FN Archimer Export Format
PT J
TI Obukhov Length Estimation From Spaceborne Radars
BT 
AF O’Driscoll, Owen
   Mouche, Alexis
   Chapron, Bertrand
   Kleinherenbrink, Marcel
   López‐Dekker, Paco
AS 1:1;2:1;3:1;4:2;5:2;
FF 1:;2:PDG-ODE-LOPS-SIAM;3:PDG-ODE-LOPS-SIAM;4:;5:;
C1 University Brest CNRS Ifremer IRD Laboratoire d’Océanographie Physique et Spatiale (LOPS) IUEM Plouzané, France
   Geoscience and Remote Sensing Civil Engineering and Geosciences Delft University of Technology Delft ,The Netherlands
C2 IFREMER, FRANCE
   UNIV DELFT, NETHERLANDS
SI BREST
SE PDG-ODE-LOPS-SIAM
UM LOPS
IF 5.2
TC 0
UR https://archimer.ifremer.fr/doc/00850/96224/104384.pdf
   https://archimer.ifremer.fr/doc/00850/96224/104385.pdf
LA English
DT Article
DE ;Obukhov length;surface-layer stability;SAR;machine learning;regression
AB Two air‐sea interaction quantification methods are employed on synthetic aperture radar (SAR) scenes containing atmospheric‐turbulence signatures. Quantification performance is assessed on Obukhov length L, an atmospheric surface‐layer stability metric. The first method correlates spectral energy at specific turbulence‐spectrum wavelengths directly to L. Improved results are obtained from the second method, which relies on a machine‐learning algorithm trained on a wider array of SAR‐derived parameters. When applied on scenes containing convective signatures, the second method is able to predict approximately 80% of observed variance with respect to validation. Estimated wind speed provides the bulk of predictive power while parameters related to the kilometer‐scale distribution of spectral energy contribute to a significant reduction in prediction errors, enabling the methodology to be applied on a scene‐by‐scene basis. Differences between these physically based estimates and parameterized numerical models may guide the latter's improvement. 
PY 2023
PD AUG
SO Geophysical Research Letters
SN 0094-8276
PU American Geophysical Union (AGU)
VL 50
IS 15
DI 10.1029/2023GL104228
ID 96224
ER

EF