Probabilistic Multisite Precipitation Downscaling by an Expanded Bernoulli–Gamma Density Network

By Alex J. Cannon. Journal of Hydrometeorology, 2008

123 citations recorded by the source corpus.

A nonlinear, probabilistic synoptic downscaling algorithm for daily precipitation series at multiple sites is presented. The expanded Bernoulli–gamma density network (EBDN) represents the conditional density of multisite precipitation, conditioned on synoptic-scale climate predictors, using an artificial neural network (ANN) whose outputs are parameters of the Bernoulli–gamma distribution. Following the methodology used in expanded downscaling, predicted covariances between sites are forced to match observed covariances through the addition of a constraint to the ANN cost function. The resulting model can be thought of as a regression-based downscaling model with a stochastic weather generator component. Parameters of the Bernoulli–gamma distribution are downscaled from the synoptic-scale circulation, and unresolved temporal variability is generated via an autoregressive noise model. Demonstrated on a multisite precipitation dataset from coastal British Columbia, Canada, the EBDN is capable of specifying the conditional distribution of precipitation at each site, modeling the occurrence and the amount of precipitation simultaneously, reproducing observed spatial relationships between sites, randomly generating realistic synthetic precipitation series, and predicting precipitation amounts in excess of those in the observational record.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/e9af1a3dedfc8b34912ca46ceb85411806911f76

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Environmental Engineering

In Global and Planetary Change

Citations in the corpus

Daily spatiotemporal precipitation simulation using latent and transformed Gaussian processes (2012, background): “…and Lall, 1999], generalized chain-dependent processes [Zheng and Katz, 2008; Zheng et al., 2010], power transformation of precipitation to normality [Sansó and Guenni, 2000; Yang et al., 2005], artificial neural networks [Cannon, 2008], or copula-based approaches [Bárdossy and Pegram, 2009].”
A bivariate mixed distribution with a heavy‐tailed component and its application to single‐site daily rainfall simulation (2013, methodology): “Discrete-continuous mixed distributions of this form have been used in the literature for daily rainfall downscaling [Cannon, 2008; Carreau and Vrac, 2011].”
Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? (2015, unknown): “method of generalized maximum likelihood (Martins and Stedinger 2000), following Cannon (2010), using the shape parameter prior of Papalexiou and Koutsoyiannis”
Quantile regression neural networks: Implementation in R and application to precipitation downscaling (2011, methodology): “(2006) and Cannon (2008) downscaled precipitation amounts using an ANN to specify parameters of a mixed Bernoulli-gamma distribution; and Cornford et al. (1999) predicted wind direction using an ANN to specify a mixture of periodic kernel functions.”