Regridding Uncertainty for Statistical Downscaling of Solar Radiation: References

Authors: (1) Maggie D. Bailey, Colorado School of Mines and National Renewable Energy Lab; (2) Douglas Nychka, Colorado School of Mines; (3) Manajit Sengupta, National Renewable Energy Lab; (4) Aron Habte, National Renewable Energy Lab; (5) Yu Xie, National Renewable Energy Lab; (6) Soutir Bandyopadhyay, Colorado School of Mines. Table of Links Abstract and Intro Data Bayesian Hierarchical Model (BHM) Solar Radiation Example Results Conclusion Appendix A: Simulation Study Appendix B: Regridding Coefficient Estimates References References Accadia, C., Mariani, S., Casaioli, M., Lavagnini, A., and Speranza, A.: Sensitivity of precipitation forecast skill scores to bilinear interpolation and a simple nearest-neighbor average method on high-resolution verification grids, Weather and forecasting, 18, 918–932, 2003. Berndt, C. and Haberlandt, U.: Spatial interpolation of climate variables in Northern Germany—Influence of temporal resolution and network density, Journal of Hydrology: Regional Studies, 15, 184–202, 2018. Chandler, R., Barnes, C., Brierley, C., and Alegre, R.: Regridding and interpolation of climate data for impacts modelling–some cautionary notes, Tech. rep., Copernicus Meetings, 2022. Cressie, N. and Wikle, C. K.: Statistics for Spatio-Temporal Data, 2011. Cressie, N. A.: Change of support and the modifiable areal unit problem, 1996. Diaconescu, E. P., Gachon, P., and Laprise, R.: On the remapping procedure of daily precipitation statistics and indices used in regional climate model evaluation, Journal of Hydrometeorology, 16, 2301–2310, 2015. Ensor, L. A. and Robeson, S. M.: Statistical characteristics of daily precipitation: comparisons of gridded and point datasets, Journal of Applied Meteorology and Climatology, 47, 2468–2476, 2008. Finley, A. O., Banerjee, S., and Gelfand, A. E.: spBayes for large univariate and multivariate point-referenced spatio-temporal data models, arXiv preprint arXiv:1310.8192, 2013. Gelfand, A. E., Zhu, L., and Carlin, B. P.: On the change of support problem for spatio-temporal data, Biostatistics, 2, 31–45, 2001. Handcock, M. S. and Stein, M. L.: A Bayesian analysis of kriging, Technometrics, 35, 403–410, 1993. Harris, T., Li, B., and Sriver, R.: Multi-model Ensemble Analysis with Neural Network Gaussian Processes, arXiv [preprint] arXiv:2202.04152, 2022. Loghmari, I., Timoumi, Y., and Messadi, A.: Performance comparison of two global solar radiation models for spatial interpolation purposes, Renewable and Sustainable Energy Reviews, 82, 837–844, 2018. McGinnis, S. and Mearns, L.: Building a climate service for North America based on the NA-CORDEX data archive, Climate Services, 22, 100 233, 2021. McGinnis, S., Mearns, L., and McDaniel, L.: Effects of Spatial Interpolation Algorithm Choice on Regional Climate Model Data Analysis, "Fall Meeting, American Geophysical Union, San Francisco", 2010. Phillips, D. L. and Marks, D. G.: Spatial uncertainty analysis: propagation of interpolation errors in spatially distributed models, Ecological Modelling, 91, 213–229, 1996. Rajulapati, C. R., Papalexiou, S. M., Clark, M. P., and Pomeroy, J. W.: The perils of regridding: examples using a global precipitation dataset, Journal of Applied Meteorology and Climatology, 60, 1561–1573, 2021. Rauscher, S. A., Coppola, E., Piani, C., and Giorgi, F.: Resolution effects on regional climate model simulations of seasonal precipitation over Europe, Climate dynamics, 35, 685–711, 2010. Sengupta, M., Xie, Y., Lopez, A., Habte, A., Maclaurin, G., and Shelby, J.: The national solar radiation data base (NSRDB), Renewable and sustainable energy reviews, 89, 51–60, 2018. Whittemore, A. S.: Errors-in-variables regression using Stein estimates, The American Statistician, 43, 226–228, 1989. This paper is under CC 4.0 license. available on arxiv