1217 Sensitivity of a TPV to a Downstream Forecast Bust

Wednesday, 25 January 2017
4E (Washington State Convention Center )
Christopher P. Riedel, University of Oklahoma, Norman, OK; and S. Cavallo

The prediction of a particular feature has downstream implications on larger-scale atmospheric evolution and forecast skill.   The Tropopause Polar Vortex (TPV) is a feature found in the Arctic that can persist for many days before ultimately exerting a major impact on weather forecasts over North America. The extended-range predictability of weather events can be sensitive to initial conditions and the accuracy with which a model represents physical processes in short-term forecasts.  Ensemble Data Assimilation is used here as a tool to investigate forecast sensitivity of TPV characteristics to an extreme weather event that was poorly forecast.  By using an Ensemble Data Assimilation System, multiple initial conditions are provided, which leads to many realizations of TPVs.  This study examines the hypothesis that small differences in initial TPV strength and location lead to significant impacts occurring 6 days later for an extreme weather event and larger-scale flow in October 2010.

            Results show that forecast spread rapidly increases in association with an equatorward-moving TPV in the Canadian Arctic that interacts with an amplifying Rossby wave on the North Atlantic jet stream.  Discussion focuses on the ensemble sensitivity of a 6-day forecast to the location and strength of TPVs in the initial conditions using the Model for Prediction Across Scales (MPAS), which offers smooth grid refinement to higher resolutions without abrupt mesh transitions.  The MPAS grid refinement feature is found to be important in this study with higher-resolution over the TPV, allowing for a better representation and evolution of TPVs.  MPAS is initialized with analyses from the Global Ensemble Forecasting System (GEFS) creating a 21-member ensemble of MPAS forecasts.  The mean of the 21 initial condition states is then found, and subtracting each member from the mean produces perturbations.  Two sets of perturbations are produced using the most- and least-accurate members of the 21 forecasts.  Perturbations surrounding the TPV of interest are then retained to isolate the TPV contributions in initial conditions to test the hypothesis that significant forecast error is due to the location and strength of the TPV.

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