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Study of aircraft icing warning algorithm based on millimeter wave radar

  • Special Collection on Aerosol-Cloud-Radiation Interactions
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Abstract

In order to avoid accidents due to aircraft icing, an algorithm for identifying supercooled water was studied. Specifically, a threshold method based on millimeter wave radar, lidar, and radiosonde was used to retrieve the coverage area of supercooled water and a fuzzy logic algorithm was used to classify the observed meteorological targets. The macrophysical characteristics of supercooled water could be accurately identified by combing the threshold method with the fuzzy logic algorithm. In order to acquire microphysical characteristics of supercooled water in a mixed phase, the unimodal spectral distribution of supercooled water was extracted from a bimodal or trimodal spectral distribution of a mixed phase cloud, which was then used to retrieve the effective radius and liquid water content of supercooled water by using an empirical formula. These retrieved macro- and micro-physical characteristics of supercooled water can be used to guide aircrafts during takeoff, flight, and landing to avoid dangerous areas.

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Acknowledgments

We thank Rutherford Appleton Laboratory for providing the data used in this study.

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Correspondence to Jinhu Wang.

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Supported by the Natural Science Foundation of Jiangsu Province (BK20170945), Open Fund of the Key Laboratory for Aerosol–Cloud–Precipitation of CMA–NUIST (KDW1703), National (Key) Basic Research and Development (973) Program of China (2014CB441405), National Natural Science Foundation of China (41275004, 61372066, and 41571348), Startup Fund for Introduced Talents of the Nanjing University of Information Science & Technology (2016r028), and Earth Science Virtual Simulation Experiment Teaching Course Construction Project (XNFZ2017C02).

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Wang, J., Ge, J., Zhang, Q. et al. Study of aircraft icing warning algorithm based on millimeter wave radar. J Meteorol Res 31, 1034–1044 (2017). https://doi.org/10.1007/s13351-017-6796-9

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  • DOI: https://doi.org/10.1007/s13351-017-6796-9

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