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In 2024, a group of researchers at Google's DeepMind AI research laboratories published a paper in Nature to descrivbe their machine-learning model, called GenCast, that is expected to produce more accurate forecasts than the best traditional weather forecasting systems. [85]
In comparison to traditional physics-based methods, machine learning (ML), or more broadly, artificial intelligence (AI) approaches, have demonstrated potential in enhancing weather forecasts (refer to the review by Shen et al. [39]). As detailed in Table 4 of Shen et al., these AI-driven models were trained with ERA5 reanalysis data and CMIP6 ...
Weather reconnaissance aircraft, such as this WP-3D Orion, provide data that is then used in numerical weather forecasts.. The atmosphere is a fluid.As such, the idea of numerical weather prediction is to sample the state of the fluid at a given time and use the equations of fluid dynamics and thermodynamics to estimate the state of the fluid at some time in the future.
The Weather Research and Forecasting (WRF) Model [1] (/ ˈ w ɔːr f /) is a numerical weather prediction (NWP) system designed to serve both atmospheric research and operational forecasting needs. NWP refers to the simulation and prediction of the atmosphere with a computer model, and WRF is a set of software for this.
Weather reconnaissance aircraft, such as this WP-3D Orion, provide data that is then used in numerical weather forecasts. The atmosphere is a fluid . As such, the idea of numerical weather prediction is to sample the state of the fluid at a given time and use the equations of fluid dynamics and thermodynamics to estimate the state of the fluid ...
Articles on forecasting and analysis techniques, forecast verification studies, and case studies useful to forecasters. In addition, submissions that report on changes to the suite of operational numerical models and statistical post-processing techniques, and articles that demonstrate the transfer of research results to the forecasting community.
Weather forecast skill is often presented in the form of seasonal geographical maps. Forecasting skill for single-value forecasts (i.e., time series of a scalar quantity) is commonly represented in terms of metrics such as correlation, root mean squared error, mean absolute error, relative mean absolute error, bias, and the Brier score, among ...
Probabilistic forecasting summarizes what is known about, or opinions about, future events. In contrast to single-valued forecasts (such as forecasting that the maximum temperature at a given site on a given day will be 23 degrees Celsius, or that the result in a given football match will be a no-score draw), probabilistic forecasts assign a probability to each of a number of different ...