Aquarius geophysical model function and combined active passive algorithm for ocean surface salinity and wind retrieval

Abstract

This paper describes the updated Combined Active-Passive (CAP) retrieval algorithm for simultaneous retrieval of surface salinity and wind from Aquarius' brightness temperature and radar backscatter. Unlike the algorithm developed by Remote Sensing Systems (RSS), implemented in the Aquarius Data Processing System (ADPS) to produce Aquarius standard products, the Jet Propulsion Laboratory's CAP algorithm does not require monthly climatology SSS maps for the salinity retrieval. Furthermore, the ADPS-RSS algorithm fully uses the National Center for Environmental Predictions (NCEP) wind for data correction, while the CAP algorithm uses the NCEP wind only as a constraint. The major updates to the CAP algorithm include the galactic reflection correction, Faraday rotation, Antenna Pattern Correction, and geophysical model functions of wind or wave impacts. Recognizing the limitation of geometric optics scattering, we improve the modeling of the reflection of galactic radiation; the results are better salinity accuracy and significantly reduced ascending-descending bias. We assess the accuracy of CAP's salinity by comparison with ARGO monthly gridded salinity products provided by the Asia-Pacific Data-Research Center (APDRC) and Japan Agency for Marine-Earth Science and Technology (JAMSTEC). The RMS differences between Aquarius CAP and APDRC's or JAMSTEC's ARGO salinities are less than 0.2 psu for most parts of the ocean, except for the regions in the Intertropical Convergence Zone, near the outflow of major rivers and at high latitudes.

Directional Spreading Function of the Gravity-Capillary Wave Spectrum Derived from Radar Observations

“…In contrast, the L-band amplitude at low and moderate wind speeds (4 and 8 m/s) along upwind (0°) or downwind (180°) directions is less than one along crosswind (90° or 270°) directions, which signifies the negative upwind-crosswind asymmetry. It is consistent with the directional feature observed by Yueh et al [17], Zhou et al [18] and Isoguchi et al [28] and. In addition, the difference of the directional spreading function between L, C and Ku band decreases with the increase of wind speed.…”

Roughness and foam signature on SMOS-MIRAS brightness temperatures: A semi-theoretical approach

“…8), where the two Aquarius e w are different at WS above 10 m s −1 and the SMOS e w is close to the one of Yueh et al (2014). However, they disagree with SMOS models derived using ECMWF wind speeds for WS above 17 m s −1 .…”

“…To derive e w , many in-situ and airborne efforts using various instruments and observation techniques have been performed, e.g., the experiment made from a tower (Hollinger, 1971), the Wind and Salinity Experiments (WISE) Etcheto et al, 2004), the airborne Passive-Active L-band Sensor (PALS) campaign (Yueh, Dinardo, Fore, & Li, 2010) and the Combined Airborne Radio instruments for Ocean and Land Studies (CAROLS) campaign (Martin et al, 2012;Martin, Boutin, Hauser, & Dinnat, 2014). Recently, new empirical roughness models were proposed using space-borne measurements such as those from the Soil Moisture and Ocean Salinity (SMOS) mission (Guimbard et al, 2012;Reul & Tenerelli, personal communication, 2012) and the Aquarius mission (Yueh et al, 2013;Yueh et al, 2014;Meissner, Wentz, & Ricciardulli, 2014). A semi-physical model was also proposed by adjusting the Durden-Vesecky (1985) wave spectrum and the Monahan and O'Muircheartaigh (1986) foam coverage model using SMOS data (Yin, Boutin, Martin, & Spurgeon, 2012a).…”

Pacific Ocean surface freshwater variability underneath the double ITCZ as seen by satellite sea surface salinity retrievals

“…More information about the algorithm and the data validation can be found in Yueh et al . [] and Tang et al . [].…”