By Adam Schaubroeck
During my first semester here at South Dakota Mines, I thoroughly enjoyed my atmospheric science introductory class. In this class my professor would often discuss current weather events and use a wide variety of information to back up what he was saying. One such source of information was atmospheric models. These atmospheric models caught my attention and have fascinated me ever since. This has led me to a bigger interest in our atmosphere from a computational perspective.
Atmospheric models are computational models that calculate how the atmosphere will change over a period of time. For example, they can attempt to predict where, when, and how much rain will fall. These models look at a bunch of variables, including, but not limited to, temperature, dew point, and precipitation. Now these models aren’t always perfect, and they can be tweaked in many different ways. When a bunch of models are put together and compared with each other it is called an ensemble. Ensembles are very interesting because they can show the spread of a forecast. This spread is found by comparing the most different forecasts and seeing how different they are. The larger the spread, the less confident we should be in the forecast put out from these machines (Whitaker and Loughe 1998). These ensembles are used by forecasters to give them an average of what is expected to happen by the models in the ensemble. The spread of the ensemble can show how confident the forecaster can be in the ensemble average.
For my capstone project, I will be looking at the spread of an ensemble and how that affects forecast error. This research has been done before and the results show that larger ensemble spread causes a larger forecast error (Grimit and Mass 2007). What I am doing that is new for the field is using a new cutting-edge ensemble that started outputting data on November 3rd, 2026. I am also doing this over the region of western South Dakota, which could be very interesting because the topography from the Black Hills could create polarizing results between different models in an ensemble run. This is because as air travels over the Black Hills it could have very different outcomes depending on the initial conditions as the air starts to lift due to the topography.
My research could eventually help us to understand uncertainties with weather systems as they go over the Black Hills and improve forecasting for the area. My research is also limit-testing the new ensemble because there is an expected outcome. Hopefully my research shows the expected outcome so that the atmospheric science community will know that this ensemble is as trustworthy as the ensembles it replaced.
References:
Grimit E. P., C. F. Mass, 2007, Measuring the Ensemble Spread–Error Relationship with a Probabilistic Approach: Stochastic Ensemble Results, Monthly Weather Review, 135, 203-221, https://doi.org/10.1175/MWR3262.1
Whitaker J. S., A. F. Loughe, 1998, The Relationship between Ensemble Spread and Ensemble Mean Skill, Monthly Weather Review, 126, 3292-3302, https://doi.org/10.1175/1520-0493(1998)126<3292:TRBESA>2.0.CO;2
Bio:
I am a senior Atmospheric and Environmental Science major with a minor in Computer Science. Throughout my whole life I have always found the weather interesting. When COVID struck and we were all forced indoors, I would spend my evenings at my local park. During this time, I would admire the clouds coming off the mountains and take pictures of them. This happened often, and continues to happen, so much so that my camera roll is usually only pictures of clouds. When I was exploring my options for college, I was lost on what I wanted to do with my life. I visited South Dakota Mines, and the Atmospheric and Environmental Science program really spoke to me. I hadn’t really realized how much the weather had fascinated me and now I get the opportunity to work with it the rest of my life. After graduation I plan to pursue a career using the skills I’ve gotten from here. I’m not sure what this will look like, but I’m open to a wide variety of options. Outside of school I enjoy playing video games, hanging out with friends, cooking, and eating great food.
