Abstract: 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.

Abstract: A weather forecasting system has a data processing system that receives weather data from one or more sources and processes such data in conjunction with a weather forecasting algorithm in order to forecast weather for one or more geographic regions. In this regard, the weather data is input into a machine learning algorithm, which applies learned weights and relationships to the inputs in order to calculate at least one score indicating a probability that precipitation or other weather event will occur in the future within a certain time period (e.g., within the next 1 hour or some other unit of time) in one or more geographic regions. For each such geographic region, the weather forecasting logic may also predict the extent to which rain or other precipitation, lightning, or other weather event will occur during the time period.

Abstract: A weather forecasting system may receive satellite image samples and identify an updraft and components of the updraft within a cloud. These satellite image samples are collected over time (e.g., at 30 second to 1 minute time intervals). The system may identify an area of rotation and/or divergence at cloud top in a cumulus cloud or mature convective storm over time by comparing the samples and determine a parameter indicative of the updraft based on the area of rotation and divergence. The system may estimate aspects of the environment related to storm development and predict the occurrence of a weather event in the future based on the parameter and generate an output indicative of the occurrence.

Abstract: A weather forecasting system has a data processing system that receives weather data from one or more sources and processes such data in conjunction with a weather forecasting algorithm in order to forecast weather for one or more geographic regions. In this regard, the weather data is input into a machine learning algorithm, which applies learned weights and relationships to the inputs in order to calculate at least one score indicating a probability that precipitation or other weather event will occur in the future within a certain time period (e.g., within the next 1 hour or some other unit of time) in one or more geographic regions. For each such geographic region, the weather forecasting logic may also predict the extent to which rain or other precipitation, lightning, or other weather event will occur during the time period.

Abstract: A weather forecasting system has memory for storing satellite image data and numerical weather prediction (NWP) model data, which indicates predicted atmospheric conditions for a geographic region. At least one processor is programmed to identify a cumulus cloud within the satellite image data and to define a zone of influence around the cumulus cloud. The zone of influence represents a boundary for the NWP model data to be used by the processor for predicting whether the cumulus cloud will produce a weather event (e.g., precipitation, convective storm, etc.) in the future.

Abstract: A weather forecasting system has a data processing system that receives weather data from one or more sources and processes such data in conjunction with a weather forecasting algorithm in order to forecast weather for one or more geographic regions. In this regard, the weather data is input into a machine learning algorithm, which applies learned weights and relationships to the inputs in order to calculate at least one score indicating a probability that precipitation or other weather event will occur in the future within a certain time period (e.g., within the next 1 hour or some other unit of time) in one or more geographic regions. For each such geographic region, the weather forecasting logic may also predict the extent to which rain or other precipitation, lightning, or other weather event will occur during the time period.

Abstract: A weather forecasting system has a data processing system that receives weather data from one or more sources and processes such data in conjunction with a weather forecasting algorithm in order to forecast weather for one or more geographic regions. In this regard, the weather data is input into a machine learning algorithm, which applies learned weights and relationships to the inputs in order to calculate at least one score indicating a probability that precipitation or other weather event will occur in the future within a certain time period (e.g., within the next 1 hour or some other unit of time) in one or more geographic regions. For each such geographic region, the weather forecasting logic may also predict the extent to which rain or other precipitation, lightning, or other weather event will occur during the time period.

Abstract: A weather forecasting system may receive satellite image samples and identify an updraft and components of the updraft within a cloud. These satellite image samples are collected over time (e.g., at 30 second to 1 minute time intervals). The system may identify an area of rotation and/or divergence at cloud top in a cumulus cloud or mature convective storm over time by comparing the samples and determine a parameter indicative of the updraft based on the area of rotation and divergence. The system may estimate aspects of the environment related to storm development and predict the occurrence of a weather event in the future based on the parameter and generate an output indicative of the occurrence.

Abstract: A weather forecasting system has memory for storing satellite image data and numerical weather prediction (NWP) model data, which indicates predicted atmospheric conditions for a geographic region. At least one processor is programmed to identify a cumulus cloud within the satellite image data and to define a zone of influence around the cumulus cloud. The zone of influence represents a boundary for the NWP model data to be used by the processor for predicting whether the cumulus cloud will produce a weather event (e.g., precipitation, convective storm, etc.) in the future.

Abstract: 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.

Abstract: A weather forecasting system has memory for storing satellite image data and numerical weather prediction (NWP) model data, which indicates predicted atmospheric conditions for a geographic region. At least one processor is programmed to identify a cumulus cloud within the satellite image data and to define a zone of influence around the cumulus cloud. The zone of influence represents a boundary for the NWP model data to be used by the processor for predicting whether the cumulus cloud will produce a weather event (e.g., precipitation, convective storm, etc.) in the future.

Abstract: A weather forecasting system may receive satellite image samples and identify an updraft and components of the updraft within a cloud. These satellite image samples are collected over time (e.g., at 30 second to 1 minute time intervals). The system may identify an area of rotation and/or divergence at cloud top in a cumulus cloud or mature convective storm over time by comparing the samples and determine a parameter indicative of the updraft based on the area of rotation and divergence. The system may estimate aspects of the environment related to storm development and predict the occurrence of a weather event in the future based on the parameter and generate an output indicative of the occurrence.

Abstract: A weather forecasting system has a data processing system that receives weather data from one or more sources and processes such data in conjunction with a weather forecasting algorithm in order to forecast weather for one or more geographic regions. In this regard, the weather data is input into a machine learning algorithm, which applies learned weights and relationships to the inputs in order to calculate at least one score indicating a probability that precipitation or other weather event will occur in the future within a certain time period (e.g., within the next 1 hour or some other unit of time) in one or more geographic regions. For each such geographic region, the weather forecasting logic may also predict the extent to which rain or other precipitation, lightning, or other weather event will occur during the time period.