ENVIRONMENTAL ENGINEERING SPRING 2016 COLLOQUIUM SERIES and ASG
Friday, February 12, 2016 • 12:15 PM • Storrs Hall, Room WW16
“Probabilistic predictions and uncertainty quantification with an analog ensemble”
Luca Delle Monache, Scientist, National Center for Atmospheric Research, Boulder, CO
Abstract: The analog of a forecast for a given location and time is defined as the observation that corresponds to a past prediction matching selected features of the current forecast. The best analogs form the analog ensemble (AnEn). The AnEn is a general method to generate probabilistic predictions that has been tested successfully for a range of applications including weather predictions, climate downscaling, renewable energy (wind and solar), air quality (ground-level ozone and particulate matter), and hurricane intensity. The recurring features found across different applications are:
- Theabilitytouseahigherresolutionmodelsinceonlyonereal-timedeterministicforecastisneeded to generate an ensemble;
- Noneedforinitialconditionormodelperturbationstrategiestogenerateanensemble;
- Reliableuncertaintyquantification,i.e.,noadditionalensemblecalibrationisrequired;
- Abilitytocapturetheflow-dependenterrorcharacteristics.
- A superior skill in predicting rare events.Examples of AnEn current applications will be shown, and a discussion on how this technique could be implemented for probabilistic predictions of weather parameters over a two-dimensional gridded domain will follow.
Presenter’s Bio:
Luca Delle Monache is the Deputy Science Director of the National Security Applications Program of the Research Applications Laboratory with the National Center for Atmospheric Research, Boulder, Colorado. He earned a Laurea (M.S.) in Mathematics from the University of Rome, Italy (1997), a M.S. in Meteorology from the San Jose State University, San Jose, California (2002), and a Ph.D. in Atmospheric Sciences from the University of British Columbia, Vancouver, Canada (2006). Before joining NCAR he worked at the Lawrence Livermore National Laboratory. His main interests include probabilistic predictions, uncertainty quantification and ensemble design, urban meteorology, mesoscale numerical weather prediction, ensemble data assimilation, boundary layer meteorology, air pollution, inverse and dispersion modeling, and renewable energy prediction and resource assessment.
To access this seminar’s live broadcast or recording please use the following link:
https://mediasite.dl.uconn.edu/Mediasite/Play/107cc29e625744038f830910b0c29a061d
Sponsors: Department of Civil and Environmental Engineering, Environmental Engineering Program, Center for Environmental Sciences and Engineering and Atmospheric Sciences Group