An automated global weather notification system is provided. The automated global weather notification system is capable of obtaining observational weather data, including data form of forecast grids, and applying business rules and conditional variables to that data. Based on the business rules and conditional variables, notifications are generated. Relevant users are identified in particular geographic areas and notifications are delivered to those users via, for example, SMS, MMS, email, or other methods of electronic information delivery.

Disclosed is a method for determining an atmospheric radiative forcing difference by optimising or preventing contrail formation caused by an aircraft. The method comprises receiving one or more weather parameters to determine contrail forecast data; receiving one or more flight parameters associated with aircraft to determine flight data; determining tentative atmospheric radiative forcing quantity, along tentative flight trajectory, based on contrail forecast data and flight data; altering one or more flight parameters to determine optimised flight trajectory having optimum atmospheric radiative forcing quantity, wherein optimised flight trajectory is validated using imagery data; and determining an atmospheric radiative forcing difference to evaluate offset value for at least one forcing parameter associated with atmospheric radiative forcing difference. Disclosed also is an apparatus for determining atmospheric radiative forcing caused by aircraft by optimising or preventing contrail formation. Further, disclosed is computer program product to carry out aforementioned method.

Methods and systems for generating a neural network (NN)-based climate forecasting model are disclosed. The methods and systems perform steps of selecting a global climate simulation dataset from a plurality of simulation datasets each generated from a global climate simulation model; training the NN-based climate forecasting model on the selected global climate simulation dataset; and validating the NN-based climate forecasting model using observational historical climate data. Embodiments of the present invention enable accurate climate forecasting without the need to run new dynamical global climate simulations on supercomputers. Also disclosed are benefits of the new methods, and alternative embodiments of implementation.

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.

A method, apparatus and computer program product for determining a reduced visibility event warning are described herein. In the context of a method, a location corresponding with a reduced visibility event warning may be identified. Information regarding visibility at one or more stationary positions based upon the location of the reduced visibility event warning may be received from one or more remote devices (e.g., sensing apparatuses). The method may determine a reduced visibility event warning confidence for the location corresponding with a reduced visibility event warning based upon the information regarding visibility. The method may cause the reduced visibility event warning to be published in an instance in which the reduced visibility event warning confidence satisfies a confidence threshold.

A method performed by at least one apparatus is inter alia disclosed, said method comprising: obtaining weather model data indicative of location-specific weather information for a first set of locations (26) on a first grid (28); obtaining an area of interest (30) associated to at least one user (32); obtaining and/or determining a second set of locations (34) based on a second grid (36) within said area of interest (30); obtaining measurement data on location-specific weather information of a measurement device associated to said at least one user located at a measurement location (38) within and/or proximate to said area of interest (30); and determining, based on at least said obtained weather model data and said obtained measurement data, location-specific weather information for said second set of locations (34) based on said second grid (36).

Surface modification control stations and methods in a globally distributed array for dynamically adjusting the atmospheric, terrestrial and oceanic properties. The control stations modify the humidity, currents, wind flows and heat removal rate of the surface and facilitate cooling and control of large area of global surface temperatures. This global system is made of arrays of multiple sub-systems that monitor climate and act locally on weather with dynamically generated local forcing & perturbations for guiding in a controlled manner aim at long-term modifications. The machineries are part of a large-scale system consisting of an array of many such machines put across the globe at locations called the control stations. These are then used in a coordinated manner to modify large area weather and the global climate as desired. The energy system installed at a control stations, with multiple machines to change the local parameters of the ocean, these stations are powered using renewable energy (RE) sources including Solar, Ocean Currents, Wind, Waves and Batteries to store energy and provide sufficient power and energy as required and available at all hours. This energy is then used to do directed work using special machines, that can be pumps for seawater to move ocean water either amplifying or changing the currents in various locations and at different depths, in addition it will have machineries for changing the vertical depth profile of the ocean of temperature, salinity and currents. Control stations will also directly use devices such as heat pumps to change the temperatures of local water either at surface or at controlled depths, or modify the humidity and salinity to change the atmospheric and oceanic properties as desired. The system will work in a globally coordinated manner applying artificial intelligence and machine learning algorithms to learn from observations to improve the control characteristics and aim to slow down the rise of global surface temperatures. These systems are used to reduce the temperatures of coral reefs, arctic glaciers and south pacific to control the El Nino oscillations.

Methods and systems for generating a neural network (NN)-based climate forecasting model are disclosed. The methods and systems perform steps of generating a multi-model ensemble of global climate simulation data by combining simulation data from at least two global climate simulation models; pre-processing the multi-model ensemble of global climate simulation data; training the NN-based climate forecasting model on the pre-processed multi-model ensemble of global climate simulation data; and validating the NN-based climate forecasting model using observational historical climate data. Embodiments of the present invention enable accurate climate forecasting without the need to run new dynamical global climate simulations on supercomputers. Also disclosed are benefits of the new methods, and alternative embodiments of implementation.

A mesoscale modeling system and method that enables meteorologists to adjust forecasts to account for known biases of weather forecasting models and outputs high-resolution images consistent with the adjusted forecasts. The mesoscale modeling system and method may also use a weather forecasting model to forecast future weather events based on one or more adjustments provided by the meteorologists.

Surface modification control stations and methods in a globally distributed array for dynamically adjusting the atmospheric, terrestrial and oceanic properties. The control stations modify the humidity, currents, wind flows and heat removal rate of the surface and facilitate cooling and control of large area of global surface temperatures. This global system is made of arrays of multiple sub-systems that monitor climate and act locally on weather with dynamically generated local forcing & perturbations for guiding in a controlled manner aim at long-term modifications. The machineries are part of a large-scale system consisting of an array of many such machines put across the globe at locations called the control stations. These are then used in a coordinated manner to modify large area weather and the global climate as desired. The energy system installed at a control stations, with multiple machines to change the local parameters of the ocean, these stations are powered using renewable energy (RE) sources including Solar, Ocean Currents, Wind, Waves and Batteries to store energy and provide sufficient power and energy as required and available at all hours. This energy is then used to do directed work using special machines, that can be pumps for seawater to move ocean water either amplifying or changing the currents in various locations and at different depths, in addition it will have machineries for changing the vertical depth profile of the ocean of temperature, salinity and currents. Control stations will also directly use devices such as heat pumps to change the temperatures of local water either at surface or at controlled depths, or modify the humidity and salinity to change the atmospheric and oceanic properties as desired. The system will work in a globally coordinated manner applying artificial intelligence and machine learning algorithms to learn from observations to improve the control characteristics and aim to slow down the rise of global surface temperatures. These systems are used to reduce the temperatures of coral reefs, arctic glaciers and south pacific to control the El Nino oscillations.