Assessment of Aquarius sea surface salinity with Argo in the Bay of Bengal. Issue 22 (17th November 2019) - Lin, Xinyu; Qiu, Yun; Cha, Jing; Guo, Xiaogang
ABSTRACT: This study validates three Aquarius sea surface salinity (SSS) Level 2 products against Argo salinity in the Bay of Bengal (BoB) for the period 26 August 2011 through 30 April 2015. The Aquarius SSS products used here include the standard product processed by the Aquarius Data Processing System (ADPS) and two data sets from the Jet Propulsion Laboratory (JPL): the Combined Active-Passive algorithm with and without rain correction (i.e. CAP and CAPRC ). The bias and root-mean-square difference (RMSD) of the ADPS, CAP and CAPRC with respect to Argo salinity is −0.14 ± 0.60 psu, 0.03 ± 0.53 psu, and 0.07 ± 0.52 psu, respectively. A large negative bias (large RMSD) value is observed in the northern bay and along the Sumatra coast in the ADPS (all three Aquarius products) with a remarkable seasonal variation. We evaluate the errors induced by the geographic displacement and the difference in depth of the measurement and show that these errors are significant in regards to the validation of Aquarius in our study region. The strong negative bias is mainly caused by the near-surface salinity stratification. Frequent heavy precipitation events in the BoB induce a freshening bias of approximately −0.13 psu (mm h −1 ) −1 that lasted for less than 6 h, contributed 21% to the overall bias of the ADPS SSS and partly caused the high RMSD values observed along the Sumatra coast. The large SSS RMSD in the northern bay is largely induced by small-scale variations in SSS, and the overall RMSD is reduced by approximately 11% when the horizontal-SSS-gradient-induced RMSD is isolated. Therefore, the potential influences of the spatial representation errors associated with the spatial displacement between the satellite and in-situ measurements need to be considered when evaluating the satellite SSS values measured over marginal seas or plume regions that have rich-precipitation or strong small-scale SSS variations.