Icing hazard for civil aviation

Bernabo, Piercesare; Cuccoli, Fabrizio; Baldini, Luca

doi:10.1109/metroaerospace.2015.7180671

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…Due to an unusually warm spring, the germination process has advanced, and losses are at historic levelsespecially for fruit and wine growers: economic losses are estimated at EUR 3.37 billion, of which about EUR 600 million are insured (Faust and Herbold 2018). In particular, super cooled droplets and high-density ice crystals are dangerous for civil aviation (Kjellsson 2015;Bernabò et al 2015).…”

Section: Snow-and Ice-related Disasters On Landmentioning

confidence: 99%

Global Snow- and Ice-Related Disaster Risk: A Review

2022

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Since the 1970s, the ongoing retreat of the global cryosphere has been affecting human societies and causing a series of snow-and ice-related disasters (SIRDs). Based on existing research results, this paper focuses on searching for the formation mechanism of SIRDs, classifies their types and spatiotemporal scales, and reveals the integrated impacts of the SIRD and its future situation on global high-hazard areas. On land, SIRDs mainly occur in the high mountainous areas of middle-low latitude and the permafrost regions of high latitude, in the behaviors of increasing frequency of glacier/snow/glacier lake outburst flood-related disasters and an expanding range of freeze-thaw disasters. The recorded frost events show a decreasing trend but the hail hazard distributions are greatly heterogenous. Overall, the frequency of rain-on-snow events is projected to increase on land in the future. In the ocean, SIRDs are mainly distributed in the Arctic coastal areas and global low-lying islands or areas, with great potential risk. Among them, coastal freeze-thaw, icebergs, and sea-level rise and its impacts are likely or expected to continue increasing in the next few decades.

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Section: Snow-and Ice-related Disasters On Landmentioning

confidence: 99%

Global Snow- and Ice-Related Disaster Risk: A Review

2022

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“…Thus, the issue of too confident ensembles in difficult-to-predict weather situations (related especially to cloud products) is often brought up by duty forecasters. On top of this, forecasting clouds in the correct place is important for generating products for atmospheric icing for wind energy, power lines and aviation (see e.g Bernabò et al 2015;Kraj and Bibeau 2010;Nygaard et al 2016). Some operational HarmonEPS configurations have opted to use multi-physics or multi-model approaches, but there is currently no universal approach to handling model uncertainties in Har-monEPS.…”

Section: Introductionmentioning

confidence: 99%

Model Uncertainty Representation in a Convection-Permitting Ensemble—SPP and SPPT in HarmonEPS

Andræ

Ollinaho

Hally

et al. 2022

Monthly Weather Review

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The Stochastically Perturbed Parameterizations scheme (SPP) is here implemented and tested in HarmonEPS - the convection-permitting limited area ensemble prediction system by the international research program High Resolution Limited Area Model (HIRLAM) group. SPP introduces stochastic perturbations to values of chosen closure parameters representing efficiencies or rates of change in parameterized atmospheric (sub)processes. The impact of SPP is compared to that of the Stochastically Perturbed Parameterization Tendencies scheme (SPPT). SPP in this first version in HarmonEPS perturbs 11 parameters, active in different atmospheric processes and under various weather conditions. The main motivation for this study is the lack of variability seen in cloud products in HarmonEPS, as reported by duty forecasters. SPP in this first version is able to increase variability in a range of weather variables, including the cloud products. However, for some weather variables the root mean squared error of the ensemble mean is increased and the mean bias is impacted, especially in winter. This indicates that (some) parameter perturbation distributions are not optimal in the current configuration, and a further sensitivity analysis is required. SPPT resulted in a smaller increase in variability in the ensemble, but the impact was almost completely masked out when combined with perturbations of the initial state, lateral boundaries and surface properties. An in-depth investigation into this lack of impact from SPPT is here presented through examining, among other things, accumulated tendencies from the model physics.

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“…Atmospheric icing is a severe risk for aviation safety and can cause financial losses for wind energy producers and electricity network operators. Aircraft dynamics degrade as ice accumulates on the wings, reducing lift, and is especially dangerous during landing and take‐off (Bernabò et al ., 2015). Wind turbine blades suffer similar aerodynamic degradation (Kraj and Bibeau, 2010), causing power production loss.…”

Section: Introductionmentioning

confidence: 99%

Evaluating atmospheric icing forecasts with ground‐based ceilometer profiles

Hämäläinen

Hirsikko

Leskinen

et al. 2020

Meteorological Applications

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Atmospheric icing can cause severe damage and danger for many different sectors of society, and accurate forecasts of icing can help mitigate this. The aim of the study is to demonstrate how new ceilometer-based icing observations can be used in icing model verification. The study focuses on icing in the lowest 2 km of the atmosphere. The lowest 400 m of the atmosphere is essential for wind energy production and electricity power lines, while the higher elevations are essential from the aviation point of view. For aviation, the most risky situations are takeoffs and landings. The ceilometer observations provide a new way to investigate atmospheric icing. Ceilometers provide vertical profiles of clouds which can be further processed in order to identify in-cloud icing conditions. The new type of ceilometer-based icing observations enables more detailed verification of icing forecasts both in the vertical and in terms of geographical location. Overall, the model shows good skill in predicting clear-sky situations. The verification results show that the icing model performs better for inland areas than for coastal areas, which is attributed to the tendency of the model to forecast clouds, and in-cloud icing, at higher altitudes than observed.

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Icing hazard for civil aviation

Cited by 5 publications

References 10 publications

Global Snow- and Ice-Related Disaster Risk: A Review

Global Snow- and Ice-Related Disaster Risk: A Review

Model Uncertainty Representation in a Convection-Permitting Ensemble—SPP and SPPT in HarmonEPS

Evaluating atmospheric icing forecasts with ground‐based ceilometer profiles

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