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• An ocean forcing dataset JRA55-do significantly improves nearshore warm SST biases. • The improvement mechanisms of the warm SST biases are quantitatively investigated. • Ocean forcing datasets are essential for ocean data assimilation systems. In the previous study, the authors have produced an eddy-resolving ocean ensemble analysis product called the local ensemble transform Kalman filter (LETKF)-based ocean research analysis (LORA) over the western North Pacific and Maritime Continent regions using an ocean data assimilation system driven by the Japanese operational atmospheric reanalysis dataset known as the JRA-55. However, the LORA includes warm biases in sea surface temperatures (SSTs) in coastal regions during the boreal winter. In this study, we perform sensitivity experiments with atmospheric forcing using an ocean forcing dataset known as the JRA55-do, which adjusts the JRA-55 to high-quality reference datasets to reduce biases and uncertainties. The results show that the nearshore warm SST biases are significantly improved by the JRA55-do. During the boreal autumn, the improvement comes from mainly two factors: (i) enhancement of surface cooling by latent heat releases caused by removing contamination of weak winds at the land grid cells, and (ii) weakening surface heating by downward shortwave radiation through the adjustment in the JRA55-do. During the boreal winter, enhanced cooling by the analysis increments suppresses the growth of the warm SST biases when the JRA55-do is used. However, if the JRA-55 dataset is used, the adaptive observation error inflation (AOEI) scheme acts negatively to keep the nearshore SST biases in winter. Based on the innovation statistics, the AOEI inflates the observation errors when the differences between the squared observation-minus-forecast innovations and the squared forecast ensemble spreads are larger than the prescribed observation error variance, and improves the accuracy in the open ocean, especially around the frontal regions. However, when substantial warm SST biases are formed in the previous season, AOEI's observation error inflation makes the analysis increments smaller and cannot suppress the warm biases. We also validate the analysis accuracy using various data such as sea surface height and horizontal velocities and find that the JRA55-do has significant advantages. Therefore, continuous maintenance and development of ocean forcing datasets are essential for ocean modeling and data assimilation. [ABSTRACT FROM AUTHOR]

This paper describes a quasi-physical method (the Q-method) for determining the sea surface temperatures (SSTs). The Q-method is a coefficient-based technique developed for processing the multiband infrared (IR) data of the geostationary Himawari-8 satellite. We applied the Q-method to the split-window data from the Second-generation Global Imager (SGLI) onboard the Global Change Observation Mission-Climate (GCOM-C) satellite. A comparison of the determined SGLI SSTs and buoy data shows a bias with a robust standard deviation of −0.097 K and 0.28 K in the daytime and −0.18 K and 0.28 K at night, respectively. Meanwhile, high biases of nearly −0.5 K were calculated for SSTs at and around 305 K. A residuals analysis suggests that the high negative bias is caused by insufficient information on the atmospheric correction brought by split-window data. This paper discusses the physical and mathematical background of the Q-method and compares it with another coefficient-based physical scheme. • Quasi-physical determination of sea surface temperature. • Physical and mathematical background of the quasi-physical determination. • Accuracy of quasi-physically determined sea surface temperature. • Result comparison with another coefficient-based physical scheme. • Physical background for the high bias. [ABSTRACT FROM AUTHOR]