Gilpin, S., T. Matsuo, and S. E. Cohn. 2023. A generalized, compactly supported correlation function for data assimilation applications Quarterly Journal of the Royal Meteorological Society 149 (754):
1953-1989
[10.1002/qj.4490]
Gilpin, S., T. Matsuo, and S. E. Cohn. 2022. Continuum Covariance Propagation for Understanding Variance Loss in Advective Systems SIAM/ASA Journal on Uncertainty Quantification 10 (3):
886-914
[10.1137/21m1442449]
Wargan, K., B. Weir, G. L. Manney, S. E. Cohn, and N. J. Livesey. 2020. The Anomalous 2019 Antarctic Ozone Hole in the GEOS Constituent Data Assimilation System With MLS Observations Journal of Geophysical Research: Atmospheres 125 (18):
[10.1029/2020jd033335]
Janjić, T., N. Bormann, M. Bocquet, et al. 2017. On the representation error in data assimilation Quarterly Journal of the Royal Meteorological Society 144 (713):
1257-1278
[10.1002/qj.3130]
Janjic, T., D. McLaughlin, S. E. Cohn, and M. Verlaan. 2014. Conservation of Mass and Preservation of Positivity with Ensemble-Type Kalman Filter Algorithms Mon. Wea. Rev. 142 (2):
755-773
[10.1175/MWR-D-13-00056.1]
Cacio, E., S. E. Cohn, and R. Spigler. 2012. Numerical treatment of degenerate diffusion equations via Feller's boundary classification, and applications Numerical Methods for Partial Differential Equations 28 (3):
807-833
[10.1002/num.20657]
Cohn, S. E. 2009. Energetic Consistency and Coupling of the Mean and Covariance Dynamics Handbook of Numerical Analysis Vol. XIV, Special Volume: Computational Methods for the Atmosphere and the Oceans Chapter 10 443-478
[10.1016/s1570-8659(08)00210-x]
Janjić, T., and S. E. Cohn. 2006. Treatment of Observation Error due to Unresolved Scales in Atmospheric Data Assimilation Monthly Weather Review 134 (10):
2900-2915
[10.1175/mwr3229.1]
Gaspari, G., S. E. Cohn, J. Guo, and S. Pawson. 2006. Construction and application of covariance functions with variable length-fields Q.J.R. Meteorol. Soc. 132 (619):
1815-1838
[10.1256/qj.05.08]
Lyster, P. M., S. E. Cohn, B. Zhang, et al. 2004. A Lagrangian trajectory filter for constituent data assimilation Q. J. R. Meteorol. Soc. 130 (601):
2315-2334
[10.1256/qj.02.234]
Yang, R., S. E. Cohn, A. da Silva, J. Joiner, and P. Houser. 2003. Tangent linear analysis of the Mosaic land surface model J Geophys Res 108 (D2):
12-1 - 12-16
Tippett, M. K., S. E. Cohn, R. Todling, and D. Marchesin. 2000. Low-dimensional representation of error covariance Tellus 52A (5):
533-553
M'enard, R., S. E. Cohn, L.-P. Chang, and P. M. Lyster. 2000. Assimilation of stratospheric chemical tracer observations using a Kalman filter. Part I: Formulation Monthly Weather Review 128 (8):
2654--2671
Gaspari, G., and S. E. Cohn. 1999. Construction of correlation functions in two and three dimensions Quarterly Journal of the Royal Meteorological Society 125 (554):
723-757
[10.1256/smsqj.55416]
Riishojgaard, L., S. E. Cohn, Y. Li, and R. M'enard. 1998. The use of spline interpolation in semi-Lagrangian transport models Monthly weather review 126 (7):
2008--2016
Cohn, S. E., A. Da Silva, J. Guo, M. Sienkiewicz, and D. Lamich. 1998. Assessing the Effects of Data Selection with the DAO Physical-Space Statistical Analysis System* Monthly Weather Review 126 (11):
2913--2926
Cohn, S. E. 1997. An introduction to estimation theory JOURNAL-METEOROLOGICAL SOCIETY OF JAPAN SERIES 2 75 147--178
Kalnay, E., D. L. Anderson, A. F. Bennett, et al. 1997. Data assimilation in the ocean and in the atmosphere: what should be next? Journal of the Meteorological Society of Japan 75 (1B):
489--496
Lyster, P., S. E. Cohn, R. Menard, et al. 1997. Parallel implementation of a Kalman filter for constituent data assimilation Monthly weather review 125 (7):
1674--1686
Cohn, S. E., and R. Todling. 1996. Approximate data assimilation schemes for stable and unstable dynamics JOURNAL-METEOROLOGICAL SOCIETY OF JAPAN SERIES 2 74 63--75
Cohn, S. E. 1993. Dynamics of short-term univariate forecast error covariances Monthly weather review 121 (11):
3123--3149
Augenbaum, J., S. E. Cohn, and D. Marchesin. 1991. Eliminating grid-orientation errors in alternating-direction implicit schemes Applied Numerical Mathematics 8 (1):
1-10
[10.1016/0168-9274(91)90094-g]
Cohn, S. E., and D. F. Parrish. 1991. The behavior of forecast error covariances for a Kalman filter in two dimensions Monthly Weather Review 119 (8):
1757--1785
Cohn, S. E., and D. P. Dee. 1989. An analysis of the vertical structure equation for arbitrary thermal profiles Quarterly Journal of the Royal Meteorological Society 115 (485):
143-171
[10.1002/qj.49711548508]
Cohn, S. E., and D. P. Dee. 1988. Observability of Discretized Partial Differential Equations SIAM Journal on Numerical Analysis 25 (3):
586-617
[10.1137/0725037]
Dee, D., S. Cohn, A. Dalcher, and M. Ghil. 1985. An efficient algorithm for estimating noise covariances in distributed systems IEEE Transactions on Automatic Control 30 (11):
1057-1065
[10.1109/tac.1985.1103837]
Augenbaum, J. M., S. E. Cohn, E. Isaacson, D. P. Dee, and D. Marchesin. 1985. A factored implicit scheme for numerical weather prediction Comm. Pure Appl. Math. 38 (5):
503-517
[10.1002/cpa.3160380504]
Cohn, S. E., D. Dee, D. Marchesin, E. Isaacson, and G. Zwas. 1985. A fully implicit scheme for the barotropic primitive equations Monthly weather review 113 (4):
436--448