Methods and systems for automating processes of receiving, prioritizing, and grouping weather data into a weather event, extent of weather event, and an associated duration for presentation on a displayed mission timeline in an aircraft having a flight plan (FP). The method includes: receiving, by a controller circuit, weather data, aircraft state data, and aircraft system status data; identifying a weather phenomenon that impacts the FP and creating an information structure for the weather phenomenon, the information structure including at least a type, a subtype, a severity, a start of impact and an end of impact. The method also includes presenting a weather event indicator overlaid on the mission timeline to indicate the weather phenomenon. The rendering of the weather event indicator on the mission timeline additionally depicts on the mission timeline: a start, an end, and duration of the weather event.

A method for determining convective cell growth from weather radar reflectivity data includes receiving first weather reflectivity values and receiving second weather reflectivity values at a point in time subsequent to receiving the first weather reflectivity values, storing the first and second weather reflectivity values in cells of a three-dimensional buffer, for each of the first and second weather reflectivity values, calculating a vertically-integrated reflectivity (VIR) value for a column of cells in the three-dimensional buffer, the column of cells being associated with a latitude/longitude position, and comparing the VIR value for the second weather reflectivity values against the VIR for the first weather reflectivity values to determine a difference in the VIR values. Furthermore, the method includes displaying a cell growth hazard indication at a weather display in an area of the weather display that corresponds to the latitude/longitude position.

Computer-implemented methods for controlling the operation of aircraft, particularly electric or hybrid electric aircraft, are described. One such method, which may be implemented on a Flight Management System of the aircraft, comprises: receiving weather data indicative of weather conditions between a flight origin and a flight destination of the aircraft; and determining, using a constrained optimization method and a weather data dependent aircraft energy usage model, a three-dimensional flight path for the aircraft from the origin to the destination. The constrained optimization method may determine a flight path constrained by, amongst other things the energy required by an Environmental Control System of the aircraft.

Examples provide a method and system for quantitatively measuring air turbulence. A turbulence measuring application on each user device in a plurality of user devices associated with a plurality of users at a plurality of different locations within an aircraft generates vibration data and positional data associated with turbulence detected at each user's location. The data is analyzed to eliminate noise due to user movements and other non-turbulence related events. An aircraft server aggregates the vibration data and the positional data generated by the user devices scattered throughout the aircraft. The aggregated data is analyzed to eliminate noise due to non-turbulence related events to create quantitative air turbulence data. The quantitative air turbulence data generated by one or more aircraft is used with weather and other related data to generate more accurate and precise turbulence predictions for aircraft.

The system as described collects and utilizes weather data sensor information in order to rapidly collect and update weather forecasts using real-time weather data collected at high rates of frequency, and use this collected high frequency weather data to rapidly correct and update the weather forecasts generated by the system.

The system as described collects and utilizes weather data sensor information in order to rapidly collect and update weather forecasts using real-time weather data collected at high rates of frequency, and use this collected high frequency weather data to rapidly correct and update the weather forecasts generated by the system.

An example real-time ad hoc network sensor system includes a plurality of sensors positioned at fixed locations on an aircraft, a plurality of mobile devices in an interior of the aircraft, and a computing device having one or more processors and a non-transitory computer readable medium having stored thereon instructions, that when executed by the one or more processors, cause the computing device to perform functions including receiving outputs from the plurality of sensors and from the sensors of the plurality of mobile devices during the flight of the aircraft, mapping the outputs to a computer model of the aircraft for association with locations in the interior of the aircraft, and based on the mapping, creating a vehicle data-signature-map of the interior of the aircraft for at least one parameter of the aircraft.

Embodiments provide functionality to remotely observe atmospheric conditions. An embodiment, in response to receiving an indication of existence of an airborne aircraft, automatically selects an antenna from a plurality of fixed antennas based on a location and an orientation of each antenna of the plurality of fixed antennas. In turn, a request for data is sent to the aircraft using the selected antenna and, in response to the request, data is received from the aircraft. The data received from the aircraft is automatically processed in a manner determining an atmospheric condition.

A device, system and method is provided for obtaining and processing turbulence data via communication devices located on-board airplanes. Turbulence data obtained by a plurality of communication devices may be received during flights on-board respective ones of a plurality of airplanes. Turbulence map data may be generated by super-positioning the turbulence data received from the plurality of communication devices onto a single tempo-spatial frame of reference. The turbulence map data may be distributed to one or more of the communication devices. A device, system and method is also provided for generating turbulence map data that may reduce or eliminate “false positive” turbulence events. A device, system and method is also provided for communicating with on-board communication devices operating in a “flight crew mode” or a “passenger mode.”

A method for employing a unified semi-supervised deep learning (DL) framework for turbulence forecasting is presented. The method includes extracting historical and forecasted weather features of a spatial region, calculating turbulence indexes to fill feature cubes, each feature cube representing a grid-based 3D region, and building an encoder-decoder framework based on convolutional long short-term memory (ConvLSTM) to model spatio-temporal correlations or patterns causing turbulence. The method further includes employing a dual label guessing component to dynamically integrate complementary signals from a turbulence forecasting network and a turbulence detection network to generate pseudo-labels, reweighing the generated pseudo-labels by a heuristic label quality detector based on KL-Divergence, applying a hybrid loss function to predict turbulence conditions, and generating a turbulence dataset including the predicted turbulence conditions.