Passive remote sensing of oceanic whitecaps: Further developments

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Monday, 5 January 2015
Magdalena D. Anguelova, NRL, Washington, DC; and M. H. Bettenhausen, W. F. Johnston, and P. W. Gaiser

Many air-sea interaction processes are quantified in terms of whitecap fraction W because oceanic whitecaps are the most visible and direct way of observing breaking of wind waves in the open ocean. Enhanced by breaking waves, surface fluxes of momentum, heat, and mass are critical for ocean-atmosphere coupling and thus affect the accuracy of models used to forecast weather and predict storm intensification and climate change.

Whitecap fraction is defined as the fraction of a unit sea surface covered with foam. It has been traditionally measured by extracting the high-intensity pixels marking white water in still photographs or video images collected from towers, ships, and aircrafts. Satellite-based passive remote sensing of whitecap fraction is a recent development that allows long term, consistent observations of whitecapping on a global scale. The remote sensing method relies on changes of ocean surface emissivity at microwave frequencies (e.g., 6 to 37 GHz) due to presence of sea foam on a rough sea surface. These changes at the ocean surface are observed from the satellite as brightness temperature TB.

The algorithm to obtain W from satellite observations of TB was developed at the Naval Research Laboratory within the framework of WindSat mission. It improved upon the feasibility study of this remote sensing technique by using independent sources for the input variables of the algorithm, physically based models for the emissivity of rough sea surface and emissivity of foam, improved rain flag, and improved atmospheric model necessary for the atmospheric correction. The database built with this algorithm compiles W for entire year 2006 matched in time and space with data for the wind vector, wave field (such as significant wave height and peak wave period), and environmental parameters (such as sea surface temperature and atmospheric stability). This database has proved useful in analyzing and quantifying the variability of W.

In this poster we will present updated algorithm for estimating W from WindSat TB data using new sources and products for the input variables. The originally used QuikSCAT data for ocean wind vector are replaced with new wind vector fields. Water vapor and cloud liquid from Remote Sensing Systems (RSS), which we use for atmospheric correction, are now generated with Version 7 (V7) of the RSS processing algorithm. V7 algorithm provides inter-calibration of TB data from different microwave sensors including SSM/I, SSM/IS, AMSR, WindSat, and TMI. The new input variables will allow extension of our W algorithm beyond 2006 and its future applicability to radiometric data from different satellite platforms. We will describe our W algorithm, present new results for whitecap fraction W, and analyze differences between the new W data and those obtained with the previous version of the algorithm.