Abstract: Apparatuses, methods, and systems for generating gridded predictions of a probability of wildfire spread for geographies are disclosed. One method includes sensing observational climate and earth surface data, obtaining historical data on wildfire spread events, obtaining gridded climate data, creating a set of input features, creating a gridded wildfire data set, training a model that learns one or more probabilistic mapping function emulators between the set of input features and the gridded wildfire data set, which predicts a first probability of wildfire occurrence and a rate and extent of wildfire spread within a geographical region and at a specified period of time, and generating gridded wildfire prediction data including a second probability of wildfire occurrence and spread within the geographical region, using the model and a new set of input features over the geographical region but for a different time period.

Abstract: Apparatuses, methods, and systems for generating simulations of physical variables of a physical system are disclosed. A method includes fusing, by the one or more computing devices, the observation data and the numeric simulation data, including training a spatial-temporal emulator model for a physical numerical model using the observation data, the numeric simulation data, and the domain interpretable data.

Abstract: Apparatuses, methods, and systems for increasing a spatial resolution of gridded spatial-temporal data on weather and climate-related physical variables are disclosed.

Abstract: Apparatuses, methods, and systems for generating simulations of physical variables of a physical system are disclosed. A method includes fusing observation data and numeric simulation data. The fusing includes preprocessing the observational data and the numeric simulation data to remove inconsistencies of the observational data and the numeric simulation data, processing the preprocessed observational data and the numeric simulation data to extract interpretable structures and patterns within that data using ground truth and labeled information to create domain interpretable data, normalizing the preprocessed observation data, the numeric simulation data, and the domain interpretable data layers.

Abstract: Apparatuses, methods, and systems for generating simulations of physical variables of a physical system are disclosed. A method includes fusing observation data and numeric simulation data. The fusing includes preprocessing the observational data and the numeric simulation data to remove inconsistencies of the observational data and the numeric simulation data, processing the preprocessed observational data and the numeric simulation data to extract interpretable structures and patterns within that data using ground truth and labeled information to create domain interpretable data, normalizing the preprocessed observation data, the numeric simulation data, and the domain interpretable data layers.

Abstract: Apparatuses, methods, and systems for generating simulations of physical variables of a physical system are disclosed. A method includes fusing observation data and numeric simulation data. The fusing includes preprocessing the observational data and the numeric simulation data to remove inconsistencies of the observational data and the numeric simulation data, processing the preprocessed observational data and the numeric simulation data to extract interpretable structures and patterns within that data using ground truth and labeled information to create domain interpretable data, normalizing the preprocessed observation data, the numeric simulation data, and the domain interpretable data layers.

Abstract: Apparatuses, methods, and systems for generating gridded predictions of a probability of wildfire spread for geographies are disclosed. One method includes sensing observational climate and earth surface data, obtaining historical data on wildfire spread events, obtaining gridded climate data, creating a set of input features, creating a gridded wildfire data set, training a model that learns one or more probabilistic mapping function emulators between the set of input features and the gridded wildfire data set, which predicts a first probability of wildfire occurrence and a rate and extent of wildfire spread within a geographical region and at a specified period of time, and generating gridded wildfire prediction data including a second probability of wildfire occurrence and spread within the geographical region, using the model and a new set of input features over the geographical region but for a different time period.

Abstract: Apparatuses, methods, and systems for increasing a spatial resolution of gridded spatial-temporal data on weather and climate-related physical variables are disclosed.

Abstract: Apparatuses, methods, and systems for generating simulations of physical variables of a physical system are disclosed. A method includes fusing observation data and numeric simulation data. The fusing includes preprocessing the observational data and the numeric simulation data to remove inconsistencies of the observational data and the numeric simulation data, processing the preprocessed observational data and the numeric simulation data to extract interpretable structures and patterns within that data using ground truth and labeled information to create domain interpretable data, normalizing the preprocessed observation data, the numeric simulation data, and the domain interpretable data layers.