I am currently employed as a Graduate Research Assistant with the Center for Analysis and Prediction of Storms (CAPS) and the School of Meteorology (SoM) at the University of Oklahoma. My funding is provided by the National Science Foundation through a large Information Technology Research grant known as Linked Environments for Atmospheric Discovery (LEAD).

My research is focused on exploring the detection of hazardous weather phenomena (i.e. detecting and tracking tornadoes, hail, damaging winds, etc.) by using advanced dynamic data assimilation techniques performed on weather RADAR data. Current detection systems rely on the "raw" RADAR data and highlight features such as azimuthal shear and high reflectivity. Unfortunately, every time a new observation platform is developed, and entirely new set of algorithms is required. The data assimilation approach produces a 4D grid of physically consistent data, based on optimal combinations of observed quantities and model physics. Since adding new observation platforms to the assimilation only increases resolution and accuracy, only one set of algorithms is needed!

Positive Aspects of Detection Using Data Assimilation:

Algorithm set does not have to change with every new observation platform Unobserved quantities are available Data is dynamically consistent Allow for seamless model initialization

Negative Aspects of Detection Using Data Assimilation:

VERY expensive computationally Data is smoothed, dulling sharp gradients Spatial resolution is limited by computational restrictions

Links on this page are to provide a overview of my research and some related topics in the research community.