No abstract

Atmospheric Motion Vectors (AMVs) calculated by six different institutions (Brazil Center for Weather Prediction and Climate Studies/CPTEC/INPE, European Organization for the Exploitation of Meteorological Satellites/EUMETSAT, Japan Meteorological Agency/JMA, Korea Meteorological Administration/KMA, Unites States National Oceanic and Atmospheric Administration/NOAA, and the Satellite Application Facility on Support to Nowcasting and Very short range forecasting/NWCSAF) with JMA’s Himawari-8 satellite data and other common input data are here compared. The comparison is based on two different AMV input datasets, calculated with two different image triplets for 21 July 2016, and the use of a prescribed and a specific configuration. The main results of the study are summarized as follows: (1) the differences in the AMV datasets depend very much on the ‘AMV height assignment’ used and much less on the use of a prescribed or specific configuration; (2) the use of the ‘Common Quality Indicator (CQI)’ has a quantified skill in filtering collocated AMVs for an improved statistical agreement between centers; (3) Among the six AMV operational algorithms verified by this AMV Intercomparison, JMA AMV algorithm has the best overall performance considering all validation metrics, mainly due to its new height assignment method: ‘Optimal estimation method considering the observed infrared radiances, the vertical profile of the Numerical Weather Prediction wind, and the estimated brightness temperature using a radiative transfer model’.

Himawari-8, a next-generation geostationary meteorological satellite that has been in operation since July 2015, incorporates significant improvements in resolution, scan frequency, and number of bands, bringing new capabilities to weather forecasting. By taking advantage of the availability of high-frequency data with high spatial resolution, an ensemble Kalman filter implemented with a mesoscale regional model assimilated rapid-scan atmospheric motion vectors (RS-AMVs) from Himawari-8. Data assimilation and ensemble forecast experiments were conducted for a heavy rainfall event that occurred in September 2015 in the Kanto and Tohoku regions of Japan. The results showed that the inclusion of RS-AMVs improved precipitation scores, especially for weak and moderate rainfall. In addition, the subsequent model forecast simulated successfully the band of heavy rainfall. Ensemble-based probabilistic forecasts showed that when RSAMVs were assimilated, the results captured the occurrence of torrential rainfall with a relatively high probability. The ensemble-based correlation analysis indicated that the strong rainfall was related to advection of moisture at low to mid levels and moisture flux convergence at lower levels. Simulations with a higher resolution model initialized by nested data assimilation showed that the assimilation of frequent RS-AMVs improved the forecast results.(Citation: Kunii, M., M. Otsuka, K. Shimoji, and H. Seko, 2016: Ensemble data assimilation and forecast experiments for the September 2015 heavy rainfall event in Kanto and Tohoku regions with atmospheric motion vectors from 12,[209][210][211][212][213][214]

Atmospheric motion vectors (AMVs) derived from 5-min rapid scan (RS) imagery of the Multi-functional Transport Satellite are expected to capture small-scale distributions of airflows better than typical AMVs derived from 30-min imagery because the observation interval of RS-AMV is shorter. The impact of these high-frequency data on the numerical forecasting of a heavy rainfall near a stationary front was investigated by conducting data assimilation experiments. As a part of preparation for the assimilation, RS-AMVs were compared with the firstguess field obtained from the Japan Meteorological Agency (JMA) nonhydrostatic model (NHM). The comparison result indicated that the RS-AMVs were of good quality and could be used in the JMA's operational NHM with 4D variational data assimilation (JNoVA). Assimilation experiments investigating a heavy rainfall event were conducted using different lengths of assimilation time slot and time intervals of spatial thinning for the assimilation of the RS-AMV data. The assimilation of RS-AMVs caused the initial wind fields to enhance the upper-level divergence and low-level convergence around the front. Consequently, the forecast of the rainfall amount was increased near the front, and the verification scores were slightly improved over the control experiment in the early forecast hours.

21The high temporal and spatial resolutions of geostationary satellite observations achieved by