NERTO Students

Steven Buckner

Steven Buckner
PhD, Atmospheric Science, Graduate, 12/31/2020

Internship Location: NWS Wakefield

Internship Date: September 23, 2019 - December 13, 2019


Buckner graduated from University of Maryland – Baltimore County in 2014 with a B.S in Physics with a minor in Mathematics. His involvement at the Center began in the summer of 2013, when he took part in the Undergraduate Research Opportunity at Hampton University. His summer project, which involved him working with Hampton's Lidar system, enabled him to continue doing his research. He hopes to work on atmospheric ozone at NOAA or a similar organization upon graduation.

NERTO Research Project Title:

Developing and Testing New A-priori Inputs for NUCAPS Ozone Profiles UsingOMPS-LP Ozone Profile Retrieval

NERTO Project Details :

Synopsis: The Ozone Mapping and Profiler Suite (OMPS) Limb Profiler makes measurements of limb-scattered solar radiances over Ultraviolet and Visible wavelengths. These measurements are used in retrieval algorithms to create high vertical resolution ozone profiles to help to monitor the evolution of the atmospheric ozone later. The NOAA Unique Combined Atmosphere Processing System (NUCAPS) is the new generational processing system for NOAA and creates high vertical resolution profiles of atmospheric parameters the NUCAPS ozone product is made using a combination of Cross-Track Infrared Sounder (CrIS) data and a tropopause-based climatology derived from ozone sondes. It is hypothesized that incorporating OMPS-LP data as a stratospheric a-priori first guess could improve the product and, subsequently, numerical weather prediction forecasts that use the data. The main objective of this project is to incorporate the NOAA Total Assimilation of Stratosphere and Troposphere (TOAST) product, which uses OMPS-LP data, into the NUCAPS algorithm as a stratospheric a-priori. Estimates of the accuracy and precision will be generated by comparing both the inputs and the resulting product with similar products, such as measurements from the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III-ISS). The work will also examine the impact of the new ozone information on the NUCAMS temperature retrievals.

NERTO Outcomes:

I was able to continue my comparisons between OMPS-LP and SAGE III ISS ozone measurements, and was also able to begin editing the a-priori file for NUCAPS. I did not have access to the a-priori file until November, and have been working through it since. This has been a key part to my project. I was also able to see how the NUCAPS ozone product is incorporated into weather forecasting and how it is represented in forecasting tools. I also learned how to use the AWIPS program, which I will explain more in the next section.

Value of NERTO to the Line Office:

Some of the most prominent benefits to having a NERTO take place at an NWS office are concepts that tend to be less tangible. My NERTO mentor valued the discussions and exchange of ideas he and I would have on how the NWS can better use satellite data. As a student who works almost entirely with satellite data, I was in a unique position to provide insight on this matter. Interactions like that, both in his office and during the morning briefings, were some of the most valuable to the office. It was also appreciated to have someone in the research community, yet outside of NWS operations, to listen in and give feedback on things, providing a sort of “educated public” viewpoint that can be difficult to come by otherwise.

NERTO Skills:

I was able to learn and work some with the Advanced Weather Interactive Processing System (AWIPS) which is one of the main forecasting tools used by the forecasters in the NWS. AWIPS is a program that combines all of the data from models and observations into one place to make creating forecasts and issuing warnings easier. I also partook in two training sessions on the Forecast Builder tool, a newer tool that can allow for somewhat more customizable forecasts, but did not work on it myself.