Contrails are ice clouds (cirrus) that form from the exhaust of aircraft [1]. Aircraft-induced cirrus potentially accounts for half of aviation derived climate impact [2]. Thus, the mitigation of contrails represents a promising pathway towards improved aviation sustainability, making accurate identification of contrail-forming regions desirable. These contrail-forming regions are largely defined by ice-supersaturated regions (ISSR) where the relative humidity with respect to ice exceeds 100%. However, current forecast techniques may overestimate persistent contrail formation by 100% to 250% due to inadequacies in ISSR identification [3]. Given the abundance of weather data and the enigmatic nature of the atmosphere, machine learning methods can make a significant impact in improving contrail prediction capabilities [4]. Advancements in scientific machine learning techniques have facilitated the automatic identification of underlying physical models from observational data [5]. These techniques promote sparsity, and are designed to identify comprehensible and parsimonious models. We propose that novel applications of SINDy (sparse identification of non-linear dynamics) designed to model contrail-forming regions using surface-based weather monitoring data and GOES-16 satellite observations can produce interpretable models that improve our understanding of aviation-induced atmospheric effects and support sustainable flight-planning strategies. REFERENCES [1] U. Schumann. On conditions for contrail formation from aircraft exhausts. Meteorologische Zeitschrift, 5(1):4–23, 1996. [2] IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [T.F. Stocker et. al. (eds.)]. CUP, Cambridge, United Kingdom and New York, NY, USA, 1535 pp. [3] A. Agarwal, V.R. Meijer, S.D. Eastham, R.L. Speth, and S.R.H. Barrett. Reanalysis-driven simulations may overestimate persistent contrail formation by 100%–250%. Environmental Research Letters, 17(1):014045, 2022. [4] V.R. Meijer, L. Kulik, S.D. Eastham, F. Allroggen, R.L. Speth, S. Karaman, and S.R.H. Barrett. Contrail coverage over the United States before and during the COVID-19 pandemic. Environmental Research Letters, 17(3):034039, 2022. [5] S.L. Brunton, J.L. Proctor, and J.N. Kutz. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. PNAS, 113(15):3932–3937, 2016.