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Ensemble Data Assimilation for the A...

Ying, Yue.

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Ensemble Data Assimilation for the Analysis and Prediction of Multiscale Tropical Weather Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Ensemble Data Assimilation for the Analysis and Prediction of Multiscale Tropical Weather Systems./
作者:	Ying, Yue.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	195 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-02, Section: B.
Contained By:	Dissertations Abstracts International80-02B.
標題:	Meteorology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10903793
ISBN:	9780438136182

Ensemble Data Assimilation for the Analysis and Prediction of Multiscale Tropical Weather Systems.
Ying, Yue.

Ensemble Data Assimilation for the Analysis and Prediction of Multiscale Tropical Weather Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 195 p.

Source: Dissertations Abstracts International, Volume: 80-02, Section: B.

Thesis (Ph.D.)--The Pennsylvania State University, 2018.

This item must not be sold to any third party vendors.

Tropical weather systems are important components of the global circulation that span a wide range of spatial and temporal scales. On the large-scale end of the spectrum, the Madden-Julian Oscillation (MJO) is found to be the dominant mode. Atmospheric wave motion due to Earth's rotation and gravity fills the spectrum from weeks to hours and from tens of thousands of kilometers to a few tens of kilometers. The thermally driven convective processes at smaller scales are chaotic in nature, which poses an intrinsic limit on the long-term predictability of tropical weather through coupling and scale interaction. This dissertation seeks to identify the predictability limits for tropical atmosphere, establishing an upper bound in expected prediction skill of these weather systems. Other scientific questions this dissertation answered are how much future satellite observations can improve the prediction skill, and how to design adaptive multiscale data assimilation methods that make better use of the available observations. Using a convection-permitting numerical model, Weather Research and Forecasting (WRF), an MJO active phase during October 2011 is simulated. The practical predictability limit is estimated from an ensemble forecast with realistic initial and boundary condition uncertainties sampled from the operational global model forecasts. Predictability limit is reached when the ensemble spread is indistinguishable from random climatological draws. Results indicate predictability is scale dependent. There is a sharp transition from slow to fast error growth at the intermediate scales (~500 km), separating the more predictable large-scale components (~2 weeks) from the less predictable small-scale components (2 weeks for larger scales and <3 days for small scales. An Observing System Simulation Experiment (OSSE) is conducted using the Ensemble Kalman Filter (EnKF) to evaluate the potential improvements in the prediction skill through assimilating current and future satellite observations. Results show that the currently available temperature, humidity profiles and wind vectors retrieved from infrared and microwave satellite sounder data can extend the skillful forecast lead time by as much as 4 days for the larger scales. With prospective improvement in resolution and complementary sampling strategies, the prediction skill can be further improved, especially for the smaller scales. These results shed lights on the need, design and cost-benefit analysis of future observing systems for better tropical weather prediction. For ensemble filtering, covariance localization and inflation methods are required to account for sampling errors due to limited ensemble size and unrepresented model errors. Tuning the localization and inflation to achieve optimal filter performance is a laborious process, thus adaptive algorithms are much favored. In this dissertation, an adaptive covariance relaxation (ACR) method is proposed and tested in the Lorenz 40-variable system. The method is able to account for observations that are irregular in spatial and temporal distribution, which is typical for the tropics. In pursuit for an optimal localization method, the sensitivity of localization distance to ensemble size, model resolution, and observing network are comprehensively tested in a multiscale quasi-geostrophic (QG) model. The best localization distance is found dependent to the dominant scale of a system, which motivates the implementation of a multiscale localization for tropical weather. Some behavior related to nonlocal and irregular observation are also documented in this dissertation.

ISBN: 9780438136182Subjects--Topical Terms:

542822
Meteorology.

Ensemble Data Assimilation for the Analysis and Prediction of Multiscale Tropical Weather Systems.
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520 $a Tropical weather systems are important components of the global circulation that span a wide range of spatial and temporal scales. On the large-scale end of the spectrum, the Madden-Julian Oscillation (MJO) is found to be the dominant mode. Atmospheric wave motion due to Earth's rotation and gravity fills the spectrum from weeks to hours and from tens of thousands of kilometers to a few tens of kilometers. The thermally driven convective processes at smaller scales are chaotic in nature, which poses an intrinsic limit on the long-term predictability of tropical weather through coupling and scale interaction. This dissertation seeks to identify the predictability limits for tropical atmosphere, establishing an upper bound in expected prediction skill of these weather systems. Other scientific questions this dissertation answered are how much future satellite observations can improve the prediction skill, and how to design adaptive multiscale data assimilation methods that make better use of the available observations. Using a convection-permitting numerical model, Weather Research and Forecasting (WRF), an MJO active phase during October 2011 is simulated. The practical predictability limit is estimated from an ensemble forecast with realistic initial and boundary condition uncertainties sampled from the operational global model forecasts. Predictability limit is reached when the ensemble spread is indistinguishable from random climatological draws. Results indicate predictability is scale dependent. There is a sharp transition from slow to fast error growth at the intermediate scales (~500 km), separating the more predictable large-scale components (~2 weeks) from the less predictable small-scale components (2 weeks for larger scales and <3 days for small scales. An Observing System Simulation Experiment (OSSE) is conducted using the Ensemble Kalman Filter (EnKF) to evaluate the potential improvements in the prediction skill through assimilating current and future satellite observations. Results show that the currently available temperature, humidity profiles and wind vectors retrieved from infrared and microwave satellite sounder data can extend the skillful forecast lead time by as much as 4 days for the larger scales. With prospective improvement in resolution and complementary sampling strategies, the prediction skill can be further improved, especially for the smaller scales. These results shed lights on the need, design and cost-benefit analysis of future observing systems for better tropical weather prediction. For ensemble filtering, covariance localization and inflation methods are required to account for sampling errors due to limited ensemble size and unrepresented model errors. Tuning the localization and inflation to achieve optimal filter performance is a laborious process, thus adaptive algorithms are much favored. In this dissertation, an adaptive covariance relaxation (ACR) method is proposed and tested in the Lorenz 40-variable system. The method is able to account for observations that are irregular in spatial and temporal distribution, which is typical for the tropics. In pursuit for an optimal localization method, the sensitivity of localization distance to ensemble size, model resolution, and observing network are comprehensively tested in a multiscale quasi-geostrophic (QG) model. The best localization distance is found dependent to the dominant scale of a system, which motivates the implementation of a multiscale localization for tropical weather. Some behavior related to nonlocal and irregular observation are also documented in this dissertation.
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