On the Value of Time-Lag-Ensemble Averaging to Improve Numerical Model Predictions of Aircraft Icing Conditions

Abstract

The High-Resolution Rapid Refresh (HRRR) model with its hourly updating cycles provides multiple weather forecasts valid at any given time. A logical combination of these individual deterministic forecasts is postulated to show more skill than any single forecast for predicting clouds containing supercooled liquid water (SLW), an aircraft icing threat. To examine the potential value of using multiple HRRR forecasts for icing prediction, a time-lag-ensemble (TLE) averaging method of combining a number of HRRR forecasts was implemented for a multiple month real-time test during the winter of 2016/17. The skills of individual HRRR and HRRR-TLE aircraft icing predictions were evaluated using icing pilot reports (PIREPs) and surface weather observations and compared with the operational Forecast Icing Product (FIP) using the Rapid Refresh (RAP) model. The HRRR-TLE was found to produce a higher capture rate of icing PIREPs and surface icing conditions of freezing drizzle or freezing rain than single deterministic HRRR forecasts. As a trade-off, the volume of airspace warned in HRRR-TLE increased, resulting in a higher false detection rate than in the deterministic HRRR forecasts. Overall, the HRRR-TLE had similar probability of detection and volume of airspace warned for icing as the operational FIP prediction for the icing probability of 25% or greater. Alternative techniques for composing TLE from multiple HRRR forecasts were tested in postseason rerun experiments. The rerun tests also included a comparison of the skills of HRRR and HRRR-TLE to the skills of RAP and RAP-TLE.

Comparison of Lightning Forecasts from the High-Resolution Rapid Refresh Model to Geostationary Lightning Mapper Observations

“…Many studies have used neighborhood and objectbased verification methods to provide meaningful measures of convective-scale forecast performance (Ebert 2009;Clark et al 2010;Wolff et al 2014;Nachamkin and Schmidt 2015;Xu et al 2019;Radford et al 2019). Davis et al (2006) used object-based verification to assess the displacement of simulated and observed areas of rainfall-a method now referred to as Method for Object-based Diagnostic Evaluation (MODE; see also Bytheway andKummerow 2015 andRadford et al 2019).…”

Using Cloud Computing to Analyze Model Output Archived in Zarr Format

“…To illustrate the utility of the Zarr format for this use case, we plot time series of forecast wind gusts from the 00, 06, 12, and 18 UTC HRRR model runs for a single point from 12 UTC 6 Sept- provides only deterministic guidance (Xu et al 2019). TLEs with sufficient lead time to be potentially useful operationally can be constructed using a set of sequential HRRR forecasts, with each model run treated as an ensemble member.…”

The Ability of the ICE-T Microphysics Scheme in HARMONIE-AROME to Predict Aircraft Icing

“…Finally, operational forecasters issue warnings based on spatial patterns of certain extents in NWP output and not based on output from single grid points. To address these issues we extend the analysis to also include calculations of hit rate and forecast frequency as functions of neighborhood areas, a method also utilized by Xu et al (2019).…”