photo of Jaison Thomas

Jaison Ambadan Thomas

Research Associate

The World Within Reach
Jaison Ambadan Thomas
Research Scientist

University at Albany
Department: Atmospheric Sciences Research Center




  • Ph.D. in Geoscience (2012):
    IMPRS-ESM, Max-Planck Institute for Meteorology, Hamburg, Deutschland

  • M.Sc. in Environmental Science (2008):
    University of Northern British Columbia Prince George, BC, Canada

Research Interests

  • Ensemble data assimilation methods, Forward operator modelling, Model/Observation error estimation techniques, Bias correction techniques
  • Land surface processes in particular snow/snowmelt-soil moisture relationship, Land-atmosphere feedback processes; Land-Atmosphere model coupling and assimilation
  • Meteorological data analysis, Forecast verification scores/methods


  1. Jaison Ambadan Thomas, Aaron A Berg, William J Merryfield, Woo-Sung Lee, (2016); Influence of snowmelt on soil moisture memory and on near surface temperature during winter-spring transition season Climate Dynamics (in review; CLDY-S-17-00169)
  2. Hachborn, Ellen; Berg, Aaron; Levison, Jana; Ambadan, Jaison, (2017); "Sensitivity of GRACE-derived estimates of groundwater level changes in southern Ontario" Hydrogeology Journal (accepted: HJ-2016-4353.R1)
  3. Wagner-Riddle, C., Congreves, K.A., Abalos, D., Berg, A.A., Brown, S.E., Ambadan, J.T., Gao, X. and Tenuta, M., (2017) Globally important nitrous oxide emissions from croplands induced by freeze-thaw cycles", Nature Geoscience vol 10, pp 279–283; doi:10.1038/ngeo2907
  4. Jaison Ambadan Thomas, Aaron A Berg, William J Merryfield, (2015); Influence of snow and soil moisture initialization on sub-seasonal predictability and forecast skill in boreal spring, Climate Dynamics, DOI:10.1007/s00382-015-2821-9
  5. Tang, Y., Ambadan, J.T., D. Chen, (2013); A modification of Kalman Gain for Nonlinear measurement function in Ensemble-Kalman filter, Advance in Atmospheric Sciences, DOI:10.1007/s00376-013-3117
  6. Ambadan, J.T. and Tang, Y., (2011); Sigma-Point Particle Filter for Parameter Estimation in a Multiplicative Noise Environment, Journal of Advances in Modelling Earth Systems, VOL. 3, M12005, 16 PP., doi:10.1029/2011MS000065
  7. Tang, Y. and Ambadan, J.T., (2009); Reply to comment on Sigma-point Kalman Filters for the assimilation of strongly nonlinear systems". J. Atmos. Sci., Vol 66(11), 3501-3503
  8. Ambadan, J.T. and Tang, Y., (2009); Sigma-point Kalman Filters for the assimilation of strongly nonlinear systems. J. Atmos. Sci., 66(2), 261-285.