Introduction An important goal in operational weather forecasting is to reduce the number of forecast failures, in particular those with large socio economic impact. Forecast failures of high-impact weather are often due to lack of observations over data sparse areas, such as the southern hemisphere, Tropics and Northern hemisphere oceans, over a prolonged period prior to the extreme event. Int...

This paper describes techniques used operationally by the National Weather Service (NWS) to prepare gridded multisensor (gauge, radar, and satellite) quantitative precipitation estimates (QPEs) for input into hydrologic forecast models and decisionmaking systems for river forecasting, flood and flash flood warning, and other hydrologic monitoring purposes. Advanced hydrologic prediction techniq...

The background error covariance matrix, B, is often used in variational data assimilation for numerical weather prediction as a static and hence poor approximation to the fully dynamic forecast error covariance matrix, P . In this paper the concept of an Ensemble Reduced Rank Kalman Filter (EnRRKF) is outlined. In the EnRRKF the forecast error statistics in a subspace defined by an ensemble of ...

This paper presents a short-term forecasting model designed to forecast the hourly power production in a grid-connected photovoltaic plant. The model is based on neuro-fuzzy systems optimized with the use of a genetic algorithm. The model uses as inputs forecasted weather variables obtained with a meso-scale numerical weather prediction model. The model was applied to forecast the hourly produc...

The purpose of the work was performing Air Quality (AQ) forecast model. The idea has made a prediction of AQ in urban-industrial agglomeration basing on weather forecasts. Mezoscale meteorological forecasting UM model (Unified Model) developed by the UKMO, artificial neural networks and regression method was used. Two-step approaches were used to AQ forecasting. The first step was based on clas...

NWP forecast performance has made great progress during the past decade due to a few important factors. First, the numerical forecast models at major numerical weather prediction (NWP) centres have improved tremendously due to more accurate physics parametrisation schemes and increased computing power, which permits the use of higher resolution forecast models. Second, more observations, more a...

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. ...

Through a collaborative partnership, principally among NCAR, NOAA, DoD, OU/CAPS, FAA, and universities, the U.S. modeling community is developing a next-generation mesoscale model, known as the Weather Research and Forecasting (WRF) model. The goals of the WRF project are to develop an advanced mesoscale forecast and data assimilation system and to accelerate research advances into operations. ...

This site report describes the HIRLAM project, which deals with the development and maintenance of a state-of-the-art operational numerical short-range weather forecasting system. The report provides an insight into the diierent components of these systems, presents examples what can be achieved, and describes brieey what kind of CSE research is carried out. The international project: Its aim a...

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...