PA51A-2200
Transitioning Empirical Dynamical Models from Research to Operational Subseasonal Forecasting
Friday, 18 December 2015
Poster Hall (Moscone South)
Matthew Newman, University of Colorado at Boulder, Boulder, CO, United States, Jon Gottschalk, Climate Prediction Center, NOAA / National Centers for Environmental Prediction, washington, DC, United States, Qin Zhang, CPC NCEP, College Park, MD, United States, Cecile Penland, NOAA Boulder, Boulder, CO, United States and Prashant D Sardeshmukh, CIRES, Boulder, CO, United States
Abstract:
CLIVAR support has been essential in the development of the Linear Inverse Model (LIM), an empirical dynamical model that can be used to both diagnose and predict climate variability on time scales as short as a week and as long as a decade. The LIM is an approximation of chaotic weather and climate variability that assumes that, under certain conditions, predictability within the system reduces to multivariate linear dynamics forced by unpredictable noise. This assumption is testable, and for those systems where it is demonstrated to be appropriate, LIM forecasts are competitive with state-of-the-art numerical forecast models. Over the course of more than twenty years of research, LIM has been developed as a forecast tool in parallel with its development for basic research in climate diagnosis, and has now reached the point where it is being transitioned to the Climate Predictions Center (CPC) as a part of their new Weeks 3 and 4 operational forecasting effort. In this presentation, we will discuss the development of the LIM as a CLIVAR-funded research effort, how it is being transitioned to CPC operations, and its part in the larger CPC subseasonal forecasting effort.
