Cory A. Wolff

Current Icing Potential: Algorithm Description and Comparison with Aircraft Observations

Abstract The “current icing potential” (CIP) algorithm combines satellite, radar, surface, lightning, and pilot-report observations with model output to create a detailed three-dimensional hourly diagnosis of the potential for the existence of icing and supercooled large droplets. It uses a physically based situational approach that is derived from basic and applied cloud physics, combined with forecaster and onboard flight experience from field programs. Both fuzzy logic and decision-tree logic are applied in this context. CIP determines the locations of clouds and precipitation and then estimates the potential for the presence of supercooled liquid water and supercooled large droplets within a given airspace. First developed in the winter of 1997/98, CIP became an operational National Weather Service and Federal Aviation Administration product in 2002, providing real-time diagnoses that allow users to make route-specific decisions to avoid potentially hazardous icing. The CIP algorithm, its individual c...

An Inferred Climatology of Icing Conditions Aloft, Including Supercooled Large Drops. Part I: Canada and the Continental United States

Because of a lack of regular, direct measurements, little information is available about the frequency and spatial and temporal distribution of icing conditions aloft, including supercooled large drops (SLD). Research aircraft provide in situ observations of these conditions, but the sample set is small and can be biased. Other techniques must be used to create a more unbiased climatology. The presence and absence of icing and SLD aloft can be inferred using surface weather observations in conjunction with vertical profiles of temperature and moisture. In this study, such a climatology was created using 14 yr of coincident, 12-hourly Canadian and continental U.S. surface weather reports and balloonborne soundings. The conditions were found to be most common along the Pacific Coast from Alaska to Oregon, and in a large swath from the Canadian Maritimes to the Midwest. Prime locations migrated seasonally. Most SLD events appeared to occur below 4 km, were less than 1 km deep, and were formed via the collision–coalescence process.

A Solo-Based Automated Quality Control Algorithm for Airborne Tail Doppler Radar Data

An automated quality control preprocessing algorithm for removing nonweather radar echoes in airborne Doppler radar data has been developed. This algorithm can significantly reduce the time and experience level required for interactive radar data editing prior to dual-Doppler wind synthesis or data assimilation. The algorithm uses the editing functions in the Solo software package developed by the National Center for Atmospheric Research to remove noise, Earth-surface, sidelobe, second-trip, and other artifacts. The characteristics of these nonweather radar returns, the algorithm to identify and remove them, and the impacts of applying different threshold levels on wind retrievals are presented. Verification was performed by comparison with published Electra Doppler Radar (ELDORA) datasets that were interactively edited by different experienced radar meteorologists. Four cases consisting primarily of convective echoes from the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX), Bow Echo and Mesoscale Convective Vortex Experiment (BAMEX), Hurricane Rainband and Intensity Change Experiment (RAINEX), and The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC)/Tropical Cyclone Structure-2008 (TCS08)field experiments were used to test the algorithm using three threshold levels for data removal. The algorithm removes 80%, 90%, or 95% of the nonweather returns and retains 95%, 90%, or 85% of the weather returns on average at the low-, medium-, and high-threshold levels. Increasing the threshold level removes more nonweather echoes at the expense of also removing more weather echoes. The low threshold is recommended when weather retention is the highestpriority,andthehighthresholdisrecommendedwhennonweatherremovalisthehighestpriority.The medium threshold is a good compromise between these two priorities and is recommended for general use. Dual-Doppler wind retrievals using the automatically edited data compare well to retrievals from interactively edited data.

The Forecast Icing Product: Recent Upgrades and Improvements

The Forecast Icing Product (FIP) has undergone a series of upgrades since the algorithm was introduced in 2003. Most of these improvements were implemented to make the FIP more similar to the Current Icing Product (CIP), both in how the algorithm uses information and the output content. New methods for determining areas of precipitation and cloud top height have allowed locations of icing to be more accurately represented, decreasing overwarning of hazardous areas. Convection identification has also been added so that icing in and around forecast thunderstorms can be better characterized. The outputs have also been changed. The former icing potential is now icing probability after undergoing a calibration procedure. Expected severity, which is a new algorithm based on how much supercooled liquid water is forecast to be present, is displayed. Finally, areas with probable SLD are highlighted on the same graphic as severity for higher “quickglance” value.

Near-real-time cloud properties and aircraft icing indices from GEO and LEO satellites

Imagers on many of the current and future operational meteorological satellites in geostationary Earth orbit (GEO) and lower Earth orbit (LEO) have enough spectral channels to derive cloud microphysical properties useful for a variety of applications. The products include cloud amount, phase, optical depth, temperature, height and pressure, thickness, effective particle size, and ice or liquid water path, shortwave albedo, and outgoing longwave radiation for each imager pixel. Because aircraft icing depends on cloud temperature, droplet size, and liquid water content as well as aircraft variables, it is possible to estimate the potential icing conditions from the cloud phase, temperature, effective droplet size, and liquid water path. A prototype icing index is currently being derived over the contiguous USA in near-real time from Geostationary Operational Environmental Satellite (GOES-10 and 12) data on a half-hourly basis and from NOAA-16 Advanced Very High Resolution (AVHRR) data when available. Because the threshold-based algorithm is sensitive to small errors and differences in satellite imager and icing is complex process, a new probability based icing diagnosis technique is developed from a limited set of pilot reports. The algorithm produces reasonable patterns of icing probability and intensities when compared with independent model and pilot report data. Methods are discussed for improving the technique for incorporation into operational icing products.

THE FORECAST ICING POTENTIAL ALGORITHM

The Forecast Icing Potential (FIP) algorithm was developed at the National Center for Atmospheric Research under the Federal Aviation Administrations Aviation Weather Research Program. The FIP examines output from a numerical weather prediction model to calculate the potential for in-flight aircraft icing conditions. This icing potential demonstrates the confidence that a three-dimensional atmospheric location, represented by a model grid box, will contain icing conditions. The algorithm uses a combination of weather scenarios and fuzzy logic membership functions to compute the icing potential.

Model-Evaluation Tools for Three-Dimensional Cloud Verification via Spaceborne Active Sensors

AbstractClouds pose many operational hazards to the aviation community in terms of ceilings and visibility, turbulence, and aircraft icing. Realistic descriptions of the three-dimensional (3D) distribution and temporal evolution of clouds in numerical weather prediction models used for flight planning and routing are therefore of central importance. The introduction of satellite-based cloud radar (CloudSat) and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) sensors to the National Aeronautics and Space Administration A-Train is timely in light of these needs but requires a new paradigm of model-evaluation tools that are capable of exploiting the vertical-profile information. Early results from the National Center for Atmospheric Research Model Evaluation Toolkit (MET), augmented to work with the emergent satellite-based active sensor observations, are presented here. Existing horizontal-plane statistical evaluation techniques have been adapted to operate on observations in th...

A Generalized Navigation Correction Method for Airborne Doppler Radar Data

AbstractUncertainties in aircraft inertial navigation system and radar-pointing angles can have a large impact on the accuracy of airborne dual-Doppler analyses. The Testud et al. (THL) method has ...

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.