According to an embodiment, an information processing device includes an acquirer configured to acquire at least one type of data within meteorological observation data indicating weather at a target location observed at each of a plurality of observation times and meteorological prediction data indicating the weather at the target location predicted using a meteorological prediction model, a deriver configured to derive an index value indicating a threat level of lightning of the target location at each of the plurality of observation times on the basis of the meteorological observation data and the like acquired by the acquirer, and a predictor configured to input the index value derived by the deriver to a model for outputting a future index value when a past or present index value is input and predict a threat of lightning of the target location at a future time later than the observation time on the basis of an output result of the model to which the index value has been input.

An aircraft-based synthetic vision system (SVS) is disclosed. In embodiments, the SVS includes avionics processors in communication with onboard lightning detection sensors, which provide the SVS with real-time lightning data (e.g., bearing to, and distance from, the aircraft) about proximate lightning strikes. Based on the real-time lightning data, the avionics processors generate flight deck effects (FDE) corresponding to identified areas of lightning activity (e.g., a sufficient quantity of strikes, exceeding a strike threshold, within a particular airspace during a time window), each FDE having a particular bearing to and distance from the aircraft. The FDE data is processed by a display system aboard the aircraft (e.g., a cockpit-based primary flight display (PFD) or head-worn/heads-up display (HWD/HUD)) which incorporates the generated FDEs into the SVS status display provided to the flight crew or pilot at the appropriate bearing and distance relative to the aircraft.

An aircraft-based synthetic vision system (SVS) is disclosed. In embodiments, the SVS includes avionics processors in communication with onboard lightning detection sensors, which provide the SVS with real-time lightning data (e.g., bearing to, and distance from, the aircraft) about proximate lightning strikes. Based on the real-time lightning data, the avionics processors generate flight deck effects (FDE) corresponding to identified areas of lightning activity (e.g., a sufficient quantity of strikes, exceeding a strike threshold, within a particular airspace during a time window), each FDE having a particular bearing to and distance from the aircraft. The FDE data is processed by a display system aboard the aircraft (e.g., a cockpit-based primary flight display (PFD) or head-worn/heads-up display (HWD/HUD)) which incorporates the generated FDEs into the SVS status display provided to the flight crew or pilot at the appropriate bearing and distance relative to the aircraft.

A system and method for detecting in real-time high risk lightning (HRL) strikes and sending out alerts to responsible personnel to allow for earlier responses to lightning caused fire ignitions to help maintain and/or reduce the chance of spread by the wildfire. The system and method allow for HRL events and fire ignitions to be detected preferably within seconds. The system and method can use a network of detectors, data from environmental satellites and/or other environmental data sources, and novel AI/algorithms for signal processing to relatively quickly locate fire ignition spots. Thus, the system and method provide for actionable wildfire intelligence in real-time and to relatively quickly and accurately send out alerts when an HRL event has been determined. Cameras and drones can be used to provide real-time visualization at the location of the HRL event to verify or monitor any fire ignition or smoldering at the area of the HRL event.

A system and method for detecting in real-time high risk lightning (HRL) strikes and sending out alerts to responsible personnel to allow for earlier responses to lightning caused fire ignitions to help maintain and/or reduce the chance of spread by the wildfire. The system and method allow for HRL events and fire ignitions to be detected preferably within seconds. The system and method can use a network of detectors, data from environmental satellites and/or other environmental data sources, and novel AI/algorithms for signal processing to relatively quickly locate fire ignition spots. Thus, the system and method provide for actionable wildfire intelligence in real-time and to relatively quickly and accurately send out alerts when an HRL event has been determined. Cameras and drones can be used to provide real-time visualization at the location of the HRL event to verify or monitor any fire ignition or smoldering at the area of the HRL event.

A method, apparatus, and system for managing sensor system for an aircraft. A presence of erroneous sensor data generated by a set of external sensors on an exterior of the aircraft is detected. A set of deployable sensors is deployed in response to the erroneous sensor data being received from the set of external sensors on the exterior of the aircraft when an undesired environmental condition adverse to the set of external sensors on the exterior of the aircraft is absent. Sensor data is received from the set of deployable sensors.

Train a machine learning model, using an image-based knowledge graph of tropical cyclone data, for implementing a surface field modeling architecture that produces images of at least surface wind fields and surface rainfall fields from images of at least tropical cyclone tracks and pressure intensities. Generate model images of a modeled surface wind field and a modeled surface rainfall field by providing images of at least a user-generated tropical cyclone track and pressure intensity to the trained machine learning model.

Train a machine learning model, using an image-based knowledge graph of tropical cyclone data, for implementing a surface field modeling architecture that produces images of at least surface wind fields and surface rainfall fields from images of at least tropical cyclone tracks and pressure intensities. Generate model images of a modeled surface wind field and a modeled surface rainfall field by providing images of at least a user-generated tropical cyclone track and pressure intensity to the trained machine learning model.

Described herein are methods and systems for locating lightning activity. A server computing device receives, from a satellite that detects lightning activity occurring in a geographic region, location coordinates and time data associated with lightning activity detected by the satellite. The server captures, from at least one of one or more ground-based lightning sensors that detect lightning activity, lightning feature data for lightning activity detected by the at least one of one or more ground-based lightning sensors. The server computing device determines a location of the lightning activity within the geographic region using the lightning feature data and the location coordinates and time data. The server computing device transmits the determined location of the lightning activity to one or more remote computing devices.

Described herein are methods and systems for locating lightning activity. A server computing device receives, from a satellite that detects lightning activity occurring in a geographic region, location coordinates and time data associated with lightning activity detected by the satellite. The server captures, from at least one of one or more ground-based lightning sensors that detect lightning activity, lightning feature data for lightning activity detected by the at least one of one or more ground-based lightning sensors. The server computing device determines a location of the lightning activity within the geographic region using the lightning feature data and the location coordinates and time data. The server computing device transmits the determined location of the lightning activity to one or more remote computing devices.