A robotic work tool system (200) comprising further comprising a charging station (210) and a robotic work tool (100), the robotic work tool system (200) being configured to determine a change in weather and to take preservation action. The robotic work tool may detect the change in weather by detecting an electrical charge buildup in the boundary wire. The robotic work tool may take the preservative action by the robotic work tool (100) distancing itself from the charging station (210).

The lightning estimation apparatus includes a first acquisition unit, a second acquisition unit and a lightning analysis unit. The first acquisition unit acquires, from a radar apparatus, radar analysis data including a characteristic detection region obtained by analyzing a characteristic region of a meteorological phenomenon observed in a predetermined cycle. The second acquisition unit acquires lightning data including an observation result of a lightning strike point. The lightning analysis unit analyzes a spatial correlation between the characteristic detection region and the lightning strike point using integrated data in which the radar analysis data and the lightning data are associated in synchronization with each other.

The lightning estimation apparatus includes a first acquisition unit, a second acquisition unit and a lightning analysis unit. The first acquisition unit acquires, from a radar apparatus, radar analysis data including a characteristic detection region obtained by analyzing a characteristic region of a meteorological phenomenon observed in a predetermined cycle. The second acquisition unit acquires lightning data including an observation result of a lightning strike point. The lightning analysis unit analyzes a spatial correlation between the characteristic detection region and the lightning strike point using integrated data in which the radar analysis data and the lightning data are associated in synchronization with each other.

Systems and methods are disclosed to detect cloud-to-ground (CG) strokes that include and/or are followed by continuing current (CC). For example, earth-based lightning data may be generated for one or more lightning pulses detected in an environmental space using multiple earth-based lightning detection sensors. Space-based lightning data may be received for one or more optical signals detected in the environmental space using one or more space-based lightning detection sensors. It may be determined from the earth-based and space-based lightning data whether a lightning pulse is a CG stroke that includes or is followed by CC. Charge transfer of a CG stroke with CC may be obtained from measurements of electromagnetic fields by an array of electromagnetic field sensors to generate training data. A machine learning model may be trained to relate space-based and/or earth-based lightning data to charge transfer where there is no proximate array of electromagnetic field sensors.

Systems and methods are disclosed to detect cloud-to-ground (CG) strokes that include and/or are followed by continuing current (CC). For example, earth-based lightning data may be generated for one or more lightning pulses detected in an environmental space using multiple earth-based lightning detection sensors. Space-based lightning data may be received for one or more optical signals detected in the environmental space using one or more space-based lightning detection sensors. It may be determined from the earth-based and space-based lightning data whether a lightning pulse is a CG stroke that includes or is followed by CC. Charge transfer of a CG stroke with CC may be obtained from measurements of electromagnetic fields by an array of electromagnetic field sensors to generate training data. A machine learning model may be trained to relate space-based and/or earth-based lightning data to charge transfer where there is no proximate array of electromagnetic field sensors.

Systems and methods are disclosed to determine the severity of a thunderstorm and/or track the path of a thunderstorm. For instance, multiple thunderstorms may be tracked by assigning detected lightning flashes to thunderstorm objects based on a number of previous lightning flashes assigned to each of the thunderstorm objects and a distance between each of the lightning flashes and each of the thunderstorm objects. In addition, an updated position may be determined for each of the thunderstorm objects based on positions and ages of lightning flashes assigned to each of the thunderstorm objects. The severity of a given thunderstorm may be determined based on lightning rates, types, and/or polarities of lightning flashes and/or lightning pulses of the thunderstorm object.

Systems and methods are disclosed to determine the severity of a thunderstorm and/or track the path of a thunderstorm. For instance, multiple thunderstorms may be tracked by assigning detected lightning flashes to thunderstorm objects based on a number of previous lightning flashes assigned to each of the thunderstorm objects and a distance between each of the lightning flashes and each of the thunderstorm objects. In addition, an updated position may be determined for each of the thunderstorm objects based on positions and ages of lightning flashes assigned to each of the thunderstorm objects. The severity of a given thunderstorm may be determined based on lightning rates, types, and/or polarities of lightning flashes and/or lightning pulses of the thunderstorm object.

Example methods and systems described herein relate to determining average arrival time of a radio wave emitted by a lightning discharge and/or determining the lobe and/or polarity of the radio wave. The determination of the average arrival time may take a weighted average of arrival times of peaks of the radio wave. The determination of the lobe and/or polarity may depend on an estimated propagation distance, a propagation path profile, and one or more waveform features.

A weather forecasting system has weather forecasting logic that receives weather data from a satellite or other source, such as radar. The weather forecasting logic processes such data to identify cumulus clouds. For each cumulus cloud identified, the weather forecasting logic applies interest field tests and feeds the results into formulas derived based on measurements from current and past weather events. The model determines a score indicating the likelihood of the cumulus cloud forming precipitation and a score indicating the likelihood of the cumulus cloud forming lightning in the future within a certain time period. Based on such scores, the weather forecasting logic predicts in which geographic regions the identified cumulus cloud will produce precipitation and/or lightning during the time period. The predictions of the weather forecasting logic may then be used to provide a weather map thereby providing users with a graphical illustration of the areas.

A weather forecasting system has weather forecasting logic that receives weather data from a satellite or other source, such as radar. The weather forecasting logic processes such data to identify cumulus clouds. For each cumulus cloud identified, the weather forecasting logic applies interest field tests and feeds the results into formulas derived based on measurements from current and past weather events. The model determines a score indicating the likelihood of the cumulus cloud forming precipitation and a score indicating the likelihood of the cumulus cloud forming lightning in the future within a certain time period. Based on such scores, the weather forecasting logic predicts in which geographic regions the identified cumulus cloud will produce precipitation and/or lightning during the time period. The predictions of the weather forecasting logic may then be used to provide a weather map thereby providing users with a graphical illustration of the areas.