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X-WR-CALNAME:Data Assimilation in The Great Lakes: Implementation of Local 
 Ensemble Kalman Filter (LETKF) for Improving Lake Erie Surface Temperature
  Prediction 
X-WR-TIMEZONE:Eastern Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260814T153709Z
UID:tag:localist.com\,2008:EventInstance_39677038352817
DTSTART:20220418T190000Z
DTEND:20220418T200000Z
DESCRIPTION:Environmental Engineering Graduate Seminar\n\nAra Hakim\, Envir
 onmental Engineering Ph.D. student\, Graduate Research Assistant\n\nBio:\n
 Ara Hakim is a graduate research assistant at the Dept. of Civil\, Environ
 mental\, and Geospatial Engineering in Michigan Tech. He has a BS degree i
 n Oceanography (2006) from Bandung Institute of Technology (Indonesia)\, a
 nd MS degrees in Coastal Geoscience & Engineering (2009) and Marine Scienc
 e & Technology (2015) from University of Kiel (Germany) and UMass Dartmout
 h respectively. He served as a consultant for various government agencies 
 and private companies in Indonesia\, being involved in various works such 
 as marine policy\, marine spatial planning\, and ocean operational forecas
 t system development. Two years prior to joining Michigan Tech\, easing hi
 s way back to academia Ara worked as a research fellow at Hydrography Rese
 arch Group in Bandung Institute of Technology and as an adjunct lecturer a
 t the School of Fisheries and Marine Science in Padjadjaran University\, b
 oth located at his hometown. He currently works under Dr. Pengfei Xue doin
 g implementation of Data Assimilation techniques in the Great Lakes\, and 
 receives CIGLR student fellowship for the year 2021.\n\nAbstract: \nLake s
 urface temperature (LST) is one of the most important physical variables i
 n the Great Lakes. It plays a major role and acts as a crucial proxy for u
 nderstanding atmosphere-lake interactions\, ecosystem dynamics\, and futur
 e projection due to the changing climate. Hence\, accurate estimation of L
 ST within the Great Lakes Operational Forecast System (GLOFS) has been und
 er continuous development since the 90s. This research uses ensemble-based
  data assimilation approach to improve the accuracy of short-term LST fore
 cast for Lake Erie. The Finite Volume Community Ocean Model (FVCOM) is the
  basis of the operational forecast system. Daily LST from Great Lakes Surf
 ace Environmental Analysis (GLSEA) database is assumed to be the true stat
 e\, and it is also used to correct initial conditions during the Data Assi
 milation (DA) phase utilizing Universal Model Domain Local Ensemble Transf
 orm Kalman Filter (UMD-LETKF). In this investigation we are using 20-membe
 r ensemble models\, which are generated using atmospheric forcing from NOA
 A’s High Resolution Ensemble Forecast (HREF). Our current work demonstra
 tes that UMD-LETKF has been successfully implemented in improving the init
 ial conditions for FVCOM forecast model run. Preliminary results from eval
 uation of 7-day forecast in the mid of August 2021 show improvement for fo
 recast simulation with DA compared to forecast simulation without DA.
GEO:47.120636;-88.546486
LOCATION:Great Lakes Research Center (GLRC)\, 202
SUMMARY:Data Assimilation in The Great Lakes: Implementation of Local Ensem
 ble Kalman Filter (LETKF) for Improving Lake Erie Surface Temperature Pred
 iction 
URL;VALUE=URI:https://events.mtu.edu/event/data_assimilation_in_the_great_l
 akes_implementation_of_local_ensemble_kalman_filter_letkf_for_improving_la
 ke_erie_surface_temperature_prediction
CATEGORIES:Academics
CATEGORIES:Lectures/Seminars
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