2.     Hu, J. F., J. Gao, C. Liu, G. Zhang, P. L. Heinselman, and J. T. Carlin, 2023: Test of Power Transformation Function to Hydrometeor and Water Vapor Mixing Ratios for Direct Variational Assimilation of Radar Reflectivity Data. Wea. Forecasting, 38, 1995-2010. doi: https://doi.org/10.1175/WAF-D-22-0158.1

3.     Zhang H., J. Gao, Q. Xu and L. Ran, 2023: Applying Time-Expended Sampling to Ensemble Assimilation of Remote-Sensing Data for Short-Term Predictions of Thunderstorms. Remote Sensing. 15(9), 2358. DOI: https://doi.org/10.3390/rs15092358

4.     Pan S. and J. Gao, 2022: A Method for Assimilating Pseudo Dewpoint Temperature as a Function of GLM Flash Extent Density in GSI-Based EnKF Data Assimilation System – A Proof of Concept study. Earth and Space Science, https://doi.org/10.1029/2022EA002378

5.     Zhao, J. and J. Gao, T. A. Jones, J. Hu, 2022: Impact of Assimilating High-Resolution Atmospheric Motion Vectors on Convective Scale Short-Term Forecasts. Part III: Experiments with Radar Reflectivity and Radial Velocity. J. Adv. in Modeling Earth Systems, https://doi.org/10.1029/2022MS003246

6.     Pan, S., J. Gao, T. A. Jones, Y. Wang, X. Wang, and J. Li, 2021: The Impact of Assimilating Satellite-derived Layered Precipitable Water, Cloud Water Path and Radar Data on Short-Range Thunderstorm Forecast. Mon. Wea. Rev. 149, 1359-1380. https://doi.org/10.1175/MWR-D-20-0040.1

7.     Zhao, J., J. Gao, T. A. Jones, J. Hu, 2021a: Impact of Assimilating High-Resolution Atmospheric Motion Vectors on Convective Scale Short-Term Forecasts. Part I: Observing System Simulation Experiment (OSSE), J. Adv. in Modeling Earth Systems. https://doi.org/10.1029/2021MS002484

8.     Zhao, J., J. Gao, T. A. Jones, J. Hu, 2021b: Impact of Assimilating High-Resolution Atmospheric Motion Vectors on Convective Scale Short-Term Forecasts. Part II: Assimilation Experiments of GOES-16 Satellite Derived Winds. J. Adv. in Modeling Earth Systems. https://doi.org/10.1029/2021MS002486

9.     Chen, H., J. Gao, Y. Wang, Y. Chen,T. Sun, J. Carlin, and Y. Zheng, 2021: Radar reflectivity data assimilation method based on background-dependent hydrometeor retrieval: The comparison with direct assimilation in real cases., Quart. J. Royal Meteorol. Soc. 147, 2409-2428. https://doi.org/10.1002/qj.4031

10.  Hu, J., J. Gao, Y. Wang, S. Pan, A. Fierro, P. S. Skinner, K. Knopfmeier, E. Mansell, and P. Heinselman, 2021: Evaluation of a Warn-on-Forecast 3DVAR analysis and forecast system on quasi-real-time short-term forecasts of high impact weather events. Quart. J. Royal Meteorol. Soc. https://doi.org/10.1002/qj.4168

11.  Zhang, G., J. Gao, and M. Du, 2021: Parameterized Forward Operators for Simulation and Assimilation of Polarimetric Radar Data with Numerical Weather Predictions, Adv. Atmos. Sci. 38, 737-754. https://doi.org/10.1007/s00376-021-0289-6

12.  Du, M., J. Gao, G. Zhang, Y. Wang, P. L. Heinselman, and C. Cui, 2021: Assimilation of Polarimetric Radar Data in Simulation of a Supercell Storm with a Variational Approach and the WRF Model, Remote Sensing. 13, 3060. DOI: https://doi.org/10.3390/rs13163060

13.  Lai, A., J. Min, J. Gao, H Ma, C. Cui, Y. Xiao and Z. Wang, 2020: Assimilation of Radar Data, Pseudo Water Vapor, and Potential Temperature in a 3DVAR Framework for Improving Precipitation Forecast of Severe Weather Events. Atmosphere, 11, doi:10.3390/atmos11020182.

14.  Wang Y., J. Gao, P. Skinner, K. H. Knopfmeier, T. A. Jones, G. J. Creager, P. L. Heinselman, and L. J. Wicker, 2019: Test of a Hybrid Dual-resolution Ensemble Variational Analysis and Forecast System During the HWT Spring Experiments in 2017. Wea. Forecasting, 34, 1807-1827. https://doi.org/10.1175/WAF-D-19-0071.1.

15.  Lai, A., J. Gao, et al, 2019: Assimilation of pseudo water vapor and radar data for convective-scale NWP in a variational framework. Mon. Wea. Rev. 147, 2877-2900. https://doi.org/10.1175/MWR-D-18-0403.1.

16.  Pan, S., J. Gao, D. J. Stensrud, X. Wang, and T. A. Jones, 2018: Assimilation of Radar Radial Velocity and Reflectivity, Satellite Cloud Water Path and Total Precipitable Water for Convective Scale NWP in OSSEs, J. Atmos. Oceanic Tech., 35, 67-89. https://doi.org/10.1175/JTECH-D-17-0081.1.

17.  Fu, C., J. Gao, Y. Wang, J. Tang, C. Zhou, C. Ye, and Z. Zhuang, 2018: The application of radar data assimilation to the analysis of severe thunderstorms which have potential to produce tornadoes. Adv. Meteorol. Sci. and Technol. 8(3), 19-37. Doi: 10.3969/j.issn.2095-1973.2018.03.002 (In Chinese with English Abstract).

18.  Gao, J., 2017: A Three-Dimensional Variational Radar Data Assimilation Scheme Developed for Convective Scale NWP. A book chapter in Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, Sasaki Memorial Volume (Editors: Seon Ki Park and Liang Xu), Springer. P285-326. doi 10.1007/978-3-319-43415-5.

19.  Carlin, J. T., J. Gao, J. C. Snyder and V. Ryzhkov, 2017: Assimilation of ZDR Columns for Improving the Spin-Up and Forecast of Convective Storms in Storm-Scale Models: Proof-of-Concept Experiments. Mon. Wea. Rev., 144, 2981-3001.https://doi.org/10.1175/MWR-D-15-0423.1.

20.  Gao, J., C. Fu, D. J. Stensrud, and J. S. Kain, 2016: OSSE experiments for An Ensemble of 3DVAR Data Assimilation System with Radar Data for Convective Storms. J. Atmos. Sci. 73, 2403-2426. doi:10.1175/JAS-D-15-0311.1.

21.  Fierro, A., J. Gao, C. Ziegler, K. Calhoun, E. Mansell, and D. MacGorman, 2016: Assimilation of flash extent data in the variational framework at convection-allowing scales: Proof-of-concept and evaluation for the short term forecast of the 24 May 2011 tornado outbreak. Mon. Wea. Rev. 144, 4373-4393. doi:10.1175/MWR-D-16-0053.1

22.  Gao, J. and D. J. Stensrud, 2014: Some Observing System Simulation Experiments with a Hybrid 3DEnVAR System for Stormscale Radar Data Assimilation, Mon. Wea. Rev., 142, 3326-3346. http://dx.doi.org/10.1175/MWR-D-14-00025.1.

23.  Fierro, A. O., J. Gao, C. Ziegler, E. Mansell, and D. MacGorman, 2014: Evaluation of a cloud scale lightning data assimilation technique and a 3DVAR method for the analysis and short-term forecast of the 29 June 2012 derecho event. Mon. Wea. Rev., 142, 183-202. https://doi.org/10.1175/MWR-D-13-00142.1

24.  Gao, J., D. J. Stensrud, L. Wicker, M. Xue and K. Zhao, 2014: Storm-scale radar data assimilation and high resolution NWP, Adv. Meteor. 2013, 1-3. http://dx.doi.org/10.1155/2013/213479.

25.  Gao, J., M. Xue, and D. J. Stensrud, 2013: The development of a hybrid EnKF-3DVAR algorithm for storm-scale data assimilation, Adv. Meteor. 2013, 1-12. http://dx.doi.org/10.1155/2013/512656.

26.  Smith, T. M., J. Gao, K. M. Calhoun, D. J. Stensrud, K. L. Manross, K. L. Ortega, C. Fu, D. M. Kingfield, K. L. Elmore, V. Lakshmanan, and C. Riedel, 2014: Performance of a real-time 3DVAR analysis system in the Hazardous Weather Testbed. Wea. Forecasting, 29, 63-77. DOI: https://doi.org/10.1175/WAF-D-13-00044.1

27.  Natenberg, E., J. Gao, M. Xue, and F. H. Carr, 2013: Multi-Doppler radar analysis and forecast of a tornadic thunderstorm using a 3D variational data assimilation technique and ARPS model. Adv. Meteor., 2013, 1-18. http://dx.doi.org/10.1155/2013/281695.

28.  Ge, G., J. Gao, M. Xue, 2013: Impacts of Assimilating Measurements of Different State Variables with a Simulated Supercell Storm and Three-Dimensional Variational Method. Mon. Wea. Rev., 141, 2759-2777. doi: http://dx.doi.org/10.1175/MWR-D-12-00193.1.

29.  Ge., G, J. Gao, and M. Xue, 2013: Impact of a diagnostic pressure equation constraint on tornadic supercell thunderstorms forecasts initialized using 3DVAR radar data assimilation. Adv. Meteor. 2013, 1-12. http://dx.doi.org/10.1155/2013/947874. 

30.  Clark, A. J., J., Gao, P. T. Marsh, T. M. Smith, J. S. Kain, J. Correia Jr., M. Xue, and F. Kong, 2013: Tornado path length forecasts from 2011 using a 3-dimensional object identification algorithm applied to ensemble updraft helicity, Wea. Forecasting, 28, 387-407. http://dx.doi.org/10.1175/WAF-D-12-00038.1.

31.  Gao, J., 2013: The contributions of Professor Jifan Chou to atmospheric data assimilation and his style of nurturing students - a tribute to his 60 years research and educational activities. Adv. Meteorol. Sci. and Technol. (Review Article, In Chinese). 

32.  Yussouf, N., J. Gao, D. J. Stensrud, and G. Ge, 2013: The impact of mesoscale environmental uncertainty on the prediction of a tornadic supercell storm using ensemble data assimilation approach. Adv. Meteor. 2013, 1-15. http://dx.doi.org/10.1155/2013/731647.

33.  Gao, J., T. M. Smith, D. J. Stensrud, C. Fu, K. Calhoun, K. L. Manross, J. Brogden, V. Lakshmanan, Y. Wang, K. W. Thomas, K. Brewster, and M. Xue, 2013: A realtime weather-adaptive 3DVAR analysis system for severe weather detections and warnings with automatic storm positioning capability. Wea. Forecasting, 28, 727-745. http://dx.doi.org/10.1175/WAF-D-12-00093.1.

34.  Ge, G., J. Gao, and M. Xue, 2012: Diagnostic pressure equation as a weak constraint in a storm-scale three dimensional variational radar data assimilation system. J. Atmos. Ocean. Tech., 29, 1075-1092. doi: http://dx.doi.org/10.1175/JTECH-D-11-00201.1.

35.  Gao, J. and D. J. Stensrud, 2012: Assimilation of Reflectivity Data in a Convective-Scale, Cycled 3DVAR Framework with Hydrometeor Classification, J. Atmos. Sci., 69, 1054-1065. Doi: http://dx.doi.org/10.1175/JAS-D-11-0162.1.

36.  Stensrud D. J. and J. Gao, 2010: Importance of horizontally inhomogeneous environmental initial conditions to ensemble storm-scale radar data assimilation and very short range forecasts. Mon. Wea. Rev., 138, 1250-1272. DOI: https://doi.org/10.1175/2009MWR3027.1

37.  Ge G., J. Gao, K. A. Brewster, and M. Xue 2010: Effects of beam broadening and earth curvature in radar data assimilation. J. Atmos. Oceanic. Technol. 27, 617-636. DOI: https://doi.org/10.1175/2009JTECHA1359.1

38.  Sriastava, K., J. Gao, K. Brewster, and S. K. Bhowmik, 2010: Assimilation of radar data for prediction of a small cyclone OGNI near India coast using the CAPS radar data assimilation system. Natural Hazards (Springer), DOI: 10.1007/s11069-010-9640-4.

39.  Gao, J. and M. Xue, 2008: An efficient dual-resolution approach for ensemble data assimilation and tests with assimilated Doppler radar data. Mon. Wea. Rev., 136, 945-963. DOI: https://doi.org/10.1175/2007MWR2120.1

40.  Gao, J., K. Brewster, and M. Xue, 2008: Sensitivity of radio reflectivity to moisture and temperature and its influence on radar ray path., Adv. Atmos. Sci., 25, 1098-1106. doi: 10.1007/s00376-008-1098-x

41.  Gao, J., M. Xue, S. Lee, A. Shapiro, Q. Xu and K. K. Droegemeier, 2006: A three-dimensional variational method for velocity retrievals from dingle-Doppler radar on supercell storms, Meteorol. Atmos. Physics, 94, 11-26. https://doi.org/10.1007/s00703-005-0170-7

42.  Gao, J., K. Brewster, and M. Xue, 2006: A comparison of the radar ray path equations and approximations for use in radar data assimilation, Adv. Atmos. Sci., 32, 190-198. doi: 10.1007/s00376-006-0190-3

43.  Phillip, L. S., and J. Gao, 2004: The use of gradient information for improving variational objective analysis. Mon. Wea. Rev. 132, 2977-2994. https://doi.org/10.1175/MWR2833.1

44.  Gao, J., M. Xue, A. Shapiro, Q. Xu and K. K. Droegemeier, 2001: Simple adjoint method for the retrieval of three-dimensional mesoscale wind fields from single-Doppler radar, J. Atmos. Oceanic. Technol., 18, 26-38. DOI: https://doi.org/10.1175/1520-0426(2001)018<0026:TDSAVR>2.0.CO;2

45.  Gao, J., K. K. Droegemeier, J. Gong and Q. Xu 2004: Retrieval of vertical wind profiles from Doppler radar radial velocity data, Mon. Wea. Rev. 132, 1399-1409. DOI: https://doi.org/10.1175/1520-0493-132.6.1399

46.  Gao, J., K. K. Droegemeier 2004: A variational technique for dealiasing Doppler radial velocity data, J. Appl. Meteor. 43, 934-940. DOI: https://doi.org/10.1175/1520-0450(2004)043<0934:AVTFDD>2.0.CO;2

47.  Gao, J., M. Xue, K. Brewster, and K. K. Droegemeier 2004: A three-dimensional variational data assimilation method with recursive filter for single-Doppler radar, J. Atmos. Oceanic. Technol. 21, 457-469. DOI: https://doi.org/10.1175/1520-0426(2004)021<0457:ATVDAM>2.0.CO;2

48.  Gao, J., M. Xue, A. Shapiro, and K. K. Droegemeier 1999: A variational analysis for the retrieval of three-dimensional mesoscale wind fields from two Doppler radars, Mon. Wea. Rev., 127, 2128-2142. https://doi.org/10.1175/1520-0493(1999)127<2128:AVMFTA>2.0.CO;2

49.  Xu, Q., and J. Gao, 1999: Generalized Adjoint for physical processes with parameterized discontinuities: minimization problems in multidimensional space, J. Atmos. Sci., 56, 994-1002. DOI: https://doi.org/10.1175/1520-0469(1999)056<0994:GAFPPW>2.0.CO;2

50.  Xu, Q., J. Gao and W. Gu, 1998: Generalized adjoint for discretized physical processes with parameterized discontinuities, Part V: Coarse-grain adjoint and problems in gradient check, J. Atmos. Sci., 55, 2130-2135. DOI: https://doi.org/10.1175/1520-0469(1998)055<2130:GAFPPW>2.0.CO;2

51.  Gao, J., J. She, J. Chou and Y. Yuan, 1996: A new method to determine the sea level dynamic height by using Radar altimeter in satellite, Journal of Lanzhou University (Natural Sciences), 32(1): 133-137 (in Chinese with English abstract).

52.  Gao, J., J. Chou, and J. She, 1995: A variational assimilation scheme with adjusting parameters of the model equation--study based on Lorenz model, Plateau Meteorology, 14, 10-18 (in Chinese with English abstract).

53.  Gao, J., C. Qiu, and J. Chou 1995: The sensitivity influence of numerical model initial values on four-dimensional assimilation - study based on Lorenz system, Acta Meteorologica Sinica (English Version), 9, 278-287.

54.  Gao, J., J. Chou, and Z. Li, 1995: The adjoint method for determining the spatial structure of meteorological variable from the temporal evolution of observation data, Chinese Journal of Atmos. Sci. (in Chinese with English abstract), 19, 113-125. doi: 10.3878/j.issn.1006-9895.1995.03.01

55.  Chou, J., and J., Gao, 1995: The long-term numerical weather prediction, published by Chinese Meteorological Press, Beijing (http://item.kongfz.com/book/51179039.html/). ISBN: 7502919732.

56.  Qiu, C., and J. Gao, 1994: A numerical study of effect of model errors upon variational data assimilation - based on shallow water equation, Plateau Meteorology, 13, 449-456 (in Chinese with English abstract).

57.  Gao, J., and J. Chou, 1994: Two kinds of inverse problems in NWP and numerical adjoint method -- ideal field experiment, Acta Meteorologica Sinica, 52, 129-137 (in Chinese with English abstract). doi: 10.11676/qxxb1994.017

58.  Gao, J., and J. Chou, 1993: A simple analysis of the adjoint equation and the optimal control technique applied to the numerical model, Atmos. Sci. Res. and Appl., 2, 31-40 (in Chinese with English abstract).

59.  Huang, J., and J. Gao, 1990: The analogous rhythms phenomena of monthly mean circulation over northern hemisphere, Plateau Meteorology, 9, 88-92 (in Chinese with English abstract).

Other Co-authored Publications:

60.  Peng, L., G. Zhang, J. Carlin, and J. Gao, 2024: A New Melting Model and its Implementation in Parameterized Forward Operators for Polarimetric Radar Data Simulation with Double Moment Microphysics Schemes. J. Geophys. Res. - Atmospheres (Submitted).

61.  Ho, J., G. Zhang, P. Bukovcic, D. Parsons, F. Xu, J. Gao, A. Ryzhkov, J. Carlin, and J. Snyder, 2023: Improving Polarimetric Radar-based Drop Size Distribution Retrieval and Rain Estimation using Deep Neural Network, J. of Hydrometeorology, 24, 2057-2073. DOI: https://doi.org/10.1175/JHM-D-22-0166.1

62.  Heinselman, L. P., and Co-authors, 2023: Warn-on-Forecast: From Vision to Reality. Wea. Forecasting. DOI: https://doi.org/10.1175/WAF-D-23-0147.1

63.  Schultz, D. M., J. Anderson, T. Benacchio, K. L. Corbosiero, M. D. Eastin, C. Evans, J. Gao, J. P. Hacker, D. Hodyss, D. Kleist, M. R. Kumjian, R. McTaggart-Cowan, Z. Meng, J. R. Minder, D. Posselt, P. Roundy, A. Rowe, M. Scheuerer, R. S. Schumacher, S.Trier, and C. Weiss, 2022: How to Be a More Effective Author, Mon. Wea. Rev., 150, 2819-2828. DOI: https://doi.org/10.1175/MWR-D-22-0277.1

64.  Hu, J., A. Fierro, Y. Wang, J. Gao, A. Clark, I. Jirak,, E. Mansell, and M. Hu, 2021: Assessment of storm-scale real time assimilation of GOES-16 GLM lightning-derived water vapor mass and radar data on short term precipitation forecasts during the 2020 Spring Forecast Experiment. J. Geophys. Res., 126, http://doi.org/10.1029/2021JD034603

65.  Liu, P., Y. Yang, A. Lai, Y. Wang, A. O. Fierro, J. Gao, and C. Wang, 2021: Assimilating FY-4A Lightning and Radar Data for Improving Short-Term Forecasts of a High-Impact Convective Event with a Dual-Resolution Hybrid 3DEnVAR Method Remote Sensing. 13(16), 3090. https://doi.org/10.3390/rs13163090.

66.  Clark, A. J., coauthors, and J. Gao, 2021: A Real-Time, Virtual Spring Forecasting Experiment to Advance Severe Weather Prediction. Bulletin of the American Meteorological Society, April 2021, E814-E816. https://doi.org/10.1175/BAMS-D-20-0268.1.

67.  Chen H., Y. Chen, J. Gao, T. Sun, J. T. Carlin, 2020: A radar reflectivity data assimilation method based on background-dependent hydrometeor retrieval: An observing system simulation experiment, Atmos. Res. 243 (2020) 105022. https://doi.org/10.1016/j.atmosres.2020.105022.

68.  Schultz D. M., et al. 2020: Data Availability Principles and Practice. 148, Mon. Wea. Rev., 4701-4702. DOI: https://doi.org/10.1175/MWR-D-20-0323.1

69.  Hu, J., Fierro, A., Y. Wang, J. Gao, and E. Mansell, 2020: An Evaluation of the Impact of Assimilating GLM-observed total lightning data on short-term forecasts of high-impact convective events. Mon. Wea. Rev. 148, 1005-1028. https://doi.org/10.1175/WAF-D-19-0071.1

70.  Liu P., Y. Yang, J. Gao, Y. Wang, C. Wang, 2020: An Approach for Assimilating FY4 Lightning and Cloud Top Height Data using 3DVAR. Front. Earth Sci., 8, https://doi.org/10.3389/feart.2020.00288.

71.  Blumberg, W. G., D. D. Turner, S. M. Cavallo, J. Gao, J. Basara, A. Shapiro, 2019: Relationships between the Mesoscale Evolution of Near-Surface Water Vapor and Vegetation during the Afternoon to Evening Transition. J. of Applied Meteorology and Climatol. 58, 2217-2234. https://doi.org/10.1175/JAMC-D-19-0062.1

72.  Fierro, A., Y. Wang, J. Gao, and E. Mansell, 2019: Variational assimilation of radar data and water vapor derived from GLM-observed total lightning for the short-term forecasts of high-impact convective events. Mon. Wea. Rev. 147, 4045-4069. https://doi.org/10.1175/MWR-D-18-0421.1

73.  Mahale, V., N., G. Zhang, M. Xue, J. Gao, and H. D. Reeves, 2019: Variational Retrieval of Rain Microphysics and Related Parameters from Polarimetric Radar Data with a Parameterized Operator, J Atmos. Oceanic Tech. (Accepted). 36, 2483-2500. https://doi.org/10.1175/JTECH-D-18-0212.1

74.  Zhang G. et al. 2019: Current Status and Future Challenges of Weather Radar Polarimetry: Bridging the Gap between Radar Meteorology/Hydrology/Engineering and Numerical Weather Prediction, Adv. Atmos. Sci., 36, 571-588. https://doi.org/10.1007/s00376-019-8172-4.

75.  Qian, W., J. L C-H Leung, W. Luo, J. Du and J. Gao, 2017: An index of anomalous covective instability to detect tornadic and hail storms, Meteor. Atmos. Physics, 131(3), 351-373https://doi.org/10.1007/s00703-017-0576-z.

76.  Fierro, A., S. Liu, G. Zhao, Y. Wang, J. Gao, K. Calhoun, C. Ziegler, E. Mansell, and D. MacGorman, 2018: Assimilation of Total Lightning with GSI and NEWS3DVAR to Improve Short-Term Forecasts of High-Impact Weather Events at Cloud-Resolving Scales. Quarterly Newsletters of Joint Center for Satellite Data Assimilation, No. 58, 5-12, Doi: https://doi.org/10.7289/V5CJ8BR2.

77.  Xu Q., L. Wei, J. Gao, Q. Zhao, K. Nai, and S. Liu, 2016: Multistep variational data assimilation: important issues and a spectral approach, Tellus A, 68, 1-26. http://dx.doi.org/10.3402/tellusa.v68.31110.

78.  Zhuang Z., N. Yussouf, and J. Gao, 2016: The Analyses and Forecasts of 24 May 2011 Oklahoma Tornadic Supercell Storms using Ensemble of 3DVAR System. Adv. in Atmos. Sci., 33, 544-558. doi: 10.1007/s00376-015-5072-0

79.  Li., H, Hong, Y., P. Xie, J. Gao , Z. Niu , P-E Kirstetter , B. Yong, 2015: Variational merged of hourly gauge-satellite precipitation in China: preliminary results, J. Geophys. Res. -Atmos.,120, 9897-9915. doi:10.1002/ 2015JD023710.

80.  Calhoun, K., M., T. M. Smith, D. M. Kingfield, J. Gao, and D. J. Stenrud, 2014: Forecaster Use and Evaluation of realtime 3DVAR analyses during Severe Thunderstorm and Tornado Warning Operations in the Hazardous Weather Testbed . Wea. Forecasting, 29, 601-613. DOI: 10.1175/WAF-D-13-00107.1.

81.  Johnson, A., X. Wang, M. Xue, F. Kong, K. Thomas, Y. Wang, K. Brewster, J. Gao, 2014: Multiscale characteristics and evolution of perturbations for warm season convection allowing precipitation forecasts: Dependence on background flow and method of perturbation. Mon. Wea. Rev., 142, 1053-1073. DOI: https://doi.org/10.1175/MWR-D-13-00204.1

82.  Zhang, Y., Y. Hong, J.J. Gourley, X-G Wang, J. Gao, H. Vergara, and B. Yong. 2013: Assimilation of Passive Microwave Streamflow Signals for Improving Flood Forecasting: A First Study in Okavango River Basin, Africa, IEEE Journal of Special Topics in Applied Earth Observations and Remote Sensing. 6, 2375-2390. 10.1109/JSTARS.2013.2251321

83.  Xue M., F. Kong, K. W. Thomas, Y. Wang, K. A. Brewster, J. Gao, and K. K. Droegemeier, 2013: Prediction of convective storms at convection-resolving 1-km resolution over continental United States with radar data assimilation: An example case of 26 May 2008. Adv. Meteor. 2013, 1-9. http://dx.doi.org/10.1155/2013/259052.

84.  Shimose, K.., M. Xue, R. D. Palmer, J. Gao, B. L. Cheong, and D. J. Bodine, 2013: Two-dimensional variational analysis of near-surface moisture from simulated radar refractivity-related phase change observations. Adv. Atmos. Sci., 30(2), 291-305. http://159.226.119.58/aas/EN/volumn/volumn_1135.shtml

85.  Stensrud, D. J., L. Wicker, M. Xue, D. T. Dawson II, N. Yussouf, D. M. Wheatley, T. E. Thompson, N. A. Snook, T. M. smith, A. D. Schenkman, C. K. Potvin, E. R. MAnsell, T. Lei, K. M. Kuhlman, Y. Jung,T. A. Jones, J. Gao, M. C. Coniglio, H. E. Brooks, K. A. Brewster, 2013: Progress and challenges with Warn-on-Forecast, Atmos. Res., 123, 2-16, doi:10.1016/j.atmosres.2012.04.004.

86.  Gasperoni, N. A., M. Xue, R. D. Palmer, J. Gao, B. L. Cheong, D. Michaud, and D. Bodine, 2013: Sensitivity of Convective Initiation Prediction to Near-Surface Moisture when Assimilating Radar Refractivity: Impact Tests using OSSEs, J. Atmos. Oceanic Technol. 30, 2281-2302. https://doi.org/10.1175/JTECH-D-12-00038.1

87.  Clark, A. J., and co-authors, 2012: An Overview of the 2010 Hazardous Weather Testbed Experimental Forecast Program Spring Experiment. Bull. Amer. Meteor. Sci., 93, 55-74. http://dx.doi.org/10.1175/BAMS-D-11-00040.1.

88.  Bodine D., D. Michaud, R. D. Palmer, P. L. Heinselman, J. Brotzge4, N. Gasperoni, B. L. Cheong, M. Xue, and J. Gao, 2011: Understanding radar refractivity: sources of uncertainty. Journal of Applied Meteorology and Climatology., 50, 2543-2560. http://dx.doi.org/10.1175/2011JAMC2648.1.

89.  Clark, A. J., J. S. Kain, D. J. Stensrud, M. Xue, F. Kong, M. C. Coniglio, K. W. Thomas, Y. Wang, K. Brewster, J. Gao, X. Wang, S. J. Weiss, D. Bright, and J. Du, 2011: Probabilistic precipitation forecast skill as a function of ensemble size and spatial scale in a convection-allowing ensemble. Mon. Wea. Rev., 139, 1410-1418. DOI: https://doi.org/10.1175/2010MWR3624.1

90.  Schenkman, A., M. Xue, A. Shapiro, K. Brewster, and J. Gao, 2011: The analysis and prediction of the 8-9 May 2007 Oklahoma tornadic mesoscale convective system by assimilating WSR-88D and CASA radar data using 3DVAR. Mon. Wea. Rev., 139, 224-246. DOI: https://doi.org/10.1175/2010MWR3336.1

91.  Schenkman, A., M. Xue, A. Shapiro, K. Brewster, and J. Gao, 2011: Impact of CASA radar and Oklahoma mesonet data assimilation on the analysis and prediction of tornadic mesovortices in a MCS. Mon. Wea. Rev., 139, 3422-3445. DOI: https://doi.org/10.1175/MWR-D-10-05051.1

92.  Kain, J. S., M. Xue, M. C. Coniglio, S. J. Weiss, F. Kong, T. L. Jensen, B. G. Brown, J. Gao, K. Brewster, K. W. Thomas, Y. Wang, C. S. Schwartz, and J. J. Levit, 2010: Assessing advances in the assimilation of radar data within a collaborative forecasting-research environment. Wea. Forecasting, 25, 1510-1521. https://doi.org/10.1175/2010WAF2222405.1

93.  Lan, W. -R, J. Zhu, M. Xue, and J. Gao, and T. Lei, 2010a: Storm-Scale Ensemble Kalman Filter Data Assimilation Experiments Using Simulated Doppler Radar Data. Part I: Perfect Model Tests. Chinese J. Atmos. Sci., 34(3), 640-652. doi: 10.3878/j.issn.1006-9895.2010.03.15

94.  Lan, W. -R, J. Zhu, M. Xue, and J. Gao, T. Lei, 2010b: Storm-Scale Ensemble Kalman Filter Data Assimilation Experiments Using Simulated Doppler Radar Data. Part II: with model errors. Chinese J. Atmos. Sci., 34(4), 737-753. doi: 10.3878/j.issn.1006-9895.2010.04.07

95.  Liu, Y, J. Zhu, J. She, S. Zhuang, W., Fu and J. Gao, 2009: Assimilating temperature and salinity profile observations using an anisotropic recursive filter in a coastal ocean model. Ocean. Modeling, 30(2), 75-87. DOI:10.1016/j.ocemod.2009.06.005

96.  Shapiro A., C. K. Potvin, and J. Gao, 2009: Use of a mesoscale vertical vorticity in variational dual-Doppler wind analysis. J. Atmos. Oceanic. Technol., 90, 2089-2106. DOI: https://doi.org/10.1175/2009JTECHA1256.1

97.  Potvin, C. K., A. Shapiro, T-Y Yu, and J. Gao, 2009: Using a low-order model to detect and characterize intense vortices in multiple-Doppler radar data. Mon. Wea. Rev. 137, 1230-1249. DOI: https://doi.org/10.1175/2008MWR2446.1

98.  Hu, M., M. Xue, J. Gao and K. Brewster, 2006: 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of Fort Worth tornadic thunderstorms. Part II: Impact of radial velocity analysis via 3DVAR, Mon. Wea. Rev. 134, 699-721. DOI: https://doi.org/10.1175/MWR3093.1

99.  Liu S., C. Qiu, Q. Xu, P. Zhang, J. Gao, and A. Shao, 2005: An improved method for Doppler wind and thermodynamic retrievals. Adv. Atmos. Sci., 22, 90-102. doi: 10.1007/BF02930872

100.        Xiao, Y., M. Xue, W. J. Martin, and J. Gao, 2005: Development of an adjoint for a complex atmospheric model, the ARPS, using TAF. Automatic Differentiation: Applications, Theory, and Tools, M. Bueckeret et al., Eds., Springer, 263-272.

101.        Shapiro, A., P. Robinson, J. Wurman, and J. Gao, 2003: Single-Doppler velocity retrieval with rapid-scan radar data, J. Atmos. Oceanic. Technol. 20, 1758-1775. DOI: https://doi.org/10.1175/1520-0426(2003)020<1758:SVRWRR>2.0.CO;2

102.        Xue, M., D. Wang, J. Gao, K. Brewster, and K. K. Droegemeier, 2003: The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation. Meteor. Atmos. Physics, 82, 139-170. DOI： 10.1007/s00703-001-0595-6

103.        Kalnay E., S. Park, Z. Pu and J. Gao, 2000: Application of the quasi-inverse method to accelerate 4-DVAR, Mon. Wea. Rev., 128, 864-875. DOI: https://doi.org/10.1175/1520-0493(2000)128<0864:AOTQIM>2.0.CO;2

104.        Xu, Q., W. Gu and J. Gao, 1998: Baroclinic eady wave and fronts, Part I: viscous semigeostrophy and the impact of boundary condition. J. Atmos. Sci., 55, 3598-3615. DOI: https://doi.org/10.1175/1520-0469(1998)055<3598:BEWAFP>2.0.CO;2