| Variational Assimilation of GPS Precipitable Water Vapor and Hourly Rainfall Observations for a Meso-β Scale Heavy Precipitation Event During the 2002 Mei-Yu Season |
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| 引用本文： | 张盟,倪允琪,张福青.Variational Assimilation of GPS Precipitable Water Vapor and Hourly Rainfall Observations for a Meso-β Scale Heavy Precipitation Event During the 2002 Mei-Yu Season[J].大气科学进展,2007,24(3):509-526. |
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| 作者姓名： | 张盟 倪允琪 张福青 |
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| 作者单位： | [1]Department of Atmospheric Sciences, Nanjing University, Nanjing 210093 [2]Department of Atmospheric Sciences, Texas A ＆ M University, College Station, Texas 77843-3150, USA [3]State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081 |
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| 基金项目： | Acknowledgements. The authors are grateful to the China Heavy Rain Experiment and Study program and Shanghai CPS Comprehensive Application Network for providing the intensive observational data. This research is supported by the National Key Basic Research and Development Program 2004CB418300, and also funded by 0ffice of Naval Research through the Young Investigator＇s Program （Award N000140410471） and the US NSF Grant No. 0205599. |
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| 摘 要： | Recent advances in Global Positioning System （GPS） remote sensing technology allow for a direct estimation of the precipitable water vapor （PWV） from delayed signals transmitted by GPS satellites, which can be assimilated into numerical models with four-dimensional variational （4DVAR） data assimilation. A mesoscale model and its 4DVAR system are used to access the impacts of assimilating GPS-PWV and hourly rainfall observations on the short-range prediction of a heavy rainfall event on 20 June 2002. The heavy precipitation was induced by a sequence of meso-β-scale convective systems （MCS） along the mei-yu front in China. The experiments with GPS-PWV assimilation cluster and also eliminated the erroneous rainfall successfully simulated the evolution of the observed MCS systems found in the experiment without 4DVAR assimilation. Experiments with hourly rainfall assimilation performed similarly both on the prediction of MCS initiation and the elimination of erroneous systems, however the MCS dissipated much sooner than it did in observations. It is found that the assimilation-induced moisture perturbation and mesoscale low-level jet are helpful for the MCS generation and development. It is also discovered that spurious gravity waves may post serious limitations for the current 4DVAR algorithm, which would degrade the assimilation efficiency, especially for rainfall data. Sensitivity experiments with different observations, assimilation windows and observation weightings suggest that assimilating GPS-PWV can be quite effective, even with the assimilation window as short as 1 h. On the other hand, assimilating rainfall observations requires extreme cautions on the selection of observation weightings and the control of spurious gravity waves.
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| 关 键 词： | GPS 可降水汽量 四维变异同化 每小时降水观测值 2002年 梅雨季节 中β尺度 暴雨活动 |
| 收稿时间： | 2006-03-22 |
| 修稿时间： | 2006-07-13 |
Variational assimilation of GPS precipitable water vapor and hourly rainfall observations for a meso-<Emphasis Type="Italic">β</Emphasis> scale heavy precipitation event during the 2002 mei-yu season |
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| Zhang?Meng,Ni?Yunqi,Zhang?Fuqing.Variational assimilation of GPS precipitable water vapor and hourly rainfall observations for a meso-β scale heavy precipitation event during the 2002 mei-yu season[J].Advances in Atmospheric Sciences,2007,24(3):509-526. |
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| Authors: | Zhang Meng Ni Yunqi Zhang Fuqing |
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| Institution: | Department of Atmospheric Sciences, Nanjing University, Nanjing 210093; Department of Atmospheric Sciences, Texas A & M University, College Station, Texas 77843-3150, USA,State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081,Department of Atmospheric Sciences, Texas A & M University, College Station, Texas 77843-3150, USA |
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| Abstract: | Recent advances in Global Positioning System (GPS) remote sensing technology allow for a direct estimation of
the precipitable water vapor (PWV) from delayed signals transmitted by GPS satellites, which can be assimilated into
numerical models with four-dimensional variational (4DVAR) data assimilation. A mesoscale model and its 4DVAR system are
used to access the impacts of assimilating GPS-PWV and hourly rainfall observations on the short-range prediction of a
heavy rainfall event on 20 June 2002. The heavy precipitation was induced by a sequence of meso-beta-scale convective
systems (MCS) along the mei-yu front in China.
The experiments with GPS-PWV assimilation successfully simulated the evolution of the observed MCS cluster and also
eliminated the erroneous rainfall systems found in the experiment without 4DVAR assimilation. Experiments with hourly
rainfall assimilation performed similarly both on the prediction of MCS initiation and the elimination of erroneous systems,
however the MCS dissipated much sooner than it did in observations. It is found that the assimilation-induced moisture
perturbation and mesoscale low-level jet are helpful for the MCS generation and development. It is also discovered that
spurious gravity waves may post serious limitations for the current 4DVAR algorithm, which would degrade the assimilation
efficiency, especially for rainfall data. Sensitivity experiments with different observations, assimilation windows and
observation weightings suggest that assimilating GPS-PWV can be quite effective, even with the assimilation window as short
as 1 h. On the other hand, assimilating rainfall observations requires extreme cautions on the selection of observation
weightings and the control of spurious gravity waves. |
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| Keywords: | GPS precipitable water vapor four-dimensional variational assimilation meso-beta -scale convective system |
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