Climate & Radiation Laboratory Seminar Series
Wednesday, February 5, 2020
Bldg. 33 Room H114
University Corporation for Atmospheric Research / NOAA Environmental Modeling Center
Challenges in Building Better Snowflake Models
Cloud cover and precipitation have great significance in modulating Earth’s radiation budget and the global hydrological cycle. When we observe clouds remotely, we are fundamentally interested in determining the amounts of ice or liquid water or aerosols that are present, but we cannot directly observe these parameters. We must instead rely on models of how cloud particles scatter light.
At any time, around 50% of the Earth’s surface is covered by snow and ice-bearing clouds. Bulk snow scattering properties emerge as a complex function of constituent particle masses, structures, orientations, and spectral frequency. Nature is highly variable, and there are many possible snowflake shapes, ranging from relatively pristine rosettes, columns and plates, to very complex aggregates. New, large datasets have been produced in the past 5-10 years that represent increasingly sophisticated attempts to account for this variability, yet further development is necessary to meet the needs of new polarization-sensitive instruments that observe at shorter wavelengths and non-nadir orientations.
This talk will highlight two broad issues. How can we disseminate and validate our models? How can we meet both operational and research needs? I will cover the development of a common reference format and programming library for storing and manipulating scattering datasets in a consistent manner. This is an outgrowth of discussions by the International Precipitation Working Group and International Summer Snowfall Workshops. Additionally, I will address the disconnect between per-particle datasets (which now may exceed tens of terabytes in size) and radiative transfer models that need a small, fast bulk parameterization of scattering properties.
Ryan Honeyager received his doctorate from Florida State University where, despite ever-sunny skies, he spent considerable time examining snow and ice clouds. This work led to participation in the NASA Convective Processes Experiment (CPEX), as well as several studies of convective and stratiform precipitation. In 2018, Ryan joined the NOAA Center for Weather and Climate Prediction and has since worked with the Microwave Integrated Retrieval System (MiRS) and the Joint Effort for Data Assimilation Integration (JEDI) projects. He is the Joint Center for Satellite Data Assimilation’s liaison to NOAA/EMC. Current interests include machine learning, big data, next-generation forecasting systems, hailstones, and microwave radiative transfer.
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