An intensification mechanism analysis and anthropogenic climate change signal identification method for terrestrial water cycle (TWC) in dry and wet regions, includes: identifying dry regions and wet regions worldwide with observed data; quantifying a precipitation increasing rate; calculating a regional warming rate and a precipitation response warming rate, and investigating a difference between dry regions and wet regions; identifying a fingerprint pattern of a precipitation increase in dry regions and wet regions in response to global warming, and calculating a signal-to-noise ratio (SNR) to quantify a possibility of an anthropogenic climate change signal in dry regions and wet regions; and detecting different external forcing signals in a precipitation change of dry regions and wet regions with an optimal fingerprinting method, and quantifying a contribution of each of the different external forcing signals to the precipitation change.

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 predictive real time and prospective environmental analysis and display system accessible by one or more client computing devices through a network to depict on the display surface of a computing device a graphical representation of a geographic environment which can be delimited into one or more two or three-dimensional zones in which visual indicators provide predicted current or prospective airflow speed or direction values associated with the geographic environment.

Disclosed is a method for siting heat wave monitoring stations based on risk evaluation, including: acquiring historical meteorological data of a target region, and preprocessing the historical meteorological data to generate a gridded associated meteorological data set; identifying historical high-temperature heat wave events based on the associated meteorological data set, and calculating parameters and summary indexes of heat wave feature of grids; evaluating station building priority of the grids based on spatial distribution features of the summary indexes; acquiring multi-source data, and evaluating a heat wave risk to generate a heat wave risk map; performing iterative computation using an optimization algorithm based on current station building information, temporal-spatial distribution features of meteorological factors and the heat wave risk map to determine alternative station building positions; and acquiring on-site survey information of each alternative station building position, and determining a position where a station is to be built.

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.

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.

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.

A method for quantifying a structural feature forecast error of a meteorological element based on a graphical similarity is based on the concept of graphical similarity to propose a normalized evaluation technique for a forecast error of a scalar meteorological element such as rainfall, radar reflectivity, temperature, visibility, or wind speed. The method can objectively and truly reflect the true capability of forecasting the meteorological element such as precipitation.

Dynamic generation of climate projections is provided. The method comprises receiving past climate data of a first spatial resolution. The past climate data of the first spatial resolution is converted to past climate data of a second spatial resolution. A machine learning model is trained with a deep learning algorithm with a training set of the data to generate a trained model object that maps a relationship between the past climate data of the first spatial resolution and the first climate data of the second spatial resolution. The trained model object is validated with a validation set of the data. The trained model object is applied to climate projections of the second spatial resolution to generate climate projections of the first spatial resolution.

Systems, methods, and computer-readable storage media for allowing users to identify impacts of climate change in a given environment. A system receives, from a user, a request to predict climate change impacts for at least one piece of infrastructure within a geographic area over a defined period of time, then executes, in response to the request, a first engine, with the first engine generating precipitation predictions over the defined period of time within the geographic area. The system then executes, in response to the request, at least one secondary engine using the precipitation predictions, where the at least one secondary engine generates a risk analysis due to climate change for the at least one piece of infrastructure within the geographic area.