A method for downscaling a weather forecast including obtaining a sky image and location data indicative of a user location via a mobile computing device; sending the location data to a weather forecast provider; generating, by the weather forecast provider, a local weather forecast for the user location; sending the local weather forecast to a server; determining, by the mobile computing device, cloud cover data and cloud type data based on the sky image, and sending the cloud cover data and cloud type data to the server or sending the sky image to the server and determining cloud cover data and cloud type data based on the sky image; increasing the resolution of the local weather forecast based on the cloud cover data and the cloud type data, thus obtaining a downscaled local weather forecast; and, sending the downscaled local weather forecast to the mobile computing device.

A method for selecting a subset of devices from a plurality of devices in a wireless network to perform: a first function comprising transmitting, receiving and/or processing a first radio frequency signal in a first frequency band for detecting atmospheric conditions; and a second function comprising transmitting, receiving and/or processing a second radio frequency signal in a second frequency band, at least partially overlapping with the first frequency band, for performing network communications; wherein the first and the second function are performed during a time period; and wherein the time period comprises a first subset of timeslots and a second subset of timeslots; wherein the method comprises: selecting the subset of devices based on a physical location of the devices relative to an area in which atmospheric conditions are to be monitored; assigning the first subset of timeslots to each of the selected devices to perform the first function, wherein the first subset of timeslots is assigned based on a priority value associated with the relative priority of performing the first function compared to the second function; assigning the second subset of timeslots to each of the selected devices to perform the second function; controlling the selected devices to execute the first function and the second function during the assigned timeslots respectively.

Techniques for using machine learning to model climatic data are disclosed. In one example, a computer implemented method comprises receiving climate data comprising a plurality of spatial components and a plurality of temporal components, and masking a portion of the climate data. A machine learning model is trained, wherein the training is based at least in part on the masked portion of the climate data. A vector representation of the climate data is generated via the machine learning model.

A data processing system is for producing a weather report. The data processing system may include external weather event databases, each external weather event database having a different weather event data set, and a server in communication with the external weather event databases. The server may be configured to import the different weather event data sets from the external weather event databases, perform a filtering process on the different weather event data sets from the external weather event databases, and generate the weather report based upon the filtered different weather event data sets from the external weather event databases and a geolocation.

A computer system and method for improving the accuracy of predictions of the amount of renewable energy, such as solar energy and wind energy, available to an electric utility, and/or refine such predictions, by providing improved integration of meteorological forecasts. Coefficient values are calculated for a renewable energy generation model by performing a regression analysis with the forecasted level of renewable energy posted by the utility, forecasted weather conditions and measures of seasonality as explanatory variables. Accuracy is further enhanced through the inclusion of a large number of time series variables that reflect the systematic nature of the energy/weather system. The model also uses the original forecast posted by the system operator as well as variables to control for season.

A movable system for automatically monitoring the correlated wind and temperature filed of a bridge, including a bridge monitoring subsystem, a cloud server, and a client. The system monitors the meteorological parameters of a bridge surface and a temperature of a bridge structure, performs data analysis and processing on a cloud server, and performs visual data interaction by using a client. A bridge surface-specific meteorological parameter monitoring module is movable, such that the location of the sensor for meteorological data monitoring can be adjusted at any time to monitor an entire bridge deck in a time-sharing manner. A lower cantilever structure has an adjustable height, such that the sensor for meteorological data monitoring can track a height of a boundary layer of the bridge surface. A bridge structure-specific temperature monitoring module adopts distributed patch-type temperature sensors, which can detect the temperature of the bridge structure in all directions.

A wireless mesh network includes a group of nodes configured to predict cloud movements based on voltage time series data. A node residing in the wireless mesh network records voltage fluctuations at a site where solar power is generated. The voltage fluctuations occur when an advancing cloud reduces solar irradiance at the site, thereby reducing solar power generation. The node correlates these voltage fluctuations with other voltage fluctuations recorded by other nodes at other sites where solar power is generated. The node computes a time offset between these voltage fluctuations that corresponds to the time needed for the cloud to advance between the different sites. Based on this time offset and the locations of the various nodes, the node estimates a wind vector. The wind vector can be used to perform near-term solar forecasting by predicting when the cloud will advance to other sites and reduce solar power generation.

A ski resort management system comprising: a data acquisition system that receives data from telemetry systems of the lift systems, of the snow generators and of the snow groomers of the ski resort; a processing station; mass storage units, containing a system database fed by the data acquisition system; and a display interface, accessible from operator terminals. The data acquisition system stores data and signals from the lift systems, the snow generators and the snow groomers in the system database. The processing station enables setting a mode of the display interface for alternatively displaying data relative to each lift system, to each snow generator or to each snow groomer, or to display, in an aggregated form, data relative to all the lift systems, all the snow generators and all the snow groomers.

A method for downscaling a weather forecast including obtaining a sky image and location data indicative of a user location via a mobile computing device; sending the location data to a weather forecast provider; generating, by the weather forecast provider, a local weather forecast for the user location; sending the local weather forecast to a server; determining, by the mobile computing device, cloud cover data and cloud type data based on the sky image, and sending the cloud cover data and cloud type data to the server or sending the sky image to the server and determining cloud cover data and cloud type data based on the sky image; increasing the resolution of the local weather forecast based on the cloud cover data and the cloud type data, thus obtaining a downscaled local weather forecast; and, sending the downscaled local weather forecast to the mobile computing device.

The systems, methods, and apparatuses described herein provide integrated weather forecast products designed to assist operations managers with operational decision-making related to a designated event or set of events. The present disclosure provides a way to process weather data from various sources and in diverse data formats containing varying spatial resolutions and temporal resolutions, in order to generate an integrated and cohesive weather projection product such that the weather projection product is continuous in both spatial and temporal domains relative to a designated event or set of events.