InFlight, Ground and Engine Icing


For the past two decades RAL scientists have worked to improve diagnoses and forecasts of icing conditions that impact aviation. The research areas include icing aloft, jet or turbine engine icing, and ground icing. Much of this work is accomplished as part of the FAA's Aviation Weather Research Program. 

One outcome from the icing aloft research is the development of operationally-available, automated in-flight icing forecasts over the CONUS and Alaska. At this time, the Current and Forecast Icing Products (CIP and FIP) developed at RAL are running at the National Weather Service's Aviation Weather Center (AWC) and are approved for unrestricted supplementary use. The outputs include expected icing severity, probability of encounter, and potential for supercooled large drop (SLD, those drops with diameters exceeding 50 microns) at 13-km resolution over the CONUS for 0-18 h.

The engine icing research falls under the FAA’s High Ice Water Content (HIWC) program. This program sponsors research to develop an algorithm called the Algorithm for Prediction of High Ice Water Content Areas (ALPHA) to diagnose atmospheric conditions conducive to engine icing events. Another aspect of the HIWC program is field experiments to characterize the atmosphere where engine icing events will occur. RAL scientists have participated in these experiments that have taken place in Darwin, Australia; Florida; and Cayenne, French Guiana.

Icing fig 1
Figure 1. Aircraft undergoing the deicing process.

The FAA also funds icing research in the terminal area after a new rule was enacted regarding flight restrictions in known icing conditions. To improve the detection and forecasting of icing in the terminal area, the Terminal Area Icing Weather Information for NextGen (TAIWIN) program is conducting research focused on two key areas: improved ground detection of icing conditions (notably the detection of freezing drizzle) and improved performance and forecasting of freezing drizzle and freezing rain in numerical models. Improved ground detection of icing conditions has focused primarily on automated detection of freezing drizzle and ice pellets and improvements to feature-tracking algorithms for radar and satellite data are also being explored. The numerical modeling tasks funded under TAIWIN have focused on development of a time-lagged ensemble for better forecasts of icing conditions; improved aerosol initialization and fluxes in the models’ identification of shortcomings of model initialization of clouds and precipitation; testing of new data assimilation methods; and improved blending of observations and nowcasts with numerical forecasts of icing conditions. The NextGen Surface Observing Capabilities (NSOC) Weather Observations Improvement (WOI) group has been developing a specification for the present weather sensor (PWS) replacement on the Automated Surface Observing System (ASOS). The current sensor, the Light Emitting Diode Weather Indicator (LEDWI), is currently at the end of its life and needs to be replaced. Various sensors are being tested to determine their accuracy with reporting drizzle/freezing drizzle and ice pellets to mitigate the gap in reporting those conditions since ASOS cannot currently report those precipitation types without being augmented by a human observer.

Ground deicing research continued with a focus on improving the NCAR snow machine to better match the indoor holdover times versus the outdoor holdover times observed in nature. The machine has typically shown more conservative (shorter) holdover times as compared to outdoor times the FAA is interested in determining why that is and correcting for it. Upgrades to the machine were also a focus for improving machine performance and reliability during fluid testing.


In 2018, RAL continued work on a number of icing aloft sub-projects for the FAA: 1) research on high-resolution NWP model with explicit microphysics and improved use of sensor data to develop drop size distribution (DSD) products for icing prediction and severity calculations with the intention of addressing FAA regulations to discriminate between freezing drizzle and freezing rain.  2) an icing product tailored for Alaska that was approved for experimental status this year by a FAA technical review panel. 3) Improvements to icing diagnosis using NEXRAD dual-polarization data. 4) Engineers worked with NCEP to transfer operational icing products to its WCOSS supercomputing environment.

RAL staff working on the High Ice Water Content (HIWC) project joined with members of the European High Altitude Ice Crystals (HAIC) to evaluate and refine methods for detecting and nowcasting areas of HIWC. Research quality data from a series of joint field campaigns in Australia, French Guiana, and Florida were analyzed by the teams and were used to assess diagnostic and forecasting capability. The evaluation process has resulted in an upgraded version of ALPHA (Algorithm for Prediction of High Ice Water Content Areas) which uses NWP output combined with satellite and radar data to diagnose cold cloud tops, warm atmosphere (compared to a standard sounding), high radar reflectivity below typical flight cruise altitudes, and other factors to determine regions conducive to the high ice water content hazard. ALPHA was applied to compile statistics on the frequency and duration of HIWC conditions. A joint effort with the Australian Bureau of Meteorology to implement ALPHA in their operational setting and distribute products to airlines continued.

The TAIWIN project made significant progress in both the modeling and observational areas. The automated algorithm for ground detection of freezing drizzle was modified to include detection of ice pellets and frost utilizing the existing Automated Surface Observing System (ASOS) infrastructure. The algorithm has gone through several iterations now, and the latest results indicate that the algorithm is likely working as expected and can be used to reprocess archived data for determining periods where freezing drizzle, ice pellets and frost may have been occurring. Spatial variability of precipitation (primarily snow thus far) in the terminal area has also been a primary focus of TAIWIN efforts with the goal of determining how far apart surface observations are needed to accurately detect the changing weather conditions within the terminal area. A series of surface weather stations has been installed at varying distances apart (2 km to 10 km) along the Colorado Front Range to examine the spatial variability of snow and provide recommendations on surface station spacing.

Radar and satellite tasking focused on automated tracking of possible derived Supercooled Large Drop (SLD) conditions from both radar and satellite data with a goal of providing a near-term nowcast of icing conditions. Considerable effort also went into the model improvement tasks with most of the work focusing on the Time-Lagged Ensembles (TLEs), cloud underproduction, and aerosol tasks. A new TLE of the HRRR model was developed and is currently being run in real-time within RAL. The modeling group is performing an initial verification of the TLE to determine if adjustments need to be made to the weighting of the different model runs. Significant progress was also made on the cloud underproduction task with efforts focused on development of a new cloud-fraction scheme for the WRF model to better forecast cloud development. A journal article summarizing these results was submitted.

RAL staff maintained sensors at four locations across North America including Boulder, CO; Grand Forks, ND; Pittsburgh, PA; and St. John’s, Newfoundland. The sites collected sensor and video data of all precipitation events during the winter of 2017-18.  NCAR staff then examined all periods where drizzle/freezing drizzle and ice pellets occurred and reviewed all the video data during those periods to develop a human observation database for precipitation type on a minute-by-minute basis for comparison with the sensor data.


The In-flight Icing Project will continue its research using state-of-the-art NWP model ouput and observations to develop icing products that provide information about cloud drop size to the aviation community. A data set to support this development and verify products will be obtained during the In-Cloud Icing and Large drop Experiment (ICICLE), an airborne and ground-based measurement effort to collect atmospheric data in supercooled large drop conditions planned for Jan-Mar 2019.

The HIWC team will complete evaluation of the diagnostic capabilities of ALPHA and continue research on HIWC forecasting techniques.  An HIWC Nowcasting Trial Exercise will be conducted in Australia under an agreement with the Bureau of Meteorology during the northern Australia monsoon season. This trial exercise will provide insight to the FAA sponsors on the feasibility of fielding an operational version of ALPHA in the United States.

TAIWIN will continue to focus its efforts on the aforementioned tasks, with the goal of publishing the results of the freezing drizzle algorithm work, the TLE work and the cloud underproduction work. An AMS presentation on efforts relating to freezing drizzle, spatial variability, model verification work, and cloud underproduction is being planned. Flight campaign goals will also be established and potential university collaborators will be considered for participation in a new proposal to NSF to support this work. A flight campaign, the In-Cloud ICing and Large-drop Experiment (ICICLE), is being planned for winter 2019 to study microphysical cloud properties in order to better understand how icing conditions (particularly freezing rain, freezing drizzle and ice pellets) form and evolve over time.

The NSOC WOI program is expected to wrap up the data analysis portion of the study and provide recommendations to the FAA on the specifications that industry sensors will need to meet in order to be considered for implementation on the ASOS system. NCAR staff will continue to contribute to the task as necessary to support data requests and attend meetings to present results in support of finding a new replacement sensor for the LEDWI. The snow machine work is expected to continue with a focus on improving the design of the tray assembly, conducting more outdoor versus indoor testing, and upgrading the APS Aviation snow machine to match the current version of the NCAR snow machine. A request is also being explored from the FAA to possibly build a third machine for use by APS Aviation at its icing facility in Montreal.