Multivariate Stochastic Downscaling for Semicontinuous Data

2018 ◽  
pp. 107-115
Author(s):  
Lucia Paci ◽  
Carlo Trivisano ◽  
Daniela Cocchi
Keyword(s):  
Semicontinuous Data ◽  
Stochastic Downscaling

scholarly journals New Approach for Bias Correction and Stochastic Downscaling of Future Projections for Daily Mean Temperatures to a High-Resolution Grid

2019 ◽  
Vol 58 (12) ◽  
pp. 2617-2632 ◽  
Author(s):  
Qifen Yuan ◽  
Thordis L. Thorarinsdottir ◽  
Stein Beldring ◽  
Wai Kwok Wong ◽  
Shaochun Huang ◽  
...  
Keyword(s):  
High Resolution ◽  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Change Signal ◽  
Climate Projections ◽  
Data Product ◽  
Temporal Features ◽  
Stochastic Downscaling

AbstractIn applications of climate information, coarse-resolution climate projections commonly need to be downscaled to a finer grid. One challenge of this requirement is the modeling of subgrid variability and the spatial and temporal dependence at the finer scale. Here, a postprocessing procedure for temperature projections is proposed that addresses this challenge. The procedure employs statistical bias correction and stochastic downscaling in two steps. In the first step, errors that are related to spatial and temporal features of the first two moments of the temperature distribution at model scale are identified and corrected. Second, residual space–time dependence at the finer scale is analyzed using a statistical model, from which realizations are generated and then combined with an appropriate climate change signal to form the downscaled projection fields. Using a high-resolution observational gridded data product, the proposed approach is applied in a case study in which projections of two regional climate models from the Coordinated Downscaling Experiment–European Domain (EURO-CORDEX) ensemble are bias corrected and downscaled to a 1 km × 1 km grid in the Trøndelag area of Norway. A cross-validation study shows that the proposed procedure generates results that better reflect the marginal distributional properties of the data product and have better consistency in space and time when compared with empirical quantile mapping.

scholarly journals Solving the Uniform Density Constraint in a Stochastic Downscaling Model

2008 ◽  
Vol 24 ◽  
pp. 97-110 ◽  
Author(s):  
C. Chauvin ◽  
S. A. Hirstoaga ◽  
P. Kabelikova ◽  
A. Rousseau ◽  
F. Bernardin ◽  
...  
Keyword(s):  
Uniform Density ◽  
Stochastic Downscaling

scholarly journals Analysis of longitudinal semicontinuous data using marginalized two-part model

2018 ◽  
Vol 16 (1) ◽  
Author(s):  
Miran A. Jaffa ◽  
Mulugeta Gebregziabher ◽  
Sara M. Garrett ◽  
Deirdre K. Luttrell ◽  
Kenneth E. Lipson ◽  
...  
Keyword(s):  
Semicontinuous Data ◽  
Part Model

A marginalized two-part model for semicontinuous data

2014 ◽  
Vol 33 (28) ◽  
pp. 4891-4903 ◽  
Author(s):  
Valerie A. Smith ◽  
John S. Preisser ◽  
Brian Neelon ◽  
Matthew L. Maciejewski
Keyword(s):  
Semicontinuous Data ◽  
Part Model

scholarly journals Bias in 2-part mixed models for longitudinal semicontinuous data

Biostatistics ◽  
2009 ◽  
Vol 10 (2) ◽  
pp. 374-389 ◽  
Author(s):  
Li Su ◽  
Brian D. M. Tom ◽  
Vernon T. Farewell
Keyword(s):  
Mixed Models ◽  
Semicontinuous Data

Joint analysis of semicontinuous data with latent variables

2020 ◽  
Vol 151 ◽  
pp. 107005
Author(s):  
Xiaoqing Wang ◽  
Xiangnan Feng ◽  
Xinyuan Song
Keyword(s):  
Latent Variables ◽  
Joint Analysis ◽  
Semicontinuous Data
Sign in / Sign up

Export Citation Format

Share Document