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.

Disclosed is a computing device for generating weather observation data for solving the problem. The computing device includes: a memory including computer executable components; and a processor executing following computer executable components stored in the memory, and the computer executable components may include an initial ground weather observation data recognition component recognizing observed initial ground weather observation data, and a weather data generation component trained to generate weather data of a gap region on the initial ground weather observation data by using a machine learning module.

Disclosed is a computing device for generating weather observation data for solving the problem. The computing device includes: a memory including computer executable components; and a processor executing following computer executable components stored in the memory, and the computer executable components may include an initial ground weather observation data recognition component recognizing observed initial ground weather observation data, and a weather data generation component trained to generate weather data of a gap region on the initial ground weather observation data by using a machine learning module.

A system for processing acoustic data to identify an event includes a receiver unit including a processor and a memory. The receiver unit is configured to receive a signal from a sensor disposed along a sensor path or across a sensor area. A processing application is stored in the memory. The processing application, when executed on the processor, configures the processor to: receive the signal from the sensor, where the signal includes an indication of an acoustic signal received at one or more lengths along the sensor path or across a portion of the sensor area and the signal is indicative of the acoustic signal across a frequency spectrum; determine a plurality of frequency domain features of the signal across the frequency spectrum; and generate an output comprising the plurality of frequency domain features.

A system for processing acoustic data to identify an event includes a receiver unit including a processor and a memory. The receiver unit is configured to receive a signal from a sensor disposed along a sensor path or across a sensor area. A processing application is stored in the memory. The processing application, when executed on the processor, configures the processor to: receive the signal from the sensor, where the signal includes an indication of an acoustic signal received at one or more lengths along the sensor path or across a portion of the sensor area and the signal is indicative of the acoustic signal across a frequency spectrum; determine a plurality of frequency domain features of the signal across the frequency spectrum; and generate an output comprising the plurality of frequency domain features.

A temperature prediction system may include a data input module configured to receive data related to climate, a prediction module having installed therein a trained model for predicting a temperature based on input data from the data input module, and an output module configured to output temperature information predicted by the prediction module.

A temperature prediction system may include a data input module configured to receive data related to climate, a prediction module having installed therein a trained model for predicting a temperature based on input data from the data input module, and an output module configured to output temperature information predicted by the prediction module.

A method for estimating a characteristic atmospheric turbulence parameter, the method including the steps of: training a machine learning model using, as learning data, values of the characteristic parameter with which are associated optical speckle images corresponding to defocused images of one or more stars, and using the trained learning model to estimate the characteristic parameter from input data containing one or more optical speckle images acquired by at least one measuring telescope, where the speckle images correspond to defocused images of one or more stars observed in real conditions by the telescope.

A method for estimating a characteristic atmospheric turbulence parameter, the method including the steps of: training a machine learning model using, as learning data, values of the characteristic parameter with which are associated optical speckle images corresponding to defocused images of one or more stars, and using the trained learning model to estimate the characteristic parameter from input data containing one or more optical speckle images acquired by at least one measuring telescope, where the speckle images correspond to defocused images of one or more stars observed in real conditions by the telescope.

A forecast engine is configured to analyze aerial and/or satellite images depicting a geographic area to identify the existence of solar panels within the geographic area at different times. Based on the installation time of each solar panel, the forecast engine estimates the solar power generation capacity of the solar panel. The forecast engine also analyzes meteorological data, including weather forecasts, to estimate a level of insolation at each solar panel within the geographic area across a range of times. The forecast engine can then determine the total amount of solar power generation within the given geographic area at a particular time using the solar power generation capacity of each solar panel and the level of insolation at each solar panel at the particular time.