Dr. M.A. (Marc) Schleiss
Dr. Marc Schleiss is an Assistant Professor in the Geoscience & Remote Department at TU-Delft. He graduated in Applied Mathematics in 2008 at Ecole Polytechnique Fédérale de Lausanne, Switzerland (EPFL) and obtained his PhD in Civil & Environmental Engineering at EPFL in 2012. Before coming to Delft in 2017, Marc spent 3 years in the Hydrometeorology Group at Princeton University in the United States.
Marc’s research focuses on the science behind the measurement, modeling and forecasting of precipitation, from the drop scale up to the global scale. His main research themes are:
- Precipitation microphysics: disdrometers, raindrop size distributions and microphysical retrievals from ground-based and spaceborne weather radar.
- Quantitative precipitation estimation using in-situ and remote sensing techniques: gauges, weather radar, opportunistic sensing, citizen science and probabilistic merging and downscaling of rainfall measurements.
- Extreme rain and flooding: space-time structures and dynamics of heavy rainfall-producing systems, short-term forecasting of rain using machine learning and sensitivity of extremes to climate change.
- CIE4603-16 Geo-signal analysis (course manager)
- CTB3366 Rainfall in the City (course manager)
- CIE5703 Urban Climate and Hydrology (guest lecturer)
- CIE4620 Climate Data Analysis (guest lecturer)
- CIE4608 Atmospheric Observation (guest lecturer)
- CIE4603 Atmospheric Remote Sensing (guest lecturer)
- AESM401B Climate & Weather (module manager and lecturer)
- AESM404B Georesources B-module (lecturer)
- ENVM1800 Atmospheric Measurements & Modeling (lecturer)
- ENVM1900 Grand Challenges in AEE (lecturer)
- Ruisdael observatory of atmospheric science https://ruisdael-observatory.nl/. Management and 24/7 operation of a network of disdrometers and micro-rain radars in Cabauw, Delft and Rotterdam.
- RainGuru: real-time nowcasting of heavy rain in the Netherlands using machine learning https://rainguru.hkvservices.nl/
- Between 2017 and 2019, Marc was the TU Delft project coordinator for MUFFIN, Multiscale Urban Flood Forecasting, https://muffin-project.eu/
- Schleiss, M., A new discrete multiplicative random cascade model for downscaling intermittent rainfall fields (2020) Hydrology and Earth System Sciences, 24 (7), art. no. 193, pp. 3699-3723. DOI: http://10.5194/hess-24-3699-2020
- Reinoso-Rondinel, R., Schleiss, M., Quantitative evaluation of polarimetric estimates from scanning weather radars using a vertically pointing micro rain radar (2021) Journal of Atmospheric and Oceanic Technology, 38 (3), pp. 481-499. DOI: http://10.1175/JTECH-D-20-0062.1
- Schleiss, M., Olsson, J., Berg, P., Niemi, T., Kokkonen, T., Thorndahl, S., Nielsen, R., Ellerbæk Nielsen, J., Bozhinova, D., Pulkkinen, S., The accuracy of weather radar in heavy rain: A comparative study for Denmark, the Netherlands, Finland and Sweden (2020) Hydrology and Earth System Sciences, 24 (6), pp. 3157-3188. DOI: http://10.5194/hess-24-3157-2020
- Tian, X., ten Veldhuis, M.-C., Schleiss, M., Bouwens, C., van de Giesen, N. Critical rainfall thresholds for urban pluvial flooding inferred from citizen observations (2019) Science of the Total Environment, 689, pp. 258-268. DOI: http://10.1016/j.scitotenv.2019.06.355
- Schleiss, M., How intermittency affects the rate at which rainfall extremes respond to changes in temperature (2018) Earth System Dynamics, 9 (3), pp. 955-968. DOI: http://10.5194/esd-9-955-2018
- Schleiss, M., Rieckermann, J., Berne, A., Quantification and modeling of wet-antenna attenuation for commercial microwave links (2013), IEEE Geoscience and Remote Sensing Letters, 10 (5), art. no. 6451115, pp. 1195-1199. http://10.1109/LGRS.2012.2236074
- Schleiss, M., Jaffrain, J., Berne, A., Stochastic simulation of intermittent DSD fields in time (2012) Journal of Hydrometeorology, 13 (2), pp. 621-637. DOI: http://10.1175/JHM-D-11-018.1