As a doctoral student at UMD, I am interested in coupled ocean-atmosphere processes, AI/ML applications in earth science, and subseasonal-to-seasonal climate prediction. I value inclusivity and diversity, and it is important to me that my work helps build a more resilient and just future. I am the primary maintainer of XCast (xcast-lib.github.io), an open source python climate forecasting library, and previously served as an associate scientist with CIRES/NOAA PSL.
Selected Research Papers/Presentations/Posters:
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Hall, K. J. C., & Acharya, N. (2022, June 27). XCast: A python climate forecasting toolkit. Frontiers. https://doi.org/10.3389/fclim.2022.953262
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Bernardet, L., Bengtsson, L., Reinecke, P. A., Yang, F., Zhang, M., Hall, K., Doyle, J., Martini, M., Firl, G., & Xue, L. (2024, June 19). Common community physics package: Fostering collaborative development in physical parameterizations and suites. AMETSOC. https://doi.org/10.1175/BAMS-D-23-0227.1 (in press
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Kowal, K. M., Slater, L. J., Li, S., Kelder, T., Hall,
K. J. C ., Moulds, S., et al. (2024). Process-informed subsampling improves subseasonal rainfall forecasts in Central America. Geophysical Research Letters, 51, e2023GL105891. https://doi.org/10.1029/2023GL105891 - Acharya, N., Ehsan, M. A., Admasu, A., Teshome, A., & Hall , K. J. C. (2021, November 20). On The next generation (nextgen) seasonal prediction system to enhance climate services over Ethiopia. Climate Services. https://doi.org/10.1016/j.
cliser.2021.100272 .