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Using ensemble data assimilation for...

Torn, Ryan.

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Using ensemble data assimilation for predictability and dynamics.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Using ensemble data assimilation for predictability and dynamics./
作者:	Torn, Ryan.
面頁冊數:	186 p.
附註:	Adviser: George J. Hakim.
Contained By:	Dissertation Abstracts International68-05B.
標題:	Atmospheric Sciences. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3265423
ISBN:	9780549040439

Using ensemble data assimilation for predictability and dynamics.
Torn, Ryan.

Using ensemble data assimilation for predictability and dynamics. - 186 p.

Adviser: George J. Hakim.

Thesis (Ph.D.)--University of Washington, 2007.

Atmospheric predictability depends in part on the sources and evolution of errors in numerical weather prediction models. As a consequence, it is important to initialize a model with the best estimate of the atmosphere and understand how errors in this initial condition will affect the forecast. The ensemble Kalman filter (EnKF) is an attractive method of initializing a forecast model because this technique uses statedependent error statistics to spread observation information to model grid points. In addition, output from an EnKF system can be used to quantify how changes to the initial conditions and observation assimilation affect scalar functions of forecast variables.

ISBN: 9780549040439Subjects--Topical Terms:

1019179
Atmospheric Sciences.

Using ensemble data assimilation for predictability and dynamics.
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245 1 0 $a Using ensemble data assimilation for predictability and dynamics.
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502 $a Thesis (Ph.D.)--University of Washington, 2007.
520 $a Atmospheric predictability depends in part on the sources and evolution of errors in numerical weather prediction models. As a consequence, it is important to initialize a model with the best estimate of the atmosphere and understand how errors in this initial condition will affect the forecast. The ensemble Kalman filter (EnKF) is an attractive method of initializing a forecast model because this technique uses statedependent error statistics to spread observation information to model grid points. In addition, output from an EnKF system can be used to quantify how changes to the initial conditions and observation assimilation affect scalar functions of forecast variables.
520 $a A pseudo-operational EnKF system is implemented for a limited-area domain that includes the eastern Pacific Ocean to test the benefit of ensemble analyses and forecasts in a region characterized by sparse in-situ data and complex topography. Comparisons against rawinsondes indicate that ensemble forecasts from this system have comparable skill to other major global NWP forecasts, even though it does not consider satellite radiance data. Forecasts of average pressure over western Washington state from this EnKF system show a region of maximum sensitivity to the west of this region. The accuracy of ensemble predictions of observation impact is verified by comparing forecasts where observations are assimilated with the control case where no observations are used. These experiments indicate that the impact of thousands of observations can be estimated by a subset of O(100) most-significant observations.
520 $a These ensemble techniques are applied to understand the initial condition sensitivity and observation impact during forecasts of western Pacific extratropical transition (ET) events, which are often characterized by large short-term forecast er rors. ET forecasts are most sensitive to the position of the tropical cyclone (TC) and to upstream mid-latitude troughs that interact with the transitioning storm and other downstream features. Observation impact calculations indicate that assimilating O(50) observations near the TC and upstream troughs can have nearly the same impact as all 12 000 available observations. Furthermore, the amount of downstream ridging that occurs during these events depends on the lower-tropospheric moisture flux east of the TC.
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