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- Author or Editor: Courtney Weeks x
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Abstract
The prediction of supercooled large drops (SLD) from the Thompson–Eidhammer (TE) microphysics scheme—run as part of the High-Resolution Rapid Refresh (HRRR) model—is evaluated with observations from the In-Cloud Icing and Large drop Experiment (ICICLE) field campaign. These observations are also used to train a random forest machine learning (ML) model, which is then used to predict SLD from several variables derived from HRRR model output. Results provide insight on the limitations and benefits of each model. Generally, the ML model results in an increase in the probability of detection (POD) and false alarm rate (FAR) of SLD compared to prediction from TE microphysics. Additionally, the POD of SLD increases with increasing forecast lead time for both models, likely since clouds and precipitation have more time to develop as forecast length increases. Since SLD take time to develop in TE microphysics and may be poorly represented in short-term (<3 h) forecasts, the ML model can provide improved short-term guidance on supercooled large-drop icing conditions. Results also show that TE microphysics predicts a frequency of SLD in cold (<−10°C) or high ice water content (IWC) environments that is too low compared to observations, whereas the ML model better captures the relative frequency of SLD in these environments.
Abstract
The prediction of supercooled large drops (SLD) from the Thompson–Eidhammer (TE) microphysics scheme—run as part of the High-Resolution Rapid Refresh (HRRR) model—is evaluated with observations from the In-Cloud Icing and Large drop Experiment (ICICLE) field campaign. These observations are also used to train a random forest machine learning (ML) model, which is then used to predict SLD from several variables derived from HRRR model output. Results provide insight on the limitations and benefits of each model. Generally, the ML model results in an increase in the probability of detection (POD) and false alarm rate (FAR) of SLD compared to prediction from TE microphysics. Additionally, the POD of SLD increases with increasing forecast lead time for both models, likely since clouds and precipitation have more time to develop as forecast length increases. Since SLD take time to develop in TE microphysics and may be poorly represented in short-term (<3 h) forecasts, the ML model can provide improved short-term guidance on supercooled large-drop icing conditions. Results also show that TE microphysics predicts a frequency of SLD in cold (<−10°C) or high ice water content (IWC) environments that is too low compared to observations, whereas the ML model better captures the relative frequency of SLD in these environments.
Abstract
Supercooled large drop (SLD) icing poses a unique hazard for aircraft and has resulted in new regulations regarding aircraft certification to fly in regions of known or forecast SLD icing conditions. The new regulations define two SLD icing categories based upon the maximum supercooled liquid water drop diameter (Dmax): freezing drizzle (100–500 μm) and freezing rain (>500 μm). Recent upgrades to U.S. operational numerical weather prediction models lay a foundation to provide more relevant aircraft icing guidance including the potential to predict explicit drop size. The primary focus of this paper is to evaluate a proposed method for estimating the maximum drop size from model forecast data to differentiate freezing drizzle from freezing rain conditions. Using in situ cloud microphysical measurements collected in icing conditions during two field campaigns between January and March 2017, this study shows that the High-Resolution Rapid Refresh model is capable of distinguishing SLD icing categories of freezing drizzle and freezing rain using a Dmax extracted from the rain category of the microphysics output. It is shown that the extracted Dmax from the model correctly predicted the observed SLD icing category as much as 99% of the time when the HRRR accurately forecast SLD conditions; however, performance varied by the method to define Dmax and by the field campaign dataset used for verification.
Abstract
Supercooled large drop (SLD) icing poses a unique hazard for aircraft and has resulted in new regulations regarding aircraft certification to fly in regions of known or forecast SLD icing conditions. The new regulations define two SLD icing categories based upon the maximum supercooled liquid water drop diameter (Dmax): freezing drizzle (100–500 μm) and freezing rain (>500 μm). Recent upgrades to U.S. operational numerical weather prediction models lay a foundation to provide more relevant aircraft icing guidance including the potential to predict explicit drop size. The primary focus of this paper is to evaluate a proposed method for estimating the maximum drop size from model forecast data to differentiate freezing drizzle from freezing rain conditions. Using in situ cloud microphysical measurements collected in icing conditions during two field campaigns between January and March 2017, this study shows that the High-Resolution Rapid Refresh model is capable of distinguishing SLD icing categories of freezing drizzle and freezing rain using a Dmax extracted from the rain category of the microphysics output. It is shown that the extracted Dmax from the model correctly predicted the observed SLD icing category as much as 99% of the time when the HRRR accurately forecast SLD conditions; however, performance varied by the method to define Dmax and by the field campaign dataset used for verification.
