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Thursday, September 9, 2021. 3:00 PM. Webinar: Investigation of Land-Atmosphere Interaction in UFS and its Influence on Model Mean Bias. Eunkyo Seo, George Mason University. Sponsored by the National Weather Service of NOAA. Register here.

Abstract: The Unified Forecast System (UFS) is a fully coupled Earth modeling system. It will be the system for NOAAs operational numerical weather prediction applications. UFS has been progressing through several prototype simulations (lately P5, P6, and P7) by improving model configurations on the way toward an operational version. The land surface model (LSM) beginning with P7 is the multi-parameterization version of Noah (Noah-MP), replacing the older Noah LSM. The main differences between these LSMs include the number of land tiles and snowpack layers, and the simulation of an aquifer below the bottom layer. This study investigates land-atmosphere interactions in the UFS prototypes and their influence on model mean state bias, with particular attention to the impact of Noah-MP on the coupled model simulation. For the model evaluations, the modeled states related to land-atmosphere interactions for two July seasons (2012-2013) simulated in sub-prototype P7a are assessed against in situ observations as well as satellite-based products with attention to consistency in vegetated land cover and coupling regimes (energy vs. moisture limit controls on surface fluxes). There is an improvement in the simulation of net radiation and the terrestrial coupling index at the land surface without a large degradation on the other state variables (e.g., surface soil moisture). A remarked improvement in surface air temperature over the central US is related to the increased soil moisture sourced by precipitation. This suppresses sensible heat flux, corresponding to weakened land-atmosphere interaction, which corrects the warm bias by improving the coupling regime.

Bio(s): Dr. Seo is currently the Post-Doctoral Research Fellow at the Center for Ocean-Land- Atmosphere Studies, at George Mason University, in Fairfax, Virginia where he worked along with Prof. Paul A. Dirmeyer. From 2019-2020 Dr. Seo was the Post-Doctoral Research Fellow at School of Urban and Environmental Engineering at Ulsan National Institute of Science and Technology (UNIST) in South Korea where he received a B. S. in Environmental Science and Engineering and moving on to obtain his Ph. D in Environmental Science and Engineering. Dr. Seo's thesis was in Data Assimilation of Remote Sensing Soil Moisture Retrievals with Local Ensemble Transform Kalman Filter scheme.