Skip to main content

Visualizing uncertainties in a storm surge ensemble data assimilation and forecasting system

Abstract

We present a novel integrated visualization system that enables the interactive visual analysis of ensemble simulations and estimates of the sea surface height and other model variables that are used for storm surge prediction. Coastal inundation, caused by hurricanes and tropical storms, poses large risks for today's societies. High-fidelity numerical models of water levels driven by hurricane-force winds are required to predict these events, posing a challenging computational problem, and even though computational models continue to improve, uncertainties in storm surge forecasts are inevitable. Today, this uncertainty is often exposed to the user by running the simulation many times with different parameters or inputs following a Monte-Carlo framework in which uncertainties are represented as stochastic quantities. This results in multidimensional, multivariate and multivalued data, so-called ensemble data. While the resulting datasets are very comprehensive, they are also huge in size and thus hard to visualize and interpret. In this paper, we tackle this problem by means of an interactive and integrated visual analysis system. By harnessing the power of modern graphics processing units for visualization as well as computation, our system allows the user to browse through the simulation ensembles in real time, view specific parameter settings or simulation models and move between different spatial and temporal regions without delay. In addition, our system provides advanced visualizations to highlight the uncertainty or show the complete distribution of the simulations at user-defined positions over the complete time series of the prediction. We highlight the benefits of our system by presenting its application in a real-world scenario using a simulation of Hurricane Ike.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  • Altaf MU, Butler T, Luo X, Dawson C, Mayo T, Hoteit I (2013) Improving short range ensemble Kalman storm surge forecasting using robust adaptive inflation. Mon Wea Rev 141:2705–2720

    Article  Google Scholar 

  • Altaf MU, Butler T, Mayo T, Luo X, Dawson C, Heemink AW, Hoteit I (2014) A comparison of ensemble Kalman filters for storm surge assimilation. Mon Wea Rev p in press

  • Anderson JL (2001) An ensemble adjustment Kalman filter for data assimilation. Mon Wea Rev 129:2884–2903

    Article  Google Scholar 

  • Berg R (2009) Tropical cyclone report: Hurricane Ike. National Hurricane Center

  • Bishop CH, Etherton BJ, Majumdar SJ (2001) Adaptive sampling with ensemble transform Kalman filter. Part I: theoretical aspects. Mon Wea Rev 129:420–436

    Article  Google Scholar 

  • Blake ES, Landsea CW, Gibney EJ (2011) The deadliest, costliest, and most intense united states tropical cyclones from 1851 to 2010 (and other frequently requested hurricane facts). NOAA Technical Memorandum NWSNHC-6

  • Brown JD, Spencer T, Moeller I (2007) Modelling storm surge flooding of an urban area with particular reference to modelling uncertainties: a case study of canvey island, united kingdom. Water Resources Research 43

  • Brown RA (2004) Animated visual vibrations as an uncertainty visualisation technique. In: Proceedings of International Conference on Computer Graphics and Interactive Techniques in Australasia and South East Asia, pp 84–89

  • Bunya S, Dietrich J, Westerink J, Ebersole B, Smith J, Atkinson J, Jensen R, Resio D, Luettich R, Dawson C, Cardone V, Cox A, Powell M, Westerink H, Roberts H (2010) A high resolution coupled riverine flow, tide, wind, wind wave and storm surge model for southern louisiana and mississippi: Part i - model development and validation. Mon Wea Rev 138:345–377

    Article  Google Scholar 

  • Burgers G, van Leeuwen PJ, Evensen G (1998) On the analysis scheme in the ensemble Kalman filter. Mon Wea Rev 126:1719–1724

    Article  Google Scholar 

  • Butler T, Altaf MU, Dawson C, Hoteit I, Luo X, Mayo T (2012) Data assimilation within the advanced circulation (ADCIRC) modeling framework for hurricane storm surge forecasting. Mon Wea Rev 140:2215–2231

    Article  Google Scholar 

  • Cubasch U, Santer B, Hellbach A, Hegerl G, Hck H, Maier-Reimer E, Mikolajewicz U, Stssel A, Voss R (1994) Monte carlo climate change forecasts with a global coupled ocean-atmosphere model. Clim Dyn 10(1–2):1–19

    Article  Google Scholar 

  • Dietrich J, Westerink J, Kennedy A, Smith J, Jensen RE, Zijlema M, Holthuijsen L, Dawson C, Luettich R, Powell M, Cardone V, Cox A, Stone G, Pourtaheri H, Hope M, Tanaka S, Westerink L, Westerink HJ, Cobell Z (2011a) Hurricane gustav (2008) waves and storm surge: Hindcast, synoptic analysis and validation in Southern Louisiana. Mon Weather Rev 139:2488–2522

    Article  Google Scholar 

  • Dietrich J, Zijlema M, Westerink J, Holtjuijsen L, Dawson C Jr (2011b) Modeling hurricane wave and storm surge using integrally-coupled, scalable computations. Coast Eng 58:45–65

    Article  Google Scholar 

  • Dietrich J, Dawson C, Proft J, Howard M, Wells G, Fleming J, Jr RL, Westerink J, Cobell Z, Vitse M, Lander H, Blanton B, Szpilka C, Atkinson J (2013) Real-time forecasting and visualization of hurricane waves and storm surge using SWAN+ADCIRC and FigureGen, vol 156

  • Dietrich JC, Bunya S, Westerink JJ, Ebersole BA, Smith JM, Atkinson JH, Jensen R, Resio DT, Luettich RA, Dawson C, Cardone VJ, Cox AT, Powell MD, Westerink HJ, Roberts HJ (2010) A high resolution coupled riverine flow, tide, wind, wind wave and storm surge model for southern louisiana and mississippi: Part ii - synoptic description and analyses of hurricanes katrina and rita. Mon Wea Rev 138:378–404

    Article  Google Scholar 

  • Djurcilov S, Kim K, Lermusiaux PFJ, Pang A (2001) Volume rendering data with uncertainty information. In: Data Visualization 2001: Proceedings of the Joint Eurographics - IEEE TCVG Symposium on Visualization, pp 243–252

  • Djurcilov S, Kim K, Lermusiaux PFJ, Pang A (2002) Visualizing scalar volumetric data with uncertainty. Comput Gr 26:239–248

    Article  Google Scholar 

  • El Serafy GY, Mynett AE (2008) Improving the operational forecasting system of the stratified flow in osaka bay using an ensemble kalman filter-based steady state kalman filter. Water Res Res 44(6):1–19

  • Evensen G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res 99:10,143–10,162

    Article  Google Scholar 

  • Ghil M (1989) Meteorological data assimilation for oceanographers. part i: description and theoretical framework. Dyn Atmos Oceans 13(3):171–218

    Article  Google Scholar 

  • Griethe H, Schumann H (2006) The visualization of uncertain data: methods and problems. In: Proceedings of SimVis

  • Heap NS (1983) Storm surges 1967–1982. Geophys J R Astron Soc 74:331–376

    Article  Google Scholar 

  • Heemink A, Kloosterhuis H (1990) Data assimilation for non-linear tidal models. Int J Numer Methods Fluids 11(8):1097–1112

    Article  Google Scholar 

  • Heemink AW (1986) Storm surge prediction using kalman filtering. PhD Thesis, TU Delft (1986)

  • Helton J (2008) Uncertainty and sensitivity analysis for models of complex systems. Comput Methods Transp 62:207–228

    Google Scholar 

  • Hintze JL, Nelson RD (1998) Violin plots: a box plot-density trace synergism. Am Stat 52(2):181–184

    Google Scholar 

  • Holland G (1980) An analytic model of the wind and pressure profiles in hurricanes. Mon Wea Rev 108:1212–1218

    Article  Google Scholar 

  • Höllt T, Chen G, Hansen CD, Hadwiger M (2013a) Extraction and visual analysis of seismic horizon ensembles. Eurographics 2013 Short Papers pp 69–72

  • Höllt T, Magdy A, Chen G, Gopalakrishnan G, Hoteit I, Hansen CD, Hadwiger M (2013b) Visual analysis of uncertainties in ocean forecasts for planning and operation of off-shore structures. In: Proceedings of the IEEE Pacific visualization symposium, pp 59–66

  • Höllt T, Magdy A, Zhan P, Chen G, Gopalakrishnan G, Hoteit I, Hansen CD, Hadwiger M (2014) Ovis: a framework for visual analysis of ocean forecast ensembles. IEEE Trans Vis Comput Graph 20(8):1114–1126

    Article  Google Scholar 

  • Hope ME, Westerink JJ, Kennedy AB, Kerr PC, Dietrich JC, Dawson C, Bender C, Smith JM, Jensen RE, Zijlema M, Holthuijsen LH, Luettich RA, Cardone MDPVJ, Cox AT, Pourtaheri H, Roberts HJ, Atkinson JH, Tanaka S, Westerink HJ, Westerink LG (2013) Hindcast and validation of hurricane ike (2008) waves, forerunner, and storm surge. J Goephys Res (Oceans) pp 4424–4460

  • Hoteit I, Pham DT, Blum J (2002) A simplified reduced order Kalman filtering and application to altimetric data assimilation in Tropical Pacific. J Mar Syst 36:101–127

    Article  Google Scholar 

  • Hoteit I, Korres G, Triantafyllou G (2005) Comparison of extended and ensemble based kalman filters with low and high-resolution primitive equations ocean models. Nonlinear Process Geophys 12:755–765

    Article  Google Scholar 

  • Hoteit I, Pham D, Triantafyllou G, Korres G (2008) A new approximate solution of the optimal nonlinear filter for data assimilation in meteorology and oceanography. Mon Weather Rev 136:317–334

    Article  Google Scholar 

  • Hoteit I, Hoar T, Gopalakrishnan G, Anderson J, Collins N, Cornuelle B, Kohl A, Heimbach P (2013) A MITgcm/DART ensemble analysis and prediction system with application to the gulf of mexico. Dyn Atmos Oceans 63:1–23

    Article  Google Scholar 

  • Houtekamer PL, Mitchell HL (1998) Data assimilation using an ensemble Kalman filter technique. Mon Wea Rev 126:796–811

    Article  Google Scholar 

  • Johnson CR, Sanderson AR (2003) A next step: visualizing errors and uncertainty. IEEE Comput Graph Appl 23(5):6–10

    Article  Google Scholar 

  • Kao D, Dungan J, Pang A (2001) Visualizing 2d probability distributions from eos satellite image-derived data sets: a case study. In: Proceedings of the IEEE visualization conference, pp 457–560

  • Kao D, Kramer M, Love A, Dungan J, Pang A (2005) Visualizing distributions from multi-return lidar data to understand forest structure 42(1):35–47

  • Kennedy A, Gravois U, Zachry B, Westerink J, Hope M, Dietrich J, Powell M, Cox A, Luettich R, Dean R (2011) Origin of the hurricane ike forerunner surge. Geophys Res Lett 38:L08608

  • Lermusiaux PFJ, Chiu CS, Gawarkiewicz GG, Abbot P, Robinson AR, Miller RN, Haley PJ, Leslie WG, Majumdar SJ, Pang A, Lekien F (2006) Quantifying uncertainties in ocean predictions. Oceanography 19:90–103

    Article  Google Scholar 

  • Love A, Pang A, Kao D (2005) Visualizing spatial multivalue data. IEEE Comput Graph Appl 25(3):69–79

    Article  Google Scholar 

  • Luettich R, Westerink J (2005) ADCIRC: A parallel advanced circulation model for oceanic, coastal and estuarine waters. Users manual for version 45.08 available at www.adcirc.org/document/ADCIRCtitlepage.html

  • Luo A, Kao D, Pang A (2003) Visualizing spatial distribution data sets. In: Proceedings of the symposium on data visualisation, pp 29–38

  • Malanotte-Rizzoli P, Young RE, Haidvogel DB (1989) Initialization and data assimilation experiments with a primitive equation model. Dyn Atmos Oceans 13(3):349–378

    Article  Google Scholar 

  • Munshi A, Gaster B, Mattson TG, Fung J, Ginsburg D (2011) OpenCL Programming guide. Addison-Wesley Professional

  • Murty TS, Flather RA, Henry R (1986) The storm surge problem in the bay of bengal. Prog Oceanogr 16(4):195–233

    Article  Google Scholar 

  • Nerger L, Janjić T, Schröter J, Hiller W (2012) A unification of ensemble square root Kalman filters. Mon Wea Rev 140:2335–2345

    Article  Google Scholar 

  • Pang AT, Wittenbrink CM, Lodha SK (1997) Approaches to uncertainty visualization. Visual Comput 13:370–390

    Article  Google Scholar 

  • Pfaffelmoser T, Reitinger M, Westermann R (2011) Visualizing the positional and geometrical variability of isosurfaces in uncertain scalar fields. Comput Graph Forum 30(3):951–960

    Article  Google Scholar 

  • Pham DT (2001) Stochastic methods for sequential data assimilation in strongly nonlinear systems. Mon Wea Rev 129:1194–1207

    Article  Google Scholar 

  • Pöthkow K, Hege HC (2011) Positional uncertainty of isocontours: condition analysis and probabilistic measures. IEEE Trans Vis Comput Graph 17(10):1393–1406

    Article  Google Scholar 

  • Pöthkow K, Weber B, Hege HC (2011) Probabilistic marching cubes. Comput Graph Forum 30(3):931–940

    Article  Google Scholar 

  • Potter K, Wilson A, Bremer PT, Williams D, Doutriaux C, Pascucci V, Johnson CR (2009) Ensemble-Vis: a framework for the statistical visualization of ensemble data. in: ieee workshop on knowledge discovery from climate data: prediction, Extremes and Impacts, pp 233–240

  • Rhodes PJ, Laramee RS, Bergeron RD, Sparr TM (2003) Uncertainty visualization methods in isosurface rendering. In: EUROGRAPHICS 2003 Short Papers, pp 83–88

  • Riveiro M (2007) Evaluation of uncertainty visualization techniques for information fusion. In: Proceedings of the 10th international conference on information fusion, pp 1–8

  • Rost RJ, Licea-Kane BM, Ginsburg D, Kessenich JM, Lichtenbelt B, Malan H, Weiblen M (2009) OpenGL shading language. Addison-Wesley Professional,

  • Sanyal J, Zhang S, Dyer J, Mercer A, Amburn P, Moorhead RJ (2010) Noodles: a tool for visualization of numerical weather model ensemble uncertainty. IEEE Trans Vis Comput Graph 16(6):1421–1430

    Article  Google Scholar 

  • Shreiner D, Sellers G, Kessenich JM, Licea-Kane BM (2013) OpenGL programming guide: the official guide to learning OpenGL. Addison-Wesley Professional

  • Sørensen JVT, Madsen H (2006) Parameter sensitivity of three kalman filter schemes for assimilation of water levels in shelf sea models. Ocean Model 11(3):441–463

    Article  Google Scholar 

  • Tippett MK, Anderson JL, Bishop CH, Hamill TM, Whitaker JS (2003) Ensemble square root filters. Mon Wea Rev 131:1485–1490

    Article  Google Scholar 

  • Westerink JJ, Luettich RA, Feyen JC, Atkinson JH, Dawson CN, Roberts HJ, Powell MD, Dunion JP, Kubatko EJ, Pourtaheri H (2008) A basin to channel scale unstructured grid hurricane storm surge model applied to southern louisiana. Mon Wea Rev 136:833–864

    Article  Google Scholar 

  • Whitaker JS, Hamill TM (2002) Ensemble data assimilation without perturbed observations. Mon Wea Rev 130:1913–1924

    Article  Google Scholar 

Download references

Acknowledgments

We would like to thank the anonymous reviewers for the constructive comments. Research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Thomas Höllt.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (mp4 33650 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Höllt, T., Altaf, M.U., Mandli, K.T. et al. Visualizing uncertainties in a storm surge ensemble data assimilation and forecasting system. Nat Hazards 77, 317–336 (2015). https://doi.org/10.1007/s11069-015-1596-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-015-1596-y

Keywords

  • Interactive Visualization
  • Visual analysis
  • Ensemble data
  • Storm surge