Evaluating Land-Atmosphere Interactions during LAFE
Atmospheric and Oceanic Sciences Departmental Seminar Series
presents
Evaluating Land-Atmosphere Interactions during LAFE
a talk by
惭别迟别辞谤辞濒辞驳颈蝉迟,听
National Oceanic and Atmospheric Administration,
U.S. Department of Commerce, Washington, DC
The interactions between the land surface and the atmosphere needs to be represented in all numerical weather prediction and climate models. The Land-Atmosphere Feedback Experiment (LAFE) was conducted at the ARM SGP to collect a comprehensive dataset to evaluate the similarity relationships used to represent these processes in most numerical models. This seminar will present an overview of the LAFE campaign, present some observational results, and demonstrate how observations like this can be used to improve similarity relationships. Furthermore, the 2017 solar eclipse that occurred in the middle of the LAFE experiment, and we show that the operational High-Resolution Rapid Refresh (HRRR) weather forecast model is not adequately capturing these land-atmosphere interactions.
Dr. David Turner received his BA and MS in mathematics at Eastern Washington University, after which he started working for the Department of Energy鈥檚 Atmospheric Radiation Measurement (ARM) program at Pacific Northwest National Lab (PNNL). After 7 years at PNNL, he went to the University of Wisconsin - Madison (UW), where he received a PhD in atmospheric science in 2003. He joined NOAA in 2010 and is meteorologist at the Earth System Research Laboratory. He is the program manager for NOAA鈥檚 Atmospheric Science for Renewable Energy Program and the lead of the Model Assessment Section within the Global Systems Division.
Dr. Turner鈥檚 research interests include improving radiative transfer models, remote sensing with active and passive sensors, retrieval theory, studying the thermodynamic and dynamic structure of the boundary layer and its evolution, characterizing the properties and processes in mixed-phase clouds and especially those in the Arctic, and using observations to improve operational weather prediction models.