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Journal Publications

(“*” denotes advised students)

27.  Zhang, Y., D. J. Stensrud, C. L. Comer, and B. C. Stouffer, 2025: Assimilating novel boundary layer observations from dual-polarization radars to improve lower-tropospheric moisture and torrential rainfall forecast. Monthly Weather Review, 153, 309–326. https://doi.org/10.1175/MWR-D-24-0154.1.

26.  Huang, L., L. Bai, and Y. Zhang, 2024: Analysis of uncertainties in convection-permitting ensemble simulations of land breeze and nocturnal coastal rainfall in South China. Journal of Meteorological Research, 38, 1047–1063. https://doi.org/10.1007/s13351-024-4075-0.

25. Stouffer, B. C., D. J. Stensrud, C. L. Comer, Y. Zhang, and M. R. Kumjian, 2024: An investigation of convective boundary layer depth and entrainment zone properties using dual-polarization radar and balloon-borne observations. Journal of Atmospheric and Oceanic Technology, 41, 1213–1228. https://doi.org/10.1175/JTECH-D-23-0165.1.

24. Comer, C. L., D. J. Stensrud, B. C. Stouffer, Y. Zhang, and M. R. Kumjian, 2024: An automated approach to estimating convective boundary layer depth from dual-polarization WSR-88D radar observations. Journal of Atmospheric and Oceanic Technology, 41, 767–780. https://doi.org/10.1175/JTECH-D-23-0166.1.

23. Zhang, Y., X. Chen, D. J. Stensrud, and E. E. Clothiaux, 2024: Enhancing severe weather prediction with all-sky microwave radiance assimilation: The 10 August 2020 Midwest derecho. Geophysical Research Letters, 51, e2023GL106602. https://doi.org/10.1029/2023GL106602.

22. *Mykolajtchuk, P. D., *K. C. Eure, D. J. Stensrud, Y. Zhang, F. Zhang, S. J. Greybush, and M. R. Kumjian, 2023: Diagnosing a missed supercell thunderstorm forecast. Weather and Forecasting, 38, 1935–1951. https://doi.org/10.1175/WAF-D-23-0010.1.

21. 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, 1415–1432. https://doi.org/10.1175/JAS-D-22-0231.1.

20. 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, 591–609. https://doi.org/10.1175/WAF-D-22-0156.1.

19. *Eure, K. C., *P. D. Mykolajtchuk, Y. Zhang, D. J. Stensrud, F. Zhang, S. J. Greybush, and M. R. Kumjian, 2023: Simultaneous assimilation of planetary boundary layer observations from radar and all-sky satellite observations to improve forecasts of convection initiation. Monthly Weather Review, 151, 795–813. https://doi.org/10.1175/MWR-D-22-0188.1.

18. Zhang, Y., H. Yu, M. Zhang, Y. Yang, and Z. Meng, 2022: Uncertainties and error growth in forecasting the record-breaking rainfall in Zhengzhou, Henan on 19–20 July 2021. Science China: Earth Sciences, 65, 1903–1920. https://doi.org/10.1007/s11430-022-9991-4.

17. Zhang, Y., E. E. Clothiaux, and D. J. Stensrud, 2022: Correlation structures between satellite all-sky infrared brightness temperatures and the atmospheric states at storm scales. Advances in Atmospheric Sciences, 39, 714–732. https://doi.org/10.1007/s00376-021-0352-3.

16. 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 of Hurricane Harvey (2017). Geophysical Research Letters, 48, e2021GL096410. https://doi.org/10.1029/2021GL096410.

15. Zhang, Y., X. Chen, and Y. Lu, 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. Monthly Weather Review, 149, 2409–2430. https://doi.org/10.1175/MWR-D-20-0369.1.

14. 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.

13. Meng, Z., F. Zhang, D. Luo, Z. Tan, J. Fang, J. Sun, X. Shen, Y. Zhang, S. Wang, W. Han, K. Zhao, L. Zhu, Y. Hu, H. Xue, Y. Ma, L. Zhang, J. Nie, R. Zhou, S. Li, H. Liu, Y. Zhu, 2019: Review of Chinese atmospheric science research over the past 70 years: Synoptic meteorology. Science China: Earth Sciences, 62, 1946–1991. https://doi.org/10.1007/s11430-019-9534-6.

12. Zhang, Y., D. J. Stensrud, and F. Zhang, 2019: Simultaneous assimilation of radar and all-sky satellite 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.

11. Hayatbini, N., K.-L. Hsu, S. Sorroshian, Y. Zhang, and 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.

10. Bai, L., Z. Meng, Y. Huang, Y. Zhang, S. Niu, and T. Su, 2019: Convection initiation resulting from the interaction between a quasi-stationary dryline and intersecting gust fronts: A case study. Journal of Geophysical Research, 124, 2379–2396. https://doi.org/10.1029/2018JD029832.

9. 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.

8. Pan, J., D. Teng, F. Zhang, L. Zhou, L. Luo, Y. Weng, and Y. Zhang, 2018: Dynamics of local extreme rainfall of super Typhoon Soudelor (2015) in East China. Science China: Earth Sciences, 61, 572–594. https://doi.org/10.1007/s11430-017-9135-6.

7. Zhang, Y., and F. Zhang, 2018: A review on the ensemble-based data assimilations for severe convective storms. Advances in Meteorological Science and Technology (in Chinese), 8, 38–52. https://doi.org/10.3969/j.issn.2095-1973.2018.03.003.

6. Zhang, Y., F. Zhang, D. J. Stensrud, and Z. Meng, 2016: Intrinsic predictability of the tornadic thunderstorm event in Oklahoma on 20 May 2013 at storm scales. Monthly Weather Review, 144, 1271–1298. https://doi.org/10.1175/MWR-D-15-0105.1.

5. Zhu, L., Q. Wan, X. Shen, Z. Meng, F. Zhang, Y. Weng, J. Sippel, Y. Gao, Y. Zhang, and J. Yue, 2016: Prediction and predictability of high-impact western Pacific landfalling tropical cyclone Vicente (2012) through convection-permitting ensemble assimilation of Doppler radar velocity. Monthly Weather Review, 144, 21–43. https://doi.org/10.1175/MWR-D-14-00403.1.

4. Zhang, Y., F. Zhang, D. J. Stensrud, and Z. Meng, 2015: Practical predictability of the 20 May 2013 tornadic thunderstorm event in Oklahoma: Sensitivity to synoptic timing and topographical influence. Monthly Weather Review, 143, 2973–2997. https://doi.org/10.1175/MWR-D-14-00394.1.

3. Zhang, Y., Z. Meng, F. Zhang, and Y. Weng, 2014: Predictability of tropical cyclone intensity evaluated through 5-yr forecasts with a convection-permitting regional-scale model in the Atlantic Basin. Weather and Forecasting, 29, 1003–1023. https://doi.org/10.1175/WAF-D-13-00085.1.

2. Meng, Z., D. Yan, and Y. Zhang, 2013: General features of squall lines in East China. Monthly Weather Review, 141, 1629–1647. https://doi.org/10.1175/MWR-D-12-00208.1.

1. Meng, Z., and Y. Zhang, 2012: On the squall lines preceding landfalling tropical cyclones in China. Monthly Weather Review, 140, 445–470. https://doi.org/10.1175/MWR-D-10-05080.1.