Abstracts of Gregory Thompson's publications

 Abstract: 

Overprediction of the spatial extent of aircraft icing is a major problem in forecaster products based on numerical model output. Dependence on relative humidity fields, which are inherently broad and smooth, is the cause of this difficulty. Using multispectral satellite analysis based on NOAA Advanced Very High Resolution Radiometer (AVHRR) data, this paper shows how the spatial extent of icing potential based on model output can be reduced where there are no subfreezing cloud tops and, therefore, where icing is unlikely. Fifty-one cases were analyzed using two scenarios: 1) model output only and 2) model output screened by a satellite cloud analysis. Average area efficiency, a statistical validation measure of icing potential using coincident pilot reports of icing, improved substantially when satellite screening was applied.

 Abstract: 

The purpose of the Federal Aviation Administration's (FAA) Icing Forecasting Improvement Program is to conduct research on icing conditions both in-flight and on the ground. This paper describes a portion of the in-flight aircraft icing prediction effort through a comprehensive icing prediction and evaluation project conducted by the Research Applications Program (RAP) at the National Center for Atmospheric Research (NCAR). During this project, in-flight icing potential was forecast using algorithms developed by RAP, the National Weather Service's (NWS) National Aviation Weather Advisory Unit (NAWAU), and the Air Force Global Weather Center in conjunction with numerical model data from the Eta, MAPS, and MM5 models. Furthermore, explicit predictions of cloud liquid water were available from the Eta and MM5 models and were also used to forecast icing potential.

To compare subjectively the different algorithms, predicted icing regions and observed pilot reports were viewed simultaneously on an interactive, real-time display. To measure objectively the skill of icing predictions, a rigorous statistical evaluation was performed in order to compare the different algorithms (details and results are provided in Part II). Both the subjective and objective comparisons are presented here for a particular case study whereas results from the entire project are found in Part II. By statistically analyzing two months worth of data, it appears that further advances in temperature and relative humidity-based algorithms are unlikely. Explicit cloud liquid water predictions, however, show promising results although still relatively new in operational numerical models.

 Abstract: 

Recent research to improve forecasts of in-flight icing conditions has involved the development of algorithms to apply to the output of numerical weather prediction models. The abilities of several of these algorithms to predict icing conditions, as verified by pilot reports (PIREPs), are compared for two numerical weather prediction models (Eta and MAPS) for the WISP94 time period (25 January to 25 March 1994). Algorithms included in the comparison were developed by the National Aviation Weather Advisory Unit [NAWAU, now the Aviation Weather Center (AWC)], the National Center for Atmospheric Research (NCAR) Research Applications Program (RAP), and the United States Air Force. Operational icing forecasts (AIRMETs) issued by AWC for the same time period are evaluated to provide a standard of comparison. The capabilities of the Eta model's explicit cloud liquid water estimates for identifying icing regions are also evaluated and compared to the algorithm results.

Because PIREPs are not systematic and are biased toward positive reports, it is difficult to estimate standard verification parameters related to overforecasting (e.g., false alarm ratio). Methods are developed to compensate for these attributes of the PIREPs. The primary verification statistics computed include the Probability of Detection of Yes and No reports, and the areal and volume extent of the forecast region.

None of the individual algorithms were able to obtain both a higher POD and a smaller Area than any other algorithm; increases in POD are associated in all cases with increases in Area. The RAP algorithm provides additional information by attempting to identify the physical mechanisms associated with the forecast icing conditions. One component of the RAP algorithm, which is designed to detect and forecast icing in regions of "warm" stratiform clouds, is more efficient at detecting icing than the other components. Cloud liquid water shows promise for development as a predictor of icing conditions, with detection rates of 30% or more in this initial study. AIRMETs detected approximately the same percentage of icing reports as the algorithms, but with somewhat smaller forecast areas and somewhat larger forecast volumes on average. The algorithms are able to provide guidance with characteristics that are similar to the AIRMETs and should be useful in their formulation.

 Abstract: 

Experience in performing real-time mesoscale numerical prediction forecasts using the Regional Atmospheric Modeling System (RAMS) over Colorado for a winter season on high-performance workstations is summarized. Performance evaluation is done for specific case studies and, statistically, for the entire winter season. RAMS forecasts were also compared with Nested Grid Model (NGM) forecasts. In addition, RAMS precipitation forecasts with a simple "dump bucket" scheme are compared with explicit, bulk microphysics parameterization schemes. The potential applications and political/social problems of having a readily accessible, real-time mesoscale forecasting capability on low-cost, high-performance workstations is discussed.