706 Evaluation of the Algorithm for Prediction of High Ice Water Content Areas (ALPHA): Methods and Results

Tuesday, 24 January 2017
Allyson Rugg, NCAR, Boulder, CO; and J. A. Haggerty, G. P. McCabe Jr., C. Kessinger, J. W. Strapp, and J. Delanoƫ

Since the 1990s, observations have shown that commercial aircraft sometimes experience engine power-loss and similar events while operating in the vicinity of deep convective clouds. Meteorological and engine performance analyses of such events indicate that high concentrations of ice crystals present a potential hazard to jet engines. A real-time nowcasting tool for detecting high ice water content (HIWC) conditions was developed by the NCAR HIWC Product Development Team with FAA sponsorship and deployed in support of a series of focused field campaigns. The Algorithm for Prediction of HIWC Areas (ALPHA) applies fuzzy logic methodology to define ranges of interest for a set of critical meteorological predictors of HIWC conditions. Input fields from satellite, model, and ground radar are then blended to yield a 3-dimensional field estimating the likelihood of HIWC conditions.

The High Altitude Ice Crystal – High Ice Water Content (HAIC-HIWC) experiments were designed to enhance understanding of ice crystal icing processes in deep convective clouds. An international consortium of researchers supported field campaigns in Darwin, Australia (2014) and Cayenne, French Guiana (2015). The airborne ice water content (IWC) measurements provide a means for evaluating ALPHA performance and improving its skill. Specifically, in situ IWC measurements from an Isokinetic Probe (IKP) and remote retrievals of IWC profiles from the RASTA cloud radar are used in this analysis.

These measurements were related to the various satellite, model, and radar products currently used in ALPHA, along with several new variables considered for inclusion, allowing for an objective evaluation of each component in the algorithm. Methods applied to objectively evaluate the relationship between a given input variable and the presence of HIWC will be described in this paper. Results of these analyses allow for informed modifications to membership functions of ALPHA input fields, thus creating a revised algorithm with improved skill. The performance of the revised nowcasting tool (ALPHA v2.0) is being evaluated using data from a third NASA field campaign conducted in Florida (2015). RASTA vertical profiles of IWC from the prior field campaigns will also be employed to assess the quality of 3-dimensional estimates of HIWC likelihood. Results of these comparisons and a summary of ALPHA performance characteristics will be presented at the conference.

* This research is in response to requirements and funding by the Federal Aviation Administration (FAA).  The views expressed are those of the authors and do not necessarily represent the official policy or position of the FAA.

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner