Abstract: A system and method of forecasting space weather (at Earth or another location) based on identifying complex patterns in solar, interplanetary, or geophysical data. These data may include current or historical measurements and/or modeled data (predicted or simulated). Data patterns (both non-event and event-related) are identified (even when another event is occurring). Such patterns may vary with recent/cyclic variations in solar (e.g. solar max/min), interplanetary, or geophysical activity. Embodiments are built around: (1) templates, (2) expert systems, (3) neural networks, (4) hybrid systems comprising combinations of (1), (2) and/or (3), and multimodal intelligent systems. Forecasts are customized and/or updated as new data arise and as systems are dynamically modified (e.g. via feedback between system parts). Numerical or other indexes are generated representing: forecasts, associated confidence levels, etc.

Abstract: A system and method of forecasting space weather (at Earth or another location) based on identifying complex patterns in solar, interplanetary, or geophysical data. These data may include current or historical measurements and/or modeled data (predicted or simulated). Data patterns (both non-event and event-related) are identified (even when another event is occurring). Such patterns may vary with recent/cyclic variations in solar (e.g. solar max/min), interplanetary, or geophysical activity. Embodiments are built around: (1) templates, (2) expert systems, (3) neural networks, (4) hybrid systems comprising combinations of (1), (2) and/or (3), and multimodal intelligent systems. Forecasts are customized and/or updated as new data arise and as systems are dynamically modified (e.g. via feedback between system parts). Numerical or other indexes are generated representing: forecasts, associated confidence levels, etc.

Abstract: A system and method of forecasting space weather (at Earth or another location) based on identifying complex patterns in solar, interplanetary, or geophysical data. These data may include current or historical measurements and/or modeled data (predicted or simulated). Data patterns (both non-event and event-related) are identified (even when another event is occurring). Such patterns may vary with recent/cyclic variations in solar (e.g. solar max/min), interplanetary, or geophysical activity. Embodiments are built around: (1) templates, (2) expert systems, (3) neural networks, (4) hybrid systems comprising combinations of (1), (2) and/or (3), and multimodal intelligent systems. Forecasts are customized and/or updated as new data arise and as systems are dynamically modified (e.g. via feedback between system parts). Numerical or other indexes are generated representing: forecasts, associated confidence levels, etc.

Abstract: A system and method of forecasting space weather (at Earth or another location) based on identifying complex patterns in solar, interplanetary, or geophysical data. These data may include current or historical measurements and/or modeled data (predicted or simulated). Data patterns (both non-event and event-related) are identified (even when another event is occurring). Such patterns may vary with recent/cyclic variations in solar (e.g. solar max/min), interplanetary, or geophysical activity. Embodiments are built around: (1) templates, (2) expert systems, (3) neural networks, (4) hybrid systems comprising combinations of (1), (2) and/or (3), and multimodal intelligent systems. Forecasts are customized and/or updated as new data arise and as systems are dynamically modified (e.g. via feedback between system parts). Numerical or other indexes are generated representing: forecasts, associated confidence levels, etc.

Abstract: A system and method of forecasting space weather (at Earth or another location) based on identifying complex patterns in solar, interplanetary, or geophysical data. These data may include current or historical measurements and/or modeled data (predicted or simulated). Data patterns (both non-event and event-related) are identified (even when another event is occurring). Such patterns may vary with recent/cyclic variations in solar (e.g. solar max/min), interplanetary, or geophysical activity. Embodiments are built around: (1) templates, (2) expert systems, (3) neural networks, (4) hybrid systems comprising combinations of (1),(2) and/or (3), and multimodal intelligent systems. Forecasts are customized and/or updated as new data arise and as systems are dynamically modified (e.g. via feedback between system parts). Numerical or other indexes are generated representing: forecasts, associated confidence levels, etc.

Abstract: A system and method of forecasting space weather (at Earth or another location) based on identifying complex patterns in solar, interplanetary, or geophysical data. These data may include current or historical measurements and/or modeled data (predicted or simulated). Data patterns (both non-event and event-related) are identified (even when another event is occurring). Such patterns may vary with recent/cyclic variations in solar (e.g. solar max/min), interplanetary, or geophysical activity. Embodiments are built around: (1) templates, (2) expert systems, (3) neural networks, (4) hybrid systems comprising combinations of (1),(2) and/or (3), and multimodal intelligent systems. Forecasts are customized and/or updated as new data arise and as systems are dynamically modified (e.g. via feedback between system parts). Numerical or other indexes are generated representing: forecasts, associated confidence levels, etc.

Abstract: A system and method of forecasting space weather (at Earth or another location) based on identifying complex patterns in solar, interplanetary, or geophysical data. These data may include current or historical measurements and/or modeled data (predicted or simulated). Data patterns, both non-event and event-related, are identified even when another event is occurring. Such patterns may vary with recent/cyclic variations in solar (e.g. solar max/min), interplanetary, or geophysical activity. Embodiments are built around: (1) templates, (2) expert systems, (3) neural networks, (4) hybrid systems comprising combinations of (1), (2) and/or (3), and multimodal intelligent systems. Forecasts are customized and/or updated as new data arise and as systems are dynamically modified, e.g. via feedback between system parts. Numerical or other indexes are generated representing forecasts, associated confidence levels, etc.