Abstract
We present a statistical method for reconstructing continuous background density profiles that embeds incomplete measurements and a physically intuitive density stratification model within a Bayesian hierarchal framework. A double hyperbolic tangent function is used as a parametric density stratification model that captures various pycnocline structures in the upper ocean and offers insight into several density profile characteristics (e.g., pycnocline depth). The posterior distribution is used to quantify uncertainty and is estimated using recent advances in Markov chain Monte Carlo sampling. Temporally evolving posterior distributions of density profile characteristics, isopycnal heights, and nonlinear ocean process models for internal gravity waves are presented as examples of how uncertainty propagates through models dependent on the density stratification. The results show 0.95 posterior interval widths that ranged from 2.5% to 4% of the expected values for the linear internal wave phase speed and 15%-40% for the nonlinear internal wave steepening parameter. The data, collected over a year from a through-the-column mooring, and code, implemented in the software package Stan, accompany the article.
| Original language | English |
|---|---|
| Pages (from-to) | 1313-1330 |
| Number of pages | 18 |
| Journal | Journal of Atmospheric and Oceanic Technology |
| Volume | 36 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - Jul 2019 |
Cite this
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Uncertainty Quantification of Density and Stratification Estimates with Implications for Predicting Ocean Dynamics. / Manderson, A.; Rayson, M. D.; Cripps, E.; Girolami, M.; Gosling, J. P.; Hodkiewicz, M.; Ivey, G. N.; Jones, N. L.
In: Journal of Atmospheric and Oceanic Technology, Vol. 36, No. 7, 07.2019, p. 1313-1330.Research output: Contribution to journal › Article
TY - JOUR
T1 - Uncertainty Quantification of Density and Stratification Estimates with Implications for Predicting Ocean Dynamics
AU - Manderson, A.
AU - Rayson, M. D.
AU - Cripps, E.
AU - Girolami, M.
AU - Gosling, J. P.
AU - Hodkiewicz, M.
AU - Ivey, G. N.
AU - Jones, N. L.
PY - 2019/7
Y1 - 2019/7
N2 - We present a statistical method for reconstructing continuous background density profiles that embeds incomplete measurements and a physically intuitive density stratification model within a Bayesian hierarchal framework. A double hyperbolic tangent function is used as a parametric density stratification model that captures various pycnocline structures in the upper ocean and offers insight into several density profile characteristics (e.g., pycnocline depth). The posterior distribution is used to quantify uncertainty and is estimated using recent advances in Markov chain Monte Carlo sampling. Temporally evolving posterior distributions of density profile characteristics, isopycnal heights, and nonlinear ocean process models for internal gravity waves are presented as examples of how uncertainty propagates through models dependent on the density stratification. The results show 0.95 posterior interval widths that ranged from 2.5% to 4% of the expected values for the linear internal wave phase speed and 15%-40% for the nonlinear internal wave steepening parameter. The data, collected over a year from a through-the-column mooring, and code, implemented in the software package Stan, accompany the article.
AB - We present a statistical method for reconstructing continuous background density profiles that embeds incomplete measurements and a physically intuitive density stratification model within a Bayesian hierarchal framework. A double hyperbolic tangent function is used as a parametric density stratification model that captures various pycnocline structures in the upper ocean and offers insight into several density profile characteristics (e.g., pycnocline depth). The posterior distribution is used to quantify uncertainty and is estimated using recent advances in Markov chain Monte Carlo sampling. Temporally evolving posterior distributions of density profile characteristics, isopycnal heights, and nonlinear ocean process models for internal gravity waves are presented as examples of how uncertainty propagates through models dependent on the density stratification. The results show 0.95 posterior interval widths that ranged from 2.5% to 4% of the expected values for the linear internal wave phase speed and 15%-40% for the nonlinear internal wave steepening parameter. The data, collected over a year from a through-the-column mooring, and code, implemented in the software package Stan, accompany the article.
KW - Thermocline
KW - In situ oceanic observations
KW - Bayesian methods
KW - Model errors
KW - Model output statistics
KW - MODEL
KW - ENERGY
KW - EVOLUTION
KW - PROFILES
KW - WAVES
U2 - 10.1175/JTECH-D-18-0200.1
DO - 10.1175/JTECH-D-18-0200.1
M3 - Article
VL - 36
SP - 1313
EP - 1330
JO - Journal of Atmospheric and Oceanic Technology
JF - Journal of Atmospheric and Oceanic Technology
SN - 0739-0572
IS - 7
ER -