Computationally efficient modeling and data assimilation of near-surface variability
Computationally efficient modeling and data assimilation of near-surface variability
Abstract:
Near-surface (< 20m) ocean exhibits high variability due to coupled interactions, for e.g., with the atmosphere, sea ice, land, etc.
Here we focus on atmospheric heat and momentum (wind) forcing, which are known to cause diurnal variability within the mixed layer.
Only recently with a combination of sufficiently high vertical/horizontal resolution (75L, 1/4deg) and sub-daily atmospheric
forcing fields, ocean models are starting to resolve this diurnal variability. However, the computation expense of such a
high vertical resolution is burdensome in the context of coupled modeling and data assimilation. An alternative approach is to
parameterize this diurnal variability with a prognostic model, that is embedded into the ocean model.
In the first part of this presentation, we will demonstrate results with the above two approaches, by comparing them to profiles of
near-surface temperature and salinity. In the context of data assimilation and reanalysis, this modeling capability opens
the door to re-examine and perhaps improve specification of background (or, ensemble) error characteristics. The
second half of this talk will focus on illustrating diurnally varying errors within an ensemble DA, and possible approaches to improve
localization (horizontal/vertical) to extract maximum possible observational information content from in-situ and satellite
observations of sea surface temperature.
Here we focus on atmospheric heat and momentum (wind) forcing, which are known to cause diurnal variability within the mixed layer.
Only recently with a combination of sufficiently high vertical/horizontal resolution (75L, 1/4deg) and sub-daily atmospheric
forcing fields, ocean models are starting to resolve this diurnal variability. However, the computation expense of such a
high vertical resolution is burdensome in the context of coupled modeling and data assimilation. An alternative approach is to
parameterize this diurnal variability with a prognostic model, that is embedded into the ocean model.
In the first part of this presentation, we will demonstrate results with the above two approaches, by comparing them to profiles of
near-surface temperature and salinity. In the context of data assimilation and reanalysis, this modeling capability opens
the door to re-examine and perhaps improve specification of background (or, ensemble) error characteristics. The
second half of this talk will focus on illustrating diurnally varying errors within an ensemble DA, and possible approaches to improve
localization (horizontal/vertical) to extract maximum possible observational information content from in-situ and satellite
observations of sea surface temperature.