Peer Review Articles
2021
Zupanski M, 2021: The Maximum Likelihood Filter with state space localization. Mon. Wea. Rev., 149 (10), 3505-3524.
The Maximum Likelihood Filter with state space localization
[Link]Wang Y, Guzik S, Zupanski M, Gao X, 2021: The Maximum Likelihood Ensemble Filter for Computational Flame and Fluid Dynamics. IMA J. Appl. Math., 86, 631-661.
The Maximum Likelihood Ensemble Filter for Computational Flame and Fluid Dynamics
[Link]2020
Goodliff, M., S. J. Fletcher, A. J. Kliewer, J. Forsythe, and A. S. Jones
Detection of Non-Gaussian Behavior Using Machine Learning Techniques: A Case Study on the Lorenz 63 Model
Journal of Geophysical Research, 125, 2, 10.1029/2019JD031551 [Link]Fletcher, S. J., A. J. Kliewer, and A. S. Jones
Quantification of Optimal Choices of Parameters in Lognormal Variational Data Assimilation and Their Chaotic Behavior
Math Geosci (2019) 51: 187. https://doi.org/10.1007/s11004-018-9765-7 [Link]2019
Wu, T.-C., M. Zupanski, L. D. Grasso, C. D. Kummerow, and S.-A. Boukabara
All-Sky Radiance Assimilation of ATMS in HWRF: A Demonstration Study.
Mon. Wea. Rev, 147, 85–106. doi: 10.1175/MWR-D-17-0337.1 [Link]2018
Grasso, L. D., D. T. Lindsey, Y.-J. Noh, C. O’Dell, T.-C. Wu, and F. Kong
Improvements to cloud top brightness temperatures computed from the CRTM at 3.9 μm
Mon. Wea. Rev, 146, 3927–3944. doi: 10.1175/MWR-D-17-0342.1 [Link]Fletcher S., A. Kliewer, and A. S. Jones
Quantification of Optimal Choices of Parameters in Lognormal Variational Data Assimilation and Their Chaotic Behavior
Mathematical Geosciences [Link]Suzuki, K. and M. Zupanski
Uncertainty in solid precipitation and snow depth prediction for Siberia using the Noah and Noah-MP land surface models
Front. Earth. Sci. [Link]2017
Suzuki, K., M. Zupanski, and D. Zupanski
A case study involving single observation experiments performed over snowy Siberia using a coupled atmosphere-land modelling system
Atmos. Sci. Lett, 18, 3, 106–111. doi: 10.1002/asl.730 [Link]Lee, Y., C. D. Kummerow, and M. Zupanski