Type
Text
Type
Dissertation
Advisor
Jiao, Xiangmin | Zhu, Wei | Xing, Haipeng | Zhang, Minghua.
Date
2012-12-01
Keywords
Statistics
Department
Department of Applied Mathematics and Statistics.
Language
en_US
Source
This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.
Identifier
http://hdl.handle.net/11401/77688
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
Understanding climate change is an increasingly urgent issue of our society. Existing global climate models are less accurate particularly in predicting severe weather and abrupt climate change, which will invariably cause dramatic losses in life and property. One of the four major impediments identified by the National Research Council is the rudimentary, stationary and often unrealistic statistical methods/models employed in climate modeling. We endeavor to improve the stochastic modeling of atmospheric data by incorporating the spatial and temporal correlations with observations in the constrained variational analysis modeling (referred to CVA model hereinafter) approach pioneered by Zhang and Lin (1997) and further developed by Zhang et al. (2001). Thus far, we have successfully incorporated some spatial correlations into our model, especially the correlations of state variables across different vertical levels. Furthermore, we have incorporated temporal correlations via an AR(1) time series model. The newly enhanced constrained variational analysis model is a significant improvement over the traditional methods in its core idea of (1) enforcing physical consistency through variational constraints, (2) stochastic modeling of the random errors, and (3) utilizing heterogeneous data through multiple nesting. Our novel integration of statistical methods and physical principles has given birth to a modern and superior climate model featuring better uncertainty quantifications and more accurate predictions as demonstrated by our final results. | 76 pages
Recommended Citation
Huang, Jun, "Constrained Variational Analysis Integrating Vertical and Temporal Correlations" (2012). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 3479.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/3479