AAARG focuses on the dynamics and physical mechanisms of high latitude atmospheric processes. We study Arctic and Antarctic processes using high-resolution regional and global numerical models such as NCAR’s Weather Research and Forecasting (WRF) model and the Model for the Prediction Across Scales (MPAS). Since these locations are relatively data-sparse regions of the globe, we also study atmospheric predictability through ensemble data assimilation techniques using tools such as the Data Assimilation Research Testbed (DART) to better understand how to extend information provided by observations to improve numerical models.
MAP’s primary interests include i) developing new techniques and novel methodologies for data assimilation and ensemble prediction; ii) applying these techniques to global scale to convective scale modeling systems to improve predictive skill; iii) improving the understanding of atmospheric predictability and dynamics from global to convective scales; iv) transitioning research and development into operations (R2O).
CCC group research falls within three topic areas of atmospheric science: Radar & Satellite Meteorology, Upper Troposphere and Lower Stratosphere (UTLS) studies, and Climate Variability and Change. Many of the topics that we work on are cross-cutting in that they require and contribute to knowledge in more than one of these areas. For example, thunderstorms are capable of reaching the tropopause: the boundary between the lowest layer of the atmosphere and that in which we live (the troposphere) and the layer immediately above (the stratosphere). If a storm overshoots the tropopause and extends into the stratosphere, it may lead to transport of air between the two layers (stratosphere-troposphere exchange or STE). STE affects the composition of the UTLS, which in turn leads to changes in the radiation budget and climate. Studying such problems enables the CCC group to broadly impact the atmospheric sciences. Additional details on our research activities and research identity can be found below.
Research in the IDEALab focuses on developing and applying data science, artificial intelligence, and machine learning techniques with a focus on high-impact real-world applications. Our research foci include the development of:
Autonomous data science techniques that can learn from real-world data
Applied data science/artificial intelligence/machine learning techniques that can be deployed in real-world settings
Intelligent autonomous agents that can successfully interact and learn while embedded in the real world
The Martin Research Group aims to further our knowledge of climate, climate variability and weather-climate interactions, with a focus on rainfall. This is accomplished through the use of observations and model simulations to provide a means to increase our understanding of essential mechanisms and processes in the tropics and subtropics.
Research interests include the following areas:
Variability of precipitation at multiple scales
Interaction of weather and climate
Representation of climate variability and change in climate models
Caribbean and African precipitation variability and change
The Salesky research group focuses on the structure and dynamics of the atmospheric boundary layer, turbulence, and interactions between Earth’s surface and the atmosphere. We use analytical methods, field experiments, and numerical simulations to address scientific questions of importance for weather and climate, air quality, water resources, and human health.
Recent topics of interest include convective boundary layers, boundary layers over complex surface topography, transport in the urban environment, and particle-laden flows. We also develop new numerical tools to study these problems using large eddy simulation.
The Applied Climate Dynamics Group is primarily focused on large-scale climate dynamics in both the atmosphere and ocean and all around the world. We focus on using observations (e.g., station data, satellite products, and reanalysis datasets) and climate models (both designed experiments and already-existing model output) for our studies. We use a variety of statistical approaches and forecasting techniques in our research with the goal of applying the results to operations in the science and in the private sector. Thus, graduate students and post-docs studying in this group have a well-rounded, application-oriented research experience.
OU-BLISS is a team of faculty and researchers with an interest in multi-faceted studies of the boundary layer. Through a variety of instrumentation and simulations, we seek to understand the dynamic nature of the lowest level of the atmosphere.