Using Saildrones to Validate Satellite-Derived Sea Surface Salinity and Sea Surface Temperature along the California/Baja Coast

Abstract

Traditional ways of validating satellite-derived sea surface temperature (SST) and sea surface salinity (SSS) products by comparing with buoy measurements, do not allow for evaluating the impact of mesoscale-to-submesoscale variability. We present the validation of remotely sensed SST and SSS data against the unmanned surface vehicle (USV)—called Saildrone—measurements from the 60 day 2018 Baja California campaign. More specifically, biases and root mean square differences (RMSDs) were calculated between USV-derived SST and SSS values, and six satellite-derived SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and three SSS (JPLSMAP, RSS40, RSS70) products. Biases between the USV SST and OSTIA/CMC/DMI were approximately zero, while MUR showed a bias of 0.3 °C. The OSTIA showed the smallest RMSD of 0.39 °C, while DMI had the largest RMSD of 0.5 °C. An RMSD of 0.4 °C between Saildrone SST and the satellite-derived products could be explained by the diurnal and sub-daily variability in USV SST, which currently cannot be resolved by remote sensing measurements. SSS showed fresh biases of 0.1 PSU for JPLSMAP and 0.2 PSU and 0.3 PSU for RMSS40 and RSS70 respectively. SST and SSS showed peaks in coherence at 100 km, most likely associated with the variability of the California Current System.

Statistical Assessment of Sea-Surface Salinity from SMAP: Arabian Sea, Bay of Bengal and a Promising Red Sea Application

“…Several studies reported that SMAP is able to retrieve SSS with some confidence in coastal regions within 500-1000 km offshore in many places around the globe [11][12][13][14][15][16][17][18][19]. The present study adds another region to that list: the Red Sea.…”

“…The SMAP results in the Red Sea presented here are promising, particularly when they are contrasted with other marginal seas. For instance, Vazquez-Cuervo et al [16] found correlations of 0.61-0.91 (except by one mooring in which correlation was around 0.3) and rmsd of 0.4-0.78 in the much wider Gulf of Mexico for the L3 SMAP products they analyzed. These numbers are the same order as those described here for the narrow Red Sea.…”

“…Although salinity variability is more prominent in the Bay of Bengal (high standard deviation and wide salinity range), SMAP is more strongly correlated with Argo here (0.89-0.96) than in the Arabian Sea (0.71-0.94). The rmsd, however, is of the same order, which means that signal-to-noise ratio (SN = std SMAP /rmsd [12,16]) is higher in the Bay of Bengal.…”

“…and histograms were calculated for each SMAP product for the collocated datasets to assess SMAP performance in the North Indian Ocean in both the spatial and temporal domains. These statistics are generally used in studies evaluating satellite salinity observations [1,3,8,9,12,13,[16][17][18]21,61].…”

Saildrone: Adaptively Sampling the Marine Environment

“…This is likely due to a transient bias present in one of the satellite SST used by the MUR analysis system. A more detailed analysis of satellite SST and SSS comparisons is presented in Vazquez-Cuervo et al (2019). Comparisons with SST indicated excellent agreement with overall biases approaching zero.…”