FN Archimer Export Format
PT J
TI Towards improved analysis of short mesoscale sea level signals from satellite altimetry
BT 
AF Quilfen, Yves
   Piolle, Jean-Francois
   Chapron, Bertrand
AS 1:1;2:1;3:1;
FF 1:PDG-ODE-LOPS-SIAM;2:PDG-ODE-LOPS-SIAM;3:PDG-ODE-LOPS-SIAM;
C1 Laboratoire d'Océanographie Physique et Spatiale (LOPS), IFREMER, Univ. Brest, CNRS, IRD, IUEM, Brest, France
C2 IFREMER, FRANCE
SI BREST
SE PDG-ODE-LOPS-SIAM
UM LOPS
IN WOS Ifremer UMR
   DOAJ
IF 11.4
TC 0
UR https://archimer.ifremer.fr/doc/00732/84389/89401.pdf
   https://archimer.ifremer.fr/doc/00732/84389/93181.pdf
LA English
DT Article
AB Satellite altimeters routinely supply sea surface height (SSH) measurements, which are key observations for monitoring ocean dynamics. However, below a wavelength of about 70 km, along-track altimeter measurements are often characterized by a dramatic drop in signal-to-noise ratio, making it very challenging to fully exploit the available altimeter observations to precisely analyze small mesoscale variations in SSH. Although various approaches have been proposed and applied to identify and filter noise from measurements, no distinct methodology has emerged for systematic application in operational products. To best address this unresolved issue, the Copernicus Marine Environment Monitoring Service (CMEMS) actually provides simple band-pass filtered data to mitigate noise contamination of along-track SSH signals. More innovative and suitable noise filtering methods are thus left to users seeking to unveil small-scale altimeter signals. As demonstrated here, a fully data-driven approach is developed and applied successfully to provide robust estimates of noise-free Sea Level Anomaly (SLA) signals. The method combines Empirical Mode Decomposition (EMD), to help analyze non-stationary and non-linear processes, and an adaptive noise filtering technique inspired by Discrete Wavelet Transform (DWT) decompositions. It is found to best resolve the distribution of SLA variability in the 30–120 km mesoscale wavelength band. A practical uncertainty variable is attached to the denoised SLA estimates that accounts for errors related to the local signal-to-noise ratio, but also for uncertainties in the denoising process, which assumes that the SLA variability results in part from a stochastic process. For the available period, measurements from the Jason-3, Sentinel-3 and Saral/AltiKa missions are processed and analyzed, and their energy spectral and seasonal distributions characterized in the small mesoscale domain. In anticipation of the upcoming SWOT (Surface Water and Ocean Topography) mission data, the SASSA data set (Satellite Altimeter Short-scale Signals Analysis, Quilfen and Piolle, 2021) of denoised SLA measurements for three reference altimeter missions already yields valuable opportunities to evaluate global small mesoscale kinetic energy distributions. 
PY 2022
PD APR
SO Earth System Science Data
SN 1866-3508
PU Copernicus GmbH
VL 14
IS 4
UT 000777684000001
BP 1493
EP 1512
DI 10.5194/essd-14-1493-2022
ID 84389
ER

EF