Introducing Along-Track Error Correlations for Altimetry Data in a Regional Ocean Prediction System

Abstract

Because of the systematic error in the processing of altimetry data, sea level anomaly (SLA) observation errors are likely affected by nonnegligible spatial correlations. To account for these, we exploit the synergy of altimetry data with in situ profiles from gliders, piloted to follow the altimetry tracks during the Long-Term Glider Mission for Environmental Characterization 2017 (LOGMEC17) observational campaign in the Ligurian Sea. The assimilation of along-track unfiltered sea level anomalies in a regional ocean analysis and forecast system is consequently optimized by means of introducing spatial correlations for the SLA observation errors. In particular, collocated data of glider and altimetry are used to derive an along-track error covariance model for the sea level anomaly assimilation, assuming that most of the covariance behavior versus separation distance stems from altimetry. Spatial scales of the altimetry error are found to have a correlation radius of about 12 km for the dataset utilized in the Ligurian Sea, using a simple Gaussian shape for the error correlation, shorter than the correlation radius found through assimilation output diagnostics. A variational data assimilation system is modified to relax the usual assumption of uncorrelated altimetry observation errors, thus allowing for along-track error correlations. Its implementation provides promising results in the regional ocean prediction system, outperforming in most verification skill scores the use of uncorrelated observational errors without compromising the analysis scheme efficiency.

Sea-state contributions to sea-level variability in the European Seas

“…In this study, we use the sealevel anomaly (SLA) from the Tailored Altimetry Products for Assimilation System (TAPAS) dataset (e.g. Pujol et al 2016;Storto et al 2019b), which provides data without along-track sub-sampling (Dufau et al 2016) and makes available the corrections applied to the signals retrieved from the altimetric waveforms (e.g. dynamic atmospheric corrections; Carrère and Lyard 2003; Taburet and SL-TAC Team 2019).…”

Observing the Disintegration of the A68A Iceberg from Space

“…When the whole track is averaged to derive the mean freeboard change at one point in time, the uncertainties of the involved grid cells are propagated. Rather than assume that our freeboard measurement errors are not correlated in space or time, we employ a more conservative approach and propagate the uncertainties using a full covariance matrix to account for their correlation (Storto et al, 2019). In the absence of independent freeboard measurements for verification, we assume that altimeter-derived freeboards recorded along the same track are 60% correlated and that the initial freeboards, which are derived from measurements acquired along several independent tracks, are 30% correlated.…”

Optimal Assimilation of Daytime SST Retrievals from SEVIRI in a Regional Ocean Prediction System

“…LOGMEC17 is an acoustical and oceanographic campaign aimed to study the variability and predictability of the oceanographic and acoustic environments on different temporal and spatial scales. The sea trial was conducted from 14 September to 14 November 2017 and two gliders were piloted, mostly to follow altimetry tracks and exploit the synergy between altimetry and gliders [20]. Here, glider data available from 21 September to 14 November 2017 were used as independent data to validate the SST data assimilation.…”

“…In practice, the relaxation coefficient smoothly decreases to −12 W m −2 K −1 at noon, following the sinusoidal shape in order to give less weight to the nudging during daytime. The tuning of these parameters was performed in previous experiments [20]. More sophisticated relationships for β(t) could also be formulated (e.g., as a function of wind speed to decrease the relaxation in conditions of large skin SST warming, or to the MLD to modulate the propagation as a function of the ocean stratification) but are not considered here, where the aim is to compare a standard form of SST nudging to variational data assimilation of daytime SST data.…”

“…We used independent data from the two gliders deployed during the LOGMEC17 sea trial to validate against the independent dataset of the experiments previously introduced. The glider tracks are shown in the top left panel of Figure 3, and were designed to follow some altimetry tracks in order to provide co-located information about sea surface height and sub-surface density fields [20]. Glider tracks cover a relatively extended area of the domain, and are thus the primary dataset for validating the SST data assimilation.…”