Peer review

1. Lin, K-J, S-C Yang and Y-P Chang, 2025: Analysis and forecast of Chanthu (2021) using an ensemble radar assimilation system, part I: factors leading to vortex spin-down. QJRMS, under review.

2. Chang, Y-P, S-C Yang, K-J Lin, T-R Wu and C-W Lin, 2025: A Real-Time Framework for Assessing Tropical Cyclone Surface Wind Speed Using a Parametric Model and Multiple Satellite Winds, IEEE TGRS (under review).

3. Chung, K-S, C-C Tasi, L-H Chen and S-C Yang, 2025: Convective-scale forecast errors over Taiwan: an ensemble analysis of error covariances and their infuences, Terrestrial, Atmospheric and Oceanic Sciences, https://doi.org/10.1007/s44195-025-00092-y.

4. Yang, S-C, S-H Chen, L. J-Y Liu, H-L Yeh, W-Y Chang, K-S Chung, P-L Chang and W-C Lee, 2024: Investigating mechanisms of an intense coastal rainfall event during TAHOPE/PRECIP-IOP3 using a multiscale radar ensemble data assimilation system, Mon. Wea. Rev.,  152, 2545–2567, https://doi.org/10.1175/MWR-D-24-0049.1.

5. Pham G., S-C Yang, C-C Chang, S-Y Chen and C-Y Huang, 2024: Estimating the refractivity bias of Formosat-7/COSMIC-II GNSS Radio Occultation in the planetary boundary layer, Atmos. Mea. Tech., 17, 3605–3623.

6. Yang,S-C , Chang, Y-P, H-W Cheng, Y-T Tsai, J-S Hong and Y-C Li, 2024: Improving the afternoon thunderstorm prediction with assimilation of ground-based observations: a case study of 22 July 2019 over northern Taiwan, Wea. Forecasting, in press.

7. Lien, T-Y , T-K Yeh, C-S Wang, N. Jiang, S-C Yang, 2024: Accuracy verification of the precipitable water vapor derived from COSMIC-2 radio occultation using ground-based GNSS, Advances in Space Research, in press.

8. Lin, K.-J., Yang, S.-C. and S. Chen, 2023: Sensitivity of Extreme Rainfall in Taiwan to SST over the South China Sea through Modulation of Marine Boundary Layer Jet: A mei-yu front event during 1-4 June 2017, Geophysical Research Letter, https://doi.org/10.1029/2023GL10444.

9. Cheng, Y.-Y., Yang, S.-C., Lin, Z.-H., and Lee, Y.-A., 2023: Using orthogonal vectors to improve the ensemble space of the EnKF and its effect on data assimilation and forecasting, Nonlin. Processes Geophys. https://doi.org/10.5194/npg-30-289-2023, in press.

10. Cheng, P. -H., C. C. -H. Lin, Y. T. Jade Morton, S. -C. Yang and T. J. -Y. Liu, 2023: A Bagged-Tree Machine Learning Model for High and Low Wind Speed Ocean Wind Retrieval from CYGNSS Measurements, in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2023.3246019.

11. Yang, S-C, Chen* and Chang: 2023: Impact of assimilating FORMOSAT-7/COSMIC-2 Radio Occultation data on tropical cyclone formation: Observing System Simulation Experiments based on Hurricane Gorden (2006), QJRMS.

12. Lin, K-J, S-C Yang*, S. Chen, 2022: Improving Analysis and Prediction of Tropical Cyclones by Assimilating Radar and GNSS-R Wind Observations: Observing System Simulation Experiments, Wea. Forecasting (accepted).

13. Chang, C-C, S-C Yang*, and S. Penny, 2022: A regional hybrid gain data assimilation system and preliminary evaluation based on Radio Occultation reflectivity assimilation, SOLA (in press).

14. Yang, S-C, F-Y Cheng, L-J Wang, S-H Wang and C-H Hsu,: 2022: Impact of lidar data assimilation on planetary boundary layer wind and PM2.5 prediction in Taiwan, Atmospheric Environment, 277, 119064. 

15. Yeh, H-L, S-C Yang*, K. Terasaki and T. Miyoshi, 2022: Including observation error correlation for ensemble radar radial wind assimilation and its impact on heavy rainfall prediction, QJRMS, (accepted). 

16. Lupo, K., R. Torn and S-C Yang, 2021: Process-Based Evaluation of Stochastic Perturbed Parameterization Tendencies on Ensemble Forecasts of Heavy Rainfall Events. Part I: Microphysics Perturbations, Mon. Wea. Rev., (in press).

17. Lin, Z-H, S-C Yang* and E. Kalnay, 2021: An Ensemble Transform Kalman Incremental Smoother and its application to Data Assimilation and Prediction, Front. Appl. Math. Stat. 7:687743. doi: 10.3389/fams.2021.687743.

18. Chen, C-H, K-S Chung, S-C Yang, H-W Cheng, P-L Lin and R. Torn, 2022: Analysis of Forecast Uncertainty by Using Different Microphysics Schemes for the Cloud-Resolving Ensemble during SoWMEX-IOP8, Mon. Wea. Rev. (in press).

19. Cheng, H-W, S-C Yang*, C-S Chen and Y-C Liou, 2020: An investigation of the sensitivity of a WRF-based convective-scale assimilation system on an afternoon thunderstorm in northern Taiwan, SOLA, (accepted).

20. Chang, C-C, S. Penny and S-C Yang, 2020: Hybrid gain data assimilation using variational corrections in the subspace orthogonal to the ensemble, Mon. Wea. Rev. (in press)

21. Lupo, K., R. Torn and S-C Yang, 2020: Evaluation of Stochastic Perturbed Parameterization Tendencies on Convective-Permitting Ensemble Forecasts of Heavy Rainfall Events in New York and Taiwan, Wea. Forecasting, 35, 5-24.

22. Wu, P-Y, S-C Yang*, C-C Tsai and H-W Cheng, 2020: convective-scale sampling error and its impact on the ensemble radar data assimilation system: a case study of heavy rainfall event on 16th June 2008 in Taiwan. Mon. Wea. Rev. 3631, doi:10.1175/MWR-D-19-0319.1 .

23. Chang, Y-P, S-C Yang*, K-J Lin, G-Y Lien and C-M Wu, 2020: Impact of tropical cyclone Initialization on its convection development and intensity: A case study of Typhoon Megi (2010), J. Atmos. Sci., DOI: 10.1175/JAS-D-19-0058.1 (in press).

24. Yang, S-C*, Z-M Huang, C-Y Huang, C-C Tsai and D-K Yeh, 2020: Convective-scale assimilation with the GNSS-ZTD and radar data and its impact on heavy rainfall prediction in Taiwan.  Mon. Wea. Rev. , 148, 1075-1098. DOI: 10.1175/MWR-D-18-0418.1.

25. Chen, S-H, S-C Yang*, C-Y Chen, C. P. van Dam, A. Cooperman, H. Shiu, C. MacDonald, J. Zack, 2019:  Application of Bias Corrections to Improve Hub-height Ensemble Wind Forecasts over the Tehachapi Wind Resource Area, Renewable energy, 140, 281-291. https://doi.org/10.1016/j.renene.2019.03.043.

26. Shin, S., J-S Kang, S-C Yang and E. Kalnay, 2019: Ensemble singular vectors as additive inflation in the Local Transform Kalman Filter (LETKF) framework with a global NWP model, QJRMS (in press).

27. Lin, K-J, S-C Yang* and S. S. Chen, 2018: Reducing TC position uncertainty in ensemble data assimilation and prediction system: A Case Study of Typhoon Fanapi (2010), Wea. Forecasting. (in press)

28. Yang, S.-C., S.-H. Chen, K. Kondo, T.Miyoshi, Y.-C. Liou, Y.-L.Teng, and H.-L. Chang (2017), Multilocalization data assimilation for predicting heavy precipitation associated with a multiscale weather system, J. Adv. Model. Earth Syst., 9, doi:10.1002/2017MS001009.

29. Tseng, Y-H, Y-H Lin, M-H Lo and S.-C. Yang, 2016: Diagnosing the dynamics controlling Sahel precipitation in the short-range ensemble community atmospheric model hindcasts. Clim. Dym. In press.

30. Huang, C-Y, Anisetty, S-Y Chen, S-C Yang, L-F Hiao, 2016: An Impact Study of GPS Radio Occultation Observations on Frontal Rainfall Prediction with a Local Bending Angle Operator. Wea. Forecating. 31, 129-150.

31. Chang, H.-L., S.-C. Yang*, H. Yuang, P-L Lin, and Y-C Liou, 2015: Analysis of relative operating characteristic and economic value using ensemble probabilistic forecasts. Mon. Wea. Rev., 140, 1496-1516..

32. Yang*, S.-C. and E. Kalnay, T. Enomoto, 2015: Ensemble singular vectors and their use as the additive inflation. Tellus A, 67, 26536.

33. Yang*, S.-C., S.-H. Chen, S.-Y. Chen, C.-Y. Huang and C.-S. Chen, 2014: Evaluating the impact of the COSMIC-RO bending angle data on predicting the heavy precipitation episode on 16 June 2008 during SoWMEX-IOP8. Mon. Wea. Rev., 142, 4139-4163.

34. Chang, C.-C., S.-C. Yang* and C. Keppenne, 2014: Applications of the mean re-recentering scheme to improve typhoon track prediction: A case study of typhoon Nanmadol (2011), JMSJ special edition on AICS-DA workshop, 92, 559-584.

35. Chang, C.-C., S.-C. Yang*, M.-C. Liang, S.-W. Hsu, Y.-H. Tseng, J.-S. Kang, 2014:  Constraining sources and sinks for trace species under an ensemble-based data assimilation framework with a regional chemical transport model: CO2 as an example. resubmission.

36. Tsai, C.-C., S.-C. Yang*, and Y.-C. Liou, 2014: Improving Short-Term QPFs with a WRF-LETKF Radar Data assimilation system: OSSEs on Typhoon Morakot (2009). Tellus A, 66, 21804.

37. Yang, S.-C., K.-J. Lin, T. Miyoshi and E. Kalnay, 2013: Improving the spin-up of regional EnKF for typhoon assimilation and forecasting with Typhoon Sinlaku (2008). Tellus A, 65, 25804. 

38. Norwood, A., E. Kalnay, K. Ide, S-C Yang, and C. Wolfe, 2013: Lyapunov, singular and bred vectors in a multi-scale system: an empirical exploration of vectors related to instabilities. Journal of Physics A, special issue of Lyapunov vectors, 46, 20.pp.

39. Ham, Y-G., M. M. Rienecker, S. Schubert, J. Marshak, S-W Yeh, and S.-C. Yang, 2012: The decadal modulation of coupled bred vectors for seasonal forecasts, Geophy. Res. Lett. (accepted).

40. Yang, S-C, E. Kalnay and T. Miyohsi, 2012: Improving EnKF spin-up for typhoon assimilation and prediction, Wea. Forecasting, 27,  878-897.

41. Yang, S-C and E. Kalnay, 2012: Handling nonlinearity and non-Gaussianity in Ensemble Kalman Filter. submitted to a special collection "Intercomparisons of 4D-Variational Assimilation and the Ensemble Kalman Filter",  Mon. Wea. Rev., 140, 2628-2646.

42. Yang, S-C, M. Rienecker, C. Keppenne, E. Kalnay, 2010: Impact on the seasonal-to-interannual forecasting from the ocean data assimilation: A case study of 2006 El Nino event. J. of Climate, 136, 4080-4095.

43. Kalnay, E. and S-C Yang, 2010: Accelerating the spin up of the LETKF, Q. J. R. Meteorol. Soc.,  136, 1644-1651.

44. Yang, S-C, C. Keppenne, M. Rienecker, E. Kalnay, 2009: Applications of coupled bred vectors to seasonal-to-interannual forecasting and ocean data assimilation. J. of Climate, 22, 2850-2870.

45. Yang, S-C, E. Kalnay, B. Hunt, N. Bowler, 2009: Weight interpolation for efficient data assimilation with the Local Ensemble Transform Kalman Filter, Q. J. R. Meteorol. Soc., 135, 251-262

46. Yang, S-C, M. Corazza, A. Carrassi, E. Kalnay, and T. Miyoshi, 2009: Comparison of ensemble-based and variational-based data assimilation schemes in a quasi-geostrophic model. Mov. Wea. Rev.,137, 693-709.

47. Ballabrera-Poy, J., E. Kalnay and S-C Yang, 2009: Data assimilation in a system with two scales. Combining two data assimilation techniques.Tellus 61A, 539-549.

48. Hoffman, M. J., E. Kalnay, J. Carton, and S-C Yang, 2009: Use of breeding to detect and explain instabilities in the global ocean, Geophys. Res. Lett., 36, L12608, doi:10.1029/2009GL037729.

49. Yang, S-C, E. Kalnay, M. Cai and M. Rienecker, 2008: Bred vectors and tropical Pacific forecast errors in the NASA coupled general circulation model. Mon. Wea. Rev. 136, 1305-1326.

50. Kalnay, E., H. Li, T. Miyoshi, S-C Yang and J. Ballabrera-Poy, 2007: 4D-Var or Ensemble Kalman Filter? Tellus A, 59, 758-773.

51. Kalnay, E., H. Li, T. Miyoshi, S-C Yang and J. Ballabrera-Poy, 2007: Response to the discussion on “4D-Var or Ensemble Kalman Filter?” Tellus A, 59, 778-780.

52. Corazza, M., E. Kalnay and S-C Yang, 2007: An implementation of the Local Ensemble Kalman Filter for a simple quasi-geostrophic model: Results and comparison with a 3D-Var data assimilation system. Nonlinear Processes in Geophysics, 14, 89-101.

53. Yang, S-C, D. Baker, H. Li, M. Huff, G. Nagpal, E. Okereke, J. Villafane, E. Kalnay and G. Duane, 2006: Data assimilation as synchronization of truth and model: experiments with the 3-variable Lorenz system. J. Atmos. Sci., 63, 2340-2354.

54. Yang, S-C, M. Cai, E. Kalnay, M. Rienecker, G. Yuan and Z. Toth, 2006: ENSO Bred Vectors in Coupled Ocean-Atmosphere General Circulation Models. J. Climate, 19, 1422-1436.

55. Evans, E., N. Bhatti, J. Kinney, L. Pann, M. Pena, S-C Yang, E. Kalnay and J. Hansen, 2004: RISE: Undergraduates Find That Regime Changes in Lorenz's Model are Predictable. Bull. Amer. Meteor. Soc., 85, 520-524.

56. Corazza, M., E. Kalnay, D. J. Patil, S-C Yang, R. Morss, M. Cai, I. Szunyogh, B. R. Hunt, and J. A. Yorke, 2002: Use of the breeding technique to estimate the structure of the analysis "error of the day". Nonlinear Processes in Geophysics, 10, 233-243.

Thesis

Yang, S-C, 2005: Bred vectors in the NASA NSIPP global coupled model and their application to coupled ensemble predictions and data assimilation. Ph. D. dissertation, University of Maryland, 173pp.

Yang, S-C, 1999: The application of NOAA Microwave-Sounding unit data to estimate the intensity of the typhoon. MS dissertation, National Central University, Taiwan.