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Dr Antje Weisheimer (she)

Principal NCAS Research Fellow

Research theme

  • Climate physics

Sub department

  • Atmospheric, Oceanic and Planetary Physics

Research groups

  • Predictability of weather and climate
Antje.Weisheimer@physics.ox.ac.uk
Telephone: 01865 (2)82441
Robert Hooke Building, room S37
ECMWF
NCAS
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Warming Stripes for Oxford from 1814-2019

Warming Stripes for Oxford from 1814-2019.

Dynamically-based seasonal forecasts of Atlantic tropical storm activity issued in June by EUROSIP

Geophysical Research Letters 34:16 (2007)

Authors:

F Vitart, MR Huddleston, M Déqué, D Peake, TN Palmer, TN Stockdale, MK Davey, S Ineson, A Weisheimer

Abstract:

Most seasonal forecasts of Atlantic tropical storm numbers are produced using statistical-empirical models. However, forecasts can also be made using numerical models which encode the laws of physics, here referred to as "dynamical models". Based on 12 years of re-forecasts and 2 years of real-time forecasts, we show that the so-called EUROSIP (EUROpean Seasonal to Inter-annual Prediction) multi-model ensemble of coupled ocean atmosphere models has substantial skill in probabilistic prediction of the number of Atlantic tropical storms. The EUROSIP real-time forecasts correctly distinguished between the exceptional year of 2005 and the average hurricane year of 2006. These results have implications for the reliability of climate change predictions of tropical cyclone activity using similar dynamically-based coupled ocean-atmosphere models.
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How good is an ensemble an capturing truth? Using bounding boxes for forecast evaluation

Quarterly Journal of the Royal Meteorological Society 133:626 A (2007) 1309-1325

Authors:

K Judd, LA Smith, A Weisheimer

Abstract:

Ensemble prediction systems aim to account for uncertainties of initial conditions and model error. Ensemble forecasting is sometimes viewed as a method of obtaining (objective) probabilistic forecasts. How is one to judge the quality of an ensemble at forecasting a system? The probability that the bounding box of an ensemble captures some target (such as 'truth' in a perfect model scenario) provides new statistics for quantifying the quality of an ensemble prediction system: information that can provide insight all the way from ensemble system design to user decision support. These simple measures clarify basic questions, such as the minimum size of an ensemble. To illustrate their utility, bounding boxes are used in the imperfect model context to quantify the differences between ensemble forecasting with a stochastic model ensemble prediction system and a deterministic model prediction system. Examining forecasts via their bounding box statistics provides an illustration of how adding stochastic terms to an imperfect model may improve forecasts even when the underlying system is deterministic. Copyright © 2007 Royal Meteorological Society.
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Historical Overview of Climate Change Science

Chapter in Intergovernmental Panel on Climate Change (IPCC), 4th Assessment Report, Working Group 1: The Physical Basis of Climate Change, (2007) 1

Authors:

H Le Treut, R Somerville, A Weisheimer

Initialisation strategies for decadal hindcasts for the 1960-2005 period within the ENSEMBLES project. ECMWF Tech Memo.

(2007) 521

Authors:

A Weisheimer, FJ Doblas-Reyes, P Rogel, N Keenlyside, MA Balmaseda, J Murphy, D Smith, M Collins, B Bhaskaran, TN Palmer

Recent Advances in Radiation Transfer Parametrizations. ECMWF Tech Memo.

(2007) 539

Authors:

J-J Morcrette, P Bechtold, A Beljaars, A Weisheimer

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