Aircraft Icing: In‐Cloud Measurements and Sensitivity to Physical Parameterizations

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.

Mountain Waves Analysis in the Vicinity of the Madrid-Barajas Airport Using the WRF Model

“…(i) Yonsei University (YSU) [36]: this scheme intensifies the boundary layer mixing in the thermally induced free convection regime, while reducing it in the mechanically induced forced convection. It has been tested in other studies related to cloudiness [37] and icing [38]. (ii) Mellor-Yamada-Janjic (MYJ) [39]: this scheme is optimized for deep convective regimes assuming a new parameter called cloud efficiency.…”

“…(ii) Mellor-Yamada-Janjic (MYJ) [39]: this scheme is optimized for deep convective regimes assuming a new parameter called cloud efficiency. It has been proven to be a good performer in similar BT [40] and icing studies [38]. (iii) Mellor-Yamada-Nakanishi-Niino (MYNN) [41]:…”

“…(i) Goddard (GOD) [43]: this scheme is optimized to simulate condensation and evaporation processes; it has been proven to be a good performer in aircraft icing studies in the Iberian Peninsula, although not specifically for mountain waves [38,44]. (ii) ompson (THO) [45]: this microphysics scheme is defined to improve the forecast of aircraft icing, since it considers mixed-phase processes relative to another bulk microphysics.…”

“…It has been used by Otkin et al [46], Bormann et al [40], and Montejo [37] for the validation of simulated clouds using BT. (iii) Morrison 2-moment (MOR) [47]: even if this microphysics scheme is not optimized for icing conditions, it has been proven to be a good performer in similar studies [27,38].…”

Heterogeneous Nucleation of Midlevel Cloud Layer Influenced by Transported Asian Dust Over Wuhan (30.5°N, 114.4°E), China

“…The yearly averaged emission of dust aerosols is estimated to be 1,000–3,000 Tg, which is far more than that of either soot (∼50 Tg) or organic material (∼60 Tg) (Seifert, 2011), and thus provides a promising opportunity to study heterogeneous nucleation in an actual atmospheric environment. Numerous studies concerning dust‐related heterogeneous ice formation have involved laboratory experiments (Atkinson et al., 2013; Yadav et al., 2019), in situ observations via aircraft (Lawson et al., 2019; Merino et al., 2019), and remote sensing techniques (Ansmann et al., 2005). Ground‐based lidar is a superior remote sensing approach for conducting long‐term ice formation observations and recording the entire process for a single event.…”