Google Scholar Citation: Zhiquan Liu

Weather-related

Schwartz, C.S., Bresch, J., Lupo, K.M., Ban, J., Guerrette, J.J., Jung, B.J., Liu, Z., Snyder, C., Vahl, S., Wu, Y., and Yu, Y.G., 2025: A first step toward global ensemble-based data assimilation at convection-allowing scales using MPAS and JEDI, Mon. Wea. Rev., accepted

Zheng, Q., Sun, W., Liu, Z., Mao, J., He, J., Li, J., and Jiang, X., 2025: Direct assimilation of ground-based microwave radiometer observations with machine learning bias correction based on developments of RTTOV-gb v1.0 and WRFDA v4.5, Geosci. Model Dev., submitted, gmd

Yang, S., Li, D., Duan, Y., Chen, Y., Liu, Z, Huang, X., 2025: Assimilating Precipitation Data via Full-Hydrometeor Scheme in WRF 4D-Var for Convective Precipitation Forecast Associated With the Northeast China Cold Vortex (NCCV), J. Geophys. Res. Atmos., 130, e2024JD042427. jgr

Yang S., Li D., Huang X.-Y., Liu Z., Pan Y., and Duan Y., 2024: Assimilation of radar reflectivity via a full-hydrometeor assimilation scheme based on the WSM6 microphysics scheme in WRF 4D-Var, Mon. Wea. Rev., 152, 1303-1320. ams

Jung, B.-J., Ménétrier, B., Snyder, C., Liu, Z., Guerrette, J. J., Ban, J., Baños, I. H., Yu, Y. G., and Skamarock, W. C., 2024: Three-dimensional variational assimilation with a multivariate background error covariance for the Model for Prediction Across Scales–Atmosphere with the Joint Effort for data Assimilation Integration (JEDI-MPAS 2.0.0-beta), Geosci. Model Dev., 17, 3879–3895. gmd

Shen, F. F., A. Q. Shu, Liu Z., H. Li, L. P. Jiang, T. Zhang, and D. M. Xu, 2024: Assimilating FY-4A AGRI Radiances with a channel-sensitive cloud detection scheme for the analysis and forecasting of multiple typhoons, Adv. Atmos. Sci., 41(5), 937-958. AAS

Xu D., Zhang X., Liu Z., and Shen F., 2023: All-sky infrared radiance data assimilation of FY-4A AGRI with different physical parameterizations for the prediction of an extremely heavy rainfall event, Atmospheric Research, 293, 106898.

Guerrette, J. J., Liu, Z., Snyder, C., Jung, B.-J., Schwartz, C. S., Ban, J., Vahl, S., Wu, Y., Banos, I. H., Yu, Y. G., Ha, S., Tremolet, Y., Auligne, T., Gas, C., Menetrier, B., Shlyaeva, A., Miesch, M., Herbener, S., Liu, E., Holdaway, D., and Johnson, B. T., 2023: Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta): ensemble of 3D ensemble-variational (En-3DEnVar) assimilations, Geosci. Model Dev., 16, 7123–7142.

Ban, J., Liu Z., D.H. Bromwich, and L. Bai, 2023: Improved regional forecasting of an extreme Arctic Cyclone in August 2016 with WRF MRI-4DVAR. Q. J. R. Meteorol. Soc., 149, 3490-3512.

Liu, Z., Snyder, C., Guerrette, J. J., Jung, B.-J., Ban, J., Vahl, S., Wu, Y., Trémolet, Y., Auligné, T., Ménétrier, B., Shlyaeva, A., Herbener, S., Liu, E., Holdaway, D., and Johnson, B. T., 2022: Data Assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 1.0.0): EnVar implementation and evaluation. Geosci. Model Dev., 15, 7859–7878. gmd

Yang, S., D. Q. Li, L. Q. Chen, Z. Liu, X.-Y. Huang, and X. Pan, 2022: The regularized WSM6 microphysical scheme and its validation in WRF 4D-Var. Adv. Atmos. Sci., 40, 483-500. AAS

Sun, W., Z. Liu, C. A.Davis, F. M. Ralph, L. D. Monache, and M. Zheng, 2022: Impacts of dropsonde and satellite observations on the forecasts of two atmospheric-river-related heavy rainfall events. Atmospheric Research, 278, 106327. ar

Sun, W., Z. Liu, G. Song, Y. Zhao, S. Guo, F. Shen, and X. Sun, 2022: Improving Wind Speed Forecasts at Wind Turbine Locations over Northern China through Assimilating Nacelle Winds with WRFDA. Weather and Forecasting , 37, 545-562. waf

Li, J., A. Geer, K. Okamoto, J. Otkin, Z. Q. Liu, W. Han, P. Wang, 2022: Satellite All-sky Infrared Radiance Assimilation: Recent Progress and Future Perspectives, Adv. Atmos. Sci., 39, 9-21. aas

Xu, D. M., Z. Q. Liu, S. Y. Fan, M. Chen, and F. F. Shen, 2021: Assimilating all-sky infrared radiances from Himawari-8 using the 3DVar method for the prediction of a severe storm over North China. Adv. Atmos. Sci., 38(4), 661-676. aas

Liu, Z., J. Ban, J.-S, Hong, and Y.-H. Kuo, 2020: Multi-resolution incremental 4D-Var for WRF: Implementation and application at convective scale, Q. J. R. Meteorol. Soc., 146, 3661-3674. qjrms

Wu, Y., Z. Liu, and D. Li, 2020: Improving Forecasts of a Record-Breaking Rainstorm in Guangzhou by Assimilating Every 10-min AHI Radiances with WRF 4DVAR, Atmospheric Research, 239, 104912. ar

Qiao, X., S. Wang, C. S. Schwartz, Z. Liu, and J. Min, 2020: A method for probability matching based on the ensemble maximum for quantitative precipitation forecasts. Mon. Wea. Rev., 148, 3379-3396. ams

Yao, B., C. Liu, Y. Yin, Z. Liu, C. Shi, H. Iwabuchi, and F. Weng, 2020: Evaluation of of cloud properties from reanalyses over East Asia with a radiance-based approach, Atmos. Meas. Tech., 13, 1033-1049. amt

Wang, S. and Z. Liu, 2019: A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data. Geosci. Model Dev., 12, 4031–4051. gmd

Gao, F., Z. Liu, J. Ma, N.A. Jcaobs, P.P Childs, and H. Wang, 2019: Variational Bias Correction of TAMDAR Temperature Observations in the WRF Data Assimilation System. Mon. Wea. Rev., 147, 1927-1945. ams

D. H. Bromwich, A. B. Wilson, L. Bai, Z. Liu, M. Barlarge, C.-F. Shih, S. Maldonado, K. M. Hines, S.-H. Wang, J. Woollen, B. Kuo, H.-C. Lin, T.-K. Wee, M. C. Serreze, and J. E. Walsh , 2018: The Arctic System Reanalysis Version 2. Bull. Amer. Meteor. Soc., 99, 805-828. bams

Wang, P., J. Li, B. Lu, T. J. Schmit, J. Lu, Y.-K. Lee, J. Li, and Z. Liu, 2018: Impact of Moisture Information From Advanced Himawari Imager Measurements on Heavy Precipitation Forecasts in a Regional NWP Model. J. Geophys. Res. Atmos., 123, 6022-6038. jgr

Wang, Y., Z. Liu, S. Yang, J. Min, L. Chen, Y. Chen, and T. Zhang, 2018: Added value of assimilating Himawari-8 AHI water vapor radiances on analyses and forecasts for "7.19" severe storm over north China. J. Geophys. Res. Atmos., 123, https://doi.org/10.1002/2017JD027697. jgr

Ban, J., Z. Liu, X. Zhang, X.-Y. Huang, and H. Wang, 2017: Precipitation data assimilation in WRFDA 4D-Var: implementation and application to convection-permitting forecasts over United States. Tellus A: Dynamic Meteorology and Oceanography, 69:1, 1368310, DOI: 10.1080/16000870.2017.1368310. tellus

Powers, J. G., J. B., Klemp, W. C. Skamarock, C. A. Davis, J. Dudhia, D. O. Gill, J. L. Coen, D. J. Gochis, R. Ahmadov, S. E. Peckham, G. A. Grell, J. Michalakes, S. Trahan, S. G. Benjamin, C. R. Alexander, G. J. Dimego, W. Wang, C. S. Schwartz, G. S. Romine, Z. Liu, C. Snyder, F. Chen, M. J. Barlage1, W. Yu, and M. G. Duda , 2017: The Weather Research and Forecasting (WRF) Model: Overview, System Efforts, and Future Directions. Bull. Amer. Meteor. Soc., 98, 1717-1737. bams

Yang, C., Z. Liu, F. Gao, P. P. Childs, and J. Min, 2017: Impact of assimilating GOES imager clear-sky radiance with a rapid refresh assimilation system for convection-permitting forecast over Mexico. J. Geophys. Res. Atmos., 122, 5472–5490, doi:10.1002/2016JD026436. pdf

Yang, C., Z. Liu, J. Bresch, S.R.H. Rizvi, X.-Y. Huang, and J. Min, 2016: AMSR2 all-sky radiance assimilation and its impact on the analysis and forecast of Hurricane Sandy with a limited-area data assimilation system. Tellus A, 68, 30917. pdf

Xing, J., J. Shi, Y. Lei, X.-Y. Huang, and Z. Liu, 2016: Evaluation of HY-2A Scatterometer Wind Vectors Using Data from Buoys, ERA-Interim and ASCAT during 2012–2014. Remote Sensing, 8(5), 390.

Schwartz, C. S., Z. Liu, X.-Y. Huang, 2015: Sensitivity of Limited-Area Hybrid Variational-Ensemble Analyses and Forecasts to Ensemble Perturbation Resolution. Mon. Wea. Rev. , 143, 3454-3477. pdf

Newman, K.M., C.S. Schwartz, Z. Liu, H. Shao, and X.-Y. Huang, 2015: Evaluating forecast impact of assimilating Microwave Humidity Sounder (MHS) radiances with a regional ensemble Kalman filter data assimilation system. Weather and Forecasting , 30, 964-983.

Zheng, J., J. Li, T. J. Schmit, J. L. Li, and Z. Q. Liu, 2015: The impact of AIRS atmospheric temperature and moisture profiles on hurricane forecasts: Ike (2008) and Irene (2011). Adv. Atmos. Sci., 32(3), 319-335,doi: 10.1007/s00376-014-3162-z. AAS

Xu, D., Huang, X.-Y., Liu, Z., Min, J. 2014: Comparisons of Two Cloud Detection Schemes for Infrared Radiance Observations. Atmospheric and Oceanic Science Letters, 7(4), 358-363, doi:10.3878/j.issn.1674-2834.14.0016.

Schwartz, C. S. and Z. Liu, 2014: Convection-permitting forecasts initialized with continuously cycling limited-area 3DVAR, ensemble Kalman filter, and "hybrid" variational-ensemble data assimilation systems. Mon. Wea. Rev., 142, 716-738. ams

Xu, D., Z. Liu, X.-Y. Huang, J. Min, and H. Wang, 2013: Impact of Assimilating IASI Radiance Observations on Forecasts of Two Tropical Cyclones. Meteorology and Atmospheric Physics, 122, 1-18. pdf

Schwartz, C. S., Z. Liu, X.-Y. Huang, Y.-H. Kuo, and C.-T. Fong, 2013: Comparing limited-area 3DVAR and hybrid variational-ensemble data assimilation methods for typhoon track forecasts: Sensitivity to outer loops and vortex relocation. Mon. Wea. Rev., 141, 4350-4372. ams

Liu, Z., C. S. Schwartz, C. Snyder, and S.-Y. Ha, 2012: Impact of assimilating AMSU-A radiances on forecasts of 2008 Atlantic tropical cyclones initialized with a limited-area ensemble Kalman filter. Mon. Wea. Rev., 140 , 4017-4034. ams pdf

Wee, T. K., Y. H. Kuo, D.-K. Lee, Z. Liu, W. Wang, and S.-Y. Chen, 2012: Two Overlooked Biases of the Advanced Research WRF (ARW) Model in Geopotential Height and Temperature. Mon. Wea. Rev., 140 , 3907-3918. ams

Barker, D. M., X.-Y. Huang, Z. Liu, T. Auligne, X. Zhang, S. Rugg, A. A. AL KATHERI, A. Bourgeios, J. Bray, Y. Chen, M. Demirtas, Y. Guo, T. Henderson, W. Huang, H.-C. Lin, J. Michalakes, S. Rizvi, X.-Y. Zhang, 2011: The Weather Research and Forecasting (WRF) Mode l's Community Variational/Ensemble Data Assimilation System: WRFDA. Bull. Amer. Meteor. Soc., 93 , 831-843. ams

Schwartz, C. S., Z. Liu, Y. Chen, and X.-Y. Huang, 2012a: Impact of assimilating microwave radiances with a limited-area ensemble dat a assimilation system on forecasts of typhoon Morakot. Wea. Forecasting., 27 , 424-437. doi: http://dx.doi.org/10.1175/WAF-D-11-00033.1 ams pdf

He, W., Z. Liu, and H. Chen, 2011: Influence of surface temperature and emissivity on AMSU-A assimilation over land. Acta Meteor. Sinica, 25(5) , 547-557. doi: 10.1007/s13351-011-0501-1. pdf

Lu, Q., W. Zhang, P. Zhang, X. Wu, F. Zhang, Z. Liu, and D. M. Barker, 2010: Monitoring the 2008 cold surge and frozen disasters snowstorm in South China based on regional ATOVS data assimilation. Sci. China Earth Sci., 53 , 1216-1228. doi: 10.1007/s11430-010-3040-1. pdf

Xu, J., S. Rugg, L. Brerle, and Z. Liu, 2009: Weather forecast by the WRF-ARW model with GSI data assimilation system in the complex terrain areas of southwest Asia. Wea. Forecasting., 24 , 987-1008. pdf

Xue J., Zhuang S., Zhu G., Zhang H., Z. Liu, Liu Y., and Zhuang Z., 2008: Scientific design and preliminary results of three-dimensional variational data assimilation system of GRAPES. China Science Bulletin, No. 22, Vol. 53, 3446-3457.

Zhu G., Xue J., Zhang H. and Z. Liu, Zhuang S., and Dong P., 2008: Direct Assimilation of satellite radiance data in GRAPES variational assimilation system. China Science Bulletin, No. 22, Vol. 53, 3465-3469.

Liu, Z., Zhang F., Wu, X. and Xue, J. 2007: A regional ATOVS radiance-bias correction scheme for radiance assimilation (in Chinese). Acta Meteorologica Sinica, No. 1, Vol. 65, 113-123

Liu, Z., and F. Rabier, 2003: The potential of high-density observations for numerical weather prediction: A study with simulated observations. Q. J. R. Meteorol. Soc., 129 , 3013-3035. pdf

Liu, Z., and F. Rabier, 2002: The interaction between model resolution, observation resolution and observation density in data assimilation: A one-dimensional study. Q. J. R. Meteorol. Soc., 128 , 1367-1386. pdf

Air Quality and Aerosol Feedback-related

Zhou, Y., Sun, W., Liu, Z., Gao, L., Chen, D., Feng, J., Zhang, T., Zhou, Z., 2025: Further development and application of the WRFDA-Chem three-dimensional variational (3DVAR) system: Joint assimilation of satellite AOD retrievals and surface observations, Atmospheric Research, 316, 107942. ar

Wei Y., Zhao X., Zhang Z., Xu J., Cheng S., Liu Z., Sun W., Chen X., Wang Z., Hao X., Li J., and Chen D., 2024: Impact of model resolution and its representativeness consistency with observations on operational prediction of PM2.5 with 3D-VAR data assimilation, Atmospheric Pollution Research, 15, 102141. APR

Gao, L., Z. Liu, W. Sun, P. Yan, Y. Chen, Z. Pu, and H. Hu, 2022: Three-dimensional variational assimilation of Lidar extinction profiles: application to PM2.5 prediction in North China. Atmospheric Environment, 269, 118828. AE

Cheng, X., Hao, Z., Zhang, Z., Liu, Z., Xu, X., Wang, S., Liu, Y., Hu, Y., and Ma, X., 2021: A new inverse modeling approach for emission sources based on the DDM-3D and 3DVAR techniques: an application to air quality forecasts in the Beijing–Tianjin–Hebei region. Atmos. Chem. Phys., 21, 13747-13761. acp

Peng, Z., Lei, L., Liu, Z., Liu, H., Chu, K., and Kou, X., 2020: Impact of assimilating meteorological observations on source emissions estimate and chemical simulations. Geophysical Research Letters, 47, e2020GL089030. grl

Sun, W., Liu, Z., Chen, D., Zhao, P., and Chen, M., 2020: Development and application of the WRFDA-Chem three-dimensional variational (3DVAR) system: aiming to improve air quality forecasting and diagnose model deficiencies, Atmos. Chem. Phys., 20, 9311-9329. acp

Ha, S., Z. Liu, Sun, W., Lee, Y., and Chang, L., 2020: Improving air quality forecasting with the assimilation of GOCI aerosol optical depth (AOD) retrievals during the KORUS-AQ period, Atmos. Chem. Phys., 20, 6015–6036. acp

Chen, D., Liu, Z., Ban, J., and Chen, M., 2019: The 2015 and 2016 wintertime air pollution in China: SO2 emission changes derived from a WRF-Chem/EnKF coupled data assimilation system, Atmos. Chem. Phys., 19, 8619–8650. acp

Chen, D., Liu, Z., Ban, J., Zhao, P., and Chen, M., 2019: Retrospective analysis of 2015–2017 winter-time PM2.5 in China: response to emission regulations and the role of meteorology, Atmos. Chem. Phys., 19, 7409-7427. acp

Kumar, R., L. D. Monache, J. Bresch, P. E. Saide, Y. Tang, Z. Liu, A. M. da Silva, S. Alessandrini, G. Pfister, D. Edwards, and P. Lee, and I. Djalalova, 2019: Towards improving short-term predictions of fine particulate matter over the United States via assimilation of satellite aerosol optical depth retrievals. J. Geophys. Res. Atmos., 124, 2753-2773. jgr

Peng, Z., Lei, L., Liu, Z., Sun, J., Ding, A., Ban, J., Chen, D., Kou, X., and Chu, K., 2018: The impact of multi-species surface chemical observations assimilation on the air quality forecasts in China, Atmos. Chem. Phys., 18, 17387–17404. acp

Chu, K., Z. Peng, Z. Liu, L. Lei, X. Kou, Y. Zhang, X. Bo, and J. Tian, 2018: Evaluating the Impact of Emissions Regulations on the Emissions Reduction during the 2015 China Victory Day Parade with an Ensemble Square Root Filter. J. Geophys. Res. Atmos. , 123 , 4122-4134. jgr

Pang, J., Z. Liu, X. Wang, J. Bresch, J. Ban, D. Chen, and J. Kim, 2018: Assimilating AOD retrievals from GOCI and VIIRS to forecast surface PM2.5 episodes over Eastern China. Atmospheric Environment, 179, 288-304. AE

Chen, D., Z. Liu, C. Davis, and Y. Gu, 2017: Dust radiative effects on atmospheric thermodynamics and tropical cyclogenesis over the Atlantic Ocean using WRF-Chem coupled with an AOD data assimilation system. Atmos. Chem. Phys., 17, 7917-7939. acp

Peng, Z., Z. Liu, D. Chen, and J. Ban, 2017: Improving PM2.5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter. Atmos. Chem. Phys., 17, 4837-4855. acp

Chen, D., Z. Liu, Fast, J., and Ban, J., 2016: Simulations of sulfate–nitrate–ammonium (SNA) aerosols during the extreme haze events over northern China in October 2014. Atmos. Chem. Phys., 16, 10707-10724. acp

Chen, D., Z. Liu, Schwartz, C. S., H.-C. Lin, J. D. Cetola, Y. Gu, and L. Xue, 2014: The impact of aerosol optical depth assimilation on aerosol forecasts and radiative effects during a wild fire event over the United States. Geosci. Model Dev., 7, 2709-2715, doi:10.5194/gmd-7-2709-2014. gmd

Pagowski, M., Liu, Z., Grell, G. A., Hu, M., Lin, H.-C., and Schwartz, C. S., 2014: Implementation of aerosol assimilation in Gridpoint Statistical Interpolation (v. 3.2) and WRF-Chem (v.3.4.1), Geosci. Model Dev. , 7, 1621-1627, doi:10.5194/gmd-7-1621-2014. gmd

Schwartz, C. S., Z. Liu, H.-C. Lin, and J.D. Cetola, 2014: Assimilating aerosol observations with a "hybrid" variational-ensemble data assimilation system. J. Geophys. Res. Atmos., , 119, 4043-4069. jgr

Jiang, Z., Z. Liu, T. Wang, C. S. Schwartz, H.-C. Lin, and F. Jiang, 2013: Probing into the impact of 3DVAR assimilation of surface PM10 observations over China using process analysis. J. Geophys. Res. Atmos. , 118, 6738-6749. jgr pdf

Saide, P., G. Carmichael, Z. Liu, H.-C. Lin, C. S. Schwartz, A. Da Silva, and E. Hyer, 2013: Aerosol optical depth assimilation for a size-resolved sectional model: Impacts of empirically corrected, multi-wavelength and fine mode retrievals on regional scale forecasts. Atmos. Chem. Phys. , 13 , 10425-10444. doi:10.5194/acp-13-10425-2013. acp

Schwartz, C. S., Z. Liu, H.-C. Lin, and S. McKeen, 2012b: Simultaneous three-dimensional variational assimilation of surface fine par ticulate matter and MODIS aerosol optical depth. J. Geophys. Res., 117 , D13202, doi:10.1029/2011JD017383. pdf

Liu, Z., Q. Liu, H.-C. Lin, C. S. Schwartz, Y.-H. Lee, and T. Wang, 2011: Three-dimensional variational assimilation of MODIS aerosol optical depth: Implementation and application to a dust storm over East Asia. J. Geophys. Res., 116 , D23206, doi:10.1029/2011JD016159. pdf

Non-referred Publications

Liu, Z., and D. Chen, 2014: A Case Study on Atlantic Tropical Cyclogenesis and Saharan Air Layer Simulated Using WRF/Chem Coupled with an AOD Data Assimilation System. Preprints, 19th International TOVS Study Conferences, Jeju Island, South Korea, 26 March - 1 April, 2014.

Zhang, Y., Z. Liu, C. S. Schwartz, H.-C. Lin, and X.-Y. Haung, 2012: The impacts of Saharan Air Layer on the Atlantic Tropical Storms: A Case Study using WRF/Chem. Preprints, 13th WRF Users' Workshop, Boulder, CO, National Center for Atmospheric Research, P40. pdf

Schwartz, C. S., Z. Liu, 2012: Bias correction and assimilation of microwave radiance measurements over the Antarctic. 16th Symposium on IOAS-AOLS, 23-26 January 2012, New Orleans, LA, 10.1. pdf

Liu, Z., Q. Liu, H.-C. Lin, C. S. Schwartz, and Y.-H. Lee, 2011: Assimilating MODIS aerosol optical depth using WRF/Chem and GSI: Application to a Chinese dust storm. Preprints, 12th WRF Users' Workshop, Boulder, CO, National Center for Atmospheric Research, 8A.4. pdf

Liu, Z., H.-C. Lin, Ying-Hwa Kuo, Dave H. Bromwich, and L. Bai, 2011: Data assimilation over Northern polar region using WRF and WRFDA. Preprints, 12th WRF Users' Workshop, Boulder, CO, National Center for Atmospheric Research, 7B.7 pdf

Schwartz, C. S., and Z. Liu, 2011: Assimilating satellite microwave radiance measurements over the Antarctic. Preprints, 12th WRF Users' Workshop, Boulder, CO, National Center for Atmospheric Research, 7B.1. pdf

Schwartz, C. S., Z. Liu, Y. Chen, and X.-Y. Huang, 2011: Satellite radiance data assimilation with a limited-area ensemble Kalman filter and 3D-Var analysis system. Preprints, 15th Symposium on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans and Land Surface, Seattle, WA, Amer. Meteor. Soc., 5.5. pdf

Mizzi, A.P., Z. Liu, and X.Y. Huang, 2011: A Hybrid GSI/ETKF Data Assimilation Scheme for WRF/ARW. 15th Symposium on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface; and the 24th Conference on Weather and Forecasting/20th Conference on Numerical Weather Prediction (J16.1), Seattle, Washington. pdf

Schwartz, C. S., Z. Liu, Y. Chen, and X.-Y. Huang, 2010: Studying typhoon Morakot with a coupled WRFDA-DART system. Preprints, 11th WRF Users' Workshop, Boulder, CO, National Center for Atmospheric Research, 3A.11. pdf

Liu Z., Zhang X., Auligne T., Lin H.-C., 2009: Variational Analysis of Hydrometeors with Satellite Radiance Observations: a Simulated Study. Preprints, 10th WRF Users Workshop, Boulder, CO, National Center for Atmospheric Research, 2A.01. pdf

Shao H., Z. Liu, T. Auligne, D. M. Barker, J. Powers, and X.-Y. Huang, 2008: Impact Studies of Satellite Observations in The Antarctic Mesoscale Prediction System: AMSU-A Radiance Measurements. Preprints, 9th WRF Users' Workshop, Boulder, CO, National Center for Atmospheric Research. pdf

Xiao Q., E. Lim, X. Zhang, J. Sun, and Z. Liu, 2008: Doppler Radar Data Assimilation with WRF 3D-VAR: IHOP Retrospective Studies. Preprints, 9th WRF Users' Workshop, Boulder, CO, National Center for Atmospheric Research. pdf

Liu Z. and Barker D. M., 2006: Radiance Assimilation in WRF-Var: Implementation and Initial Results, Preprints, 7th WRF Users' Workshop, Boulder, CO, National Center for Atmospheric Research. pdf

Liu Z. and Qi C., 2005: Robust Variational Inversion with simulated ATOVS radiances, Preprints, ITSC-14 Proceedings, Beijing, China. pdf

Rabier F. and Liu Z., 2003: Variational Data Assimilation: Theory and Overview. ECMWF seminar proceedings on Recent developments in data assimilation for atmosphere and ocean, ECMWF, Reading, UK. pdf