Publications

Academic papers

• Nur Ratri, D., Weerts, A., Muharsyah, R., Whan, K., Klein Tank, A., Aldrian, E., and Hadi Hariadi, M. 2023. Calibration of ECMWF SEAS5 based streamflow forecast in Seasonal hydrological forecasting for Citarum river basin, West Java, Indonesia. Journal of Hydrology: Regional Studies. https://doi.org/10.1016/j.ejrh.2022.101305
• Garcia-Marti., I., Overeem, A., Noteboom, J.W., de Vos, L., de Haij, M., and Whan, K. 2022. From proof-of-concept to proof-of-value: approaching third-party data to operational workflows of national meteorological services. International Journal of Climatology (accepted).
• van Straaten, C., Whan, K., Coumou, D., van den Hurk, B., and Schmeits, M. 2022. Using Explainable Machine Learning Forecasts to Discover Subseasonal Drivers of High Summer Temperatures in Western and Central Europe. Monthly Weather Review.
• Nur Ratri, D., Whan, K., and Schmeits, M. 2021. Calibration of ECMWF Seasonal Ensemble Precipitation Reforecasts in Java (Indonesia) Using Bias-Corrected Precipitation and Climate Indices. Weather and Forecasting. https://doi.org/10.1175/WAF-D-20-0124.1
• Chen, J., Saunders, K. and Whan, K. 2021. Quality control and bias correction of citizen science wind observations. QJRMS.
• Whan, K., Zscheischler, J., Jordan, A. and Ziegel, J. 2021. Novel multivariate quantile mapping methods forensemble post-processing of medium-range forecasts. Weather and Climate Extremes.
• Veldkamp, S, Whan, K., Dirksen, S., and Schmeits, M. 2020. Statistical post-processing of wind speed forecasts using convolutional neural networks. Monthly Weather Review. DOI: 10.1175/MWR-D-20-0219.1
• Vannitsem, S. et al. 2020. Statistical Postprocessing for Weather Forecasts — Review, Challenges and Avenues in a Big Data World. BAMS.
• Schaller, N., J. Sillmann, M. Mueller, R. Haarsma, W. Hazeleger, T. Jahr Hegdahl, T. Kelder, G. van den Oord, A. Weerts, and K. Whan, 2020: The role of spatial and temporal model resolution in a flood event storyline approach in Western Norway, Weather and Climate Extremes, 29, DOI: 10.1016/j.wace.2020.100259.
• van Straaten, C., Whan, K., Coumou, D., van den Hurk, B., and Schmeits, M. 2020. The influence of aggregation and statistical post-processing on the sub-seasonal predictability of European temperatures. QJRMS.
• Whan, K., Sillmann, J., Schaller, N. and Haarsma, R. 2020. Future changes in atmospheric rivers and extreme precipitation in Norway. Climate Dynamics. https://doi.org/10.1007/s00382-019-05099-z
• Bakker, K., Whan, K., Knap, W., and Schmeits, M. 2019. Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation. Solar Energy, 191, 138-150. DOI: 10.1016/j.solener.2019.08.044.
• Nur Ratri, D., Whan, K., and Schmeits, M. 2019. A comparative verification of raw and bias-corrected ECMWF seasonal ensemble precipitation reforecasts in Java (Indonesia). Journal of Applied Meteorology and Climatology.
• van Straaten, C., Whan, K., and Schmeits, M. 2018. Statistical post-processing and multivariate structuring of high-resolution ensemble precipitation forecasts. Journal of Hydrometerology., DOI: 10.1175/JHM-D-18-0105.1
• Rahimi, M., Mohammadian, N., Vanashi, AR. and Whan, K. 2018. Trends in Indices of Extreme Temperature and Precipitation in Iran over the Period 1960-2014. Open Journal of Ecology, 8 (07), 396. DOI: 10.4236/oje.2018.87024.
• Whan, K., and Schmeits, M. 2018. Comparing area-probability forecasts of (extreme) local precipitation using parametric and machine learning statistical post-processing methods. Monthly Weather Review. DOI: 10.1175/MWR-D-17-0290.1. 
• Drake, J., Patti, A.F., Whan, K., Jackson, W.R. and Cavagnaro, T.R. 2018. Can we maintain productivity on broad acre dairy farms during early stage transition from mineral to compost fertilisation? Agriculture, Ecosystems & Environment. DOI: 10.1016/j.agee.2017.12.022.
• Philip, S., Kew, S., Hauser, M., Guillod, B., Teuling, A., Whan, K., Uhe, P. and Van Oldenborgh, GJ. 2017. Western US high June 2015 temperatures and their relation to global warming and soil moisture. Climate Dynamics, 1-15. DOI: 10.1007/s00382-017-3759-x
• van der Wiel, K., Kapnick, S. B., van Oldenborgh, G. J., Whan, K., Philip, S., Vecchi., G. A., Singh, R. K., Arrighi, J. and Cullen, H. 2017. Rapid attribution of the August 2016 flood-inducing extreme precipitation in south Louisiana to climate change. Hydrology and Earth System Sciences Discussions. DOI: 10.5194/hess-2016-448
• Teufel, B., Diro, G.T., Whan, K., Milrad, S., Jeong, D. I., Ganji, A., Huziy, O., Winger., K., Gyakum, J. R.,  de Elia, R., Zwiers, F. and Sushama, L. 2016. Investigation of the 2013 Alberta flood from weather and climate perspectives. Climate Dynamics. DOI: 10.1007/s00382-016-3229-8
• Whan, K. and Zwiers, F. 2016. The impact of ENSO and the NAO on extreme winter precipitation in North America in observations and regional climate models, Climate Dynamics. DOI: 10.1007/s00382-016-3148-x
• Whan, K., Zwiers, F.  and Sillmann, J. 2016. The influence of atmospheric blocking on extreme winter minimum temperatures in North America, Journal of Climate. DOI: 10.1175/JCLI-D-15-0493.1.
• Curry, C., Tencer, B., Whan, K., Weaver, A., Giguere, M. and Wiebe, E. 2016. Searching for added value in simulating climate extremes with a high-resolution regional climate model over Western Canada. Atmosphere-Ocean, DOI:10.1080/07055900.2016.1158146
• Curry, C., Tencer, B., Whan, K., Weaver, A., Giguere, M. and Wiebe, E. 2016. Searching for added value in simulating climate extremes with a high-resolution regional climate model over Western Canada. II. Basin-scale results. Atmosphere-Ocean, DOI:10.1080/07055900.2016.1215287
• Whan, K. and Zwiers, F. 2015. Evaluation of extreme rainfall and temperature over North America in CanRCM4 and CRCM5. Climate Dynamics, DOI: 10.1007/s00382-015-2807-8.
• Whan, K., Zscheischler, J.,  Orth, R.,  Shongwe, M., Rahimi., M., Asare, E. and  Seneviratne, S.I. 2015. Impact of soil moisture on extreme maximum temperatures in Europe, Weather and Climate Extremes
• Whan, K., L. Alexander, A. Imielska, S. McGree, D. Jones, E. Ene, S. Finaulahi, K. Inape, L. Jacklick, R. Kumar, V. Laurent, H. Malala, P. Malsale, R. Mitiepo, M. Ngemaes, A. Peltier, A. Porteous, S. Seuseu, E. Skilling, L. Tahani, U. Toorua and M. Vaiimene 2013. Trends and variability of temperature extremes in the tropical Western Pacific, International Journal of Climatology, 34, 8, 2585–2603
• McGree, S., K. Whan, D. Jones, A. Imielska, L. Alexander, H. Diamond, E. Ene, S. Finaulahi, K. Inape, L. Jacklick, R. Kumar, V. Laurent, H. Malala, P. Malsale, R. Mitiepo, T. Moniz, M. Ngemaes, A. Peltier, A. Porteous, S. Seuseu, E. Skilling, L. Tahani, F. Teimitsi, U. Toorua and M. Vaiimene 2013. An updated Assessment of Trends and Variability in total and extreme rainfall in the tropical Western Pacific, International Journal of Climatology, 34, 8, 2775–2791
  
• Whan, K., B. Timbal and J. Lindesay, 2013. Linear and nonlinear statistical analysis of the impact of sub-tropical ridge intensity and position on south-east Australian rainfall, International Journal of Climatology, 34, 2, 326–342. DOI: 10.1002/job.3689
• Timbal, B., K. Whan and M. Raupach 2009. Climate change influence of changes in evapotranspiration, runoff and drainage across SEA, through both physical and ecological processes. Final Report for Project 1.3.1P. South Eastern Australian Climate initiative.
• Timbal, B., J. Arblaster, K. Braganza, E. Fernandez, H. Hendon, B. Murphy, M. Raupach, C. Rakich, I. Smith, K. Whan and M. Wheeler 2010. Understanding the anthropogenic nature of the observed rainfall decline across South Eastern Australia. The CAWCR – Bureau of Meteorology Contribution to SEACI -1 Theme 1 From January 2006 to June 2009. Melbourne: SEACI

Student projects (masters and internships)

• Daniel Teixeira Soares Tolomei (2022, UU): Multivariate Postprocessing of Temporal Dependencies with Autoregressive and LSTM Neural Networks. Co-supervised with Dr Sjoerd Dirksen (UU) and Dr Maurice Schmeits (KNMI).
• Ludo Diender (2022, WUR): Estimating precipitation from Commercial Microwave Link signals using an all-encompassing neural network in the Netherlands. Co-supervised with Dr Hidde Leijnse, and Ruben Imhoff.
• Francesco Guardamagna (2022, TUE): Convolutional neural networks for statistical post-processing of wind gusts speed. Co-supervised with Dr Elisa Perrone (TUE) and Dr Roberto Fontana (Politecnico di Torino).
• Jeroen Sonnemans (2021, WUR): Future changes in poleward moisture transport variability associated with atmospheric rivers. Co-supervised with Dr Richard Bintanja (UoG, KNMI), Marlen Kolbe (UoG, KNMI), Dr Karin van der Wiel (KNMI), Dr Imme Benedict (WUR).
• Ellen Stevens (2021, TUE): Multivariate postprocessing methods for temperature forecasts and examination of their limitations. Co-supervised with Dr Elisa Perrone (TUE) and Dr Edwin van den Heuvel (TUE).
• Koert Schreurs (2021, Radboud): Precipitation Nowcasting using GenerativeAdversarial Networks. Co-supervised with Dr Yuliya Shapovalova (Radboud) and Dr Maurice Schmeits (KNMI).
• Jieyu Chen (2020, UU): Quality Control and Verification of Citizen Science Wind Observations. Co-supervised with Dr Jason Frank (UU) and Dr Kate Saunders (TU Delft).
• Simon Veldkamp (2019, UU): Statistical Post-processing of wind speed forecasts using convolutional neural networks. Co-supervised with Dr Sjoerd Dirksen (UU) and Dr Maurice Schmeits (KNMI).
• Edward Groot (2019, UU): Probabilistic thunderstorm forecasts using statistical post-processing: Comparison of logistic regression and quantile regression forests and an investigation of physical predictors. Co-supervised with Dr Willem Jan van de Berg (UU) and Dr Maurice Schmeits (KNMI).
• Daan van Dijk (2019, TU Delft): Road temperatures with random forests. Co-supervised with Marcel Molendijk (KNMI).
• Kilian Bakker (2018, UU): Improving solar radiation forecasts using advanced statistical post-processing methods. Published paper. Co-supervised with Dr Jason Frank (UU), Dr Maurice Schmeits (KNMI), and Dr Wouter Knap (KNMI).
• Eleftherios Ioannidis (2018): Probabilistic wind speed forecasting using parametric and non-parametric statistical post-processing methods.  Co-supervised with Dr Maurice Schmeits (KNMI).
• Romà Domènech Masana (2017, Radboud) : Improving the GLAMEPS wind speed forecasts using a machine learning technique. Co-supervised with Dr Eric Cator (Radboud) and Dr Maurice Schmeits (KNMI).
• Chiem van Straaten (2017, UU): Statistical Post-processing and Multivariate Structuring of High-Resolution Ensemble Precipitation Forecasts. Co-supervised with Dr Maurice Schmeits (KNMI).