Geomagnetic storms, which are governed by the plasma magnetohydrodynamics of solar-interplanetary-magnetosphere system, entail a formidable challenge for physical forward modeling. Yet, abundance high-quality observational data has been amenable to application data-hungry neural networks geomagnetic storm forecasting. Almost all applications forecasting have utilized solar wind observations fro...

Appropriate precautions in the case of flood occurrence often require long lead times (several days) in hydrological forecasting. This in turn implies large uncertainties that are mainly inherited from the meteorological precipitation forecast. Here we present a case study of the extreme flood event of August 2005 in the Swiss part of the Rhine catchment (total area 34 550 km2). This event caus...

This COMET proposal describes a two-year project, beginning on 6/1/00, that will create, evaluate, and implement the first precipitation-type probabilistic forecast system, using ensemble forecasting and consensus forecasting concepts, at the Hydrometeorological Prediction Center (HPC) and the Storm Prediction Center (SPC). The research component of this project will evaluate the quality of var...

A framework for short-term probabilistic forecasting of watershed flood stage conditions using a distributed rainfall-runoff model is proposed. A stochastic rainfall pattern simulation model capable of generating input for a distributed rainfall-runoff model is developed. Generation of rainfall patterns over a 6-hour period is achieved using a translation vector rainfall forecasting process mod...

Recent developments in dynamical seasonal forecasting of potential relevance to agricultural management are discussed. These developments emphasize the importance of using a fully probabilistic approach at all stages of the forecasting process, from the dynamical ocean–atmosphere models used to predict climate variability at seasonal and interannual time scales, through the models used to downs...

Before and during the 2010 Soccer World Cup, participants made probabilistic forecasts for the outcomes of the tournament. We examine the relationship between depression levels and performance in this real-world forecasting task. Across two different waves of predictions, for both continuous and categorical classifications of depression, and with multiple measures of prediction accuracy, we fin...

Abstract This article describes two R packages for probabilistic weather forecasting, ensembleBMA, which offers ensemble postprocessing via Bayesian model averaging (BMA), and ProbForecastGOP, which implements the geostatistical output perturbation (GOP) method. BMA forecasting models use mixture distributions, in which each component corresponds to an ensemble member, and the form of the compo...

The application of a flash-flood prediction chain, developed by CIMA, to some testcases for the Tanaro river basin in the framework of the EU project HYDROPTIMET is presented here. The components of the CIMA chain are: forecast rainfall depths, a stochastic downscaling procedure and a hydrological model. Different meteorological Limited Area Models (LAMs) provide the rainfall input to the hydro...