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.

This invention addresses the problem of Global Warming, expressed as the environmental condition of unintended and imperceptible levels of Vapor Pressure Deficit, (VPD) in Nursing Homes and Hospitals. The invention teaches an art form which addresses Global Warming as expressed by Vapor Pressure deficit and resistance to medication. The invention identifies the ideal conditions for fungal and bacteria growth and in particular a new highly resistant fatal form of Candida Fungus, referred to as Candida Auris (C. Auris). Existing HVAC technology does not address this problem, since it is novel in that it identifies a unique interaction between Global Warming with the problem of resistances to medication. The invention is also novel and unobvious in that it teaches an art form indicating that certain levels of imperceptible VPD require continued HVAC, A/C dehumidification and temperature reduction even throughout tepid temperatures when such equipment may be turned off. As well as teaches an art form to alert medical staff and administration as to when these conditions are occurring and help plan treatments during periods of favorable ambient indoor and outdoor environmental conditions.

Methods and systems for generating a multi-model ensemble of global climate simulation data from a plurality of pre-existing global climate simulation model (GCM) datasets, are disclosed. The methods and systems perform steps of computing a GCM dataset validation measure based on at least one sample statistic for at least one climate variable from the pre-existing GCM dataset; selecting a validated subset of the plurality of pre-existing GCM datasets; selecting a subset of GCM datasets; generating one or more candidate ensembles of GCM datasets; computing an ensemble forecast skill score for each candidate ensemble of GCM datasets; generating the multi-model ensemble of GCM datasets by selecting a candidate ensemble of GCM datasets with a best ensemble forecast skill score; and training the NN-based climate forecasting model using the multi-model ensemble of GCM datasets. Embodiments of the present invention enable accurate climate forecasting without the need to run new dynamical global climate simulations on supercomputers.

Improved mechanisms for collecting information from a diverse suite of sensors and systems, calculating the current precipitation, atmospheric water vapor, atmospheric liquid water content, or precipitable water and other atmospheric-based phenomena, for example presence and intensity of fog, based upon these sensor readings, predicting future precipitation and atmospheric-based phenomena, and estimating effects of the atmospheric-based phenomena on visibility, for example by calculating runway visible range (RVR) estimates and forecasts based on the atmospheric-based phenomena.

A worksite control system includes a communication system configured to receive weather data corresponding to a worksite, a weather model generation logic configured to generate a weather model based on the weather data, a worksite action identification logic configured to identify a worksite action based on the weather model, and a control signal generator configured to generate a machine control signal that controls a machine associated with the worksite based on the identified worksite action.

Provided are a prediction value correction method and apparatus. The prediction value correction method includes steps of: (a) determining a prediction condition to be predicted; (b) receiving past prediction values and past measurement values according to the determined prediction condition; (c) filtering the past prediction values and the past measurement values by using an H-infinity filter to obtain an output value for a final time point; (d) estimating a future bias for a date and time point to be predicted by using the output value of the H-infinity filter; and (e) correcting a future prediction value for the date and time point to be predicted by using the estimated future bias to obtain a corrected future prediction value for the date and time point to be predicted.

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.

Methods and systems for generating a multi-model ensemble of global climate simulation data from a plurality of global climate simulation models (GCMs), to be used in training a neural network (NN)-based climate forecasting model, are disclosed. The methods and systems perform steps of computing a GCM validation measure for each GCM; selecting a validated subset of the GCMs, by comparing each computed GCM validation measure to a validation threshold determined based on observational historical climate data; computing a forecast skill score for each validated GCM, based on a first forecast function; selecting a validated and skillful subset of GCMs; generating one or more candidate ensembles by combining simulation data from at least two validated and skillful GCMs; computing an ensemble forecast skill score for each candidate ensemble, based on a second forecast function; and selecting a best-scored candidate ensemble. Embodiments of the present invention enable accurate climate forecasting without the need to run new dynamical global climate simulations on supercomputers.

The invention relates to training a machine learning algorithm and predicting a value for a weather data variable, preferably at a field or sub-field level. In this respect, according to the invention, a method for predicting a value for at least one weather data variable for at least one instant of time in the future, is provided, the method comprising the following method steps: feeding a machine learning algorithm with a predicted weather dataset that comprises at least one predicted value for the said at least one weather data variable for the said at least one instant of time in the future and for at least one grid point of a first grid covering at least a part of the Earth's surface, feeding the machine learning algorithm with an observed environmental dataset that comprises at least one ground truth value for at least one environmental data variable for at least one grid point of a second grid covering at least the said part of the Earth's surface, and outputting by the machine learning algorithm a predicted value for the said at least one weather data variable for the said at least one instant of time in the future. In this way, a possibility for field specific weather predictions for providing field zone specific treatment recommendations at a small-meshed grid level may be provided.

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, where the pre-processing comprises at least one action of spatial re-gridding, temporal homogenization, and data augmentation; 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.