “risk”: any localized meteorological phenomenon geographically (i.e., latitude, longitude, altitude) and temporary that may result in material damage, physical damage, discomfort to the crew and / or passengers, and additional non-standard maneuvers and / or procedures (e.g., changes to the flight plan and / or destination) during a commercial flight. These meteorological risks are, for example: turbulence, lightning, hail, heavy rain, icing conditions, the presence of convective cells, the presence of volcanic ash, areas likely to produce condensation trails, etc. These risks may be associated with levels of intensity, such as: light (“LIGHT”), moderate (“MODERE”), high (“HEAVY”);

[0035] - “meteorological data”: any data provided by a source information and can be correlated or transformed into meteorological information (i.e., information concerning a meteorological phenomenon, for example: type of phenomenon, location, intensity level, etc.) and therefore directly or indirectly into meteorological risk. These meteorological data are, for example: i. data coming from (non-exhaustive list): sensors on board the aircraft (e.g., linear and angular accelerations, temperature, pressure, position (i.e., latitude, longitude, altitude), relative speed with respect to the ground, relative speed with respect to the air mass); from a meteorological radar on board the aircraft (e.g., reflectivity, etc.); from a camera on board the aircraft (e.g., visible image, infrared, etc.); from a LiDAR (“light / laser imaging detection and ranging”) on board the aircraft; ii. external environmental data from systems external to the aircraft, such as (non-exhaustive list): visible satellite images; infrared images; ground weather radar data (e.g., reflectivity, etc.); data extracted from airport weather reports (e.g.: ATIS or “Automatic Terminal Information Service”, TAF or “Terminal Aerodrome Forecast”, etc.); data extracted from areas of risks (for example: Polygons, SIGMET, NOTAM or “Notice to airmen”); data extracted from 4D meteorological models (4 dimensions) including for example: temperature, pressure, humidity, horizontal wind, vertical wind, dew point, etc. Thus, we subsequently consider that meteorological data is representative of a meteorological risk (presence of a risk) or of a non-meteorological risk (absence of risk).

[0036] - “source of information”: all sensors (for example temperature sensor, atmospheric pressure, etc.), systems or installations (e.g.: weather radar, weather station, etc.) which provide one or more meteorological data.

[0037] [Fig.l] schematically illustrates, in side view, an aircraft 100 equipped with a system for monitoring meteorological risks in a predefined perimeter around a flight path of the aircraft 100, according to one embodiment. Subsequently, for simplicity, this system for monitoring meteorological risks in a predefined perimeter around a flight path of the aircraft 100 is called “monitoring system 101”.

[0038] According to the embodiment of [Fig.l], the monitoring system 101 belongs to the avionics system of the aircraft 100.

[0039] According to another embodiment (not shown), the monitoring system 101 belongs to a system external to the aircraft 100, but on board, such as for example an EFB (“Electronic FlightBag”).

[0040] According to another embodiment (not shown), the surveillance system 101 belongs to a ground system communicating with the aircraft 100 via air-ground communication.

[0041] [Fig.2] schematically illustrates an example of a hardware platform making it possible to implement, in the form of electronic circuitry, the monitoring system 101, according to one embodiment.

[0042] The hardware platform comprises, connected by a communication bus 210: a processor or CPU (Central Processing Unit) 201; a random access memory (RAM) 202; a read-only memory 203, for example of the ROM (Read Only Memory) or EEPROM (Electrically Erasable Programmable ROM) type, such as a Flash memory; a storage unit, such as a hard disk (HDD) 204, or a storage media reader, such as an SD (Secure Digital) card reader; and an I / f interface manager 205.

[0043] The I / f interface manager 205 allows the surveillance system 101 to interact with different sources of information external or internal to the aircraft 100, and the avionics systems of the aircraft 100. In particular, the I / f interface manager 205 allows the surveillance system 101 to interact with a human-machine interface of the cockpit of the aircraft 101 and / or a human-machine interface of a system external to the aircraft 100 of the EFB type and / or a human-machine interface of a ground surveillance system communicating with the aircraft 100 via air-ground communication to inform the crew and / or ground personnel of the presence of a meteorological risk in the predefined perimeter around the trajectory of the aircraft 100.

[0044] The processor 201 is capable of executing instructions loaded into the RAM 202 from the ROM 203, an external memory, a storage medium (such as an SD card), or a communications network. When the hardware platform is powered on, the processor 201 is capable of reading instructions from the RAM 202 and executing them. These instructions form a computer program causing the processor 201 to implement all or part of the method steps described herein.

[0045] All or part of the method steps described here can thus be implemented in software form by executing a set of instructions by a programmable machine, for example a DSP (Digital Signal Processor) type processor or a microcontroller, or be implemented in hardware form by a machine or a dedicated electronic component (chip) or a dedicated set of electronic components (chipset), for example an FPGA (Field Programmable Gate Array) or ASIC (Application Specified Integrated Circuit) component. Generally speaking, the monitoring system 101 comprises electronic circuitry adapted and configured to implement all or part of the method steps described here.

[0046] We now present in connection with [Fig. 3], in diagrammatic form, the steps of a method for monitoring meteorological risks in the predefined perimeter around the trajectory of the aircraft 100, which is implemented by the monitoring system 101, according to one embodiment.

[0047] During a step 301, denoted ENS_DATA, the monitoring system 101 collects heterogeneous meteorological data over a predefined collection period, from certain information sources. For example, these meteorological data are obtained either directly from information sources (for example: sensors on board the aircraft 100 etc.), or indirectly via the extraction of these meteorological data from meteorological reports issued by an information source (for example: SIGMET message) or from meteorological models.

[0048] The meteorological data and their information sources are pre-filtered by the monitoring system 101 on the basis of the collection period predefined and a predefined geographic area.

[0049] In particular, the predefined collection period extends from a predefined time t to a time t+At corresponding to the time at which the collection of the meteorological data begins (for example the day of preparation of the flight plan or the day of the flight of the aircraft 100). In one example, the meteorological data from 6 hours, or 3 days, before the preparation of the flight plan, until the day of preparation of the flight plan are recovered. It is thus possible to collect and store (for example in the storage unit 204 of the monitoring system 101) only the meteorological data dated from this collection period. This predefined collection period may be dependent on the type of information source.

[0050] Furthermore, the monitoring system 101 collects the meteorological data produced during this predefined collection period and only from certain information sources in a particular location. More particularly, the monitoring system 101 collects and stores only the meteorological data produced by one or more information sources located in the predefined geographical area. In one example, this predefined geographical area is a country that the aircraft 100 flies over during its flight.

[0051] In one embodiment, during the collection of the meteorological data, the monitoring system 101 processes (or reformats) the meteorological data. In particular, the monitoring system 101 applies one or more or a combination of the following processing techniques to the meteorological data: - sub-sampling, - filtering, - consolidation. This makes it possible to reduce the amount of meteorological data from a single information source. In addition, it is possible to collect meteorological data with a frequency relatively similar to that used to perform the calculations described in connection with steps 305 RSK_DET to 307 CONS_MNTR described below.

[0052] In an example of a subsampling application, a weather radar takes measurements every second, so it is advantageous to “undersample” these measurements because the calculations in steps 305 RSK_DET to 307 CONS_MNTR can be done every 30 to 60 seconds, or even every 5 minutes.

[0053] In an example of filtering, for a meteorological radar of the aircraft 100 providing reflectivity values ​​every second, it is possible to have parasitic variations in the geographical position of the meteorological risks between two measurements, especially when these are far from the aircraft 100 itself (for example during turns where the aircraft 100 changes direction quickly, or when the direction of the aircraft 100 is not necessarily well synchronized with the direction of the weather radar). A "filtering" is therefore applied to follow the general evolution of the risk without being hindered by these parasitic variations from one measurement to another. For example, an average of the reflectivity values ​​over a time interval and / or over a given geographical area can be made in this case.

[0054] In an example of consolidation, from one measurement to the next, a weather radar can identify new small areas of meteorological risks, but which are parasitic areas only linked to the processing of signals by the weather radar. They generally only last a few seconds, or even tens of seconds. “Consolidation” makes it possible to eliminate these untimely weather risk areas if they do not appear consistently over a certain number of consecutive measurements.

[0055] In a particular embodiment, the monitoring system 101 pre-processes the meteorological data by placing them in the same spatial reference frame (for example as described in connection with step 302 DATA_TR below). It is thus possible to facilitate the sub-sampling and / or filtering and / or consolidation processing described previously.

[0056] In another particular embodiment, additionally or alternatively to one of the above processing techniques, the monitoring system 101 processes the meteorological data by reducing the amount of information, for example by applying a delimitation of zones (or "contouring" in English), to define and keep only the contour delimiting zones of a certain measurement value, rather than having to keep each value at each point.

[0057] Thus, at the end of step 301 ENS_DATA, the monitoring system 101 records in its storage unit 204 a set of meteorological data collected over a predefined collection period and from the information sources present in the predefined geographical area.

[0058] During a step 302, noted DATA_TR, the monitoring system 101 aggregates together the recorded meteorological data. For this, the monitoring system 101 processes the meteorological data from each information source in order to put them in one or more common reference systems, such as:

[0059] - a time reference allowing all meteorological data to be brought back logical data from each information source to the same time base. In one example, all weather data from each information source is converted to Universal Time Clock (UTC). In another example, all weather data from each information source is converted to deltaTime relative to the time the weather data was collected. meteorological by the monitoring system 101. The “deltaTime” corresponds to a relative incremental time system: when meteorological data are collected by the monitoring system 101 at a time t = 0, then their “deltaTime” = 0. At each new collection cycle by the monitoring system 101 (for example if the monitoring system 101 collects meteorological data from information sources every 10s), the “deltaTime” of the meteorological data is incremented accordingly;

[0060] - a spatial reference system allowing all meteorological data to be brought back logic of each information source to a single location system. In one example, weather data is brought back to a location characterized by latitude, longitude and altitude;

[0061] - a meteorological risk reference system based on the identification of types of risks associated with the meteorological data from each information source, such as: turbulence; lightning; hail; rain; freezing conditions; convection zone; area likely to produce condensation trails. Indeed, as described previously, meteorological data is representative of a meteorological risk or a meteorological non-risk. It is therefore possible to reduce each of them to either a meteorological risk or a meteorological non-risk. In one example, meteorological data, such as a satellite image, is transformed into polygons, where each polygon is a simplified geographical representation of a meteorological risk previously identified by the information source from which this satellite image originates.In another example, a meteorological data, such as an image from a camera, is projected into a 3D space, and the colors of the pixels are used to determine the presence of cumulonimbus, lightning, rain and therefore the meteorological risks of convection, rain, lightning, etc. over the geographical area observed by the camera.

[0062] It should be noted that a meteorological risk can be identified from meteorological data either by certain sources which directly indicate a meteorological risk in their report (for example: this is the case of METAR, SIGMET), or by the monitoring system 101 (for example: by interpreting a certain reflectivity value of a radar, a certain temperature, etc.).

[0063] In a particular embodiment, the monitoring system 101 further applies a second filtering or, if no filtering has been done previously in step 301 ENS_DATA, then a first filtering of the meteorological data placed in one or more common reference systems. This filtering is as described previously in connection with step 301 ENS_DATA.

[0064] Subsequently, in order to illustrate the implementation of the method as presently described, it is considered that the meteorological data of each information source are processed by the 101 monitoring system so that all meteorological data is put into a common meteorological risk repository.

[0065] In one embodiment, during this step 302 DATA_TR, the monitoring system 101 also applies a spatial propagation rule to the meteorological data thus processed (i.e., placed in a common reference system, such as a meteorological risk reference system).

[0066] In one embodiment, during this step 302 DATA_TR, the monitoring system 101 also applies a temporal propagation rule to the meteorological data thus processed (i.e., placed in a common reference system, such as a meteorological risk reference system).

[0067] These propagation rules are based on the physical reality of physical phenomena behind the punctual weather measurements and reports provided by an information source.

[0068] In one example, a spatial propagation rule corresponds to the spatial propagation of a meteorological risk in a 40km cylinder around the information source, when the information source is a weather station. For example, for meteorological data representative of a “hail” risk received by a weather station whose position in terms of latitude and longitude is known, the weather monitoring system 101 spatially “propagates” this “hail” risk so that said hail risk is applicable in a 40km cylindrical area around this weather station.

[0069] In another example, for a camera image or satellite image representative of a meteorological risk, the image of which is only available on a surface layer, the monitoring system 101 propagates this meteorological risk spatially beyond this surface layer of the image, over a predefined depth. According to a more specific example, for a satellite image which gives a view “from above” of risk zones, a spatial propagation rule makes it possible to extrapolate these meteorological risks onto lower layers of the atmosphere (for example, only the top of a cumulonimbus is seen via a satellite image, whereas this cloud can be 10-15 km high).

[0070] In yet another example, if a temperature probe emits meteorological data measurements representative of a risk of icing at a time t, and this temperature probe stops emitting, it is possible, thanks to a time propagation rule, to still consider and use the last measured meteorological data to construct an extrapolation of meteorological data for a future period (for example, the time window of the next 5 minutes). It is also possible to consider and use the last measured meteorological data to construct an extrapolation of meteorological data for an area wider geographical area (for example, within a 5km radius around the location of the temperature probe).

[0071] Thus, by applying spatial and / or temporal propagation rules, it is possible to extend over a geographical area and / or respectively a time window a meteorological risk which would have been seen only partially and / or punctually by a measurement of meteorological data representative of the meteorological risk provided by an information source.

[0072] During this step 302 DATA_TR, the monitoring system 101 also selects, from among all the sources of information previously filtered in step 301 ENS_DATA and therefore from among all the meteorological data previously processed, one or more sources of information of interest and therefore meteorological data of interest.

[0073] More particularly, the monitoring system 101 selects the meteorological data of interest provided by one or more sources of information of interest present at a point of interest corresponding, for example, to a point in the predefined perimeter around the trajectory of the aircraft 100. Furthermore, the monitoring system 101 selects the meteorological data of interest representative of the absence or presence of a meteorological risk of interest at one or more instants (present or future) of interest corresponding, for example, to the instant of passage of the aircraft 100 in the predefined perimeter around its trajectory (i.e., meteorological risk relevant for the trajectory of the aircraft 100 currently or to be studied).

[0074] In other words, the monitoring system 101 selects meteorological data representative of a risk of interest (or of a non-risk of interest, if applicable) at a point in a 4D space, called a “point of calculation of a probability of risk”, or more simply “point of calculation”. This point of calculation corresponds to a geographical location of interest and a time of interest for which a probability of a meteorological risk of interest (or of a non-risk of interest) and / or of a level of intensity of this risk of interest at this time of interest and this location of interest will be determined subsequently in step 305 RSK_DET, by the monitoring system 101.

[0075] It is thus possible to keep only the meteorological data relevant to an aircraft trajectory 100 (i.e., trajectory determined or to be determined), that is to say “linked” and “neighboring” a location of interest and a time of interest (for example, the monitoring system 101 seeks to determine the probability of a risk of hail in the Mediterranean region at a time t + 1H. It is therefore useless to retrieve and consider reports dating from t - 3 days from the northern region of France). This geographical location of interest and this time of interest correspond, for example, to a particular point in the predefined perimeter (latitude, longitude, altitude) (different or similar to the predefined geographic area described in connection with step 301 ENS_DATA) around the trajectory of the aircraft 100 and at a time (flight time) of the aircraft 100 along this flight trajectory.

[0076] This “calculation point” is therefore a point in a corresponding 4D space:

[0077] - either at a point of a 4D mesh comprising a set of points for which the monitoring system 101 determines one or more probabilities that a risk of interest occurs, or not (i.e., probability of a non-risk of interest). This 4D mesh can have an adaptive resolution which can be: automatic depending on the locations of application of the different available meteorological data, automatic depending on the times of application of the different available meteorological data and / or manual based on a configuration by the user.In one example, this 4D mesh is representative of the predefined perimeter around the trajectory of the aircraft 100 and each calculation point is then a point (defined by a latitude, longitude altitude) belonging to the predefined perimeter around the trajectory of the aircraft 100 and for which the probability of a meteorological risk of interest (or of a non-risk of interest) in particular and / or of a particular intensity level of this risk of interest at this instant of interest and this location of interest is determined (step 305 RSK_DET) by the monitoring system 101; .

[0078] - either at a point characterized by a location and / or an instant on or around the trajectory of the aircraft 100, which are manually configured by a user.

[0079] During a step 303, denoted PRA, in one embodiment, the monitoring system 101 assigns a preliminary level of “relevance” to each meteorological data item selected during step 302 DATA_TR. In a variant, alternatively or additionally, the monitoring system 101 first assigns a level of relevance to information sources (for example: depending on the type of information source) and then assigns the same level of relevance to the meteorological data from these information sources.

[0080] “Relevance” means the relevance of the information source with respect to a meteorological phenomenon. Thus, when the monitoring system 101 assigns a preliminary level of relevance to a meteorological datum, this amounts to assigning a “weight” to this meteorological datum on the basis of the relevance of its information source.

[0081] This preliminary level of relevance preferably includes two parameters whose values ​​are between 0 and 1:

[0082] - a rate of “true positives” of the meteorological data, i.e. at what proportion, a posteriori, this meteorological data indicated the presence of a meteorological risk when it was actually occurring,

[0083] - a rate of “true negatives” of the meteorological data, i.e. at what proportion, a posteriori, this meteorological data indicated the absence of a meteorological risk when this was not actually occurring.

[0084] In other words, the preliminary level of relevance can be expressed as a pair of two values ​​between 0 and 1, one of the values ​​expressing the rate of true negatives, and the other value expressing the rate of true positives.

[0085] Thus, varying or modifying this preliminary level of relevance amounts to varying or modifying one or both of these rates.

[0086] The monitoring system 101 assigns a preliminary level of relevance to a meteorological datum based on one or the other, or a combination of the following rules:

[0087] a) the monitoring system 101 assigns a preliminary level of relevance (therefore preferentially, a rate of true positives and a rate of true negatives) by default to meteorological data or to information sources. In other words, the monitoring system 101 assigns a preliminary level of relevance specific to each source of information or to each meteorological data item. For this, the monitoring system has, in its possession, for example recorded in its storage unit 204, a list of information sources for which a preliminary level of relevance by default is assigned. This preliminary level of relevance by default may depend on parameters such as: the type of the information source, the location of the information source, the location with which the meteorological data item itself is associated, the time of applicability of the meteorological data item.For example, the monitoring system 101 assigns a default preliminary relevance level of 0.98 (more precisely 0.98 in true positive rate and 0.98 in true negative rate) to a particular weather station for hail risks.

[0088] b) the monitoring system 101 assigns a preliminary level of relevance (i.e., a rate of true positives and a rate of true negatives) to a source of information, or to meteorological data, on the basis of a level of relevance which has been previously transmitted to it by the source of information itself;

[0089] c) the monitoring system 101 assigns a preliminary level of relevance (therefore preferentially, a rate of true positives and a rate of true negatives) to a source of information, or to meteorological data, on the basis of a level of relevance configured and adjustable by a user.

[0090] Thus, the preliminary level of relevance is easily configurable (for example: by a user or via tables internal to the monitoring system 101 or internal to the information sources) to act on the contribution of an information source, or of the meteorological data provided by this information source, in obtaining a probability of a particular meteorological risk (or non-risk). This flexibility makes it possible to adapt the preliminary level of relevance to developments relevance of information sources. For example, if an information source loses consistency, it is possible to attenuate its contribution in obtaining a probability of a particular meteorological risk (or non-risk). The relevance of an information source can also, for example, differ from one season to another, from one year to another (e.g., due to aging), etc.

[0091] Then, during a step 304, noted RC, the monitoring system 101 determines a final level of relevance for each meteorological data item selected during step 302 DATA_TR, for a meteorological risk of interest, a geographical location of interest and a time of interest, as defined previously.

[0092] For this, during two sub-steps R_SPC and R_TM implemented by the monitoring system 101 one after the other, or concomitantly, the monitoring system 101 determines the final level of relevance for each meteorological data selected in step 302 DATA_TR, on the basis of one or more variation rule(s) (i.e., decrease or increase) of the preliminary level of relevance assigned to these meteorological data in step 303 PRA. These rules for variations of the preliminary level of relevance define a predetermined variation, spatial (sub-step R_SPC) and / or temporal (sub-step R_TM), of the preliminary level of relevance.

[0093] It should be noted that the final level of relevance of each meteorological data is less than or equal to the preliminary level of relevance of this meteorological data.

[0094] In one embodiment, this predetermined variation (e.g., degradation or improvement) of the preliminary level of relevance depends on one or the other, or a combination of the conditions below.

[0095] 1) According to the rules of variation of the preliminary level of relevance as a function of space, also called spatial variation rules, the predetermined variation of the preliminary level of relevance depends on: a. of an application location with which meteorological data are associated. Indeed, the location with which the meteorological data itself is associated (i.e., location of applicability of the meteorological data) may be different from the location of the calculation point (i.e., geographical location of interest), while being in a close perimeter. Thus, the variation of the preliminary level of relevance may depend on a spatial distance, whether a direct, vertical or horizontal distance, between the application position of the meteorological data (i.e. the initial position of the source of information for this meteorological data), and the different calculation points as described previously; b. the presence of physical phenomena and / or obstacles in a predefined area around the location of application of the meteorological data. logical, that is to say:

[0096] - either near the position of application of the meteorological data itself- same (for example: local increase of a preliminary level of relevance of the meteorological data when viewing a particularly dark storm cell with a camera);

[0097] - either further away and located between the position of the information source and the position of application of meteorological data, and which hinder the observation of meteorological phenomena.

[0098] [Fig.4A] schematically illustrates an example of application of a spatial variation rule of the preliminary level of relevance of a meteorological data item representative of a meteorological risk (for example: hail risk) provided by an information source (for example: ST_WETH weather station). As described previously, a preliminary level of relevance of 0.85 is assigned to a meteorological data item representative of the presence of a hail risk at its application position (i.e., true positive rate at the ST_WETH weather station). The hail risk is propagated in a cylindrical Y zone of 40km around the ST_WETH weather station. The preliminary level of relevance of this meteorological data item associated with the hail risk decreases with increasing distance from the ST_WETH weather station (i.e., initial application position of the meteorological data item).

[0099] [Fig.4B] schematically illustrates another example of application of a rule of spatial variation of the preliminary level of relevance of a meteorological data item representative of a meteorological risk (for example: risk of hail), and provided by an information source (for example: RAD weather radar). A preliminary level of relevance of 0.98 is assigned to a meteorological data item measured by the RAD weather radar and representative of a hail risk. This preliminary level of relevance of 0.98 corresponds to the preliminary level of relevance of the meteorological data item at its initial position of application (i.e., at the RAD weather radar). This hail risk is propagated in a zone Z around the RAD weather radar. The preliminary level of relevance of this meteorological data item representative of a hail risk decreases with increasing distance from the RAD radar.

[0100] [Fig.5] illustrates in diagram form the application of a rule of spatial variation of the preliminary level of relevance of meteorological data representative of a meteorological risk (for example: risk of hail), and provided by an information source (for example: meteorological radar RAD) in the presence of an obstacle O. The observed phenomenon or obstacle O disturbs and degrades the preliminary level of relevance of the meteorological data measured by the weather radar RAD meteorological radar “behind” this obstacle O. Thus, at the level of a zone P_A behind the obstacle O relative to the position of the RAD meteorological radar, the preliminary level of relevance of the meteorological data measured by the RAD meteorological radar is adapted to take into account radar masking generated by the observed phenomenon or obstacle O.

[0101] 2) According to the rules of variation of the preliminary level of relevance depending on the time, also called time variation rules, the predetermined variation of the preliminary level of relevance depends on:

[0102] a. of an instant of application of each of the meteorological data. Indeed, the instant of application of the meteorological data may be different from the instant of interest, while being in a close time interval. Thus, the variation (for example: degradation or improvement) of the preliminary level of relevance may depend on the duration between the instant of reception or effective application of the meteorological data provided by the information source and the different calculation points as described previously. For this, several methods are possible, including for example: - reuse of meteorological data with an adapted preliminary level of relevance: reuse of “old” meteorological data applicable to a particular location, by temporally degrading their preliminary level of relevance depending on the age of the meteorological data. The location of the meteorological data does not change during its reuse. It should be noted that it is possible to use consolidated and filtered meteorological data as described in connection with step 301 ENS_DATA, when these are available; - prediction of meteorological data with an adapted preliminary level of relevance: when in a meteorological report information on the speed of movement of the risk and / or a general trend (for example: risk increasing in size and moving at 40km / h in a South-West direction) is available, then the variation of the preliminary level of relevance of the meteorological data extracted from this meteorological report uses an extrapolation of this speed and / or trend information to obtain the risk at the present moment. At the same time, the preliminary level of relevance of the meteorological data provided by the information source from which the meteorological report comes is degraded over time. - interpolation with a suitable preliminary level of relevance: this type of “past” weather report received at time T_report includes meteorological data representative of the form and location of the risk at a future time (for example at time T_report+6h). At the time of the termination of the final relevance level (e.g. at time T_rapport+3h), an interpolation between the risk described at T_rapport and the risk described at T_rapport+6h is performed to describe the meteorological risk at T_rapport+3h. In parallel, the preliminary relevance level of the meteorological data measured by the information source from which the weather report comes is degraded over time.

[0103] b. of a model of evolution of meteorological data from an information source over time. Such a model of evolution over time is notably applied for meteorological data which may already include a future evolution over time (for example: “NowCasting” or immediate forecasting, which indicates the future movement of meteorological risk polygons for the next few hours).

[0104] In one example, [Fig.6] illustrates in graphic form the application of a temporal variation rule of a preliminary level of relevance of a meteorological data representative of a meteorological risk. In this example, the preliminary level of relevance of a meteorological data of an information source deteriorates during a predefined period T to go from a value A to a lower value B, as long as a new measurement of the meteorological data is not available. A new measurement of the meteorological data is for example available periodically according to a predefined period T, that is to say that a new measurement of the meteorological data is available at times t1, t2, t3, etc.

[0105] [Fig.7] schematically illustrates an example of application of a combination of a spatial variation rule and a temporal variation rule of a preliminary level of relevance of a meteorological data representative of a meteorological risk (for example: risk of hail). In particular, in this example the preliminary level of relevance of the meteorological data representative of a meteorological risk, and provided by the meteorological station ST_WETH, decreases over time and with distance from the meteorological station ST_WETH. In this example, a temporal variation rule and a spatial variation rule are applied to translate the spatial and temporal evolution of the relevance of the meteorological data.

[0106] Alternatively, or additionally, like the preliminary relevance level described above, this predetermined variation of the preliminary relevance level depends on either, or a combination of the following conditions:

[0107] - type of the information source,

[0108] - the location of the information source itself, independently of the loca lization of calculation points (i.e., geographical location of interest),

[0109] - location with which the meteorological data itself is associated, independent- during calculation points, and

[0110] - instant of applicability of the meteorological data, independently of the duration between the time of receipt or effective application of the meteorological data provided by the information source and the different times of interest.

[0111] Once one or more spatial and / or temporal variation rules of the preliminary level of relevance are applied for each meteorological data of each information source, during a sub-step SE_CONC, the monitoring system 101 determines from the preliminary level of relevance of each meteorological data and the spatial or temporal variation rules applicable to each meteorological data, the final level of relevance of each meteorological data of each information source at each calculation point.

[0112] [Fig.8A] schematically illustrates a first example of the result of a determination of the presence or absence of a meteorological risk (here: risk of hail) based on meteorological data, and of an application of a rule for the spatial propagation of the presence of the risk. The meteorological data are provided by an information source (for example: a RAD weather radar). In particular, [Fig.8A] presents: - in dark gray, the determination of the presence of a risk of hail (HAIL), noted HAIL_MES, in a particular area and at a particular time, based on meteorological data from the RAD radar, - in light gray, the result of the application of a spatial propagation rule (for example as described in step 302 DATA_TR) of the presence of a hail risk (noted HAIL_PRO) behind the HAIL_MES zone, - in black, the determination of the absence of a risk of hail, noted NO_HAIL, in a particular geographical area and at a particular time, based on meteorological data from the RAD radar, - an area marked NO_MES for which the RAD weather radar has not taken any measurements.

[0113] [Fig.8B] schematically illustrates a second example of the result of a determination of a final level of relevance for two meteorological data respectively representative of the presence or absence of a meteorological risk (for example presence or absence of hail). In particular, [Fig.8B] illustrates the final level of relevance in time and space for the hail risk of the associated information source RAD, more precisely here the rate of “true negatives”, after application of the different variation rules of step 304 RC.

[0114] [Fig.8C] schematically illustrates a third example of the result of a determination of a final level of relevance for two meteorological data respectively representative of the presence or absence of a meteorological risk (for example example presence or absence of hail). In particular, [Fig.8C] illustrates the final level of relevance in time and space for the hail risk of the associated information source RAD, more precisely the rate of “true positives”, after application of the different variation rules of step 304 RC.

[0115] Optionally, during this sub-step SE_CONC, the monitoring system 101 implements in a sub-step SE_SCR, an additional filtering which removes the meteorological data for which the final relevance level is lower than a predefined relevance threshold (denoted SP). It is thus possible to remove the meteorological data which are unreliable, or even irrelevant (i.e., which no longer provide any interesting information). Furthermore, it is possible to reduce the computational load necessary for the step 305 RSK_DET described below.

[0116] Thus, at the end of step 304 RC, the monitoring system 101 records in its storage unit 104, for each calculation point (for example: each point of a 4D mesh) and for each source of information selected in step 302 DATA_TR:

[0117] - an indication of the presence or absence of a meteorological risk of interest;

[0118] - a final level of relevance of each meteorological data, determined in step 304 RC and representative: of a final true positive rate and a final true negative rate for each meteorological data.

[0119] During step 305, noted RSK_DET, the monitoring system 101 determines a probability for the different meteorological risks (or non-risks) of interest, in the location of interest and the time of interest (i.e., for each calculation point).

[0120] For this, the monitoring system 101 determines the probability of occurrence of meteorological risks at this time of interest and this geographical location of interest from the final level of relevance of each meteorological data at this time of interest and geographical location of interest. For this, the monitoring system 101 implements a Bayesian estimation, or inference, for example.

[0121] This determination of the probability of risk (or non-risk) is made for each meteorological risk (or non-risk) of interest and, where appropriate, for each intensity level of the meteorological risk of interest. In examples, the monitoring system 101 determines the probability of “icing conditions”, of “hail” according to a “moderate” or “high” intensity level, or of turbulence according to a “high” intensity level.

[0122] Thus, at the end of this step 305 RSK_DET, a probability is therefore determined for each risk (or non-risk) of interest and for each calculation point (for example: each point of a 4D mesh). It should be noted that this probability determination by the monitoring system 101 is made for a particular calculation point independently of the rest of the calculation points.

[0123] In one embodiment, this determination of the probability of the risk (or non-risk) of interest is carried out for each point of an evolving mesh, spatially and temporally (for example: tight mesh near the aircraft 100 engaged on its flight path, and which spaces out as one moves away from the aircraft 100).

[0124] In another embodiment, this determination of probability of the risk (or non-risk) of interest is carried out on demand, for example upon detection of a user action, for a geographical location of interest and a time of interest chosen by the user (for example: for a particular point along the trajectory of the aircraft 100).

[0125] In another embodiment, this determination of the probability of the risk (or non-risk) of interest is carried out periodically to update a global mesh that can be consulted by a large number of users. For example, this global mesh covers the flight trajectory but also the entire area reachable by the aircraft 100, to cover a possible change of destination. In this embodiment, this global mesh (and the associated risk probabilities) can be consulted by an independent trajectory calculator in order to determine in advance an optimized trajectory towards one or more alternative destination airports.

[0126] At the end of this step 305 RSK_DET, the monitoring system 101 records in its storage unit 204 a plurality of probabilities for a risk (or non-risk) of interest for a set of calculation points (for example for all the points of the 4D mesh).

[0127] During a step 306, RSK_POST-TR, the monitoring system 101 post-processes the probabilities of the meteorological risk (or non-risk) of interest. For this, the monitoring system 101 implements a post-processing corresponding to a smoothing by neighborhood. This smoothing by neighborhood makes it possible to correct “extreme” values ​​of a 3D representation by relativizing them with respect to the data of the zones which are close to them. The monitoring system 101 takes the probabilities determined at the calculation points (for example: each point of a 4D mesh), for each level of intensity of each risk of interest, and comes to homogenize or apply a smoothing on all of the results.

[0128] In one embodiment, after post-processing, the monitoring system 101 further determines one or more subsets of calculation points (i.e., subsets of particular points of the set of points of the 4D mesh) corresponding to contours / regions / volumes grouping together all the calculation points whose probability of the meteorological risk (or non-meteorological risk) of interest is greater (respectively less) than a configurable probability configuration threshold (denoted SProb). The monitoring system 101 then constructs lines and / or polygons and / or volumes sharing the mesh of points with respect to this threshold. configurable SProb probability configuration.

[0129] At the end of step 306 RSK_POST-TR, the monitoring system 101 records in its storage unit 204 for each calculation point the final probability of presence or absence of a risk of interest. In addition, the lines and / or polygons and / or volumes created during post-processing are also recorded.

[0130] [Fig.9] illustrates an example of graphical representations of the probabilities of a meteorological risk before and after post-processing of these probabilities by the monitoring system 101. In particular, [Fig.9] represents on graph (A) the probabilities for a risk of interest according to a geographical location of interest. Graph (B) represents the resulting probability after the application of smoothing by neighborhood, as well as the creation of spatial delimitations based on probabilities greater than probability configuration thresholds corresponding to respectively 0.27 / 0.34 / 0.41 / 0.48 / ... / 0.9.

[0131] It should be noted that the smoothing by neighborhood is presented here in 2D (with axis 1 of the mesh and axis 2 of the mesh), but that it can very well be done by considering other axes (for example: also using an axis representative of time and / or by taking a 3rd geographical axis for example vertical).

[0132] It is thus easier to provide the user with a delimitation of geographical zones which reflect a probability of “Hail” meteorological risk greater than 80%, for example.

[0133] During a step 307, noted CONS_MNTR, the monitoring system 101 checks the consistency of the input data in steps 305 RSK_DET (i.e., indication of presence or absence of a risk of interest, final level of relevance of each meteorological data) with respect to the result obtained at the end of step 306 RSK_POST-TR. Where appropriate, if an inconsistency is detected by the monitoring system 101, then an indication of an inconsistency between the meteorological data is generated and transmitted to the user's attention via a message described below.

[0134] In particular, the monitoring system 101 automatically checks the consistency between:

[0135] - meteorological data from different information sources between them;

[0136] - meteorological data from different information sources with the pro probabilities of presence or absence of interest risks.

[0137] In other words, step 307 CONS_MNTR consists of determining for each calculation point that there is an inconsistency when the meteorological data(s) which indicated(s):

[0138] - “presence of the risk of interest” while the final probability of “presence” of this interest risk is lower than a first predefined SI threshold (for example: 0.5), and in payment, “absence of interest risk” while the final probability of “presence of interest risk” is greater than the first predefined SI threshold (for example: 0.5);

[0139] - “presence of the risk of interest” while one or more other meteorological data- meteorological data indicated “absence of the risk of interest” with a final level of relevance (i.e., final true negative rate) higher than a second predefined threshold S2 (for example: 0.75), and conversely, “absence of the risk of interest” while one or more other meteorological data indicated “presence of the risk of interest” with a final level of relevance (i.e., final true positive rate) higher than the second predefined threshold S2 (for example: 0.75).,

[0140] Optionally, these inconsistencies can be recorded by the monitoring system 101 during a step 3071, noted ST_CONS_MNTR, in the storage unit 204 of the monitoring system 101. This makes it possible to carry out analyses after the fact and to improve the determination of the preliminary levels of relevance of the information sources over the long term and to improve the rules of spatial and / or temporal variation of the preliminary level of relevance for a source of information or meteorological data.

[0141] During a step 308, denoted TRANS, the monitoring system 101 generates, then transmits a set of information corresponding to the result of the determination of the probabilities of a risk of interest for each calculation point of the set of calculation points (for example: on the set of points of the 4D mesh). In one embodiment, this information corresponds to the result of the determination of the probabilities of a risk of interest for each calculation point of at least one subset of calculation points (i.e., subset of the 4D mesh). This subset being determined as described previously in step 306, RSK_POST-TR, i.e. by configuring a probability configuration threshold SProb.

[0142] This information or set of information is, for example, transmitted in the form of a message. Thus, subsequently, a message corresponds to information representative of the probability of the meteorological risk of interest of each point of a set of calculation points or of at least a subset of calculation points.

[0143] This result can be expressed for example in the form of a graph as represented in [Fig. 10], in the form of point clouds or via the display on a human-machine interface of the lines and / or polygons and / or volumes determined in step 306 RSK_POST-TR.

[0144] [Fig. 10] illustrates in graphic form an example of the result of a determination of probabilities of a risk of interest on a geographical location of interest and for a time of interest, which can be displayed on a screen of a human-machine interface. The user can select a “volume” or a “section” of the location or risk of interest to display the probabilities of the risk (or non- risk) of interest in this area. In this example, the graph represents a hail risk with a high probability (HG) of this risk being present at this location and time.

[0145] Optionally, this message further includes the indication of inconsistencies in certain meteorological data, when an inconsistency is detected.

[0146] The monitoring system 101 transmits this message to a user system. Such a user system is for example a human-machine interface of the cockpit of the aircraft 100 and / or of a ground control system via air-ground communication, or a system external to the aircraft 100 of the EFB type.

[0147] In one embodiment, this message is displayed on a screen of the human-machine interface of the cockpit of the aircraft 100 and / or of a ground control system. For example, once determined, the probability levels can be displayed to the pilot as described in the Applicant's patent FR3013444A1.

[0148] In a particular embodiment, the monitoring system 101 transmits this message to a human-machine interface of a third-party application (for example EFB) and the probabilities of the meteorological risk of interest can be used by the crew via this third-party application.

[0149] In a particular embodiment, the probabilities of a particular meteorological risk can be combined with the status of the aircraft 100 to derive areas with a general danger level for the aircraft 100. For example: a probability of “icing condition” does not have the same danger depending on whether the aircraft 100 has operational “de-icing” capabilities or not. There is therefore additional processing necessary before the action decision when using this probability of meteorological risk.

[0150] In a particular embodiment, the probabilities of the meteorological risk of interest can also be used by the avionics systems of the aircraft 100.

[0151] In one embodiment, during a phase of preparing the flight plan of the aircraft 100, a flight trajectory of the aircraft 101 is determined as a function of the probabilities of the risk of interest on all of the calculation points (eg, for all of the points in the predefined perimeter around the trajectory of the aircraft 101). In particular:

[0152] - below a first predefined threshold of probability of meteorological risks (eg: 35%), the flight path may pass through the identified meteorological risk of interest,

[0153] - above this first predefined threshold of probability of meteorological risks, a trajectory avoiding the meteorological risk is determined.

[0154] Alternatively, or additionally, during a flight phase, an alternative flight trajectory of the aircraft 101 (i.e., trajectory different from that initially planned in the flight plan) is determined based on the probabilities of the risk of interest. In particular:

[0155] - below a second predefined threshold of probability of meteorological risks (different or identical to the first threshold above), the aircraft 100 can continue its course along the trajectory defined in its flight plan, and pass through the identified meteorological risk of interest,

[0156] - above this second predefined threshold of probability of meteorological risks, the alternative flight path, avoiding the weather risk, is determined.

[0157] It is thus possible to determine a flight trajectory (i.e., initial trajectory in a flight plan or alternative during the flight of the aircraft 100) as a function of the probabilities of meteorological risks that may be encountered by the aircraft 100 during its flight.

[0158] Then, maneuvers are applied to the aircraft 100 (manual or automatic flight) to follow the flight path thus determined with regard to the meteorological risks treated as detailed above.

Claims

1. Claims Method for monitoring meteorological risks in a predefined perimeter around a trajectory of an aircraft (100), the method being implemented by a monitoring system (101) in the form of electronic circuitry, the method comprising: - collecting (301) from a plurality of information sources a plurality of heterogeneous meteorological data, - aggregating (302) said heterogeneous meteorological data collected by placing them all in at least one common reference frame, then by selecting from among the heterogeneous meteorological data collected and placed in at least one common reference frame, a set of meteorological data representative of a risk of interest potentially present in the predefined perimeter around the trajectory of the aircraft (100), in a geographical location of interest and at a time of interest, corresponding to a point among a set of points in the predefined perimeter around the trajectory of the aircraft (100), - assign (303) a preliminary level of relevance to each meteorological data representative of the meteorological risk of interest, - determine (304) a final level of relevance for each of the meteorological data representative of the meteorological risk of interest by applying at least one rule for variation of the preliminary level of relevance which is representative of a predetermined variation in space and / or time of the preliminary level of relevance, and which depends on: - a distance between a location of application of each of the meteorological data representative of the meteorological risk of interest and the geographical location of interest, and / or - a duration between a time of application of each of the meteorological data representative of the meteorological risk of interest and the time of interest, - determining (305), for each point of said set of points in the predefined perimeter around the trajectory of the aircraft (100), from the final level of relevance of each of the meteorological data representative of the meteorological risk of interest, a probability of said meteorological risk of interest, - transmitting (308) to a user system, information corresponding to the probability of said meteorological risk of interest for each point of at least one subset of points of said set of points.

2. A method according to claim 1, wherein the predetermined variation in space and / or time of the preliminary relevance level further depends on: - the presence of physical phenomena and / or obstacles in a predefined area around the location of application of each of the meteorological data representative of the meteorological risk of interest, and / or - a model of the evolution of each of the meteorological data representative of the meteorological risk of interest over time, and / or - a type of information source and / or a location of the information sources and / or a location with which each of the meteorological data representative of the meteorological risk of interest is associated, independently of the geographical location of interest, and / or - a time of applicability of each of the meteorological data representative of the meteorological risk of interest independently of the time of interest.

3. Method according to one of claims 1 and 2, further comprising: determining an inconsistency (307) between the meteorological data representative of the meteorological risk of interest and the probability of the meteorological risk of interest when the meteorological data representative of the meteorological risk of interest indicate: - “presence of the risk of interest” while the probability of the meteorological risk of interest is lower than a first predefined SI threshold, and vice versa, - “non-presence of the risk of interest” while the probability of the meteorological risk of interest is higher than the first predefined threshold SI, - “presence of the risk of interest” while at least one meteorological data among the meteorological data representative of the meteorological risk of interest indicates “non-presence of the risk” with the final level of relevance higher than a second predefined threshold S2, and vice versa, - “non-presence of the risk of interest” while at least one meteorological data among the meteorological data representative of the meteorological risk of interest indicates “presence of the risk of interest” with the final level of relevance higher than the second threshold S2 predefined.

4. Method according to one of claims 1 to 3, in which aggregating the collected heterogeneous meteorological data further comprises: propagating each of the heterogeneous meteorological data according to at least one spatial propagation rule and / or one temporal propagation rule.

5. Method according to any one of claims 1 to 4, in which the set of points in the predefined perimeter around the trajectory of the aircraft (100) corresponds to a set of points of a mesh of the space, each of said points of said mesh being representative of a location of interest and of an instant of interest in the predefined perimeter around the trajectory of the aircraft (100).

6. The method of any one of claims 1 to 5, wherein collecting (301) from the plurality of information sources the plurality of heterogeneous meteorological data comprises: processing the heterogeneous meteorological data.

7. System for monitoring (101) meteorological risks in a predefined perimeter around a trajectory of an aircraft (100), the monitoring system (101) comprising electronic circuitry configured to implement: - collecting (301) from a plurality of information sources a plurality of heterogeneous meteorological data, - aggregating (302) said collected heterogeneous meteorological data by placing them all in at least one common reference frame, then by selecting from the heterogeneous meteorological data collected and placed in at least one common reference frame, a set of meteorological data representative of a risk of interest potentially present in the predefined perimeter around the trajectory of the aircraft (100), in a geographical location of interest and at a time of interest, corresponding to a point among a set of points in the predefined perimeter around the trajectory of the aircraft (100),- assigning (303) a preliminary level of relevance to each meteorological data representative of the meteorological risk of interest, - determining (304) a final level of relevance for each of the meteorological data representative of the meteorological risk of interest by applying at least one rule for variation of the preliminary level of relevance which is representative of a predetermined variation in space and / or time of the pre-level of relevance, preliminary, and which depends on: - a distance between a location of application of each of the meteorological data representative of the meteorological risk of interest and the geographical location of interest, and / or - a duration between an instant of application of each of the meteorological data representative of the meteorological risk of interest and the instant of interest, - determining (305), for each point of said set of points in the predefined perimeter around the trajectory of the aircraft (100), from the final level of relevance of each of the meteorological data representative of the meteorological risk of interest, a probability of said meteorological risk of interest, - transmitting (308) to a user system, information corresponding to the probability of said meteorological risk of interest for each point of at least one subset of points of said set of points.

8. An aircraft (100) comprising a surveillance system (101) according to claim 7.

9. Computer program product comprising instructions causing the execution, by a processor, of the method according to any one of claims 1 to 6, when said instructions are executed by the processor.

10. Storage medium, storing a computer program comprising instructions causing a processor to execute the method according to any one of claims 1 to 6, when said instructions are read and executed by the processor.

Images