Martin Verlaan
Martin Verlaan
Professor Data Assimilation, Mathematics, Technical University Delft and senior researcher Deltares
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A global reanalysis of storm surges and extreme sea levels
S Muis, M Verlaan, HC Winsemius, JCJH Aerts, PJ Ward
Nature communications 7 (1), 1-12, 2016
Global probabilistic projections of extreme sea levels show intensification of coastal flood hazard
MI Vousdoukas, L Mentaschi, E Voukouvalas, M Verlaan, S Jevrejeva, ...
Nature communications 9 (1), 1-12, 2018
Tidal flow forecasting using reduced rank square root filters
M Verlaan, AW Heemink
Stochastic Hydrology and Hydraulics 11 (5), 349-368, 1997
Extreme sea levels on the rise along Europe's coasts
MI Vousdoukas, L Mentaschi, E Voukouvalas, M Verlaan, L Feyen
Earth's Future 5 (3), 304-323, 2017
Variance reduced ensemble Kalman filtering
AW Heemink, M Verlaan, AJ Segers
Monthly Weather Review 129 (7), 1718-1728, 2001
The impact of future sea-level rise on the global tides
MD Pickering, KJ Horsburgh, JR Blundell, JJM Hirschi, RJ Nicholls, ...
Continental Shelf Research 142, 50-68, 2017
Nonlinearity in data assimilation applications: A practical method for analysis
M Verlaan, AW Heemink
Monthly weather review 129 (6), 1578-1589, 2001
Operational storm surge forecasting in the Netherlands: developments in the last decade
M Verlaan, A Zijderveld, H de Vries, J Kroos
Philosophical Transactions of the Royal Society of London A: Mathematicalá…, 2005
Inverse 3D shallow water flow modelling of the continental shelf
AW Heemink, EEA Mouthaan, MRT Roest, EAH Vollebregt, ...
Continental Shelf Research 22 (3), 465-484, 2002
Improved water-level forecasting for the Northwest European Shelf and North Sea through direct modelling of tide, surge and non-linear interaction
F Zijl, M Verlaan, H Gerritsen
Ocean Dynamics 63 (7), 823-847, 2013
Reduced rank square root filters for large scale data assimilation problems.
M Verlaan
Second International Symposium on Assimilation of Observations iná…, 1995
Compound simulation of fluvial floods and storm surges in a global coupled river‐coast flood model: Model development and its application to 2007 C yclone S idr in B angladesh
H Ikeuchi, Y Hirabayashi, D Yamazaki, S Muis, PJ Ward, HC Winsemius, ...
Journal of Advances in Modeling Earth Systems 9 (4), 1847-1862, 2017
Conservation of Mass and Preservation of Positivity with Ensemble-Type Kalman Filter Algorithms
T Janjić, D McLaughlin, SE Cohn, M Verlaan
Monthly Weather Review 142 (2), 755-773, 2014
Non-uniqueness in probabilistic numerical identification of bacteria
M Gyllenberg, T Koski, E Reilink, M Verlaan
Journal of Applied Probability 31 (2), 542-548, 1994
A comparison of two global datasets of extreme sea levels and resulting flood exposure
S Muis, M Verlaan, RJ Nicholls, S Brown, J Hinkel, D Lincke, AT Vafeidis, ...
Earth's Future 5 (4), 379-392, 2017
Classification of binary vectors by stochastic complexity
M Gyllenberg, T Koski, M Verlaan
Journal of Multivariate Analysis 63 (1), 47-72, 1997
Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?
M Mazzoleni, M Verlaan, L Alfonso, M Monego, D Norbiato, M Ferri, ...
Hydrology and Earth System Sciences 21 (2), 839-861, 2017
An iterative ensemble Kalman filter for reservoir engineering applications
MV Krymskaya, RG Hanea, M Verlaan
Computational Geosciences 13 (2), 235-244, 2009
A modified RRSQRT-filter for assimilating data in atmospheric chemistry models
AJ Segers, AW Heemink, M Verlaan, M van Loon
Environmental Modelling & Software 15 (6-7), 663-671, 2000
Application of data assimilation for improved operational water level forecasting on the northwest European shelf and North Sea
F Zijl, J Sumihar, M Verlaan
Ocean Dynamics 65 (12), 1699-1716, 2015
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