IASC - Spatio-temporal downscaling emulator for regional climate models
| Date | 15 Nov 2023 |
| Time | 14:00 GMT+01:00 - 14:30 GMT+01:00 |
| Level of instruction | advanced |
| Instructor |
Luis A. Barboza
|
| Registration fee | |
Regional climate models (RCM) describe the mesoscale global atmospheric and oceanic dynamics and serve as dynamical downscaling models. They are computationally demanding and, depending on the application, require several orders of magnitude of compute time more than statistical climate downscaling. In this article, we describe how to use a spatio-temporal statistical model with varying coefficients (VC), as a downscaling emulator for a RCM using VC. In order to estimate the proposed model, two options are compared: INLA, and varycoef. We set up a simulation to compare the performance of both methods for building a statistical downscaling emulator for RCM, and then show that the emulator works properly for NARCCAP data. The results show that the model is able to estimate non-stationary marginal effects, which means that the downscaling output can vary over space.
Instructors
About the instructor
Luis A. Barboza is a professor at the School of Mathematics and a researcher at the Center for Research in Pure and Applied Mathematics at the University of Costa Rica. After completing his bachelor's degree in Actuarial Sciences and a Master of Science in Applied Mathematics at the University of Costa Rica, he earned a Ph.D. in Statistics at Purdue University in Indiana, USA, specializing in Bayesian Analysis and Inference of Stochastic Processes, particularly State-Space Processes. He is currently interested in statistical modeling of environmental issues, such as those related to climate, epidemiology, and social models.