cumulus parameterization in numerical weather prediction models can significantly affect severe weather forecasts, such as hurricanes, flash floods, and winter storms. the role of convection is essential in stabilizing an unstable atmosphere through vertically transferring moisture, energy, chemical species and momentum. cumulus parameterization schemes use simple one-dimensional convective clo...

The use of look-up-tables (LUTs) to represent parameterizations within numerical weather prediction and other atmospheric models is presented. We discuss several approaches as to how the use of LUTs can be optimized in order to retain the physical representation of the parameterization, yet be much more computationally efficient than the parent parameterization from which they are derived. tras...

Accurate predictions of cloud and precipitation processes in the marine boundary layer are critical to U.S. Navy operations, as well as being more broadly important to improving seasonable predictability and the performance of NWP models. The major goal of the project is to develop and test state-of-theart boundary layer and microphysical parameterizations in order to better represent the conti...

We analyze the impacts of adopting advanced weather forecasting systems at different levels of the decision-making hierarchy of the power grid. Using case studies, we show that state-of-the-art numerical weather prediction (NWP) models can provide high-precision forecasts and uncertainty information that can significantly enhance the performance of planning, scheduling, energy management, and f...

cumulus convection, as a meso-scale phenomenon, results in the release of latent heat and vertical drift of energy that by part could affect the large scale field of energy, humidity, and momentum of the atmosphere. since the current numerical models are unable to resolve the singular cumulus convective clouds that are important from a meteorological point of view, it is used to apply some sort...

Microphysics parameterization plays an important role in Numerical Weather Prediction (NWP). Most NWP models, including the Weather Research Forecast (WRF) model, use single-moment (bulk water) schemes derived based on an assumption of the exponential distribution with a fixed intercept parameter for raindrop size distributions (DSDs) such as the Marshall-Palmer (M-P) DSD model (Marshall and Pa...

Weather forecasting is a complex process that involves numerous specialized fields of expertise. The output from computationally intensive numerical weather prediction (NWP) models forms the starting point of the forecasting process. Expert forecasters have both a general knowledge of large-scale weather systems and specific knowledge about the idiosyncratic behavior of local scale weather phen...

Complex numerical weather prediction models incorporate a variety of physical processes, each described by multiple alternative physical schemes with specific parameters. The selection of the physical schemes and the choice of the corresponding physical parameters during model configuration can significantly impact the accuracy of model forecasts. There is no combination of physical schemes tha...

With the advent of digital computers and their continuous increasing processing power, the ‘Numerical Weather Prediction’ (NWP) models which solve a close set of equations of atmospheric model, have been adopted by most of the meteorological services to issue day to day weather forecasts. These forecasts are issued for public in general. But there are many limitations inherent to this technique...