Mr Sam Allen
I am a final year PhD student studying the statistical post-processing of weather forecasts. Dynamical weather prediction models typically comprise complex partial differential equations that seek to explain the atmosphere's evolution. Unfortunately, due to limited computational resources and incomplete knowledge of atmospheric processes, these models are inherently imperfect. The removal of systematic model errors is achieved by statistically post-processing the output. This can be thought of as a statistical modelling technique that maps dynamical model output to real world observations.
My research has, so far, focussed on model biases that can be attributed to the manifestation of certain atmospheric circulation patterns. These 'atmospheric regimes' have a pronounced effect on local weather systems, and a failure to capture their occurrence can result in misleading predictions of both surface and upper-air weather variabes. My recent work has therefore considered extending conventional post-processing methods to utilise additional regime information, and investigating in what situations such extensions would be most beneficial.
Allen S, Ferro CAT, Kwasniok F. (2020) Recalibrating wind speed forecasts using regime-dependent Ensemble Model Output Statistics, Quarterly Journal of the Royal Meteorological Society. DOI:10.1002/qj.3806. [PDF]
Allen S, Ferro CAT, Kwasniok F. (2019) Regime-dependent statistical post-processing of ensemble forecasts, Quarterly Journal of the Royal Meteorological Society, 145, 3535-3552. DOI:10.1002/qj.3638. [PDF]