Abstract: Method, apparatus, and computer program product are provided for generating synthetic bathymetry using one or more fractal techniques. In some embodiments, real terrain data generated from sensor-based measurement of terrain associated with but outside a water body are received. This data may include, for example, real terrain data generated from sensor-based measurement (e.g., LiDAR) of at least a portion of an area proximate the water body. One or more fractal dimensions is/are extracted from the real terrain data, and synthetic bathymetry of the water body is generated based on the one or more fractal dimensions using one or more fractal terrain generation techniques. In some embodiments, one or more simulated geological processes is/are applied to the synthetic bathymetry. For example, a simulated sedimentation process and/or a simulated erosion process may be applied to soften the synthetic bathymetry.

Abstract: Method, apparatus, and computer program product are provided for generating synthetic bathymetry using one or more fractal techniques. In some embodiments, real terrain data generated from sensor-based measurement of terrain associated with but outside a water body are received. This data may include, for example, real terrain data generated from sensor-based measurement (e.g., LiDAR) of at least a portion of an area proximate the water body. One or more fractal dimensions is/are extracted from the real terrain data, and synthetic bathymetry of the water body is generated based on the one or more fractal dimensions using one or more fractal terrain generation techniques. In some embodiments, one or more simulated geological processes is/are applied to the synthetic bathymetry. For example, a simulated sedimentation process and/or a simulated erosion process may be applied to soften the synthetic bathymetry.

Abstract: A computer implemented method of predicting ice coverage on a body of water includes generating a first ice cover prediction with a thermodynamics module and generating a second ice cover prediction with a machine learning module. The first ice cover prediction is combined with the second ice cover prediction to generate a combined ice cover prediction. Error statistics are computed based on a comparison of the combined ice cover prediction with an ice coverage observation and the combined ice cover prediction is updated based on the error statistics.

Abstract: Method, apparatus, and computer program product are provided for assessing road surface condition. In some embodiments, candidate locations each forecast to have a dangerous road surface condition are determined, an optimized flight path is determined comprising a sequence of sites corresponding to the candidate locations, dispatch is made to a first site within the sequence, and a road surface condition at the first site is assessed using an onboard sensor (e.g., spectroradiometer). In some embodiments, a check for new information is performed before dispatch is made to a second site. In some embodiments, the candidate locations are determined using both a model forecast and data-mined locations considered hazardous. In some embodiments, the optimized flight path is determined using TSP optimization constrained by available flight time and prioritized by frequency of historical incident and severity of forecast road surface condition.

Abstract: A system and method to augment model-to-model coupling include obtaining an output signal fs from an upstream model. The method also includes obtaining an observation signal gob from a region of interest, extracting a high-frequency signal g from the observation signal gob using a linear operator, and providing to a downstream model the high-frequency signal g in addition to information based on the output signal fs from the upstream model. The downstream model is implemented to obtain a prediction or estimation of one or more parameters of interest or drive a mechanical process.

Abstract: Method, apparatus, and computer program product are provided for assessing road surface condition. In some embodiments, candidate locations each forecast to have a dangerous road surface condition are determined, an optimized flight path is determined comprising a sequence of sites corresponding to the candidate locations, dispatch is made to a first site within the sequence, and a road surface condition at the first site is assessed using an onboard sensor (e.g., spectroradiometer). In some embodiments, a check for new information is performed before dispatch is made to a second site. In some embodiments, the candidate locations are determined using both a model forecast and data-mined locations considered hazardous. In some embodiments, the optimized flight path is determined using TSP optimization constrained by available flight time and prioritized by frequency of historical incident and severity of forecast road surface condition.

Abstract: A system and method to augment model-to-model coupling include obtaining an output signal fs from an upstream model. The method also includes obtaining an observation signal gob from a region of interest, extracting a high-frequency signal g from the observation signal gob using a linear operator, and providing to a downstream model the high-frequency signal g in addition to information based on the output signal fs from the upstream model. The downstream model is implemented to obtain a prediction or estimation of one or more parameters of interest or drive a mechanical process.