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A previous study proposed an adaptive observation error inflation (AOEI) method for an ensemble Kalman filter (EnKF)-based atmospheric data assimilation system to assimilate all-sky infrared brightness temperatures. Brightness temperature differences between clear- and cloudy-sky radiances are large, and observation-minus-forecast differences (or innovations) are therefore likely to be large around boundaries between clear- and cloudy-sky regions. The AOEI method mitigates these discrepancies by adaptively inflating observation errors. Ocean frontal regions have similar characteristics to the borders between clear- and cloudy-sky regions with large innovations. Consequently, we have implemented the AOEI with an EnKF-based regional ocean data assimilation system, in which the assimilation interval is set to 1 d to utilize frequent satellite observations. We conducted sensitivity experiments to investigate the impacts of the AOEI on salinity structure, geostrophic balance, and accuracy. A control run, in which the AOEI is not applied, shows the degradation of low-salinity North Pacific Intermediate Water around the Kuroshio Extension region, where the innovation amplitude and forecast ensemble spread are large in association with the fronts and eddies. The resulting large temperature and salinity increments weaken the density stratification, leading to large vertical diffusivity. As a result, the low-salinity water in the intermediate layer is lost through strong vertical diffusion. When the AOEI is used, the salinity structure in the ocean interior is preserved because the AOEI suppresses the salinity degradation by reducing the temperature and salinity increments. We also demonstrate that the AOEI provides significant improvement of the geostrophic balance and the analysis accuracy of temperature, salinity, and surface-flow fields. [ABSTRACT FROM AUTHOR]

This study develops an ensemble Kalman filter (EnKF)-based regional ocean data assimilation system in which the local ensemble transform Kalman filter (LETKF) is implemented with version 1.0 of the Stony Brook Parallel Ocean Model (sbPOM) to assimilate satellite and in situ observations at a daily frequency. A series of sensitivity experiments are performed with various settings of the incremental analysis update (IAU) and covariance inflation methods, for which the relaxation-to-prior perturbations and spread (RTPP and RTPS, respectively) and multiplicative inflation (MULT) are considered. We evaluate the geostrophic balance and the analysis accuracy compared with the control experiment in which the IAU and covariance inflation are not applied. The results show that the IAU improves the geostrophic balance, degrades the accuracy, and reduces the ensemble spread, and that the RTPP and RTPS have the opposite effect. The experiment using a combination of the IAU and RTPP results in a significant improvement for both balance and analysis accuracy when the RTPP parameter is 0.8–0.9. The combination of the IAU and RTPS improves the balance when the RTPS parameter is ≤0.8 and increases the analysis accuracy for parameter values between 1.0 and 1.1, but the balance and analysis accuracy are not improved significantly at the same time. The experiments with MULT inflating the forecast ensemble spread by 5 % do not demonstrate sufficient skill in maintaining the balance and reproducing the surface flow field regardless of whether the IAU is applied or not. The 11 d ensemble forecast experiments show consistent results. Therefore, the combination of the IAU and RTPP with a parameter value of 0.8–0.9 is found to be the best setting for the EnKF-based ocean data assimilation system. [ABSTRACT FROM AUTHOR]

The adequacy of hourly rainfall sampling was examined in terms of the detection of diurnal variations using 8 yr (1998–2005) of data observed by the precipitation radar on the Tropical Rainfall Measuring Mission (TRMM) satellite. It was found that the monthly and hourly rain samples for each 0.2° grid point over the 8-yr period are composed of multiple precipitation systems. In this study, a “3-h-significant diurnal peak” was defined as the time of maximum rainfall with consecutive positive anomalies for more than 3 h. The fraction of the analyzed area with a 3-h-significant diurnal peak increased annually and accounted for 43% of the total global tropics at 0.2° resolution over the 8-yr period. The diurnal signature over Tibet and the Amazon showed a high degree of spatial uniformity (at >10° scale). The degree of similarity and locations of the regional diurnal characteristics are described in terms of seasonal variations and at multiple resolutions based on spatial uniformity. For example, uniform early-afternoon peaks generally appear over the coastal land and areas of high relief, whereas a seasonally invariant early-afternoon peak over the low-lying Amazon basin is recognized as a regional characteristic. In areas of coastal ocean, early-morning peaks appear in certain regions such as the area surrounding the so-called Maritime Continent and the area off the west coast of Mexico. These peaks are distinct from the global characteristics of late-morning rainfall maxima recorded over most coastal oceans and early-morning peaks recorded over open ocean. The results are also compared with those derived from TRMM Microwave Imager (TMI) data. In addition to obtaining a coherent signal, regional differences in the timing of maximum rainfall over the Tibetan Plateau were addressed; this discrepancy is attributed to limitations of the scattering algorithm used for TMI data in terms of detecting shallow convection and screening cold surfaces. [ABSTRACT FROM AUTHOR]