Zhaoxia Pu's Publication

Prof. Zhaoxia Pu

Peer-Reviewed Articles ; Books ; Thesis and dissertation

  • Peer-Reviewed Research Articles
    • [129] Ming, J., J. Zhang, X. Li, and Z. Pu, 2022: Vertical turbulent mixing in the boundary layer of landfalling tropical cyclones: Observations and effects on numerical predictions (in preparation)
    • [128] Li, X., Z. Pu, 2022: The Structure and Dynamics of Roll Vortices and Associated Coherent Turbulence in Hurricane Harvey during Landfall Using a Large Eddy Simulation. To be submitted to .
    • [127] Li, X., Z. Pu, J. Zhang, Z. Zhang, 2022: A Modified Vertical Eddy Diffusivity Parameterization in the HWRF Model based on Large Eddy Simulations and its Impacts on Prediction of Landfalling Hurricanes. Submitted to Weather and Forecasting .
    • [126] Feng, C., Z. Pu, 2022: A Bias Correction Scheme with the Symmetric Cloud Proxy Variable and Its Influence on Assimilating All-Sky GOES-16 Brightness Temperatures. Mon. Wea. Rev. , (under revision).
    • [125] Li, X., and Z. Pu, 2022: Turbulence effects on the formation of cold fog over complex terrain with large-eddy simulation. Geophysical Research Letters. https://doi.org/10.1029/2022GL098792
    • [124] Li, X.; Pu, Z.; Zhang, J.A.; Emmitt, G.D., 2022: Combined Assimilation of Doppler Wind Lidar and Tail Doppler Radar Data over a Hurricane Inner Core for Improved Hurricane Prediction with the NCEP Regional HWRF System. Remote Sens. 14, 2367. https://www.mdpi.com/2072-4292/14/10/2367
    • [123] Pu, Z., Y. Wang, X. Li, C. Ruf, L. Bi, A. Mehra, 2022: Impacts of Assimilating CYGNSS Satellite Ocean Surface Wind on Prediction of Landfalling Hurricanes with the HWRF Model. Remote Sensing, 2022, 14(9), 2118. https://doi.org/10.3390/rs14092118 .

    • [122] Zhu, B., Z. Pu, A.W. Putra, Z. Gao, 2021: Assimilating C-band Radar Data for High-resolution Simulations of Precipitation: Case Studies over Western Sumatra. Remote Sensing , 2022, 14, 42. https://doi.org/10.3390/rs14010042
    • [121] Wei, Y., and Z. Pu 2021: Moisture Variation with Cloud Effects during an BSISO over the Eastern Maritime Continent in Cloud Permitting-Scale Simulations. Journal of Atmospheric Sciences. 78,1869-1888. https://doi.org/10.1175/JAS-D-20-0210.1
    • [120] Hock, N., F. Zhang, Z. Pu , 2021: Numerical Simulations of a Florida Sea Breeze and Its Interactions with Associated Convection: Effects of Geophysical Representations and Model Resolution. Advances in Atmospheric Sciences, Accepted. https://doi.org/10.1007/s00376-021-1216-6
    • [119] Wei, Y., and Z. Pu, 2021: Diurnal Cycle of Precipitation and Near-surface Atmospheric Conditions over the Maritime Continent: Land-Sea Contrast and Impacts of Ambient Winds in Cloud-Permitting Simulation. Climate Dynamics, https://doi.org/10.1007/s00382-021-06012-3 .
    • [118] Li, X., Z. Pu, and Z. Gao, 2021: Effects of Roll Vortices on the Evolution of Hurricane Harvey During Landfall. Journal of Atmospheric Sciences. 78, 1847-1867. https://doi.org/10.1175/JAS-D-20-0270.1
    • [117] Li, X., Z. Pu, and Z. Gao, 2021: The Combination of Monte Carlo and Ensemble Probabilities in Tropical Cyclone Forecasts near Landfall. Journal of Meteorological Research. 35, doi: 10.1007/s13351-021-0128-9
    • [116] Wang, Y., Z. Pu, 2021: Assimilation of Radial Velocity from Coastal NEXRAD into HWRF for Improved Forecasts of Landfalling Hurricanes. Weather and Forecasting. 36, 587-599.https://doi.org/10.1175/WAF-D-20-0163.1
    • [115] Pu, Z., 2021: Improving Near-Surface Weather Forecasts with Strongly Coupled Land- Atmosphere Data Assimilation. Book Chapter, "Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV)", Ed. By S.K. Park and L. Xu, Springer. ISBN: 978-3-030-77722-7. DOI: https://doi.org/10.1007/978-3-030-77722-7

    • [114] Zhang, F., Z. Pu, and C. Wang, 2020: Land-Surface Diurnal Effects on the Asymmetric Structures of a Post-Landfall Tropical Storm. Journal of Geophysical Research -Atmospheres, https://doi.org/10.1029/2020JD033842
    • [113] Li, X., Z. Pu, 2020: Vertical eddy diffusivity parameterization based on a large eddy simulation and its impact on prediction of hurricane landfall. Geophysical Research Letters , https://doi.org/10.1029/2020GL090703 .
    • [112] Wei, Y., Z. Pu, C. Zhang, 2020: Diurnal Cycle of Precipitation over the Maritime Continent under Modulation of MJO: Perspectives from Cloud-Permitting Simulations. Journal of Geophysical Research - Atmospheres, 25, e2020JD032529, https://doi.org/10.1029/2020JD032529
    • [111] Lin, L-F, and Z. Pu,2020: Improving Near-surface Short-Range Weather Forecasts Using Strongly Coupled Land-Atmosphere Data Assimilation with GSI-EnKF, Mon. Wea. Rev., 148, 2863-2888. https://doi.org/10.1175/MWR-D-19-0370.1.
    • [110] Cui, Z., Z. Pu, G. D. Emmitt, S. Greco, 2020: The Impact of Airborne Doppler Aerosol WiNd lidar (DAWN) Wind Profiles on Numerical Simulations of Tropical Convective Systems during the NASA Convective Processes Experiment (CPEX). Journal of Atmospheric and Oceanic Technology, 37, 705-722. https://doi.org/10.1175/JTECH-D-19-0123.1
    • [109] Zhang,S., Z. Pu, 2020:Evaluation of the Four-Dimensional Ensemble-Variational Hybrid Data Assimilation with Self-Consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts. Atmosphere,2020, 11, 1007; https://www.mdpi.com/2073-4433/11/9/1007/htm doi:10.3390/atmos11091007.
    • [108] Xue, J., F. Mohammadi, X. Li, M. Sahraei-Ardakani, G. Ou, and Z. Pu, 2020: Impact of Transmission Tower-Line Interaction to the Bulk Power System during Hurricane, Reliability Engineering and System Safety,203,https://doi.org/10.1016/j.ress.2020.107079.
    • [107] Lackmann, G., B. Ancell, M. Bunkers, B. Kirtman, K. Kosiba, A. McGovern, L. McMurdie, Z. Pu , E. Ritchie, H. P. Huntington, 2020: Editorial: Data Availability Principles and Practice. Weather and Forecasting , 35. 2217.
    • [106] Feng, J., Y. Duan, Q. Wan, H. Hu, Z.Pu, 2020: Improved Prediction of Landfalling Tropical Cyclone Precipitation in China Based on Assimilation of Radar Radial Winds with New Super-Observation Processing. Weather and Forecasting. 35,2523-2539. https://doi.org/10.1175/WAF-D-20-0002.1
    • [105] Zhang, T., C. Zhao, C. Gong, Z. Pu, 2020: Simulation of Wind Speed Based on Different Driving Datasets and Parameterization Schemes Near Dunhuang Wind Farms in Northwest of China. Atmosphere, 11, 647, http://dx.doi.org/10.3390/atmos11060647.

    • [104] Zhang, F., C. Wang, Z. Pu, 2019: Genesis of Tibetan Plateau Vortex: Roles of Surface Diabatic and Atmospheric Condensational Latent Heating. Journal of Applied Meteorology and Climatology, 53, 2633-2651. https://doi.org/10.1175/JAMC-D-19-0103.1
    • [103] Gultepe, I., Sharman, R., Williams, P.D. et al. 2019: A review of high impact weather for aviation meteorology. Pure Appl. Geophys., https://doi.org/10.1007/s00024-019-02168-6
    • [102] Ma, M., Y. Chen, F. Ding, Z. Pu , and X. Liang, 2019: Representative Analysis of Air Quality Monitoring Sites in Urban Areas of a Mountainous City. J. Meteor. Res., 32 , 219-235. doi: 10.1007/s13351-019-8145-7. http://www.cmsjournal.net:8080/Jweb_jmr/EN/10.1007/s13351-019-8145-7
    • [101] Liu, J., and Z. Pu, 2019: Does Soil Moisture Have an Influence on Near-Surface Temperature? Journal of Geophysical Research - Atmospheres, 124, 6444-6466. https://doi.org/10.1029/2018JD029750
    • [100] Cui, Z., Z. Pu, V. Tallapragada, R. Atlas, C. Ruf, 2019: A Preliminary Impact Study of CYGNSS Ocean Surface Wind Speeds on Numerical Simulations of Hurricanes. Geophysical Research Letters, 46, https://doi.org/10.1029/2019GL082236 .
    • [99] Lin, L-F, and Z. Pu, 2019: Examining the Impact of SMAP Soil Moisture Retrievals on Short-Range Weather Prediction under Weakly and Strongly Coupled Data Assimilation with WRF-Noah. Monthly Weather Review, 147 4345-4366. http://journals.ametsoc.org/doi/10.1175/MWR-D-19-0017.1.
    • [98] Hodges, D., and Z. Pu , 2019: Characteristics and Variations of Low-Level Jets and Environmental FactorsAssociated with Summer Precipitation Extremes over the Great Plains. Journal of Climate , 32, 5123-5144. https://doi.org/10.1175/JCLI-D-18-0553.1
    • [97] Zhang, S., and Z. Pu, 2019:Numerical Simulation of Rapid Weakening of Hurricane Joaquin with Assimilation of High-Definition Sounding System Dropsondes During the Tropical Cyclone Intensity Experiment: Comparison of 3DEnVar and 4DEnVar. Weather and Forecasting, 34, 521-538. https://doi.org/10.1175/WAF-D-18-0151.1
    • [96] Zhang, F. and Z. Pu , 2019:Sensitivity of Numerical Simulations of Near-surface Atmospheric Conditions During an Ice Fog Event Over Heber Valley to Snow Depth and Surface Albedo. Journal of Applied Meteorology and Climatology, 58,797-811. https://doi.org/10.1175/JAMC-D-18-0064.1
    • [95] Saunders, P., Y. Yu, Z. Pu, 2019: Sensitivity of numerical simulations of Hurricane Joaquin (2015) to cumulus parameterization schemes: Implications for processes controlling hairpin turn in the track., Journal of Meteorological Society of Japan, doi:10.2151/jmsj.2019-030. 97, 577-595. https://doi.org/10.2151/jmsj.2019-030
    • [94] Zhang, F, Z. Pu, and C. Wang, 2019: Impacts of Initial Soil Moisture on the Numerical Simulation of a Post-landfall Storm. Journal of Meteorological Research , 33, 206-218. doi: 3 10.1007/s13351-019-8002-8.
    • [93] Pu, Z., C. Yu, V. Tallapragada, J. Jin, W. McCarty, 2019: The Impact of Assimilation of GPM Microwave Imager Clear-Sky Radiance on Numerical Simulations of Hurricanes Joaquin (2015) and Matthew (2016) with the HWRF model. Mon. Wea. Rev., 147,175-198. https://journals.ametsoc.org/doi/abs/10.1175/MWR-D-17-0200.1

      Book Chapter
    • [92] Pu, Z. and E. Kalnay, 2018: Numerical Weather Prediction Basics: Models, Numerical Methods, and Data Assimilation. In: Duan Q., Pappenberger F., Thielen J., Wood A., Cloke H., Schaake J. (eds) Handbook of Hydrometeorological Ensemble Forecasting. Springer, Berlin, Heidelberg.
      Journal articles
    • [91] Lin, L-F, and Z. Pu, 2018: Characteristics of Background Error Covariance of Soil Moisture and Atmospheric States in Strongly Coupled Land-Atmosphere Data Assimilation. Journal of Applied Meteorology and Climatology, 57, 2507-2529. https://doi.org/10.1175/JAMC-D-18-0050.1
    • [90] Zhang, S., Z. Pu , C. Velden, 2018: Impact of enhanced atmospheric motion vectors on HWRF hurricane analysis and forecasts with different data assimilation configurations. Monthly Weather Review, 146, 1549-1569. https://doi.org/10.1175/MWR-D-17-0136.1
    • [89] Hodegs, D. and Z. Pu , 2018: Characteristics and variations of low-level jets in the contrasting warm season precipitation extremes of 2006 and 2007 over the Southern Great Plains. Theoretical and Applied Climatology. DOI: 10.1007/s00704-018-2492-7
    • [88] Li, Y., C. Zhao, T. Zhang, W. Wang, H. Duan, Y. Liu, Y. Ren, Z. Pu, 2018: Impacts of land-use data on the simulation of surface air temperature in Northwest China. J. Meteor. Res., 32, 896-908.
    • [87] Ren, Y., Y. Li, Z. Pu, T. Zhang, H. Duan, W. Wang, 2018: Effect of updated RegCM4 land use data on near-surface temperature simulation in China, J. Meteor. Res., 32,758-767.
    • [86] Duan, H., Y. LI, T. ZHANG, Z. Pu, C. Zhao, Y. Liu, 2018: Evaluation of the forecast accuracy of near-surface temperature and wind in Northwest China based on the WRF model. J. Meteor. Res., 32, 469-490. doi: 10.1007/s13351-018-7115-9
    • [85] Li, Y., Z. Pu, J. Feng, 2018: The influence of ENSO on upper-Level Jets. Journal of Lanzhou University (Natural Sciences) . 53, 127-136.
    • [84] Pu, Z., C. Lin, X. Dong, S. Krueger, 2018: Sensitivity of numerical simulations of a mesoscale convective system to ice hydrometeors in bulk microphysical parameterization. Pure and Applied Geophysics, , DOI: 10.1007/s00024-018-1787-z
    • [83]Chachere, C. and Z. Pu , 2018: Numerical Simulations of an Inversion Fog Event in the Salt Lake Valley During the MATERHORN-Fog Field Campaign. Pure and Applied Geophysics, DOI: 10.1007/s00024-018-1770-8

    • [82] Pu, Z, C. Yu, V. Tallapragada, J. Jin, W. McCarty, 2017: Assimilation of GPM microwave Imager clear-sky radiance in improving hurricane forecasts. JCSDA Quarterly Newsletter, Fall 2017. https://doi.org/10.7289/V50P0X8R
    • [81] Doyle, J., J. Moskaitis, J. Feldmeier, R. Ferek, M. Beaubien, M. Bell, D. Cecil, R. Creasey, P. Duran, R. Elsberry, W. Komaromi, J. Molinari, D. Ryglicki, D. Stern, C. Velden, X. Wang, T. Allen, B. Barrett, P. Black, J. Dunion, K. Emanuel, P. Harr, L. Harrison, E. Hendricks, D. Herndon, W. Jeffries, S. Majumdar, J. Moore, Z. Pu, R. Rogers, E. Sanabia, G. Tripoli, and D. Zhang, 2017: A View of Tropical Cyclones from Above: The Tropical Cyclone Intensity (TCI) Experiment. Bull. Amer. Meteor. Soc. , Oct. 2017, 2113-2134, doi:10.1175/BAMS-D-16-0055.1. http://journals.ametsoc.org/doi/pdf/10.1175/BAMS-D-16-0055.1
    • [80] Lin, C-Y, Z. Zhang, Z. Pu , F. Wang, 2017: Numerical Simulations of an Advection Fog Event over the Shanghai Pudong Airport with the WRF Model. Journal of Meteorological Research, 31 , 874-889
    • [79] Zhang, F., Z. Pu , and C. Wang, 2017: Effects of boundary layer vertical mixing strength on the evolution of hurricanes over land. Mon. Wea. Rev., 145, 2343-2361. https://doi.org/10.1175/MWR-D-16-0421.1
    • [78] Zhang, F. and Z. Pu , 2017: Effects of vertical eddy diffusivity parameterization on the evolution of landfalling hurricanes. Journal of Atmospheric Sciences,74,1879-1905. https://doi.org/10.1175/JAS-D-16-0214.1
    • [77] Li, Y., P. Ye, Z. Pu , J. Feng, B. Ma. 2017: Historical statistics and future changes in long-duration blocking highs in key regions of Eurasia. Theoretical and Applied Climatology, 1-13. DOI: 10.1007/s00704-017-2079-8. http://dx.doi.org/10.1007/s00704-017-2079-8
    • [76] Li, Y., P. Ye, J. Feng, J. Wang, L. Yao, and Z. Pu . 2017: Simulation and projection of blocking highs in key regions of the Eurasia by CMIP5 models. J. Meteorl. Soc. Japan, 95, 147-165. http://doi.org/10.2151/jmsj.2017-008
    • [75] Zhang, S., Z. Pu , D. J. Posselt, and R. Atlas. 2017: Impact of CYGNSS ocean surface wind speeds on numerical simulations of a hurricane in observing system simulation experiments. Journal of Atmospheric and Oceanic Technology, 34, 375-383. http://dx.doi.org/10.1175/JTECH-D-16-0144.1
    • [74] Liu, J., F. Zhang*, Z. Pu , 2017: Numerical simulations of the rapid intensification of Hurricane Katrina (2005): Sensitivity to boundary layer parameterization schemes. Advances in Atmospheric Sciences, 34 (4), 482-296, doi: 10.1007/s00376-016-6209-5. , full text ,
      Book Chapter
    • [73] Pu, Z., 2017: Surface data assimilation and near-surface weather prediction over complex terrain. Book Chapter, "Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. III)", Ed. By S.K. Park and L. Xu, Springer. pp.219-240.
      DOI 10.1007/978-3-319-43415-5_10.
    • [72] Pu, Z., L. Zhang, S. Zhang, B. Gentry, D. Emmitt, B. Demoz, R. Atlas, 2017: The impact of Doppler wind lidar measurements on high-impact weather forecasting: Regional OSSE and data assimilation studies. Book Chapter, "Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. III)", Ed. By S. K. Park and L. Xu, Springer, pp.259-283.
      DOI 10.1007/978-3-319-43415-5_12.

    • [71] Boukabara, S. A.; T. Zhu; H. Tolman; S. Lord; S. Goodman; R. Atlas; M. Goldberg; T. Auligne; B. Pierce; L. Cucurull; M. Zupanski; M. Zhang; I. Moradi; J. Otkin; D. Santek; B. Hoover; Z. Pu; X. Zhan; C. Hain; E. Kalnay; D. Hotta; S. Nolin; E. Bayler; A. Mehra; S. Casey; D. Lindsey; L. Grasso; K. Kumar; A. Powell; J. Xu; T. Greenwald; J. Zajic; J. Li; J. Li; B. Li; J. Liu; L. Fang; P. Wang; T.-C. Chen 2016: S4: An O2R/R2O infrastructure for optimizing satellite utilization in NOAA numerical modeling systems. Bull. Amer. Meteor. Soc., 98, 2359-2378. http://dx.doi.org/10.1175/BAMS-D-14-00188.1
    • [70] Pu, Z., S. Zhang, M. Tong and V. Tallapragada. 2016: Influence of the self-consistent regional ensemble background error covariance on hurricane inner-core data assimilation with the GSI-based hybrid system for HWRF, J. Atmos. Sci., 73, 4911-4925. http://dx.doi.org/10.1175/JAS-D-16-0017.1
    • [69] Pu, Z. , C. Chachere, S. Hoch, E. Pardyjak, I. Gultepe, 2016: Numerical Prediction of Cold Season Fog Events Over Complex Terrain: The Performance of the WRF Model During MATERHORN-Fog and Early Evaluation, Pure and Applied Geophysics, doi:10.1007/s00024-016-1375-z; https://link.springer.com/article/10.1007/s00024-016-1375-z .
    • [68] Chachere, C., and Z. Pu , 2016: Connection between cold air pools and mountain valley fog events in Salt Lake City, Pure and App. Geo. , doi:10.1007/s00024-016-1316-x. http://link.springer.com/article/10.1007/s00024-016-1316-x .
    • [67] Gultepe, I., H. J. S. Fernando, E. Pardyjak, S. Hoch, Z. Silver, E. Creegan, L. Leo, Z. Pu, S. De Weker, and Chaoxun Hang, 2016: Mountain ice fog: Observations and predictability. Pure and Applied Geophy. doi:10.1007/s00024-016-1374-0. https://link.springer.com/article/10.1007/s00024-016-1374-0 .

    • [66] Hodges, D., and Z. Pu, 2015: The climatology, frequency, and distribution of cold season fog events in northern Utah. Pure and Applied Geophysics, doi:10.1007/s00024-015-1187-6. http://link.springer.com/article/10.1007/s00024-015-1187-6
    • [65] Pu, Z. , and C. Lin, 2015: Evaluation of double-moment representation of ice hydrometeors in bulk microphysical parameterization: Comparison between WRF numerical simulations and UND-Citation data during MC3E, Geoscience Letters, 2015, 2:11. DOI: 10.1186/s40562-015-0028-x
    • [64] Fernando, H. J. S., E. R. Pardyjak, S. Di Sabatino, F. K. Chow, S. F. J. De Wekker, S. W. Hoch, J. Hacker, J. C. Pace, T. Pratt, Z. Pu , W. J. Steenburgh, C. D. Whiteman, Y. Wang, D. Zajic, B. Balsley, R. Dimitrova, G. D. Emmitt, C. W. Higgins, J. C. R. Hunt, J. C. Knievel, D. Lawrence, Y. Liu, D. F. Nadeau, E. Kit, B. W. Blomquist, P. Conry, R. S. Coppersmith, E. Creegan, M. Felton, A. Grachev, N. Gunawardena, C. Hang, C. M. Hocut, G. Huynh, M. E. Jeglum, D. Jensen, V. Kulandaivelu, M. Lehner, L. S. Leo, D. Liberzon, J. D. Massey, K. McEnerney, S. Pal, T. Price, M. Sghiatti, Z. Silver, M. Thompson, H. Zhang, and T. Zsedrovits, 2015: The MATERHORN - Unraveling the intricacies of mountain weather. Bull. Amer. Meteor. Soc., 96, 1945-1967.

    • [63] Thatcher, L., and Z. Pu , 2014: Characteristics of tropical cyclone genesis forecasts and underdispersion in high-resolution ensemble forecasting with a stochastic kinetic energy backscatter scheme. Tropical Cyclone Research and Review, 3(4):203-217.
    • [62] Li, Z., Z. Pu , J. Sun and W-C. Lee, 2014: Impacts of 4D-VAR Assimilation of Airborne Doppler Radar Observations on Numerical Simulations of the Genesis of Typhoon Nuri (2008). Journal of Applied Meteorology and Climatology, 53 , 2325-2343. http://dx.doi.org/10.1175/JAMC-D-14-0046.1
    • [61] Li, Z. and Z. Pu , 2014: Numerical simulations of the genesis of Typhoon Nuri (2008): Sensitivity to initial conditions and implications for the roles of intense convection and moisture conditions. Wea. Forecasting. 29,1402-1424 http://dx.doi.org/10.1175/WAF-D-14-00003.1.
    • [60] Zhang, H. and Z. Pu , 2014: Influence of assimilating surface observations on the predicton of landfalls of Hurricane Katrina (2005) with ensemble Kalman filter. Mon. Wea. Rev., 142 , 2915-2934. doi: http://dx.doi.org/10.1175/MWR-D-14-00014.1
    • [59] Baker, W., R. Atlas, C. Cardinail, A. Clement, G. Emmitt, R. Ferrare, B. Gentry, R. Hardesty, E. Kallen, M. Kayaya, R. Langland, M. Masutani, W. McCarty, R. Pierce, Z. Pu , L. Riishojgaard, J. Ryan, S. Tucker, M. Weissman, and J. Yoe, 2014: Lidar-measured wind profiles - The missing link in the global observing system. Bull. Amer. Meteorol. Soc., 95, 543-564. link

    • [58] Thatcher, L.* and Z. Pu , 2013: Evaluation of tropical cyclone genesis precursors with relative operating characteristics (ROC) in high-resolution ensemble forecasts: Hurrican Ernesto. Tropical Cyclone Research and Review, 2 (3): 131-148. link
    • [57] Zhang, H., Z. Pu and X. Zhang*, 2013: Examination of errors in near-surface temperature and wind from WRF numerical simulations in regions of complex terrain. Wea. Forecasting. 28, 893-914. link
    • [56] Wei, L., Z. Pu, and S. Wang, 2013: Numerical simulation of the life cycle of a persistent wintertime inversion over Salt Lake City. Boundary Layer Meteorology, 148, 399-418. DOI 10.1007/s10546-013-9821-2. [pdf]
      [55] Duan, H.-X., Y.-H. Li, and Z. Pu , 2013: An application of assimilated dust concentration data and AMSU Satellite radiance data in GRAPES_SDM model. Journal of Desert Research, 4. 1150-1159.
    • [54] Luo, J., W. Tian, Z. Pu, P. Zhang, L. Shang, M. Zhang, J. Hu, 2013: Characteristics of Stratosphere-troposphere Exchange During the Meiyu Season. J. Geophy. Res., 118, doi:10.1029/2012JD018124. link , [pdf]
    • [53] Pu, Z., H. Zhang, and J. A. Anderson, 2013: Ensemble Kalman filter assimilation of near-surface observations over complex terrain: Comparison with 3DVAR for short-range forecasts. Tellus A, 65,19620. link

    • [52] Thatcher, L.,Y. Takayabu, C. Yokoyama, Z. Pu, 2012: Characteristics of tropical cyclone precipitation features over the western Pacific warm pool region. J. Geophy. Res., 117, D16208, doi:10.1029/2011JD017351., [pdf]
    • [51] Zhang, L., Z. Pu , W.-C. Lee, and Q. Zhao, 2012: The influence of airborne Doppler radar data quality control on numerical simulations of a tropical cyclone. Weather and Forecasting. 27, 231-239., [pdf].

    • [50] Zhang, L., and Z. Pu, 2011: Four-dimensional Assimilation of Multi-time Wind Profiles Over a Single Station and Numerical Simulation of a Mesoscale Convective System Observed During IHOP_2002 Mon. Wea. Rev. , 139, 3369-3388,[pdf].
    • [49] Thatcher, L. and Z. Pu, 2011: How vertical wind shaer affacts tropical cyclone intensity change: An overview(Book Chapter). Recent Hurricane Research - Climate, Dynamics, and Societal Impacts, Anthony Lupo (Ed.), ISBN: 978-953-307-238-8, InTech, Available from: link )
    • [48] Pu, Z., 2011: Improving Hurricane Intensity Forecasting through Data Assimilation: Environmental Conditions Versus the Vortex Initialization (Book Chapter), Recent Hurricane Research - Climate, Dynamics, and Societal Impacts, Anthony Lupo (Ed.), ISBN: 978-953-307-238-8, InTech, (Available from:link )
    • [47] Snyder, A., Z. Pu, and C. A. Reynolds, 2011: Impact of stochastic convection on ensemble forecasts of tropical cyclone development. Mon. Wea. Rev., 139 , 620-626. link , [pdf]
    • [46] Ma, M., Z. Pu, S. Wang and Q. Zhang, 2011: Characteristics and numerical simulations of extremely large atmospheric boundary-layer heights over an arid region in north-west China. Boundary Layer Meteorology ,140,163-176, DOI:10.1007/s10546-011-9608-2, link ,[pdf]

    • [45] Pu, Z., S.-Y. Hong, Y. Li, and H.-M. H. Juang, 2010: Advanced Data Assimilation and Predictability Studies on High-Impact Weather and Climate, Advances in Meteorology , vol. 2010, Article ID 121763, 2 pages, 2010. doi:10.1155/2010/121763 link
    • [44] Pu, Z., and L. Zhang*, 2010: Validation of AIRS temperature and moisture profiles over tropical oceans and their impact on numerical simulations of tropical cyclones, J. Geophys. Res., 115 , D24114, doi:10.1029/2010JD014258 link, [pdf]
    • [43] Zhang, H. and Z. Pu, 2010: Beating the uncertainties: Ensemble forecasting and ensemble based data assimilation (review article), Advances in Meteorology, Vol. 2010, Article ID 432160, 10pp., doi:10.1155/2010/432160. Link
    • [42] Zhang, L. and Z. Pu, 2010: An Observing System Simulation Experiment (OSSE) to assess the impact of Doppler wind lidar (DWL) measurements on the numerical simulation of a tropical cyclone, Advances in Meteorology, Vol. 2010, Article ID 743863, 14pp, doi:10.1155/2010/743863. link , [pdf]
    • [41] Pu, Z., L. Zhang, and G. D. Emmitt, 2010: Impact of airborne Doppler Wind Lidar data on numerical simulation of a tropical cyclone , Geophy. Res. Lett., 37, L05801, doi:10.1029/2009GL041765. link, [pdf]
    • [40] Snyder, A., Z. Pu and Y. Zhu, 2010: Tracking and verification of East Atlantic tropical cyclone genesis in NCEP global ensemble: Case studies during NASA African monsoon multi-disciplinary analyses. Wea. Forecasting, 25 , 1397-1411. link, [pdf]

    • [39] Pu, Z, X. Li and J. Sun, 2009: Impact of airborne Doppler radar data assimilation on the numerical simulation of intensity changes of Hurricane Dennis near a landfall. J. Atmos. Sci., 66 , 3351-3365. link, [pdf]
    • [38] Pu, Z., X. Li, and E. J. Zipser, 2009: Diagnosis of the initial and forecast errors in the numerical simulation of rapid intensification of Hurricane Emily, Wea Forecasting, 24, 1236-1251. link , [pdf]
    • [37] Pu, Z. and J. Hacker, 2009: Ensemble-based Kalman filters in strongly nonlinear dynamics, Advances in Atmos. Sci., 26 , 373-380, dio: 10.1007/s00376-009-0373-9. link, [pdf]
    • [36] Li, X. and Z. Pu, 2009: Sensitivity of numerical simulation of early rapid intensification of Hurricane Emily (2005) to cumulus parameterization schemes in different model horizontal resolutions, J. Meteor. Soc. Japan., 87, 403-421. link, [pdf]
    • [35] Pu, Z., 2009: Assimilation of satellite data in improving numerical simulations of tropical cyclones: progress, challenge and development. Book chapter for "Data Assimilation for Atmospheric, Oceanic, and Hydrologic Applications", The Springer-Verlag, 2009, Ed. by S. K. Park. and L. Xu. pp163-176.
    • [34] Pu, Z., 2009: Book review:"Eye to the sky: Exploring our atmosphere", Bull. Amer. Meteorol. Soc., 90, No.4, 541-542
    • [33] Wang, J., J. Feng, L. Yang, J. Guo, Z. Pu , 2009: Runoff-denoted drought index and its relationship to the yields of spring wheat in the arid area of Hexi corridor, Northwest China . Agricultural Water Management , 96, 666-676 [pdf]
    • [32] Pu, Z. and L. Xu, 2009: MODIS/Terra Observed Snow Cover Over the Tibet Plateau: Distribution, Variation and Possible Connection with the East Asian Summer Monsoon (EASM) , Theor. Appl. Climatol. 97, 265-278. [pdf]

    • [31] Li, X. and Z. Pu, 2008: Sensitivity of numerical simulation of early rapid intensification of Hurricane Emily (2005) to cloud microphysical and planetary boundary layer parameterizations . Mon. Wea. Rev. 136, 4819-4838, link, [pdf]
    • [30] Pu, Z., X. Li, C. Velden,S. Aberson, W. T. Liu, 2008: Impact of aircraft dropsonde and satellite wind data on the numerical simulation of two landfalling tropical storms during TCSP . Wea. Forecasting 23, 62-79 link , [PDF]

    • [29] Pu, Z., 2007: Applications of data assimilation in climate modeling: a perspective from regional climate studies over western China . Advances in Earth Science, 22, 1177-1184.
    • [28] Pu, Z., L. Xu, V. Salomonson, 2007: MODIS/Terra observed seasonal variations of snow cover over the Tibet Plateau . Geophys. Res. Lett. 34, L06706, doi:10.1029/2007GL029262. [PDF]

      Before 2006
    • [27] Pu, Z., 2006: Book review: "Atmospheric modeling, data assimilation and predictability", Bull. Amer. Meteorol. Soc., 87, 98-100 [PDF]
    • [26] Braun, S., M. Montgomery, Z. Pu, 2006: The organization of vertical motion in asymmetric hurricane ---- Bonnie (1998). J. Atmos. Sci., 63, 19-42. [PDF]
    • [25] Pu, Z., and W.-K. Tao, 2004: Mesoscale assimilation of TMI data with 4DVAR: Sensitivity study. J. Meteor. Soc. Japan, 82, 1389-1397. [PDF]
    • [24] Tao, W.-K., R. Adler, D. Baker, S. Braun, M.-D. Chou, M.F. Jasinski, Y. Jia, R. Kakar, M. Karyampudi, S. Lang, W. Lau, B. Lynn, Z. Pu , M. Shepherd, J. Simpson, D. Starr, Y. Wang, P. Wetzel, and J. Weinman, 2003: Regional-Scale Modeling in NASA Goddard Space Flight Center. Research Signpost, 37/661(2).
    • [23] Jorgensen, D. P., Z. Pu, O. Persson and W.-K. Tao, 2003: Variations associated with cores and gaps of a pacific narrow cold frontal rainband. Mon. Wea. Rev.,131,2705-2729. [PDF]
    • [22] Pu, Z., W.-K. Tao, S. Braun, J. Simpson, Y. Jia, J. Halverson, A. Hou, and W. Olson, 2002: The impact of TRMM data on mesoscale numerical simulation of Supertyphoon Paka. Mon. Wea. Rev. 130, 2248-2258 [PDF]
    • [21] Pu, Z.-X., and S. Braun, 2001: Evaluation of bogus vortex techniques with four -dimensional variational data assimilation. Mon. Wea. Rev. , 129, 2023-2039. [PDF]
    • [20] Kalnay, E., S. K. Park, Z.-X. Pu, and J. Gao, 2000: Application of quasi inverse method to data assimilation. Mon. Wea. Rev. , 128, 864-875. [PDF]
    • [19] Pu, Z.-X., and E. Kalnay, 1999: Targeting observation with the quasi-inverse linear and adjoint NCEP global models: performance during FASTEX. Q. J. Roy. Meteorol. Soc., 125, 3329-3338. [PDF]
    • [18] Pu, Z.-X., S. J. Lord, and E. Kalnay, 1998: Forecast sensitivity with dropwindsonde data and targeted observations. Tellus, 50A, 391-410. [PDF]
    • [17] Pu, Z.-X., E. Kalnay, J. Sela, and I. Szunyogh, 1997: Sensitivity of forecast error to initial conditions with a quasi inverse linear method. Mon. Wea. Rev., 125, 2479-2503 [PDF]
    • [16] Pu, Z.-X., E. Kalnay, J. C. Derber, and J. Sela, 1997: Using forecast sensitivity patterns to improve future forecast skill. Q. J. Roy. Meteorol. Soc., 123, 1035-1053 [PDF]
    • [15] Pu, Z.-X., E. Kalnay, D. Parrish, W. Wu, and Z. Toth, 1997: The use of bred vectors in the NCEP global 3 dimensional variational analysis system. Wea. Forecasting, 12, 689-695. [PDF]
    • [14] Parrish, D., J. Derber, J. Purser, W. S. Wu, and Z.-X. Pu, 1997: The NCEP global analysis system: recent improvements and future plans. J. Meteorol. Soc. Japan, 75, 359-365.
    • [13] Pu, Z.-X. and J. Chou, 1994: The adjoint method and its numerical applications to four dimensional assimilation of mesoscale remote sensing data. Plateau Meteorology, 13,419-429.
    • [12] Pu, Z.-X., 1992: The nonlinearity of atmospheric dynamics and long-range weather forecast (translation from Russian). Meteorological Science and Technology , No.1, 23-29.
    • [11] Chen, Q., and Z.-X. Pu, 1992: Evolutions of the mesoscale rainstorm system in Beijing Tianjin Hebei areas. The 1st Annual Summary of Severe Storm Laboratory, Chinese Academy of Meteorological Sciences, 38-52
    • [10] Pu, Z.-X., and C. Qiu, 1991: Two dimensional numerical simulation of the mountain Valley circulation over the Lanzhou area. Journal of Lanzhou University (Natural Version) , 27. 169-175.
    • [9] Qiu, C, and Z.-X. Pu, 1991: The transportation and dispersion of air pollutants controlledby mountain valley circulation: two dimensional numerical simulation. Plateau Meteorology, 10, 362-370
  • Books and Office Notes
    • [8] Pu, Zhaoxia, Fuquan Yang, Beisheng Deng, Huaigang Xu, and Xiaogang Zhou, 2005: Atmospheric modeling, data assimilation and predictability (Translated from E. Kalnay'book in English), China Meteorological Press. 300pp. (ISBN 7-5029-4007-3/P.1434)
    • [7] Pu, Z.-X., S. J. Lord, and E. Kalnay, 1997: Forecast sensitivity with dropwindsonde data and targeted observations. NCEP Office Note , No. 421, NCEP, NWS/NOAA, 50pp
    • [6] Pu, Z.-X., E. Kalnay, D. Parrish, W. Wu, Z. Toth, 1997: The Use of Bred Vectors in the NCEP Global 3-D Variational Analysis System. NCEP Office Note , No. 416, NCEP, NWS/NOAA
    • [5] Pu, Z.-X., and E. Kalnay, 1996: An inexpensive technique for using past forecast errors to improve future forecast skill: Part II --- a quasi-inverse linear method. NCEP Office Note, No. 415b, NCEP, NWS/NOAA, 38pp
    • [4] Pu, Z-X., E. Kalnay, J. Derber, and J. Sela, 1996: An inexpensive technique for using past forecast errors to improve future forecast skill: Part I --- adjoint method. NCEP Office Note, No. 415a, NCEP, NWS/NOAA, 33pp

  • Thesis and Dissertation
    • [3] Pu, Zhaoxia, Application of adjoint and quasi-inverse linear models of the NCEP operational global spectral model to sensitivity analysis and variational data assimilation. Ph.D. Dissertation, Lanzhou University, 1997. (Dissertation completed at EMC/NCEP, NWS/NOAA, Camp Springs, MD, USA)
    • [2] Pu, Zhaoxia, Four-dimensional variational assimilation of mesoscale remote sensing data with adjoint method, M.S. Thesis, Lanzhou University, 1992
    • [1] Pu, Zhaoxia, Numerical simulation of mountain-valley circulations in Lanzhou area , B.S. Thesis, Lanzhou University, 1989