The Aquarius end-of-mission (Version 5) salinity data set was released in December 2017. This article gives a comprehensive overview of the main steps of the Level 2 salinity retrieval algorithm. In particular, we will discuss the corrections for wind induced surface roughness, atmospheric oxygen absorption, reflected galactic radiation and side-lobe intrusion from land surfaces. Most of these corrections have undergone major updates from previous versions, which has helped mitigating temporal and zonal biases. Our article also discusses the ocean target calibration for Aquarius Version 5. We show how formal error estimates for the Aquarius retrievals can be obtained by perturbing the input to the algorithm. The performance of the Aquarius Version 5 salinity retrievals is evaluated against salinity measurements from the ARGO network and the HYCOM model. When stratified as function of sea surface temperature or sea surface wind speed, the difference between Aquarius Version 5 and ARGO is within ±0.1 psu. The estimated global RMS uncertainty for monthly 100 km averages is 0.128 psu for the Aquarius Version 5 retrievals. Finally, we show how the Aquarius Version 5 salinity retrieval algorithm is adapted to retrieve salinity from the Soil-Moisture Active Passion (SMAP) mission.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission was launched in January 2015 and has been providing science data since April 2015. Though designed to measure soil moisture, the SMAP radiometer has an excellent capability to measure ocean winds in storms at a resolution of 40 km with a swath width of 1,000 km. SMAP radiometer channels operate at a very low microwave frequency (L band, 1.41 GHz, 21.4 cm), which has good sensitivity to ocean surface wind speed even in very high winds and with very little impact by rain. This gives SMAP a distinct advantage over many spaceborne ocean wind sensors such as C-band [Advanced Scatterometer (ASCAT)] or Ku-band [Rapid Scatterometer (RapidScat)] scatterometers and radiometers operating at higher frequencies [Special Sensor Microwave Imager (SSM/I), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), WindSat, Advanced Microwave Scanning Radiometer (AMSR), and Global Precipitation Measurement (GPM) Microwave Imager (GMI)], which either lose sensitivity at very high winds or degrade in rainy conditions. This article discusses the major features of a new ocean wind vector retrieval algorithm designed for SMAP. We compare SMAP wind fields in recent intense tropical cyclones with wind measurements from current scatterometer missions as well as WindSat. The most important validation source in hurricanes is the airborne stepped frequency microwave radiometer (SFMR), whose wind speeds are matched with SMAP in space and time. A comparison between SMAP and SFMR winds for eight storms in 2015, including Patricia, one of the strongest hurricanes ever recorded, shows excellent agreement up to 65 m s–1 without degradation in rain.

In order to achieve the required accuracy in sea surface salinity (SSS) measurements from L‐band radiometers such as the Aquarius/SAC‐D or SMOS (Soil Moisture and Ocean Salinity) mission, it is crucial to accurately correct the radiation that is emitted from the ocean surface for roughness effects. We derive a geophysical model function (GMF) for the emission and backscatter of L‐band microwave radiation from rough ocean surfaces. The analysis is based on radiometer brightness temperature and scatterometer backscatter observations both taken on board Aquarius. The data are temporally and spatially collocated with wind speeds from WindSat and F17 SSMIS (Special Sensor Microwave Imager Sounder) and wind directions from NCEP (National Center for Environmental Prediction) GDAS (Global Data Assimilation System). This GMF is the basis for retrieval of ocean surface wind speed combining L‐band H‐pol radiometer and HH‐pol scatterometer observations. The accuracy of theses combined passive/active L‐band wind speeds matches those of many other satellite microwave sensors. The L‐band GMF together with the combined passive/active L‐band wind speeds is utilized in the Aquarius SSS retrieval algorithm for the surface roughness correction. We demonstrate that using these L‐band wind speeds instead of NCEP wind speeds leads to a significant improvement in the SSS accuracy. Further improvements in the roughness correction algorithm can be obtained by adding VV‐pol scatterometer measurements and wave height (WH) data into the GMF.

Since 2009, three low frequency microwave sensors have been launched into space with the capability of global monitoring of sea surface salinity (SSS). The European Space Agency’s (ESA’s) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), onboard the Soil Moisture and Ocean Salinity mission (SMOS), and National Aeronautics and Space Administration’s (NASA’s) Aquarius and Soil Moisture Active Passive mission (SMAP) use L-band radiometry to measure SSS. There are notable differences in the instrumental approaches, as well as in the retrieval algorithms. We compare the salinity retrieved from these three spaceborne sensors to in situ observations from the Argo network of drifting floats, and we analyze some possible causes for the differences. We present comparisons of the long-term global spatial distribution, the temporal variability for a set of regions of interest and statistical distributions. We analyze some of the possible causes for the differences between the various satellite SSS products by reprocessing the retrievals from Aquarius brightness temperatures changing the model for the sea water dielectric constant and the ancillary product for the sea surface temperature. We quantify the impact of these changes on the differences in SSS between Aquarius and SMOS. We also identify the impact of the corrections for atmospheric effects recently modified in the Aquarius SSS retrievals. All three satellites exhibit SSS errors with a strong dependence on sea surface temperature, but this dependence varies significantly with the sensor. We show that these differences are first and foremost due to the dielectric constant model, then to atmospheric corrections and to a lesser extent to the ancillary product of the sea surface temperature.

Aquarius was the first NASA satellite to observe the sea surface salinity (SSS) over the global ocean. The mission successfully collected data from 25 August 2011 to 7 June 2015. The Aquarius project released its final version (Version-5) of the SSS data product in December 2017. The purpose of this paper is to summarize the validation results from the Aquarius Validation Data System (AVDS) and other statistical methods, and to provide a general view of the Aquarius SSS quality to the users. The results demonstrate that Aquarius has met the mission target measurement accuracy requirement of 0.2 psu on monthly averages on 150 km scale. From the triple point analysis using Aquarius, in situ field and Hybrid Coordinate Ocean Model (HYCOM) products, the root mean square errors of Aquarius Level-2 and Level-3 data are estimated to be 0.17 psu and 0.13 psu, respectively. It is important that caution should be exercised when using Aquarius salinity data in areas with high radio frequency interference (RFI) and heavy rainfall, close to the coast lines where leakage of land signals may significantly affect the quality of the SSS data, and at high-latitude oceans where the L-band radiometer has poor sensitivity to SSS.