Comparison of GHRSST SST Analysis in the Arctic Ocean and Alaskan Coastal Waters Using Saildrones

Abstract

There is high demand for complete satellite SST maps (or L4 SST analyses) of the Arctic regions to monitor the rapid environmental changes occurring at high latitudes. Although there are a plethora of L4 SST products to choose from, satellite-based products evolve constantly with the advent of new satellites and frequent changes in SST algorithms, with the intent of improving absolute accuracies. The constant change of these products, as reflected by the version product, make it necessary to do periodic validations against in situ data. Eight of these L4 products are compared here against saildrone data from two 2019 campaigns in the western Arctic, as part of the MISST project. The accuracy of the different products is estimated using different statistical methods, from standard and robust statistics to Taylor diagrams. Results are also examined in terms of spatial scales of variability using auto- and cross-spectral analysis. The three products with the best performance, at this point and time, are used in a case study of the thermal features of the Yukon–Kuskokwim delta. The statistical analyses show that two L4 SST products had consistently better relative accuracy when compared to the saildrone subsurface temperatures. Those are the NOAA/NCEI DOISST and the RSS MWOI SSTs. In terms of the spectral variance and feature resolution, the UK Met Office OSTIA product appears to outperform all others at reproducing the fine scale features, especially in areas of high spatial variability, such as the Alaska coast. It is known that L4 analyses generate small-scale features that get smoothed out as the SSTs are interpolated onto spatially complete grids. However, when the high-resolution satellite coverage is sparse, which is the case in the Arctic regions, the analyses tend to produce more spurious small-scale features. The analyses here indicate that the high-resolution coverage, attainable with current satellite infrared technology, is too sparse, due to cloud cover to support very high resolution L4 SST products in high latitudinal regions. Only for grid resolutions of ~9–10 km or greater does the smoothing of the gridding process balance out the small-scale noise resulting from the lack of high-resolution infrared data. This scale, incidentally, agrees with the Rossby deformation radius in the Arctic Ocean (~10 km).

Validation of NASA Sea Surface Temperature Satellite Products Using Saildrone Data

“…Our study showed higher bias and RMSE between SST MUR and Saildrone for most of the trajectories compared to a previous study conducted along the Baja California (bias of 0.32 deg Celsius and RMSE of 0.42 deg Celsius) [27]. However, compared to another study that compared MUR SST with two Saildrone deployments over the Arctic and Alaskan coastal waters which derived RMSE of about 7 and 9 deg Celsius [28], our results are more promising in terms of the accuracy of the MUR products (we found a maximum RMSE of 2.57 K).…”

“…Existing literature has already demonstrated the efficiency of averaging Saildrone values for co-registration with the satellite products [27][28][29]. The Euclidean distance coregistration method was also tested on our dataset and gave similar results to the averaging co-registration method.…”

Validation of OSCAR Surface Currents in the Western Arctic Marginal Seas Against Saildrone Observations

“…The near surface waters are remarkably colder and fresher in the Beaufort Sea than those on the Bering and Chukchi shelves (Figures 1a, 1b and 2). The saildrones also measured the very warm and fresh surface waters, associated with seasonal warming and freshening, west of Yukon‐Kuskokwim delta (Y‐K delta) in early June 2019 (Vazquez‐Cuervo et al., 2021, 2022). The surface waters on the shelf are significantly warmed in 3 months of the saildrone deployment (Figures 1 and 2); the Bering Sea shelf is freshened especially near the Alaskan coast.…”

Validation of OSCAR Surface Currents in the Western Arctic Marginal Seas against Saildrone Observations

“…The evaluation of the differences between OSCAR and saildrone current speed are quantified objectively by several statistical metrics including bias, root-mean-square error (RMSE), standard deviation of the errors (SDE), and signal-to-noise ratio (SNR). The definitions of the statistical metrics follow Vazquez-Cuervo et al (2022). The vector correlation is the cosine similarity as the cosine of the angle between the collocated OSCAR and saildrone current vectors.…”

“…These observations are instrumental in advancing our knowledge for further development and verification of satellite observation, satellite-derived data products, and numerical models. Recent studies used two saildrones to validate various satellite SST products and SMAP SSS products in the western Arctic (Vazquez-Cuervo et al, 2021, Vazquez-Cuervo et al, 2022. As the satellite-based products evolve constantly with emergence of new satellites and frequent changes of algorithms for improving their absolute accuracy, it is vital to do periodic validation against in situ data.…”