A device may receive historical network behavior data associated with a network that includes network devices and may receive historical weather data associated with a geographical location of the network. The device may receive historical action data identifying historical actions taken for the network in response to the historical weather data, and may train a correlation model, with the historical network behavior data, the historical weather data, and the historical action data, to generate a trained correlation model. The device may receive a weather event forecast associated with the geographical location, and may process the weather event forecast, with the trained correlation model, to determine an anticipated behavior of the network in response to the weather event forecast. The device may process data identifying the anticipated behavior, with the trained correlation model, to identify actions to take in response to the anticipated behavior and may perform the actions.

A device may receive historical network behavior data associated with a network that includes network devices and may receive historical weather data associated with a geographical location of the network. The device may receive historical action data identifying historical actions taken for the network in response to the historical weather data, and may train a correlation model, with the historical network behavior data, the historical weather data, and the historical action data, to generate a trained correlation model. The device may receive a weather event forecast associated with the geographical location, and may process the weather event forecast, with the trained correlation model, to determine an anticipated behavior of the network in response to the weather event forecast. The device may process data identifying the anticipated behavior, with the trained correlation model, to identify actions to take in response to the anticipated behavior and may perform the actions.

A system is disclosed for exploiting a transmitted signal from an aircraft to determine characteristics of any of the aircraft's motion or meteorological conditions in which the aircraft is moving. The system includes a plurality of platforms for detecting Doppler shift information of the transmitted signal at each of the plurality of platforms, and a processing system for determining characteristics of any of the aircraft's motion or meteorological conditions in which the aircraft is moving.

A storm damage response system includes a storm ensemble database that stores ensemble forecast models associated with respective potential storm paths of a given storm across a geographic area and an equipment database that stores equipment data associated with location and characteristics of power-providing equipment in the geographic area. A storm damage model algorithm generates a storm response plan comprising an operational procedure for repairing or maintaining power transmission and distribution electric systems to mitigate storm damage impact based on generating a probabilistic model for each of the ensemble forecast models based on the equipment data and calculating a statistical impact value associated with the probabilistic model based on an aggregate of iterative probabilistic simulations for the respective ensemble forecast model. The storm response plan can be generated based on the relative statistical impact value of the probabilistic model of each of the ensemble forecast models.

A method for a platform-based scheduling of a job flow is provided, which relates to the technology field of satellites. Specifically, the method includes: acquiring satellite remote sensing data according to transit time of a satellite and triggering a processing flow of the satellite remote sensing data; determining an execution order of the processing flow of the satellite remote sensing data according to a constraint relationship; and allocating processing resources to the processing flow in a hierarchical scheduling manner according to the execution order, and executing the processing flow with the processing resources. Due to a reasonable determination of the execution order of the processing flow of the satellite remote sensing data and a scheduling of the processing resources in the hierarchical scheduling manner, a reasonable allocation of the processing resources is realized. Thus, A timeliness of the processing of the satellite remote sensing data may be improved.

A computer-implemented method that includes receiving a request for a target vehicle including input data associated with an operator, a geographic location, and the target vehicle. The method includes determining a depreciation rate based on third-party transaction data retrieved from databases in accordance with the input data associated with the target vehicle, and a decommission rate based on third-party ownership data retrieved from databases in accordance with the input data associated with the target vehicle. The method includes determining a deterioration rate based on local environmental data retrieved from databases in accordance with the input data associated with the geographic location, and generating a regression model configured to compute an estimated value of the target vehicle during a lifetime of the target vehicle using the input data, depreciation rate, decommission rate, and deterioration rate. The method includes determining an output in response to the request for the target vehicle.

Techniques for forecasting solar power generation include determining, by a computing device, respective proximity scores for a plurality of measurement devices, wherein the respective proximity scores are based on proximity of the measurement devices to a photovoltaic installation; determining, by the computing device, respective bearing scores for the plurality of measurement devices, wherein the respective bearing scores are based on respective angular offsets between the plurality of measurement devices and an azimuth of the photovoltaic installation; selecting, by the computing device, a first measurement device in the plurality of measurement devices using the respective proximity scores and the respective bearing scores; and predicting, by the computing device, a solar power generation level for the photovoltaic installation based on data obtained from the first measurement device.

Techniques for forecasting solar power generation include determining, by a computing device, respective proximity scores for a plurality of measurement devices, wherein the respective proximity scores are based on proximity of the measurement devices to a photovoltaic installation; determining, by the computing device, respective bearing scores for the plurality of measurement devices, wherein the respective bearing scores are based on respective angular offsets between the plurality of measurement devices and an azimuth of the photovoltaic installation; selecting, by the computing device, a first measurement device in the plurality of measurement devices using the respective proximity scores and the respective bearing scores; and predicting, by the computing device, a solar power generation level for the photovoltaic installation based on data obtained from the first measurement device.

Systems, devices, and methods including an aerial vehicle having a global positioning system (GPS) and at least one trace-gas sensor configured to generate gas data; and a processor having addressable memory, the processor configured to: determine a flight envelope based on a received spatial location, a received spatial location of the one or more potential gas sources, a received desired level of confidence, and a received wind data; determine a flight path for the aerial vehicle, where the flight path covers a portion of the determined flight envelope; and determine based on a received gas data whether a gas leak is present in the received spatial location to the received desired level of confidence.

An example method for processing wind field data includes generating wind field base data using the preliminary data and one or more empirical equations based on climatology. The wind field base data includes multiple data sets each associated with a different time-point in a first set of time-points. The method also includes performing spatial interpolation and temporal interpolation over the wind field base data to generate a sequence of two-dimensional wind field representations each associated with a different time-point in a second set of time-points, and visualizing the sequence of two-dimensional wind field representations.