Aircraft icing forecasts with the ICE-T microphysics scheme
Transcript of Aircraft icing forecasts with the ICE-T microphysics scheme
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Aircraft icing forecasts with the ICE-T microphysics scheme
Bjørg Jenny Engdahl, Morten Køltzow, MET-Norway, Tim Carlsen, Trude Storelvmo, University of Oslo
Photo: Greg Thompson
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RecapImplemented parts of the Thompson microphysics scheme into ICE3
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RecapImplemented parts of the Thompson microphysics scheme into ICE3
ICE3 + Thompson scheme = ICE-T
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RecapImplemented parts of the Thompson microphysics scheme into ICE3
ICE3 + Thompson scheme = ICE-T
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RecapImplemented parts of the Thompson microphysics scheme into ICE3
ICE3 + Thompson scheme = ICE-TGoal: Improve the representation of supercooled liquid water and forecasts of atmospheric icing
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RecapImplemented parts of the Thompson microphysics scheme into ICE3
ICE3 + Thompson scheme = ICE-TGoal: Improve the representation of supercooled liquid water and forecasts of atmospheric icing
Validated against observations of ice loads at transmission lines
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Next step: Up into the air!
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Up into the air!What? Check the effects higher up in the atmosphere
Goal: Check if ICE-T can be used for forecasts of aircraft icing
How? Validate the model simulations against pilot reports (aireps) and satellite retrieved profiles of Liquid and Ice Water Content (LWC and IWC) over a 3 month
winter season
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3 month winter simulation
Dec 1 2016 - Feb 28 2017
CTRL and ICE-T
No data assimilation, input forecast ECMWF, Cold start, 00+36h (use 12-36)
2.5km grid spacing, 65 vertical levels, 949x739 grid points covering Norway, Sweden and parts of Finland
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Aireps
111 reported icing events in total
12 FBL (light), 78 MOD (moderate), 21 SEV (severe)
Time, location, height interval, severity
Problem: Biased and subjective
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Icing algorithmCurrently at MET-Norway: Based on the ice accretion model described by Makkonen (2000)
Input from NWP:
Cloud waterRain(Snow, graupel, cloud ice)Specific humidityPressureTemperature
Icingmodel
Icing indices:
1: Trace (not used)2: FBL (light)3: MOD (moderate)4: SEV (severe)
Thresholds appear somewhat arbitrary. Could easily be adjusted to fit model output.
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Icing frequency
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NeighbourhoodLocations given in aireps may be inaccurate, moreover icing often occur in a larger area
Define a set of neighbourhoods of different sizes around the aireps0, 1, 3, 5, 10, and 15 grid boxes in each direction from the closest point (largest area cover 31 x 31 grid boxes, or ~6000 km²)
Find hit rate for all neighbourhood sizes (Icing/no-icing), thresholds:
< 0 % (any grid points with icing inside area)
< 5 %
< 10 %
< 15 %
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Hit rate and icing frequencies CTRL (solid lines) and ICE-T (dashed lines)
Shaded areas represent 98% confidence intervals
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Satellite retrieved observations
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Modelled and satellite retrieved (CloudSat-CALIPSO) distribution of LWC and IWC DJF 2016-2017 (cloud only)
merp
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Liquid water path
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Main conclusionsICE-T has more supercooled liquid water than CTRL, and subsequently higher icing frequency
ICE-T has a higher hit rate than CTRL, but higher false alarm ratio can not be ruled out for most thresholds and neighbourhoods
ICE-T improves the vertical distribution of IWC, and LWC at lower levels
LWP is improved with ICE-T
Lack of LWC at lower levels should be investigated further
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Questions?