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SUMMARY:On the Development of an Ensemble Data Assimilation and Forecastin
 g System for the Red Sea  - Ibrahim Hoteit\, King Abdullah University of S
 cience and Technology (KAUST)
DTSTART:20191024T140000Z
DTEND:20191024T150000Z
UID:TALK133624@talks.cam.ac.uk
CONTACT:Edriss S. Titi
DESCRIPTION:With a growing interest in exploiting the Red Sea resources an
 d protecting its fragile ecosystem\, there is more and more pressing deman
 d for building an operational system to predict its circulation. This is a
  challenging task due to the dominant strong seasonal variability and shor
 t-living mesoscales in this basin. This talk will present our approach for
  building this system within an ensemble Kalman filtering (EnKF) framework
 \, combining a (i) one-step-ahead smoothing formulation to enhance the ens
 embles sampling with the future observations\, (ii) a hybrid formulation o
 f the filter prior covariance for implementation with reasonable-size ense
 mbles\, and (iii) a second-order exact sampling of the observations pertur
 bations for efficient implementation of (i) and (ii) with a stochastic EnK
 F. I will discuss the relevance of each of these schemes and demonstrate t
 heir performances with various applications.
LOCATION:MR 14
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