FN Archimer Export Format
PT J
TI Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes
BT 
AF Kolodziejczyk, Nicolas
   Hamon, Michel
   Boutin, Jacqueline
   Vergely, Jean-Luc
   Reverdin, Gilles
   Supply, Alexandre
   Reul, Nicolas
AS 1:4;2:1;3:2;4:3;5:2;6:2;7:1;
FF 1:;2:PDG-ODE-LOPS-TOIS;3:;4:;5:;6:;7:PDG-ODE-LOPS-SIAM;
C1 University of Brest, LOPS Laboratory, IUEM, UBO-CNRS-IRD-Ifremer, rue Dumont D’Urville, Plouzané, 29280, France
   Sorbonne University, LOCEAN Laboratory, CNRS-IRD-MNHM, Paris, France
   ACRI-ST, Guyancourt, France
   University of Brest, LOPS Laboratory, IUEM, UBO-CNRS-IRD-Ifremer, rue Dumont D’Urville, Plouzané, 29280, France
C2 IFREMER, FRANCE
   UNIV SORBONNE, FRANCE
   ACRI-ST, FRANCE
   UBO, FRANCE
SI BREST
   TOULON
SE PDG-ODE-LOPS-TOIS
   PDG-ODE-LOPS-SIAM
UM LOPS
IN WOS Ifremer UMR
   WOS Cotutelle UMR
   copubli-france
   copubli-univ-france
IF 2.531
TC 9
UR https://archimer.ifremer.fr/doc/00665/77702/79785.pdf
LA English
DT Article
DE ;Ocean;Salinity;In situ oceanic observations;Satellite observations;Surface observations;Interpolation schemes
AB Ten years of L-Band radiometric measurements have proven the capability of satellite Sea Surface Salinity (SSS) to resolve large scale to mesoscale SSS features in tropical to subtropical ocean. In mid to high latitude, L-Band measurements still suffer from large scale and time systematic errors. Here, a simple method is proposed to mitigate the large scale and seasonal varying biases. First, an Optimal Interpolation (OI) using a large correlation scale (~500 km) is used to map independently Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) Level 3 data. The mapping is compared to the equivalent mapping of in situ observations to estimate the large scale and seasonal biases. A second mapping is performed on adjusted SSS at the scale of SMOS/SMAP spatial resolution (~45 km). This procedure merges both products, and increases the signal to noise ratio of the absolute SSS estimates, reducing the RMSD of in situ-satellite products by about 26-32% from mid to high latitude, respectively, in comparison to the existing SMOS and SMAP L3 products. However, in the Arctic Ocean, some issues on satellite retrieved SSS related to e.g. radio frequency interferences, land-sea contamination, ice-sea contamination remain challenging to reduce given the low sensitivity of L-Band radiometric measurements to SSS in cold water. Using the thermodynamic equation of state (TEOS-10), the resulting L4 SSS satellite product is combined with satellite-microwave SST products to estimate sea surface density, spiciness, haline contraction and thermal expansion coefficients. For the first time, we illustrate how useful are these satellite derived parameters to fully characterize the surface ocean water masses at large mesoscale. 
PY 2021
PD MAR
SO Journal Of Atmospheric And Oceanic Technology
SN 0739-0572
PU American Meteorological Society
VL 38
IS 3
UT 000646372600001
BP 405
EP 421
DI 10.1175/JTECH-D-20-0093.1
ID 77702
ER

EF