• Air Weather Service 1980. Forecasters’ guide on aircraft icing. Air Weather Service Rep. AWS/TR-80/001, 58 pp. [Available from U.S. Air Force, Scott AFB, IL 62225.].

  • Benjamin, S G. Coauthors 2004. An hourly assimilation–forecast cycle: The RUC. Mon. Wea. Rev. 132:495518.

  • Bernstein, B C. 1996. A new technique for identifying locations where supercooled large droplets are likely to exist: The stovepipe algorithm. Preprints, 15th Conf. on Weather Analysis and Forecasting, Norfolk, VA, Amer. Meteor. Soc., 5–8.

  • Bernstein, B C., T A. Omeron, F. McDonough, and M K. Politovich. 1997. The relationship between aircraft icing and synoptic scale weather conditions. Wea. Forecasting 12:742762.

    • Search Google Scholar
    • Export Citation
  • Brown, B G., G. Thompson, R T. Bruintjes, R. Bullock, and T. Kane. 1997. Intercomparison of in-flight icing algorithms. Part II: Statistical verification results. Wea. Forecasting 12:890914.

    • Search Google Scholar
    • Export Citation
  • Brown, B G., T L. Kane, R. Bullock, and M K. Politovich. 1999. Evidence of improvements in the quality of in-flight icing algorithms. Preprints, Eighth Conf. on Aviation, Range, and Aerospace Meteorology, Dallas, TX, Amer. Meteor. Soc., 48–52.

  • Brown, B G., J L. Mahoney, R. Bullock, T L. Fowler, J. Henderson, and A. Loughe. 2001. Quality assessment report: Integrated Icing Diagnostic Algorithm (IIDA). AWIT Technology Review Panel Internal Report, 35 pp. [Available from B. Brown, Research Applications Program, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000.].

  • Carriere, J., S. Alquier, C. LeBot, and E. Moulin. 1997. Some results of a statistical evaluation of forecast icing risk algorithms. Meteor. Appl. 4:115130.

    • Search Google Scholar
    • Export Citation
  • Cober, S G., G A. Isaac, and J W. Strapp. 1995. Aircraft icing measurements in East Coast winter storms. J. Appl. Meteor. 34:88100.

    • Search Google Scholar
    • Export Citation
  • Cober, S G., J W. Strapp, and G A. Isaac. 1996. An example of supercooled drizzle drops formed through a collision–coalescence process. J. Appl. Meteor. 35:22502260.

    • Search Google Scholar
    • Export Citation
  • Cober, S G., G A. Isaac, and J W. Strapp. 2001. Characterizations of aircraft icing environments that include supercooled large drops. J. Appl. Meteor. 40:19842002.

    • Search Google Scholar
    • Export Citation
  • Ellrod, G P. 1996. The use of GOES-8 multispectral imagery for the detection of aircraft icing regions. Preprints, Eighth Conf. on Satellite Meteorology and Oceanography, Atlanta, GA, Amer. Meteor. Soc., 168–171.

  • Forbes, G S., Y. Hu, B G. Brown, B C. Bernstein, and M K. Politovich. 1993. Examination of soundings in the proximity of pilot reports of aircraft icing during STORM-FEST. Preprints, Fifth Conf. on Aviation Weather Systems, Vienna, VA, Amer. Meteor. Soc., 282–286.

  • Geresdi, I., R. Rasmussen, W. Grabowski, and B. Bernstein. 2005. Sensitivity of freezing drizzle formation in stably stratified clouds to ice processes. Meteor. Atmos. Phys. 88:91105.

    • Search Google Scholar
    • Export Citation
  • Guan, H., S G. Cober, and G A. Isaac. 2001. Verification of supercooled cloud water forecasts with in situ aircraft measurements. Wea. Forecasting 16:145155.

    • Search Google Scholar
    • Export Citation
  • Hanesiak, J M. and R E. Stewart. 1995. The mesoscale and microscale structure of a severe ice pellet storm. Mon. Wea. Rev. 123:31443162.

    • Search Google Scholar
    • Export Citation
  • Hill, G E. 1980. Seeding-opportunity recognition in winter orographic clouds. J. Appl. Meteor. 19:13711381.

  • Isaac, G A., S G. Cober, J W. Strapp, A V. Korolev, A. Tremblay, and D L. Marcotte. 2001. Recent Canadian research on in-flight aircraft icing. Can. Aeronaut. Space J. 47:19.

    • Search Google Scholar
    • Export Citation
  • Jensen, C E. 1963. Prediction of large-scale cloudiness and airframe icing conditions by machine methods. J. Appl. Meteor. 2:337344.

    • Search Google Scholar
    • Export Citation
  • Kelsch, M. and L. Wharton. 1996. Comparing PIREPs with NAWAU turbulence and icing forecasts: Issues and results. Wea. Forecasting 11:385390.

    • Search Google Scholar
    • Export Citation
  • Knight, C A. and P. Squires. 1982. Case Studies of the National Hail Research Experiment. Vol. 2, Hailstorms of the Central High Plains, Colorado Associated University Press, 245 pp.

    • Search Google Scholar
    • Export Citation
  • Korolev, A V., G A. Isaac, S G. Cober, J W. Strapp, and J. Hallett. 2003. Microphysical characterization of mixed-phase clouds. Quart. J. Roy. Meteor. Soc. 129:3965.

    • Search Google Scholar
    • Export Citation
  • Le Bot, C. 2004. SIGMA: System of Icing Geographic Identification in Meteorology for Aviation. Preprints, 11th Conf. on Aviation, Range and Aerospace Meteorology, Hyannis, MA, Amer. Meteor. Soc., CD-ROM.

  • Lee, T F., F J. Turk, and K. Richardson. 1997. Stratus and fog products using GOES-8–9 3.9-m data. Wea. Forecasting 12:664677.

  • Marwitz, J D., M K. Politovich, B C. Bernstein, F M. Ralph, P J. Neiman, R. Ashenden, and J. Bresch. 1997. Meteorological conditions associated with the ATR-72 aircraft accident near Roselawn, Indiana, on 31 October 1994. Bull. Amer. Meteor. Soc. 78:4152.

    • Search Google Scholar
    • Export Citation
  • Mason, I B. 1982. A model for assessment of weather forecasts. Aust. Meteor. Mag. 30:291303.

  • Miller, D., T. Ratvasky, B. Bernstein, F. McDonough, and J W. Strapp. 1998. NASA/FAA/NCAR supercooled large droplet icing flight research: Summary of winter 96-97 flight operations. Extended Abstracts, 36th Aerospace Science Meeting and Exhibit, Reno, NV, American Institute of Aeronautics and Astronautics, AIAA 98-0557, 20 pp.

  • National Transportation Safety Board 1996. Aircraft accident report. In-flight icing encounter and loss of control, Simmons Airlines, d.b.a. American Eagle flight 4184, Avions de Transport Regional (ATR) Model 72-212, N401MA, Roselawn, IN, October 31 1994, Vol. 1, Safety Board Rep. NTSB/AAR-96/01, PB96-910401, DCA95MA001, 322 pp. [Available from National Transportation Safety Board, Public Inquiries Section RE-51, 490 L’Enfant Plaza S.W., Washington, DC 20594.].

  • National Transportation Safety Board 1998. Aircraft accident report. In-flight icing encounter and uncontrolled collision with terrain, COMAIR flight 3272, Embraer EMB-120RT, N265 CA, Monroe, MI, January 9, 1997, Safety Board Rep. NTSB/AAR-98/04, PB08-910404, DCA97MA017, 348 pp. [Available from National Transportation Safety Board, Public Inquiries Section RE-51, 490 L’Enfant Plaza S.W., Washington, DC 20594.].

  • Pobanz, B M., J D. Marwitz, and M K. Politovich. 1994. Conditions associated with large-drop regions. J. Appl. Meteor. 33:13661372.

    • Search Google Scholar
    • Export Citation
  • Politovich, M K. and B C. Bernstein. 1995. Production and depletion of supercooled liquid water in a Colorado winter storm. J. Appl. Meteor. 34:26312648.

    • Search Google Scholar
    • Export Citation
  • Rasmussen, R. Coauthors 1992. Winter Icing and Storms Project (WISP). Bull. Amer. Meteor. Soc. 73:951974.

  • Rasmussen, R M., B C. Bernstein, M. Murakami, G. Stossmeister, J. Reisner, and B. Stankov. 1995. The 1990 Valentine’s Day Arctic outbreak. Part I: Mesoscale and microscale structure and evolution of a Colorado Front Range shallow upslope cloud. J. Appl. Meteor. 34:14811511.

    • Search Google Scholar
    • Export Citation
  • Rauber, R M., L S. Olthoff, M K. Ramamurthy, and K E. Kunkel. 2000. The relative importance of warm rain and melting processes in freezing precipitation events. J. Appl. Meteor. 39:11851195.

    • Search Google Scholar
    • Export Citation
  • Reinking, R F., S Y. Matrosov, R T. Bruintjes, and B E. Martner. 1997. Identification of hydrometeors with elliptical and linear polarization Ka-band radar. J. Appl. Meteor. 36:322339.

    • Search Google Scholar
    • Export Citation
  • Reisner, J., R M. Rasmussen, and R T. Bruintjes. 1998. Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quart. J. Roy. Meteor. Soc. 124:10711107.

    • Search Google Scholar
    • Export Citation
  • Rogers, R R. and M K. Yau. 1989. A Short Course in Cloud Physics. 3d ed. Pergamon Press, 151 pp.

  • Rosenfeld, D. and W L. Woodley. 2000. Deep convective clouds with sustained supercooled liquid water down to −37.5°C. Nature 405:440442.

    • Search Google Scholar
    • Export Citation
  • Ryerson, C., M K. Politovich, K L. Rancourt, G G. Koenig, R F. Reinking, and D R. Miller. 2000. Overview of Mt. Washington icing sensors project. Extended Abstracts, 38th AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, American Institute of Aeronautics and Astronautics, Paper AIAA-2000-0488.

  • Sand, W R., W A. Cooper, M K. Politovich, and D L. Veal. 1984. Icing conditions encountered by a research aircraft. J. Climate Appl. Meteor. 23:14271440.

    • Search Google Scholar
    • Export Citation
  • Schultz, P. and M K. Politovich. 1992. Toward the improvement of aircraft-icing forecasts for the continental United States. Wea. Forecasting 7:491500.

    • Search Google Scholar
    • Export Citation
  • Smith Jr, W L., P. Minnis, B. Bernstein, and P W. Heck. 2002. Supercooled liquid water cloud properties derived from GOES: Comparisons with in-situ aircraft measurements. Preprints, 10th Conf. on Aviation, Range and Aerospace Meteorology, Portland, OR, Amer. Meteor. Soc., 89–92.

  • Tafferner, A., T. Hauf, C. Leifeld, T. Hafner, H. Leykauf, and U. Voigt. 2003. ADWICE: Advanced Diagnosis and Warning System for Aircraft Icing Environments. Wea. Forecasting 18:184203.

    • Search Google Scholar
    • Export Citation
  • Thompson, G., R T. Bruintjes, B G. Brown, and F. Hage. 1997a. Intercomparison of in-flight icing algorithms. Part I: WISP94 real-time icing prediction and evaluation program. Wea. Forecasting 12:878889.

    • Search Google Scholar
    • Export Citation
  • Thompson, G., R. Bullock, and T F. Lee. 1997b. Using satellite data to reduce spatial extent of diagnosed icing. Wea. Forecasting 12:185190.

    • Search Google Scholar
    • Export Citation
  • Thompson, G., R M. Rasmussen, and K. Manning. 2004. Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon. Wea. Rev. 132:519542.

    • Search Google Scholar
    • Export Citation
  • Tremblay, A. and A. Glazer. 2000. An improved modeling scheme for freezing precipitation forecasts. Mon. Wea. Rev. 128:12891308.

  • Turk, J., J. Vivekanandan, T. Lee, P. Durkee, and K. Nielsen. 1998. Derivation and applications of near-infrared cloud reflectances from GOES-8 and GOES-9. J. Appl. Meteor. 37:819831.

    • Search Google Scholar
    • Export Citation
  • Vivekanandan, J., S M. Ellis, R. Oye, D S. Zrnic, A V. Ryzhkov, and J. Straka. 1999. Cloud microphysics retrieval using S-band dual-polarization radar measurements. Bull. Amer. Meteor. Soc. 80:381388.

    • Search Google Scholar
    • Export Citation
  • Wade, C G. 2003. A multi-sensor approach to detecting drizzle on ASOS. J. Atmos. Oceanic Technol. 20:820832.

  • View in gallery

    CIP conceptual diagram. Precipitation types: snow (asterisks), rain (large open circles), and freezing drizzle (small gray circles).

  • View in gallery

    Flowchart of the CIP process.

  • View in gallery

    (a) Standard and convective temperature interest maps (Tmap, Tmap-convective), and normalized icing PIREP ratio (frequency of PIREPs at a given temperature divided by the frequency of occurrence of that temperature in the model, normalized to a range of 0–1). (b) As in (a), but for CTT. (c) As in (a), but for RH. (d) As in (a), but for VV.

  • View in gallery

    The pilot-report interest map, showing change in horizontal and vertical distance. An “x” marks the location of a pilot report of icing.

  • View in gallery

    (a) CIP plot for icing potential at 3000 ft (915 m) MSL for 2100 UTC 15 Feb 2003. Color bar shows the range of icing potentials from 0.05 to 1.00. NASA Twin Otter location is marked with an “x.” (b) CIP flight-route cross section from Louisville, KY, (SDF) to Washington—Dulles International Airport (IAD) for 2100 UTC 15 Feb 2003. SLD icing conditions were encountered by the NASA Twin Otter near Huntington, WV, (HTS) below 2500 ft (760 m) MSL (marked with an “x”). Color bar shows the range of icing and SLD potentials from 0.05 to 1.00. RUC topography is filled with black. Gray areas are those where icing was diagnosed but there was no direct indication of SLD [SLD potential is unknown (UKN)].

  • View in gallery

    Relationship between PODy [moderate-or-greater-severity (mog) PIREPs] and 1 – PODn for CIP, using data from the winter of 2000, with all valid times combined. The thresholds used (starting in the upper-right corner) are 0.0, 0.02, 0.05, 0.15, 0.25, . . . , 0.95.

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Current Icing Potential: Algorithm Description and Comparison with Aircraft Observations

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  • a Research Applications Program, National Center for Atmospheric Research,* Boulder, Colorado
  • | b NASA Glenn Research Center, Cleveland, Ohio
  • | c Research Applications Program, National Center for Atmospheric Research,* Boulder, Colorado
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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 components, and the logic behind them are described.

* The National Center for Atmospheric Research is sponsored by the National Science Foundation

Corresponding author address: Ben C. Bernstein, National Center for Atmospheric Research, Research Applications Program, P.O. Box 3000, Boulder, CO 80307-3000. bernstei@rap.ucar.edu

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 components, and the logic behind them are described.

* The National Center for Atmospheric Research is sponsored by the National Science Foundation

Corresponding author address: Ben C. Bernstein, National Center for Atmospheric Research, Research Applications Program, P.O. Box 3000, Boulder, CO 80307-3000. bernstei@rap.ucar.edu

Introduction

In-flight icing is a significant threat to aircraft, resulting in loss of lift, reduced airspeed, and, in some cases, loss of control. This threat has been underscored by two recent accidents. An ATR-72 (all acronyms in this paper are defined in the appendix) holding in icing conditions crashed near Roselawn, Indiana, in 1994, and an EMB-120 descending through icing conditions crashed on final approach to Detroit, Michigan, in 1997, resulting in the deaths of all 68 and 29 people on board, respectively. Following a brief icing encounter in 2001, another commuter aircraft lost control and plunged 8000 ft (2440 m), but regained control and made an emergency landing.

Following the ATR-72 crash, the National Transportation Safety Board (NTSB) recommended that the Federal Aviation Administration (FAA) “continue to sponsor the development of methods to produce weather forecasts that both define specific locations of atmospheric icing conditions (including freezing drizzle and freezing rain) and produce short-range forecasts (‘nowcasts’) that identify icing conditions for a specific geographic area with a valid time of 2 hours or less” (National Transportation Safety Board 1996). To that end, the National Center for Atmospheric Research (NCAR), under the FAA’s Aviation Weather Research Program, expanded their in-flight icing research efforts on the diagnosis and forecasting of supercooled large droplets (SLD; droplet diameters greater than 50 μm). Through case studies (e.g., Politovich and Bernstein 1995; Rasmussen et al. 1995), examination of icing-related aircraft accidents (e.g., Marwitz et al. 1997), and directing research aircraft into icing, including SLD (Rasmussen et al. 1992; Miller et al. 1998; Ryerson et al. 2000; Isaac et al. 2001), NCAR meteorologists have identified robust signatures in observational and model datasets that are linked to the presence and intensity of icing conditions.

One result of this work has been the development of improved icing diagnosis and forecast techniques, including the “current icing potential” (CIP). CIP combines satellite, radar, surface, lightning, and pilot-report observations with numerical model output to create an hourly three-dimensional diagnosis of the potential for icing and SLD. First developed during the winter of 1997/98, it became an official FAA and National Weather Service (NWS) product in 2002 (available online at http://adds.aviationweather.gov). Since that time, numerous upgrades have been made that will be implemented in the operational system in 2005. In this paper, the upgraded version of CIP is described, including the information extracted from each dataset it employs, the application of fuzzy-logic membership functions and a decision tree, and how the data are integrated to diagnose icing. CIP’s abilities and shortcomings are demonstrated using example cases sampled by a research aircraft and a brief verification using pilot reports. A more in-depth verification of CIP was completed as part of its assessment for FAA and NWS approval (Brown et al. 2001).

Recent in-flight icing diagnosis and forecast techniques

Forecasters have used rules of thumb, observations, and numerical weather prediction model output to diagnose and forecast in-flight icing for many years (e.g., Jensen 1963; Air Weather Service 1980). The advent of modern observation networks and improved numerical models has led to recent advances in this arena. Schultz and Politovich (1992) found combinations of model temperature T and relative humidity RH that were coincident with icing by comparing pilot reports (PIREPs) with Nested Grid Model output. They created a simple, two-level icing product based on T and RH thresholds. Forbes et al. (1993) and Thompson et al. (1997a) furthered this approach by characterizing four meteorological situations for icing, based on combinations of T, RH, and vertical thermodynamic structure. In an attempt to find more intense icing, Carriere et al. (1997) combined upward vertical motion with favorable T and RH ranges. Although these purely model-based algorithms capture a large percentage of PIREPs, they tend to overforecast icing, even indicating it in cloud-free areas (Brown et al. 1997; Thompson et al. 1997a, b).

Another purely model-based approach to forecasting icing is the use of microphysics schemes to forecast supercooled liquid water (SLW) explicitly (e.g., Reisner et al. 1998; Tremblay and Glazer 2000; Thompson et al. 2004). Verification of the first two schemes showed that they captured less than one-half of the icing observed but were very efficient because of the small volume of airspace they covered (Guan et al. 2001; Brown et al. 2001).

Several purely observation-based icing diagnosis techniques have also been developed. For example, Lee et al. (1997), Ellrod (1996), and Smith et al. (2002) used multispectral satellite data to discriminate between cloudy and cloud-free areas and to identify cloud tops likely to contain supercooled liquid water. These techniques work well for single-layer clouds that are illuminated by sunlight, but alternative techniques used at night are not as robust, and both break down near the solar terminator (the transition between areas that are and are not illuminated by sunlight). The techniques also miss potential icing clouds obscured from satellite view by higher cloud layers. Radar-based techniques have used polarization signals in attempts to differentiate between water droplets and ice crystals (e.g., Reinking et al. 1997; Vivekanandan et al. 1999). It is clear that a variety of data sources can be used to diagnose icing, each of which has its strengths and weaknesses. Rather than using a single data source, a system that uses multiple data sources is likely to diagnose and forecast icing conditions with greater accuracy (Carriere et al. 1997).

Subsequent efforts attempted to combine datasets to mitigate the problems associated with single-data-source approaches to detection and forecasting of icing. Bernstein (1996) used a gridded analysis of surface observations to limit model-based icing diagnoses to cloudy and precipitating areas. Some situations conducive to the presence of SLD were identified, based on the principle that surface observations of freezing drizzle, freezing rain, and ice pellets provide direct indications of the existence of SLD aloft (e.g., Hanesiak and Stewart 1995; Politovich and Bernstein 1995). Thompson et al. (1997b) compared multispectral satellite data with model temperature grids to eliminate cloud-free areas and altitudes from their T/RH-based icing scheme. Tafferner et al. (2003) used a version of the four-category Thompson et al. (1997a) scheme and then confirmed or corrected the first-guess icing mechanism through comparison with surface observations and radar data. Le Bot (2004) combined satellite and radar data with model output, associated more severe icing with warm cloud tops, and related radar reflectivity to droplet size. The latter two systems demonstrate that the intelligent combination of data from multiple data sources can be useful in the diagnosis of icing.

All of the techniques described above apply hard thresholds to create their diagnoses, indicating icing at T = −14.9°C but not at T = −15.1°C, for example. Thus, small changes in temperature, relative humidity, or other parameters could result in abrupt changes in the icing field. Such an approach is not representative of the more gradual transitions from icing to nonicing environments that exist in nature. Like many weather phenomena, icing has both discontinuous and continuous aspects. Abrupt discontinuities occur at cloud boundaries, where icing can change from significant to nonexistent over a few hundred meters, especially in the vertical direction. Icing conditions can also change gradually in time and space. An example is a gradual increase in temperature from −6° to 0°C as an aircraft flies at a constant altitude within an otherwise similar cloud. The nature of icing is suited well to a hybrid approach to its diagnosis. Black-and-white decisions make sense for some aspects, such as the presence or absence of clouds, whereas other aspects such as temperature lend themselves well to shades of gray.

To maximize the value of multiple data sources and to represent better the hybrid nature of icing conditions, CIP merges satellite, surface, radar, lightning, and PIREP observations with model forecasts of T, RH, SLW, and vertical velocity and then uses fuzzy-logic and decision-tree techniques to determine the likelihood of icing and SLD at each location. This approach is designed to maximize the strengths and to minimize the weaknesses of each dataset while mimicking manual techniques used by NCAR meteorologists to direct research aircraft into icing and SLD. The goal is to indicate the maximum potential for icing and SLD conditions in each part of the 3D model domain. The icing and SLD potential values range from 0.0 to 1.0. Although they are not calibrated as true probabilities, high (low) values indicate a relatively high (low) chance for icing and SLD to be present.

The CIP technique

CIP determines icing and SLD potentials in a stepwise fashion (see Fig. 1 for a conceptual diagram and Fig. 2 for a flowchart of the process) and will be described in this way. In step 1, the datasets are placed onto a common grid. In step 2, the 3D locations of clouds and precipitation are found using satellite, surface, and radar observations. In step 3, fuzzy-logic membership functions are applied to icing-related fields to create interest maps. In step 4, the physical icing situation is determined by using a decision tree. In step 5, the initial icing and SLD potentials are calculated by situationally combining interest maps from basic fields (e.g., T, RH). In step 6, the final icing potential is calculated by increasing or decreasing the initial icing potential using the vertical velocity, SLW, and PIREP interest maps.

Step 1: Place the datasets onto a common grid

The first step in the process is to map current satellite, surface, radar, lightning, and PIREP observations and explicit model forecasts of supercooled liquid water content to the Rapid Update Cycle (RUC) model (Benjamin et al. 2004) pressure grid. The RUC pressure grid supplies the temperature, relative humidity, vertical velocity, and geopotential height fields with 25-hPa vertical and 20-km horizontal grid spacing. Fields from the RUC 3-h forecast are typically used, but CIP could be run using other forecast lengths and even different models. The 0-h RUC diagnosis is not used because moisture parameters, including cloud microphysics, typically need several hours to spin up.

Satellite data

To determine cloud locations and cloud-top temperatures (CTT), CIP uses observations from the following NWS Geostationary Operational Environmental Satellite (GOES) imager channels and combinations thereof: visible, shortwave and longwave infrared, shortwave reflectance (Turk et al. 1998), and the NCAR satellite-icing algorithm (Thompson et al. 1997b). The high-resolution satellite data are projected to a Lambert-conformal 5-km grid. Sixteen pixels are matched to each 20-km RUC model grid box. The visible and shortwave infrared reflectance fields are dependent on solar reflection, and so the day is broken into three distinct periods (day, night, and solar terminator; see Table 1) to apply appropriate cloud detection tests that will be described in section 3b.

Surface observations

Surface observations (METARs) are used to determine ceiling height, precipitation occurrence, and precipitation type. Although generally accurate, these point observations are made at irregularly spaced locations, and the number of stations reporting in the vicinity of a grid box may vary. For this reason, CIP incorporates information from surface stations using a concentric-circle approach, assuming that data from the surface observations closest to a given grid box are most representative of the conditions present within it. CIP initially searches for METARs within 40 km of the center of the grid box for at least one observation of cloud cover and each of six precipitation types. If such data are not found within 40 km, then the search continues using incrementally larger circles up to a maximum radius of 125 km.

Radar mosiac

Mosaics of NWS Next-Generation Weather Radar (NEXRAD) data are used to refine the precipitation fields. They are available at 4-km resolution, and 25 pixels are mapped to each RUC grid box. The percentage of the grid box filled with echoes exceeding 18 dBZ (light precipitation) is saved. The 18-dBZ threshold is used because until recently it was the minimum reflectivity available for use in the development code. Future versions of CIP will take advantage of the full range of reflectivity, which can be useful in the diagnosis of icing when applied correctly.

Explicit SLW from the RUC native grids

RUC’s explicit microphysics fields, including SLW, are not included in the pressure grids, and so they must be extracted from the native, “hybrid b” grids. Horizontal spacing and valid times are the same for both grids, and so the SLW amounts are linearly interpolated in the vertical direction to the pressure grid.

Pilot reports and lightning

PIREPs indicate point observations of the presence (or absence) and severity of icing. Each report contains the geographic location, altitude, and time of the icing. If a PIREP occurred within the last hour, then its horizontal and vertical distances from nearby RUC grid points (within 150 km), as well as the reported icing severity, are saved. The influence of a given PIREP within this range decreases with increasing distance. This fact will be described in greater detail later.

Lightning observations provide the geographic location and time of the strikes. Those strikes that occurred in close proximity to an RUC grid point (within 25 km) during the 15 min prior to CIP valid time are mapped to the grid. The relatively small space and time scales used for lightning are appropriate for deep convection, because it typically results in relatively small scale icing conditions.

Step 2: Find the 3D locations of clouds and precipitation

Once the datasets have been mapped to the model grid, the matched data are examined to determine whether clouds are present in each model grid box. If adequate cloudiness is found, then cloud-base and cloud-top heights, as well as the presence of precipitation and its type, are assessed.

Cloudiness

The presence of clouds is determined using an extension of the Lee et al. (1997) and Thompson et al. (1997a) technique, which applies time-of-day-specific thresholds on data from the various satellite channels (see Table 1). During the day and night, the model grid box is considered to be a candidate for icing if at least 40% of the 16 matched satellite pixels are cloudy, because icing rarely occurs with less than broken cloud cover (Bernstein et al. 1997). In the solar terminator, satellite data are not used alone to determine whether grid boxes are cloudy because several fields that are dependent on solar radiation are contaminated. During these narrow time windows around sunrise and sunset, the model grid box is considered to be cloudy if one or more of the following are true: the satellite-measured infrared temperature is greater than −35°C and surface observations mapped to it indicate at least broken sky conditions, the infrared temperature was less than −35°C, or the satellite-measured infrared temperature was at least 15°C colder than the RUC model–predicted surface temperature. The latter two tests infer cloud presence based on temperatures that are very different than those expected at the surface within the continental United States. The −35°C threshold would not apply in particularly cold climates. Grid boxes that do not meet the cloudiness parameters described above are considered to be ice free (icing and SLD potentials = 0.0 throughout the column).

Cloud-top height

Cloud-top height is estimated by finding the coldest satellite-measured infrared temperature among the cloudy pixels and comparing it with the profile of model temperature in a top-down manner [similar to Thompson et al. (1997b)]. Once a model temperature greater than the satellite-measured temperature is found, cloud top is set to the next model level above, because clouds are likely to exist somewhere between these two levels. All altitudes above the highest cloud top are considered to be ice free. When strong inversions are present in the model at temperatures close to the satellite-measured cloud-top temperature, this method can overestimate the cloud-top height and, in some cases, the top of the icing layer.

Cloud-base height, precipitation presence, and precipitation type

Using the concentric-circle approach described above, CIP searches for METARs that provide observations of ceiling height and precipitation type. Ceiling height is typically measured well by all weather station platforms, and so cloud-base height is set to the lowest value (m MSL) of those reported by all of the stations within the first circle in which ceiling observations are found. In the absence of precipitation, all altitudes below cloud base are considered to be ice free.

It is important to determine the presence of precipitation and its type, because subfreezing liquid precipitation within and below the clouds can also pose an icing hazard. To make this determination, CIP searches the concentric circles for information about the presence or absence of six categories of precipitation: freezing drizzle, freezing rain, ice pellets, rain, drizzle, and snow. A report of any of the first five precipitation types means that altitudes below cloud base need to be considered for possible icing and SLD, because subfreezing liquid precipitation may be present.

The determination of precipitation occurrence and type from METARs is somewhat complicated, because the precipitation information available varies among station types. Automated stations, even of the same type, may have very different capabilities. This situation is accounted for by determining whether information on each of the six precipitation types is available within each concentric circle. Once good information on a given precipitation type is found within a given circle, the search for that type ceases. For example, information on the presence or absence of freezing rain is found if a manual station or an automated station with a working freezing-rain sensor is present within the circle. That same automated station may not be capable of reporting freezing drizzle (Wade 2003), and so the search for that precipitation type must continue.

In the absence of METARs reporting precipitation, the subcloud layer may also be further considered for possible icing based on the presence of radar echoes. Because the radar cannot directly provide information on precipitation type, the potential for supercooled liquid precipitation below cloud base cannot be negated.

Step 3: Apply fuzzy-logic membership functions to icing-related fields

An important element of the CIP technique is the use of fuzzy-logic membership functions to develop interest maps for the T, RH, CTT, vertical velocity, SLW, and PIREP fields. Rather than applying thresholds, CIP attempts to handle uncertainties evident in the datasets it employs and to mimic the gradual transition from icing to nonicing environments associated with each field, based on cloud physics principles, experience gained from in-flight icing field programs, and distributions of icing PIREPs relative to these parameters.

Temperature

Both research flight observations and basic cloud physics concepts support the notion that clouds and precipitation are more likely to contain SLW at certain temperatures. SLW is most common at temperatures close to freezing, becomes less frequent with decreasing temperature, and is relatively rare at temperatures less than −25°C (Korolev et al. 2003), except in deep convection and isolated “clean” clouds (e.g., Cober et al. 2001). Conversely, ice crystals are unlikely to form at temperatures close to freezing and become increasingly common as temperature decreases beyond −10°C (Rogers and Yau 1989; Rauber et al. 2000; Cober et al. 2001; Korolev et al. 2003). Data collected during several field programs indicate that icing occurred most often at temperatures between −15° and −3°C (Sand et al. 1984; Schultz and Politovich 1992; Cober et al. 1995). Based on cloud physics principles, observations, and forecaster experience, the temperature interest map (Tmap) is designed to indicate the likelihood of SLW that may freeze onto an aircraft, given only temperature from the model. Here, Tmap is maximized at temperatures at which SLW is most frequently expected and drops off as glaciation becomes more likely on the cold side and as SLW becomes less likely to freeze onto an aircraft because of compressional heating on the warm side (Fig. 3a). The convective Tmap curve allows for a much greater chance for icing on the cold end of the spectrum, down to −30°C, because strong upward motion associated with convection allows SLW to exist at relatively cold temperatures (Rosenfeld and Woodley 2000).

Comparisons of RUC temperatures with 19 057 icing PIREPs made between November of 2002 and March of 2003 indicated that peak frequencies were centered on −7°C, with a gradual decrease on the cold side and a sharp decrease on the warm side of that peak. About 4.6% of the PIREPs occurred at temperatures colder than −25°C, but indicating icing at such cold temperatures would result in a gross overforecast, except in areas of deep convection. This fact is borne out by dividing the PIREP counts in each temperature bin (e.g., −8°C < T < −6°C) by the overall frequency of occurrence of model forecasts of that temperature range. This calculation results in a tiny value (on the order of 1 × 10−4) for each bin. The distribution of these relative PIREP frequency values by temperature is then normalized to a 0–1 range. The result is the “normalized PIREP curve,” which provides an indication of the likelihood of icing for a forecast of a given temperature (or other variable; see sections that follow). It shows that icing is most likely for a model forecast of −8°C < T < −6°C but does not imply that icing will always be found at such temperatures. Icing is relatively infrequent, but is not completely absent, when relatively cold temperatures are forecast (Fig. 3a). The curve nicely matches the independently developed Tmap curve, except at temperatures for which glaciation is expected to be likely (T < −20°C) and at above-freezing temperatures, for which icing should not occur. The fact that 3.5% of the PIREPs occurred at above-freezing temperatures is likely due to errors in reported icing altitudes (Brown et al. 1999) and to incorrect model forecasts.

Cloud-top temperature

The phase of hydrometeors at cloud top can affect the composition of the cloud below. Relatively warm cloud tops imply that the cloud layer is likely to be dominated by liquid water (Rauber et al. 2000). Conversely, if cloud tops are cold enough to produce ice crystals, these crystals can grow and fall through the clouds below, sometimes leading to complete glaciation [as in Hill (1980) and Politovich and Bernstein (1995)]. Geresdi et al. (2005) found observational evidence of a gradual transition from liquid- to ice-dominated precipitation as cloud-top temperature decreased.

The cloud-top temperature interest map (CTTmap) was developed to estimate the likelihood that clouds contain liquid water rather than being glaciated for a given CTT (Fig. 3b). Values are maximized for CTT > −12°C, because liquid water is likely to dominate in such warm clouds, and drop off gradually with decreasing CTT but never reach zero. Though cold cloud tops imply that ice crystals are likely to dominate, liquid water can still exist within such clouds if its production rate exceeds the depletion rate. The normalized distribution of CTT for 7855 icing PIREPs made in single-layer clouds (as diagnosed by CIP) shows that the normalized frequency of icing peaks around −12°C (Fig. 3b). It drops off sharply as CTT increases beyond about −8°C, because it becomes increasingly difficult to have sufficiently deep layers with good icing temperatures beneath such warm tops. CTTmap does not decrease at CTT > −8°C because such warm cloud tops imply liquid-dominated cloud processes. The distribution drops off gradually with decreasing cloud-top temperatures between −12° and about −30°C and then flattens out. CTTmap appears to drop off a little too much on the cold end of the spectrum, but many of the PIREPs associated with these cold CTTs are associated with unresolved multilayered situations in which the icing occurred in a lower deck with higher CTT.

Relative humidity

CIP also employs a relative humidity map (RHmap; Fig. 3c) in many situations to assess the likelihood that clouds exist between the observed cloud top and base given the model RH forecast. Icing PIREPs matched to RUC RH forecasts indicate that they occur most frequently with high RH, as expected, and decrease gradually with decreasing RH. The relatively low RH values matched to some PIREPs are mostly due to poor RH forecasts, but some may be caused by errors in PIREP locations (Brown et al. 1999). Overall, 74.9% of all PIREPs occurred with RUC RH in excess of 70%, and only 1.7% occurred with RH less than 25%. The RHmap roughly matches the normalized PIREP distribution, though it is perhaps a little too generous at moderately high RH values (70%–90%). The relationship between model RH and icing is mostly a function of model accuracy rather than icing physics. Note that the meaning of a given model RH forecast is highly dependent on the model’s handling of moisture. Adjustments to RHmap and, perhaps, to other interest maps (e.g., vertical velocity and explicit SLW) may be needed to handle differences among models.

Vertical velocity

Given that a cloud is present, upward vertical velocity can aid the production of liquid water, even in what might normally be an ice-dominated environment. Downward motion may indicate that liquid water is waning. The CIP vertical velocity membership function (VVmap; Fig. 3d) is designed with these concepts in mind, and it is used to adjust the initial estimates of positive icing potential upward or downward by as much as 25%, because upward and downward motion similarly affect forecaster confidence that icing will be present at ideal temperatures within a cloud. Distributions of PIREP occurrence with vertical velocity (VV) forecasts from the RUC show that 65.6% of all icing occurs in areas of forecast upward motion, but the normalized distribution shows that forecasts of moderately strong upward motions (<−0.5 μbar s−1) are much more likely to be associated with icing than forecasts of weak upward or any downward motions, as expected. The stronger upward motions are relatively uncommon in the model, but when they are present, icing is likely to occur. The shape of the VVmap curve roughly matches the normalized VV distribution, though it was developed independently, using forecaster experience and cloud physics principles. The slight increase in the normalized PIREP distribution at strong downward motions (>0.5 μbar s−1) is associated with very few data points.

Explicit supercooled liquid water predictions

The interest map for explicitly predicted supercooled liquid water content (cloud and rain water content at subfreezing temperatures) from the model is designed somewhat differently because of the characteristics of this field in the RUC. Brown et al. (2001) showed that the RUC liquid water predictions captured ∼40% of all icing PIREPs while warning for a small volume of airspace. Practical experience has shown that icing is likely to be present when the model predicts SLW, but the lack of an SLW prediction has not proven to work well as a negative indicator of icing. Because the CIP icing potential field is intended to diagnose the presence of SLW, rather than its amount, the SLW interest map (SLWmap, not shown) is set to unity when any SLW is predicted and to zero when it is not. The SLWmap is used to boost but not to decrease the initial icing potential, because the lack of SLW in a forecast is not a robust indicator of a lack of icing. Planned upgrades to the RUC microphysics package described by Thompson et al. (2004) may allow for better use of the SLW field, including forecasts of a lack of SLW.

Pilot reports

A valuable piece of information for any icing forecaster is an actual icing report. PIREPs provide the forecaster with the approximate time, location, and altitude of icing. PIREP shortcomings are documented well and include nonuniformity in time or space and contamination by errors in location, altitude, and time (Brown et al. 1997; Kelsch and Wharton 1996). When a positive icing PIREP is found in a location at which icing is expected, however, it supplies increased confidence that the diagnosis is correct. The relevance of a PIREP decreases with increasing distance (horizontally and vertically) and time since it was made. The PIREP membership function considers the horizontal and vertical distances from the center of each model grid box to the nearest PIREP. In CIP, the influence of a given PIREP can only extend to 150 km horizontally, 300 m vertically, and 1 h temporally. Its influence decreases rapidly with increasing distance and height differences (see Fig. 4). The choices of range and height difference are somewhat arbitrary but are reasonable, based on forecaster experience. A PIREP’s influence could be related to the uniformity of the clouds that surround it, but this concept is yet to be implemented. The age of a PIREP would ideally also be a factor, but all PIREPs made within the last hour are treated equally in this version of CIP.

Because the icing potential field is intended to show the potential for any icing conditions, rather than a specific icing severity, all reported severity levels are treated equally and PIREPs of “no icing” are not used. Negative-icing PIREPs often exist within areas of icing and are sometimes indicative of embedded icing-free pockets. An example of this situation is the group of reports from aircraft that flew similar approach paths within minutes of an EMB-120 aircraft that crashed on its descent into Detroit on 9 January 1997. The pilots reported that the icing was “not present,” “light,” “moderate,” and “extremely heavy to severe.” The apparent disagreement was attributed to small-scale variations in the clouds and how the aircraft traversed them (National Transportation Safety Board 1998). This example demonstrates that small variations in location, time, and approach can be the difference between no icing and icing that contributed to an accident that killed 29 people.

Steps 4 and 5: Determine the physical icing scenario and calculate the initial icing and SLD potentials

Icing and SLD conditions result from many processes, and so the meteorological structure that is present must be identified and the data and interest maps need to be applied in an appropriate manner. This situational approach is critical, because the meaning of an individual piece of data can be very different for different situations. CIP identifies five distinct icing situations: single-layer clouds, multiple-layer clouds, cloud-top temperature gradients, classical freezing rain, and deep convection. The methods used to assess the presence of icing for each situation are described below. The equations used can be found in Table 2.

Single-layer clouds

The simplest of all icing situations is a single-layer, nonprecipitating cloud. A single-layer cloud is considered to be present when high RH is found at all levels between the satellite-derived cloud top and the METAR-derived cloud base. An initial assessment of cloud phase is made using the CTTmap, and this assessment is combined with the Tmap and RHmap values at each level to calculate the initial icing potential. When there is no indication of the presence of precipitation-sized liquid water droplets in radar and surface observations, the SLD potential is set to “unknown” (−9.9) for all altitudes within cloud, because it is uncertain whether SLD exists there.

The icing situation is more complex when precipitation is observed beneath a cloud layer. Liquid precipitation in combination with warm cloud tops implies that the collision–coalescence process is active (Cober et al. 1996; Rauber et al. 2000). Thus, the potential for icing should be high, both within the lowest cloud layer and beneath its base, if the temperatures are in the proper range. Large droplets are clearly present from the surface up to at least cloud base and often extend upward well into the cloud, sometimes to its top [as in Pobanz et al. (1994)].

In a similar situation in which only snow is reported at the surface, ice crystals are clearly present beneath and within the lowest cloud layer. These crystals scavenge SLW through riming and may completely glaciate the cloud (Geresdi et al. 2005). In such cases, CIP decreases the maximum possible icing potential somewhat by including a snow factor in the equation (see Table 2). When the snow is associated with widespread radar echoes of greater than 18 dBZ, there is likely to be an abundance of large ice crystals aloft, implying more riming, and the icing potential is further lowered. As more of the grid box is filled with snow echoes, this factor becomes stronger, further decreasing the potential for icing.

Multiple cloud layers

When multiple cloud layers are present and no precipitation from the upper layer falls into the lower layer, the icing situation for each layer should be considered separately. An example of this is a cirrus layer passing over a low stratus layer. The top of the upper cloud layer is observed by satellite, and so satellite data can be used to determine its icing potential. The lower layer is obscured from view, and so its location and cloud top must be determined by other means. CIP infers the presence of an intervening dry layer with model RH of less than 50% over at least 75 hPa of depth and infers a lower cloud layer by RH of more than 70% beneath the dry layer. The lower cloud layer has its own cloud-top temperature, and CIP separately calculates the icing and SLD potentials for that layer. When precipitation is present at the surface, its attributes are only applied to the lowest cloud layer and the altitudes beneath it.

Cloud-top temperature gradient

When a CTT gradient is present within a grid box, it implies that a transition may be occurring. One portion of the grid box may contain clouds with relatively warm tops (e.g., −12°C), for which supercooled liquid water is expected, while another portion may contain clouds with relatively cold tops (e.g., −25°C), for which partially or completely glaciated conditions are expected. An aircraft flying at constant altitude across such a grid box at a typical icing temperature (e.g., −8°C) is likely to find liquid water in portions with warm cloud tops and mixed-phase or glaciated conditions in portions with cold cloud tops. An aircraft traversing the grid box at a higher, colder (e.g., −20°C) level may encounter clear air in the portions where it is above the warm clouds and glaciated conditions where it enters deeper clouds with relatively cold tops.

The icing potential field is designed to find the maximum potential for supercooled liquid water expected at each altitude anywhere across the grid box. With a good chance for icing at low altitudes in some portions and little chance for icing at higher altitudes across the entire grid box, CIP treats each altitude differently. For each altitude, the temperature is compared with the CTT distribution present within the grid box and an appropriate CTT and temperature combination is found. In the example described above, a CTT of −12°C is applied to all altitudes with temperatures greater than or equal to −12°C. It is in this portion of the grid box where icing is most likely, because it has the most ideal combination of temperature and CTT. Thus, a large icing potential is indicated. At altitudes with colder temperatures, correspondingly colder CTT values are used and lower icing potentials result.

Classical freezing rain

The classical freezing-rain structure consists of a layer of above-freezing temperatures (the warm layer) between two layers of subfreezing temperatures (cold layers). Snow formed in the upper cold layer falls into the warm layer, where it melts to form rain, and subsequently falls into the lower cold layer to form freezing rain. Depending on the temperature and depth of these layers, the resulting precipitation at the surface is typically freezing rain, ice pellets, or rain (Hanesiak and Stewart 1995).

Using the model temperature profile, the column is separated into three layers: above, below, and within the warm layer. Because no melting has taken place above the warm layer, any cloud residing there is treated like a single-layer cloud. Cloud tops are typically cold (<−20°C), and so the potential for ice crystals to dominate is large and the resulting icing potential is often small. If the cloud tops are relatively warm, the icing potential is larger.

Altitudes beneath the warm layer are treated separately, because melting is involved. When liquid precipitation or ice pellets are observed at the surface in the presence of this temperature structure, icing and SLD are likely to be present anywhere in the lower cold layer, regardless of the relative humidity and cloud-top temperature. Thus, CIP does not use those two interest maps to calculate the icing or SLD potentials at these altitudes. The presence of widespread radar echoes >18 dBZ further suggests the potential for larger and/or more droplets falling through the lower cold layer. This information is used to increase both icing and SLD potentials there (see Table 2). Temperatures within the warm layer are too warm for icing, and so the icing potential is zero.

Deep convection

Icing is one of many hazards associated with deep convective situations, and CIP uses observations of lightning from the national lightning network to identify them. Strong upward motion allows thunderstorms to produce large amounts of supercooled liquid water (e.g., Knight and Squires 1982) and, in many cases, SLD. Icing conditions in convective turrets typically have small horizontal but large vertical extents from the freezing level up to unusually cold temperatures (Rosenfeld and Woodley 2000). Because of this, CIP uses a special temperature map that allows for icing at temperatures as cold as −30°C, rather than the −25°C lower limit employed for other clouds (Fig. 3a). This map also dramatically increases the interest at temperatures between −30° and −12°C. The lower bound could be extended to as cold as −40°C to capture the relatively rare icing at these temperatures, but it would also result in more false alarms. Cloud-top temperature is not considered to be an important factor in assessing the icing potential in deep convection, because the strong lift can allow supercooled liquid water production to exceed depletion normally expected with cold cloud tops. Relative humidity is also not used because numerical models often underestimate the moisture associated with subgrid-scale convection.

Step 6: Calculate the final icing potential using boosting factors

In the final step, the situationally derived initial icing potential (0.0–1.0 scale) is adjusted using recent pilot reports of icing and model forecasts of vertical velocity and supercooled liquid water. As described earlier, these fields are used in manual forecasting to increase or decrease confidence that icing will be present. Recent reports of icing and forecasts of upward motion and SLW can all increase the icing potential, while only a forecast of downward motion can decrease it. The maximum amount of increase is the difference between the initial icing potential and unity (e.g., 0.6 for an initial icing potential of 0.4). SLWmap, PIREPmap, and VVmap can contribute boosts of as much as 40%, 35%, and 25% of this value, respectively. When downward motion is forecast, the maximum decrease from VVmap is 25% of the difference between the initial icing potential and zero (see Table 2). These factors are not applied to the SLD potential field, because they have not been shown to be well correlated with the presence of SLD.

Examination of four sample cases using research aircraft data

To demonstrate CIP’s abilities and shortcomings in a variety of environments, icing and SLD diagnoses from real-time runs are compared with observations made by the National Aeronautics and Space Administration (NASA) Glenn Research Center Twin Otter research aircraft (Miller et al. 1998) during flights through 1) freezing rain, 2) a single-layer precipitating cloud, and 3) a cloud with a cloud-top temperature gradient. A flight through a single-layer, nonprecipitating cloud that CIP diagnosed poorly is also discussed.

Freezing rain

At ∼2100 UTC 15 February 2003, the Twin Otter flew through freezing rain over Huntington, West Virginia (station identifier KHTS). A classical freezing-rain temperature structure was present in the RUC, and surface observations indicated freezing rain and 0.2-km ceilings while satellite-measured cloud-top temperatures were near −45°C. CIP indicated high icing and SLD potentials in the lower cold layer, and zero and low icing potentials were diagnosed within and above the warm (above freezing) layer, respectively. The aircraft encountered freezing rain from the base of the warm layer (1.1 km) down to the minimum vectoring altitude of 0.8 km. Given the temperature structure that was present and the KHTS surface observation of freezing rain, SLD is likely to have also existed between 0.8 km and the surface. Rough, bumpy ice formed on the aircraft, extending back to 20% chord, and a ridge of ice formed at the trailing edge of the ice protection boot, leading the pilot to report moderate-to-severe clear icing. Horizontal and vertical cross sections through this location show that CIP diagnosed the icing and SLD conditions well (Fig. 5).

Single-layer, precipitating cloud

At ∼1420 UTC 21 February 2002, the NASA Twin Otter descended through precipitating clouds north of Mansfield, Ohio. The satellite-measured cloud-top temperature was −11°C and (above freezing) drizzle was reported at nearby Cleveland, Ohio. CIP’s initial icing and SLD potentials were high for this case of collision–coalescence SLD aloft. RUC predicted slight upward vertical velocities throughout the layer and SLW from just above cloud base (0.3 km) to 1.4 km, and recent PIREPs of icing were found between ∼0.9 and 2.1 km. These factors boosted the high initial icing potentials to near unity. The Twin Otter found a single-layer, all-water cloud, with drops as large as 300 μm in diameter near 1.6 km. The aircraft accreted mixed icing that extended beyond the ice protection boots. The cloud-top temperature measured by the Twin Otter was −9°C, which was somewhat warmer than that indicated by satellite. This measurement error, in combination with RUC temperature forecasts that were too warm near cloud top, caused CIP to indicate that the icing extended well above the actual cloud top.

Cloud-top temperature gradient

At ∼1815 UTC 26 February 2002, the Twin Otter flew across a single RUC model grid box in which cloud-top temperatures changed from −17° to −22°C. No precipitation was falling from the warmer clouds at the eastern end of the grid box, while snow and radar echoes were present beneath the colder tops at the western end. As the aircraft traversed the grid box from east to west at ∼1.3 km (T = −5.5°C level), there was a clear transition from a mixed-phase icing situation to a glaciated/icing-free one. As described in section 3d(3), CIP applied the warmest CTT (−17°C) to the −5.5°C flight level. This result was combined with at least one recent PIREP that indicated icing at this level and RUC predictions of both SLW and upward motion there, resulting in an icing potential of ∼0.9. The high icing potential agrees with Twin Otter observations of icing in the eastern part of the grid box.

Poorly diagnosed, single-layer cloud

At ∼1922 UTC 6 March 2004, the Twin Otter encountered icing conditions between 0.8 and 1.7 km to the southwest of Cleveland. Peak water contents of 0.6–0.8 g m−3 were found below cloud top at 1.6 km MSL and −4°C. Satellite data indicated that cloud-top temperatures were near −7°C, resulting in an estimated cloud top of ∼2.8 km. Though the top of the layer sampled on this flight was well below this level, onboard researchers indicated that a second cloud layer was present above the sampled clouds. CIP indicated icing from cloud top to cloud base, with an icing potential of 0.82 at 1.2 km but only 0.28 at 1.6 km. The relatively low icing potential resulted from forecasts of moderate relative humidity (74%), temperatures that were slightly too warm (−2.9°C) in a critical temperature range (between −4° and 0°C; see Fig. 3a), weak downward motion, and a lack of both model-predicted SLW and recent PIREPs. Such a case demonstrates the value of the fuzzy-logic approach. One field (CTT) provided a good indication of icing, but several others were less favorable. When presented with conflicting information, the CIP technique still indicated that icing was possible but was relatively unlikely at this level.

Verification using PIREPs

Sample cases provide useful demonstrations, but it is important to assess the overall ability of CIP to capture icing occurrences and to differentiate between icing and nonicing environments. To accomplish these goals, 44 376 positive (yes; 7638 with moderate or greater reported severity) and 10 057 negative (no) icing PIREPs collected during January–March of 2003 are compared with CIP diagnoses valid at 0000, 0300, 1200, 1500, 1800, and 2100 UTC (late-night hours are excluded because of a lack of PIREPs). The basic verification approach is described in Brown et al. (1997) and extended in Brown et al. (1999, 2001). Probability of detection (POD) is computed for both yes and no PIREPs, with the resulting statistics denoted PODy and PODn, respectively. PODy can be interpreted as the proportion of yes PIREPs that are correctly diagnosed to be in regions with icing; PODn is the proportion of no-icing PIREPs that are correctly diagnosed to be in regions with no icing. Only PIREPs with moderate-or-greater icing severity are used to compute PODy here, but tests using PIREPs of all severities yielded very similar results.

Because CIP values cover a continuous range from 0.0 to 1.0, the verification analyses are based on applying an array of thresholds to create yes/no icing diagnoses. That is, a yes diagnosis is inferred at a grid box if the CIP value equals or exceeds the threshold; a no diagnosis is inferred if the value at a grid box is less than the threshold. The relationship between PODy and 1 − PODn for different algorithm thresholds is the basis for the verification approach known as the “signal detection theory” (e.g., Mason 1982). The curve joining the points for different thresholds is known as the “relative operating characteristic” (ROC) curve; the area under this curve is a measure of skill (0.5 indicates no skill). In the ideal case, the ROC curve will lie above the diagonal no-skill line, toward the upper left corner of the diagram (see Fig. 6).

The verification system compares each PIREP with the largest icing potential value found within the nearby grid boxes (±1 grid box horizontally and ±0.3 km vertically). As described earlier, CIP incorporates PIREPs from the hour prior to the valid time. Thus, the verification analyses only used PIREPs from the hour following the forecast valid time.

The ROC curve indicates that CIP is skillful in discriminating between yes and no PIREPs. In fact, the area under this curve is ∼0.78, which is significantly larger than the no-skill value of 0.50. At a low threshold (0.05), CIP was able to diagnose correctly 83.4% of the (moderate or greater) yes and 62.4% of the explicit no PIREPs, respectively. Statistics were very similar when all positive icing severities were examined.

Summary

CIP combines satellite, surface, radar, lightning, and pilot observations with model output to provide a detailed three-dimensional hourly diagnosis of the potential for icing and SLD. It uses a physically based situational approach derived from basic and applied cloud physics principles, combined with forecaster and onboard flight experience from field programs. CIP uses a conservative approach to the determination of the locations of clouds and precipitation and in its depiction of the potential for icing and SLD, showing the worst possible conditions that are likely to exist within a given portion of airspace (3D grid volume). The technique was demonstrated using examples of icing encountered by a research aircraft, and its quality was validated using pilot reports for a 3-month period.

CIP provides users with accurate, high-resolution depictions of icing and SLD potential, allowing them to make route-specific decisions that can help aircraft to avoid icing, including that associated with SLD. The use of the operational CIP in combination with high-resolution diagnoses and forecasts of convection, turbulence, ceiling and visibility, flight-level winds, and so on in an easy-to-use graphical form may soon allow pilots and dispatchers to choose their routes and altitudes appropriately to allow for more efficient and, most important, safer flights.

Acknowledgments

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

The authors appreciate the thoughtful comments supplied by reviewers, including Alexi Korolev and Roy Rasmussen. Engineers, pilots, and others at the NASA Glenn Research Center provided invaluable in-flight research data and the opportunity to gain experience by flying on board their aircraft. Thanks are given to regional airlines Air Wisconsin, SkyWest, COMAIR, Peninsula Airways, and Atlantic Coast Airlines for serving as test users for CIP, providing valuable feedback and participating in algorithm assessments. Greg Thompson and Mike Dixon provided significant help with real-time ingest of satellite and radar data. Gary Cunning was instrumental in the development of the high-resolution and operational versions of CIP. The verification group at NCAR and NOAA Forecast Systems Laboratory and the weather assessment group at the FAA Technical Center made high-quality algorithm verifications and evaluations. Last, we thank the FAA’s Aviation Weather Research Program managers for their strong support of the in-flight icing program at NCAR, the development of the CIP, and its subsequent passage into the operational environment.

REFERENCES

  • Air Weather Service 1980. Forecasters’ guide on aircraft icing. Air Weather Service Rep. AWS/TR-80/001, 58 pp. [Available from U.S. Air Force, Scott AFB, IL 62225.].

  • Benjamin, S G. Coauthors 2004. An hourly assimilation–forecast cycle: The RUC. Mon. Wea. Rev. 132:495518.

  • Bernstein, B C. 1996. A new technique for identifying locations where supercooled large droplets are likely to exist: The stovepipe algorithm. Preprints, 15th Conf. on Weather Analysis and Forecasting, Norfolk, VA, Amer. Meteor. Soc., 5–8.

  • Bernstein, B C., T A. Omeron, F. McDonough, and M K. Politovich. 1997. The relationship between aircraft icing and synoptic scale weather conditions. Wea. Forecasting 12:742762.

    • Search Google Scholar
    • Export Citation
  • Brown, B G., G. Thompson, R T. Bruintjes, R. Bullock, and T. Kane. 1997. Intercomparison of in-flight icing algorithms. Part II: Statistical verification results. Wea. Forecasting 12:890914.

    • Search Google Scholar
    • Export Citation
  • Brown, B G., T L. Kane, R. Bullock, and M K. Politovich. 1999. Evidence of improvements in the quality of in-flight icing algorithms. Preprints, Eighth Conf. on Aviation, Range, and Aerospace Meteorology, Dallas, TX, Amer. Meteor. Soc., 48–52.

  • Brown, B G., J L. Mahoney, R. Bullock, T L. Fowler, J. Henderson, and A. Loughe. 2001. Quality assessment report: Integrated Icing Diagnostic Algorithm (IIDA). AWIT Technology Review Panel Internal Report, 35 pp. [Available from B. Brown, Research Applications Program, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000.].

  • Carriere, J., S. Alquier, C. LeBot, and E. Moulin. 1997. Some results of a statistical evaluation of forecast icing risk algorithms. Meteor. Appl. 4:115130.

    • Search Google Scholar
    • Export Citation
  • Cober, S G., G A. Isaac, and J W. Strapp. 1995. Aircraft icing measurements in East Coast winter storms. J. Appl. Meteor. 34:88100.

    • Search Google Scholar
    • Export Citation
  • Cober, S G., J W. Strapp, and G A. Isaac. 1996. An example of supercooled drizzle drops formed through a collision–coalescence process. J. Appl. Meteor. 35:22502260.

    • Search Google Scholar
    • Export Citation
  • Cober, S G., G A. Isaac, and J W. Strapp. 2001. Characterizations of aircraft icing environments that include supercooled large drops. J. Appl. Meteor. 40:19842002.

    • Search Google Scholar
    • Export Citation
  • Ellrod, G P. 1996. The use of GOES-8 multispectral imagery for the detection of aircraft icing regions. Preprints, Eighth Conf. on Satellite Meteorology and Oceanography, Atlanta, GA, Amer. Meteor. Soc., 168–171.

  • Forbes, G S., Y. Hu, B G. Brown, B C. Bernstein, and M K. Politovich. 1993. Examination of soundings in the proximity of pilot reports of aircraft icing during STORM-FEST. Preprints, Fifth Conf. on Aviation Weather Systems, Vienna, VA, Amer. Meteor. Soc., 282–286.

  • Geresdi, I., R. Rasmussen, W. Grabowski, and B. Bernstein. 2005. Sensitivity of freezing drizzle formation in stably stratified clouds to ice processes. Meteor. Atmos. Phys. 88:91105.

    • Search Google Scholar
    • Export Citation
  • Guan, H., S G. Cober, and G A. Isaac. 2001. Verification of supercooled cloud water forecasts with in situ aircraft measurements. Wea. Forecasting 16:145155.

    • Search Google Scholar
    • Export Citation
  • Hanesiak, J M. and R E. Stewart. 1995. The mesoscale and microscale structure of a severe ice pellet storm. Mon. Wea. Rev. 123:31443162.

    • Search Google Scholar
    • Export Citation
  • Hill, G E. 1980. Seeding-opportunity recognition in winter orographic clouds. J. Appl. Meteor. 19:13711381.

  • Isaac, G A., S G. Cober, J W. Strapp, A V. Korolev, A. Tremblay, and D L. Marcotte. 2001. Recent Canadian research on in-flight aircraft icing. Can. Aeronaut. Space J. 47:19.

    • Search Google Scholar
    • Export Citation
  • Jensen, C E. 1963. Prediction of large-scale cloudiness and airframe icing conditions by machine methods. J. Appl. Meteor. 2:337344.

    • Search Google Scholar
    • Export Citation
  • Kelsch, M. and L. Wharton. 1996. Comparing PIREPs with NAWAU turbulence and icing forecasts: Issues and results. Wea. Forecasting 11:385390.

    • Search Google Scholar
    • Export Citation
  • Knight, C A. and P. Squires. 1982. Case Studies of the National Hail Research Experiment. Vol. 2, Hailstorms of the Central High Plains, Colorado Associated University Press, 245 pp.

    • Search Google Scholar
    • Export Citation
  • Korolev, A V., G A. Isaac, S G. Cober, J W. Strapp, and J. Hallett. 2003. Microphysical characterization of mixed-phase clouds. Quart. J. Roy. Meteor. Soc. 129:3965.

    • Search Google Scholar
    • Export Citation
  • Le Bot, C. 2004. SIGMA: System of Icing Geographic Identification in Meteorology for Aviation. Preprints, 11th Conf. on Aviation, Range and Aerospace Meteorology, Hyannis, MA, Amer. Meteor. Soc., CD-ROM.

  • Lee, T F., F J. Turk, and K. Richardson. 1997. Stratus and fog products using GOES-8–9 3.9-m data. Wea. Forecasting 12:664677.

  • Marwitz, J D., M K. Politovich, B C. Bernstein, F M. Ralph, P J. Neiman, R. Ashenden, and J. Bresch. 1997. Meteorological conditions associated with the ATR-72 aircraft accident near Roselawn, Indiana, on 31 October 1994. Bull. Amer. Meteor. Soc. 78:4152.

    • Search Google Scholar
    • Export Citation
  • Mason, I B. 1982. A model for assessment of weather forecasts. Aust. Meteor. Mag. 30:291303.

  • Miller, D., T. Ratvasky, B. Bernstein, F. McDonough, and J W. Strapp. 1998. NASA/FAA/NCAR supercooled large droplet icing flight research: Summary of winter 96-97 flight operations. Extended Abstracts, 36th Aerospace Science Meeting and Exhibit, Reno, NV, American Institute of Aeronautics and Astronautics, AIAA 98-0557, 20 pp.

  • National Transportation Safety Board 1996. Aircraft accident report. In-flight icing encounter and loss of control, Simmons Airlines, d.b.a. American Eagle flight 4184, Avions de Transport Regional (ATR) Model 72-212, N401MA, Roselawn, IN, October 31 1994, Vol. 1, Safety Board Rep. NTSB/AAR-96/01, PB96-910401, DCA95MA001, 322 pp. [Available from National Transportation Safety Board, Public Inquiries Section RE-51, 490 L’Enfant Plaza S.W., Washington, DC 20594.].

  • National Transportation Safety Board 1998. Aircraft accident report. In-flight icing encounter and uncontrolled collision with terrain, COMAIR flight 3272, Embraer EMB-120RT, N265 CA, Monroe, MI, January 9, 1997, Safety Board Rep. NTSB/AAR-98/04, PB08-910404, DCA97MA017, 348 pp. [Available from National Transportation Safety Board, Public Inquiries Section RE-51, 490 L’Enfant Plaza S.W., Washington, DC 20594.].

  • Pobanz, B M., J D. Marwitz, and M K. Politovich. 1994. Conditions associated with large-drop regions. J. Appl. Meteor. 33:13661372.

    • Search Google Scholar
    • Export Citation
  • Politovich, M K. and B C. Bernstein. 1995. Production and depletion of supercooled liquid water in a Colorado winter storm. J. Appl. Meteor. 34:26312648.

    • Search Google Scholar
    • Export Citation
  • Rasmussen, R. Coauthors 1992. Winter Icing and Storms Project (WISP). Bull. Amer. Meteor. Soc. 73:951974.

  • Rasmussen, R M., B C. Bernstein, M. Murakami, G. Stossmeister, J. Reisner, and B. Stankov. 1995. The 1990 Valentine’s Day Arctic outbreak. Part I: Mesoscale and microscale structure and evolution of a Colorado Front Range shallow upslope cloud. J. Appl. Meteor. 34:14811511.

    • Search Google Scholar
    • Export Citation
  • Rauber, R M., L S. Olthoff, M K. Ramamurthy, and K E. Kunkel. 2000. The relative importance of warm rain and melting processes in freezing precipitation events. J. Appl. Meteor. 39:11851195.

    • Search Google Scholar
    • Export Citation
  • Reinking, R F., S Y. Matrosov, R T. Bruintjes, and B E. Martner. 1997. Identification of hydrometeors with elliptical and linear polarization Ka-band radar. J. Appl. Meteor. 36:322339.

    • Search Google Scholar
    • Export Citation
  • Reisner, J., R M. Rasmussen, and R T. Bruintjes. 1998. Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quart. J. Roy. Meteor. Soc. 124:10711107.

    • Search Google Scholar
    • Export Citation
  • Rogers, R R. and M K. Yau. 1989. A Short Course in Cloud Physics. 3d ed. Pergamon Press, 151 pp.

  • Rosenfeld, D. and W L. Woodley. 2000. Deep convective clouds with sustained supercooled liquid water down to −37.5°C. Nature 405:440442.

    • Search Google Scholar
    • Export Citation
  • Ryerson, C., M K. Politovich, K L. Rancourt, G G. Koenig, R F. Reinking, and D R. Miller. 2000. Overview of Mt. Washington icing sensors project. Extended Abstracts, 38th AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, American Institute of Aeronautics and Astronautics, Paper AIAA-2000-0488.

  • Sand, W R., W A. Cooper, M K. Politovich, and D L. Veal. 1984. Icing conditions encountered by a research aircraft. J. Climate Appl. Meteor. 23:14271440.

    • Search Google Scholar
    • Export Citation
  • Schultz, P. and M K. Politovich. 1992. Toward the improvement of aircraft-icing forecasts for the continental United States. Wea. Forecasting 7:491500.

    • Search Google Scholar
    • Export Citation
  • Smith Jr, W L., P. Minnis, B. Bernstein, and P W. Heck. 2002. Supercooled liquid water cloud properties derived from GOES: Comparisons with in-situ aircraft measurements. Preprints, 10th Conf. on Aviation, Range and Aerospace Meteorology, Portland, OR, Amer. Meteor. Soc., 89–92.

  • Tafferner, A., T. Hauf, C. Leifeld, T. Hafner, H. Leykauf, and U. Voigt. 2003. ADWICE: Advanced Diagnosis and Warning System for Aircraft Icing Environments. Wea. Forecasting 18:184203.

    • Search Google Scholar
    • Export Citation
  • Thompson, G., R T. Bruintjes, B G. Brown, and F. Hage. 1997a. Intercomparison of in-flight icing algorithms. Part I: WISP94 real-time icing prediction and evaluation program. Wea. Forecasting 12:878889.

    • Search Google Scholar
    • Export Citation
  • Thompson, G., R. Bullock, and T F. Lee. 1997b. Using satellite data to reduce spatial extent of diagnosed icing. Wea. Forecasting 12:185190.

    • Search Google Scholar
    • Export Citation
  • Thompson, G., R M. Rasmussen, and K. Manning. 2004. Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon. Wea. Rev. 132:519542.

    • Search Google Scholar
    • Export Citation
  • Tremblay, A. and A. Glazer. 2000. An improved modeling scheme for freezing precipitation forecasts. Mon. Wea. Rev. 128:12891308.

  • Turk, J., J. Vivekanandan, T. Lee, P. Durkee, and K. Nielsen. 1998. Derivation and applications of near-infrared cloud reflectances from GOES-8 and GOES-9. J. Appl. Meteor. 37:819831.

    • Search Google Scholar
    • Export Citation
  • Vivekanandan, J., S M. Ellis, R. Oye, D S. Zrnic, A V. Ryzhkov, and J. Straka. 1999. Cloud microphysics retrieval using S-band dual-polarization radar measurements. Bull. Amer. Meteor. Soc. 80:381388.

    • Search Google Scholar
    • Export Citation
  • Wade, C G. 2003. A multi-sensor approach to detecting drizzle on ASOS. J. Atmos. Oceanic Technol. 20:820832.

APPENDIX

Abbreviations

  • ATR  Avions de Transport Régional

  • CIP  Current icing potential algorithm

  • CTT  Cloud-top temperature

  • CTTmap  Cloud-top temperature interest map

  • DZ  Drizzle

  • EMB  Embraer (Empresa Brasileira de Aeronáutica S.A.)

  • FAA  Federal Aviation Administration

  • FZDZ  Freezing drizzle

  • FZRA  Freezing rain

  • GOES  Geostationary Operational Environmental Satellites

  • METAR  Aviation Routine Weather Report

  • NASA  National Aeronautics and Space Administration

  • NCAR  National Center for Atmospheric Research

  • NEXRAD  Next-Generation Weather Radar

  • NTSB  National Transportation Safety Board

  • NWS  National Weather Service

  • PE  Ice pellets

  • PIREP  Pilot report

  • PIREPmap  Pilot-report interest map

  • POD  Probability of detection (suffixes “y” and “n” indicate “yes” and “no,” respectively)

  • RA  Rain

  • RH  Relative humidity with respect to water

  • RHmap  Relative humidity interest map

  • ROC  Relative operating characteristic

  • RUC  Rapid Update Cycle numerical weather prediction model

  • SLD  Supercooled large droplets

  • SLW  Supercooled liquid water

  • SLWmap  Supercooled liquid water interest map

  • SN  Snow

  • T  Temperature

  • Tmap  Temperature interest map

  • UTC  Universal coordinated time

  • VV  Vertical velocity

  • VVmap  Vertical velocity interest map

Fig. 1.
Fig. 1.

CIP conceptual diagram. Precipitation types: snow (asterisks), rain (large open circles), and freezing drizzle (small gray circles).

Citation: Journal of Applied Meteorology 44, 7; 10.1175/JAM2246.1

Fig. 2.
Fig. 2.

Flowchart of the CIP process.

Citation: Journal of Applied Meteorology 44, 7; 10.1175/JAM2246.1

Fig. 3.
Fig. 3.

(a) Standard and convective temperature interest maps (Tmap, Tmap-convective), and normalized icing PIREP ratio (frequency of PIREPs at a given temperature divided by the frequency of occurrence of that temperature in the model, normalized to a range of 0–1). (b) As in (a), but for CTT. (c) As in (a), but for RH. (d) As in (a), but for VV.

Citation: Journal of Applied Meteorology 44, 7; 10.1175/JAM2246.1

Fig. 4.
Fig. 4.

The pilot-report interest map, showing change in horizontal and vertical distance. An “x” marks the location of a pilot report of icing.

Citation: Journal of Applied Meteorology 44, 7; 10.1175/JAM2246.1

Fig. 5.
Fig. 5.

(a) CIP plot for icing potential at 3000 ft (915 m) MSL for 2100 UTC 15 Feb 2003. Color bar shows the range of icing potentials from 0.05 to 1.00. NASA Twin Otter location is marked with an “x.” (b) CIP flight-route cross section from Louisville, KY, (SDF) to Washington—Dulles International Airport (IAD) for 2100 UTC 15 Feb 2003. SLD icing conditions were encountered by the NASA Twin Otter near Huntington, WV, (HTS) below 2500 ft (760 m) MSL (marked with an “x”). Color bar shows the range of icing and SLD potentials from 0.05 to 1.00. RUC topography is filled with black. Gray areas are those where icing was diagnosed but there was no direct indication of SLD [SLD potential is unknown (UKN)].

Citation: Journal of Applied Meteorology 44, 7; 10.1175/JAM2246.1

Fig. 6.
Fig. 6.

Relationship between PODy [moderate-or-greater-severity (mog) PIREPs] and 1 – PODn for CIP, using data from the winter of 2000, with all valid times combined. The thresholds used (starting in the upper-right corner) are 0.0, 0.02, 0.05, 0.15, 0.25, . . . , 0.95.

Citation: Journal of Applied Meteorology 44, 7; 10.1175/JAM2246.1

Table 1.

Satellite cloud diagnosis (adapted from Thompson et al. 1997b). Channel 1 = 0.65 μm, channel 2 = 3.9 μm, and channel 4 = 10.7 μm for GOES imagers.

Table 1.
Table 2.

Icing and SLD potential equations. Precipitation abbreviations are defined in the appendix.

Table 2.
Table 2.

(Continued)

Table 2.
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