Jonathan Poterjoy Data Assimilation · University of Maryland

Publications

Peer-reviewed articles, book chapters, and preprints. Names marked with * are graduate students or postdocs in our group.

32Journal articles
12Led by advisees
13First-authored
15Since 2022

Journal articles

2026

  1. Cheung*, A., D. P. Stern, P. M. Finocchio, and J. Poterjoy, 2026: Using IMERG to Understand the Relationship Between Precipitation Structure and Intensification in Tropical Cyclones. Mon. Wea. Rev., 154, 2151–2172. DOI ↗Advisee-led
  2. Wang, X., P. J. van Leeuwen, J. Poterjoy, D. J. Stensrud, S. J. Greybush, Y. Zhang, H. Santer*, Y. Wang, Z. Pu, X. Chen, B. C. Stouffer, S. Chiao, K. Ide, C.-H. Lu, A. Johnson, C. Liu, N. Gasperoni, Y. Yang, J. Kim, D. Kleist, S. Frolov, and F. Carr, 2026: The Multi-University Consortium for Advanced Data Assimilation Research and Education (CADRE). Bull. Amer. Meteor. Soc., 107, E2048–E2064. DOI ↗

2025

  1. Santer*, H., J. Poterjoy, and M. El Gharamti, 2025: Innovation-Based Methods for Online Estimates of Observation Error Variances Within Ensemble Data Assimilation Cycles. Mon. Wea. Rev., 153, 1781–1792. DOI ↗Advisee-led
  2. Choi, Y., A. Petty, D. Felikson, and J. Poterjoy, 2025: Estimation of the State and Parameters in Ice Sheet Model Using an Ensemble Kalman Filter and Observing System Simulation Experiments. The Cryosphere, 19, 5423–5444. DOI ↗
  3. Kurosawa*, K., and J. Poterjoy, 2025: Augmented Flow-Dependent Perturbations to Mitigate Sampling Errors: Experiments for a Regional Application of the NOAA Unified Forecast System. Wea. Forecasting, 40, 1147–1161. DOI ↗Advisee-led

2024

  1. McCurry*, J., and J. Poterjoy, 2024: Optimizing Numerical Weather Prediction Utility of the Maryland Mesonet With Observing System Simulation Experiments. Wea. Forecasting, 39, 1849–1867. DOI ↗Advisee-led

2023

  1. Knisely*, J., and J. Poterjoy, 2023: Implications of Self-Contained Radiance Bias Correction for Data Assimilation Within the Hurricane Analysis and Forecasting System (HAFS). Wea. Forecasting, 38, 1719–1738. DOI ↗Advisee-led
  2. Walsworth*, A., J. Poterjoy, and E. Satterfield, 2023: Challenges for Inline Observation Error Estimation in the Presence of Misspecified Background Uncertainty. Mon. Wea. Rev., 151, 2295–2306. DOI ↗Advisee-led
  3. Santer*, H., J. Poterjoy, and J. McCurry*, 2023: Evaluating Contour Band Depth as a Method for Understanding Ensemble Uncertainty. Mon. Wea. Rev., 151, 2097–2113. DOI ↗Advisee-led
  4. McCurry*, J., J. Poterjoy, K. Knopfmeier, and L. Wicker, 2023: An Evaluation of Non-Gaussian Data Assimilation Methods in Moist Convective Regimes. Mon. Wea. Rev., 151, 1609–1629. DOI ↗Advisee-led
  5. Kurosawa*, K., and J. Poterjoy, 2023: A Statistical Hypothesis Testing Strategy for Adaptively Blending Particle Filters and Ensemble Kalman Filters for Data Assimilation. Mon. Wea. Rev., 151, 105–125. DOI ↗Advisee-led

2022

  1. Schwartz*, C. S., J. Poterjoy, J. R. Carley, D. C. Dowell, G. S. Romine, and K. Ide, 2022a: Comparing Partial and Continuously Cycling Ensemble Kalman Filter Data Assimilation Systems for Convection-Allowing Ensemble Forecast Initialization. Wea. Forecasting, 37, 85–112. DOI ↗Advisee-led
  2. Poterjoy, J., 2022a: Implications of Multivariate Non-Gaussian Data Assimilation for Multiscale Weather Prediction. Mon. Wea. Rev., 150, 1475–1493. DOI ↗
  3. Poterjoy, J., 2022b: Regularization and Tempering for a Moment-Matching Localized Particle Filter. Quart. J. Roy. Meteor. Soc., 148, 2631–2651. DOI ↗
  4. Schwartz*, C. S., J. Poterjoy, G. S. Romine, D. C. Dowell, J. R. Carley, and J. Bresch, 2022b: Short-Term Convection-Allowing Ensemble Precipitation Forecast Sensitivity to Resolution of Initial Condition Perturbations and Central Initial States. Wea. Forecasting, 37, 1259–1286. DOI ↗Advisee-led

2021

  1. Kurosawa*, K., and J. Poterjoy, 2021: Data Assimilation Challenges Posed by Nonlinear Operators: A Comparative Study of Ensemble and Variational Filters and Smoothers. Mon. Wea. Rev., 149, 2369–2389. DOI ↗Advisee-led
  2. 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. DOI ↗

2020

  1. 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. DOI ↗

2019

  1. Poterjoy, J., L. J. Wicker, and M. Buehner, 2019: Progress Toward the Application of a Localized Particle Filter for Numerical Weather Prediction. Mon. Wea. Rev., 147, 1107–1126. DOI ↗

2018

  1. Morzfeld, M., D. Hodyss, and J. Poterjoy, 2018: Variational Particle Smoothers and Their Localization. Quart. J. Roy. Meteor. Soc., 144, 806–825. DOI ↗

2017

  1. 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. DOI ↗

2016

  1. 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. DOI ↗
  2. 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. DOI ↗
  3. Poterjoy, J., 2016: A Localized Particle Filter for High-Dimensional Nonlinear Systems. Mon. Wea. Rev., 144, 59–76. DOI ↗

2015

  1. 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. DOI ↗

2014

  1. Poterjoy, J., and F. Zhang, 2014a: Intercomparison 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. DOI ↗
  2. Poterjoy, J., and F. Zhang, 2014b: Predictability and Genesis of Hurricane Karl (2010) Examined Through the EnKF Assimilation of Field Observations Collected During PREDICT. J. Atmos. Sci., 71, 1260–1275. DOI ↗
  3. 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. DOI ↗
  4. Zhang, X., X.-Y. Huang, J. Liu, 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. DOI ↗

2013

  1. 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., 141, 900–917. DOI ↗
  2. 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. DOI ↗

2011

  1. 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. DOI ↗

Book chapters

  • Poterjoy, J., 2026: Data Assimilation. Encyclopedia of Atmospheric Sciences, 3rd ed., W. A. Robinson, Ed., Vol. 1, Academic Press, 237–240. DOI ↗

Preprints

  • Jung*, H.-S., J. Poterjoy, and A. Petty, 2026: A State-Dependent Error Covariance Model of Surface Atmospheric Forcings Over the Arctic Sea Ice. EarthArXiv [preprint; submitted to J. Atmos. Oceanic Technol.]. DOI ↗