Models for the growth of rime, glaze, icicles and wet snow on structures

Makkonen, Lasse

doi:10.1098/rsta.2000.0690

Cited by 288 publications

(251 citation statements)

References 71 publications

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“…Radiation is simulated using the RRTM longwave radiation scheme and the Dudhia shortwave radiation scheme. (2000): droplet flow around a cylinder (taken from Makkonen, 2000). …”

Section: Forecasts Of Icingmentioning

confidence: 99%

“…In the current study, icing simulations are based on results of the weather research and forecasting (WRF) model coupled to an icing algorithm (Makkonen, 2000). WRF is a state-of-the-art non-hydrostatic numerical model for high resolution weather forecasts (Skamarock et al, 2008).…”

Section: Forecasts Of Icingmentioning

confidence: 99%

“…Weather forecast models made a strong development in the recent years allowing increased resolution due to grown computing resources and using improved cloud microphysics parameterizations. In the current study, icing forecasts are calculated based on results of the weather research and forecasting model WRF (Skamrock et al, 2008) driving an accretion model (Makkonen, 2000). Icing simulations show a time dependent, threedimensional picture of icing conditions.…”

Section: Introductionmentioning

confidence: 99%

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Wind turbines in icing conditions: performance and prediction

2011

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Abstract. Icing on structures is an important issue for wind energy developments in many regions of the world. Unfortunately, information about icing conditions is mostly rare due to a lack of measurements. Additionally, there is not much known about the operation of wind turbines in icing conditions. It is the aim of the current study to investigate the effect of icing on power production and to evaluate the potential of icing forecasts to help optimizing wind turbine operation.A test site with two Enercon E-82 turbines was set up in the Jura region in Switzerland in order to study the turbines' behaviour in icing conditions. Icing forecasts were performed by using an accretion model driven by results of the mesoscale weather forecast model WRF.The icing frequency at the test site is determined from pictures of a camera looking at the measurement sensors on the nacelle. The results show that the site is affected by frequent icing: 11.5 days/year of meteorological icing and 41.5 days/year of instrumental icing were observed corresponding to a factor of about four. The comparison of power production with and without blade heating shows that blade heating results in a 3.5 % loss and operation without blade heating results in a 10 % loss of the annual power production due to icing. Icing forecasts are performed for winter 2009/2010. Simulated and observed icing events agree well and also coincide with periods of power drop. Thus, the results suggest that icing forecasts can help to optimize the operation of wind parks in icing conditions.

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“…Radiation is simulated using the RRTM longwave radiation scheme and the Dudhia shortwave radiation scheme. (2000): droplet flow around a cylinder (taken from Makkonen, 2000). …”

Section: Forecasts Of Icingmentioning

confidence: 99%

Section: Forecasts Of Icingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wind turbines in icing conditions: performance and prediction

2011

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“…where M is the mass of ice, t is the time, α 1 ,α 2 and α 3 are efficiency coefficients, W is liquid water content, v is wind speed and A is the cross-sectional area of the cylinder on which the ice accumulation is calculated (Makkonen, 2000). The 20 efficiency coefficients take into account aspects of the object where the ice is accumulated, such as the possibility for water adhering to the surface.…”

mentioning

confidence: 99%

“…The 20 efficiency coefficients take into account aspects of the object where the ice is accumulated, such as the possibility for water adhering to the surface. A detailed description of the coefficients is found in Makkonen (2000). Meteorological inputs needed for the ice calculations are temperature, wind speed, liquid water content and median volume droplet size.…”

mentioning

confidence: 99%

Probabilistic forecasting of wind power production losses in cold climates: A case study

Söderman¹,

Körnich²,

Olsson³

et al. 2017

Preprint

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Abstract.The problem of icing on wind turbines in cold climates is addressed using probabilistic forecasting to improve next-day forecasts of icing and related production losses. A case study of probabilistic forecasts was generated for a two-week period.Uncertainties in initial and boundary conditions are represented with an ensemble forecasting system, while uncertainties in the spatial representation are included with a neighbourhood method. Using probabilistic forecasting instead of one single 5 forecast was shown to improve the forecast skill of the ice-related production loss forecasts and hence the icing forecasts.The spread of the multiple forecasts can be used as an estimate of the forecast uncertainty and of the likelihood for icing and severe production losses. Best results, both in terms of forecast skill and forecasted uncertainty, were achieved using both the ensemble forecast and the neighbourhood method combined. This demonstrates that the application of probabilistic forecasting for wind power in cold climate can be valuable when planning next-day energy production, in the usage of de-icing systems, 10 and for site safety.

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Analysis of a seeder‐feeder and freezing drizzle event

et al. 2015

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Surface icing can cause dramatic consequences on human activities. What is more, numerical weather prediction models are not very accurate in determining freezing drizzle, which creates uncertainty when forecasting this type of weather phenomenon. Therefore, it is essential to improve the forecast accuracy of these models for such phenomena to mitigate risks caused by unforeseen freezing drizzle events. On 5 February 2012, an episode of freezing drizzle took place in the Guadarrama Mountains, at the center of the Iberian Peninsula. This episode was preceded by weak snowfall. After the freezing drizzle, moderate snowfall was recorded in the study area. This event was simulated using the Weather Research and Forecasting model. Through this analysis, we identified the meteorological factors at both synoptic scale and mesoscale that caused this episode. Wind perpendicular to an orographic barrier‐generated updrafts and retention of moisture upwind, which caused orographic clouds to appear on the north side of the Guadarrama Mountains. Atmospheric stability prevented cloud formation at midlevels at the time of the freezing drizzle, which maintained cloud top temperatures warmer than −15°C during the episode. The entrance of moisture and instability at midlevels caused cloud top temperatures substantially colder than −15°C, which coincided with snow in the mountain range. Cloud top temperature and thickness control the efficiency of the glaciation process, thereby determining the type of precipitation at the surface. Freezing drizzle risk and in‐cloud icing algorithms were developed with the aim of predicting similar events in the study area, which could mitigate impacts on human activities.

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Models for the growth of rime, glaze, icicles and wet snow on structures

Cited by 288 publications

References 71 publications

Wind turbines in icing conditions: performance and prediction

Wind turbines in icing conditions: performance and prediction

Probabilistic forecasting of wind power production losses in cold climates: A case study

Analysis of a seeder‐feeder and freezing drizzle event

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