Publications Since Center Established

2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014




2023

  1. Mykolajtchuk, P. D.*, K. C. Eure*, Y. Zhang, D. J. Stensrud, F. Zhang, S. J. Greybush, and M. R. Kumjian, 2023: Diagnosing a missed convective initiation forecast by assimilating GOES-16 satellite radiances and WSR-88D radar observations. Weather and Forecasting, 38(10), 1935-1951, doi:10.1175/WAF-D-23-0010.1.
  2. Eure, K. C.*, P. D. Mykolajtchuk*, Y. Zhang, D. J. Stensrud, F. Zhang, S. J. Greybush, and M. R. Kumjian, 2023: Simultaneous Assimilation of Radar and Satellite Observations to Improve Ensemble Forecasts of Severe Weather. Monthly Weather Review, 151(3), 795-813, doi:10.1175/MWR-D-22-0188.1.
  3. Wang, Y.*, M. Gueye, S. J. Greybush, H. Greatrex, A. J. Whalen, P. Ssentongo, F. Zhang, G. Jenkins, and S. J. Schiff, 2023: Verification of Operational Numerical Weather Prediction Model Forecasts of Precipitation Using Satellite Rainfall Estimates over Africa. Meteorological Applications, 30(1), 22p, https://doi.org/10.1002/met.2112.
  4. Chan, M.-Y.*, X. Chen, and J. Anderson, 2023: The potential benefits of handling mixture statistics via a bi-Gaussian EnKF: Tests with all-sky satellite infrared radiances. Journal of Advances in Modeling Earth Systems, 15, e2022MS003357.
  5. Chen, X., L. R. Leung, Z. Feng, and Q. Yang, 2023: Diurnal MCS precedes the genesis of tropical cyclone Mora (2017): the role of convectively forced gravity waves. Journal of the Atmospheric Sciences, 80, 1463–1479.
  6. Chen, X., L. R. Leung, Z. Feng, and Q. Yang, 2023: Environmental controls on MCS lifetime rainfall over tropical oceans. Geophysical Research Letters, 50, e2023GL103267.
  7. Hartman, C.*, Chen, X., and Chan, M.-Y.*, 2023: Improving tropical cyclogenesis forecasts of Hurricane Irma (2017) through the assimilation of all-sky infrared brightness temperatures. Monthly Weather Review, 151, 837–853.
  8. He, J., X. Ma, and Chen, X., 2023: Benefit of assimilating satellite all-sky infrared radiances on the cloud and precipitation prediction of a long-lasting mesoscale convective system over the Tibetan plateau. Quarterly Journal of the Royal Meteorological Society, 149, 2742–2760.
  9. Peng, C.-H.*, and Chen, X., 2023: Warm-season afternoon precipitation peak in the central Bay of Bengal: Process-oriented diagnostics. Journal of Geophysical Research: Atmospheres, 128, e2022JD038398.
  10. Zhang, Y., 2023: Sensitivity of Intrinsic Error Growth to Large-Scale Uncertainty Structure in a Record-Breaking Summertime Rainfall Event. Journal of the Atmospheric Sciences,80,5,1415-1432.
    11. doi:10.1175/JAS-D-22-0231.1
  11. Zhang, Y., X. Chen, and M. M. Bell, 2023: Improving Short-Term QPF Using Geostationary Satellite All-Sky Infrared Radiances: Real-Time Ensemble Data Assimilation and Forecast during the PRECIP 2020 and 2021 Experiments. Weather and Forecasting , 38, 4, 591-609. doi:10.1175/WAF-D-22-0156.1

2022

  1. Fan, D.*, S. J. Greybush, X. Chen, Y. Lu, G. S. Young, and F. Zhang, 2022: Exploring the Role of Deep Moist Convection in the Wavenumber Spectra of Atmospheric Kinetic Energy and Brightness Temperature. Journal of the Atmospheric Sciences, 79(10), 2721-2737, doi:10.1175/JAS-D-21-0285.1.
  2. McMurdie, L. A., G. M. Heymsfield, J. E. Yorks, S. A. Braun, G. Skofronick-Jackson, R. Rauber, S. Yuter, B. Colle, G. M. McFarquahr, M. Poellot, D. R. Novak, T. J. Lang, R. Kroodsma, M. McLinden, M. Oue, P. Kollias, M. R. Kumjian, S. J. Greybush, A. J. Heymsfield, J. A. Finlon, V. McDonald, S. Nicholls, 2022: Chasing Snowstorms: The Investigation of Microphysics and Preciptation for Atlantic Coast-threatening Snowstorms (IMPACTS) Campaign. BAMS, 103(5), E1243-E1269, doi:10.1175/BAMS-D-20-0246.1.
  3. Mooring, T. A., G. E. Davis, and S. J. Greybush, 2022: Low-level jets and the convergence of Mars data assimilation algorithms. J. Geophys. Res. Planets, 127, 2, doi:10.1029/2021JE006968.
  4. Seibert, J. J.*, S. J. Greybush, J. Li, Z. Zhang, and F. Zhang, 2022: Applications of the Geometry-Sensitive Ensemble Mean for Lake-Effect Snowbands and Other Weather Phenomena. Mon. Wea. Rev., 150, 2, 409-429, doi:10.1175/MWR-D-21-0212.1.
  5. Chan, M.*, Chen, X., & Leung, L. R., 2022: A High-Resolution Tropical Mesoscale Convective System Reanalysis (TMeCSR). Journal of Advances in Modeling Earth Systems, 14, e2021MS002948. doi:10.1029/2021MS002948
  6. Song, F., Leung, L. R., Feng, Z., Chen, X., & Yang, Q., 2022: Observed and projected changes of large-scale environments conducive to spring MCS initiation over the US Great Plains. Geophys. Res. Lett., 49, e2022GL098799. doi:10.1029/2022GL098799
  7. Rao, X., Zhao, K., Chen, X., Huang, A., Hu, S., Hu, D., & Liu, X., 2022: Roles of multi-scale orography in triggering nocturnal convection at a summer rainfall hotspot over the South China coast: A case study. J. Geophys. Res. Atmos., 127, e2022JD036503. https://doi.org/10.1029/2022JD036503
  8. Chen, X., Leung, L. R., Feng, Z., & Yang, Q., 2022: Precipitation-moisture coupling over tropical oceans: Sequential roles of shallow, deep, and mesoscale convective systems. Geophys. Res. Lett., 49, e2022GL097836. https://doi.org/10.1029/2022GL097836
  9. Chen, X., Leung, L. R., Feng, Z., & Song, F., 2022: Crucial Role of Mesoscale Convective Systems in the Vertical Mass, Water, and Energy Transports of the South Asian Summer Monsoon, J. Climate, 35(1), 91-108. https://doi.org/10.1175/JCLI-D-21-0124.1
  10. Chan, M.* and Chen, X. (2022). Improving Analyses and Forecasts of a Tropical Squall Line using Upper Tropospheric Infrared Satellite Observations, Adv. Atmos. Sci., in press, https://doi.org/10.1007/s00376-021-0449-8
  11. Ruppert, J. H., Jr., Koch, S. E., Chen, X., Du, Y., Seimon, A., Sun, Y. Q., Wei, J., & Bosart, L. F., 2022: Mesoscale Gravity Waves and Midlatitude Weather: A tribute to Fuqing Zhang , Bull. Amer. Meteor. Soc., https://journals.ametsoc.org/view/journals/bams/aop/BAMS-D-20-0005.1/BAMS-D-20-0005.1.xml
  12. Zhang, Y., E. E. Clothiaux, and D. J. Stensrud, 2022: Correlation structures between satellite all-sky infrared brightness temperatures and the atmospheric state at storm scales. Advances in Atmospheric Sciences, 39, 5, 714-732. doi:10.1007/s00376-021-1018-x
  13. Zhang, Y., Yu, H., Zhang, M., Yang, Y., and Z. Meng, 2022: Uncertainties and error growth in forecasting the record-breaking rainfall in Zhengzhou, Henan on 19–20 July 2021. Sci. China Earth Sci. 65, 1903–1920 (2022). doi:10.1007/s11430-022-9991-4

2021

  1. Zhang, Y., S. B. Sieron*, Y. Lu, X. Chen, R. G. Nystrom, M. Minamide, M.-Y. Chan*, C. M. Hartman*, Z. Yao*, J. H. Ruppert, Jr., A. Okazaki*, S. J. Greybush, E. E. Clothiaux, and F. Zhang, 2021: Ensemble-Based Assimilation of Satellite All-Sky Microwave Radiances Improves Intensity and Rainfall Predictions for Hurricane Harvey (2017). Geophys. Res. Lett., 48, 24, doi:10.1029/2021GL096410.
  2. Okazaki, A., T. Miyoshi, K. Yoshimura, S. J. Greybush, and F. Zhang, 2021: Revisiting online and offline data assimilation comparison for paleoclimate reconstruction: an idealized OSSE study. J. Geophys. Res. Atmos., 126, 16, doi:10.1029/2020JD034214.
  3. Nystrom, R. G.*, S. J. Greybush, X. Chen, and F. Zhang, 2021: Potential for new constraints on tropical cyclone surface exchange coefficients through simultaneous ensemble-based state and parameter estimation. Mon. Wea. Rev., 149, 2213-2230, doi:10.1175/MWR-D-20-0259.1.
  4. Ssentongo, P., C. Fronterre, A. Geronimo, S. J. Greybush, P. M. Mbabazi, J. Muvawala, S. Nahalamba, P. O. Omadi, B. T. Opar, S. A. Sinnar, Y. Wang*, A. J. Whalen, L. Held, C. Jewell, A. J. B. Muwanguzi, H. Greatrex, M. N. Norton, P. Diggle, and S. J. Schiff, 2021: Tracking and Predicting the African COVID-19 Pandemic. Proceedings of the National Academies of Science, 118 (28) e2026664118, doi:10.1073/pnas.2026664118.
  5. Chen, X., R.G. Nystrom*, C.A. Davis, and C.M. Zarzycki , 2021: Dynamical Structures of Cross-Domain Forecast Error Covariance of a Simulated Tropical Cyclone in a Convection-Permitting Coupled Atmosphere-Ocean Model. Monthly Weather Review, 149, 41-63. https://doi.org/10.1175/MWR-D-20-0116.1 .
  6. Munsell, E.R., S.A. Braun, and F. Zhang, 2021: GOES-16 observations of rapidly-intensifying tropical cyclones: Hurricanes Harvey (2017), Maria (2017), and Michael (2018). Monthly Weather Review, 149, Early Online Release. https://doi.org/10.1175/MWR-D-19-0298.1.
  7. Wang, C., K. Zhao, A. Huang, X. Chen and X.Rao 2021: The Crucial Role of Synoptic Pattern in Determining the Spatial Distribution and Diurnal Cycle of Heavy Rainfall over the South China Coast. Journal of Climate, 34, 2441–2458. https://doi.org/10.1175/JCLI-D-20-0274.1.
  8. Wu, D., F. Zhang, X. Chen, A.Ryzhkov. K. Zhao, M.R. Kumjian, X. Chen et al., 2021: Evaluation of Microphysics Schemes in Tropical Cyclones using Polarimetric Radar Observations: Convective Precipitation in an Outer Rainband development . Monthly Weather Review, 149, Early Online Release. https://doi.org/10.1175/MWR-D-19-0378.1.
  9. Zhang, Y.,D.J. Stensrud and E.E. Clothiaux, 2021: Benefits of the Advanced Baseline Imager (ABI) for Ensemble-Based Analysis and Prediction of Severe Thunderstorms. Monthly Weather Review , 149, 313–332. https://doi.org/10.1175/MWR-D-20-0254.1.
  10. Yu, C-L.*, A.C. Didlake, F. Zhang and R.G. Nystrom, 2021: Asymmetric Rainband Processes Leading to Secondary Eyewall Formation in a Model Simulation of Hurricane Matthew (2016). Journal of the Atmospheric Sciences, 78, 29-49. https://doi.org/10.1175/JAS-D-20-0061.1.
  11. Song, F., Feng, Z., Leung, L. R., Pokharel, B., Simon Wang, S.-Y., Chen, X., et al., 2021: Crucial roles of eastward propagating environments in the summer MCS initiation over the U.S. Great Plains. J. Geophys. Res. Atmos., 126, e2021JD034991. https://doi.org/10.1029/2021JD034991
  12. Zhang, Y., Chen, X., and Lu, Y., 2021: Structure and Dynamics of Ensemble Correlations for Satellite All-Sky Observations in an FV3-Based Global-to-Regional Nested Convection-Permitting Ensemble Forecast of Hurricane Harvey, Mon. Wea. Rev., https://doi.org/10.1175/MWR-D-20-0369.1
  13. Li, Z., Chan, J. C. L., Zhao, K., & Chen, X., 2021: Impacts of urban expansion on the diurnal variations of summer monsoon precipitation over the south China coast. J. Geophys. Res. Atmos., 126, e2021JD035318. https://doi.org/10.1029/2021JD035318
  14. Chen, X., Leung, L. R., Feng, Z., Song, F., & Yang, Q., 2021: Mesoscale convective systems dominate the energetics of the South Asian summer monsoon onset. Geophys. Res. Lett., 48, e2021GL094873. https://doi.org/10.1029/2021GL094873
  15. Yang, Q., Leung, L. R., Feng, Z., Song, F., and Chen, X., 2021: A Simple Lagrangian Parcel Model for the Initiation of Summer-time Mesoscale Convective Systems over the Central United States . J. Atmos. Sci., https://doi.org/10.1175/JAS-D-21-0136.1
  16. Hartman, C. M.*, Chen, X., Clothiaux, E. E., and Chan, M.*, 2021: Improving the Analysis and Forecast of Hurricane Dorian (2019) with Simultaneous Assimilation of GOES-16 All-Sky Infrared Brightness Temperatures and Tail Doppler Radar Radial Velocities, Mon. Wea. Rev., https://doi.org/10.1175/MWR-D-20-0338.1

2020

  1. Bao, X., F. Zhang, Y. Zhao, Y. Chen 2020: The Impact of the Observation Data Assimilation on Atmospheric Reanalyses over Tibetan Plateau and Western Yunnan-Guizhou Plateau. Atmosphere, 12, 1-13. https://doi.org/10.3390/atmos12010038.
  2. Minamide, M.*, F. Zhang and E.E. Clothiaux, 2020: Nonlinear Forecast Error Growth of Rapidly Intensifying Hurricane Harvey (2017) Examined through Convection-Permitting Ensemble Assimilation of GOES-16 All-Sky Radiances. Journal of the Atmospheric Sciences, 77, 4277–4296. https://doi.org/10.1175/JAS-D-19-0279.1.
  3. Chan, M., J.L., Anderson and X. Chen, 2020: An Efficient Bi-Gaussian Ensemble Kalman Filter for Satellite Infrared Radiance Data Assimilation. Monthly Weather Review , 148, 5087–5104. https://doi.org/10.1175/MWR-D-20-0142.1.
  4. Chen, X., R.G., Nystrom*, C.A., Davis and C.M. Zarzycki, 2020: The Crucial Role of Synoptic Pattern in Determining the Spatial Distribution and Diurnal Cycle of Heavy Rainfall over the South China Coast. Monthly Weather Review , 149, 41-63. https://doi.org/10.1175/MWR-D-20-0116.1.
  5. Zeng, X., R. Atlas, R.J. Birk, F.H. Carr, M.J. Carrier, L. Cucurull, W.H. Hooke, E. Kalnay, R. Murtugudde, D.J. Posselt, J.L. Russell, D.P. Tyndall, R.A. Weller, and F. Zhang, 2020: Use of Observing System Simulation Experiments in the United States. Bulletin of the American Meteorological Society, 101, 1427-1438. https://doi.org/10.1175/BAMS-D-19-0155.1 .
  6. Nystrom, R.G.*, R. Rotunno, C.A. Davis and F. Zhang, 2020: Consistent Impacts of Surface Enthalpy and Drag Coefficient Uncertainty between an Analytical Model and Simulated Tropical Cyclone Maximum Intensity and Storm Structure. Journal of the Atmospheric Sciences, 77, 3059–3080. https://doi.org/10.1175/JAS-D-19-0357.1.
  7. Chan, M., F. Zhang, X. Chen and L.R. Leung, 2020: Potential Impacts of Assimilating All-Sky Satellite Infrared Radiances on Convection-Permitting Analysis and Prediction of Tropical Convection. Monthly Weather Review , 148, 3203-3224. https://doi.org/10.1175/MWR-D-19-0343.1 .
  8. Kotsuki, S., A. Pensoneault, Okazaki, A. and T. Miyoshi, 2020: Weight structure of the Local Ensemble Transform Kalman Filter: A case with an intermediate atmospheric general circulation model. Quarterly Journal of the Royal Meteorological Society , 146, 3399-3415. https://doi.org/10.1002/qj.3852 .
  9. Qian, T., F. Zhang, J. Wei, J. He and Y. Lu, 2020: Diurnal Characteristics of Gravity Waves over the Tibetan Plateau in 2015 Summer Using 10-km Downscaled Simulations from WRF-EnKF Regional Reanalysis . Journal of Climate , 11, 1-12. https://doi.org/10.3390/atmos11060631>.
  10. Sun, Y.Q. and F. Zhang, 2020: A New Theoretical Framework for Understanding Multiscale Atmospheric Predictability. Journal of the Atmospheric Sciences, 77, 2297–2309. https://doi.org/10.1175/JAS-D-19-0271.1.
  11. Fu, J., F. Zhang and T.D. Hewson, 2020: Object-Oriented Composite Analysis of Warm-Sector Rainfall in North China. Monthly Weather Review , 148, 2719–2735. https://doi.org/10.1175/MWR-D-19-0038.1 .
  12. Minola, L., F. Zhang, C. Azorin‐Molina, A. A. Safaei Pirooz, R. G. J. Flay, H. Hersbach and D. Chen, 2020: Near‐surface mean and gust wind speeds in ERA5 across Sweden: towards an improved gust parametrization. Climate Dynamics , 55, 887–907. https://doi.org/10.1007/s00382-020-05302-6 .
  13. Yang, J., K. Zhao, X. Chen, A. Huang, Y. Zheng and K. Sun, 2020: Subseasonal and Diurnal Variability in Lightning and Storm Activity over the Yangtze River Delta, China, during Mei-yu Season . Journal of Climate , 33, 5013–5033. https://doi.org/10.1175/JCLI-D-19-0453.1 .
  14. J Zhu, L., X. Chen, , and L. Bai, , 2020: Relative roles of low‐level wind speed and moisture in the diurnal cycle of rainfall over a Tropical Island under monsoonal flows. Geophysical Research Letters , 47, 1-9. https://doi.org/10.1029/2020GL087467.
  15. Nystrom, R. G.*, X. Chen, F. Zhang and C.A. Davis, 2020: Nonlinear impacts of surface exchange coefficient uncertainty on tropical cyclone intensity and air‐sea interactions. Geophysical Research Letters, 47, 1-10. https://doi.org/10.1029/2019GL085783 .
  16. Ruppert, J.H, and X. Chen, 2020: Island Rainfall Enhancement in the Maritime Continent . Geophysical Research Letters , 47, 1-10. https://doi.org/10.1029/2019GL086545 .
  17. T. Ou, D., Chen, X. Chen, C.,Lin, K.,Yang, H., Lai, F. Zhang, 2020: Simulation of summer precipitation diurnal cycles over the Tibetan Plateau at the gray-zone grid spacing for cumulus parameterization. Climate Dynamics, 54, 3525–3539. https://doi.org/10.1007/s00382-020-05181-x.
  18. Pal, S., K.J. Davis , T. Lauvaux , E.V. Browell, B.J. Gaudet, D.R. Stauffer, M.D. Obland, Y. Choi, J.P. DiGangi, S. Feng, B. Lin, Natasha L. MilesR.M. Pauly, S.J. Richardson and F. Zhang, 2020: Observations of Greenhouse Gas Changes Across Summer Frontal Boundaries in the Eastern United States. Journal of Geophysical Research: Atmospheres, 125, 1-19. https://doi.org/10.1029/2019JD030526.
  19. Chen, Y-S., J.Y. Harrington, J. Verlinde, F. Zhang, and M. Oue, 2020: Observations of Dynamical Response of an Arctic Mixed‐Phase Cloud to Ice Precipitation and Downwelling Longwave Radiation From an Upper‐Level Cloud. Journal of Geophysical Research: Atmospheres, 125, 1-17. https://doi.org/10.1029/2019JD031089.
  20. Sun, Y.Q., F. Zhang, L. Magnusson, R. Buizza, J-H. Chen, and K. Emanuel, 2020: Reply to “Comments on ‘What Is the Predictability Limit of Midlatitude Weather?’”. Journal of the Atmospheric Sciences, 77, 787–793. https://doi.org/10.1175/JAS-D-19-0308.1.
  21. Ruppert, J. H., A. A. Wing, X. Tang, and E. L. Duran 2020: The critical role of cloud-infrared radiation feedback in tropical cyclone development . Proceedings of the National Academy of Sciences , 117, 27884–27892. https://doi.org/10.1073/pnas.2013584117 .
  22. Wing, A.A., Stauffer, C.L., Becker, T., Reed, K.A., Ahn, M.S., Arnold, N.P., etal., Ruppert, J.H., et al., 2020: Clouds and convective self‐aggregation in a multi-model ensemble of radiative‐convective equilibrium simulations. Journal of Advances in Modeling Earth Systems, 12, 1-38. https://doi.org/ 10.1029/2020MS002138 .
  23. Ruppert, J.H, and X. Chen, 2020: Convectively forced diurnal gravity waves in the maritime continent. Journal of the Atmospheric Sciences, 77, 1119-1136. https://doi.org/10.1175/JAS-D-19-0236.1.
  24. Lai, H-W.*,F. Zhang, E.E. Clothiaux, D. R. Stauffer, B. J. Gaudet, J. Verlinde, and D. L. Chen, 2020: Modeling Arctic Boundary Layer Cloud Streets at Grey-zone Resolutions. ADVANCES IN ATMOSPHERIC SCIENCES , 37, 42-56. https://doi.org/10.1007/s00376-019-9105-y .
  25. Chen, X. O.M. Pauluis, L.R. Leung and F. Zhang , 2020: Significant Contribution of Mesoscale Overturning to Tropical Mass and Energy Transport Revealed by the ERA5 Reanalysis. Geophysical Research Letters, 47, 1-10. https://doi.org/10.1029/2019GL085333 .

2019

  1. Ruppert, J.H, and M. O’Neill, 2019: Diurnal Cloud and Circulation Changes in Simulated Tropical Cyclones. Geophysical Research Letters , 46, 502-511. https://doi.org/10.1029/2018GL081302.
  2. Ruppert, J.H, and D. Klocke, 2019: The Two Diurnal Modes of Tropical Upward Motion. Geophysical Research Letters , 46, 2911-2921. https://doi.org/10.1029/2018GL081806.
  3. Chen, A., K.A. Emanuel, D. Chen, C. Lin and F. Zhang, 2019: Rising future tropical cyclone-induced extreme winds in the Mekong River Basin. Science Bulletin, 15, 419-424. https://doi.org/10.1016/j.scib.2019.11.022.
  4. Alley, R. B., K. Emanuel and F. Zhang, 2019: Advances in Weather Prediction. Science, 363, 342-344. doi: 10.1126/science.aav7274 ; [Public press release at AAAS and EurekAlert!]; [Podcast at Science Friday with Richard Alley]
  5. Zhang, F., Y.Q. Sun*, L. Magnusson, R. Buizza, S.-J. Lin, J.-H. Chen and K. Emanuel, 2019: What is the Predictability Limit of Midlatitude Weather? Journal of the Atmospheric Sciences, 76, 1077-1091. doi:10.1175/JAS-D-18-0269.1
  6. Minamide, M.*, and F. Zhang, 2019: Adaptive Backgroun Error Inflation for Assimilating All-sky Satellite Radiance. Quarterly Journal of the Royal Meteorological Society, 145, 805-823. https://doi.org/10.1002/qj.3466.
  7. Tao, D.*, and F. Zhang, 2019: Evolution of dynamic and thermodynamic structures before and during rapid intensification of tropical cyclones: sensitivity to vertical wind shear. Monthly Weather Review, 147, 1171-1191, https://doi.org/10.1175/MWR-D-18-0173.1.
  8. Chen, X., F. Zhang, and J. Ruppert, 2019: Modulations of Coastal Rainfall Diurnal Cycle over South China by the Boreal Summer Intraseasonal Oscillation. Journal of Climate ,32, 2089-2108, https://doi.org/10.1175/JCLI-D-18-0786.1
  9. Chen, X., and F. Zhang, 2019: Relative roles of preconditioning moistening and global circumnavigating mode on the MJO convective initiation during DYNAMO. Geophysical Research Letters, 46, 1079–1087. https://doi.org/10.1029/2018GL080987.
  10. Tapiador, F.J., R Roca, A. del Genio, B. deWitte, W. Petersen, F. Zhang, 2018: Is Precipitation a Good Metric of Model Performance? Bulletin of the American Meteorological Society, 100, 223-233, https://doi.org/10.1175/BAMS-D-17-0218.1
  11. Tang, X.*, Z. Tan, J. Fang, E. B. Munsell and F. Zhang, 2019: Impact of the Diurnal Radiation Contrast on the Contraction of Radius of Maximum Wind during Intensification of Hurricane Edouard (2014) Journal of the Atmospheric Sciences, 76, 421-432, https://doi.org/10.1175/JAS-D-18-0131.1.
  12. Keller, J., C. Grams, M. Riemer, H. Archambault, L. Bosart, J. Doyle, J. Evans, T.Galarneau, K. Griffin, P. Harr, N. Kitabatake, R. McTaggart-Cowan, F. Pantillon, J.Quinting, C. Reynolds, E. Ritchie, R. Torn, and F. Zhang, 2019: The Extratropical Transition of Tropical Cyclones Part II: Interaction with the midlatitude flow, downstream impacts, and implications for predictability , Monthly Weather Review, 147, 1077-1106, https://doi.org/10.1175/MWR-D-17-0329.1.
  13. Schuster D., M. Mayernik, C. Hou, G. Stossmeister, F. Zhang, T. Bgyuen, R. Downs, D. Kinkade and M. Ramamurthy, 2019: Challenges and Future Directions for Data Management in the Geosciences. Bulletin of the American Meteorological Society, 100, 909-912, https://doi.org/10.1175/BAMS-D-18-0319.1
  14. Du, Y.*, and F. Zhang, 2019: Banded Convective Activity Associated with Mesoscale Gravity Waves over Southern China. Journal of Geophysical Research - Atmospheres, 124, 1912-1930, https://doi.org/10.1029/2018JD029523 .
  15. Greybush, S. J., H. E. Gillespie*, and R. J. Wilson, 2019: Transient Eddies in the TES/MCS Ensemble Mars Atmosphere Reanalysis System (EMARS). ICARUS 317, 158-181. https://doi.org/10.1016/j.icarus.2018.07.001.
  16. Zhang, F., M. Minamide*, R. Nystrom*, X. Chen, S.-J. Lin, L. Harris, 2019: Improving Harvey forecasts with next-generation weather satellites. Bulletin of the American Meteorological Society, 100, 1217-1222, https://doi.org/10.1175/BAMS-D-18-0149.1
  17. Wang, Y.*, K. Yang, X. Zhou, B. Wang, D. Chen, H. Liu, C. Lin and F. Zhang, 2019: The formation of a dry-belt in the north side of central Himalaya Mountains. Geophysical Research Letters, 46, 2993-3000, https://doi.org/10.1029/2018GL081061
  18. Hayatbini, N.*, K. Hsu, S. Sorooshian, Y. Zhang, F. Zhang 2019: Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS. Journal of Hydrometeorology, 20, 901-913, https://doi.org/10.1175/JHM-D-18-0197.1.
  19. Chen, H. W.*, F. Zhang, T. Lauvaux, K.J. Davis, S. Feng, M.P. Butler, R.B. Alley, 2019: Characterization of Regional-Scale CO2 Transport Uncertainties in an Ensemble with Flow-Dependent Transport Errors. Geophysical Research Letters,46, 4049-4058, https://doi.org/10.1029/2018GL081341
  20. Alley, R.B., W. Li, B. Parizek, and F. Zhang, 2019: Evaluation of ice-stream model sensitivities for parameter estimation. Earth and Planetary Science Letters, 516, 49-55, https://doi.org/10.1016/j.epsl.2019.03.035.
  21. Fang, J., O. Pauluis, F. Zhang, 2019: The thermodynamic cycles and associated energetics of Hurricane Edouard (2014) during its intensification. Journal of the Atmospheric Sciences, 76, 1769-1784, https://doi.org/10.1175/JAS-D-18-0221.1
  22. Li, X.*, F Zhang, Q. Zhang and M.R. Kumjian, 2019: Sensitivity of hail precipitation to ensembles of uncertainties of representative initial environmental conditions from ECMWF. Journal of Geophysical Research , 124, 6929-6948, https://doi.org/10.1029/2018JD029899.
  23. Thomas, A.*, A. Huff, X. Hu and F. Zhang, 2019: Quantifying uncertainties in WRF-Chem Simulations of Ground-Level Ozone in the Mid-Atlantic Region of the United States for Air Quality Prediction. Journal of Advances in Modeling Earth Systems, 11, 1100-1116, https://doi.org/10.1029/2018MS001457.
  24. Tao, D.*, K. Emanuel, F. Zhang, R. Rotunno, M. Bell and R.G. Nystrom*, 2019: Evaluation of the assumptions in the steady-state tropical cyclone self-stratified outflow using three-dimensional convection-allowing simulations. Journal of the Atmospheric Sciences, 76, 2995-3009, https://doi.org/10.1175/JAS-D-19-0033.1 .
  25. Lu, Y., and F. Zhang, 2019: Towards ensemble assimilation of hyperspectral satellite observations with data compression and dimension reduction using principal component analysis. Monthly Weather Review, 147, 3505-3518, https://doi.org/10.1175/MWR-D-18-0454.1.
  26. Chen, W. H.*, L.N. Zhang, F. Zhang, K.J. Davis, T. Lauvaux, S. Pal, B. Gaudet, and J.P. DiGangi , 2019: Evaluation of regional CO2mole fractions in the ECMWF CAMS real-time atmospheric analysis and NOAA CarbonTracker Near-Real Time reanalysis with airborne observations from ACT-America field campaigns. Journal of Geophysical Research: Atmospheres, 124, 8119-8133, https://doi.org/10.1029/2018JD029992.
  27. Nystrom,R.G.* and F. Zhang, 2019: Practical Uncertainties in the Limited Predictability of the Record-Breaking Intensification of Hurricane Patricia (2015). Monthly Weather Review, 147, 401-423, https://doi.org/10.1175/ MWR-D-18-0450.1.
  28. Ruppert, J.H. and F. Zhang, 2019: Diurnal Forcing and Phase Locking of Gravity Waves in the Maritime Continent. Journal of the Atmospheric Sciences,76, 2815-2835, https://doi.org/10.1175/JAS-D-19-0061.1 .
  29. Magnusson, L., J.D. Doylt, W.A. Komaromi, R. Torn, C.K.Tang, J.V.L.Chan, M.Yamaguchi, and F. Zhang 2019: Advances in understanding difficult cases of track forecasts. Tropical Cyclone Research and Review.
  30. He, J.*, Fuqing Zhang, Xingchao Chen, Xinghua Bao, Deliang Chen, Hyun Mee Kim, Hui‐Wen Lai, L. Ruby Leung, Xulin Ma, Zhiyong Meng, Tinghai Ou, Ziniu Xiao, Eun‐Gyeong Yang, and Kun Yang, 2019: Development and Evaluation of an Ensemble‐Based Data Assimilation System for Regional Reanalysis Over the Tibetan Plateau and Surrounding Regions. Journal of Advances in Modeling Earth Systems, 11, 2503–2522. https://doi.org/10.1029 /2019MS001665 .
  31. Chen, X. and Fuqing Zhang, 2019: Development of a Convection-Permitting Air-Sea-Coupled Ensemble Data Assimilation System for Tropical Cyclone Prediction. Journal of Advances in Modeling Earth Systems, 11, 3474-3496. https://doi.org/10.1029/2019MS001795 .
  32. Zhang, Y., David J. Stensrud, and Fuqing Zhang, 2019: Simultaneous Assimilation of Radar and All-Sky Satellite Infrared Radiance Observations for Convection-Allowing Ensemble Analysis and Prediction of Severe Thunderstorms. Monthly Weather Review, 147, 4389-4409. https://doi.org/10.1175/MWR-D-19-0163.1.
  33. Du,Y.*, Richard Rotunno and Fuqing Zhang, 2019: Impact of Vertical Wind Shear on Gravity Wave Propagation in the Land–Sea-Breeze Circulation at the Equator. Journal of the Atmospheric Sciences, 76, 3247-3265. https://doi.org/10.1175/JAS-D-19-0069.1.
  34. Eipper, D. T., S. J. Greybush, G. S. Young, S. Saslo*, T. D. Sikora, R. D. Clark, 2019: Lake-Effect Snowbands in Baroclinic Environments. Weather and Forecasting, 34, 1657-1674. https://doi.org/10.1175/WAF-D-18-0191.1.
  35. Greybush, S. J., E. Kalnay, R. J. Wilson, R. N. Hoffman, T. Nehrkorn, M. Leidner, J. Eluszkiewicz, H. E. Gillespie*, M. Wespetal, Y. Zhao, M. Hoffman, P. Dudas, T. McConnochie, A. Kleinboehl, D. Kass, D. McCleese, and T. Miyoshi, 2019: The Ensemble Mars Atmosphere Reanalysis System (EMARS) Version 1.0. Geoscience Data Journal, https://doi.org/10.18113/D3W375.
  36. Greybush, S. J., H. E. Gillespie*, and R. J. Wilson, 2019: Transient eddies in the TES/MCS Ensemble Mars Atmosphere Reanalysis System (EMARS). Icarus, 317, 158-181, https://doi.org/10.1016/j.icarus.2018.07.001.
  37. Sharma*, S., H. Gall, J. Gironas, and A. Mejia, 2019: Seasonal hydroclimatic ensemble forecasts anticipate nutrient and suspended sediment loads using a dynamical-statistical approach. Environmental Research Letters, 14(8), 1-16. https://doi.org/10.1088/1748- 9326/ab2c26.
  38. Sharma*, S., R. Siddique*, S. Reed, P. Anhert, A. Mejia, 2019: Hydrological model diversity enhances streamflow forecast skill at short- to medium-range timescales. Water Resources Research, 55, 5010-5030, https://doi.org/10.1029/2018WR023197.
  39. Gomez*, M., S. Sharma*, S. Reed, A. Mejia 2019: Skill of ensemble flood inundation forecasts at short- to medium-range timescales. Journal of Hydrology, 568, 207-220, https://doi.org/10.1016/j.jhydrol.2018.10.063.
  40. Zhang,H., X. Li, and J. Harlim, 2019: A parameter estimation method using linear response statistics: Numerical scheme. Chaos, 29, 033101, https://doi.org/10.1063/1.5081744.
  41. Jiang, S.W., and J. Harlim, 2019: https://doi.org/10.3390/e21060559. Entropy, 21(6), 559, https://doi.org/10.3390/e21060559.

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2018

  1. Zhang, F, K. Emanuel, 2018: Promises in air-sea fully-coupled data assimilation for future hurricane prediction Geophysical Research Letters45, 13,173-13,177, https://doi.org/10.1029/2018GL080970 .
  2. Tang*, X.,Z. Tan, J. Fang, E. B. Munsell and F. Zhang, 2018: Impact of the Diurnal Radiation Contrast on the Contraction of Radius of Maximum Wind during Intensification of Hurricane Edouard (2014) Journal of the Atmospheric Sciences, , https://doi.org/10.1175/JAS-D-18-0131.1 .
  3. Keller*, J., C. Grams, M. Riemer, H. Archambault, L. Bosart, J. Doyle, J. Evans, T.Galarneau, K. Griffin, P. Harr, N. Kitabatake, R. McTaggart-Cowan, F. Pantillon, J.Quinting, C. Reynolds, E. Ritchie, R. Torn, and F. Zhang, 2018: The Extratropical Transition of Tropical Cyclones Part II: Interaction with the 4 midlatitude flow, downstream impacts, and implications for predictability Journal of the Atmospheric Sciences, ,Preliminary accepted version https://doi.org/10.1175/MWR-D-17-0329.1 .
  4. Sieron*, S. B., F. Zhang, E. E. Clothiaux, L. N. Zhang, and Y. Lu, 2018: Representing Precipitation Ice Species with Both Spherical and Non-Spherical Particles for Microphysics-Consistent Cloud Microwave Scattering Properties. Journal of Advances in Modeling Earth Systems, 10, 1011–1028. https://doi.org/10.1002/2017MS001226 .
  5. Nystrom*, R., F. Zhang, E. B. Munsell, S. A. Braun, J. A. Sippel, Y. Weng, and K. Emanuel, 2018: Predictability and dynamics of Hurricane Joaquin (2015) explored through convection-permitting ensemble sensitivity experiments. Journal of the Atmospheric Sciences, 75, 401-424 https://doi.org/10.1175/JAS-D-17-0137.1.
  6. Munsell, E.B.*, F Zhang, S.A. Braun, J.A. Sippel, and A.C. Didlake, 2018: The inner-core temperature structure of Hurricane Edouard (2014): Observations and ensemble variability. Monthly Weather Review, 146, 135–155, https://doi.org/10.1175/MWR-D-17-0095.1
  7. Pan*, J., D. Teng*, L. Zhou, Y. Zhang, Y. Weng* and F. Zhang, 2018: Dynamical processes of heavy local rainfall over East China induced by Super Typhoon Soudelor (2015). Science China – Earth Science, https://doi.org/10.1007/s11430-017-9135-6.
  8. Ying, Y.*, F. Zhang, J. L. Anderson, 2018: On the selection of localization radius in ensemble filtering for multi-scale quasi-geostrophic dynamics. Monthly Weather Review,14, 543-560, https://doi.org/10.1175/MWR-D-17-0336.1
  9. Ying, Y.*, F. Zhang, 2018: Potentials in improving predictability of multiscale tropical weather systems evaluated through ensemble assimilation of simulated satellite-based observations. Journal of the Atmospheric Sciences, 75, 1675–1698, https://doi.org/10.1175/JAS-D-17-0245.1
  10. Chen, X., O. Pauluis, F. Zhang, 2018: Regional Simulation of Indian summer Monsoon Intraseasonal Oscillations at Gray Zone Resolution. Atmospheric Chemistry and Physics, 18, 1003-1022, https://www.atmos-chem-phys.net/18/1003/2018/acp-18-1003-2018.pdf.
  11. Chen, X., O. Pauluis, F. Zhang, 2018: Atmospheric overturning across multiple scales of an MJO event during the CINDY/DYNAMO Campaign. Journal of the Atmospheric Sciences, 75, 381-399. https://journals.ametsoc.org/doi/pdf/10.1175/JAS-D-17-0060.1.
  12. Chen, X., O. Pauluis, L. R. Leung, F. Zhang, 2018: Multiscale Atmospheric Overturning of Indian Summer Monsoon as Seen through Isentropic Analysis.Journal of the Atmospheric Sciences, 75, 3011-3010, https://journals.ametsoc.org/doi/pdf/10.1175/JAS-D-18-0068.1.
  13. Chen*, Y., F. Zhang, B. W. Green, and Y. Xu, 2018: Impacts of Ocean Cooling and Reduced Wind Drag on Hurricane Katrina (2005) Based on Numerical Simulations. Monthly Weather Review, 146, 287-306. https://journals.ametsoc.org/doi/pdf/10.1175/MWR-D-17-0170.1.
  14. Zhang, Y., F. Zhang, C. A. Davis and J. Sun, 2018: Diurnal evolution and structure of long-lived mesoscale convective vortices along the Mei-yu front over the East China Plains. Journal of the Atmospheric Sciences, 75, 1005–1025, https://doi.org/10.1175/JAS-D-17-0197.1 .
  15. Zhang, Y., and F. Zhang, 2018: A Review on the Ensemble-Based Data Assimilations for Severe Convective Storms". Advances in Meteorological Science and Technology, 8 (3), 38-52, https://doi.org/10.3969/j.issn.2095-1973.2018.03.003 .
  16. Lu, Y., and F. Zhang, 2018: A novel channel-synthesizing method for reducing uncertainties in satellite radiative transfer modeling. Geophysical Research Letters, 45,5115-5125, https://doi.org/10.1029/2018GL077342
  17. Li, J., F. Zhang, 2018: Geometry-Sensitive Ensemble Mean based on Wasserstein Barycenters: Proof-of-Concept on Cloud Simulations. Journal of Computational Statistics,27, 785-797, 10.1080/10618600.2018.1448831.
  18. Wu, D., K. Zhao, M. R. Kumjian, X. Chen, H. Huang, M. Wang, A. C. Didlake, Y. Duan, and F. Zhang, 2018: Kinematics and Microphysics of Convection in the Outer Rainband of Typhoon Nida (2016) revealed by Polarimetric Radar. Monthly Weather Review, 146, 2147-2159. https://journals.ametsoc.org/doi/pdf/10.1175/MWR-D-17-0320.1.
  19. Wang, M., K. Zhao, W.-C. Lee, F. Zhang, 2018: Microphysical and kinemaiitic structure of convective-scale elements in the inner rainband of Typhoon Matmo (2014) after landfall. Journal of Geophysical Research – Atmospheres,123, http://doi.org/10.1029/2018JD028578 .
  20. Liu, S., D. Tao*, K. Zhao, M. Minamide, and F. Zhang, 2018: Dynamics and predictability of the rapid intensification of Super Typhoon Usagi (2013). Journal of Geophysical Research – Atmospheres, 123 ,2147-2159.https://doi.org/10.1029/2018JD028561.
  21. Guo, L., B. Fan, F. Zhang, H. Lin and J. Zhao, 2018: The variability of severe dust storm occurrence in China from 1958 to 2007. Journal of Geophysical Research – Atmospheres,123 ,8035-8046. https://doi.org/10.1029/2018JD029042.
  22. Minamide, M.*, and F. Zhang, 2018: Assimilation of all-sky infrared radiances from Himawari-8 and impacts of moisture and hydrometer initialization on convection-permitting tropical cyclone prediction.Monthly Weather Review,, 146 ,3241-3258, https://doi.org/10.1175/MWR-D-17-0367.1.
  23. Zhang, Y., F. Zhang, and D. J. Stensrud, 2018: Assimilating All-Sky Infrared Radiances from GOES-16 ABI using an Ensemble Kalman Filter for Convection-Allowing Severe Thunderstorms Prediction. Monthly Weather Review,, 146 ,3363-3381, https://doi.org/10.1175/MWR-D-18-0062.1.
  24. Tapiador, F.J., R Roca, A del Genio, B deWitte, W Petersen, F Zhang, 2018: Is Precipitation a Good Metric of Model Performance? Bulletin of the American Meteorological Society, https://doi.org/10.1175/BAMS-D-17-0218.1
  25. Taraphdar*, S., F. Zhang, L. R. Lueng, O. Pauluis and X. Chen, 2018: MJO Affects the Monsoon Onset Timing over the Indian Region. Geophysical Research Letters, 45, 10,011-10,018, https://doi.org/10.1029/2018GL078804
  26. Eipper, D. T., G. S. Young, S. J. Greybush, S. Saslo*, T. D. Sikora, and R. D. Clark, 2018: Predicting the Inland Penetration of Long-Lake-Axis Parallel Snowbands. Weather and Forecasting, 33, 1435-1451. https://doi.org/10.1175/WAF-D-18-0033.1.
  27. Zarzar, C.M., H. Hosseiny, R. Siddique*, M. Gomez*, V. Smith, A. Mejia, and J. Dyer, 2018: A hydraulic multi-model ensemble framework for visualizing flood inundation uncertainty, Journal of the American Water Resources Association (JAWRA), 54(4), 07-819, https://doi.org/10.1111/1752-1688.12656.
  28. Sharma*, S., R. Siddique*, S. Reed, P. Ahnert, P. Mendoza, A. Mejia, 2018: Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system, Hydrology and Earth System Sciences, 22(3), 1831-1849, https://doi.org/10.5194/hess-22-1831-2018.
  29. De La Chevrotière, M.* and J. Harlim, 2018: Data-driven localization mappings in filtering the monsoon-Hadley multicloud convective flows . Monthly Weather Review, 146(4), 1197-1218, https://doi.org/10.1175/MWR-D-17-0381.1.
  30. Harlim, J. and H. Yang, 2018: Diffusion Forecasting Model with Basis Functions from QR-Decomposition, Journal of Nonlinear Science, 28(3), 847-872, https://doi.org/10.1007/s00332-017-9430-1.

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2017

  1. Emanuel, K. and F. Zhang, 2017: The Role of Inner Core Moisture in Tropical Cyclone Predictability and Practical Forecast Skill, Journal of the Atmospheric Sciences, 74, doi: 10.1175/JAS-D-17-0008.1.
  2. Pauluis, O., and F. Zhang, 2017: Reconstruction of thermodynamic cycles in a high resolution simulation of a hurricane. Journal of the Atmospheric Sciences, 74, 3357-3381. doi:10.1175/JAS-D-16- 0353.1
  3. Fang, J*, O Pauluis, F Zhang, 2017: Isentropic analysis on the intensification of Hurricane Edouard (2014). Journal of the Atmospheric Sciences,, 74, 4177–4197, doi:10.1175/JAS-D-17-0092.1
  4. Tang, X.*, Z. Tan, J. Fang*, Y.Q. Sun* and F. Zhang, 2017: Impacts of diurnal radiation cycle on secondary eyewall formation. Journal of the Atmospheric Sciences, 74, 3079-3098.
  5. Munsell, E. B.*, F. Zhang, J. A. Sippel, S. A. Braun, Y. Weng*, 2017: Dynamics and predictability of the intensification of Hurricane Edouard (2014). Journal of he Atmospheric Sciences, 74, 573-595.
  6. Sun, Y.Q.*, R. Rotunno, and F. Zhang, 2017: Contributions of moist convection and internal gravity waves to building the atmospheric "-5/3" kinetic energy spectra, Journal of the Atmospheric Sciences, 74, 185-201.
  7. Ying, Y.*, F. Zhang, 2017: Practical and intrinsic predictability of multi-scale weather and convectively-coupled equatorial waves during the active phase of an MJO. Journal of the Atmospheric Sciences, 74, 3771-3785, doi:10.1175/JAS-D-17-0157.1.
  8. Chen, X., F. Zhang, K. Zhao, 2017: Influence of Monsoonal Wind Speed and Moisture Content on Intensity and Diurnal Variations of the Mei-yu Season Coastal Rainfall over South China. Journal of the Atmospheric Sciences, 74, 2835-2856.
  9. Stern, D. P.*, J. Vigh, D. S. Nolan, and F. Zhang, 2017: Reply to Comments On “Revisiting the Relationship Between Eyewall Contraction and Intensification”. Journal of the Atmospheric Sciences, 74, 4275-4286, https://doi.org/10.1175/JAS-D-17-0120.1
  10. Zhang, F., D. Tao*, Y.Q. Sun* and J. D. Kepert, 2017: Dynamics and predictability of secondary eyewall formation in sheared tropical cyclones. Journal of Advances in Modeling Earth Systems (JAMES), 9, DOI: 10.1002/2016MS000729.
  11. Minamide, M.*, and F. Zhang, 2017: Adaptive Observation Error Inflation for Assimilating All-sky Satellite Radiance. Monthly Weather Review,145, 1063-1081.
  12. Evans, C., K. Wood, S. Aberson, H. Archambault, S. Milrad, L. Bosart, K.,Corbosiero, C. Davis, J. Dias Pinto, J. Doyle, C. Fogarty, T. Galarneau, Jr., C.,Grams, K. Griffin, J. Gyakum, R. Hart, N. Kitabatake, H. Lentink, R. McTaggartCowan,W. Perrie, J. Quinting, C. Reynolds, M. Riemer, E. Ritchie, Y. Sun, F. Zhang, 2017: The Extratropical Transition of Tropical Cyclones. Part I: Cyclone Evolution and Direct Impacts. Monthly Weather Review, 145, 4317-4344, doi:10.1175/MWR-D-17-0027.1.
  13. Melhauser, C.*, F. Zhang, Y. Weng, Y. Jin, H. Jin and Q. Zhao, 2017: A Multiple-Model Convection-permitting Ensemble Examination of the Probabilistic Prediction of Tropical Cyclones: Hurricanes Sandy (2012) and Edouard (2014). Weather and forecasting, 32, 665-668.
  14. Zhao, K., M. Wang, P. Fu, Z. Yang, J. Wen, W-C Lee, and F. Zhang, 2017: Doppler radar analysis of a tornadic miniature supercell during the Landfall of Typhoon Mujigae (2015) in South China. Bulletin of the American Meteorological Society, 98, 1821-1831, http://dx.doi.org/10.1175/BAMS-D-15-00301.1.
  15. Zhu*, L., Z. Meng, F. Zhang, and P. M. Markowski, 2017: The influence of sea- and land-breeze circulations on the diurnal variability of precipitation over a tropical island. Atmospheric Chemistry and Physics, 17, 13213-13232, https://doi.org/10.5194/acp-17-13213-2017.
  16. Li*, M., F. Zhang, Q. Zhang, J. Harrington and M. R. Kumjian, 2017: Nonlinear Response of hail precipitation rate to environmental moisture content: a real case modeling study of an episodic midlatitude severe convective event. Journal of Geophysical Research - Atmosphere, 122, doi:10.1002/2016JD026373.
  17. Zhang, F., S. Talaphdar, and S. Wang, 2017: The role of global circumnavigating mode in the MJO initiation and propagation. Journal of Geophysical Research - Atmosphere, 122, 5837-5856.
  18. Sieron S. B.*, E.E. Clothiaux, F. Zhang, Y.  Lu and J. Otkin, 2017: Fast Radiative Transfer Modeling for All-Sky Microwave Satellite Radiances: Modifying CRTM with Microphysics-Consistent Cloud Optical Properties. Journal of Geophysical Research - Atmosphere, 122, 7027-7046, DOI: 10.1002/2017JD026494.
  19. Cohen Y., N. Harnik, E. Heifetz, D. S. Nolan, D. Tao and F. Zhang, 2017: On the Violation of Gradient Wind Balance at the top of Tropical Cyclones. Geophysical Research Letters, 44, doi:10.1002/2017GL074552.
  20. Sun, J.*, and F. Zhang, 2017: Daily extreme precipitation and trends over China. Science China – Earth Science, 60, 2190-2203, https://link.springer.com/article/10.1007/s11430-016-9117-8.
  21. Zhang, Q.*, X Ni* and F. Zhang, 2017, Decreasing trend in severe weather occurrence over China during the past 50 years. Nature Scientific Reports, 7, 42310, doi:10.1038/srep42310.
  22. Navarro, T., F. Forget, E. Millour, S. J. Greybush, E. Kalnay, and T. Miyoshi, 2017: The Challenge of Atmospheric Data Assimilation on Mars. Earth and Space Science, 4, 690-722. https://doi.org/10.1002/2017EA000274.
  23. Kotsuki, S., S. J. Greybush, and T. Miyoshi, 2017: Can We Optimize the Assimilation Order in the Serial Ensemble Kalman Filter? A Study with the Lorenz-96 Model. Monthly Weather Review, 145, 4977-4995. https://doi.org/10.1175/MWR-D-17-0094.1.
  24. Saslo, S.*, and S. J. Greybush, 2017: Prediction of Lake-Effect Snow Using Convection-Allowing Ensemble Forecasts and Regional Data Assimilation. Weather and Forecasting, 32, 1727-1744. https://doi.org/10.1175/WAF-D-16-0206.1.
  25. Greybush, S. J., S. Saslo*, and R. Grumm, 2017: Assessing the Ensemble Predictability of Precipitation Forecasts for the January 2015 and 2016 East Coast Winter Storms. Weather and Forecasting, 32, 1057-1078. https://doi.org/10.1175/WAF-D-16-0153.1.
  26. Yang, X.*, R. Siddique, S. Sharma*, S. J. Greybush, and A. Mejia, 2017:Postprocessing of GEFS Precipitation Ensemble Reforecasts over the U.S. Mid-Atlantic Region. Monthly Weather Review, 145, 1641-1658. https://doi.org/10.1175/MWR-D-16-0251.1.
  27. Siddique*, R. and A. Mejia, 2017: Ensemble streamflow forecasting across the U.S. middle Atlantic region with a distributed hydrological model forced by GEFS reforecasts, Journal of Hydrometeorology, 18(7), 1905-1928, https://doi.org/10.1175/JHM-D-16-0243.1.
  28. Sharma*, S., R. Siddique*, N. Balderas*, J. D. Fuentes, S. Reed, P. Ahnert, R. Shedd, B. Astifan, R. Cabrera, A. Laing, M. Klein, and A. Mejia, 2017: Eastern U.S. verification of ensemble precipitation forecasts, Weather and Forecasting, 32, 117–139, https://doi.org/10.1175/WAF-D-16-0094.1.
  29. Berry, Tyrus* and John Harlim, 2017: Correcting Biased Observation Model Error in Data Assimilation, Monthly Weather Review, 145(7), 2833-2853, https://doi.org/10.1175/MWR-D-16-0428.1.
  30. De La Chevrotière, M.* and J. Harlim, 2017: A Data-Driven Method for Improving the Correlation Estimation in Serial Ensemble Kalman Filters. Monthly Weather Review, 145(3), 985-1001, https://doi.org/10.1175/MWR-D-16-0109.1.
  31. Harlim, J. 2017: Model error in data assimilation, Cambridge University Press, 276-317, Cambridge University Press.
  32. Harlim,J., X. Li, and H. Zhang, 2017: A Parameter Estimation Method Using Linear Response Statistics. Journal of Statistical Physics, 168, 146–170, https://doi.org/10.1007/s10955-017-1788-9.

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2016

  1. Zhang, F., and K. A. Emanuel, 2016: On the role of surface fluxes and WISHE in tropical cyclone intensification. Journal of the Atmospheric Sciences, 73, 2011-2019.
  2. Emanuel, K. and F. Zhang, 2016: On the Predictability and Error Sources of Tropical Cyclone Intensity Forecasts, Journal of the Atmospheric Sciences, 73, 3739-3747.
  3. Chen, H. W.*, F. Zhang, R. B. Alley, 2016: Nonlinear atmospheric response to Arctic sea-ice loss under different sea ice scenarios, Journal of Climate, 29, 7831-7849.
  4. Tang, X.*, and F. Zhang, 2016: Impacts of the Diurnal Radiation Cycle on the Formation, Intensity and Structure of Hurricane Edouard (2014). Journal of the Atmospheric Sciences, 73, 2871-2892.
  5. Wei, J.*, F. Zhang, and J. H. Richter, 2016: Toward Improving Nonorographic Gravity Wave Parameterizations: An Analysis of Gravity Wave Spectral Characteristics in Moist Baroclinic Jet-Front Systems. Journal of the Atmospheric Sciences, 73, 3133-3155.
  6. Poterjoy, J.*, and F. Zhang, 2016: Comparison of hybrid four-dimensional data assimilation methods with and without the tangent linear and adjoint models for predicting the life cycle of Hurricane Karl (2010). Monthly Weather Review, 144, 1449-1468.
  7. Zhang, F., M. Minamide*, E.E. Clothiaux, 2016: Potential Impacts of Assimilating All-sky Satellite Radiances from GOES-R on Convection-Permitting Analysis and Prediction of Tropical Cyclones. Geophysical Research Letters, 43, doi:10.1002/2016GL068468.
  8. Sun, Y.Q.*, and F. Zhang, 2016: Intrinsic versus practical limits of atmospheric predictability and the significance of the butterfly effect. Journal of the Atmospheric Sciences, 73, 1419-1438.
  9. Dong, L.*, and F. Zhang, 2016: OBEST: An observation-based ensemble setting technique for tropical cyclone track forecasting. Weather and Forecasting, 31, 57–70.
  10. Zhu, L.*, Q. Wang, X. Shen, Z. Meng, F. Zhang, Y. Weng*, Y. Gao*, Y. Zhang, J. Yue, 2016: Prediction and Predictability of a High-impact Western Pacific Landfalling Typhoon Vicente (2012) through Convection-permitting Ensemble Assimilation of Doppler Radar Velocity. Monthly Weather Review, 144, 21-43.
  11. Zhao, K. Q. Li, W-C Lee, Y.Q. Sun* and F. Zhang, 2016: Doppler Radar Analysis of Triple Eyewalls in Typhoon Usagi (2013). Bulletin of the American Meteorological Society, 97, 25-30.
  12. Mrowiec, A.A., O. M. Pauluis and F. Zhang, 2016: Isentropic analysis of a simulated hurricane. Journal of the Atmospheric Sciences,73, 1857-1870.
  13. Qiu, X*, and F. Zhang, 2016: Prediction and Predictability of an extreme local rainfall event through EnKF assimilation of radar observations. Science China - Earth Sciences, 59, 518-532.
  14. Zhang, Y. J., F. Zhang, Z. Meng, D. J. Stensrud 2016: Intrinsic Predictability of the 20 May 2013 Tornadic Thunderstorm Event in Oklahoma at Storm Scales. Monthly Weather Review, 144,1273-1298.
  15. Melhauser, C.*, and F. Zhang, 2016: Application of a Simplified Co-plane Wind Retrieval Using Dual-Beam Airborne Doppler Radar Observations for Tropical Cyclone Prediction. Monthly Weather Review, 144, 2645-2666.
  16. Taraphdar, S., P. Mukhopadhyay, R. L. Lueng, F. Zhang, S. Abhilash, and B. N. Goswami, 2014: The Role of Moist Processes in the Intrinsic Predictability of Indian Ocean Cyclones. Journal of Geophysical Research-Atmosphere, 119, doi:10.1002/2013JD021265
  17. Zhen, Y.*, and F. Zhang, 2014: A Probabilistic Approach of Adaptive Covariance Localization For Serial Ensemble Square-root Filters.Monthly Weather Review, 142, 4499-4518. doi: http://dx.doi.org/10.1175/MWR-D-13-00390.1
  18. Zhang, Y. C.*, F. Zhang, and J. Sun, 2014: Comparison of the diurnal variations of warm-season precipitation for East Asia versus North America downstream of the Tibetan Plateau versus the Rocky Mountains. Atmospheric Chemistry and Physics, 14, 10741-10759.
  19. Sippel, J.A., F. Zhang, Y. Weng*,Lin Tian, Gerald M. Heymsfield, and Scott A. Braun, 2014: Ensemble Kalman Filter Assimilation of HIWRAP Observations of Hurricane Karl (2010) from the Unmanned Global Hawk Aircraft. Monthly Weather Review, 142, 4559-4580.
  20. Zhang, X., X.-Y Huang, J. Liu, J. Poterjoy*, Y. Weng* and F. Zhang, 2014: Development of an efficient regional four-dimensional variational data assimilationsystem for WRF. Journal of Atmospheric and Oceanic Technology, 31, 2777-2794.
  21. Plougonven, R., and F. Zhang, 2014: Internal gravity waves from atmospheric jets and fronts. Encyclopedia of Atmospheric Sciences (2nd edition), Academic Press, Volume 3, 164-170.
  22. Meng, Z, and F. Zhang, 2014: Ensemble-based data assimilation. Encyclopedia of Atmospheric Sciences (2nd edition), Academic Press, Volume 2, 241-247.

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