The CWRF has been developed as the Climate extension of the Weather Research and Forecasting model (WRF, Skamarock et al. 2008) by incorporating numerous improvements in representation of physical processes and integration of external (top, surface, lateral) forcings that are crucial to climate scales, including interactions between land–atmosphere–ocean, convection–microphysics and cloud–aeros...

Numerical weather models generate a vast amount of information which requires human interpretation to generate local weather forecasts. Convolutional Neural Networks (CNN) can extract features from images showing unprecedented results in many different domains. In this work, we propose the use of CNN models to interpret numerical weather model data which, by capturing the spatial and temporal r...

Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed in this article include how weather forecasting and atmospheric chemistry models are integrated i...

Numerical weather prediction (NWP) models produce time series data of basic weather parameters which human forecasters use as guidance while writing textual forecasts. Our studies of humans writing textual weather forecasts led us to build SUMTIME-MOUSAM, a text generator that produces textual marine weather forecasts for offshore oilrig applications. SUMTIME-MOUSAM separates control and proces...

Weather forecasting is based on the use of numerical weather prediction (NWP) models that are able to perform the necessary calculations that describe/predict the major atmospheric processes. One common problem in weather forecasting derives from the uncertainty related to the chaotic behaviour of the atmosphere. A solution to that problem is to perform in addition to “deterministic” forecasts,...

Numerical weather prediction models as well as the atmosphere itself can be viewed as nonlinear dynamical systems in which the evolution depends sensitively on the initial conditions. The fact that estimates of the current state are inaccurate and that numerical models have inadequacies, leads to forecast errors that grow with increasing forecast lead time. The growth of errors depends on the f...

During the period 24-25 July 2004, elevated convection produced rain totaling up to 5 inches across east-central Missouri. No rain was forecast by the 12 km Eta model over this area. The presence of two concurrent mesoscale convective systems rendered this event a particularly challenging one for numerical weather prediction. An analysis of the physical differences between the model forecast an...

Soil moisture can significantly influence atmospheric evolution. However the soil moisture state predicted by land surface models, and subsequently used as the boundary condition in atmospheric models, is often unrealistic. New remote sensing technologies are able to observe surface soil moisture at the scales and coverage required by numerical weather prediction (NWP), and there is potential t...

The Weather Research and Forecasting (WRF) Model has been designed to be an efficient and flexible simulation system for use across a broad range of weather-forecast and idealized-research applications. Of particular interest is the use of WRF in nonhydrostatic applications in which moist-convective processes are treated explicitly, thereby avoiding the ambiguities of cumulus parameterization. ...

Ensemble model output statistics (EMOS) is a statistical tool for post-processing forecast ensembles of weather variables obtained from multiple runs of numerical weather prediction models in order to produce calibrated predictive probability density functions. The EMOS predictive probability density function is given by a parametric distribution with parameters depending on the ensemble foreca...