Numerical weather prediction is a computationally expensive task that requires not only the numerical solution to a complex set of non-linear partial differential equations, but also the creation of a parameterization scheme to estimate sub-grid scale phenomenon. This paper outlines an alternative approach to developing a mesoscale meteorological model – a modified recurrent neural network that...

Global and regional wave forecasts are routinely conducted nowadays thanks to advances in numerical weather prediction, satellite scatterometers and computers. The demand for further improvements in such models comes from engineering communities, as well as climate research communities. A number of researchers have suggested the possibility of wave concentration caused by the ocean current. But...

The spatial resolution of publicly available numerical weather forecasts is largely limited by the computing resources of the originating weather services. Only selected parameters are available to the public from many weather services. We present a scalable system for distributed computation of high resolution symbiotic numerical weather forecasts. Each forecast is computed on the user’s deskt...

thunderstorm is one of the most dangerous phenomena in aviation because the greatest number weather hazards such as icing, turbulence, wind shear, lightning hail are combined in one single source, the thunderstorm. spatial characteristics of thunderstorms which can be associated with irreparable outcomes are known by most of the forecasters. tornadoes, microbursts, very strong winds and flash f...

Despite the centrality of the median voter prediction in political economy models, overwhelming empirical evidence shows that legislators regularly take positions that diverge significantly from the preferences of the median voter in their districts. However, all these empirical studies to date lack the necessary data to directly measure the preferences of the median voter. We utilize a unique ...

Short-range forecasts of precipitation fields are needed in a wealth of agricultural, hydrological, ecological and other applications. Forecasts from numerical weather prediction models are often biased and do not provide uncertainty information. Here we present a postprocessing technique for such numerical forecasts that produces correlated probabilistic forecasts of precipitation accumulation...

Variational data assimilation is used at major weather prediction centers to produce the initial conditions for 7to 10-day weather forecasts. This technique requires the solution of a very large data-fitting problem in which the major element is a set of partial differential equations that models the evolution of the atmosphere over a time window for which observational data has been gathered. ...

Hail causes billions of dollars in losses by damaging buildings, vehicles, and crops. Improving the spatial and temporal accuracy of hail forecasts would allow people to mitigate hail damage. We have developed an approach to forecasting hail that identifies potential hail storms in storm-scale numerical weather prediction models and matches them with observed hailstorms. Machine learning models...