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  • Author or Editor: Qingnong Xiao x
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Qingnong Xiao
,
Liqiang Chen
, and
Xiaoyan Zhang

Abstract

A tropical cyclone bogus data assimilation (BDA) scheme is built in the Weather Research and Forecasting three-dimensional variational data assimilation system (WRF 3D-VAR). Experiments were conducted (21 experiments with BDA in parallel with another 21 without BDA) to assess its impacts on the predictions of seven Atlantic Ocean basin hurricanes observed in 2004 (Charley, Frances, Ivan, and Jeanne) and in 2005 (Katrina, Rita, and Wilma). In addition, its performance was compared with the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane initialization scheme in a case study of Hurricane Humberto (2007). It is indicated that hurricane initialization with the BDA technique can improve the forecast skills of track and intensity in the Advanced Research WRF (ARW). Among the three hurricane verification parameters [track, central sea level pressure (CSLP), and maximum surface wind (MSW)], BDA improves CSLP the most. The improvement of MSW is also considerable. The track has the smallest, but still noticeable, improvement. With WRF 3D-VAR, the initial vortex produced by BDA is balanced with the dynamical and statistical balance in the 3D-VAR system. It has great potential for improving the hurricane intensity forecast. The case study on Hurricane Humberto (2007) shows that BDA performs better than the GFDL bogus scheme in the ARW forecast for the case. Better definition of the initial vortex is the main reason for the advanced skill in hurricane track and intensity forecasting in this case.

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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
,
Dale M. Barker
, and
Eunha Lim

Abstract

A radar reflectivity data assimilation scheme was developed within the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) three-dimensional variational data assimilation (3DVAR) system. The model total water mixing ratio was used as a control variable. A warm-rain process, its linear, and its adjoint were incorporated into the system to partition the moisture and hydrometeor increments. The observation operator for radar reflectivity was developed and incorporated into the 3DVAR. With a single reflectivity observation, the multivariate structures of the analysis increments that included cloud water and rainwater mixing ratio increments were examined. Using the onshore Doppler radar data from Jindo, South Korea, the capability of the radar reflectivity assimilation for the landfalling Typhoon Rusa (2002) was assessed. Verifications of inland quantitative precipitation forecasting (QPF) of Typhoon Rusa (2002) showed positive impacts of assimilating radar reflectivity data on the short-range QPF.

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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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