Advances in Weather Forecasting

Probabilistic Weather Forecasting in R

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...

Long-range Weather Forecasting

Forecasting Space Weather

Forecasting space weather events presents the ultimate challenge to a space physics model. Not only should physical constraints be satisfied, but also practical issues such as timeliness, accuracy, and reliability must be adequately addressed. To address these needs, modern space weather forecasters and users rely on a great variety of space weather models, ranging from simple regressions and e...

Model error in weather forecasting

Operational forecasting is hampered both by the rapid divergence of nearby initial conditions and by error in the underlying model. Interest in chaos has fuelled much work on the first of these two issues; this paper focuses on the second. A new approach to quantifying state-dependent model error, the local model drift, is derived and deployed both in examples and in operational numerical weath...

Weather and Forecasting

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