Seminar on Computational Learning and
Adaptation
Automating the estimation of meteorological parameters from multiple data sources using machine learning techniques
Mike Hadjimichael
Naval Research Laboratory, Monterey
hadjimic@nrlmry.navy.mil
U.S. Navy weather observing and forecasting operations would be
greatly assisted with the immediate assessment of meteorological
parameters when ground observations are not available. We attempt to
solve this problem by combining four types of coincident data:
numerical weather model, satellite, climatological, and observed,
into a single database to which machine learning methods are
applied. Supervised machine learning techniques, including decision
tree, neural network, K-nearest neighbor, etc., are used to discover
patterns in the data and develop associated classification and
parameter estimation algorithms. The parameters of initial interest
are cloud ceiling height and rain accumulation. There are 45
locations studied, over three geographical regions: the U.S. west
coast, the Adriatic Sea, and the Korean peninsula. Initial results
show that analysis using data from a combination of model and
satellite parameters yields a better estimation than either individual
source, and better than current methods. This presentation will
discuss the problem to be solved, the technical obstacles involved in
working with such data, solution methods applied, and initial
results.
Date: Thursday, February 7
|
Time: 4:15-5:30PM
|
Place: Cordura 100
|
Return to the seminar schedule