Assessment and Improvement of Global Gridded Sea Surface Temperature Datasets in the Yellow Sea Using In Situ Ocean Buoy and Research Vessel Observations

Abstract

The sea surface temperature (SST) is essential data for the ocean and atmospheric prediction systems and climate change studies. Five global gridded sea surface temperature products were evaluated with independent in situ SST data of the Yellow Sea (YS) from 2010 to 2013 and the sources of SST error were identified. On average, SST from the gridded optimally interpolated level 4 (L4) datasets had a root mean square difference (RMSD) of less than 1 °C compared to the in situ observation data of the YS. However, the RMSD was relatively high (2.3 °C) in the shallow coastal region in June and July and this RMSD was mostly attributed to the large warm bias (>2 °C). The level 3 (L3) SST data were frequently missing in early summer because of frequent sea fog formation and a strong (>1.2 °C/12 km) spatial temperature gradient across the tidal mixing front in the eastern YS. The missing data were optimally interpolated from the SST observation in offshore warm water and warm biased SST climatology in the region. To fundamentally improve the accuracy of the L4 gridded SST data, it is necessary to increase the number of SST observation data in the tidally well mixed region. As an interim solution to the warm bias in the gridded SST datasets in the eastern YS, the SST climatology for the optimal interpolation can be improved based on long-term in situ observation data. To reduce the warm bias in the gridded SST products, two bias correction methods were suggested and compared. Bias correction methods using a simple analytical function and using climatological observation data reduced the RMSD by 19–29% and 37–49%, respectively, in June.

An Extreme Marine Heatwave Event in the Yellow Sea during Winter 2019/20: Causes and Consequences

“…The temporal extent is from 1 January 1982 to 31 December 2021. The OSTIA SST data have been shown to be more accurate than other SST products in the YS [20].…”

Evaluation and Improvement of FY-4A/AGRI Sea Surface Temperature Data

“…A piecewise regression SST was designed by Petrenko et al [20] to estimate the sensor-specific error statistic for SST in the advanced clear-sky processor for oceans. To reduce the 2 > JSTARS-2022-00924.R2< bias in the coastal region, Kwon et al [21] designed two spatial-temporal bias correction functions. The first bias correction consisted of an exponential function depending on distance from the land and a cosine function depending on time, the second function was defined as the difference between the two daily climatology datasets on day.…”