Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11765/11681
Aircraft icing: in‐cloud measurements and sensitivity to physical parameterizations
Title: | Aircraft icing: in‐cloud measurements and sensitivity to physical parameterizations |
Authors: | Merino Suances, Andrés; García Ortega, Eduardo; Fernández-González, Sergio
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Keywords: | Supercooled cloud drops; Aircraft; Atmospheric icing conditions |
Issue Date: | 2019 |
Publisher: | American Geophysical Union |
Citation: | Geophysical Research Letters. 2019, 46(20), p. 11559-11567 |
Publisher version: | https://dx.doi.org/10.1029/2019GL084424 |
Abstract: | The prediction of supercooled cloud drops in the atmosphere is a basic tool for aviation safety, owing to their contact with and instant freezing on sensitive locations of the aircraft. One of the main disadvantages for predicting atmospheric icing conditions is the acquisition of observational data. In this study, we used in‐cloud microphysics measurements taken during 10 flights of a C‐212 research aircraft under winter conditions, during which we encountered 37 regions containing supercooled liquid water. To investigate the capability of the Weather Research and Forecasting model to detect regions containing supercooled cloud drops, we propose a multiphysics ensemble approach. We used four microphysics and two planetary boundary layer schemes. The Morrison parameterization yielded superior results, whereas the planetary boundary layer schemes were essential in evaluating the presence of liquid water content. The Goddard microphysics scheme best detected the presence of ice water content but tended to underestimate liquid water content. |
Sponsorship : | This research was carried out in the framework of the SAFEFLIGHT project, financed by MINECO (CGL2016‐78702) and LE240P18 project (Junta de Castilla y León). |
URI: | http://hdl.handle.net/20.500.11765/11681 |
ISSN: | 0094-8276 1944-8007 |
Appears in Collections: | Artículos científicos 2019-2022 |
Files in This Item:
File | Description | Size | Format | ||
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![]() | 2019GL084424.pdf | 378,32 kB | Adobe PDF | ![]() View/Open |
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