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.
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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).
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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
- Uncertainty visualization
- Fuzzy clustering
- Weather forecast