SHU-CHIH YANG
Department of Atmospheric Sciences
National Central University
No. 300 Zhongda Rd., Zhongli District, Taoyuan city, 320317, Taiwan
Tel: +886.3.4227151#65515, email: shuchih.yang@atm.ncu.edu.twhttps://sites.google.com/view/ncu-predictability/home
Education
08/2001-05/2005

University of Maryland, U.S.A, Ph.D in Meteorology,
Department of Meteorology,
Dissertation title: “Bred vectors in the NASA NSIPP global
coupled model and their application to coupled ensemble
predictions and data assimilation.”
Advisor: Prof. Eugenia Kalnay

09/1997-06/1999

09/1993-06/1997

National Central University, Taiwan, M.S. degree,
Institute of Atmospheric Physics,
Dissertation title: “The application of NOAA MicrowaveSounding unit data to estimate the intensity of the typhoon.”
Advisor: Professor Liu, Gin-Rong
National Central University, Taiwan, B.S. degree
Department of Atmospheric Science

Professional Experience
Aug 2019-present

Professor

Department of Atmospheric Sciences,
National Central University, Taiwan

Aug 2013-present

Visiting Scientist

RIKEN advanced institute for computational
science, data assimilation team, JapanAug 2012-present

Associate Professor

July 2008-Jul 2012

Assistant Professor

Department of Atmospheric Sciences, National
Central University, TaiwanDepartment of Atmospheric Sciences, National
Central University, Taiwan

Sep 2005- May 2008

Post-doctorial
Research Associate

Global Modeling and Assimilation Office,NASA/GSFC and University of Maryland,
U.S.A
Ø Implement the coupled breeding method for
ensemble forecasting system of NASA
coupled GCM
Ø Investigate the impact of bred vectors inensemble ENSO prediction and its
application in ocean data assimilation
Ø Advance applications of the Ensemble
Kalman Filter

2002-2005

Research assistant

Department of Meteorology/ The Institute forPhysical Science and Technology, Universityof Maryland
Ø Examine the characteristic of ENSO-related
bred vectors derived from the independently
developed CGCMs
Ø Investigate the implications of bred vectors
and singular vectors for the ensemble-based
and variational-based data assimilation
methods

1999-2001

Software engineer

Institute of space science, National CentralUniversity, ROCSAT-1 IPEI Laboratory of
Science Data Distribution Center
Software program development ofROCSAT1-IPEI data processing and crosscheck the data validity

Awards/Honors received
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President Luo Jia-Lun (羅家倫校⾧) young outstanding research award, 2018 (3 receiversin NCU)
Academia Sinica research award for junior research investigator, 2015
Outstanding young research award in National Central University, 2014-2016
Outstanding teaching award of College of Earth Science in National Central University,
2014
Outstanding research award in National Central University, 2012-2016, 2018-2021
Young outstanding women in Science, 2013 (2 receivers nationwide)
The Da-You Wu award, National Science Council, 2012•••

Outstanding new faculty in National Central University, 2009-2011
Outstanding performance Award from NASA/GSFC GMAO, 2007
Scholarship of government sponsorship for overseas study, Ministry of Education, Taiwan
2001•Invited speeches (2017 to present):o Impact of Assimilating the GNSS-Reflectometry Sea Surface Wind on Severe WeatherPrediction the Sasaki symposium, the 19th annual meeting of Asia Oceania Geosciences
Society, 2022
o Improving the Afternoon Thunderstorm Prediction with Assimilation of the GNSS-ZTD
Data in Taiwan: a Case Study on 22 July 2019, the Sasaki symposium, the 18th annual
meeting of Asia Oceania Geosciences Society, 2021
o Including observation error correlation for assimilating radar radial wind and its impact
on heavy rainfall prediction, Dept. of Atmospheric Sciences, University of Hawaii at
Manoa, 10th Mar, 2021 (virtual)o Recent progress of the regional ensemble data assimilation system for severe weather
prediction in Taiwan, RIKEN, Japan, 1st Oct, 2020 (virtual)o Recent development of the regional ensemble data assimilation system for high-impact
weather prediction in Taiwan, Academia Sinica, 4th July 2020o Recent development of the regional ensemble data assimilation system for high-impact
weather prediction in Taiwan, RIKEN, Japan, 10th Sep, 2019o Recent development of the ensemble radar data assimilation system for heavy
prediction in Taiwan, Kyoto University, 6th Sep, 2019o From Chaos to Predictability: applying data assimilation to improve severe weather
prediction in Taiwan, the NCU distinguish lecture, 2019o Impact of regional moisture analysis on heavy precipitation prediction in Taiwan, 2017
KIAPS international workshop on Real-Time NWP Forecast System, Jeju, South Korea.

Roles/positions and services•Journal editors:o Topical editor of Geophysical model development (July 2022-present)o Editor of Scientific Online Letters on the Atmosphere (2015-present)o Guest editor of “A FORMOSAT-7/COSMIC-2 Special issue after 10year onorbit, Terrestrial, Atmospheric and Oceanic Science (2021-2022)•Team leader of Software Analysis and application of Taiwan Space Union••

Review panel of discipline of atmospheric science, MOST (2018-2020)
Supervisors of MS (16 graduated, 6 currently enrolled) and Ph.D students (4 graduated, 4currently enrolled)•Referee of Journals: Monthly Weather Review, Journal of Atmospheric Science, Journal of
the Meteorological Society of Japan, Weather and Forecasting, IEEE Transactions ofGeoscience and Remote Sensing, Advances in Atmospheric Sciences, Journal of Advancesin Modeling Earth Systems, Terrestrial, Atmospheric and Oceanic Science, ScientificOnline Letters on the Atmosphere, Journal of Meteorological Research, Tellus A,Nonlinear Processes in Geophysics, Quarterly journal of the Royal MeteorologicalSociety, Journal of Atmospheric and Oceanic Technology, Journal of geophysical research
Atmosphere, Asia-Pacific Journal of Atmospheric Sciences, Frontier, AtmosphericResearch, Atmosphere-Ocean, Remote sensing, 大氣科學.

•International conference conveners/committee:o Session of “From Weather Predictability to Controllability”, the 19th AOGSo Science committee member of International Symposium of Data Assimilation(2017, 2019)o Science Committee member of the Ensemble Kalman Filter workshop (2018)

Publication List(a) Referred papers
1. 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 (in press).
2. 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, 18, 33-40. https://doi.org/10.2151/sola.2022-006.
3. Yang, S-C, F-Y Cheng, L-J Wang, S-H Wang and C-H Hsu,: 2022: Impact of lidar dataassimilation on planetary boundary layer wind and PM2.5 prediction in Taiwan,
Atmospheric Environment, 277, 119064.
4. Chang, C-C and S-C Yang*, 2022: Impact of assimilating the Formosat-7/COSMIC-II

GNSS Radio Occultation data on predicting the heavy rainfall event in Taiwan on 13
August 2019. Terrestrial, Atmospheric and Oceanic Sciences Journal, Exploring theterrestrial and space weather using an operational radio occultation satellite constellation -A FORMOSAT-7/COSMIC-2 Special Issue after 1-year on orbit, TAO, 33, 7,
https://doi.org/10.1007/s44195-022-00004-4
5. Yeh, H-L, S-C Yang*, K. Terasaki, T. Miyoshi, and Y-C Liou, 2022: Including

observation error correlation for ensemble radar radial wind assimilation and its impact onheavy rainfall prediction, Quart. J. Roy. Meteor. Soc., (in press),
https://doi.org/10.1002/qj.4302
6. Lupo, K., R. Torn, and S-C Yang, 2022: Process-Based Evaluation of Stochastic

Perturbed Microphysics Parameterization Tendencies on Ensemble Forecasts of Heavy
Rainfall Events, Mon. Wea. Rev., 150, 175-191.
7. Chen, C.-H., K.-S. Chung, S.-C. Yang, L.-H. Chen, P.-L. Lin, R. D. Torn, 2021:

Sensitivity of forecast uncertainty to different microphysics schemes within a convectionallowing ensemble during SoWMEX-IOP8. Mon. Wea. Rev., 149, 4145–4166.
8. 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.
9. 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., 148, 3631-3652.
10. Cheng, H-W, S-C Yang*, C-S Chen and Y-C Liou, 2020: An investigation of thesensitivity of a WRF-based convective-scale assimilation system on an afternoon
thunderstorm in northern Taiwan, SOLA, 16, 97–103, https://doi.org/10.2151/sola.2020017.
11. 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., 148,2331-2350.
12. 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.
13. 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 ofTyphoon Megi (2010), J. Atmos. Sci., 77, 443-464., DOI: 10.1175/JAS-D-19-0058.1.

14. Yang, S-C*, T-M Huang, C-Y Huang and C-C Tsai, 2019: Convective-scale assimilation

with the GPS-ZTD and radar data and its impact on heavy rainfall prediction in Taiwan,
Mon. Wea. Rev., 148, 1075-1098.
15. 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.
16. Shin, S., J-S Kang, S-C Yang and E. Kalnay, 2019: Ensemble singular vectors as additive

inflation in the Local Ensemble Transform Kalman Filter (LETKF) framework with aglobal NWP model, Quart. J. Roy. Meteor. Soc., 145, 258-272,
https://doi.org/10.1002/qj.3429
17. Lin, K-J, S-C Yang* and S. S. Chen, 2018: Reducing TC position uncertainty in anensemble data assimilation and prediction system: A Case Study of Typhoon Fanapi(2010), Wea. Forecasting., 33, 561-582.
18. Yang, S-C, S-H Chen, K. Kondo, T. Miyoshi, Y-C Liou, Y-L Deng, and H-L Chang, 2017:

Multilocalization data assimilation for predicting heavy precipitation associated with amultiscale weather system. Journal of Advances in Modeling Earth Systems, 9,doi:10.1002/2017MS00100.
19. 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. 1-18, doi:10.1007/s00382-016-2995-9.
20. Yang*, S-C, E. Kalnay and T. Enomoto, 2015: Ensemble Singular Vectors and their use asadditive inflation in EnKF, Tellus A, 67, 26536.
21. Chang, H.-L., S.-C. Yang*, H. Yuang, P-L Lin and Y-C Liou, 2015: Analysis of therelative operating characteristic and economic value using the LAPS ensemble prediction
system in Taiwan. Mon. Wea. Rev., 140, 1496-1516.
22. 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.
23. 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.
24. 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.

25. Huang, Z-K, Z. Peng, H-N Liu, M-G Zhang, X-G Ma, S-C Yang, S-D Lee, and S-YKim, 2014: Development of CMAQ for East Asia CO2 data assimilation under an EnKF
framework: a first result. Chinese Science Bulletin, 10.1007/s11434-014 -0348-9.
26. Yang*, S.-C., K.-J. Lin, T. Miyoshi and E. Kalnay, 2013: Improving the spin-up ofregional EnKF for typhoon assimilation and forecasting with Typhoon Sinlaku (2008).
Tellus A, 65, 25804.
27. Yang*, S-C, E. Kalnay and T. Miyohsi, 2012: Improve EnKF spin-up for typhoon

assimilation and prediction, Wea. Forecating. 27, 878-897.
28. Norwood, A., E. Kalnay, K. Ide, S-C Yang, and C. Wolfe, 2013: Lyapunov, singular andbred vectors in a multi-scale system: an empirical exploration of vectors related toinstabilities. Journal of Physics A, special issue of Lyapunov vectors, in press.
29. Ham, Y-G, M. M. Rienecker, S. Schubert, J. Marshak, S-W Yeh, and S.-C. Yang, 2012:
30. The Decadal Modulation of Coupled Bred Vectors for Seasonal Forecasts. Geophys. Res.
Lett., 39, 20712.

31. Yang*, S-C, E. Kalnay and B. Hunt, 2012: Handling nonlinearity and non-Gaussianity inEnsemble Kalman Filter. ", Mon. Wea. Rev. Special collection "Intercomparisons of 4DVariational Assimilation and the Ensemble Kalman Filter. 140, 2628-2646.
32. Yang*, S-C, M. Rienecker, C. Keppenne, 2010: Impact on the seasonal-to -interannual

forecasting from the ocean data assimilation: A case study of 2006 El Nino event. J. ofClimte, 23, 4080-4095.
33. Kalnay, E. and S-C Yang, 2010: Accelerating the spin up of Ensemble Kalman Filtering,

Q. J. R. Meteorol. Soc., 136, 1644-1651.
34. Yang*, S-C, E. Kalnay, B. Hunt, N. Bowler, 2009: Weight interpolation for efficient data

assimilation with the Local Ensemble Transform Kalman Filter. Quar. Quart. J. Roy.
Meteor. Soc., 135, 251-262. 2009.
35. 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. Climate, 22,2850-2870.
36. Ballabrera-Poy, J., E. Kalnay and S-C Yang, 2009: Data assimilation in a system withtwo scales. Combining two data assimilation techniques. Tellus, 61A, 539-549.
37. Hoffman, M. J., E. Kalnay, J. Carton, and S-C Yang, 2009: Use of breeding to detect andexplain instabilities in the global ocean. Geophys. Res. Lett., 36, L12608. 2009
38. Yang*, S-C, M. Corazza, A. Carrassi, E. Kalnay, and T. Miyoshi, 2008: Comparison ofensemble-based and variational-based data assimilation schemes in a quasi-geostrophic
model. Mov. Wea. Rev., 137, 693-709.

39. Yang*, S-C, E. Kalnay, M. Cai and M. Rienecker, 2008: Bred vectors and tropical Pacificforecast errors in the NASA coupled general circulation model. Mon. Wea. Rev. 136,1305-1326.
40. Kalnay, E., H. Li, T. Miyoshi, S-C Yang and J. Ballabrera-Poy, 2007: 4D-Var orEnsemble Kalman Filter? Tellus-A, 59, 758-773, 2007
41. Kalnay, E., H. Li, T. Miyoshi, S-C Yang and J. Ballabrera-Poy, 2007: Resphonse to thediscussion on 4D-Var or Ensemble Kalman Filter? By Nils Gustafsson. Tellus-A, 59, 778780.
42. 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 3DVar data assimilation system. Nonlinear Processes in Geophysics, 14, 89-101.
43. 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: experimentswith the 3-variable Lorenz system. J. Atmos. Sci., 63, 2340-2354.
44. 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.
45. 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.
46. Corazza, M., E. Kalnay, D. J. Patil, S-C Yang, R. Morss, M. Cai, I. Szunyogh, B. R.

Hunt, and J. A. Yorke, 2003: Use of the breeding technique to estimate the structure of
the analysis "error of the day". Nonlinear Processes in Geophysics, 10, 233-243.
(b) Book Chapter:Yang S.-C., 2022: Convective-Scale Data Assimilation and Precipitation Prediction with a Local
Ensemble Transform Kalman Filter Radar Assimilation System Over Complex Terrain: AThorough Investigation with the Heavy Rainfall in Taiwan on 16 June 2008, DataAssimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV), ISBN: 9783-030-77721-0, Springer.