David D. Turner, Ph.D.
I am a physical scientist in the Global Systems Division in the Earth System Research Laboratory. I am also affiliated with the University of Oklahoma's School of Meteorology, where I am an adjunct professor.
The focus of my research at NOAA is to better understand various processes that act upon and within the boundary layer (e.g., convective initiation, turbulent redistribution of water vapor, aerosol, and energy, etc) so that these processes are better represented within numerical weather prediction and climate models. Part of this work includes continuing to develop and mature different ground-based profiling technologies (both active lidar and passive multi-frequency infrared and microwave sensing techniques) to measure the temperature and humidity structure of the boundary layer, and using these observations to gain insight into how well NWP models (such as the rapid refresh models RAP and HRRR which are developed in GSD) are representing atmospheric processes and evolution.
I am intricately involved with the Department of Energy's Atmospheric Radiation Measurement (ARM) and Atmospheric Systems Research (ASR) programs. ARM and ASR together compose the infrastructure and science team for the largest observational-based climate research program within the DOE. I am a principal investigator in these programs, have served as the chair of the ARM User Executive Committee and the ARM Climate Research Facility Science Board. I am also a co-PI of a large, multi-year project called ICECAPS that is funded by the National Science Foundation to collect a long-term, ground-based dataset to characterize the atmospheric state, cloud properties, and radiation above Summit, Greenland. I have been a member of the US Global Change Research Program's (USGCRP) Water Cycle Science Steering Group, and have participated in a number of other steering committees and community activites.
Science relies on observations to develop theories about nature, and ultimately to evaluate and validate these theories. These observations come from our natural senses and from instruments that we have developed. The sustained development of advanced instrumentation continues to open new horizons in our understanding about how nature, including the multitude of processes in our atmosphere, really operates. In fact, instrument development and scientific advancement typically progress hand-in-hand.
I am an observationalist and an atmospheric physicist, and a budding modeler. I use a wide variety of in-situ and remote sensing techniques to characterize, understand, and quantify processes that occur in the atmosphere. Improving our understanding of these processes, and how they interact with each other and the environment, is critically important to improving our ability to represent these processes in models (both numerical weather prediction and climate models). In particular, I have worked with state-of-the-art remote sensors such as an automated water vapor and aerosol Raman lidar, Doppler lidar, Atmospheric Emitted Radiance Interferometer, multi-frequency microwave radiometer, and millimeter-wave cloud radar in my research. These instruments provide a unique view of a broad range of atmospheric phenomenon.
I utilize observations from these and other more traditional instruments, typically in synergistic fashion, to gain insight into such topics as evolution of the boundary layer's turbulent structure; shallow convective cloud processes; the interaction between clouds, aerosols, radiation, precipitation, and the thermodynamic environment in the boundary layer; mixed-phase clouds; radiative transfer in the atmosphere; and other topics. Numerical models, such as those used for climate and weather prediction, have large uncertainties in all of these areas, and it is my objective to use these observations to improve our understanding and representation of these processes.
Another scientific field that I am extremely interested in advances simultaneously with instrument development and atmospheric science: retrieval theory. It is very seldom that an instrument measures exactly the geophysical variable that is desired; more often it observes something else (e.g., a voltage) that is related to your desired variable. Thus, our community uses retrieval algorithms to essentially invert what we observe into what we ultimately desire.
Retrieval scientists, such as myself, utilize data from several instruments that offer complementary information about the geophysical variables that are desired, and develop techniques that are able to quantify the information content of the observations. Furthermore, the accurate specification of the uncertainties in the retrieved geophysical variables is a challenging, yet critical, aspect of retrieval science and is vitally important for using these derived variables to drive and evaluate atmospheric models. I believe that the study of retrieval theory is essential and just as important as developing new instruments or parameterizations of atmospheric processes.