Weather prediction systems
Schwartz, C. S., J. Poterjoy, J. R. Carley, D. C. Dowell, G. S. Romine, and K. Ide, 2021: Comparing partial and continuously cycling ensemble Kalman filter data assimilation systems for convection-allowing ensemble forecast initialization. Wea. Forecasting, Published online 22 Sept. 2021.
Poterjoy, J., G. J. Alaka, Jr., and H. R. Winterbottom 2021: The irreplaceable utility of sequential data assimilation for numerical weather prediction system development: Lessons learned from an experimental HWRF system. Wea. Forecasting, 36, 661 – 677.
Feng, J., X. Wang., and J. Poterjoy, 2020: A Comparison of Two Local Moment-Matching Nonlinear Filters: Local Particle Filter (LPF) and Local Nonlinear Ensemble Transform Filter (LNETF)., Mon. Wea. Rev., 148, 4377 – 4395.
Poterjoy, J., L. J. Wicker, and M. Buehner, 2019: Progress in the development of a localized particle filter for data assimilation in high-dimensional geophysical systems., Mon. Wea. Rev. 147, 1107 – 1126.
Morzfeld, M., D. Hodyss, J. Poterjoy, 2018: Variational particle smoothers and their localization., Q. J. R. Meteorol. Soc. 2018, 144:806 – 825.
Poterjoy, J., R. A. Sobash, and J. L. Anderson, 2017: Convective-scale data assimilation for the Weather Research and Forecasting model using the local particle filter., Mon. Wea. Rev. 145, 1897 – 1918.
Poterjoy, J., and J. L. Anderson, 2016: Efficient assimilation of simulated observations in a high-dimensional geophysical system using a localized particle filter. Mon. Wea. Rev., 144, 2007 – 2020.
Poterjoy, J., 2016: A localized particle filter for high-dimensional nonlinear systems. Mon. Wea. Rev., 144, 59 – 76.
Four-dimensional data assimilation
Kurosawa, K., and Poterjoy, J., 2021: Data assimilation challenges posed by nonlinear operators: A comparative study of ensemble and variational filters and smoothers, Mon. Wea. Rev. In press.
Poterjoy, J. and F. Zhang, 2016: Comparison of hybrid four-dimensional data assimilation methods with and without the tangent linear and adjoint models for predicting the life cycle of Hurricane Karl (2010). Mon. Wea. Rev. 144, 1449 – 1468.
Poterjoy, J. and F. Zhang, 2015: Systematic comparison of four-dimensional data assimilation methods with and without a tangent linear model using hybrid background error covariance: E4DVar versus 4DEnVar. Mon. Wea. Rev., 143, 1601 – 1621.
Poterjoy, J. and F. Zhang, 2014: Inter-comparison and coupling of ensemble and four-dimensional variational data assimilation methods for the analysis and forecasting of Hurricane Karl (2010). Mon. Wea. Rev., 142, 3347 – 3364.
Zhang, X., X.-Y. Huang, L. Yianyu,J. Poterjoy, Y. Weng, F. Zhang, and H. Wang, 2014: Development of an efficient regional four-dimensional variational data assimilation system for WRF. J. Atmos. Oceanic Technol., 31, 2777 – 2794.
Zhang, F., M. Zhang, and J. Poterjoy, 2013: E3DVar: Coupling an ensemble Kalman filter with three-dimensional variational data assimilation in a limited-area weather prediction model and comparison to E4DVar. Mon. Wea. Rev., 140, 900 – 917.
Tropical cyclone dynamics explored via ensemble methods
Poterjoy, J. and F. Zhang, 2014: Predictability and genesis of Hurricane Karl (2010) examined through the EnKF assimilation of field observations collected during PREDICT. J. Atmos. Sci., 71, 1260 – 1275.
Poterjoy, J., F. Zhang, and Y. Weng, 2014: The effects of sampling errors on the EnKF assimilation of inner-core hurricane observations. Mon. Wea. Rev., 142, 1609 – 1630.
Xie, B., F. Zhang, Q. Zhang, J. Poterjoy, and Y. Weng, 2013: Observing strategy and observation targeting for tropical cyclones using ensemble-based sensitivity analysis and data assimilation. Mon. Wea. Rev., 141, 1437 – 1453.
Poterjoy, J. and F. Zhang, 2011: Dynamics and structure of forecast error covariance in the core of a developing hurricane. J. Atmos. Sci., 68, 1586 – 1606.