Data-driven versus self-similar parameterizations for stochastic advection by Lie transport and location uncertainty
Abstract
Abstract. Stochastic subgrid parameterizations enable ensemble forecasts of fluid dynamic systems and ultimately accurate data assimilation (DA). Stochastic advection by Lie transport (SALT) and models under location uncertainty (LU) are recent and similar physically based stochastic schemes. SALT dynamics conserve helicity, whereas LU models conserve kinetic energy (KE). After highlighting general similarities between LU and SALT frameworks, this paper focuses on their common challenge: the parameterization choice. We compare uncertainty quantification skills of a stationary heterogeneous data-driven parameterization and a non-stationary homogeneous self-similar parameterization. For stationary, homogeneous surface quasi-geostrophic (SQG; QG) turbulence, both parameterizations lead to high-quality ensemble forecasts. This paper also discusses a heterogeneous adaptation of the homogeneous parameterization targeted at a better simulation of strong straight buoyancy fronts.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 16, 2020
- Source ID
- 10.5194/npg-27-209-2020
Entities
People
- Baylor Fox-Kemper
- Valentin Resseguier
- Wei Pan
Organizations
- Division of Ocean Sciences
- Engineering and Physical Sciences Research Council
- Office of Naval Research Global