気象集誌. 第2輯
Online ISSN : 2186-9057
Print ISSN : 0026-1165
ISSN-L : 0026-1165
Articles
Forecast and Observation-Impact Experiments in the Navy Global Environmental Model with Assimilation of ECWMF Analysis Data in the Global Domain
Brett T. HOOVERRolf H. LANGLAND
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2017 年 95 巻 6 号 p. 369-389

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 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.

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© 2017 by Meteorological Society of Japan
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