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Monte Carlo strategies for calibrati...

The University of New Mexico., Statistics.

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Monte Carlo strategies for calibration in climate models.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Monte Carlo strategies for calibration in climate models./
作者:	Villagran-Hernandez, Alejandro.
面頁冊數:	88 p.
附註:	Adviser: Gabriel Huerta.
Contained By:	Dissertation Abstracts International70-06B.
標題:	Atmospheric Sciences. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3359801
ISBN:	9781109221466

Monte Carlo strategies for calibration in climate models.
Villagran-Hernandez, Alejandro.

Monte Carlo strategies for calibration in climate models. - 88 p.

Adviser: Gabriel Huerta.

Thesis (Ph.D.)--The University of New Mexico, 2009.

Intensive computational methods have been used by Earth scientists in a wide range of problems in data inversion and uncertainty quantification such as earthquake epicenter location and climate projections. To quantify the uncertainties resulting from a range of plausible model configurations it is necessary to estimate a multidimensional probability distribution. The computational cost of estimating these distributions for geoscience applications is impractical using traditional methods such as Metropolis/Gibbs algorithms as simulation costs limit the number of experiments that can be obtained reasonably. Several alternate sampling strategies have been proposed that could improve on the sampling efficiency including Multiple Very Fast Simulated Annealing (MVFSA) and Adaptive Metropolis algorithms. As a goal of this research, the performance of these proposed sampling strategies are evaluated with a surrogate climate model that is able to approximate the noise and response behavior of a realistic atmospheric general circulation model (AGCM). The surrogate model is fast enough that its evaluation can be embedded in these Monte Carlo algorithms. The goal of this thesis is to show that adaptive methods can be superior to MVFSA to approximate the known posterior distribution with fewer forward evaluations. However, the adaptive methods can also be limited by inadequate sample mixing. The Single Component and Delayed Rejection Adaptive Metropolis algorithms were found to resolve these limitations, although challenges remain to approximating multi-modal distributions. The results show that these advanced methods of statistical inference can provide practical solutions to the climate model calibration problem and challenges in quantifying climate projection uncertainties. The computational methods would also be useful to problems outside climate pre- diction, particularly those where sampling is limited by availability of computational resources.

ISBN: 9781109221466Subjects--Topical Terms:

1019179
Atmospheric Sciences.

Monte Carlo strategies for calibration in climate models.
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520 $a Intensive computational methods have been used by Earth scientists in a wide range of problems in data inversion and uncertainty quantification such as earthquake epicenter location and climate projections. To quantify the uncertainties resulting from a range of plausible model configurations it is necessary to estimate a multidimensional probability distribution. The computational cost of estimating these distributions for geoscience applications is impractical using traditional methods such as Metropolis/Gibbs algorithms as simulation costs limit the number of experiments that can be obtained reasonably. Several alternate sampling strategies have been proposed that could improve on the sampling efficiency including Multiple Very Fast Simulated Annealing (MVFSA) and Adaptive Metropolis algorithms. As a goal of this research, the performance of these proposed sampling strategies are evaluated with a surrogate climate model that is able to approximate the noise and response behavior of a realistic atmospheric general circulation model (AGCM). The surrogate model is fast enough that its evaluation can be embedded in these Monte Carlo algorithms. The goal of this thesis is to show that adaptive methods can be superior to MVFSA to approximate the known posterior distribution with fewer forward evaluations. However, the adaptive methods can also be limited by inadequate sample mixing. The Single Component and Delayed Rejection Adaptive Metropolis algorithms were found to resolve these limitations, although challenges remain to approximating multi-modal distributions. The results show that these advanced methods of statistical inference can provide practical solutions to the climate model calibration problem and challenges in quantifying climate projection uncertainties. The computational methods would also be useful to problems outside climate pre- diction, particularly those where sampling is limited by availability of computational resources.
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