The summer school was organized by the Dynamical Processes and Predictability Working Group (PDP WG) of THORPEX1. THORPEX is a 10-year international research and development program to accelerate improvements in the accuracy of one-day to two-week high impact weather forecasts for the benefit of society, the economy and the environment. The PDP WG provides the connection between the operational...

due to its complexity and destructive nature, convection has been subject of many studies and researches among weather phenomena throughout the world. scientists have attempted for decades to generate different types of models to provide the possibility of abating or, at least, reducing convective weather phenomena effects on people’s lives. in south or southwest of iran, people are familiar wi...

We evaluate Machine Learning techniques for Green energy (wind, solar and biomass) prediction based on weather forecasts. Weather is constituted by multiple attributes: temperature, cloud cover, wind speed / direction which are discrete random variables. One of our objectives is to predict the weather based on the previous weather data. Additionally we are interested in finding correlation (dep...

Abstract The simulated radar reflectivity used by current mesoscale numerical weather prediction models can reflect the grid precipitation but cannot subgrid generated a cumulus parameterization scheme. To solve this problem, study developed new calculation method to obtain corresponding subgrid-scale and grid-scale based on Global/Regional Assimilation Prediction System (GRAPES) model of China...

Density forecasts for weather variables are useful for the many industries exposed to weather risk. Weather ensemble predictions are generated from atmospheric models and consist of multiple future scenarios for a weather variable. The distribution of the scenarios can be used as a density forecast, which is needed for pricing weather derivatives. We consider one to 10 dayahead density forecast...

The last decade has seen the success of stochastic parameterizations in short-term, mediumrange and seasonal ensembles: operational weather centers now routinely use stochastic parameterization schemes to better represent model inadequacy and improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations no...

Complex numerical weather prediction models incorporate a variety of physical processes, each described by multiple alternative schemes with specific parameters. The selection the and choice corresponding parameters during model configuration can significantly impact accuracy forecasts. There is no combination that works best for all times, at locations, under conditions. It therefore considera...

Our ongoing work focuses on systems for and applications of operational mesoscale numerical weather prediction. In particular, our goal is to provide weather forecasts at a level of precision and fast enough to address specific weather-sensitive operations. Hence, we are addressing problems of high-performance computing , visualization, and automation while designing, evaluating and optimizing ...

Can a relatively small numerical weather prediction ensemble produce any more forecast information than can be reproduced by a Gaussian probability density function (PDF)? This question is examined using site-specific probability forecasts from the UK Met Office. These forecasts are based on the 51-member Ensemble Prediction System of the European Centre for Medium-range Weather Forecasts. Veri...

The sub-grid-scale parameterization of clouds is one of the weakest aspects of weather and climate modeling today, and the explicit simulation of clouds will be one of the next major achievements in numerical weather prediction. Research cloud models have been in development over the last 45 years and they continue to be an important tool for investigating clouds, cloud-systems, and other small...