Publications Since Center Established

2016 | 2015 | 2014




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, accepted.
  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.

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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, in press.

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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.

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