Bayesian Inference of Drag Parameters Using AXBT Data from Typhoon Fanapi
Sraj
I.
author
Iskandarani
M.
author
Srinivasan
A.
author
Thacker
W.C.
author
Winokur
J.
author
Alexanderian
A.
author
Lee
C.-Y.
author
Chen
S.S.
author
Knio
O.M.
author
2013
The authors introduce a three-parameter characterization of the wind speed dependence of the drag coefficient and apply a Bayesian formalism to infer values for these parameters from airborne expendable bathythermograph (AXBT) temperature data obtained during Typhoon Fanapi. One parameter is a multiplicative factor that amplifies or attenuates the drag coefficient for all wind speeds, the second is the maximum wind speed at which drag coefficient saturation occurs, and the third is the drag coefficient's rate of change with increasing wind speed after saturation. Bayesian inference provides optimal estimates of the parameters as well as a non-Gaussian probability distribution characterizing the uncertainty of these estimates. The efficiency of this approach stems from the use of adaptive polynomial expansions to build an inexpensive surrogate for the high-resolution numerical model that couples simulated winds to the oceanic temperature data, dramatically reducing the computational burden of the Markov chain Monte Carlo sampling. These results indicate that the most likely values for the drag coefficient saturation and the corresponding wind speed are about 2.3 × 10−3 and 34 m s−1, respectively; the data were not informative regarding the drag coefficient behavior at higher wind speeds.
Atmosphere-ocean interaction
Hurricanes
Ocean circulation
Bayesian methods
Inverse methods
Statistical techniques
exported from refbase (http://deep-c.org/library/show.php?record=477), last updated on Mon, 15 Jul 2013 08:58:08 -0400
text
http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-12-00228.1
http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-12-00228.1
10.1175/MWR-D-12-00228.1
Sraj_etal2013
Monthly Weather Review
Mon. Wea. Rev.
2013
continuing
periodical
academic journal
141
7
2347
2367
0027-0644