Thursday, 14 January 2016
Hall D/E ( New Orleans Ernest N. Morial Convention Center)
One or several extreme weather events (EWEs) during a single season can contribute disproportionately to temperature and precipitation anomaly statistics for that particular season. This disproportionate contribution suggests that EWEs need to be considered in describing and understanding the dynamical and thermodynamic processes that operate at the weather–climate intersection and in constructing operational probabilistic temperature and precipitation forecasts for the 8–10 day time period. On the basis of this suggestion, two EWEs over the continental United States, the 22–23 December 2013 ice storm and the extended period of record cold temperatures during November 2014, will be investigated in an effort (1) to evaluate the skill of NCEP GFS deterministic and ensemble week two forecasts for these particular events, and (2) to identify the governing atmospheric flow patterns that were essential to the evolution of each event.
Predicated on the results garnered from the above case studies, a methodology to objectively identify EWEs over the continental United States is presented as part of an investigation into the skill of week two GFS forecasts of extreme temperature and precipitation events. In this investigation, extreme will be defined on the basis of observations in terms of standardized anomalies for temperature and percentiles for precipitation, and events will be defined on the basis of the spatial coverage and duration of the extreme values of temperature and precipitation. Following their identification, EWEs will be stratified in a synoptically meaningful manner that permits their classification into characteristic event types. Knowledge of both the predictive skill of week two model forecasts and the governing atmospheric flow patterns for each event type has the potential to provide forecasters with a “first alert” to the possibility of the occurrence of extreme temperature and precipitation events within the 8–10 day time period.
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