TY - JOUR
T1 - Evaluation of thermosalinograph and VIIRS data for the characterization of near-surface temperature fields
AU - Schloesser, Fabian
AU - Cornillon, Peter
AU - Donohue, Kathleen
AU - Boussidi, Brahim
AU - Iskin, Emily
N1 - Funding Information:
We acknowledge support by the Oleander Project and thank Tom Rossby, Charly Flagg, Alejandra Sanchez-Franks, Baylor Fox-Kemper, and two anonymous reviewers for their valuable feedback related to this work. We are grateful to NOAA AOML and the VIIRS project for making their data available. The work was also supported through NSF Grants OCE 0825845 and OCE 0851794. Salary support for P. C. was provided by the state of Rhode Island and Providence Plantations. B. B. was supported by Le Collège doctoral international de l'Université européenne de Bretagne.
Publisher Copyright:
© 2016 American Meteorological Society.
PY - 2016
Y1 - 2016
N2 - Detailed understanding of submesoscale processes and their role in global ocean circulation is constrained, in part, by the lack of global observational datasets of sufficiently high resolution. Here, the potential of thermosalinograph (TSG) and Visible Infrared Imager Radiometer Suite (VIIRS) data is evaluated, to characterize the submesoscale structure of the near-surface temperature fields in the Gulf Stream and Sargasso Sea. In addition to spectral density, the structure function is considered, a statistical measure less susceptible to data gaps, which are common in the satellite-derived fields. The structure function is found to be an unreliable estimator, especially for steep spectral slopes, nominally between 2 and 3, typical of the Gulf Stream and Sargasso regions. A quality-control threshold is developed based on the number and size of gaps to ensure reliable spectral density estimates. Analysis of the impact of gaps in the VIIRS data on the spectra shows that both the number of missing values and the size of gaps affect the results, and that the steeper the spectral slope the more significant the impact. Furthermore, the TSG, with a nominal resolution of 75 m, captures the spectral characteristics of the fields in both regions down to scales substantially smaller than 1 km, while the VIIRS fields, with a nominal resolution of 750 m, reproduce the spectra well down to scales of about 20 km in the Sargasso Sea and 5 km in the Gulf Stream. The scales at which the VIIRS and TSG spectra diverge are thought to be determined by sensor and retrieval noise.
AB - Detailed understanding of submesoscale processes and their role in global ocean circulation is constrained, in part, by the lack of global observational datasets of sufficiently high resolution. Here, the potential of thermosalinograph (TSG) and Visible Infrared Imager Radiometer Suite (VIIRS) data is evaluated, to characterize the submesoscale structure of the near-surface temperature fields in the Gulf Stream and Sargasso Sea. In addition to spectral density, the structure function is considered, a statistical measure less susceptible to data gaps, which are common in the satellite-derived fields. The structure function is found to be an unreliable estimator, especially for steep spectral slopes, nominally between 2 and 3, typical of the Gulf Stream and Sargasso regions. A quality-control threshold is developed based on the number and size of gaps to ensure reliable spectral density estimates. Analysis of the impact of gaps in the VIIRS data on the spectra shows that both the number of missing values and the size of gaps affect the results, and that the steeper the spectral slope the more significant the impact. Furthermore, the TSG, with a nominal resolution of 75 m, captures the spectral characteristics of the fields in both regions down to scales substantially smaller than 1 km, while the VIIRS fields, with a nominal resolution of 750 m, reproduce the spectra well down to scales of about 20 km in the Sargasso Sea and 5 km in the Gulf Stream. The scales at which the VIIRS and TSG spectra diverge are thought to be determined by sensor and retrieval noise.
KW - Circulation/Dynamics
KW - Error analysis
KW - Fourier analysis
KW - In situ oceanic observations
KW - Mathematical and statistical techniques
KW - Observational techniques and algorithms
KW - Satellite observations
KW - Ship observations
KW - Subgrid-scale processes
UR - http://www.scopus.com/inward/record.url?scp=84992401724&partnerID=8YFLogxK
U2 - 10.1175/JTECH-D-15-0180.1
DO - 10.1175/JTECH-D-15-0180.1
M3 - Article
AN - SCOPUS:84992401724
VL - 33
SP - 1843
EP - 1858
JO - Journal of Atmospheric and Oceanic Technology
JF - Journal of Atmospheric and Oceanic Technology
SN - 0739-0572
IS - 9
ER -