Bayesian inference of drag parameters using AXBT data from typhoon fanapi

Ihab Sraj, Mohamed Iskandarani, Ashwanth Srinivasan, W. Carlisle Thacker, Justin Winokur, Alen Alexanderian, Chia Ying Lee, Shuyi S. Chen, Omar M. Knio*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


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 3 10-3 and 34ms-1, respectively; the data were not informative regarding the drag coefficient behavior at higher wind speeds.

Original languageEnglish (US)
Pages (from-to)2347-2367
Number of pages21
JournalMonthly Weather Review
Issue number7
StatePublished - 2013

ASJC Scopus subject areas

  • Atmospheric Science


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