Applied Mathematics graduate student, Shay Gilpin, was awarded Best Student Presentation at the 23rd Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS) Conference at the 2019 American Meteorological Society (AMS) Annual Meeting for her presentation titled "Reducing Representativeness Errors during Radio Occultation-Radiosonde Comparisons."
This year’s annual AMS IOAS-AOLS Conference was held during January 5-11, in Phoenix Arizona. Presenters were evaluated on the quality of slides and delivery, innovation, maturity, and a deep understanding of the research. According to one of the conference program chairs, Sharan Majumdar, “Shay excelled in all of these!”
The work Gilpin presented was based on findings from her and her co-authors' recent paper, "Reducing Representativeness Errors during Radio Occultation-Radiosonde Comparisons," published in Atmospheric Measurement Techniques in May 2018. The paper discusses various methods the authors tested and found to reduce representativeness errors that occur during radio occultation and radiosonde comparisons. Instruments that observe Earth’s atmosphere play an important role in our everyday lives- they collect data that help predict the weather. These predictions are used in the weather apps on our phones and to predict severe weather events like hurricanes and winter storms. Comparing these instruments with each other is crucial in ensuring the accuracy of these instruments and can ultimately better our understanding of our atmosphere.
Radiosondes (RS), instruments attached to weather balloons, are considered as a standard observing system due to their long history (since the 1930s) of measuring atmospheric quantities, such as temperature, pressure, and relative humidity. Radio occultation (RO), a relatively new method of atmospheric measurement started in the 1990s, observes the atmosphere through the use of Global Positioning System (GPS) and low-Earth orbiting satellites, obtaining observations in the ionosphere, stratosphere, and troposphere. Comparisons between RO and RS are conducted to characterize the errors of both observing systems and allow for a better understanding of our atmosphere. During such comparisons, RO and RS observations are not taken at the exact same time or location, introducing “sampling errors,” errors caused by the observations sampling different atmospheric states. Differences in observation type also contribute in the form of “representativeness errors,” differences in how the RO and RS observations represent the atmosphere they are measuring. Both sampling and representativeness errors accrue during RO-RS comparisons and can inhibit the overall error analysis. This paper, and the presentation, detail three methods Gilpin and her co-authors developed to reduce sampling and representativeness errors during RO-RS comparisons. The methods developed have applications beyond RO-RS comparisons and can be used to reduce sampling and representativeness errors in comparisons between other observing systems. You can click here to read the entire paper.
Given the outstanding presentation of her research, Shay Gilpin was selected as the winner of the Best Student Presentation award, comprising of a certificate and small monetary prize.