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• Hacker, J. P., S.-Y. Ha, C. Snyder, J. Berner, F. A. Eckel, E. Kuchera, M. Pocernich, S. Rugg, J. Schramm, and X. Wang, 2011: The U.S. Air Force Weather Agency's mesoscale ensemble: Scientific description and performance results. Tellus, 63A, 625–641, https://doi.org/10.1111/j.1600-0870.2010.00497.x.
  

• 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, 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, https://doi.org/10.1175/2010MWR3624.1.
  

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• Johnson, A.*, X. Wang, F. Kong, and M. Xue, 2011: Hierarchical cluster analysis of a convection-allowing ensemble during the Hazardous Weather Testbed 2009 Spring Experiment. Part I: Development of object-oriented cluster analysis method for precipitation fields. Mon. Wea. Rev., 139, 3673–3693, https://doi.org/10.1175/MWR-D-11-00015.1.
  

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• Clark, A. J., S. J. Weiss, J. S. Kain, I. L. Jirak, M. Coniglio., C. J. Melick, C. Siewert, R. A. Sobash, P. T. Marsh, A. R. Dean, M. Xue, F. Kong, K. W. Thomas, Y. Wang*, K. Brewster, J. Gao, X. Wang, J. Du, D. R. Novak, F. Barthold, M. J. Bodner, J. J. Levit, C. B. Entwistle, T. L. Jensen, and J. Correia Jr., 2012: An overview of the 2010 hazardous weather testbed experimental forecast program spring experiment. Bull. Amer. Meteor. Soc., 93, 55–74, https://doi.org/10.1175/BAMS-D-11-00040.1.
  

• Johnson, A.*, and X. Wang, 2012: Verification and calibration of neighborhodd and object-based probablistic precipitation forecasts from a multi-model convection-allowing ensemble. Mon. Wea. Rev., 140, 3054–3077, https://doi.org/10.1175/MWR-D-11-00356.1.
  

• Li, Y.*, X. Wang, and M. Xue, 2012: Assimilation of radar radial velocity data with the WRF hybrid ensemble-3DVAR system for the prediction of hurricane Ike (2008). Mon. Wea. Rev., 140, 3507–3524, https://doi.org/10.1175/MWR-D-12-00043.1.
  

• Thompson, E. T.*, L. J. Wicker, and X. Wang, 2012: Impact from a volumetric radar-sampling operator for radial velocity observations within EnKF supercell assimilation. J. Atmos. Oceanic Technol., 29, 1417–1427, https://doi.org/10.1175/JTECH-D-12-00088.1.
  

• Johnson, A.*, and X. Wang, 2013: Object-based evaluation of a storm scale ensemble during the 2009 NOAA Hazardous Weather Testbed Spring Experiment. Mon. Wea. Rev., 141, 1079–1098, http://doi.org/10.1175/MWR-D-12-00140.1.
  

• Holland, B.*, and X. Wang, 2013: Effects of sequential or simultaneous assimilation of observations and localization methods on the performance of the ensemble Kalman filter. Q. J. R. Meteo. Soc., 139, 758–770, https://doi.org/10.1002/qj.2006.
  

• Zhang, Y.*, Y. Hong, X. Wang, J. J. Gourley, J. Gao, H. Vergara*, and B. Yong, 2013: Assimilation of passive microwave streamflow signals for improving flood forecasting: A first study in Cubango River Basin, Africa. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6, 2375–2390, https://doi.org/10.1109/JSTARS.2013.2251321.
  

• Wang, X., D. Parrish, D. Kleist, and J. S. Whitaker, 2013: GSI 3DVar-based ensemble–variational hybrid data assimilation for NCEP Global Forecast System: Single-resolution experiments. Mon. Wea. Rev., 141, 4098–4117, https://doi.org/10.1175/MWR-D-12-00141.1.
  

• Johnson, A.*, X. Wang, F. Kong, and M. Xue, 2013: Object-based evaluation of the impact of horizontal grid spacing on convection-allowing forecasts. Mon. Wea. Rev., 141, 3413–3425, https://doi.org/10.1175/MWR-D-13-00027.1.
  

• Zhu, K.*, Y. Pan*, M. Xue, X. Wang, J. S. Whitaker, S. G. Benjamin, S. S. Weygandt, and M. Hu, 2013: A regional GSI-based ensemble Kalman filter data assimilation system for the rapid refresh configuration: Testing at reduced resolution. Mon. Wea. Rev., 141, 4118–4139, https://doi.org/10.1175/MWR-D-13-00039.1.
  

• Zhang, Y.*, Y. Hong, J. Gourley, X. Wang, G. R. Brakenridge, T. D. Groeve, and H. Vergara*, 2013: Impact of assimilating spaceborne microwave signals for improving hydrological prediction in ungauged basins. Remote Sensing of the Terrestrial Water Cycle, 439–450, https://doi.org/10.1002/9781118872086.ch27.
  

• Johnson, A.*, X. Wang, M. Xue, F. Kong, G. Zhao, Y. Wang*, K. Thomas, K. Brewster, and 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, https://doi.org/10.1175/MWR-D-13-00204.1.
  

• Duda, J.*, X. Wang, F. Kong, and M. Xue, 2014: Using varied microphysics to account for uncertainty in warm-season QPF in a convection-allowing ensemble. Mon. Wea. Rev., 142, 2198–2219, https://doi.org/10.1175/MWR-D-13-00297.1.
  

• Wang, X., and T. Lei*, 2014: GSI-based four dimensional ensemble-variational (4DEnsVar) data assimilation: formulation and single resolution experiments with real data for NCEP Global Forecast System. Mon. Wea. Rev., 142, 3303–3325, https://doi.org/10.1175/MWR-D-13-00303.1.
  

• Pan, Y.*, K. Zhu*, M. Xue, X. Wang, J. S. Whitaker, S. G. Benjamin, S. S. Weygandt, and M. Hu, 2014: A GSI-based coupled EnSRF-En3DVar hybrid data assimilation system for the operational rapid refresh model: Tests at a reduced resolution. Mon. Wea. Rev., 142, 3756–3780, https://doi.org/10.1175/MWR-D-13-00242.1.
  

• Chen, S., X. Wang, N. Chang, and Y. Hong, 2014: Data assimilation techniques and inverse modeling. Book chapter for Environmental Informatics Series (I): Advances in hydrological remote sensing to monitor global changes, N. Chang and Y. Hong (eds.), Springer Publisher. accepted with minor revision.
  

• Zhang, Y.*, Y. Hong, X. Wang, J. Gourley, X. Xue, Y. Huang, and S. Chen, 2015: Hydrometeorological analysis and remote sensing of extremes: Was the July 2012 Beijing flood event detectable and predictable by global satellite observing and global weather modeling systems? J. Hydrometeor., 16, 381–395, https://doi.org/10.1175/JHM-D-14-0048.1.
  

• Kerr, C. A.*, D. J. Stensrud, and X. Wang, 2015: Assimilation of cloud-top temperature and radar observations of an idealized splitting supercell using an observing system simulation experiment. Mon. Wea. Rev., 143, 1018–1034, https://doi.org/10.1175/MWR-D-14-00146.1.
  

• Thompson, T.*, L. J. Wicker, X. Wang, and C. Potvin, 2015: A comparison between the Local Ensemble Transform Kalman Filter and the Ensemble Square Root Filter for the assimilation of radar data in convective-scale models. Q. J. R. Meteo. Soc., 141, 1163–1176, https://doi.org/10.1002/qj.2423.
  

• Gasperoni, N. A.*, and X. Wang, 2015: Adaptive localization for the ensemble-based observation impact estimate using regression confidence factors. Mon. Wea. Rev., 143, 1981–2000, https://doi.org/10.1175/MWR-D-14-00272.1.
  

• Johnson, A.*, X. Wang, J. Carely, L. Wicker, and C. Karstens, 2015: A comparison of multiscale GSI-based EnKF and 3DVar data assimilation using radar and conventional observations for midlatitude convective-scale precipitation forecasts. Mon. Wea. Rev., 143, 3087–3108, https://doi.org/10.1175/MWR-D-14-00345.1.
  

• Kutty, G.*, and X. Wang, 2015: A comparison of the impacts of radiosonde and AMSU radiance observations in GSI based 3DEnsVar and 3DVar data assimilation systems for NCEP GFS. Adv. Meteor., 2015, 1–17, http://dx.doi.org/10.1155/2015/280546.
  

• Bishop, C. H., B. Huang*, and X. Wang, 2015: A nonvariational consistent hybrid ensemble filter. Mon. Wea. Rev., 143, 5073–5090, https://doi.org/10.1175/MWR-D-14-00391.1.
  

• Moon, Y. *, X. Wang, and L. Leslie, 2015: On storm-scale structures at the onset of rapid intensification of hurricane Edouard (2014) in HWRF forecasts. Geophysical Research Letters, in revision
  

• Duda, J.*, X. Wang, F. Kong, M. Xue, and J. Berner, 2016: Impact of a stochastic kinetic energy backscatter scheme on warm season convection-allowing ensemble forecasts. Mon. Wea. Rev. , 144, 1887–1908, https://doi.org/10.1175/MWR-D-15-0092.1.
  

• Johnson, A.*, and X. Wang, 2016: A study of multiscale initial condition perturbation methods for convection-permitting ensemble forecasts. Mon. Wea. Rev., 144, 2579–2604, https://doi.org/10.1175/MWR-D-16-0056.1.
  

• Parsons, D. B., M. Beland, D. Burridge, P. Bougeault, G. Brunet, J. Caughey, M. Charron, H. C. Davies, A. Diongue-Niang, V. Ducrocq, P. Gauthier, T. M. Hamill, P. A. Harr, S. Jones, R. Langland, S. Majumdar, B. Mills, M. Moncrieff, T. Nakazawa, F. Rabier, J. Redelsperger, R. Saunders, M. A. Shapiro, R. Swinbank, I. Szunyogh, C. D Thorncroft, A. J. Thorpe, X. Wang., D. E. Waliser, H. Wernli, Z. Toth, and T. Paccagnella, 2017: THORPEX research and the science of prediction. Bull. Amer. Meteor. Soc., 98, 807–830, https://doi.org/10.1175/BAMS-D-14-00025.1.
  

• Lu, X.*, X. Wang, Y. Li*, M. Tong, and X. Ma, 2017: GSI-based ensemble–variational hybrid data assimilation for HWRF for hurricane initialization and prediction: Impact of various error covariances for airborne radar observation assimilation. Quart. J. Roy. Meteor. Soc., 143, 223–239, https://doi.org/10.1002/qj.2914.
  

• Geerts, B., D. Parsons, C. Ziegler, T. Weckwerth, D. Turner; J. Wurman, R. Rauber, M. Parker, R. Schumacher, M. Coniglio, K. Haghi, W. Gallus, B. Demoz, K. Knupp, R. Ferrare, X. Wang, J. Hanesiak, R. Clark, G. McFarquhar, M. Biggerstaff, A. Nehrir, J. Pinto, J. J. Moore; K. Kosiba, and P. Klein, 2017: The 2015 plains elevated convection at night field project. Bull. Amer. Meteor. Soc., 98, 767–786, https://doi.org/10.1175/BAMS-D-15-00257.1.
  

• Johnson, A.*, and X. Wang, 2017: Design and implementation of a GSI-based convection-allowing ensemble data assimilation and forecast system for the PECAN field experiment. Part I: Optimal configurations for nocturnal convection prediction using retrospective cases. Wea. Forecasting, 32, 289–315, https://doi.org/10.1175/WAF-D-16-0102.1.
  

• Johnson, A.*, X. Wang, and S. Degelia*, 2017: Design and implementation of a GSI-based convection-allowing ensemble data assimilation and forecast system for the PECAN field experiment. Part II: Overview and evaluation of a real-time system. Wea. Forecasting, 32, 1227–1251, https://doi.org/10.1175/WAF-D-16-0201.1.
  

• Duda, J. D.*, X. Wang, and M. Xue, 2017: Sensitivity of convection-allowing forecasts to land-surface model perturbations and implications for ensemble design. Mon. Wea. Rev., 145, 2001–2025, https://doi.org/10.1175/MWR-D-16-0349.1.
  

• Kerr, C. A.*, D. J. Stensrud, and X. Wang, 2017: Verification of convection-allowing model ensemble analyses of near-storm environments using MPEX upsonde observations. Mon. Wea. Rev., 145, 857–875, https://doi.org/10.1175/MWR-D-16-0287.1.
  

• James D. Doyle, Jon Moskaitis, Joel Feldmeier, Ronald Ferek, Mark Beaubien, Michael Bell, Daniel Cecil, Robert Creasey, Patrick Duran, Russ Elsberry, Will Komaromi, John Molinari, David Ryglicki, Daniel Stern, Xuguang Wang, Chris Velden, Brad Barrett, Todd Allen, Peter Black, Kerry Emanuel, Jason Dunion, Eric Hendricks, Pat Harr, Lee Harrison, Derrick Herndon, William Jeffries, Sharan Majumdar, James Moore, Zhaoxia Pu, Robert Rogers, Elizabeth Sanabia, Greg Tripoli, and Da-Lin Zhang, 2017: A view of tropical cyclones from above: The Tropical Cyclone Intensity Experiment. Bull. Amer. Meteor. Soc., 98, 2113–2134, https://doi.org/10.1175/BAMS-D-16-0055.1.
  

• Wang, Y.*, and X. Wang, 2017: Direct assimilation of radar reflectivity without tangent linear and adjoint of the nonlinear observation operator in the GSI-Based EnVar System: Methodology and experiment with the 8 May 2003 Oklahoma City tornadic supercell. Mon. Wea. Rev., 145, 1447–1471, https://doi.org/10.1175/MWR-D-16-0231.1.
  

• Lu, X.*, X. Wang, M. Tong, and V. Tallapragada, 2017: GSI-based, continuously cycled, dual-resolution hybrid ensemble-variational data assimilation system for HWRF: System description and experiments with Edouard (2014). Mon. Wea. Rev., 145, 4877–4898, https://doi.org/10.1175/MWR-D-17-0068.1.
  

• Pan, S.*, J. Gao, D. Stensrud, X. Wang, and T. Jones, 2017: Assimilation of radar radial velocity and reflectivity, satellite cloud water path, and total precipitable water for convective-scale NWP in OSSEs. J. Atmos. Ocean. Technol., 35, 67–89, https://doi.org/10.1175/JTECH-D-17-0081.1.
  

• Degelia, S.*, X. Wang, D. Stensrud, and A. Johnson*, 2018: Understanding the impact of radar and in-situ observations on the prediction of a nocturnal convection initiation event on 25 June 2013 using an ensemble-based multiscale data assimilation system. Mon. Wea. Rev., 146, 1837–1859, https://doi.org/10.1175/MWR-D-17-0128.1.
  

• Johnson, A.*, X. Wang, K. Haghi, and D. Parsons, 2018: Evaluation of forecasts of a convectively generated bores using an intensively observed case study from PECAN. Mon. Wea. Rev., 146, 3097–3122, https://doi.org/10.1175/MWR-D-18-0059.1.
  

• Jones, T., X. Wang, P. Skinner, A. Johnson*, and Y. Wang*, 2018: Assimilation of GOES-13 imager clear-sky water vapor (6.5 um) radiances into a Warn-on-Forecast system. Mon. Wea. Rev., 146, 1077–1107, https://doi.org/10.1175/MWR-D-17-0280.1.
  

• Huang, B.*, and X. Wang, 2018: On the use of cost-effective valid-time-shifting (VTS) method to increase ensemble size in the GFS Hybrid 4DEnVar system. Mon. Wea. Rev., 146, 2973–2998, https://doi.org/10.1175/MWR-D-18-0009.1.
  

• Chipilski, H. G.*, X. Wang, and D. B. Parsons, 2018: Object-based algorithm for the identification and tracking of convective outflow boundaries in numerical models. Mon. Wea. Rev., 146, 4179–4200, https://doi.org/10.1175/MWR-D-18-0116.1.
  

• Gasperoni, N. A.*, X. Wang, K. A. Brewster, and F. H. Carr, 2018: Assessing impacts of the high-frequency assimilation of surface observations for the forecast of convection initiation on 3 April 2014 within the Dallas-Fort Worth testbed. Mon. Wea. Rev., 146, 3845–3872, https://doi.org/10.1175/MWR-D-18-0177.1.
  

• Du, J., J. Berner, R. Buizza, M. Charron, P. Houtekamer, D. Hou, I. Jankov, M. Mu, X. Wang, M. Wei, and H. Yuan, 2019: Ensemble methods for meteorological predictions. In: Duan Q., Pappenberger F., Wood A., Cloke H., Schaake J. (eds) Handbook of Hydrometeorological Ensemble Forecasting. Springer, Berlin, Heidelberg, pp99-149, https://doi.org/10.1007/978-3-642-39925-1_13.
  

• Duda, J.*, X. Wang, Y. Wang*, and J. Carley, 2019: Comparing the assimilation of radar reflectivity using the direct GSI based ensemble-variational (EnVar) and indirect cloud analysis methods in convection-allowing forecasts over the continental United States. Mon. Wea. Rev., 147, 1655–1678, https://doi.org/10.1175/MWR-D-18-0171.1.
  

• Haghi, K. R., B. Geerts, H. G. Chipilski*, A. Johnson*, S. Degelia*, D. Imy, D. B. Parsons, R. Adams-Selin, D. D. Turner, and X. Wang, 2019: Bore-ing into nocturnal convection. Bull. Amer. Meteor. Soc., 100, 1103–1121, https://doi.org/10.1175/BAMS-D-17-0250.1.
  

• Kerr, C. A.*, D. J. Stensrud, and X. Wang, 2019: Diagnosing convective dependencies on near-storm environments using ensemble sensitivity analyses. Mon. Wea. Rev., 147, 495–517, https://doi.org/10.1175/MWR-D-18-0140.1.
  

• Lu, X.*, and X. Wang, 2019: Improving hurricane analyses and predictions with TCI, IFEX field campaign observations, and CIMSS AMVs using the advanced hybrid data assimilation system for HWRF. Part I: What is missing to capture the rapid intensification of Hurricane Patricia (2015) when HWRF is already initialized with a more realistic analysis? Mon. Wea. Rev., 147, 1351–1373, https://doi.org/10.1175/MWR-D-18-0202.1.
  

• Degelia, S. K.*, X. Wang, and D. J. Stensrud, 2019: An evaluation of the impact of assimilating AERI retrievals, kinematic profilers, and surface observations on a forecast of a nocturnal convection initiation event during the PECAN field campaign. Mon. Wea. Rev., 147, 2739–2764, https://doi.org/10.1175/MWR-D-18-0423.1.
  

• Johnson, A.*, and X. Wang, 2019: Multicase assessment of the impacts of horizontal and vertical grid spacing, and turbulence closure model, on subkilometer-scale simulations of atmospheric bores during PECAN. Mon. Wea. Rev., 147, 1533–1555, https://doi.org/10.1175/MWR-D-18-0322.1.
  

• Weckwerth, T. M., J. Hanesiak, J. W. Wilson, X. Wang, S. B. Trier, R. D. Roberts, W. A. Gallus, and S. K. Degelia*, 2019: Nocturnal convection initiation during PECAN 2015. Bull. Amer. Meteor. Soc., 100, 2223–2239, https://doi.org/10.1175/BAMS-D-18-0299.1.
  

• Gharamti, M., K. Raeder, J. Anderson, and X. Wang, 2019: Comparing adaptive prior and posterior inflation for ensemble filters using an atmospheric general circulation model. Mon. Wea. Rev., 147, 2535–2553, https://doi.org/10.1175/MWR-D-18-0389.1.
  

• Lyu, X.*, X. Wang, and L. Leslie, 2019: The dependence of Northwest Pacific tropical cyclone intensification rates on environmental factors. Adv. Meteor., 2019, 1–18, https://doi.org/10.1155/2019/9456873.
  

• Chipilski*, H. G., I. Tsonevsky, S. Georgiev, T. Dimitrova, L. Bocheva, and X. Wang, 2019: Analysis of a case of super cellular convection over Bulgaria: Observations and numerical simulations. Atmos., 10, 1–29, https://doi.org/10.3390/atmos10090486.
  

• Feng, J.*, and X. Wang, 2019: Impact of assimilating upper-level dropsonde observations collected during the TCI field campaign on the prediction of intensity and structure of Hurricane Patricia (2015). Mon. Wea. Rev., 147, 3069–3089, https://doi.org/10.1175/MWR-D-18-0305.1.
  

• Feng, J.*, X. Wang, and J. Poterjoy, 2020: A comparison of two local moment matching nonlinear filters: local particle filter (LPF) and local nonlinear ensemble transform filter (LNETF). Mon. Wea. Rev., 148, 4377–4395, https://doi.org/10.1175/MWR-D-19-0368.1.
  

• Huang, B.*, X. Wang, and C. H. Bishop, 2019: The high-rank ensemble transform Kalman filter. Mon. Wea. Rev., 147, 3025–3043, https://doi.org/10.1175/MWR-D-18-0210.1.
  

• Hu, J., N. Yussouf, D. D. Turner, T. A. Jones, and X. Wang, 2019: Impact of ground-based remote sensing boundary layer observations on short-term probabilistic forecasts of a tornadic supercell event. Wea. Forecasting, 34, 1453–1476, https://doi.org/10.1175/WAF-D-18-0200.1.
  

• Potvin, C., J. R. Carley, A. Clark, L. J. Wicker, P. S. Skinner, A. E. Reinhart, B. T. Gallo, J. S. Kain, G. Romine; E. Aligo, K. Brewster, D. C. Dowell, L. M. Harris, I. L. Jirak, F. Kong, T. A. Supinie, K. W. Thomas, X. Wang, Y. Wang*, and M. Xue, 2019: Systematic comparison of convection-allowing models during the 2017 NOAA HWT Spring Forecasting Experiment. Wea. Forecasting, 34, 1395–1416, https://doi.org/10.1175/WAF-D-19-0056.1.
  

• Lu, X.*, and X. Wang, 2020: Improving hurricane analyses and predictions with TCI, IFEX field campaign observations, and CIMSS AMVs using the advanced hybrid data assimilation system for HWRF. Part II: Observation impacts on the analyses and predictions of Patricia (2015). Mon. Wea. Rev., 148, 1407–1430, https://doi.org/10.1175/MWR-D-19-0075.1.
  

• Kay, J.*, and X. Wang, 2020: A multiresolution ensemble hybrid 4DEnVar for global numerical prediction. Mon. Wea. Rev., 148, 825–847, https://doi.org/10.1175/MWR-D-19-0002.1.
  

• Gasperoni, N. A.*, X. Wang, and Y. Wang*, 2020: A comparison of methods to sample model errors for convection-allowing ensemble forecasts in the setting of multiscale initial conditions produced by the GSI-based EnVar assimilation system. Mon. Wea. Rev., 148, 1177–1203, https://doi.org/10.1175/MWR-D-19-0124.1.
  

• Wang, Y.*, and X. Wang, 2020: Prediction of Tornado-Like Vortex (TLV) Embedded in the 8 May 2003 Oklahoma City Tornadic Supercell Initialized from the Subkilometer Grid Spacing Analysis Produced by the Dual-Resolution GSI-Based EnVar Data Assimilation System. Mon. Wea. Rev., 148, 2909–2934, https://doi.org/10.1175/MWR-D-19-0179.1.
  

• Kerr, C.*, and X. Wang, 2020: Ensemble-based targeted observation method applied to radar radial velocity observations on idealized supercell low-level rotation forecasts: A proof of concept. Mon. Wea. Rev., 148, 877–890, https://doi.org/10.1175/MWR-D-19-0197.1.
  

• Johnson, A*., and X. Wang, 2020: Interactions between physics diversity and multiscale initial condition perturbations for storm-scale ensemble forecasting. Mon. Wea. Rev., 148, 3549–3565, https://doi.org/10.1175/MWR-D-20-0112.1.
  

• Johnson, A*., X. Wang, Y. Wang*, A. Reinhart, A. J. Clark, and I. L. Jirak, 2020: Neighborhood- and object-based probabilistic verification of the OU MAP ensemble forecasts during 2017 and 2018 Hazardous Weather Testbeds. Wea. Forecasting, 35, 169–191, https://doi.org/10.1175/WAF-D-19-0060.1.
  

• Chipilski*, H. G., X. Wang, and D. P. Parsons, 2020: Impact of assimilating PECAN profilers on the prediction of bore-driven nocturnal convection: A multi-scale forecast evaluation for the 6 July 2015 case study. Mon. Wea. Rev., 148, 1147–1175, https://doi.org/10.1175/MWR-D-19-0171.1.
  

• Jones, T. A., P. Skinner, N. Yussouf, K. Knopfmeier, A. Reinhart, X. Wang, K. Bedka, W. Smith Jr., and R. Palikonda, 2020: Assimilation of GOES-16 radiances and retrievals into the Warn-on-Forecast System. Mon. Wea. Rev., Mon. Wea. Rev., 148, 1829–1859, https://doi.org/10.1175/MWR-D-19-0379.1.
  

• Huang, Y.*, X. Wang, C. Kerr, A. Mahre, T. Yu, and D. Bodine, 2020: Impact of assimilating future clear-air radial velocity observations from phased array radar on a supercell thunderstorm forecast: An observing system simulation experiment study. Mon. Wea. Rev., 148, 3825–3845, https://doi.org/10.1175/MWR-D-19-0391.1.
  

• McFarquhar, G., E. Smith, E. Pillar-Little, K. Brewster, P. Chilson, T. Lee, S. Waugh, N. Yussouf, X. Wang, M. Xue, G. Boer, J. Gibbs, C. Fiebrich, B. Baker, J. Brotzge, F. Carr; H. Christopherson, M. Fengler, P. Hall, T. Hock, A. Houston, R. Huck, J. Jacob, R. Palmer, P. Quinn, M. Wagner, Y. Zhang, and D. Hawk, 2020: Current and future uses of UAS for improved fprecasts/warnings and scientific studies. Bull. Amer. Meteor. Soc., 101, E1322–E1328, https://doi.org/10.1175/BAMS-D-20-0015.1.
  

• Bannister, R. N., H. G. Chipilski*, and O. Martinez-Alvarado, 2020: Techniques and challenges in the assimilation of atmospheric water observations for numerical weather prediction towards convective scales. Quart. J. Roy. Meteor. Soc., 146, 1–48, https://doi.org/10.1002/qj.3652.
  

• Roberts, B., B. Gallo, I. Jirak, A. Clark, D. Dowell, X. Wang, and Y. Wang*, 2020: What makes a convection allowing ensemble of opportunity so good at forecasting thunderstorms? Wea. Forecasting, 35, 2293–2316, https://doi.org/10.1175/WAF-D-20-0069.1.
  

• Degelia, S. K.*, X. Wang, D. J. Stensrud, and D. D. Turner, 2020: Systematic evaluation of the impact of assimilating a network of ground-base remote sensing profilers for forecasts of nocturnal convection initiation during PECAN. Mon. Wea. Rev., 148, 4703–4728, https://doi.org/10.1175/MWR-D-20-0118.1.
  

• Feng, J.*, and X. Wang, 2020: Impact of increasing horizontal and vertical resolution during the HWRF hybrid EnVar data assimilation on the analysis and prediction of Hurricane Patricia (2015). Mon. Wea. Rev., 149, 419–441, https://doi.org/10.1175/MWR-D-20-0144.1.
  

• Huang, B.*, X. Wang, D. Kleist, and T. Lei, 2020: A Simultaneous Multi-scale Data Assimilation using Scale Dependent Localization in GSI-based Hybrid 4DEnVar for NCEP FV3-based GFS. Mon. Wea. Rev., 149, 479–501, https://doi.org/10.1175/MWR-D-20-0166.1.
  

• Wang, X., H. G. Chipilski*, C. H. Bishop, E. Satterfield, N. Baker, J. Whittaker, 2021: A Multiscale Local Gain Form Ensemble Transform Kalman Filter (MLGETKF). Mon. Wea. Rev., 149, 605–622, https://doi.org/10.1175/MWR-D-20-0290.1.
  

• Wang, Y.*, and X. Wang, 2021: Development of Convective-Scale Static Background Error Covariance within GSI-Based Hybrid EnVar System for Direct Radar Reflectivity Data Assimilation. Mon. Wea. Rev., 149, 2713–2736, https://doi.org/10.1175/MWR-D-20-0215.1.
  

• Pan, S., J. Gao, T. A. Jones, Y. Wang, X. Wang, and J. Li, 2021: Impact of assimilating satellite derived layered precipitable water, cloud water path and radar data on short-range tunderstorm forecasts. Mon. Wea. Rev., 149, 1359–1380, https://doi.org/10.1175/MWR-D-20-0040.1.
  

• Johnson, A.* and X. Wang, 2021: Observation impact study of an Arctic cyclone associated with a TPV-induced Rossby wave initiation event. Mon. Wea. Rev., 149, 1577–1591, https://doi.org/10.1175/MWR-D-20-0285.1.
  

• Huang, Y.*, W. Wu, G. M. McFarquhar, X. Wang, H. Morrison, A. Ryzhkov, Y. Hu, M. Wolde, C. Nguyen, A. Schwarzenboeck, J. Milbrandt, A. V. Korolev, and I. Heckman, 2021: Microphysical processes producing high ice water contents (HIWCs) in tropical convective clouds during the HAIC-HIWC field campaign: Evaluation of simulations using bulk microphysical schemes. Atmospheric Chemistry and Physics, 21, 6919–6944, https://doi.org/10.5194/acp-21-6919-2021.
  

• Davis, B.*, X. Wang, and X., Lu*, 2021: A comparison of HWRF 6-hourly 4DEnVar and hourly 3DEnVar assimilation of inner core Tail Doppler Radar observations for the prediction of Hurricane Edouard (2014). Atmosphere, 12, 942, https://doi.org/10.3390/atmos12080942.
  

• Wang, Y.*, and X. Wang, 2021: Rapid update with EnVar direct radar reflectivity data assimilation for the NOAA regional convection-allowing NMMB model over the CONUS: System description and initial experiment results. Atmosphere, 12, 1286, https://doi.org/10.3390/atmos12101286.
  

• Wilkins, A.*, A. Johnson*, X. Wang, N. A. Gasperoni*, and Y. Wang*, 2021: Multi-scale object-based probabilistic forecast evaluation of WRF-based CAM ensemble configurations. Atmosphere, 12, 1630, https://doi.org/10.3390/atmos12121630.
  

• Han, F.*, and X. Wang, 2021: An object-based method for tracking convective storms in convection allowing models. Atmosphere, 12, 1535, https://doi.org/10.3390/atmos12111535.
  

• Lu, X.*, and X. Wang, 2021: Improving the four-dimensional incremental analysis update (4DIAU) with the HWRF 4DEnVar data assimilation system for rapidly evolving hurricane prediction. Mon. Wea. Rev., 149, 4027–4043, https://doi.org/10.1175/MWR-D-21-0068.1.
  

• Weber, M., K. Hondl, N. Yussouf, Y. Jung, D. Stratman, B. Putnam, X. Wang, T. Schuur, C. Kuster, Y. Wen, J. Sun, J. Keeler, Z. Ying, J. Cho, J. Kurdzo, S. Torres, C. Curtis, D. Schvartzman, J. Boettcher, F. Nai, H. Thomas, D. Zrnic, I. Ivic, D. Mirkovic, C. Fulton, J. Salazar, G. Zhang, R. Plamer, M. Yeary, K. Cooley, M. Istok, and M. Vincint, 2021: Towards the next generation operational meteorological radar. Bull. Amer. Meteor. Soc., 102, E1357–E1383, https://doi.org/10.1175/BAMS-D-20-0067.1.
  

• Palmer, R. D., D. Whelan, D. Bodine, P. Kirstetter, M. Kumjian, J. Metcalf, M. Yeary, T. Yu, R. Rao, J. Cho, D. Draper, S. Durden, S. English, P. Kollias, K. Kosiba, M. Wada, J. Wurman, W. Blackwell, H. Bluestein, S. Collis, J. Gerth, A. Tuttle, X. Wang, D. Zrnic, 2021: The need for spectrum and the impact on weather observations. Bull. Amer. Meteor. Soc., 102, E1402–E1407, https://doi.org/10.1175/BAMS-D-21-0009.1.
  

• Gasperoni, N. A.*, X. Wang, and Y. Wang*, 2022: Using a cost-effective approach to increase background ensemble member size within the GSI-based EnVar system for improved radar analyses and forecasts of convective systems. Mon. Wea. Rev., 150, 667–689, https://doi.org/10.1175/MWR-D-21-0148.1.
  

• Chandramouli, K.*, X. Wang, A. Johnson, J. Oktin, 2022: Online nonlinear bias correction in ensemble Kalman filter to assimilate GOES-R all-sky radiances for the analysis and prediction of rapidly developing supercells. Journal of Advances in Modeling Earth Systems, 14, e2021MS002711, https://doi.org/10.1029/2021MS002711.
  

• Chipilski, H*., X. Wang, D. Parsons, S. Degelia*, A. Johnson*, 2022: The value of assimilating different ground-based profiling networks on the forecasts of bore-generating nocturnal convection. Mon. Wea. Rev., 150, 1273–1292, https://doi.org/10.1175/MWR-D-21-0193.1.
  

• Huang, Y.*, X. Wang, A. Mahre, T. Yu and D. Bodine, 2022: Impacts of assimilating future phased array radar clear-air radial velocity observations on convection initiation forecasts. Mon. Wea. Rev., 150, 1563–1583, https://doi.org/10.1175/MWR-D-21-0199.1.
  

• Green, T.*, X. Wang and X. Lu*, 2022: Impact of assimilating ground-based and airborne radar observations for the analysis and prediction of the eyewall replacement cycle of Hurricane Matthew (2016) using the HWRF hybrid 3DEnVar system. Mon. Wea. Rev., 150, 1157–1175, https://doi.org/10.1175/MWR-D-21-0234.1.
  

• Kay, J. K.*, X. Wang, and Y. Masaya, 2022: An Observing System Simulation Experiment (OSSE) to study the impact of ocean surface observation from the Micro Unmanned Robot Observation Network (MURON) on tropical cyclone forecast. Atmosphere, 13, 779, https://doi.org/10.3390/atmos13050779.
  

• Kollias, P., R. Palmer, D. Bodine, T. Adachi, H. Bluestein, J. Y. N. Cho, C. Griffin, J. Houser, P. E. Kirstetter, M. R. Kumjian, J. M. Kurdzo, W. C. Lee, E. P. Luke, S. Nesbitt, M. Oue, A. Shapiro, A. Rowe, J. Salazar, R. Tanamachi, K. S. Tuftedal, X. Wang, D. Zrnic, and B. P. Treserras, 2022: Science applications of phased array radars. Bull. Amer. Meteor. Soc., 103, E2370–E2390, https://doi.org/10.1175/BAMS-D-21-0173.1.
  

• Lu, X.*, B. Davis*, and X. Wang, 2022: Improving the assimilation of enhanced atmospheric motion vectors for hurricane intensity predictions with HWRF. Remote Sensing, 14, 2040, https://doi.org/10.3390/rs14092040.
  

• Johnson, A.*, X. Wang, and T. Jones, 2022: Impacts of assimilating GOES-16 ABI channels 9 and 10 clear air and cloudy radiance observations with additive inflation and adaptive observation error in GSI-EnKF for a case of rapidly evolving severe supercells. JGR-Atmos., 127, e2021JD036157, https://doi.org/10.1029/2021JD036157.
  

• Wang, Y.*, and X. Wang, 2023: A multivariate additive inflation approach to improve storm-scale ensemble-based data assimilation and forecasts: Methodology and experiment with a tornadic supercell. Journal of Advances in Modeling Earth Systems, 15, e2022MS003307, https://doi.org/10.1029/2022MS003307.
  

• Yang, Y.*, and X. Wang, 2023: Impact of radar reflectivity data assimilation frequency on convection-allowing forecasts of diverse cases over the Continental United States. Mon. Wea. Rev., 151, 341–362, https://doi.org/10.1175/MWR-D-22-0095.1.
  

• Johnson, A.*, F. Han*, Y. Wang*, X. Wang, 2023: Scale-dependent Verification of the OU MAP convection allowing ensemble initialized with multi-scale and large-scale perturbations during the 2019 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. Atmosphere, 14, 255, https://doi.org/10.3390/atmos14020255.
  

• Zhong, Q.*, X. Wang, R. Ding, X. Lu*, Y. Huang*, W. Duan, and L. Liu, 2023: Impact of the low wavenumber structure in the initial vortex wind analyses on the prediction of the intensification of Hurricane Patricia (2015). JGR-Atmos., 128, e2022JD037082, https://doi.org/10.1029/2022JD037082.
  

• Degelia, S. K.*, and X. Wang, 2023: Impacts of methods for estimating the observation error variance for the frequent assimilation of thermodynamic profilers on convective-scale forecasts. Mon. Wea. Rev., 151, 855–875, https://doi.org/10.1175/MWR-D-21-0049.1.
  

• Lu, X.*, and X. Wang, 2023: A study of simultaneous assimilation of coastal ground-based and airborne radar observations on the prediction of Harvey (2017) with the hourly 3DEnVar system for HWRF. JGR-Atmos., 128, e2022JD037681, https://doi.org/10.1029/2022JD037681.
  

• Gasperoni, N. A.*, X. Wang, and Y. Wang*, 2023: Valid time shifting for an experimental RRFS convection-allowing EnVar data assimilation and forecast system: Description and systematic evaluation in real-time. Mon. Wea. Rev., 151, 1229–1245, https://doi.org/10.1175/MWR-D-22-0089.1.
  

• Jones, E. A.*, and X. Wang, 2023: A multi-resolution ensemble hybrid 4DEnVar with variable ensemble sizes to improve global and tropical cyclone track numerical prediction. Mon. Wea. Rev., 151, 1145–1166, https://doi.org/10.1175/MWR-D-22-0186.1.
  

• Wang, Y.*, and X. Wang, 2023: Simultaneous multiscale data assimilation using scale- and variable-dependent localization in EnVar for convection allowing analyses and forecasts: Methodology and experiments for a tornadic supercell. Journal of Advances in Modeling Earth Systems, 15, e2022MS003430, https://doi.org/10.1029/2022MS003430.
  

• Johnson, A.*, X. Wang, B. Blake, E. Rogers, Y. Wang, J. Beck, C. Alexander, J. Carley, A. Clark, 2023: Verification and model configuration sensitivity of simulated ABI radiance forecasts with the FV3-LAM model. Earth and Space Science, 10, e2022EA002651, https://doi.org/10.1029/2022EA002651. Dataset
  

• Zhong, Q.*, X. Lu*, X. Wang, R. Ding, W. Duan and Z. Hou, 2023: Role of the thermodynamic structure of the inner core in predicting the intensification of Hurricane Patricia (2015). JGR-Atmos., 128, e2023JD038645, https://doi.org/10.1029/2023JD038645.
  

• Degelia, S. K.*, X. Wang, Y. Wang*, and A. Johnson*, 2023: Assimilation of GOES-16 ABI all-sky radiance observations in RRFS using EnVar: System development and impacts for a severe convective event. Mon. Wea. Rev., 151, 2739–2758, https://doi.org/10.1175/MWR-D-23-0057.1.
  

• Gasperoni, N. A.*, X. Wang, Y. Wang*, and T. Li*, 2023: Evaluating the multiscale implementation of valid time shifting within a real-time EnVar data assimilation and forecast system for the 2022 HWT Spring Forecasting Experiment. Wea. Forecasting, 38, 2343–2362, https://doi.org/10.1175/WAF-D-23-0096.1.
  

• Brotzge, J. A., D. Berchoff, D. L. Carlis, F. H. Carr, R. H. Carr, J. J. Gerth, B. D. Gross, T. H. Hamill, S. E. Haupt, N. Jacob, A. McGovern, D. J. Stensrud, I. Suzunyogh, G. Szatkowski, and X. Wang, 2023: Challenges and opportunities in numerical weather prediction. Bull. Amer. Meteor. Soc., 104, E698–E705, https://doi.org/10.1175/BAMS-D-22-0172.1.
  

• Hu, G., S. L. Dance, A. Fowler, D. Simonin, J. Walker, S. Healy, D. Hotta, U. Lohnert, T. Miyoshi, N. C. Prive, O. Stiller, X. Wang, and M. Weissmann, 2023: On methods for assessment of the influence and impact of observations in convection-permitting numerical weather prediction. Atmos. Ocea. Phys., published, https://doi.org/10.48550/arXiv.2309.16433.
  

• Yang, Y.*, and X. Wang, 2024: A comparison of 3DEnVar and 4DEnVar for convective-scale direct radar reflectivity data assimilation in the context of filter and smoother. Mon. Wea. Rev., 152, 59–78, https://doi.org/10.1175/MWR-D-23-0082.1.
  

• Johnson, A.*, and X. Wang, 2024: Impacts of initial condition perturbation blending in 10- and 40-member convection-allowing ensemble forecasts. Mon. Wea. Rev., 152, 1421–1441, https://doi.org/10.1175/MWR-D-23-0188.1.
  

• Kim, Y.-S.*, X. Wang, and A. Johnson*, 2024: Impact of microphysics parameterization schemes on the assimilation of GOES-16 ABI all-Sky infrared radiances for a bow echo analysis and prediction. JGR-Atmos., 129, e2023JD040046, https://doi.org/10.1029/2023JD040046.
  

• Gasperoni, N.*, X. Wang, F. Carr, and K. Brewster, 2024: Exploring ensemble forecast sensitivity to observations for a convective-scale data assimilation system over the Dallas-Fort Worth testbed. Mon. Wea. Rev., 152, 571–588, https://doi.org/10.1175/MWR-D-23-0091.1.
  

• Li, T.-H.*, X. Wang, and X. Lu*, 2024: Impact of direct radar reflectivity data assimilation on the simulation of mesoscale descending inflow and secondary eyewall formation in Hurricane Matthew (2016). Geophysical Research Letter, 51, e2023GL106514, https://doi.org/10.1029/2023GL106514.
  

• Lyu, X.*, and X. Wang, 2024: The impact of High-Density Airborne Observations and Atmospheric Motion Vector observation assimilation on the prediction of rapid intensification of Hurricane Matthew (2016). Atmosphere, 15, 395, https://doi.org/10.3390/atmos15040395.
  

• Wang, Y.*, and X. Wang, 2024: Improving CONUS convective-scale forecasting with simultaneous multiscale data assimilation. JGR-Atmos., 129, e2023JD040206, https://doi.org/10.1029/2023JD040206. GSIsrc
  

• Lu, X.*, and X. Wang, 2024: Simultaneous multiscale 4DEnVar with scale dependent localization (SDL) in hurricane analysis and forecast system (HAFS). JGR-Atmos., 129, e2023JD040550, https://doi.org/10.1029/2023JD040550.
  

• Chen, Z.*, A. Johnson*, and X. Wang, 2024: Impact of data assimilation in sensitive features on the predictability of the 2012 Great Arctic Cyclone. JGR-Atmos., 129, e2023JD038851, https://doi.org/10.1029/2023JD038851.
  

• Yokota, S., J. R. Carley, T. Lei, S. Liu, D. T. Kleist, Y. Wang*, and X. Wang, 2024: Scale- and variable-dependent localization for 3DEnVar data assimilation in the rapid refresh forecast system. Journal of Advances in Modeling Earth Systems, 16, e2023MS004098, https://doi.org/10.1029/2023MS004098.
  

• Johnson, A.*, X. Wang, T. Hutchinson, and J. Creus-Costa, 2024: Impact of WindBorne observation assimilation on prediction of a TPV merger case from THINICE. JGR-Atmos., 129, e2024JD041395, https://doi.org/10.1029/2024JD041395.
  

• Feng, J., F. Judt, J. Zhang, and X. Wang, 2024: Influence of region-dependent error growth on the predictability of track and intensity of Typhoon Chan-hom (2020) in high-resolution HWRF ensembles. Atmos. Rea., 308, 107536, https://doi.org/10.1016/j.atmosres.2024.107536.
  

• Feng, X., D. Schvartzman, B. Parajuli, and X. Wang, 2024: Enhancing meteorological mobile radar observations through radar location optimization. IEEE Transactions on Geoscience and Remote Sensing, 62, 4108611, https://doi.org/10.1109/TGRS.2024.3447550.
  

• Riviere, G., J. Delanoë, J. D. Doyle, J. Methven; C. Barrell, M. Fearon, S. Gray, A. Johnson, O. Jourdan, T. Lachlan-Cope, I. Renfrew, R. Torn, A. Volonté, A. Weiss, M. Wimmer, C. Aubry, A. Baudoux, E. Bazile, D. Beeden, M. Bennett, K. Biernat, C. M Bitz, E. Blanchard-Wrigglesworth, S. Bounissou, M. Bray, T. Burg, J. Burzdak, S. Businger, P. Capute, C. Caudoux, S. Cavallo, L. Cossalter, C. Cozzolino, H. Croad, V. Douet, A. Elvidge, P. Finocchio, C. Gourbeyre, B. Harvey, K. Huet, T. Hutchinson, R. Ladkin, K. Marshland, O. Martinez-Alvarado, G. Mioche, F. Pantillon, C. Paquette, D. B Parsons, O. Persson, L. Raillard, J. Raut, Y. Seity, J. Trules, E. Vignon, and X. Wang, 2024: The THINICE field campaign: Interactions between Arctic cyclones, tropopause polar vortices, clouds and sea ice in summer. Bull. Amer. Meteor. Soc., 105, E2330–E2354, https://doi.org/10.1175/BAMS-D-23-0143.1.
  

• Chen, Z.*, A. Johnson*, and X. Wang, 2024: Impact of assimilating WindBorne observations following different parts of a TPV on the predictability of an Arctic Cyclone during THINICE. JGR-Atmos., 129, e2024JD042196, https://doi.org/10.1029/2024JD042196.
  

• Johnson, A.*, and X. Wang, 2025: Impact of NWP model configuration and training sample characteristics on random forest based day-1 convective outlook guidance. Earth and Space Science, 12, e2024EA003822, https://doi.org/10.1029/2024EA003822.
  

• Lu, X.*, and X. Wang, 2025: GOES‐16 ABI All‐Sky Brightness Temperature Into the HAFS Dual‐Resolution Self‐Consistent EnVar DA System: Methods for Observation Error Estimation and Impact on Hurricane Laura (2020). Earth and space sciences, 12, e2024EA004058, https://doi.org/10.1029/2024EA004058.
  

• Li, T.-H.*, N. Gasperoni*, X. Wang, and Y. Wang*, 2025: Assessing the impact of the valid-time-shifting method on the EnVar analysis of the storm environment for convection initiation cases. Wea. Forecasting, 40, 2003–2022, https://doi.org/10.1175/WAF-D-24-0096.1.
  

• Shearer, A.*, A. Johnson*, and X. Wang, 2025: Flow-dependent evaluation and training of random forest-based probabilistic forecasts of severe weather hazards. Wea. Forecasting, 40, 2357–2379, https://doi.org/10.1175/WAF-D-24-0204.1.
  

• Kim, Y.-S.*, and X. Wang, 2025: Simultaneous multiscale data assimilation of GOES-16 ABI all-sky radiances and radar reflectivity on a dryline convection event. JGR-Atmos., 130, e2025JD043682, https://doi.org/10.1029/2025JD043682.
  

• Yang, Y.*, X. Wang, and Y. Wang*, 2025: Cost-effective convective-scale static background-error covariances: Methodology and experiment in RRFS hybrid EnVar system for direct radar reflectivity data assimilation over the CONUS. Journal of Advances in Modeling Earth Systems, 17, e2024MS004917, https://doi.org/10.1029/2024MS004917.
  

• Degelia, S.*, and X. Wang, 2025: Impacts of accounting for flow-dependent, interchannel observation error correlations when assimilating GOES-16 ABI all-sky radiance observations for forecasts of a severe convective event. Mon. Wea. Rev., accepted, https://doi.org/10.1175/MWR-D-25-0205.1.
  

• Thiruvengadam, P.*, and X. Wang, 2025: A comparison of En4DVar and 4DEnVar in convective-scale supercell data assimilation: A study of the 8 May 2003 Oklahoma City tornadic supercell. Q. J. R. Meteo. Soc., e70071, https://doi.org/10.1002/qj.70071.
  

• Yang, Y.*, and X. Wang, 2025: Direct assimilation of GOES-16 ABI all-sky radiances in HAFS self-cycled dual-resolution EnVar system: System description and a case study of Hurricane Laura (2020). Wea. Forecasting, accepted, https://doi.org/10.1175/WAF-D-25-0055.1.
  

• Chipilski, H. G.*, and X. Wang, 2020: Sampling the initial condition uncertainty in multiscale dynamical systems. J. Atmo. Sci., submitted.
  

• Kubalek, D.*, A. Johnson*, and X. Wang, 2024: Impacts of multiscale predictors on random forest based probabilistic forecasts of severe weather hazards. Wea. Forecasting, submitted.
  

• Xu, N.*, X. Wang and Y. Wang*, 2024: Scale dependent inflation to improve multiscale ensemble based data assimilation. Journal of Advances in Modeling Earth Systems, submitted.
  

• Gasperoni, N. A.*, X. Wang, T.-H. Li*, and Y. Wang*, 2025: A blending method for multiscale valid time shifting within an RRFS-like ensemble-based data assimilation system. Wea. Forecasting, submitted.
  

• Li, T.-H.*, X. Wang, X. Lu, Y. Yang*, J. A. Zhang, and H.-S. Kim, 2025: Using novel observations to evaluate background ensemble forecasts at the air-sea interface from a self-cycled HAFS data assimilation system for Hurricane Fiona (2022). JGR-Atmos., submitted.
  

• Wang, Y.*, and X. Wang, 2025: Cost-effective data assimilation using machine learning: A proof of concept for convective-scale analysis and forecast. Quart. J. Roy. Meteor. Soc., submitted.
  

• Jones, E. A.*, and X. Wang, 2025: Vertical flow-dependent localization in hybrid 4DEnVar for GFS-based global numerical weather prediction: Methodology and cycling experiment results. Mon. Wea. Rev., submitted.
  

• Thiruvengadam, P.*, X. Wang, and Y. Wang*, 2025: Improving background error covariance with the convolution neural network (CNN) in the gain form ensemble transform Kalman filter (GETKF). Geophysical Research Letters, submitted.
  

* student, postdoc, and research scientist authors from MAP