Meetings: Documents

A MWR Ocean Roughness Correction Algorithm for the Aquarius SSS Retrieval
[13-Nov-14] Jones, L., Hejazin, Y., and Jones, L.
Presented at the 2014 Aquarius/SAC-D Science Team Meeting
The AQ retrieval of sea surface salinity (SSS) is based upon the smooth ocean surface brightness temperature (Tb). Since AQ measurements are at the top of the ionosphere, there are many corrections applied to achieve the desired quantity. Of the various corrections, the uncertainty in ocean Tb due to surface roughness (caused by surface wind speed) is the worst. The AQ baseline approach to provide the roughness correction using the AQ scatterometer ocean radar backscatter to infer excess ocean emissivity. This poster presents a second approach, which is derived from independent coincident Tb measurements from the CONAE MicroWave Radiometer (MWR).
In this poster, a MWR derived sea surface roughness correction algorithm is presented that uses a new semi-empirical microwave Radiative Transfer Model (RTM) to estimate the excess ocean emissivity using ancillary data such as sea surface temperature (SST) and ocean surface wind vector. This RTM has been tuned using 2-years of observed AQ and MWR Tb's and corresponding atmospheric and oceanic environmental conditions from numerical weather models (GDAS). The ocean roughness correction algorithm uses this RTM and collocated MWR Ka-band Tb's and available ancillary data (e.g., sea surface temperature, surface wind vector, and HYCOM SSS); and the outputs are the corresponding roughness corrections for each AQ footprint.
Results of independent comparisons (not used in the RTM tuning process) are presented between the baseline AQ scatterometer derived ocean roughness correction and the MWR roughness correction algorithm. Also SSS retrievals using these two independent approaches will be compared to the Hybrid Coordinate Ocean Model (HYCOM) salinity and collocated AQ Validation Data System (AVDS) buoy SSS measurements. Results suggest advantages of combining both roughness corrections (AQ SCAT and MWR) to achieve improved SSS retrievals.

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