Effects of the ICE-T Microphysics Scheme in HARMONIE-AROME on Estimated Ice Loads on Transmission Lines

In-cloud icing can cause damage to infrastructure and is challenging to forecast due to lack of a good representation of supercooled liquid water (SLW) in numerical weather prediction (NWP) models. We validate the new microphysics scheme, ICE-T, implemented into the NWP model HARMONIE-AROME, in full 3D simulations running over a 3 month period from December 1st 2016 to February 28th 2017. Output from the model simulations are first compared with conventional observations to evaluate the overall quality, and then used as input to an ice accretion model (IAM) and compared against measured ice loads at the two test sites Hardingnuten and Ålvikfjellet. The results show a clear shift towards more cloud water and snow, and less graupel and cloud ice. This shift leads to less precipitation along the coast and more inland. The estimated ice loads based on the cloud water from the simulations are generally increased. We also focus on two different icing events during January 9–18 and February 1–14. During the first event, both the run in its original configuration and the run with ICE-T overestimated the ice loads, while the second event was underestimated. For Ålvikfjellet ICE-T gives the best estimates, while for Hardingnuten the ice loads are overestimated when the wind direction is from the southeast. This is due to local terrain shielding not captured by the model. During the Feb 1–14 event, the wind direction was generally easterly, which makes comparison between the simulations and the observations more reliable. In this case, ICE-T gives a better ice load estimate. Although there are major uncertainties, especially concerning the number concentration of cloud droplets, and local terrain effects, ICE-T appears to give a better estimate of the ice loads.publishedVersio

Varsling av atmosfærisk ising

Abstract Supercooled cloud droplets combined with strong wind, can produce heavy ice accretions on unheated structures. It is called ”in-cloud atmospheric icing”, and is a well known problem at high elevations in wintertime. Atmospheric icing on wind turbines is a challenge that has to be considered when erecting wind turbines at hills and ridges at high latitudes, for example in Norway. Ice accretion on the turbine blades can reduce the production significantly and large amounts can stop the turbine entirely. The need of a method to predict atmospheric icing events is increasing since there is a growing interest for building wind turbines along the windy coastline of Norway. In this study we have tested the ability of a mesoscale numerical weather pre- diction model (Weather Research and Forecasting (WRF) modeling system) to predict in-cloud atmospheric icing events. The simulations were executed with a fine spatial resolution for a selected area, and with use of a detai- led second-moment parameterization-scheme for the microphysical processes. Two mountains have been used as test sites, Ylläs in Finland and Gamlemsve- ten in Norway, where measurements of icing are available for selected cases. The overall results showed a fairly good agreement between the measure- ments and the simulations for most of the icing events. The experiment at Ylläs, where accurate measurements of supercooled cloud water were direct- ly compared to the modeled cloud water content, gave the best results. The ratio between modeled and measured values was about 1.3 for all the cases in the finest grid, and about 0.8 in all the cases when the model’s spatial resolution was decreased by a factor 4. The results from the experiment at Gamlemsveten are a bit more intricate to analyze because the modeled ice loads, which is compared to the measu- rements, is calculated from temperature and wind speed in addition to cloud water. There are also several uncertainties regarding the comparison between the calculated and the observed ice loads. The agreement between the mo- deled and the observed ice loads seems to be best in weather situations with low stratus clouds containing mostly liquid cloud water. The model seems to underestimate the icing rate in a period with convective clouds in cold air masses, when cloud ice is mixed into the cloud

Effects of the ICE-T Microphysics Scheme in HARMONIE-AROME on Estimated Ice Loads on Transmission Lines

In-cloud icing can cause damage to infrastructure and is challenging to forecast due to lack of a good representation of supercooled liquid water (SLW) in numerical weather prediction (NWP) models. We validate the new microphysics scheme, ICE-T, implemented into the NWP model HARMONIE-AROME, in full 3D simulations running over a 3 month period from December 1st 2016 to February 28th 2017. Output from the model simulations are first compared with conventional observations to evaluate the overall quality, and then used as input to an ice accretion model (IAM) and compared against measured ice loads at the two test sites Hardingnuten and Ålvikfjellet. The results show a clear shift towards more cloud water and snow, and less graupel and cloud ice. This shift leads to less precipitation along the coast and more inland. The estimated ice loads based on the cloud water from the simulations are generally increased. We also focus on two different icing events during January 9–18 and February 1–14. During the first event, both the run in its original configuration and the run with ICE-T overestimated the ice loads, while the second event was underestimated. For Ålvikfjellet ICE-T gives the best estimates, while for Hardingnuten the ice loads are overestimated when the wind direction is from the southeast. This is due to local terrain shielding not captured by the model. During the Feb 1–14 event, the wind direction was generally easterly, which makes comparison between the simulations and the observations more reliable. In this case, ICE-T gives a better ice load estimate. Although there are major uncertainties, especially concerning the number concentration of cloud droplets, and local terrain effects, ICE-T appears to give a better estimate of the ice loads