Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation

Abstract

The ever increasing geophysical data streams pouring from earth observation satellite missions and numerical simulations along with the development of dedicated big data infrastructure advocate for truly exploiting the potential of these datasets, through novel data-driven strategies, to deliver enhanced satellite-derived gapfilled geophysical products from partial satellite observations. We here demonstrate the relevance of the analog data assimilation (AnDA) for an application to the reconstruction of cloud-free level-4 gridded Sea Surface Temperature (SST). We propose novel AnDA models which exploit auxiliary variables such as sea surface currents and significantly reduce the computational complexity of AnDA. Numerical experiments benchmark the proposed models with respect to state-of-the-art interpolation techniques such as optimal interpolation and EOF-based schemes. We report relative improvement up to 40%/50% in terms of RMSE and also show a good parallelization performance, which supports the feasibility of an upscaling on a global scale.filled and the fine-scale variability of the SST fields. Physically-driven data assimilation models [20] may outperform OI if relevant dynamical priors can be defined [21]. The trade-off to be considered between the complexity and genericity of this physical prior remains however complex, especially when considering the assimilation of a single sea surface tracer as SST.Besides model-driven schemes, the ever increasing availability of satellite-derived data and of simulation data from high-resolution ocean models has paved the way for the development of data-driven methods. EOF-based models were among the early and perhaps most popular data-driven methods applied to the reconstruction of SST fields from cloudy SST data [14,15,22] as well of other sea surface tracers such as ocean colour [22]. EOF-based approaches are particularly appealing for ocean remote sensing as they relate to a model of the covariance structure of the considered fields and may adapt to any type of geometry of missing data and interpolation grid. Their use is also motivated by their ability to decompose the spatiotemporal variability of the sea surface fields according to different modes, which may be interpreted geophysically. A renewed interest can also be noticed for analog schemes and applications to forecasting and assimilation issues [23,24]. Analog schemes, proposed a long time ago in geoscience [25], rely on the idea that the dynamics of a given system may repeat to some extent. Given a set of previously observed or analysed data, one may retrieve examples similar to a current state in this set, such that the future of this current state may be forecasted from the known evolution of these similar situations. The lack of large-scale dataset along with the computational complexity of analog methods has long limited their applicability. In this context, we recently introduced the analog data assimilation (AnDA) and demonstrated its relevance for the reconstruction of complex dynamical systems fo...

Observational Needs of Sea Surface Temperature

“…Optimal Interpolation (OI) and data assimilation techniques are traditionally used to provide gap-free datasets. These approaches are often complex and based on a number of assumptions and parameterizations (Miles and He, 2010;Zhao and He, 2012;Fablet et al, 2018). An alternate approach is to use Data Interpolation Empirical Orthogonal functions (DINEOF) FIGURE 14 | The scatter plot between in situ data and reconstructed SST using DINEOF and in situ validation data for user case study 3 during the period 2006-2015.…”

An Adaptive Optimal Interpolation Based on Analog Forecasting: Application to SSH in the Gulf of Mexico

“…The catalog is used for inferring the system dynamics and for building estimates of the system state at unobserved locations and times. Realistic applications to oceanic data include the interpolation of SST (Fablet et al 2018b) and the interpolation of SSH (Lguensat et al 2019). Lguensat et al (2019) have shown in particular how AnDA can be used for improving OIbased SSH fields at fine scale.…”

Synergy between Ocean Variables: Remotely Sensed Surface Temperature and Chlorophyll Concentration Coherence

“…To achieve this goal, data interpolation and noise reduction are commonly performed using univariate methods as, for example, [1]. Multivariate methods have also been applied to generate uniform products that combine information from different sensors; examples of multivariate approaches include optimal interpolation [2], empirical orthogonal functions [3,4], classification methods [5] and more recently using data-diven methods as analog data assimilation [6,7]. The method discussed in this paper is based on the empirical perception that fronts, eddies, and filaments are identifiable in the satellite imagery of different ocean variables (sea level, ocean color, sea surface temperature, sea surface salinity) and, sometimes, even in the raw radiance recorded by the satellites [8,9].…”