Analysis of a statistically initialized fuzzy logic scheme for classifying the severity of convective storms in Finland

Abstract

This paper proposes a method for classifying the severity of individual convective storms with real-time weather radar and lightning location data. The algorithm is based on a statistically initialized fuzzy logic model with human-oriented linguistic inference rules. When combined with an object-oriented convective storm tracking algorithm, the severity classification uses the past severity values in addition to the current state of the storm. Furthermore, the proposed method can be customized to correspond to the user-specific needs of different end user groups.The membership functions of the fuzzy logic model are initialized using the statistical analysis of various storm attributes derived from radar and lightning data, which potentially allows the adaptation of the model to different climates. The statistically initialized severity classification is also stable with respect to systematic errors in the measurements. To adjust the model to the Finnish climate, approximately 40 000 storms were tracked with a weather radar-based object-oriented convective storm tracking algorithm. The tracking was performed over a relatively large study area, with the composite of eight C-band weather radars, enabling also the analysis of relatively long-lived intense storms, such as mesoscale convective systems. In addition, the presented work illustrates the statistical characteristics of convective storms in Finland. The study also discusses the importance of careful data quality control when conducting a statistical analysis with an object-oriented storm tracking algorithm.

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Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms

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