An ensemble Kalman filter-based ocean data assimilation system improved by adaptiveobservation error inflation (AOEI)

A previous study proposed an adaptive observation error inflation (AOEI)method for an ensemble Kalman filter (EnKF)-based atmospheric data assimilationsystem to assimilate all-sky infrared brightness temperatures. Brightnesstemperature differences between clear- and cloudy-sky radiances are large,and observation-minus-forecast differences (or innovations) are thereforelikely to be large around boundaries between clear- and cloudy-sky regions.The AOEI method mitigates these discrepancies by adaptively inflatingobservation errors. Ocean frontal regions have similar characteristics tothe borders between clear- and cloudy-sky regions with large innovations.Consequently, we have implemented the AOEI with an EnKF-based regional oceandata assimilation system, in which the assimilation interval is set to 1 d to utilize frequent satellite observations. We conducted sensitivityexperiments to investigate the impacts of the AOEI on salinity structure,geostrophic balance, and accuracy. A control run, in which the AOEI is notapplied, shows the degradation of low-salinity North Pacific IntermediateWater around the Kuroshio Extension region, where the innovation amplitudeand forecast ensemble spread are large in association with the fronts andeddies. The resulting large temperature and salinity increments weaken thedensity stratification, leading to large vertical diffusivity. As a result,the low-salinity water in the intermediate layer is lost through strongvertical diffusion. When the AOEI is used, the salinity structure in theocean interior is preserved because the AOEI suppresses the salinitydegradation by reducing the temperature and salinity increments. We alsodemonstrate that the AOEI provides significant improvement of thegeostrophic balance and the analysis accuracy of temperature, salinity, andsurface-flow fields.