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.

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.

Provided are an apparatus and method for calculating a sensible temperature in consideration of outdoor ground heating and a heatwave warning apparatus and method based on a sensible temperature in consideration of outdoor ground heating. The method of calculating a sensible temperature in consideration of outdoor ground heating includes classifying data which includes a globe temperature, an atmospheric temperature, a relative humidity, and a ground surface temperature and is observed by an automated synoptic observing system (ASOS) for a certain period of time, as precipitation data and non-precipitation data according to whether there is precipitation, clustering the non-precipitation data into K clusters, and deriving K+1 sensible temperature calculation formulae by performing regression analysis on the K clusters and the precipitation data.

The Global Warming Monitor measures the effect of greenhouse gases on the temperature of a closed biosphere. Global warming of planet earth is then forecasted by comparing the values obtained by this method and apparatus with changes in global greenhouse gas levels.

The effect of greenhouse gases on the temperature of a biosphere is determined by correlating the incremental velocity of the bottom plate of the pressure controller with the initial, intermediate, and final temperature in the controller. A laser, timer, meter chart, and video camera are used to provide the incremental velocities.

The Global Warming Monitor provides a quantitative measurement of the effect greenhouse gases have on the velocity of the controller plate, vis--vis temperature. Global warming is then forecasted comparing the rates of increase of temperature from greenhouse gases in a biosphere with the change of concentration of atmospheric greenhouse gases.

A method and system for determining cloud seeding potential comprises receiving a temperature and a liquid water content (LWC). A seeding index is calculated based on the temperature T, a temperature membership function (T), the LWC, and a liquid water content membership function (LWC) at the plurality of grid points to create a seeding index set. A target region potential flag is set based on the seeding index set.

Disclosed is an environmental sensor apparatus in which a plurality of light shielding plates including an insertion space portion providing an insertion space of the circuit board and a guide portion guiding the insertion of the circuit board to the inner surface of the insertion space portion are stacked, and a circuit board is mounted inside a light shielding portion in which the plurality of light shielding plates are stacked.

Methods, systems, and computer programs are presented for flood-risk analysis and mapping. One method includes operations for presenting, in a graphical user interface (GUI), options for calculating a flood risk map, and receiving, via the GUI, input identifying a geographical region and a weather scenario. Further, the method includes operations for dividing the geographical region into cells; calculating, utilizing a hydrological model, an inflow and an outflow of water between cells in the geographical region based on the weather scenario; and calculating, utilizing a hydraulic model, water depth in each cell based on the weather scenario and the inflow and outflow of water between cells. The flood risk map, generated based on the calculated water depth in each cell, shows the probability that each cell in the geographical region will be inundated with water under the weather scenario. The flood risk map is presented in the GUI.

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.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining atmospheric conditions. In one aspect, a method includes receiving data relating to atmospheric conditions collected at a particular altitude located within the stratosphere, providing the data to a machine-learned model that has been trained using a training data set collected in a controlled environment, determining, by the machine-learned model, atmospheric conditions data for the particular altitude based on the data collected for the particular altitude and storing the atmospheric conditions data for the particular altitude.

A carbon credit is a generic term for any tradable certificate or permit representing the right to emit one ton of carbon dioxide or the mass of another greenhouse gas with a carbon dioxide equivalent (tCO2e) equivalent to one ton of carbon dioxide.

Carbon credits and carbon markets are a component of national and international attempts to mitigate the growth in concentrations of greenhouse gases (GHGs). One carbon credit is equal to one ton of carbon dioxide, or in some markets, carbon dioxide equivalent gases. Carbon trading is an application of an emissions trading approach. Greenhouse gas emissions are capped and then markets are used to allocate the emissions among the group of regulated sources.

Carbon credits can be generated by any process that conforms to ISO 14064-66 standards. Once generated, carbon credits can be stored in a distributed, Cloud-based ledger. The ledger entries can serve as a registry for carbon credits as well as the data source for an Internet-enabled trading system or financial exchange that allows the carbon credits to be sold and bought as part of the same system. The distributed ledger can provide records that combine the details of the carbon credits' origin, transaction history, and financial instructions associated with trading of the carbon credits via a distributed ledger system.