A visual uncertainty analytics approach for weather forecast similarity measurement based on fuzzy clustering

Abstract

Forecast calibration methods based on historical similar atmospheric state are effective means weather forecast accuracy. Conventional approaches search similar forecasts on the basis of predefined similarity formulas and provide calibration recommendations to forecasters. However, these approaches ignore the uncertainty of similarity measurement, which affects calibration efficacy significantly. This study proposes a similarity weight adaptive algorithm for high-dimensional data on the basis of fuzzy clustering to characterize the uncertainty of similarity measurements. Without prior knowledge, the algorithm computes the uncertainty of the similarity between data in the fuzzy set space iteratively on the basis of membership and then determine weight distribution by maximizing the differentiating ability of each dimension. This study further presents a visual analysis framework on the basis of the weight adaptive algorithm for the exploration of uncertainty in meteorological data and the optimization of similarity measurement method. This framework has coordinated views and intuitive interactions to enable the visualization of the similarity uncertainty distribution and support the iterative visual analysis of similarity weight distribution in each dimension that combines domain knowledge. We illustrate a case study using real-world meteorological data to verify the efficacy of the proposed approach.

Graphic abstract

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. Berthold MR, Hall LO (2003) Visualizing fuzzy points in parallel coordinates. IEEE Trans Fuzzy Syst 11(3):369–374

    Article  Google Scholar 

  2. Bezdek JC (2013) Pattern recognition with fuzzy objective function algorithms. Springer, Berlin

    Google Scholar 

  3. Bonneau G-P, Hege H-C, Johnson CR, Oliveira MM, Potter K, Rheingans P, Schultz T (2014) Overview and state-of-the-art of uncertainty visualization. In: Shriver B (ed) Scientific visualization. Springer, Berlin, pp 3–27

    Google Scholar 

  4. Du J, Kang Z (2014) A survey on forecasters view about uncertainty in weather forecasts. Adv Meteorol Sci Technol 4(1):60–69

    Google Scholar 

  5. Ferstl F, Bürger K, Westermann R (2016a) Streamline variability plots for characterizing the uncertainty in vector field ensembles. IEEE Trans Vis Comput Gr 22(1):767–776

    Article  Google Scholar 

  6. Ferstl F, Kanzler M, Rautenhaus M, Westermann R (2016b) Visual analysis of spatial variability and global correlations in ensembles of iso-contours. In: Hauser H, Benes B (eds) Computer graphics forum, vol 35. Wiley, New York, pp 221–230

    Google Scholar 

  7. Ferstl F, Kanzler M, Rautenhaus M, Westermann R (2017) Time-hierarchical clustering and visualization of weather forecast ensembles. IEEE Trans Visual Comput Gr 23(1):831–840

    Article  Google Scholar 

  8. Gasch AP, Eisen MB (2002) Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biol 3(11):research0059.1

    Article  Google Scholar 

  9. Glahn HR, Lowry DA (1972) The use of model output statistics (MOS) in objective weather forecasting. J Appl Meteorol 11(8):1203–1211

    Article  Google Scholar 

  10. Gong C, Chen L, Zhu Z (2016) A visualization system for calibrating multimodel ensembles in weather forecast. J Vis 19(4):769–782

    Article  Google Scholar 

  11. Hamill TM, Whitaker JS (2006) Probabilistic quantitative precipitation forecasts based on reforecast analogs: theory and application. Mon Weather Rev 134(11):3209–3229

    Article  Google Scholar 

  12. Hamill TM, Bates GT, Whitaker JS, Murray DR, Fiorino M, Galarneau TJ Jr, Zhu Y, Lapenta W (2013) Noaa’s second-generation global medium-range ensemble reforecast dataset. Bull Am Meteorol Soc 94(10):1553–1565

    Article  Google Scholar 

  13. Hamill TM, Scheuerer M, Bates GT (2015) Analog probabilistic precipitation forecasts using GEFS reforecasts and climatology-calibrated precipitation analyses. Mon Weather Rev 143(8):3300–3309

    Article  Google Scholar 

  14. Hou D, Charles M, Luo Y, Toth Z, Zhu Y, Krzysztofowicz R, Lin Y, Xie P, Seo D-J, Pena M et al (2014) Climatology-calibrated precipitation analysis at fine scales: statistical adjustment of stage IV toward CPC gauge-based analysis. J Hydrometeorol 15(6):2542–2557

    Article  Google Scholar 

  15. Leutbecher M, Palmer TN (2008) Ensemble forecasting. J Comput Phys 227(7):3515–3539

    MathSciNet  Article  Google Scholar 

  16. Liao H, Chen L, Song Y, Ming H (2016) Visualization-based active learning for video annotation. IEEE Trans Multimed 18(11):2196–2205

    Article  Google Scholar 

  17. Liao H, Wu Y, Chen L, Chen W (2018) Cluster-based visual abstraction for multivariate scatterplots. IEEE Trans Vis Comput Gr 24(9):2531–2545

    Article  Google Scholar 

  18. Liao H,  Wu Y, Chen L, Hamill TM, Wang Y, Dai K, Zhang H, Chen W (2015) A visual voting framework for weather forecast calibration. In: 2015 IEEE scientific visualization conference (SciVis), pp 25–32

  19. Mao J, Jain AK (1995) Artificial neural networks for feature extraction and multivariate data projection. IEEE Trans Neural Netw 6(2):296–317

    Article  Google Scholar 

  20. Mirzargar M, Whitaker RT, Kirby RM (2014) Curve boxplot: generalization of boxplot for ensembles of curves. IEEE Trans Vis Comput Gr 20(12):2654–2663

    Article  Google Scholar 

  21. Pfaffelmoser T, Reitinger M, Westermann R (2011) Visualizing the positional and geometrical variability of isosurfaces in uncertain scalar fields. In: Hauser H, Benes B (eds) Computer graphics forum, vol 30. Wiley, New York, pp 951–960

    Google Scholar 

  22. Pfaffelmoser T, Mihai M, Westermann R (2013) Visualizing the variability of gradients in uncertain 2d scalar fields. IEEE Trans Vis Comput Gr 19(11):1948–1961

    Article  Google Scholar 

  23. Potter K, Wilson A, Bremer P-T, Williams D,  Doutriaux C,  Pascucci V, Johnson CR (2009) Ensemble-vis: a framework for the statistical visualization of ensemble data. In: 2009 IEEE international conference on data mining workshops, pp 233–240

  24. Raftery AE, Gneiting T, Balabdaoui F, Polakowski M (2005) Using Bayesian model averaging to calibrate forecast ensembles. Mon Weather Rev 133(5):1155–1174

    Article  Google Scholar 

  25. Rueda L, Zhang Y (2006) Geometric visualization of clusters obtained from fuzzy clustering algorithms. Pattern Recognit 39(8):1415–1429

    Article  Google Scholar 

  26. Ruspini EH (1969) A new approach to clustering. Inf Control 15(1):22–32

    Article  Google Scholar 

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

    Article  Google Scholar 

  28. Sharko J, Grinstein G, Marx KA (2008) Vectorized radviz and its application to multiple cluster datasets. IEEE Trans Vis Comput Gr 14(6):1427–1444

    Article  Google Scholar 

  29. Sharko J,  Grinstein G (2009) Visualizing fuzzy clusters using radviz. In: 2009 IEEE 13th International conference information visualisation, pp 307–316

  30. Wang Y, Fan C, Zhang J, Niu T, Zhang S, Jiang J (2015) Forecast verification and visualization based on Gaussian mixture model co-estimation. In: Hauser H, Benes B (eds) Computer graphics forum, vol 34. Wiley, New York, pp 99–110

    Google Scholar 

  31. Wang J, Liu X, Shen H-W, Lin G (2017) Multi-resolution climate ensemble parameter analysis with nested parallel coordinates plots. IEEE Trans Vis Comput Gr 23(1):81–90

    Article  Google Scholar 

  32. Whitaker RT, Mirzargar M, Kirby RM (2013) Contour boxplots: a method for characterizing uncertainty in feature sets from simulation ensembles. IEEE Trans Vis Comput Gr 19(12):2713–2722

    Article  Google Scholar 

  33. Zadeh LA, Klir GJ, Yuan B (1996) Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers, vol 6. World Scientific, Singapore

    Google Scholar 

  34. Zhao Y, Luo F, Chen M, Wang Y, Xia J, Zhou F, Wang Y, Chen Y, Chen W (2018) Evaluating multi-dimensional visualizations for understanding fuzzy clusters. IEEE Trans Vis Comput Gr 25(1):12–21

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful for the valuable feedback and comments provided by the anonymous reviewers. This research is partially supported by the National Natural Science Foundation of China (Grant Nos. 61972221, 61572274) and NNW2018-ZT6B12 (National Numerical Windtunnel project).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Li Chen.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Huang, R., Chen, L. & Yuan, X. A visual uncertainty analytics approach for weather forecast similarity measurement based on fuzzy clustering. J Vis (2021). https://doi.org/10.1007/s12650-020-00709-z

Download citation

Keywords

  • Uncertainty visualization
  • Fuzzy clustering
  • Weather forecast