Daniel R. Adriaansen

Improving Diagnoses of In-Flight Icing Conditions in Regions of Sparsely Distributed Surface Observations

The accurate diagnosis of in-flight icing conditions is dependent on surface observations of cloud coverage, cloud base height, and surface precipitation type. However, the network for collecting these data over the the United States is neither contiguous nor evenly distributed. Surface observational gaps exist over much of the domain where in-flight icing conditions are diagnosed. Due to the way these observations are treated when diagnosing inflight icing conditions, the result is often circles where icing conditions are possible next to areas with no icing conditions diagnosed due to absence of surface observations. To avoid these visually unappealing and scientifically inconsistent artifacts, a method was developed to create surrogate surface observation data from numerical weather prediction model output. Using the individual condensate fields, accumulated precipitation, and temperature all three of the required datasets used from surface observations in diagnosing in-flight icing conditions were derived. The Current Icing Product (CIP) shows in-flight icing diagnoses created with the model derived surface observations that are similar to those created when using only real observations.

Diagnosing and Forecasting In-Flight Icing Conditions in Alaska

In-flight icing is a significant hazard in Alaska as the atmospheric environment is complex and ranges from maritime to continental and temperate to polar. An analysis of radiosonde data conditions for different climate zones reveals a high frequency of icing conditions year-round, varying with season and altitude. Many locations in Alaska depend on air travel for transportation, especially in smaller aircraft that fly at icing-prone altitudes. Thus, accurate diagnoses and forecasts of the icing environment, tuned to these varying conditions, are needed. Icing products are currently under development that are anticipated to meet the needs of aviation users in Alaska. The forecast product will be available first and is based on the Forecast Icing Product, originally developed for use in the CONUS, and predicts icing probability, supercooled large drop potential, and severity. The current spatial resolution is 13 km; high-resolution (3-km) model runs have also been used in the Alaska forecast algorithm to assess their value. An icing diagnosis algorithm that combines observations with model output, much like the Current Icing Product, is also in early development. To improve that product, the use of polar orbiting satellite data is being explored. These observations may be added to the diagnosis algorithm to provide observations where geostationary satellite data are not available.

Recent and Future Improvements in Automated Icing Algorithms

The goal of icing forecasting research being conducted at the National Center for Atmospheric Research (NCAR) for the Federal Aviation Administration’s (FAA) Aviation Weather Research Program (AWRP) is to provide timely and accurate forecasts of inflight icing conditions. The flying public wants to know not only where icing conditions are likely to reside, but also the probability of their occurrence and expected severity. Automated diagnosis and forecast icing products have been developed at NCAR and deployed at the Aviation Weather Center (AWC), where they provide this information to pilots, forecasters, and dispatchers. These products, known as the Current and Forecast Icing Products (CIP and FIP, respectively), have been approved for operational decision making by these groups. Recently, changes were made to both algorithms to accommodate the transition in numerical weather prediction (NWP) models, from the Rapid Update Cycle (RUC) to the Weather Research and Forecasting Rapid Refresh (WRF-RAP). This transition required some changes to the algorithms to handle updated model information and a verification of the results. The verification study confirmed that the new model had the desired effects on the icing products and also brought to light some interesting information on the handling of convection and supercooled large drops (SLD). During this transition other upgrades and changes were also made to the algorithms dealing with icing severity at night, the use of radar data, and the development of an algorithm testbed.

Performance Analysis of the Current Icing Product (CIP) Algorithm under Variations in Icing Relevant Observational Datasets

The Current Icing Product (CIP) uses five observational datasets in addition to numerical weather prediction (NWP) model data to diagnose a three-dimensional icing probability and severity. By default, there are thresholds that limit the age of the observational data that CIP uses. If the data time does not meet the threshold and it is a required dataset (surface observations, satellite), then CIP will not run. By utilizing the probability of detection (POD) value of only the positive icing pilot reports (PODy) for both of the required input datasets, it was found that the current thresholds are adequate. Satellite data that are 30 minutes old produce a PODy of 0.82, but that drops to 0.78 when the age of the satellite data reaches 120 minutes. Similar results were found using surface observation data ranging from 60 to 180 minutes old. Understanding the behavior and performance of CIP when it is required to use less than optimal input data is crucial for making improvements to the algorithm and testing additional datasets. A methodology is presented to assist with these tasks in the future, as well as a more in depth look at the effect of varying input dataset age has on PODy of both CIP icing probability and severity.