The present invention provides for a cognitive system using an autonomous vehicle includes a plurality of sensors configured to obtain the weather forecast for a pollution detectable area; a cognitive input to determine the pollution detectable area having highest sensitivity of pollution; a light detecting and ranging system configured to spatially probe pollution levels distributed in the pollution detectable area; an evaluation system to evaluate the probed pollution levels in the pollution detectable area; and a recommendation system for recommending an action to be taken based on evaluation system results of the probed pollution levels in the pollution detectable area, wherein the pollution levels are detected based light emitted by the light detecting and ranging system.

Systems and aircraft are provided. An avionics system includes a storage device and one or more data processors. The storage device stores instructions for monitoring an actual performance of the aircraft. The one or more data processors are configured to execute the instructions to: determine a first measured value of a flight characteristic of the aircraft at a first position of the aircraft; execute at least one flight maneuver between the first position and a second position of the aircraft; generate a predicted energy change between the first position and the second position based on the at least one flight maneuver and an energy state model; determine a second measured value of the flight characteristic of the aircraft at the second position; and generate an adjustment to the energy state model based on the first measured value, the second measured value, and the predicted energy change.

Systems and aircraft are provided. An avionics system includes a storage device and one or more data processors. The storage device stores instructions for monitoring an actual performance of the aircraft. The one or more data processors are configured to execute the instructions to: determine a first measured value of a flight characteristic of the aircraft at a first position of the aircraft; execute at least one flight maneuver between the first position and a second position of the aircraft; generate a predicted energy change between the first position and the second position based on the at least one flight maneuver and an energy state model; determine a second measured value of the flight characteristic of the aircraft at the second position; and generate an adjustment to the energy state model based on the first measured value, the second measured value, and the predicted energy change.

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.

A learning system of a precipitable water vapor estimation model includes a radio wave intensity acquisition part, a precipitable water vapor acquisition part, and a learning part. The radio wave intensity acquisition part acquires radio wave intensities of a plurality of frequencies among radio waves received by a microwave radiometer. The precipitable water vapor acquisition part acquires a precipitable water vapor calculated based on an atmospheric delay of a GNSS signal received by a GNSS receiver. Based on the radio wave intensities of the plurality of frequencies and the precipitable water vapor at a plurality of time points in a particular period, the learning part subjects an estimation model to machine learning such that an input data based on the radio wave intensities of the plurality of frequencies is taken as an input to output the precipitable water vapor.

A learning system of a precipitable water vapor estimation model includes a radio wave intensity acquisition part, a precipitable water vapor acquisition part, and a learning part. The radio wave intensity acquisition part acquires radio wave intensities of a plurality of frequencies among radio waves received by a microwave radiometer. The precipitable water vapor acquisition part acquires a precipitable water vapor calculated based on an atmospheric delay of a GNSS signal received by a GNSS receiver. Based on the radio wave intensities of the plurality of frequencies and the precipitable water vapor at a plurality of time points in a particular period, the learning part subjects an estimation model to machine learning such that an input data based on the radio wave intensities of the plurality of frequencies is taken as an input to output the precipitable water vapor.

A method of operating an optical icing conditions sensor includes transmitting a first light beam with a first transmitter and a second light beam with a second transmitter, thereby illuminating two illumination volumes. A first receiver receives the first light beam. A second receiver receives the second light beam. A controller measures the intensity of light received by the first and second receivers. The controller compares the intensities to threshold values and determines if either intensity is greater than the threshold values. The controller determines a cloud is present if either intensity is greater than the threshold values. The controller calculates a ratio of the intensities if a cloud is present. The controller determines, using the ratio, whether the cloud contains liquid water droplets, ice crystals, or a mixture of liquid water droplets and ice crystals.

A method of operating an optical icing conditions sensor includes transmitting a first light beam with a first transmitter and a second light beam with a second transmitter, thereby illuminating two illumination volumes. A first receiver receives the first light beam. A second receiver receives the second light beam. A controller measures the intensity of light received by the first and second receivers. The controller compares the intensities to threshold values and determines if either intensity is greater than the threshold values. The controller determines a cloud is present if either intensity is greater than the threshold values. The controller calculates a ratio of the intensities if a cloud is present. The controller determines, using the ratio, whether the cloud contains liquid water droplets, ice crystals, or a mixture of liquid water droplets and ice crystals.

An automated method for managing weather related energy usage in a physical structure having and address using preexisting smart meters, preexisting weather stations and preexisting energy portals and with a dynamic energy model connected to a network.