2B.4 Understanding African Easterly Wave Forecast Intensification Differences in the ECMWF Ensemble Prediction System

Monday, 16 April 2018: 11:15 AM
Masters ABCD (Sawgrass Marriott)
Travis J. Elless, Univ. at Albany, SUNY, Albany, NY; and R. D. Torn

During the boreal summer, African Easterly Waves (AEWs) are the primary synoptic-scale feature that influences North African weather, and are associated with the majority of the rainfall observed in this region. Previous studies have shown that AEW intensification occurs through a mixed barotropic-baroclinic energy conversion, while more recent studies have described how diabatic processes within AEW convection can also influence AEW intensification. However, these studies primarily focused on observations and idealized models, few studies have explored intensity differences of AEWs in operational forecasts. Furthermore, the processes leading to the growth of uncertainty within these forecasts remains unknown.

This project utilizes a climatology of AEW forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system, which are available through the THORPEX Interactive Grand Global Ensemble (TIGGE) dataset, during July-August-September 2007—2009 and 2011—2013. Previous work with this climatology suggests larger intensity variability occurs with a more convectively favorable downstream thermodynamic environment, which subsequently yields an increase in near-wave convection. While this result may be valid for a large number of cases, it is worth understanding what drives AEW intensification differences within individual forecasts. This study investigates the growth of forecast variability for several cases exhibiting large intensity variability . For each case, the ensemble members are subdivided into the 10 strongest members and the 10 weakest members to diagnose the kinematic and thermodynamic sources of forecast variability in a wave-centric framework. Moreover, differences in barotropic, baroclinic, and diabatic processes will be compared to identify which process(es) likely lead(s) to AEW intensity variability.

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