A Hierarchical Analysis of the Impact of Methodological Decisions on Statistical Downscaling of Daily Precipitation and Air Temperatures

TitleA Hierarchical Analysis of the Impact of Methodological Decisions on Statistical Downscaling of Daily Precipitation and Air Temperatures
Publication TypeJournal Article
Year of Publication2019
AuthorsPryor, Sara C., and Schoof Justin T.
JournalInternational Journal of Climatology
Volume39
Number6
Pages2880-2900
Date Published01/2019
Abstract / Summary

Despite the widespread application of statistical downscaling tools, uncertainty remains regarding the role of model formulation in determining model skill for daily maximum and minimum temperature (Tmax and Tmin), and precipitation occurrence and intensity. Impacts of several key aspects of statistical transfer function form on model skill are evaluated using a framework resistant to model over-specification. We focus on; 1) Model structure: Simple (generalized linear models, GLM) versus complex (artificial neural networks, ANN) models. 2) Predictor selection: Fixed number of predictors chosen a priori versus stepwise selection of predictors and inclusion of grid point values versus predictors derived from the application of principal components analysis (PCA) to spatial fields. We also examine the influence of domain size on model performance. For precipitation downscaling, we consider the role of the threshold used to characterize a wet day and apply three approaches (Poisson and Gamma distributions in GLM, and ANN) to downscale wet day precipitation amounts. While no downscaling formulation is optimal for all predictands and at ten locations representing diverse U.S. climates, and due to the exclusion of variance inflation all of the downscaling formulations fail to reproduce the range of observed variability, models with larger suites of prospective predictors generally have higher skill. For temperature downscaling, ANNs generally outperform GLM, with greater improvements for Tmin than Tmax. Use of PCA-derived predictors does not systematically improve model skill, but does improve skill for temperature extremes. Model skill for precipitation occurrence generally increases as the wet day threshold increases and models using PCA-derived predictors tend to outperform those based on grid cell predictors. Each model for wet day precipitation intensity overestimates annual total precipitation and underestimates the proportion derived from extreme precipitation events, but ANN-based models and those with larger predictor suites tend to have the smallest bias.

URLhttp://doi.org/10.1002/joc.5990
DOI10.1002/joc.5990
Journal: International Journal of Climatology
Year of Publication: 2019
Volume: 39
Number: 6
Pages: 2880-2900
Date Published: 01/2019

Despite the widespread application of statistical downscaling tools, uncertainty remains regarding the role of model formulation in determining model skill for daily maximum and minimum temperature (Tmax and Tmin), and precipitation occurrence and intensity. Impacts of several key aspects of statistical transfer function form on model skill are evaluated using a framework resistant to model over-specification. We focus on; 1) Model structure: Simple (generalized linear models, GLM) versus complex (artificial neural networks, ANN) models. 2) Predictor selection: Fixed number of predictors chosen a priori versus stepwise selection of predictors and inclusion of grid point values versus predictors derived from the application of principal components analysis (PCA) to spatial fields. We also examine the influence of domain size on model performance. For precipitation downscaling, we consider the role of the threshold used to characterize a wet day and apply three approaches (Poisson and Gamma distributions in GLM, and ANN) to downscale wet day precipitation amounts. While no downscaling formulation is optimal for all predictands and at ten locations representing diverse U.S. climates, and due to the exclusion of variance inflation all of the downscaling formulations fail to reproduce the range of observed variability, models with larger suites of prospective predictors generally have higher skill. For temperature downscaling, ANNs generally outperform GLM, with greater improvements for Tmin than Tmax. Use of PCA-derived predictors does not systematically improve model skill, but does improve skill for temperature extremes. Model skill for precipitation occurrence generally increases as the wet day threshold increases and models using PCA-derived predictors tend to outperform those based on grid cell predictors. Each model for wet day precipitation intensity overestimates annual total precipitation and underestimates the proportion derived from extreme precipitation events, but ANN-based models and those with larger predictor suites tend to have the smallest bias.

DOI: 10.1002/joc.5990
Citation:
Pryor, SC, and JT Schoof.  2019.  "A Hierarchical Analysis of the Impact of Methodological Decisions on Statistical Downscaling of Daily Precipitation and Air Temperatures."  International Journal of Climatology 39(6): 2880-2900.  https://doi.org/10.1002/joc.5990.