Machine learning in Hydrology: 1) Studying the applicability of machine learning based rainfall-runoff models in non-stationary conditions – O et al (JHYD, 2020) shows that "diverse" training data can significantly improve the model performance across contrasting conditions, e.g., water-limited vs energy-limited environments. 2) Generating a global-scale gridded soil moisture product from in situ measurements using long short-term memory (LSTM); see Global soil moisture from in-situ measurements using machine learning
Hydroclimatic Extreme Events: 1) Investigating the characteristic anomaly patterns of soil moisture and biomass before wildfires (O et al, 2020). 2) Understanding land-atmospheric feedbacks during droughts.
Uncertainty in Precipitation Data: 1) Evaluating performance of gridded precipitation data using ground reference (O and Kirstetter, 2018, Fallah et al, 2019). 2) Spatial uncertainty in rainfall meausrements during heavy rainfall events (O and Foelsche, 2019). 3) Impact of precipitation uncertainty in hydrologic modelling (Fallah et al, 2020)
O, Sungmin et al. (2020) Robustness of Process-Based versus Data-Driven Modeling in Changing Climatic Conditions, J. Hydrometeor., doi: 10.1175/JHM-D-20-0072.1
O, Sungmin et al. (2020) Observational evidence of wildfire-promoting soil moisture anomalies, Sci Rep, doi: 10.1038/s41598-020-67530-4
O, Sungmin and Kirstetter P.E. (2018) Evaluation of diurnal variation of GPM IMERG derived summer precipitation over the contiguous US using MRMS data, Q. J. R. Meteorol. Soc., doi: 10.1002/qj.3218
FULL LIST (updated May 2020)
Reviewer activity for J. Hydrol, J. Hydrometeorol, Hydrol. Earth Syst. Sci., Remote Sensing.
Lecture on Climate Change
Member of AGU Precipitation Technical Committee, https://aguprecipitation.com
Co-author of DK climate change doctoral students’ blog, https://climatefootnotes.com