Formulation and demonstration of an extended-3DVAR multi-scale data assimilation system for the SWOT altimetry era
Abstract. A state-of-the-art data assimilation system for a high-resolution model has been developed to address the opportunities and challenges posed by the upcoming Surface Water and Ocean Topography (SWOT) satellite mission. A new ‘extended’ three-dimensional variational data assimilation scheme (extended-3DVAR) is formulated to assimilate observations over a time interval, and integrated using a multi-scale approach (hereafter MSDA). The new MSDA scheme specifically enhances the efficacy of the assimilation of satellite along-track altimetry observations, which are limited by large repeat time intervals. This developed system is computationally highly efficient, and thus can be applied to a very high-resolution model. A crucial consideration of the system is first to assimilate routinely available observations, including satellite altimetry, sea surface temperature (SST) and temperature/salinity vertical profiles, to constrain large scales and large mesoscales. High-resolution (dense) observations and future SWOT measurements can then be effectively and seamlessly assimilated to constrain the smaller scales. Using this system, a reanalysis dataset was produced for the SWOT pre-launch field campaign that took place in the California Current System from September through December, 2019. An evaluation of this system with assimilated and withheld data demonstrates its ability to effectively utilize both routine and campaign observations. These results suggest a promising avenue for data assimilation development in the SWOT altimetry era, which will require the capability to efficiently assimilate large-volume datasets resolving small-scale ocean processes.
This preprint has been withdrawn.
Zhijin Li et al.
Zhijin Li et al.
Reanalysis and sensitivity experiments using a multi-scale data assimilation system during a field campaign in the California current system https://doi.org/10.5281/zenodo.4602095
Zhijin Li et al.
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This manuscript introduces a high-resolution ocean data assimilation system for generating effective ocean state estimation with SWOT data. Authors adopt an extended 3DVAR and multi-scale data assimilation strategies, and mathematical background of those theories are described. They also evaluate the effectiveness of assimilating along-track satellite SSH data, and dense TS profiles obtained by an observational campaign.
In the manuscript, the current status of SWOT and its evaluation at the cross-over point are well described, which provides readers valuable information. They also show that the system is appropriately represent small mesoscale and submesoscale phenomena. However, I feel that some essential information on the configuration of the data assimilation system is missing (see major comment A), and therefore it is difficult to understand how the adopted data assimilation strategies work in their system. And they do not appropriately evaluate the impacts of introducing the extended 3DVAR (see major comment B). Therefore, I recommend to publish this manuscript after a major revision.
A) Some essential information on the configuration of the data assimilation is missing in the manuscript. It is not clear what prediction variables in the ocean model are modified by the data assimilation. Does the system modify only TS and SSH, or does it also modify the velocity fields? And I did not find the description on how the DA system links the SSH increments to the TS increments. And there is no information how the analysis increments are inserted in the ocean model. Does the system adopt Incremental Analysis Updates or just direct insertion? And how often does the system perform the analysis?
Also, some information is missing on the observation and background errors. For example, I cannot find the information on the observation errors for the regular TS profiles. And, more importantly, authors should describe how they deal with the representativeness errors because a sophisticated setting is required for their multi-scale DA methods. For example, the standard deviations of the small mesoscale and submesoscale variations must be included in the representativeness errors of the regular TS profiles because the data are assimilated as the large mesoscale observations. The background errors for the three scales also have to be determined very carefully. These informations are required to evaluate the results shown in this manuscript correctly.
b) The impact of extended 3DVAR is not appropriately evaluated although authors insist it is the unique point of their system. In order to show the impacts of introducing extended 3DVAR, authors should compare the results with experiments in which a regular observation time window is used or inflation of the observation errors due to the sampling time errors are not applied. In addition, I would like to say that the extended 3DVAR cannot draw full values of observation data because larger observation errors are assigned to the data observed at the time deviated from the analysis time. It would be valuable if authors address why the extended 3DVAR is selected instead of FGAT, which is widely used in ocean data assimilation systems.