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The North Sea Storm Surge Atlas The North Sea Storm Surge Atlas Performance assessment and uncertainty analysis Jurgen Klein February 2015 The North Sea Storm Surge Atlas Performance assessment and uncertainty analysis Master thesis Civil Engineering and Management Faculty of Engineering Technology University of Twente Author: Jurgen Klein Location and date: Amersfoort, February 26, 2015 Exam committee: Graduation supervisor Prof. dr. S.J.M.H. Hulscher University of Twente Daily supervisor Dr. ir. P.C. Roos University of Twente External supervisors Dr. ir. M. Van Ledden Royal HaskoningDHV Ir. N.J.F. Van den Berg Royal HaskoningDHV Dr. ir. H.W. Van den Brink Royal Dutch Meteorological Institute Abstract Current storm surge forecasting is often carried out by detailed real-time computer modeling. These models are accurate, but also have long calculation times. Also, the forecast horizon is short and the possibilities for scenario assessment are limited. For safety issues, such as the operation of storm surge barriers, as well as for shipping, forecasting on the mid-term (4-10 days ahead) is important. In 2014, a North Sea Storm Surge Atlas was developed by Royal HaskoningDHV, KNMI and Deltares, which uses a new and innovative method to predict storm surges in the North Sea area. As such, it provides quick insight of storm surges for five to ten days ahead, based on a large offline database of predefined and pre-calculated storms, rather than performing all calculations real-time. Currently, a pilot version of the North Sea Storm Surge Atlas is operational. Parallel to the operational method of the Storm Surge Atlas, another method to use the database for storm surge forecasting is developed. This research focuses on the validation and uncertainty identification of two possible methods for the North Sea Storm Surge Atlas. This is done by hindcasting 33 historical storms and comparing these simulations to observed water levels. The Storm Surge Atlas uses an EOF analysis to decompose pressure fields in to time coeffi- cients (principal components) of derived spatial patterns (EOFs). With this method, a pressure field can be described by 50 principal components, rather than over 2000 grid points. The first method (A - resampling) is based on comparing the principal components of pressure fields of a weather forecast to those of the pressure fields in the database. When the best matching pres- sure is found, the corresponding pre-calculated storm surge is retrieved and used as a forecast of the storm surge. The matched storm surges for every pressure field in the weather forecast are concatenated to make a storm surge forecast for several days. The second method (B - regression) uses a multiple linear regression model. The direct correlation between the principal components of the pressure fields in the database and the pre- calculated surges is used for the regression. To forecast the storm surge from a forecasted pressure field, the regression coefficients are multiplied with the principal components of the forecasted pressure field. To assess the performance of both Storm Surge Atlas methods A and B, 33 historical storms have been selected and hindcasted and compared to observed water levels. The performance assessment is done for 11 locations, of which 6 are located at the North Sea coast of the United Kingdom and 5 at the Dutch coast. A comparison between method A and B is then made for the performance on peak water level, duration of the storm water level and timing of the peak. In general, the results show that method A performs slightly better than method B. When looking more in-depth to the results of peak water levels, we see that method A has a more structural underestimation with less variability, whereas method B has a larger variability in i under- and overestimation. At Hoek van Holland the Storm Surge Atlas performs rather well. Heavy storms as in De- cember 1999 and December 2013 are underestimated by method A, whereas method B is better able to predict the storm surge. However, in other situations method B has much larger devia- tions. At this location also simulated water levels made by the state-of-the-art numerical model WAQUA are available (using the same input data). This model is also used to build the database of pre-calculated storm surges. Method A performs better at Hoek van Holland compared to the WAQUA simulations, although a systematic underestimation (bias) of 12 centimeters is found. The next step in this research is the identification and classification of different sources of uncertainty in methods A and B in the Storm Surge Atlas. A list of uncertainties is made and several of these sources are assessed for their sensitivity regarding the performance of the Storm Surge Atlas. From this analysis, it is found that method A can be improved by including the barometric average of the pressure field in finding the best match. Also, improvements are found when the database is enlarged. Last, improvement is found when no smoothening function is used. This smoothening function corrects for any discontinuities in the concatenated storm surge. This indicates that the peak storm surge is quite influenced by any bad matches prior to the peak water level. It can be concluded that the North Sea Storm Surge Atlas is able to hindcast historical storm surges quite well, with large time savings compared to real-time modeling. The North Sea Storm Surge Atlas can be a valuable addition in the field of storm surge forecasting, e.g. for scenario analysis, quick assessments of possible developments of a storm. It is recommended to use Storm Surge Atlas method A (resampling) as basis for further development. Further improvement of the Storm Surge Atlas may be found by including the pressure gradients or wind fields in the matching with the database. Furthermore, a coupling of a parameterization of extra-tropical storms and the Storm Surge Atlas may provide additional information in finding a matching storm in the database. ii Preface This thesis is the final part of my master Civil Engineering and Management at the University of Twente. The thesis focuses on a performance assessment and uncertainty analysis of the North Sea Storm Surge Atlas. The North Sea Storm Surge Atlas is a new and innovative way of storm surge forecasting, developed by Royal HaskoningDHV, in collaboration with KNMI and Deltares. Writing this preface marks the end of a great and very interesting period. I worked with great pleasure on the subject and learned a lot about meteorology in combination with hydrodynamics. In the beginning I had to put a lot of effort in learning Python, Fortran, LATEX and of course refreshing MATLAB, but in the end it paid off and I really enjoyed it. First, I would like to thank all my supervisors. To start with Mathijs for his enthusiasm and interesting ideas and discussions during my graduation project. Thank you Niels, for all your help on daily basis and your hands-on tips and tricks. Henk, thank you for all your feedback and good ideas, I really enjoyed our meetings at KNMI. Pieter, you were the one who tipped me about this topic and this graduation project. Thank you for this and all your feedback and motivation in past months. Suzanne, thank you for your critical view and questions during our meetings. I am very grateful to have worked at Royal HaskoningDHV during this graduation project. It was great to work with so many interesting and smart people around me and I want to thank all my colleagues in who helped me and gave me a great time. The winners certificate of `Wie is de Mol' in Utrecht still hangs proudly on the wall! Special thanks to Erwin, who helped me during the difficult start of the project to get the Storm Atlas working. Last, but not least, I would like to thank all my family, friends and fellow master students for their support and the great time during my years at the university! Jurgen Klein Amersfoort, February 26, 2015 iii iv Contents Abstract i Preface iii 1 Introduction 1 1.1 Storms surge forecasting . .1 1.2 North Sea Storm Surge Atlas . .2 1.3 Research objective and research questions . .2 1.4 Research methodology . .3 1.5 Report outline . .4 2 Storms, storm surges and storm surge forecasting 5 2.1 Introduction . .5 2.2 The development of a storm . .5 2.3 The development of a storm surge . .7 2.4 Current storm surge forecasting . .9 3 The North Sea Storm Surge Atlas 13 3.1 Introduction . 13 3.2 Building the database of pre-calculated storm surges . 14 3.3 Description of pressure fields by EOF analysis . 16 3.4 Method A - resampling from database . 18 3.5 Method B - multiple linear regression . 23 4 Storm Atlas performance 29 4.1 Introduction . 29 4.2 Performance indicators . 29 4.3 Selection of locations . 32 4.4 Selection of historical storms . 34 4.5 Data selection . 34 4.6 Validation . 36 v 4.7 Comparison of methods A and B . 47 5 Uncertainty analysis 49 5.1 Introduction . 49 5.2 Identification and classification of sources of uncertainty . 49 5.3 Overview of classification of uncertainties . 55 5.4 Prioritization of sources of uncertainty . 57 6 Discussion 59 6.1 Performance assessment methodology . 59 6.2 Performance of the Storm Atlas compared to real-time models . 62 6.3 Method A - resampling . 62 6.4 Method B - regression . 62 7 Conclusions and recommendations 63 7.1 Conclusions . 63 7.2 Recommendations . 65 Bibliography 67 Appendices 71 A EOF analysis 71 A.1 Introduction . 71 A.2 Preprocessing . 72 A.3 Finding the Empirical Orthogonal Functions (EOFs) .
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