Embodiments of the present disclosure disclose a method and apparatus for processing a weather alert text, an electronic device, a computer readable storage medium, and a computer program product, relate to the field of artificial intelligence technology such as natural language processing, cloud services, and computer vision. An implementation of the method includes: acquiring a to-be-processed weather alert text; extracting actual weather alert elements from the to-be-processed weather alert text using preset element matching templates, the element matching templates being obtained by clustering from contexts of a sample weather alert elements; and performing normalization processing on the actual weather alert elements, and combining obtained normalized alert elements in a preset order to obtain a key weather alert text. The application of this implementation may improve an accuracy of extracting weather alert elements and a generalization processing capability for various to-be-processed weather alert texts.

Disclosed in present invention are a method for supporting aviation safety and a server for same, the method comprising the steps of: (a) the server for supporting aviation safety obtaining observation data measured from each of a plurality of weather observation apparatuses located in a specific zone; and (b) the server for supporting aviation safety predicting the weather state in the specific zone at a certain time after the current time by analyzing a generated map by referencing a machine learning database.

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 computer-implemented method for forecasting weather uses a trained machine learning model to determine the error in a weather forecast, e.g., for a selected ocean region. The machine learning model is configured to determine the predicted forecasting error given the weather forecast and a set of existing conditions. The weather forecast is adjusted using the predicted forecasting error to produce an augmented weather forecast. Training the machine learning model may include the utilization of hindcasting methods. Determination of existing conditions and other modeling may include the use of data from an array of metocean sensor nodes dispersed on a body of water.

A distributed weather system includes a storage, a plurality of wireless weather stations, a server, and an interface. Each of the plurality of wireless weather stations is associated with a user and has a battery, a location sensor generating location information, an anemometer generating apparent wind speed, a transmitter transmitting the location information with the apparent wind speed to a network at periodic intervals, and a receiver receiving control commands that include a length of the periodic intervals. The server receives the location information with the apparent wind speed and stores them in the storage. The interface is accessible by a mobile computer, and receives the control commands from a user and sends them to the receiver of the wireless weather station associated with the user. The interface displays a true wind speed for each of the plurality of wireless weather stations, which is calculated using the apparent wind speed, the location information, and historical location information.

A system for sensing and responding to detected activity or an event in a region is provided. The system may comprise: a modular edge computing platform configured to provide a predetermined functionality for a particular application, the modular edge computing platform is configured to process sensor data to generate processed data, and transmit the processed data; and a remote entity that comprises (i) a cloud analytic configured to receive the processed data from the modular edge computing platform and analyze the processed data, and (ii) a cloud user interface module configured to provide a graphical user interface on a user device, the graphical user interface displays one or more results generated by the cloud analytic upon analyzing the processed data.

Systems, apparatuses, and methods for acquiring information regarding weather conditions along a flightpath from other aircraft and incorporating that information into a flight path optimization and planning system. The system and methods may acquire substantially real-time information from pilots or other crew who have encountered a weather system or event (e.g., a wind direction or speed measurement, an observation of a storm or lightning, an observation of a difficulty in controlling an aircraft, unexpected excessive turbulence, etc.) and share that information with other airborne pilots or crew, either directly or using a ground-based server. The server may receive and process the acquired information and determine which aircraft may be likely to encounter or be impacted by a weather system or event for which it has received additional weather-related data and information.

A weather parameter prediction model training method, a weather parameter prediction method, an electronic device and a storage medium are provided, and relate to the technical field of artificial intelligence, such as deep learning and big data. The method includes: establishing a weather parameter prediction model according to spatial correlation information among a plurality of monitoring stations; and adjusting the weather parameter prediction model according to observation values of a weather parameter for the plurality of monitoring stations and prediction values of the weather parameter for the plurality of monitoring stations output by the weather parameter prediction model. The present disclosure can improve an accuracy of predicting weather parameters.

A system (100) for using mobile data to improve weather information is provided. The system (100) includes a weather prediction station (120) configured to receive stationary observation data provided by a plurality of stationary weather stations (110) along with data from a plurality of input weather models (115) and generate unified weather model estimates based on the stationary observation data, the input weather model data, and a processor (130). The processor (130) is configured to aggregate mobile observation data provided by a plurality of non-stationary sensors (140) and use the aggregated mobile observation data to adjust the weather model estimates.

The technology relates to determining general weather conditions affecting the roadway around a vehicle, and how such conditions may impact driving and route planning for the vehicle when operating in an autonomous mode. For instance, the on-board sensor system may detect whether the road is generally icy as opposed to a small ice patch on a specific portion of the road surface. The system may also evaluate specific driving actions taken by the vehicle and/or other nearby vehicles. Based on such information, the vehicle's control system is able to use the resultant information to select an appropriate braking level or braking strategy. As a result, the system can detect and respond to different levels of adverse weather conditions. The on-board computer system may share road condition information with nearby vehicles and with remote assistance, so that it may be employed with broader fleet planning operations.