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People

Sam Shen

Sam Shen

CESSRST Faculty and Scientists

SDSU Campus Deputy PI

Samuel S. P. Shen is Distinguished Professor of Mathematics and Statistics at San Diego State University, and Visiting Research Mathematician at Scripps Institution of Oceanography, University of California, San Diego. Formerly, he was McCalla Professor of Mathematical and Statistical Sciences at the University of Alberta, Canada, and President of the Canadian Applied and Industrial Mathematics Society. He has held visiting positions at the NASA Goddard Space Flight Center, the NOAA Climate Prediction Center, and the University of Tokyo. Shen holds a B.Sc. degree in Engineering Mechanics from East China Engineering Institute, and M.A. and Ph.D. degrees in Applied Mathematics from the University of Wisconsin–Madison.

NOAA Mission Aligned Research Interests

  • Development of big data software for fast delivery and visualization of NOAA climate data to classrooms, museums and households
  • Statistical and machine learning methods for historical climate data reconstruction and climate prediction for NOAA products
  • Modernizing the learning and teaching of data science, computing, statistics, mathematics, Python, and R for climate science in the big data era

Key NOAA Partners and Collaborators

  • Thomas M. Smith, NOAA/STAR
  • Huai-Min Zhang, NOAA/NCEI
  • Alexander Tardy, NOAA/NWS San Diego

Publications

Books

1.Shen, S.S.P, and R.C.J. Somerville, 2019: Climate Mathematics: Theory and Applications, Cambridge University Press, Cambridge, 391pp.

2.Shen, S.S.P, and R.C.J. Somerville, 2019: R-Version Solutions Manual for Climate Mathematics: Theory and Applications, Cambridge University Press, 101pp, pdf file only.

3.Shen, S.S.P, and R.C.J. Somerville, 2020: Python-Version Solutions Manual for Climate Mathematics: Theory and Applications, Cambridge University Press, 116pp, pdf file only.

4.Shen, S.S.P., 2017: R Programming for Climate Data Analysis and Visualization: Computing and Plotting for NOAA Aata Applications. The first revised edition. San Diego State University, San Diego, USA, 152pp.

5.Huang, N.E. and S.S.P. Shen (ed.), 2014: Hilbert-Huang Transform and Its Applications, 2nd edition, World Scientific, 425pp.

6.Huang, N.E. and S.S.P. Shen (ed.), 2005: Hilbert-Huang Transform and Its Applications, World Scientific, 360pp.

7.Shen, S.S.P., 1993: A Course on Nonlinear Waves, Springer, 327pp.

Journal Papers (2017-present)

1.Mu, Y., T. Biggs, and S.S.P. Shen, 2021: Satellite-based precipitation estimates using a dense rain gauge network over thesouthwestern Brazilian Amazon: Implication for identifying trends in dry season rainfall. Atmospheric Research, Accepted for publication.

2.Yang, L., J.W. Liu, S.-P. Xie, and S.S.P. Shen, 2021: Transition from fog to stratus over the northwest Pacific Ocean: Large-eddy simulation. Monthly Weather Review, Accepted for publication.

3.Xue, Y., et al. (S.S.P. Shen as a co-author), 2021: Impact of initialized land surface temperature and snowpack on subseasonal to seasonal prediction project, Phase I (LS4P-I): Organization and experimental design. Geoscientific Model Development, accepted for publication.

4.Wang, G., S.S.P. Shen, Y. Chen, Y. Bai, H. Qin, Z. Wang, B. Chen, X. Guo, M. Dai, 2021: Feasibility of reconstructing the summer basin–scale sea surface partial pressure of carbon dioxide from sparse in situ observations over the South China Sea. Earth System Science Data, 13, 1403–1417. https://doi.org/10.5194/essd-13-1403-2021

5.You, Q., Z. Cai, F. Wu, Z. Jiang, N. Pepin, and S.S.P. Shen, 2021: Temperature datasets of CMIP6 models over China: evaluation, trend and uncertainty. Climate Dynamics, https://doi.org/10.1007/s00382-021-05691-2

6.Roberts, E.G., M. Dai, Z. Cao. W. Zhai, L. Guo, S.S.P. Shen and C. Du, 2021: The carbonate system of the northern South China Sea: Seasonality and exchange with the western North Pacific. Progress in Oceanography, 191, https://doi.org/10.1016/j.pocean.2020.102464

7.Wang, M., S-P. Xie, S. S.P. Shen and Yan Du, 2020: Rossby and Yanai modes of tropical instability waves in the equatorial Pacific Ocean and a diagnostic model for surface currents. Journal of Physical Oceanography, 50, 3009-3024, https://doi.org/10.1175/JPO-D-20-0063.1

8.Shen, S.S.P., J. Pierret, I. Dorado and S. Ilawe, 2020: 4DVD visualization and delivery of the 20th Century Reanalysis data: Methods and examples. Theoretical and Applied Climatology, 142, 243-254, https://doi.org/10.1007/s00704-020-03288-z

9.Tucker, T., D. Giglio, M. Scanderbeg, and S.S.P. Shen, 2020: Argovis: A web application for fast delivery, visualization and analysis of Argo data. Journal of Atmospheric and Oceanic Technology, 37, 401- 416, https://doi.org/10.1175/JTECH-D-19-0041.1

10.Shen, S.S.P, 2019: A representativeness assessment of the Angell–Korshover 63-station network sampling based on reanalysis temperature data. Advances in Data Science and Adaptive Analysis, 11, https://doi.org/10.1142/S2424922X19500013

11.Yao, T., Y. Xue, D. Chen, F. Chen, L. Thompson, P. Cui, T. Koike, W. K.-M. Lau, D. Lettenmaier, V. Mosbrugger, R. Zhang, B. Xu, J. Dozier, T. Gillespie, Y. Gu, S. Kang, S. Piao, S. Sugimoto, K. Ueno, L. Wang, F. Zhang, Y. Sheng, W. Guo, W. Wang, Ailikun, X. Yang, Y. Ma, S.S.P. Shen, Z. Su, F. Chen, S. Liang, Y. Liu, V. Singh, K. Yang, D. Yang, X. Zhao, Y. Zhang, Q. Li, 2019: Recent Third Pole's rapid warming accompanies cryospheric melt and water cycle intensification and interactions between monsoon and environment: multi-disciplinary approach with observation, modeling and analysis. Bulletin of the American Meteorological Society, 100, 423-444, https://doi.org/10.1175/BAMS-D-17-0057.1

12.Shen, S.S.P., G. J. Clarke, T.D. Yao, B.W. Shen, 2019: Spatiotemporal variations of the 20th century Tibetan Plateau precipitation based on the monthly 2.5-degree reconstructed data, Theoretical and Applied Climatology, 135, 71-83, https://doi.org/10.1007/s00704-017-2357-5

13.Zhang, G., T. Yao, W. Chen, G. Zheng, C.K. Shum, K. Yang, S. Piao, Y. Sheng, S. Yi, J. Li, C. M. O'Reilly, S. Qi, S. S.P. Shen, H. Zhang, and Y. Jia, 2018: Regional differences of lake evolution across China during 1960s−2015 and its natural and anthropogenic causes. Remote Sensing of Environment, 221, 386-404, https://doi.org/10.1016/j.rse.2018.11.038

14.Leung, K., A. Subramanian, and S.S.P. Shen, 2018: Statistical characteristics of long-term high resolution precipitable water vapor data at Darwin. Advances in Data Science and Adaptive Analysis, 10,https://doi.org/10.1142/S2424922X18500109

15. Lammlein, L.J., and S.S.P. Shen, 2018: A multivariate regression reconstruction of the quasi-global annual precipitation on 1-degree grid from 1900 to 2015. Advances in Data Science and Adaptive Analysis, 10, https://doi.org/10.1142/S2424922X18500080

16.Tucker, T., and S.S.P. Shen, 2018: A toolkit for snow cover area calculation and display based on the Interactive Multisensor Snow and Ice Mapping System and an example for the Tibetan Plateau region. Advances in Data Science and Adaptive Analysis, 10, https://doi.org/10.1142/S2424922X18500031

17.Shen, S.S.P., G. Behm, T.Y. Song, and T.D. Qu, 2017: A dynamically consistent reconstruction of ocean temperature. Journal of Atmospheric and Oceanic Technology, 34, 1061-1082, https://doi.org/10.1175/JTECH-D-16-0133.1

18.Pierret, J., and S.S.P. Shen, 2017: 4D visual delivery of big climate data: A fast web database application system. Advances in Data Science and Adaptive Analysis, 9, https://doi.org/10.1142/S2424922X17500061

19.Smith, T.M., S.S.P. Shen, and R. Ferraro, 2017: Super-ensemble statistical forecasting of monthly precipitation over the contiguous US, with improvements from ocean-area precipitation predictors. Journal of Hydrometeorology, 17: 2699-2711,http://dx.doi.org/10.1175/JHM-D-16-0018.1

20.Hua, W., S.S.P. Shen, A. Weithmann, and H. Wang, 2017: Estimation of sampling error uncertainties in observed surface air temperature change in China. Theoretical and Applied Climatology, 129, 1133-1144, https://doi.org/10.1007/s00704-016-1836-4

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CESSRST Consortium

CESSRST is led by The City University of New York and brings together Hampton University, VA; University of Puerto Rico at Mayaguez, PR; San Diego State University, CA; University of Maryland Baltimore County, MD; University of Texas at El Paso, TX.