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7 papers · 1 filter
How is model-related uncertainty quantified and reported in different disciplines?
Emily G. Simmonds, Kwaku Peprah Adjei, Christoffer Wold Andersen +40
How do we know how much we know? Quantifying uncertainty associated with our modelling work is the only way we can answer how much we know about any phenomenon. With quantitative s…
A framework for probabilistic weather forecast post-processing across models and lead times using machine learning
Charlie Kirkwood, Theo Economou, Henry Odbert +1
Forecasting the weather is an increasingly data intensive exercise. Numerical Weather Prediction (NWP) models are becoming more complex, with higher resolutions, and there are incr…
A stochastic model for the lifecycle and track of extreme extratropical cyclones in the North Atlantic
Paul Sharkey, Jonathan A. Tawn, Simon J. Brown
Extratropical cyclones are large-scale weather systems which are often the source of extreme weather events in Northern Europe, often leading to mass infrastructural damage and cas…
Spatio-temporal bivariate statistical models for atmospheric trace-gas inversion
Andrew Zammit-Mangion, Noel Cressie, Anita L. Ganesan +2
Atmospheric trace-gas inversion refers to any technique used to predict spatial and temporal fluxes using mole-fraction measurements and atmospheric simulations obtained from compu…
A Bayesian framework for verification and recalibration of ensemble forecasts: How uncertain is NAO predictability?
Stefan Siegert, David B. Stephenson, Philip G. Sansom +3
Predictability estimates of ensemble prediction systems are uncertain due to limited numbers of past forecasts and observations. To account for such uncertainty, this paper propose…
Controlling for unmeasured confounding and spatial misalignment in long-term air pollution and health studies
Duncan Lee, Christophe Sarran
The health impact of long-term exposure to air pollution is now routinely estimated using spatial ecological studies, due to the recent widespread availability of spatial reference…