I. Radar Radial Velocity & Reflectivity Data Assimilation (DA):
1) 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
2) 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
3) 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
4) 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
5) Hu, J. F., J. Gao, C. Liu, G. Zhang, P. L. Heinselman, and J. T. Carlin, 2022: Test of Power Transformation Function to Hydrometeor and Water Vapor Mixing Ratios for Direct Variational Assimilation of Radar Reflectivity Data. Wea. Forecasting (Conditionally accepted).
II. Radar Dual-polarimetric DA:
1) 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.
2) 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
3) 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
III. Radar Data Quality Control & Forward Operators:
1) 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
2) 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
3) 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
4) 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
IV. Satellite DA: (GOES-16 Precipitable Water & Atmosphere Motion Vectors):
1) 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.
2) 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/JTECH-D-17-0081.1
3) 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
4) 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
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
V. Ground- & Satellite- based Lightning DA:
1) 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
2) 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
3) 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
VI. Variational, Ensemble & Hybrid DA (focusing on methods for convective scale):
1) 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
2) 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
3) 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.
4) 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.
5) 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.
6) 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.
7) 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.
8) 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.
VII. Warn-on-Forecast (Warning & Short-term Forecast) Applications:
1) 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
2) Gao, J., et al., 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.
3) 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
4) Lai, A., J. Gao, S. E. Koch, Y. Wang, S. Jie, A. O. Fierro, C. Cui and J. Min, 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.
5) 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.
6) Hu, J., J. Gao, Y. Wang, S. Pan, A. Fierro, P. S. Skinner, K. Knopfmeier, E. Mansell, and P. Heiselman, 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
7) Gao, J., et al, 2022: Testing of the Warn-on-Forecast Hybrid Data Assimilation and Forecasting System at 1.5-km Resolution during the HWT Spring Forecasting Experiment in 2021, 12th Conference on Transition of Research to Operations. 11B.6. Jan. 2022.