By comparison with satellite and field observations, the comprehensive performance and potential utility of near real-time forecasts using Nonhydrostatic Icosahedral Atmospheric Model (NICAM) are demonstrated by exploiting the Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY2011) / Dynamics of the Madden–Julian Oscillation (DYNAMO) campaign. A week-long forecast was run each day using a regionally stretched version of NICAM, with the finest mesh size of 14 km over the tropical Indian Ocean (IO), throughout the intensive observation period (IOP).

 The simulated precipitation time series fairly represented the evolution and propagation of the observed Madden-Julian Oscillation (MJO) events, although a 30 % overprediction of precipitation over the IO domain (60–90°E, 10°S–10°N) was found on average. Frequencies of strong (> 40 mm day⁻¹) precipitation were overpredicted, while those of weak precipitation were underpredicted against satellite observations. Compared with the field observations at Gan Island, the biases in precipitation frequency were less obvious, whereas the growth of lower to middle tropospheric dry (∼ 1 g kg⁻¹) and warm (∼ 1 K) biases were found. Despite these mean biases, temporal variations of the moisture and zonal wind profiles including the MJO events were reasonably simulated.

 Using the forecast data the moisture and energy budgets during the IOP were investigated. The diagnosis using the 7-day-mean fields captured the observed features of the MJO events. Meanwhile, significant upward transport of moisture by the grid-resolved high-frequency variability was detected throughout the IOP. The relationship between these high-frequency effects and the simulated MJO or mean biases is also discussed.

 This study examines analysis and forecast impacts in the Navy Global Environmental Model (NAVGEM) from direct assimilation of temperature and wind “pseudo-raob” profiles derived from analysis fields of the European Centre for Medium-Range Weather Forecasting-Integrated Forecast System (ECWMF-IFS). The pseudo-raob profiles are provided on eight vertical levels from 250 hPa to 1000 hPa on a 1° × 1° resolution rectilinear grid and are assimilated as synthetic observation data by NAVGEM at 0000 UTC and 1200 UTC for an experimental time period of 48 days. The pseudo-raob observations are assumed in these experiments to have observation errors identical to temperature and wind data provided by conventional radiosonde observations.

 In this diagnostic context, the assimilation of pseudo-raob profiles significantly reduces temperature and height biases in the NAVGEM analysis and provides general improvements to forecast skill when verified against both self-analysis and rawinsondes. Reduction of NAVGEM temperature bias is most evident in southern hemisphere high latitudes, where the assimilation of pseudo-raob information mitigates NAVGEM temperature bias and indicates sub-optimal bias correction of radiance data in the NAVGEM Control analysis. Despite the revisiting of assimilated observation information when assimilating pseudo-raobs from the IFS analysis into the NAVGEM analysis, improvement to the NAVGEM analyses and forecasts is both statistically significant and consistent across several verification techniques. This suggests that there are likely small effects from any correlations between pseudo-raob data and the NAVGEM background. The assimilation of pseudo-raob data also reduces the total observation impact in NAVGEM as estimated by the adjoint model, which is an indicator of general improvement to analysis and forecast quality.

 A mesoscale convective system (MCS) is organized thunderstorms with connected anvils, which has a significant impact on the global climate. By focusing on MCSs over the Maritime Continent of Indonesia, this study aims to gain a better understanding on the properties of the MCSs over the study area. The “Grab ‘em Tag ‘em Graph ‘em” (GTG) tracking algorithm is applied to hourly Multi-functional Transport Satellite-1R data for two years to observe the distribution of MCSs and the evolution of MCSs along their lifetime. The results of MCS identification by using GTG are combined with CloudSat data products to study the vertical structure of the MCSs at various MCS life stages: developing, mature, and dissipating.

 The distribution of MCSs over Indonesia has a seasonal variation and distinct diurnal cycle. The life stages of the observed MCSs are characterized by distinct cloud microphysics at each stage. In the developing stage, the upper level of the MCS raining region shows the presence of precipitating ice particles. As the MCS progresses to the mature stage, the proportion of the raining area becomes small and the intensity of rain is reduced, accompanied by increasing occurrence of small-sized ice particles at the upper level. In the dissipating stage, large hydrometeors no longer exist at the upper part of the raining region. Within the MCS anvils, the dissipating stage shows a more uniform distribution of ice-particle effective radius compared to that shown by the developing and mature stages.

 MCS characteristics over the land and ocean differ on the basis of the minimum brightness temperature, the equivalent radius, the maximum rain rate, and the rain fraction that varies along the MCS evolution.

 Eddy transport of atmospheric water vapor from the tropics is important for rainfall and related natural disasters in the middle latitudes. Atmospheric rivers (ARs), intense moisture plumes that are typically associated with extratropical cyclones, often produce heavy precipitation upon encountering topography on the west coasts of mid-latitude North America and Europe. ARs also occur over the northwestern Pacific and sometimes cause floods and landslides over East Asia, but the climatological relationship between ARs and heavy rainfall in this region remains unclear. Here we evaluate the contribution of ARs to the hydrological cycle over East Asia using high-resolution daily rainfall observations and an atmospheric reanalysis during 1958-2007. Despite their low occurrence, ARs account for 14-44 % of the total rainfall and 20-90 % of extreme heavy-rainfall events during spring, summer, and autumn. AR-related extreme rainfall is especially pronounced over western-to-southeastern slopes of terrains over the Korean Peninsula and Japan, owing to strong orographic effects and a stable direction of low-level moisture flows. A strong relationship between warm-season AR heavy rainfall and preceding-winter El Niño is identified since the 1970s, suggesting the potential of predicting heavy-rainfall risk over Korea and Japan at seasonal leads.

 An observation operator to assimilate satellite radiances with the non-hydrostatic icosahedral atmospheric model (NICAM)-based local ensemble transform Kalman filter (LETKF) is newly developed using the radiative transfer for the TIROS Operational Vertical Sounder (RTTOV, version 11.1). Here we assimilate the Advanced Microwave Sounding Unit-A (AMSU-A) brightness temperature observations that are known to bring a large improvement to global numerical weather prediction. We apply the online estimation of bias correction for both airmass and scan biases, or the biases originating from the atmospheric state and scan position. Comparing the two experiments with and without the AMSU-A radiances, we find that the adaptive bias correction methods work appropriately, and that the analysis is significantly improved by assimilating the AMSU-A radiances. This is an important step toward assimilating different types of satellite radiances with NICAM-LETKF.

 We investigated the predictability of plume advection in the lower troposphere and the impact of AMeDAS surface wind data assimilation by using radioactive cesium emitted by the Fukushima nuclear accident in March 2011 as an atmospheric tracer. We conducted two experiments of radioactive plume predictions over eastern Japan for March 15, 2011 with a 3-km horizontal resolution using the Japan Meteorological Agency non-hydrostatic weather forecast model and local ensemble transform Kalman filter (JMANHM-LETKF) data assimilation system. The assimilated meteorological data were obtained from the standard archives collected for the Japan Meteorological Agency operational numerical weather prediction and the AMeDAS surface wind observations. The standard archives do not contain land-surface wind observations. The modeled radioactive cesium concentrations were examined for plume arrival times at 40 observatories. The mean error of the plume arrival times for the standard experiment (assimilating only the standard archives) was 82.0 min with a 13-h lead-time on an average. In contrast, the mean error of the AMeDAS experiment (assimilating both the standard archives and AMeDAS surface wind observations) was 72.8 min, which was 9.2 min (11 %) better than that of the standard experiment. This result indicates that the plume prediction has a reasonable accuracy for the environmental emergency response and the prediction can be significantly improved by the surface wind data assimilation.