HR: 1340h
AN: H33A-0965    [Abstracts]
TI: A copula-based method to fill in missing data for daily rainfall
AU: * LANDOT, T
EM: tl2273@columbia.edu
AF: Columbia University, W 116th St, New York, NY 10027, United States
AU: LALL, U
EM: uls2@columbia.edu
AF: Columbia University, W 116th St, New York, NY 10027, United States
AU: Pathak, C
EM: cpathak@sfwmd.gov
AB: In this article, we describe and test a method to fill in missing values in daily rainfall datasets. This method is based on a copula-based bivariate models. Copulae are functions that are commonly used in statistics to approximate multivariate distributions. We describe how to infere the parameters of this model using historical data and validate it. Then, for a given day, this probabilistic model is used to estimate the probability of rainfall event at a rain gage station given another one and the expected value of rainfall amount. These quantities are then integrated by a logistic regression for the probabilities and a linear regression for the expected amounts in order to get a final estimate of the missing value of rainfall. This model is tested against commonly used methods, such as direct linear regression or ordinary kriging on a dataset of 43 rain gage station from Florida. Cross-validation shows that this model provides a good estimate of missing values, with a significant departure from linear models for heavy rainfall events.
DE: 1800 HYDROLOGY
DE: 1847 Modeling
DE: 1849 Numerical approximations and analysis
DE: 1854 Precipitation (3354)
SC: Hydrology [H]
MN: 2007 Fall Meeting