Abstract
Abstract. Observation operators (OOs) are a central component of any data assimilation system. As they project the state variables of a numerical model into the space of the observations, they also provide an ideal opportunity to correct for effects that are not described or are insufficiently described by the model. In such cases a dynamical OO, an OO that interfaces to a secondary and more specialised model, often provides the best results. However, given the large number of observations to be assimilated in a typical atmospheric or oceanographic model, the computational resources needed for using a fully dynamical OO mean that this option is usually not feasible. This paper presents a method, based on canonical correlation analysis (CCA), that can be used to generate highly efficient statistical OOs that are based on a dynamical model. These OOs can provide an approximation to the dynamical model at a fraction of the computational cost. One possible application of such an OO is the modelling of the diurnal cycle of sea surface temperature (SST) in ocean general circulation models (OGCMs). Satellites that measure SST measure the temperature of the thin uppermost layer of the ocean. This layer is strongly affected by atmospheric conditions, and its temperature can differ significantly from the water below. This causes a discrepancy between the SST measurements and the upper layer of the OGCM, which typically has a thickness of around 1 m. The CCA OO method is used to parameterise the diurnal cycle of SST. The CCA OO is based on an input dataset from the General Ocean Turbulence Model (GOTM), a high-resolution water column model that has been specifically tuned for this purpose. The parameterisations of the CCA OO are found to be in good agreement with the results from the GOTM and improve upon existing parameterisations, showing the potential of this method for use in data assimilation systems.
Highlights
Data assimilation (DA) strives to improve the forecast skill of a numerical model by combining the model with observations
The modelling of the diurnal cycle of sea surface temperature (SST) is described in Pimentel et al (2019), while the current paper focuses on the method for constructing the OO
During days of low wind and/or high insolation conditions the amplitude of the SST diurnal cycle can be larger than the combined accuracy of the model and observations, causing a straightforward assimilation of SST to degrade the performance of the model (Marullo et al, 2016)
Summary
Data assimilation (DA) strives to improve the forecast skill of a numerical model by combining the model with observations. One could even consider a dynamical OO that wraps a second numerical model to locally refine the results of the parent model The latter solution may very well provide the most accurate results, but the vast number of observations that need to be assimilated in a typical atmospheric or oceanographic model means that this approach would require a prohibitive amount of computing resources. This limits OOs in most practical applications to relatively simple parameterisations in terms of the model state variables.
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