Example methods and systems for detecting weather conditions using vehicle onboard sensors are provided. An example method includes receiving laser data collected for an environment of a vehicle, and the laser data includes a plurality of laser data points. The method also includes associating, by a computing device, laser data points of the plurality of laser data points with one or more objects in the environment, and determining given laser data points of the plurality of laser data points that are unassociated with the one or more objects in the environment as being representative of an untracked object. The method also includes based on one or more untracked objects being determined, identifying by the computing device an indication of a weather condition of the environment.

Example methods and systems for detecting weather conditions using vehicle onboard sensors are provided. An example method includes receiving laser data collected for an environment of a vehicle, and the laser data includes a plurality of laser data points. The method also includes associating, by a computing device, laser data points of the plurality of laser data points with one or more objects in the environment, and determining given laser data points of the plurality of laser data points that are unassociated with the one or more objects in the environment as being representative of an untracked object. The method also includes based on one or more untracked objects being determined, identifying by the computing device an indication of a weather condition of the environment.

Apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score. More particularly, apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score based on hail data. The apparatuses, systems and methods may generate a probable roof loss confidence score. The apparatuses, systems and methods may generate verified probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance underwriting data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance claims data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance loss mitigation data based on probable roof loss confidence score data.

Apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score. More particularly, apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score based on hail data. The apparatuses, systems and methods may generate a probable roof loss confidence score. The apparatuses, systems and methods may generate verified probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance underwriting data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance claims data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance loss mitigation data based on probable roof loss confidence score data.

A method for a whole-process numerical simulation and hazard forecast of a mountain disaster is provided. The method includes: S1, a high space-time rainfall forecast of a mountain area; S2, a hydrodynamic process and numerical simulation: establishing a hydrodynamic process model and solving the hydrodynamic process model; S3, a motion model and numerical simulation of a mountain torrent and debris flow disaster; and S4, a risk analysis and hazard forecast of a small watershed disaster. The present invention predicts disaster hazard and dynamically and quantitatively evaluates risk loss according to a whole-process scenario simulation of the disaster driven by a climate forecast result, improves current disaster level forecasts to hazard forecasts, and serves for accurate disaster preventions and accurate rescues.

A method for a whole-process numerical simulation and hazard forecast of a mountain disaster is provided. The method includes: S1, a high space-time rainfall forecast of a mountain area; S2, a hydrodynamic process and numerical simulation: establishing a hydrodynamic process model and solving the hydrodynamic process model; S3, a motion model and numerical simulation of a mountain torrent and debris flow disaster; and S4, a risk analysis and hazard forecast of a small watershed disaster. The present invention predicts disaster hazard and dynamically and quantitatively evaluates risk loss according to a whole-process scenario simulation of the disaster driven by a climate forecast result, improves current disaster level forecasts to hazard forecasts, and serves for accurate disaster preventions and accurate rescues.

A local productivity prediction and management system including a weather monitoring device and a productivity prediction device. The weather monitoring device 10 including at least one of the following sensors adapted to take weather measurements of local weather conditions. The sensors include a temperature sensor 12, a humidity sensor 13, a rainfall sensor 14 and a sunlight and/or ultraviolet light sensor 15. Wherein, the productivity prediction device is adapted to over time collect local actual livestock production values. The productivity prediction device is also adapted to apply a productivity prediction model which uses one or more correlating patterns between weather measurements and actual livestock production values, whether either are local and/or offsite to provide a set of one or more predicted livestock production values. The productivity prediction device is also adapted to manage a logistical function of livestock product collection and transport with regard to capacity and timing in response to the predicted livestock production value.

A local productivity prediction and management system including a weather monitoring device and a productivity prediction device. The weather monitoring device 10 including at least one of the following sensors adapted to take weather measurements of local weather conditions. The sensors include a temperature sensor 12, a humidity sensor 13, a rainfall sensor 14 and a sunlight and/or ultraviolet light sensor 15. Wherein, the productivity prediction device is adapted to over time collect local actual livestock production values. The productivity prediction device is also adapted to apply a productivity prediction model which uses one or more correlating patterns between weather measurements and actual livestock production values, whether either are local and/or offsite to provide a set of one or more predicted livestock production values. The productivity prediction device is also adapted to manage a logistical function of livestock product collection and transport with regard to capacity and timing in response to the predicted livestock production value.

A hierarchical early-warning system for landslide probability issues a first level warning based on measured rainfall amounts exceeding a determined threshold, a second level warning, after the first level warning, based additionally on measured soil moisture content measured at different levels, and Factor of safety derived from forecasted pore pressure (FPP) each exceeding a determined threshold, a third level warning, after the first and the second level warnings, based additionally on ground movement measurements compared to a determined threshold, and a fourth level warning after the first, second and third level warnings, based additionally on data from movement-based sensors including strain gauge data.

A hierarchical early-warning system for landslide probability issues a first level warning based on measured rainfall amounts exceeding a determined threshold, a second level warning, after the first level warning, based additionally on measured soil moisture content measured at different levels, and Factor of safety derived from forecasted pore pressure (FPP) each exceeding a determined threshold, a third level warning, after the first and the second level warnings, based additionally on ground movement measurements compared to a determined threshold, and a fourth level warning after the first, second and third level warnings, based additionally on data from movement-based sensors including strain gauge data.