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W. R. Cotton, R. A. PielkeSr, R. L. Walko, G. E. Liston, C. J. Tremback, H. Jiang, R. L. McAnelly, J. Y. Harrington, M. E. Nicholls, C. G. Carrio and J. P. McFadden, “RAMS 2001: Current Status and Future Directions,” Meteorological and Atmospheric Physics, Vol. 82, No. 1-4, 2003, pp. 5-29.

has been cited by the following article:

  • TITLE: Preliminary Results of a Data Assimilation System

    AUTHORS: Stefano Federico

    KEYWORDS: Meteorological Analysis; Forecasting Systems; Short-Term Forecast of Winds; Variational Methods

    JOURNAL NAME: Atmospheric and Climate Sciences, Vol.3 No.1, January 30, 2013

    ABSTRACT: A data assimilation system combines all available information on the atmospheric state in a given time-window to produce an estimate of atmospheric conditions valid at a prescribed analysis time. Nowadays, increased computing power coupled with greater access to real-time asynoptic data is paving the way toward a new generation of high-resolution (i.e. on the order of 10 km) operational mesoscale analyses and forecasting systems. Moreover, better initial conditions are increasingly considered of the utmost importance for Numerical Weather Prediction (NWP) at the short range (0 - 12 h). This paper presents a general-purpose data assimilation system, which is coupled with the Regional Atmospheric Modelling System (RAMS) to give the analyses for: zonal and meridional wind components, temperature, relative humidity, and geopotential height. In order to show its potential, the data assimilation systems applied to produce analyses over Central Europe. For this application the background field is given by a short-range forecast (12 h) of the RAMS and analyses are produced by 2D-Var with 0.25? horizontal resolution. Results show the validity of the analyses because they are closer to the observations, consistently with the settings of the data assimilation system. To quantify the impact of improved initial conditions on the forecast, the analyses are then used as initial conditions of a short-range (6 h) forecast of the RAMS model. The results show that the RMSE is effectively reduced for the one- and two hours forecast, with some improvement for the three-hours forecast.