Abstract Details
Activity Number:
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469
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Type:
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Contributed
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Date/Time:
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Wednesday, August 6, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #311731
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View Presentation
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Title:
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Logit-Normal Mixed Model for Indian Monsoon Rainfall Extremes
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Author(s):
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Lindsey Dietz*+ and Snigdhansu Chatterjee
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Companies:
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University of Minnesota and University of Minnesota
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Keywords:
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Climate Change ;
Indian Monsoon ;
Generalized Linear Mixed Model ;
Logit-Normal Mixed Model
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Abstract:
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Describing the nature and variability of Indian monsoon precipitation is a topic of much debate in current literature. We suggest the use of a generalized linear mixed model (GLMM), specifically, the logit-normal mixed model, to describe the underlying structure of this complex climatic event. Several GLMM algorithms - approximate likelihood, method of moments, and Bayesian paradigms- were vetted in simulations before application to Indian rainfall data. Logit-normal models were fit with fixed covariates of latitude, longitude, elevation, minimum and maximum temperature, tropospheric temperature difference, Nino-3.4 index, and Indian dipole mode index (DMI) with a random intercept by weather station. The output indicated the model structure aligned with understood monsoon physics and found a non-negligible random effect by weather station. This work provides a valuable starting point for extending GLMM for use in climate data analysis.
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