Statistical methods for simulation and prediction of space-time geophysical data

Event Sponsor: 
Mathematics and Computer Science Division LANS Seminar
Start Date: 
Apr 19 2017 - 3:00pm
Building/Room: 
Building 240/Room 1404-1405
Location: 
Argonne National Laboratory
Speaker(s): 
Julie Bessac
Speaker(s) Title: 
Postdoctoral Appointee, MCS

We will discuss statistical methods for predicting and simulating spatio-temporal data and the uncertainty associated with various data sources. We propose a statistical space-time model for predicting atmospheric wind speed based on deterministic numerical weather predictions (NWP) and historical measurements. The wind speed forecast is represented as stochastic predictive scenarios that are targeted for power grid applications where they account for the uncertainty associated with renewable energy generation. As forecast evaluation is a crucial step in the prediction process, we will discuss the importance of benchmark data. In particular, we model the error associated with the verification dataset and investigate the effect of this error on a decision-making process.

Miscellaneous Information: 

Coffee and goodies will be served.

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Upcoming Seminars:

April 26, 2017, "Hybrid quantum-classical computation for chemistry and materials" Jarrod McClean, Alvarez Fellow, Lawrence Berkeley National Laboratory, [more info]

May 16, 2017, "TBD" Julian Hall, Senior Lecturer, The University of Edinburgh, [more info]