Abstract ID: 194
On the source of model biases for subseasonal tropical cyclone precipitation prediction
Lead Author: Jorge L. Garcia Franco
Columbia University , United States of America, Mexico
Keywords: tropical cyclones, mean-state biases, genesis indices, large-scale, mean and extreme precipitation
Abstract: In this presentation, we investigate the causes of mean model biases relevant for predicting tropical cyclone precipitation (TCP) at subseasonal time scales. We will analyze reforecasts from the NASA Goddard Earth Observing System-Subseasonal to Seasonal version 2 (GEOS-S2S) prediction system and those from global models that participate in the WMO S2S project. We will examine the source of biases from large-scale environmental conditions and storm-scale processes. Specifically, we examine the relationship between the simulated large-scale environmental conditions, measured through indices such as the Genesis Potential index (GPI) and Tropical Cyclone Genesis Indices (TCGI) and the simulated TC activity. We will also compare the relationship between relative sea-surface temperatures, precipitable water vapor and the mean and maximum TCP derived from observations to those derived from reforecasts. Lastly, we diagnose the degree to which these mean-state biases can help explain TCP biases as the forecast lead time increases. Our findings will provide insight into the relative importance of biases in large-scale conditions and storm-scale convective processes for subseasonal TCP prediction skill.
Co-authors:
Lee, Chia-Ying (Columbia University)
Camargo, Suzana (Columbia University)
Tippett, Michael (Columbia University)
Molod, Andrea (Goddard Space Flight Center, NASA, Greenbelt, MD, USA)
Lim, Young-Kwon (Goddard Space Flight Center, NASA, Greenbelt, MD, USA)
Kim, Daehyun (University of Washington)
