Variational Bayes Estimation of Hidden Markov Models for Daily Precipitation with Semi-Continuous Emissions
Loading...
Permanent Link
Author/Creator
Author/Creator ORCID
Date
2021
Type of Work
Department
Program
Citation of Original Publication
Majumder, Reetam; Variational Bayes Estimation of Hidden Markov Models for Daily Precipitation with Semi-Continuous Emissions; UMBC High Performance Computing Facility (HPCF)
Technical Report, 2021; http://hpcf-files.umbc.edu/research/papers/MajumderHPCF20218.pdf
Rights
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Abstract
Stochastic precipitation generators can simulate dry and wet rainfall stretches for
long durations. Generated precipitation time series data are used in climate projections, impact assessment of extreme weather events, and water resource and agricultural management. Daily precipitation is specified as a semi-continuous distribution
with a point mass at zero and a mixture of Exponential or Gamma distributions for
positive precipitation. Our generators are obtained as hidden Markov models (HMM)
where the underlying climate conditions form the states. Maximum likelihood estimation for HMMs has historically relied on the Baum-Welch algorithm. We implement
variational Bayes as an alternative for parameter estimation in HMMs. In our simulation study for a 3-state HMM with positive rainfall specified as a mixture of 2 Exponential distributions, we get good posterior estimates when the model is initialized with
the correct number of states and mixture components. We also fit a similar model to a
single grid point within the Chesapeake Bay watershed based on GPM-IMERG remote
sensing data for the wet season between July to September from 2000–2019. Synthetic
data generated from the fitted model is able to replicate the monthly proportion of dry
days at the location, as well as the total monthly precipitation.