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Kun-Il Jang, X. Zou, M. S. F. V. De Pondeca, M. Shapiro, C. Davis, and A. Krueger

Abstract

In this study, a methodology is proposed for incorporating total column ozone data from the Total Ozone Mapping Spectrometer (TOMS) into the initial conditions of a mesoscale prediction model. Based on the strong correlation between vertical mean potential vorticity (MPV) and TOMS ozone (O3) that was found in middle latitudes at both 30- and 90-km resolutions, using either analyses or 24-h model forecasts, a statistical correlation model between O3 and MPV is employed for assimilating TOMS ozone in a four-dimensional variational data assimilation (4DVAR) procedure. A linear relationship between O3 and MPV is first assumed: O3 = α(MPV) + β. The constants α and β are then found by a regression method. The proposed approach of using this simple linear regression model for ozone assimilation is applied to the prediction of the 24–25 January 2000 East Coast winter storm. Three 4DVAR experiments are carried out assimilating TOMS ozone, radiosonde, or both types of observations. It is found that adjustments in model initial conditions assimilating only TOMS ozone data are confined to the upper levels and produce almost no impact on the prediction of the storm development. However, when TOMS ozone data are used together with radiosonde observations, a more rapid deepening of sea level pressure of the simulated storm is observed than with only radiosonde observations. The predicted track of the winter storm is also altered, moving closer to the coast. Using NCEP multisensor hourly rainfall data as verification, the 36-h forecasts with both TOMS ozone and radiosonde observations outperform the radiosonde-only experiments. These results indicate that TOMS ozone data contain valuable meteorological information, which can be used to improve cyclone prediction.

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Manuel S. F. V. De Pondeca, Geoffrey S. Manikin, Geoff DiMego, Stanley G. Benjamin, David F. Parrish, R. James Purser, Wan-Shu Wu, John D. Horel, David T. Myrick, Ying Lin, Robert M. Aune, Dennis Keyser, Brad Colman, Greg Mann, and Jamie Vavra

Abstract

In 2006, the National Centers for Environmental Prediction (NCEP) implemented the Real-Time Mesoscale Analysis (RTMA) in collaboration with the Earth System Research Laboratory and the National Environmental, Satellite, and Data Information Service (NESDIS). In this work, a description of the RTMA applied to the 5-km resolution conterminous U.S. grid of the National Digital Forecast Database is given. Its two-dimensional variational data assimilation (2DVAR) component used to analyze near-surface observations is described in detail, and a brief discussion of the remapping of the NCEP stage II quantitative precipitation amount and NESDIS Geostationary Operational Environmental Satellite (GOES) sounder effective cloud amount to the 5-km grid is offered. Terrain-following background error covariances are used with the 2DVAR approach, which produces gridded fields of 2-m temperature, 2-m specific humidity, 2-m dewpoint, 10-m U and V wind components, and surface pressure. The estimate of the analysis uncertainty via the Lanczos method is briefly described. The strength of the 2DVAR is illustrated by (i) its ability to analyze a June 2007 cold temperature pool over the Washington, D.C., area; (ii) its fairly good analysis of a December 2008 mid-Atlantic region high-wind event that started from a very weak first guess; and (iii) its successful recovery of the finescale moisture features in a January 2010 case study over southern California. According to a cross-validation analysis for a 15-day period during November 2009, root-mean-square error improvements over the first guess range from 16% for wind speed to 45% for specific humidity.

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