Articles | Volume 21, issue 5
Nonlin. Processes Geophys., 21, 939–953, 2014
https://doi.org/10.5194/npg-21-939-2014

Special issue: Physics-driven data mining in climate change and weather...

Nonlin. Processes Geophys., 21, 939–953, 2014
https://doi.org/10.5194/npg-21-939-2014

Research article 12 Sep 2014

Research article | 12 Sep 2014

Logit-normal mixed model for Indian monsoon precipitation

L. R. Dietz and S. Chatterjee L. R. Dietz and S. Chatterjee
  • University of Minnesota, School of Statistics, Minneapolis, Minnesota, USA

Abstract. Describing the nature and variability of Indian monsoon precipitation is a topic of much debate in the 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. Four GLMM algorithms are described and simulations are performed to vet these algorithms before applying them to the Indian precipitation data. The logit-normal model was applied to light, moderate, and extreme rainfall. Findings indicated that physical constructs were preserved by the models, and random effects were significant in many cases. We also found GLMM estimation methods were sensitive to tuning parameters and assumptions and therefore, recommend use of multiple methods in applications. This work provides a novel use of GLMM and promotes its addition to the gamut of tools for analysis in studying climate phenomena.