A Tool for Remote Detection and Nowcasting of In-Flight Icing Using Satellite Data

2023-01-1489

06/15/2023

Features
Event
International Conference on Icing of Aircraft, Engines, and Structures
Authors Abstract
Content
In-flight icing is a major weather hazard to aviation; therefore, the remote detection of meteorological conditions leading to icing is a very aspired goal for the scientific community. In 2017, the Meteorological Laboratory of CIRA has developed a satellite-based tool for in-flight icing detection in collaboration with Italian Air Force Meteorological Service. Then, in the framework of the European project SENS4ICE, a further maturation of the previously developed algorithm has been achieved, in order to consider also Supercooled Large Drop (SLD) Icing Conditions. The tool relies on high-resolution satellite products based on Meteosat Second Generation (MSG) data. The aim of this product is to identify areas potentially affected by in-flight icing hazard, using information about the properties of clouds, remotely inferred from satellite, and the set of experimental curves and envelopes describing the interrelationship of icing-related cloud variables, that represent the icing reference certification rules, namely Appendix C and Appendix O to FAA 14 CFR Part 25 / EASA CS-25. Furthermore, starting from this detection product, a nowcasting tool has been developed with the aim to perform a forecast of the current icing conditions over a short period ahead. In the present work an overall description of the implemented tools for detection and nowcasting of icing conditions is provided. These tools will be used during the SENS4ICE flight test campaign, to be held in April 2023, which represents a good opportunity to validate them and to identify steps for future enhancements.
Meta TagsDetails
DOI
https://doi.org/10.4271/2023-01-1489
Pages
10
Citation
Zollo, A., and Bucchignani, E., "A Tool for Remote Detection and Nowcasting of In-Flight Icing Using Satellite Data," SAE Technical Paper 2023-01-1489, 2023, https://doi.org/10.4271/2023-01-1489.
Additional Details
Publisher
Published
Jun 15, 2023
Product Code
2023-01-1489
Content Type
Technical Paper
Language
English