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  • Author or Editor: Qingnong Xiao x
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Xiaolei Zou
,
Qingnong Xiao
,
Alan E. Lipton
, and
George D. Modica

Abstract

The influence of Geostationary Operational Environmental Satellite (GOES) brightness temperature data on the numerical simulations of Hurricane Felix is investigated. Satellite data are included as an augmentation to a bogus data assimilation (BDA) procedure using a mesoscale adjoint modeling system. The assimilation of satellite data modified not only the environmental flow but also the structure of the initial vortex, which is located over a region devoid of satellite data. This modification resulted in a reduction of the 12-h forecast errors verified by radiosonde data. Despite the fact that the forecast using only the bogus surface low at the initial time was very good, track and intensity forecasts beyond 2 days of model integration were shown to be improved further by including satellite data in the initialization procedure. Differences in the prediction of Hurricane Felix with and without satellite data were also found in the prediction of the upper-level jet, the cold temperature trough ahead of the hurricane, the size of the hurricane eye, and the location of the maximum hydrometeor. Although the focus of this study is to assess the effect of the direct use of satellite brightness temperature data on hurricane prediction, it is also noted that the BDA experiment including only the bogus data shows a positive effect of the BDA vortex on the environmental flow during the forecast period, as verified by satellite observations.

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Qingnong Xiao
,
Ying-Hwa Kuo
,
Juanzhen Sun
,
Wen-Chau Lee
,
Eunha Lim
,
Yong-Run Guo
, and
Dale M. Barker

Abstract

In this paper, the impact of Doppler radar radial velocity on the prediction of a heavy rainfall event is examined. The three-dimensional variational data assimilation (3DVAR) system for use with the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) is further developed to enable the assimilation of radial velocity observations. Doppler velocities from the Korean Jindo radar are assimilated into MM5 using the 3DVAR system for a heavy rainfall case that occurred on 10 June 2002. The results show that the assimilation of Doppler velocities has a positive impact on the short-range prediction of heavy rainfall. The dynamic balance between atmospheric wind and thermodynamic fields, based on the Richardson equation, is introduced to the 3DVAR system. Vertical velocity (w) increments are included in the 3DVAR system to enable the assimilation of the vertical velocity component of the Doppler radial velocity observation. The forecast of the hydrometeor variables of cloud water (qc ) and rainwater (qr ) is used in the 3DVAR background fields. The observation operator for Doppler radial velocity is developed and implemented within the 3DVAR system. A series of experiments, assimilating the Korean Jindo radar data for the 10 June 2002 heavy rainfall case, indicates that the scheme for Doppler velocity assimilation is stable and robust in a cycling mode making use of high-frequency radar data. The 3DVAR with assimilation of Doppler radial velocities is shown to improve the prediction of the rainband movement and intensity change. As a result, an improved skill for the short-range heavy rainfall forecast is obtained. The forecasts of other quantities, for example, winds, are also improved. Continuous assimilation with 3-h update cycles is important in producing an improved heavy rainfall forecast. Assimilation of Doppler radar radial velocities using the 3DVAR background fields from a cycling procedure produces skillful rainfall forecasts when verified against observations.

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