| Abstract: |
The Aquarius satellite mission was developed to measure the ocean’s sea surface salinity (SSS) field and to investigate the links between changes in the global water cycle, ocean circulation and climate variability. The mission ended in June 2015 because of a power supply malfunction on the satellite. A recent data re-calibration and re-processing as produced Aquarius data version V5.0, released in December 2017. Key Aquarius science objectives were to (1) map the mean SSS field, (2) measure the annual SSS cycle, and (3) document interannual variations, within a three-year minimum duration. This presentation addresses objectives (2) and (3) by using co-located in situ data to verify that there is no significant spurious radiometer calibration drift on these time scales. The analysis converts the in situ salinity and temperature data (primarily from Argo floats) to an expected radiometer brightness temperature (Tb), and computes the differences of these from the Aquarius radiometer-derived Tb. The crux of the analysis is separating the sensor drift from the varying environmental corrections in the retrieval algorithm. The approach is to aggregate these co-located Tb differences within geographical zones (for example northern and southern hemispheres, ascending and descending passes), and then comparing the differences between these zones (double-difference). Next, regression analyses isolate the sensor variations from the environmental ones. The key assumption is that the long term (seasonal to interannual) radiometer calibration drift is a common signal among the various zones. This presentation will explain this calculation and present the results achieved with Aquarius V5.0 ocean salinity data release. Calibration histories for each of the six Aquarius radiometer channels are derived. The residuals are attributed to environmental model errors within each zone. Understanding these remains more problematic. The future study will be to adapt the technique to SMAP, and eventually SMOS measurements to enable a systematic cross-calibration of the different satellite systems and obtain a reliable combined multi-year time series for studying ocean trends. |
