Publications Since Center Established

2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014




2020

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2019

  1. Alley, R. B., K. Emanuel and F. Zhang, 2019: Advances in Weather Prediction. Science, 363, 342-344. doi: 10.1126/science.aav7274 ; [Public press release at AAAS and EurekAlert!]; [Podcast at Science Friday with Richard Alley]
  2. Zhang, F., Y.Q. Sun*, L. Magnusson, R. Buizza, S.-J. Lin, J.-H. Chen and K. Emanuel, 2019: What is the Predictability Limit of Midlatitude Weather? Journal of the Atmospheric Sciences, 76, 1077-1091. doi:10.1175/JAS-D-18-0269.1
  3. Minamide, M.*, and F. Zhang, 2019: Adaptive Backgroun Error Inflation for Assimilating All-sky Satellite Radiance. Quarterly Journal of the Royal Meteorological Society, 145, 805-823. https://doi.org/10.1002/qj.3466.
  4. Tao, D.*, and F. Zhang, 2019: Evolution of dynamic and thermodynamic structures before and during rapid intensification of tropical cyclones: sensitivity to vertical wind shear. Monthly Weather Review, 147, 1171-1191, https://doi.org/10.1175/MWR-D-18-0173.1.
  5. Chen, X., F. Zhang, and J. Ruppert, 2019: Modulations of Coastal Rainfall Diurnal Cycle over South China by the Boreal Summer Intraseasonal Oscillation. Journal of Climate ,32, 2089-2108, https://doi.org/10.1175/JCLI-D-18-0786.1
  6. Chen, X., and F. Zhang, 2019: Relative roles of preconditioning moistening and global circumnavigating mode on the MJO convective initiation during DYNAMO. Geophysical Research Letters, 46, 1079–1087. https://doi.org/10.1029/2018GL080987.
  7. Tapiador, F.J., R Roca, A. del Genio, B. deWitte, W. Petersen, F. Zhang, 2018: Is Precipitation a Good Metric of Model Performance? Bulletin of the American Meteorological Society, 100, 223-233, https://doi.org/10.1175/BAMS-D-17-0218.1
  8. Tang, X.*, Z. Tan, J. Fang, E. B. Munsell and F. Zhang, 2019: Impact of the Diurnal Radiation Contrast on the Contraction of Radius of Maximum Wind during Intensification of Hurricane Edouard (2014) Journal of the Atmospheric Sciences, 76, 421-432, https://doi.org/10.1175/JAS-D-18-0131.1.
  9. Keller, J., C. Grams, M. Riemer, H. Archambault, L. Bosart, J. Doyle, J. Evans, T.Galarneau, K. Griffin, P. Harr, N. Kitabatake, R. McTaggart-Cowan, F. Pantillon, J.Quinting, C. Reynolds, E. Ritchie, R. Torn, and F. Zhang, 2019: The Extratropical Transition of Tropical Cyclones Part II: Interaction with the midlatitude flow, downstream impacts, and implications for predictability , Monthly Weather Review, 147, 1077-1106, https://doi.org/10.1175/MWR-D-17-0329.1.
  10. Schuster D., M. Mayernik, C. Hou, G. Stossmeister, F. Zhang, T. Bgyuen, R. Downs, D. Kinkade and M. Ramamurthy, 2019: Challenges and Future Directions for Data Management in the Geosciences. Bulletin of the American Meteorological Society, 100, 909-912, https://doi.org/10.1175/BAMS-D-18-0319.1
  11. Du, Y.*, and F. Zhang, 2019: Banded Convective Activity Associated with Mesoscale Gravity Waves over Southern China. Journal of Geophysical Research - Atmospheres, 124, 1912-1930, https://doi.org/10.1029/2018JD029523 .
  12. Greybush, S. J., H. E. Gillespie*, and R. J. Wilson, 2019: Transient Eddies in the TES/MCS Ensemble Mars Atmosphere Reanalysis System (EMARS). ICARUS 317, 158-181. https://doi.org/10.1016/j.icarus.2018.07.001.
  13. Zhang, F., M. Minamide*, R. Nystrom*, X. Chen, S.-J. Lin, L. Harris, 2019: Improving Harvey forecasts with next-generation weather satellites. Bulletin of the American Meteorological Society, 100, 1217-1222, https://doi.org/10.1175/BAMS-D-18-0149.1
  14. Wang, Y.*, K. Yang, X. Zhou, B. Wang, D. Chen, H. Liu, C. Lin and F. Zhang, 2019: The formation of a dry-belt in the north side of central Himalaya Mountains. Geophysical Research Letters, 46, 2993-3000, https://doi.org/10.1029/2018GL081061
  15. Hayatbini, N.*, K. Hsu, S. Sorooshian, Y. Zhang, F. Zhang 2019: Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS. Journal of Hydrometeorology, 20, 901-913, https://doi.org/10.1175/JHM-D-18-0197.1.
  16. Chen, H. W.*, F. Zhang, T. Lauvaux, K.J. Davis, S. Feng, M.P. Butler, R.B. Alley, 2019: Characterization of Regional-Scale CO2 Transport Uncertainties in an Ensemble with Flow-Dependent Transport Errors. Geophysical Research Letters,46, 4049-4058, https://doi.org/10.1029/2018GL081341
  17. Alley, R.B., W. Li, B. Parizek, and F. Zhang, 2019: Evaluation of ice-stream model sensitivities for parameter estimation. Earth and Planetary Science Letters, 516, 49-55, https://doi.org/10.1016/j.epsl.2019.03.035.
  18. Fang, J., O. Pauluis, F. Zhang, 2019: The thermodynamic cycles and associated energetics of Hurricane Edouard (2014) during its intensification. Journal of the Atmospheric Sciences, 76, 1769-1784, https://doi.org/10.1175/JAS-D-18-0221.1
  19. Li, X.*, F Zhang, Q. Zhang and M.R. Kumjian, 2019: Sensitivity of hail precipitation to ensembles of uncertainties of representative initial environmental conditions from ECMWF. Journal of Geophysical Research , 124, 6929-6948, https://doi.org/10.1029/2018JD029899.
  20. Thomas, A.*, A. Huff, X. Hu and F. Zhang, 2019: Quantifying uncertainties in WRF-Chem Simulations of Ground-Level Ozone in the Mid-Atlantic Region of the United States for Air Quality Prediction. Journal of Advances in Modeling Earth Systems, 11, 1100-1116, https://doi.org/10.1029/2018MS001457.
  21. Tao, D.*, K. Emanuel, F. Zhang, R. Rotunno, M. Bell and R.G. Nystrom*, 2019: Evaluation of the assumptions in the steady-state tropical cyclone self-stratified outflow using three-dimensional convection-allowing simulations. Journal of the Atmospheric Sciences, 76, 2995-3009, https://doi.org/10.1175/JAS-D-19-0033.1 .
  22. Lu, Y., and F. Zhang, 2019: Towards ensemble assimilation of hyperspectral satellite observations with data compression and dimension reduction using principal component analysis. Monthly Weather Review, 147, 3505-3518, https://doi.org/10.1175/MWR-D-18-0454.1.
  23. Chen, W. H.*, L.N. Zhang, F. Zhang, K.J. Davis, T. Lauvaux, S. Pal, B. Gaudet, and J.P. DiGangi , 2019: Evaluation of regional CO2mole fractions in the ECMWF CAMS real-time atmospheric analysis and NOAA CarbonTracker Near-Real Time reanalysis with airborne observations from ACT-America field campaigns. Journal of Geophysical Research: Atmospheres, 124, 8119-8133, https://doi.org/10.1029/2018JD029992.
  24. Nystrom,R.G.* and F. Zhang, 2019: Practical Uncertainties in the Limited Predictability of the Record-Breaking Intensification of Hurricane Patricia (2015). Monthly Weather Review, 147, 401-423, https://doi.org/10.1175/ MWR-D-18-0450.1.
  25. Ruppert, J.H. and F. Zhang, 2009: Diurnal Forcing and Phase Locking of Gravity Waves in the Maritime Continent. Journal of the Atmospheric Sciences,76, 2815-2835, https://doi.org/10.1175/JAS-D-19-0061.1 .
  26. Magnusson, L.*, J.D. Doylt, W.A. Komaromi, R. Torn, C.K.Tang, J.V.L.Chan, M.Yamaguchi, and F. Zhang 2019: Advances in understanding difficult cases of track forecasts. Tropical Cyclone Research and Review.
  27. He, J.*, Fuqing Zhang, Xingchao Chen, Xinghua Bao, Deliang Chen, Hyun Mee Kim, Hui‐Wen Lai, L. Ruby Leung, Xulin Ma, Zhiyong Meng, Tinghai Ou, Ziniu Xiao, Eun‐Gyeong Yang, and Kun Yang, 2019: Development and Evaluation of an Ensemble‐Based Data Assimilation System for Regional Reanalysis Over the Tibetan Plateau and Surrounding Regions. Journal of Advances in Modeling Earth Systems, 11, 2503–2522. https://doi.org/10.1029 /2019MS001665 .
  28. Chen, X. and Fuqing Zhang, 2019: Development of a Convection-Permitting Air-Sea-Coupled Ensemble Data Assimilation System for Tropical Cyclone Prediction. Journal of Advances in Modeling Earth Systems https://doi.org/10.1029/2019MS001795 .
  29. Zhang, Y., David J. Stensrud, and Fuqing Zhang, 2019: Simultaneous Assimilation of Radar and All-Sky Satellite Infrared Radiance Observations for Convection-Allowing Ensemble Analysis and Prediction of Severe Thunderstorms. Monthly Weather Review 147, 4389-4409. https://doi.org/10.1175/MWR-D-19-0163.1.
  30. Du,Y.*, Richard Rotunno and Fuqing ZHang, 2019: Impact of Vertical Wind Shear on Gravity Wave Propagation in the Land–Sea-Breeze Circulation at the Equator. Journal of the Atmospheric Sciences, 76, 3247-3265. https://doi.org/10.1175/JAS-D-19-0069.1.
  31. Eipper, D. T., S. J. Greybush, G. S. Young, S. Saslo*, T. D. Sikora, R. D. Clark, 2019: Lake-Effect Snowbands in Baroclinic Environments. Weather and Forecasting, 34, 1657-1674. https://doi.org/10.1175/WAF-D-18-0191.1.
  32. Greybush, S. J., E. Kalnay, R. J. Wilson, R. N. Hoffman, T. Nehrkorn, M. Leidner, J. Eluszkiewicz, H. E. Gillespie*, M. Wespetal, Y. Zhao, M. Hoffman, P. Dudas, T. McConnochie, A. Kleinboehl, D. Kass, D. McCleese, and T. Miyoshi, 2019: The Ensemble Mars Atmosphere Reanalysis System (EMARS) Version 1.0. Geoscience Data Journal, https://doi.org/10.18113/D3W375.
  33. Greybush, S. J., H. E. Gillespie*, and R. J. Wilson, 2019: Transient eddies in the TES/MCS Ensemble Mars Atmosphere Reanalysis System (EMARS). Icarus, 317, 158-181, https://doi.org/10.1016/j.icarus.2018.07.001.
  34. Sharma*, S., H. Gall, J. Gironas, and A. Mejia, 2019: Seasonal hydroclimatic ensemble forecasts anticipate nutrient and suspended sediment loads using a dynamical-statistical approach. Environmental Research Letters, 14(8), 1-16. https://doi.org/10.1088/1748- 9326/ab2c26.
  35. Sharma*, S., R. Siddique*, S. Reed, P. Anhert, A. Mejia, 2019: Hydrological model diversity enhances streamflow forecast skill at short- to medium-range timescales. Water Resources Research, 55, 5010-5030, https://doi.org/10.1029/2018WR023197.
  36. Gomez*, M., S. Sharma*, S. Reed, A. Mejia 2019: Skill of ensemble flood inundation forecasts at short- to medium-range timescales. Journal of Hydrology, 568, 207-220, https://doi.org/10.1016/j.jhydrol.2018.10.063.
  37. Zhang,H., X. Li, and J. Harlim, 2019: A parameter estimation method using linear response statistics: Numerical scheme. Chaos, 29, 033101, https://doi.org/10.1063/1.5081744.
  38. Jiang, S.W., and J. Harlim, 2019: https://doi.org/10.3390/e21060559. Entropy, 21(6), 559, https://doi.org/10.3390/e21060559.

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2018

  1. Zhang, F, K. Emanuel, 2018: Promises in air-sea fully-coupled data assimilation for future hurricane prediction Geophysical Research Letters45, 13,173-13,177, https://doi.org/10.1029/2018GL080970 .
  2. Tang*, X.,Z. Tan, J. Fang, E. B. Munsell and F. Zhang, 2018: Impact of the Diurnal Radiation Contrast on the Contraction of Radius of Maximum Wind during Intensification of Hurricane Edouard (2014) Journal of the Atmospheric Sciences, , https://doi.org/10.1175/JAS-D-18-0131.1 .
  3. Keller*, J., C. Grams, M. Riemer, H. Archambault, L. Bosart, J. Doyle, J. Evans, T.Galarneau, K. Griffin, P. Harr, N. Kitabatake, R. McTaggart-Cowan, F. Pantillon, J.Quinting, C. Reynolds, E. Ritchie, R. Torn, and F. Zhang, 2018: The Extratropical Transition of Tropical Cyclones Part II: Interaction with the 4 midlatitude flow, downstream impacts, and implications for predictability Journal of the Atmospheric Sciences, ,Preliminary accepted version https://doi.org/10.1175/MWR-D-17-0329.1 .
  4. Sieron*, S. B., F. Zhang, E. E. Clothiaux, L. N. Zhang, and Y. Lu, 2018: Representing Precipitation Ice Species with Both Spherical and Non-Spherical Particles for Microphysics-Consistent Cloud Microwave Scattering Properties. Journal of Advances in Modeling Earth Systems, 10, 1011–1028. https://doi.org/10.1002/2017MS001226 .
  5. Nystrom*, R., F. Zhang, E. B. Munsell, S. A. Braun, J. A. Sippel, Y. Weng, and K. Emanuel, 2018: Predictability and dynamics of Hurricane Joaquin (2015) explored through convection-permitting ensemble sensitivity experiments. Journal of the Atmospheric Sciences, 75, 401-424 https://doi.org/10.1175/JAS-D-17-0137.1.
  6. Munsell, EB*, F Zhang, SA Braun1, JA Sippel, and AC Didlake, 2018: The inner-core temperature structure of Hurricane Edouard (2014): Observations and ensemble variability. Monthly Weather Review, 146, 135–155, https://doi.org/10.1175/MWR-D-17-0095.1
  7. Pan*, J., D. Teng*, L. Zhou, Y. Zhang, Y. Weng* and F. Zhang, 2018: Dynamical processes of heavy local rainfall over East China induced by Super Typhoon Soudelor (2015). Science China – Earth Science, https://doi.org/10.1007/s11430-017-9135-6.
  8. Ying, Y.*, F. Zhang, J. L. Anderson, 2018: On the selection of localization radius in ensemble filtering for multi-scale quasi-geostrophic dynamics. Monthly Weather Review,14, 543-560, https://doi.org/10.1175/MWR-D-17-0336.1
  9. Ying, Y.*, F. Zhang, 2018: Potentials in improving predictability of multiscale tropical weather systems evaluated through ensemble assimilation of simulated satellite-based observations. Journal of the Atmospheric Sciences, 75, 1675–1698, https://doi.org/10.1175/JAS-D-17-0245.1
  10. Chen, X., O. Pauluis, F. Zhang, 2018: Regional Simulation of Indian summer Monsoon Intraseasonal Oscillations at Gray Zone Resolution. Atmospheric Chemistry and Physics, 18, 1003-1022, https://www.atmos-chem-phys.net/18/1003/2018/acp-18-1003-2018.pdf.
  11. Chen, X., O. Pauluis, F. Zhang, 2018: Atmospheric overturning across multiple scales of an MJO event during the CINDY/DYNAMO Campaign. Journal of the Atmospheric Sciences, 75, 381-399. https://journals.ametsoc.org/doi/pdf/10.1175/JAS-D-17-0060.1.
  12. Chen, X., O. Pauluis, L. R. Leung, F. Zhang, 2018: Multiscale Atmospheric Overturning of Indian Summer Monsoon as Seen through Isentropic Analysis.Journal of the Atmospheric Sciences, 75, 3011-3010, https://journals.ametsoc.org/doi/pdf/10.1175/JAS-D-18-0068.1.
  13. Chen*, Y., F. Zhang, B. W. Green, and Y. Xu, 2018: Impacts of Ocean Cooling and Reduced Wind Drag on Hurricane Katrina (2005) Based on Numerical Simulations. Monthly Weather Review, 146, 287-306. https://journals.ametsoc.org/doi/pdf/10.1175/MWR-D-17-0170.1.
  14. Zhang, Y., F. Zhang, C. A. Davis and J. Sun, 2018: Diurnal evolution and structure of long-lived mesoscale convective vortices along the Mei-yu front over the East China Plains. Journal of the Atmospheric Sciences, 75, 1005–1025, https://doi.org/10.1175/JAS-D-17-0197.1 .
  15. Zhang, Y., and F. Zhang, 2018: A Review on the Ensemble-Based Data Assimilations for Severe Convective Storms". Advances in Meteorological Science and Technology, 8 (3), 38-52, https://doi.org/10.3969/j.issn.2095-1973.2018.03.003 .
  16. Lu, Y., and F. Zhang, 2018: A novel channel-synthesizing method for reducing uncertainties in satellite radiative transfer modeling. Geophysical Research Letters, 45,5115-5125, https://doi.org/10.1029/2018GL077342
  17. Li, J., F. Zhang, 2018: Geometry-Sensitive Ensemble Mean based on Wasserstein Barycenters: Proof-of-Concept on Cloud Simulations. Journal of Computational Statistics,27, 785-797, 10.1080/10618600.2018.1448831.
  18. Wu, D., K. Zhao, M. R. Kumjian, X. Chen, H. Huang, M. Wang, A. C. Didlake, Y. Duan, and F. Zhang, 2018: Kinematics and Microphysics of Convection in the Outer Rainband of Typhoon Nida (2016) revealed by Polarimetric Radar. Monthly Weather Review, 146, 2147-2159. https://journals.ametsoc.org/doi/pdf/10.1175/MWR-D-17-0320.1.
  19. Wang, M., K. Zhao, W.-C. Lee, F. Zhang, 2018: Microphysical and kinemaiitic structure of convective-scale elements in the inner rainband of Typhoon Matmo (2014) after landfall. Journal of Geophysical Research – Atmospheres,123, http://doi.org/10.1029/2018JD028578 .
  20. Liu, S., D. Tao, K. Zhao, M. Minamide, and F. Zhang, 2018: Dynamics and predictability of the rapid intensification of Super Typhoon Usagi (2013). Journal of Geophysical Research – Atmospheres, 123 ,2147-2159.https://doi.org/10.1029/2018JD028561.
  21. Guo, L., B. Fan, F. Zhang, H. Lin and J. Zhao, 2018: The variability of severe dust storm occurrence in China from 1958 to 2007. Journal of Geophysical Research – Atmospheres,123 ,8035-8046. https://doi.org/10.1029/2018JD029042.
  22. Minamide, M.*, and F. Zhang, 2018: Assimilation of all-sky infrared radiances from Himawari-8 and impacts of moisture and hydrometer initialization on convection-permitting tropical cyclone prediction.Monthly Weather Review,, 146 ,3241-3258, https://doi.org/10.1175/MWR-D-17-0367.1.
  23. Zhang, Y., F. Zhang, and D. J. Stensrud, 2018: Assimilating All-Sky Infrared Radiances from GOES-16 ABI using an Ensemble Kalman Filter for Convection-Allowing Severe Thunderstorms Prediction. Monthly Weather Review,, 146 ,3363-3381, https://doi.org/10.1175/MWR-D-18-0062.1.
  24. Tapiador, F.J., R Roca, A del Genio, B deWitte, W Petersen, F Zhang, 2018: Is Precipitation a Good Metric of Model Performance? Bulletin of the American Meteorological Society, https://doi.org/10.1175/BAMS-D-17-0218.1
  25. Taraphdar*, S., F. Zhang, L. R. Lueng, O. Pauluis and X. Chen, 2018: MJO Affects the Monsoon Onset Timing over the Indian Region. Geophysical Research Letters, 45, 10,011-10,018, https://doi.org/10.1029/2018GL078804
  26. Eipper, D. T., G. S. Young, S. J. Greybush, S. Saslo*, T. D. Sikora, and R. D. Clark, 2018: Predicting the Inland Penetration of Long-Lake-Axis Parallel Snowbands. Weather and Forecasting, 33, 1435-1451. https://doi.org/10.1175/WAF-D-18-0033.1.
  27. Zarzar, C.M., H. Hosseiny, R. Siddique*, M. Gomez*, V. Smith, A. Mejia, and J. Dyer, 2018: A hydraulic multi-model ensemble framework for visualizing flood inundation uncertainty, Journal of the American Water Resources Association (JAWRA), 54(4), 07-819, https://doi.org/10.1111/1752-1688.12656.
  28. Sharma*, S., R. Siddique*, S. Reed, P. Ahnert, P. Mendoza, A. Mejia, 2018: Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system, Hydrology and Earth System Sciences, 22(3), 1831-1849, https://doi.org/10.5194/hess-22-1831-2018.
  29. De La Chevrotière, M. and J. Harlim, 2018: Data-driven localization mappings in filtering the monsoon-Hadley multicloud convective flows . Monthly Weather Review, 146(4), 1197-1218, https://doi.org/10.1175/MWR-D-17-0381.1.
  30. Harlim, J. and H. Yang, 2018: Diffusion Forecasting Model with Basis Functions from QR-Decomposition, Journal of Nonlinear Science, 28(3), 847-872, https://doi.org/10.1007/s00332-017-9430-1.

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2017

  1. Emanuel, K. and F. Zhang, 2017: The Role of Inner Core Moisture in Tropical Cyclone Predictability and Practical Forecast Skill, Journal of the Atmospheric Sciences, 74, doi: 10.1175/JAS-D-17-0008.1.
  2. Pauluis, O., and F. Zhang, 2017: Reconstruction of thermodynamic cycles in a high resolution simulation of a hurricane. Journal of the Atmospheric Sciences, 74, 3357-3381. doi:10.1175/JAS-D-16- 0353.1
  3. Fang, J*, O Pauluis, F Zhang, 2017: Isentropic analysis on the intensification of Hurricane Edouard (2014). Journal of the Atmospheric Sciences,, 74, 4177–4197, doi:10.1175/JAS-D-17-0092.1
  4. Tang, X.*, Z. Tan, J. Fang*, Y.Q. Sun* and F. Zhang, 2017: Impacts of diurnal radiation cycle on secondary eyewall formation. Journal of the Atmospheric Sciences, 74, 3079-3098.
  5. Munsell, E. B.*, F. Zhang, J. A. Sippel, S. A. Braun, Y. Weng*, 2017: Dynamics and predictability of the intensification of Hurricane Edouard (2014). Journal of he Atmospheric Sciences, 74, 573-595.
  6. Sun, Y.Q.*, R. Rotunno, and F. Zhang, 2017: Contributions of moist convection and internal gravity waves to building the atmospheric "-5/3" kinetic energy spectra, Journal of the Atmospheric Sciences, 74, 185-201.
  7. Ying, Y.*, F. Zhang, 2017: Practical and intrinsic predictability of multi-scale weather and convectively-coupled equatorial waves during the active phase of an MJO. Journal of the Atmospheric Sciences, 74, 3771-3785, doi:10.1175/JAS-D-17-0157.1.
  8. Chen, X., F. Zhang, K. Zhao, 2017: Influence of Monsoonal Wind Speed and Moisture Content on Intensity and Diurnal Variations of the Mei-yu Season Coastal Rainfall over South China. Journal of the Atmospheric Sciences, 74, 2835-2856.
  9. Stern, D. P.*, J. Vigh, D. S. Nolan, and F. Zhang, 2017: Reply to Comments On “Revisiting the Relationship Between Eyewall Contraction and Intensification”. Journal of the Atmospheric Sciences, 74, 4275-4286, https://doi.org/10.1175/JAS-D-17-0120.1
  10. Zhang, F., D. Tao*, Y.Q. Sun* and J. D. Kepert, 2017: Dynamics and predictability of secondary eyewall formation in sheared tropical cyclones. Journal of Advances in Modeling Earth Systems (JAMES), 9, DOI: 10.1002/2016MS000729.
  11. Minamide, M.*, and F. Zhang, 2017: Adaptive Observation Error Inflation for Assimilating All-sky Satellite Radiance. Monthly Weather Review,145, 1063-1081.
  12. Evans, C., K. Wood, S. Aberson, H. Archambault, S. Milrad, L. Bosart, K.,Corbosiero, C. Davis, J. Dias Pinto, J. Doyle, C. Fogarty, T. Galarneau, Jr., C.,Grams, K. Griffin, J. Gyakum, R. Hart, N. Kitabatake, H. Lentink, R. McTaggartCowan,W. Perrie, J. Quinting, C. Reynolds, M. Riemer, E. Ritchie, Y. Sun, F. Zhang, 2017: The Extratropical Transition of Tropical Cyclones. Part I: Cyclone Evolution and Direct Impacts. Monthly Weather Review, 145, 4317-4344, doi:10.1175/MWR-D-17-0027.1.
  13. Melhauser, C.*, F. Zhang, Y. Weng, Y. Jin, H. Jin and Q. Zhao, 2017: A Multiple-Model Convection-permitting Ensemble Examination of the Probabilistic Prediction of Tropical Cyclones: Hurricanes Sandy (2012) and Edouard (2014). Weather and forecasting, 32, 665-668.
  14. Zhao, K., M. Wang, P. Fu, Z. Yang, J. Wen, W-C Lee, and F. Zhang, 2017: Doppler radar analysis of a tornadic miniature supercell during the Landfall of Typhoon Mujigae (2015) in South China. Bulletin of the American Meteorological Society, 98, 1821-1831, http://dx.doi.org/10.1175/BAMS-D-15-00301.1.
  15. Zhu*, L., Z. Meng, F. Zhang, and P. M. Markowski, 2017: The influence of sea- and land-breeze circulations on the diurnal variability of precipitation over a tropical island. Atmospheric Chemistry and Physics, 17, 13213-13232, https://doi.org/10.5194/acp-17-13213-2017.
  16. Li*, M., F. Zhang, Q. Zhang, J. Harrington and M. R. Kumjian, 2017: Nonlinear Response of hail precipitation rate to environmental moisture content: a real case modeling study of an episodic midlatitude severe convective event. Journal of Geophysical Research - Atmosphere, 122, doi:10.1002/2016JD026373.
  17. Zhang, F., S. Talaphdar, and S. Wang, 2017: The role of global circumnavigating mode in the MJO initiation and propagation. Journal of Geophysical Research - Atmosphere, 122, 5837-5856.
  18. Sieron S. B.*, E.E. Clothiaux, F. Zhang, Y.  Lu and J. Otkin, 2017: Fast Radiative Transfer Modeling for All-Sky Microwave Satellite Radiances: Modifying CRTM with Microphysics-Consistent Cloud Optical Properties. Journal of Geophysical Research - Atmosphere, 122, 7027-7046, DOI: 10.1002/2017JD026494.
  19. Cohen Y., N. Harnik, E. Heifetz, D. S. Nolan, D. Tao and F. Zhang, 2017: On the Violation of Gradient Wind Balance at the top of Tropical Cyclones. Geophysical Research Letters, 44, doi:10.1002/2017GL074552.
  20. Sun, J.*, and F. Zhang, 2017: Daily extreme precipitation and trends over China. Science China – Earth Science, 60, 2190-2203, https://link.springer.com/article/10.1007/s11430-016-9117-8.
  21. Zhang, Q.*, X Ni* and F. Zhang, 2017, Decreasing trend in severe weather occurrence over China during the past 50 years. Nature Scientific Reports, 7, 42310, doi:10.1038/srep42310.
  22. Navarro, T., F. Forget, E. Millour, S. J. Greybush, E. Kalnay, and T. Miyoshi, 2017, The challenge of atmospheric data assimilation on Mars. Earth and Space Science, 4,12, 690-722, https://doi.org/10.1002/2017EA000274
  23. Kotsuki, S., S. J. Greybush, and T. Miyoshi, 2017, Can we optimize the assimilation order in the serial ensemble Kalman filter? A study with the Lorenz-96 model. Monthly Weather Review, 145, 4977-4995, https://doi.org/10.1175/MWR-D-17-0094.1
  24. Saslo, S., and S. J. Greybush, 2017, Prediction of Lake-Effect Snow using Convection-Allowing Ensemble Forecasts and Regional Data Assimilation. Weather and Forecasting, 32, 1727-1744, https://doi.org/10.1175/WAF-D-16-0206.1
  25. S. J. GreybushS. Saslo, and R. Grumm, 2017, Assessing the Ensemble Predictability of Precipitation Forecasts for the January 2015 and 2016 East Coast Winter storms. Weather and Forecasting, 32, 1057-1078, https://doi.org/10.1175/WAF-D-16-0153.1
  26. Yang, X., R. Siddique, S. Sharma, S. J. Greybush, and A. Mejia, 2017, Postprocessing of GEFS precipitation ensemble reforecasts over the U.S. middle-Atlantic region. Monthly Weather Review, 145, 1641-1658, https://doi.org/10.1175/MWR-D-16-0251.1
  27. Navarro, T., F. Forget, E. Millour, S. J. Greybush, E. Kalnay, and T. Miyoshi, 2017: The Challenge of Atmospheric Data Assimilation on Mars. Earth and Space Science, 4, 690-722. https://doi.org/10.1002/2017EA000274.
  28. Kotsuki, S., S. J. Greybush, and T. Miyoshi, 2017: Can We Optimize the Assimilation Order in the Serial Ensemble Kalman Filter? A Study with the Lorenz-96 Model. Monthly Weather Review, 145, 4977-4995. https://doi.org/10.1175/MWR-D-17-0094.1.
  29. Saslo, S.*, and S. J. Greybush, 2017: Prediction of Lake-Effect Snow Using Convection-Allowing Ensemble Forecasts and Regional Data Assimilation. Weather and Forecasting, 32, 1727-1744. https://doi.org/10.1175/WAF-D-16-0206.1.
  30. Greybush, S. J., S. Saslo*, and R. Grumm, 2017: Assessing the Ensemble Predictability of Precipitation Forecasts for the January 2015 and 2016 East Coast Winter Storms. Weather and Forecasting, 32, 1057-1078. https://doi.org/10.1175/WAF-D-16-0153.1.
  31. Yang, X., R. Siddique, S. Sharma, S. J. Greybush, and A. Mejia, 2017:Postprocessing of GEFS Precipitation Ensemble Reforecasts over the U.S. Mid-Atlantic Region. Monthly Weather Review, 145, 1641-1658. https://doi.org/10.1175/MWR-D-16-0251.1.
  32. Siddique*, R. and A. Mejia, 2017: Ensemble streamflow forecasting across the U.S. middle Atlantic region with a distributed hydrological model forced by GEFS reforecasts, Journal of Hydrometeorology, 18(7), 1905-1928, https://doi.org/10.1175/JHM-D-16-0243.1.
  33. Yang*, X., S. Sharma*, R. Siddique*, S. Greybush, A. Mejia, 2017: Postprocessing of GEFS precipitation ensemble reforecasts over the U.S. middle Atlantic region, Monthly Weather Review, 145, 1641-1658, https://doi.org/10.1175/MWR-D-16-0251.1.
  34. Sharma*, S., R. Siddique*, N. Balderas*, J. D. Fuentes, S. Reed, P. Ahnert, R. Shedd, B. Astifan, R. Cabrera, A. Laing, M. Klein, and A. Mejia, 2017: Eastern U.S. verification of ensemble precipitation forecasts, Weather and Forecasting, 32, 117–139, https://doi.org/10.1175/WAF-D-16-0094.1.
  35. Berry, Tyrus* and John Harlim, 2017: Correcting Biased Observation Model Error in Data Assimilation, Monthly Weather Review, 145(7), 2833-2853, https://doi.org/10.1175/MWR-D-16-0428.1.
  36. De La Chevrotière, M. and J. Harlim, 2017: A Data-Driven Method for Improving the Correlation Estimation in Serial Ensemble Kalman Filters. Monthly Weather Review, 145(3), 985-1001, https://doi.org/10.1175/MWR-D-16-0109.1.
  37. Harlim, J. 2017: Model error in data assimilation, Cambridge University Press, 276-317, Cambridge University Press.
  38. Harlim,J., X. Li, and H. Zhang, 2017: A Parameter Estimation Method Using Linear Response Statistics. Journal of Statistical Physics, 168, 146–170, https://doi.org/10.1007/s10955-017-1788-9.

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2016

  1. Zhang, F., and K. A. Emanuel, 2016: On the role of surface fluxes and WISHE in tropical cyclone intensification. Journal of the Atmospheric Sciences, 73, 2011-2019.
  2. Emanuel, K. and F. Zhang, 2016: On the Predictability and Error Sources of Tropical Cyclone Intensity Forecasts, Journal of the Atmospheric Sciences, 73, 3739-3747.
  3. Chen, H. W.*, F. Zhang, R. B. Alley, 2016: Nonlinear atmospheric response to Arctic sea-ice loss under different sea ice scenarios, Journal of Climate, 29, 7831-7849.
  4. Tang, X.*, and F. Zhang, 2016: Impacts of the Diurnal Radiation Cycle on the Formation, Intensity and Structure of Hurricane Edouard (2014). Journal of the Atmospheric Sciences, 73, 2871-2892.
  5. Wei, J.*, F. Zhang, and J. H. Richter, 2016: Toward Improving Nonorographic Gravity Wave Parameterizations: An Analysis of Gravity Wave Spectral Characteristics in Moist Baroclinic Jet-Front Systems. Journal of the Atmospheric Sciences, 73, 3133-3155.
  6. 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). Monthly Weather Review, 144, 1449-1468.
  7. Zhang, F., M. Minamide*, E.E. Clothiaux, 2016: Potential Impacts of Assimilating All-sky Satellite Radiances from GOES-R on Convection-Permitting Analysis and Prediction of Tropical Cyclones. Geophysical Research Letters, 43, doi:10.1002/2016GL068468.
  8. Sun, Y.Q.*, and F. Zhang, 2016: Intrinsic versus practical limits of atmospheric predictability and the significance of the butterfly effect. Journal of the Atmospheric Sciences, 73, 1419-1438.
  9. Dong, L.*, and F. Zhang, 2016: OBEST: An observation-based ensemble setting technique for tropical cyclone track forecasting. Weather and Forecasting, 31, 57–70.
  10. Zhu, L.*, Q. Wang, X. Shen, Z. Meng, F. Zhang, Y. Weng*, Y. Gao*, Y. Zhang, J. Yue, 2016: Prediction and Predictability of a High-impact Western Pacific Landfalling Typhoon Vicente (2012) through Convection-permitting Ensemble Assimilation of Doppler Radar Velocity. Monthly Weather Review, 144, 21-43.
  11. Zhao, K. Q. Li, W-C Lee, Y.Q. Sun* and F. Zhang, 2016: Doppler Radar Analysis of Triple Eyewalls in Typhoon Usagi (2013). Bulletin of the American Meteorological Society, 97, 25-30.
  12. Mrowiec, A.A., O. M. Pauluis and F. Zhang, 2016: Isentropic analysis of a simulated hurricane. Journal of the Atmospheric Sciences,73, 1857-1870.
  13. Qiu, X*, and F. Zhang, 2016: Prediction and Predictability of an extreme local rainfall event through EnKF assimilation of radar observations. Science China - Earth Sciences, 59, 518-532.
  14. Zhang, Y. J., F. Zhang, Z. Meng, D. J. Stensrud 2016: Intrinsic Predictability of the 20 May 2013 Tornadic Thunderstorm Event in Oklahoma at Storm Scales. Monthly Weather Review, 144,1273-1298.
  15. Melhauser, C.*, and F. Zhang, 2016: Application of a Simplified Co-plane Wind Retrieval Using Dual-Beam Airborne Doppler Radar Observations for Tropical Cyclone Prediction. Monthly Weather Review, 144, 2645-2666.
  16. Stern, D. P.*, and F. Zhang, 2016: The Warm Core Structure of Hurricane Earl (2010). Journal of the Atmospheric Sciences,73, 3305-3328.
  17. Fang, J. and F. Zhang, 2016: Contribution of tropical waves to the formation of Super Typhoon Megi (2010). Journal of the Atmospheric Sciences,73, 4387-4405.
  18. Weng, Y.* and F. Zhang, 2016: Advances in Convection-permitting Tropical Cyclone Analysis and Prediction through EnKF Assimilation of Reconnaissance Aircraft Observations. Journal of Metrological Society of Japan, 94, 345-358.
  19. Zhang, F., W. Li* and M. E. Mann, 2016: Scale-dependent Regional Climate Predictability over North America Inferred from CMIP3 and CMIP5 Ensemble Simulations. Advances in Atmospheric Sciences, 33, 905-918.
  20. Sippel, J. A., F. Zhang, S. A. Braun, and D. Cecil, 2016: Further Exploring the Potential for Unmanned Aircraft to Benefit Hurricane Analyses and Forecasts. Tropical Cyclone Research and Review, 2, 64-70. https://doi.org/10.6057/2015TCRR02.02.
  21. Li, M.*, Q. Zhang and F. Zhang, 2016: Hail Frequency and its Association with Atmospheric Circulation Patterns in Mainland China during 1960-2012. Journal of Climate, 29, 7027-7044.
  22. Houtekamer, P. L. and F. Zhang, 2016: Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation. Monthly Weather Review, DOI: http://dx.doi.org/10.1175/MWR-D-15-0440.1
  23. Chen, X., F. Zhang, K. Zhao, 2016: Diurnal variations of land-sea breeze and its related precipitation over South China, Journal of the Atmospheric Sciences, http://dx.doi.org/10.1175/JAS-D-16-0106.1
  24. Yueh, S., A Fore, W Tang, H Akiko, B Stiles, N Reul,Y Weng and F Zhang, 2016: SMAP L-Band Passive Microwave Observations Of Ocean Surface Wind During Severe Storms. IEEE Transactions on Geoscience and Remote Sensing, 54, 7339-7350.
  25. Waugh, D. W., A. Toigo, S. Guzewich, S. J. Greybush, R. J. Wilson, and L. Montabone, 2016: Martian polar vortices: Comparison of reanalyses, JGR: Planets , 121, 1770-1785. https://doi.org/10.1002/2016JE005093.
  26. Berry, T. and J. Harlim, 2016: Semiparametric modeling: Correcting low-dimensional model error in parametric models. Journal of Computational Physics , 308, 305-321, https://doi.org/10.1016/j.jcp.2015.12.043.

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2015

  1. Zhang, F., and Y. Weng*, 2015: Predicting Hurricane Intensity and Associated Hazards: A Five-Year Real-Time Forecast Experiment with Assimilation of Airborne Doppler Radar Observations. Bulletin of the American Meteorological Society, 96, 25-32.
  2. Wei, J* and F. Zhang, 2015: Tracking gravity waves in moist baroclinic waves. Journal of Advances in Modeling Earth Systems (JAMES), 7, 67-91.
  3. Qian, T., P. Zhao, and F. Zhang, X. Bao, 2015: Rainy season precipitation over Sichuan Basin. Monthly Weather Review, 143, 383–394.
  4. Chi, Y.*, F. Zhang, W. Li*, J. He, and Z. Guan, 2015: Correlation between the Onset of the East Asian Subtropical Summer Monsoon and the Eastward Propagation of the Madden–Julian Oscillation Journal of the Atmospheric Sciences, 72, 1200–1214.
  5. Wang, S., A. H. Sobel, F. Zhang, Y. Qiang Sun*, Y. Yue*, L. Zhou, 2015: Regional Simulation of the October and November MJO Events Observed during the CINDY/DYNAMO Field Campaign at Gray Zone Resolution. Journal of Climate, 28, 2097–2119.
  6. Stern, D. P.*, J. Vigh, D.S. Nolan, and F. Zhang, 2015: Revisiting the Relationship Between Eyewall Contraction and Intensification. Journal of the Atmospheric Sciences, 72, 1283–1306.
  7. Yun*, Y., Q. Zheng, B. W. Green*, and F. Zhang, 2015: Mitigating atmospheric effects in the InSAR measurement through high-resolution data assimilation and numerical simulations with a weather prediction model. International Journal of Remote Sensing, 36, 2129-2147.
  8. Poterjoy, J.*, and F. Zhang, 2015a: Systematic comparison of tangent-linear and ensemble-based four-dimensional data assimilation methods using hybrid background error covariance: E4DVar versus 4DEnVar. Monthly Weather Reviewi, 143, 1601-1621.
  9. Green, B.W.*, and F. Zhang, 2015a: .Idealized Large Eddy Simulations of a Tropical Cyclone-Like Boundary Layer. Journal of the Atmospheric Sciences, 72, 1743-1764.
  10. Green, B.W.*, and F. Zhang, 2015b: Numerical simulations of Hurricane Katrina (2005) in the turbulent gray zone. Journal of Advances in Modeling Earth Systems, 7, 142-161.
  11. Zhang, F., J. Wei*, M. Zhang*, K.B. Bowman, L.L. Pan, E. Atlas, and S.C. Wofsy, 2015: Aircraft Measurements of Gravity Waves in the Upper Troposphere and Lower Stratosphere during the START08 Field Experiment. Atmospheric Chemistry and Physics, 15, 7667-7684.
  12. Zhang, Y. J., F. Zhang, Z. Meng, D. J. Stensrud 2015: Predictability of the 20 May 2013 Tornadic Thunderstorm Event in Oklahoma: Sensitivity to Synoptic Timing and Topographical Forcing. Monthly Weather Review, 143, 2973–2997.
  13. Munsell, E. B.*, J. A. Sippel, S. A. Braun, Y. Weng*, F. Zhang, 2015: Dynamics and predictability of Hurricane Nadine (2012) evaluated through convection-permitting ensemble analysis and forecasts. Monthly Weather Review, 143, 4514–4532.
  14. Ying, Y.*, and F. Zhang, 2015: An adaptive covariance relaxation method for ensemble data assimilation. Quarterly Journal of the Royal Meteorological Society, 141, 2898–2906.
  15. Colle, B. A., M. H. Bowman, K. J. Roberts, M. J. Bowman, C. N. Flagg, and J. Kuang, Y. Weng*, E.B. Munsell*, F. Zhang, 2015: Exploring the sensitivity of water level predictions for Metropolitan New York during Sandy (2012) using ensemble storm surge predictions. J. Marine Science & Engineering, 3, 428-443.
  16. Shi, Y.*, K. J. Davis, F. Zhang, C. J. Duffy, and X. Yu, 2015: Parameter estimation of a physically-based land surface hydrologic model using an ensemble Kalman filter: A multivariate real-data experiment. Advances in Water Resources, 83, 421-427.
  17. Shu*, S. and F. Zhang, 2015: Influence of Equatorial Wave Disturbances on the Genesis of Super Typhoon Haiyan (2013). Journal of the Atmospheric Sciences, 72, 4591–4613.
  18. Tao, D.*, and F. Zhang, 2015: Effects of Vertical Wind Shear on the Predictability of Tropical Cyclones: Practical versus Intrinsic Limit. Journal of Advances in Modeling Earth Systems (JAMES), DOI: 10.1002/2015MS000474.
  19. Zhang, F., C. Melhauser*, D. Tao*, Y. Q. Sun*, E. B. Munsell*, Y. Weng* and J. A. Sippel*, 2015: Predictability of Severe Weather and Tropical Cyclones at the Mesoscales. To appear in Dynamics and Predictability of Large-scale, High-impact Weather and Climate Events (eds, J. Li, R. Swinbank, H. Volkert and R. Grotjahn). Cambridge University Press.
  20. Plougonven, R., and F. Zhang, 2015: Gravity waves generated by jets and fronts and their relevance for clear-air turbulence. Aviation Turbulence: Processes, Measurement (Eds R. Sharman and T. Lane), Springer.
  21. Zhao, Y., S. J. Greybush, R. J. Wilson, R. N. Hoffman, and E. Kalnay, 2015: Impact of assimilation window length on diurnal features in a Mars atmospheric analysis. Tellus, 67, 26042. https://doi.org/10.3402/tellusa.v67.26042.
  22. Siddique*, R., A. Mejia, J. Brown, S. Reed, and P. Anhert. 2015: Verification of precipitation forecasts from two numerical weather prediction models in the Middle Atlantic region of the USA: A precursory analysis to hydrologic forecasting, Journal of Hydrology, 529(3), 1390-1406, https://doi.org/10.1016/j.jhydrol.2015.08.042.
  23. Zhen, Y.* and J. Harlim, 2015: Adaptive error covariances estimation methods for ensemble Kalman filters. Journal of Computational Physics, 294, 619-638, https://doi.org/10.1016/j.jcp.2015.03.061.
  24. Harlim, J. 2017: Framework and mathematical strategies for filtering or data assimilation. Springer, 559-563, Springer.
  25. Berry, T.*, D. Giannakis, and J. Harlim, 2015: Nonparametric forecasting of low-dimensional dynamical systems. PHYSICAL REVIEW E, 91, 032915, https://doi.org/10.1103/PhysRevE.91.032915.
  26. Berry, T, and J. Harlim, 2015: Nonparametric uncertainty quantification for stochastic gradient flows. SIAM/ASA Journal on Uncertainty Quantification, 3(1), 484-508, https://doi.org/10.1137/14097940X.
  27. Harlim, John and Xiantao Li, 2015: Parametric reduced models for the nonlinear Schrödinger equation. PHYSICAL REVIEW E, 91, 053306, https://doi.org/10.1103/PhysRevE.91.053306.

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2014

  1. Munsell, E. B.*, and F. Zhang, 2014: Prediction and uncertainty of Hurricane Sandy (2012) explored through a real-time cloud-permitting ensemble analysis and forecast system assimilating airborne Doppler observations. Journal of Advances in Modeling Earth Systems (JAMES), 6, 1-20.
  2. Plougonven, R., and F. Zhang, 2014: Internal gravity waves from atmospheric jets and fronts. Reviews of Geophysics, 52, 33-76.
  3. Wei, J.*, and F. Zhang, 2014: Mesoscale gravity waves in moist baroclinic jet-front systems. Journal of the Atmospheric Sciences, 71, 929-952.
  4. Melhauser, C.*, and F. Zhang, 2014: Diurnal radiation cycle impact on the pre-genesis environment of Hurricane Karl (2010). Journal of the Atmospheric Sciences, 71, 1241-1259.
  5. Poterjoy, J.*, and F. Zhang, 2014: Predictability and genesis of Hurricane Karl (2010) examined through the EnKF assimilation of field observations collected during PREDICT. Journal of the Atmospheric Sciences, 71, 1260-1275.
  6. Poterjoy, J.*, F. Zhang, and Y. Weng*, 2014: The effects of sampling errors on the EnKF assimilation of inner-core hurricane observations .Monthly Weather Review, 142, 1609-1630 .
  7. Shi, Y.*, K. J. Davis, F. Zhang, and C. J. Duffy, 2014: Evaluation of the Parameter Sensitivities of a Coupled Land Surface Hydrologic Model at a Critical Zone Observatory. Journal of Hydrometeorology, 15, 279-299.
  8. Green, B.W.*, and F. Zhang, 2014: Sensitivity of Tropical Cyclone Simulations to Parametric Uncertainties in Air-Sea Fluxes and Implications for Parameter Estimation. Monthly Weather Review, 142, 2290-2308, doi: http://dx.doi.org/10.1175/MWR-D-13-00208.1
  9. Bei, N., and F. Zhang, 2014: Mesoscale Predictability of Moist Baroclinic Waves:Variable and Scale Dependent Error Growth. Advances in Atmospheric Sciences, doi: 10.1007/s00376-014-3191-7.
  10. Shu, S.*, F. Zhang, J. Ming, and Y. Wang, 2014: Environmental Influences on the Intensity Changes of Tropical Cyclones over the Western North Pacific. Atmospheric Chemistry and Physics, 14, 6329–6342, 2014. doi:10.5194/acp-14-6329-2014(ACPDdoi:10.5194/acpd-13-31815-2013).
  11. Shi, Y.*, K. J. Davis, F. Zhang,C. J. Duffy, and X. Yu, 2014: Parameter Estimation of a Physically-Based Land Surface Hydrologic Model Using the Ensemble Kalman Filter: A Synthetic Experiment.Water Resources Research, 50, 706-724, doi:10.1002/2013WR014070.
  12. Tao, D.*, and F. Zhang, 2014: Effect of environmental shear, sea-surface temperature and ambient moisture on the formation and predictability of tropical cyclones: an ensemble-mean perspective. Journal of Advances in Modeling Earth Systems (JAMES), DOI:10.1002/2014MS000314.
  13. Zhang, Y. J., Z. Meng, Y. Weng*, and F. Zhang, 2014: Predictability of Tropical Cyclone Intensity Evaluated through 5-year Forecasts with a Convection-permitting Regional-scale Model in the Atlantic Basin. Weather and Forecasting, 29, 1003-1023. CORRIGENDUM: doi: http://dx.doi.org/10.1175/WAF-D-14-00103.1
  14. Poterjoy, J.*, and F. Zhang, 2014: Inter-comparison and coupling of ensemble and variational data assimilation approaches forfor the analysis and forecasting of Hurricane Karl (2010). Monthly Weather Review, 142, 3347-3364.
  15. Taraphdar, S., P. Mukhopadhyay, R. L. Lueng, F. Zhang, S. Abhilash, and B. N. Goswami, 2014: The Role of Moist Processes in the Intrinsic Predictability of Indian Ocean Cyclones. Journal of Geophysical Research-Atmosphere, 119, doi:10.1002/2013JD021265
  16. Zhen, Y.*, and F. Zhang, 2014: A Probabilistic Approach of Adaptive Covariance Localization For Serial Ensemble Square-root Filters.Monthly Weather Review, 142, 4499-4518. doi: http://dx.doi.org/10.1175/MWR-D-13-00390.1
  17. Zhang, Y. C.*, F. Zhang, and J. Sun, 2014: Comparison of the diurnal variations of warm-season precipitation for East Asia versus North America downstream of the Tibetan Plateau versus the Rocky Mountains. Atmospheric Chemistry and Physics, 14, 10741-10759.
  18. Sippel, J.A., F. Zhang, Y. Weng*,Lin Tian, Gerald M. Heymsfield, and Scott A. Braun, 2014: Ensemble Kalman Filter Assimilation of HIWRAP Observations of Hurricane Karl (2010) from the Unmanned Global Hawk Aircraft. Monthly Weather Review, 142, 4559-4580.
  19. Zhang, X., X.-Y Huang, J. Liu, J. Poterjoy*, Y. Weng* and F. Zhang, 2014: Development of an efficient regional four-dimensional variational data assimilationsystem for WRF. Journal of Atmospheric and Oceanic Technology, 31, 2777-2794.
  20. Plougonven, R., and F. Zhang, 2014: Internal gravity waves from atmospheric jets and fronts. Encyclopedia of Atmospheric Sciences (2nd edition), Academic Press, Volume 3, 164-170.
  21. Meng, Z, and F. Zhang, 2014: Ensemble-based data assimilation. Encyclopedia of Atmospheric Sciences (2nd edition), Academic Press, Volume 2, 241-247.
  22. Navarro, T., F. Forget, E. Millour, and S. J. Greybush, 2014: Detection of detached dust layers in the Martian atmosphere from their thermal signature using assimilation. Geophysical Research Letters, 41, 6620-6626. https://doi.org/10.1002/2014GL061377.
  23. John Harlim, Adam Mahdib and Andrew J.Majdac, 2014: An ensemble Kalman filter for statistical estimation of physics constrained nonlinear regression models. Journal of Computational Physics, 257, 782-812, https://doi.org/10.1016/j.jcp.2013.10.025.
  24. Tyrus Berry and John Harlim, 2014: Linear theory for filtering nonlinear multiscale systems with model error. Proceedings of Royal Society, 470,, 20140168, https://doi.org/10.1098/rspa.2014.0168.

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