Operational products used by the U.S. Federal Aviation Administration to alert pilots of areas of hazardous icing provide nowcast and short-term forecast ...
PROGRESS ON THE INTEGRATION OF ADVANCED SATELLITE CLOUD PRODUCTS INTO AN OPERATIONAL AIRCRAFT ICING NOWCASTING SYSTEM
J.A. Haggerty, F. McDonough, J. Black, S. Landolt, C.A. Wolff, D.B. Johnson National Center for Atmospheric Research, Boulder, Colorado, USA P. Minnis NASA Langley Research Center, Hampton, Virginia, USA
Abstract Operational products used by the U.S. Federal Aviation Administration to alert pilots of areas of hazardous icing provide nowcast and short-term forecast estimates of the potential for the presence of supercooled liquid water and supercooled large droplets. The Current Icing Product (CIP) employs basic satellite-derived information, including a cloud mask and cloud top temperature estimates, together with multiple other data sources to produce a gridded, three-dimensional, hourly depiction of icing probability, the potential for supercooled large drops, and icing severity. Advanced satellitederived cloud products developed at the NASA Langley Research Center (LaRC) provide a more detailed description of cloud properties (primarily at cloud top) compared to the basic satellite-derived information used currently in CIP. Certain LaRC products can be related to icing probability. This paper describes the status of efforts to integrate these advanced cloud products into CIP.
INTRODUCTION Operational products used by the U.S. Federal Aviation Administration to alert pilots of areas of hazardous icing provide nowcast and short-term forecast estimates of icing probability, severity, and the potential for supercooled large droplets (SLD). The Current Icing Product (CIP) system combines multiple data sources using fuzzy logic methods to produce a gridded, three-dimensional, hourly depiction of icing-related conditions (Bernstein et al., 2005). This paper describes efforts to integrate a new data set into CIP. Advanced satellite-derived cloud products developed at the NASA Langley Research Center (LaRC) provide a more detailed description of cloud properties (primarily at cloud top) compared to the basic satellite-derived information used currently in CIP.
SATELLITE CLOUD PRODUCTS Cloud hydrometeor phase, liquid water path (LWP), droplet effective radius (Re), cloud effective temperature (Te), and cloud top height (Tz) as estimated by the LaRC algorithms (Minnis et al., 2004) have been evaluated in icing conditions for possible application in CIP. Various comparisons including forecasters’ anecdotal observations (Wolff et al., 2005), correlations between these products and pilot reports of icing severity (Black et al., 2008), and direct comparisons with in situ measurements (Haggerty et al., 2005) have established the quality of LaRC products in meteorological conditions associated with aircraft icing. Examples are shown in Figure 1.
Figure 1: Selected LaRC cloud products derived from GOES-12 data over southern Quebec and northeastern United States on 30 November 2003 at 1815 UTC. (a) Aircraft flight tracks on the visible channel image. Flight altitude shown in lower left. (b) Hydrometeor phase (light blue = supercooled liquid water), cloud boundaries overlaid on aircraft altitude. (c) cloud top height (d) Liquid Water Path. (e) Re.
CURRENT ICING PRODUCT (CIP) ALGORITHM As shown in Figure 2, CIP combines data from surface observations, models, radar, pilot reports, and satellite sensors to arrive at three-dimensional estimates of icing probability, the potential for supercooled large drops (SLD), and icing severity over the continental United States. Fuzzy logic methods and decision-tree techniques are applied to determine the likelihood of icing and SLD at each location, thereby maximizing the strengths of each dataset. Currently, the satellite component of CIP uses simple combinations of GOES Imager reflectivity and brightness temperatures to estimate cloud properties of interest for icing detection. Replacement of the satellite component with LaRC cloud
products offers a means to incorporate additional micro- and macro-physical cloud information that is not currently available to CIP with the goal of improving the probability of detecting icing conditions in specific meteorological scenarios.
Model
Surface Observatio
Satellite
Pilot Reports
Radar
Lightning
Match data to each 3-D model grid box
Cloudy?
N O
ICING=0.0 SLD=0.0
YES
Determine Vertical Cloud Structure and Weather Scenario
Single cloud w/o precip
Single cloud w/precip
Multiple cloud layers
Cloud top temperature gradient
Classical freezing rain
Convective
Apply interest maps. Calculate initial icing & supercooled large drop (SLD) potentials.Adjust icing potential using info from PIREPs, model supercooled liquid water content and vertical velocity.
FINAL ICING PROBABILITY & SUPERCOOLED LIQUID DROP POTENTIAL
Figure 2: Diagram showing the Current Icing Potential system which uses multiple sources of data as input and combines them using fuzzy logic methods and decision tree technology to produce estimates of icing probability and the potential for supercooled large drops.
PRODUCT INTEGRATION METHODS CLOUD MASK AND HYDROMETEOR PHASE In an experimental version of CIP, the current cloud masking technique is replaced with a new cloud screening method that uses the LaRC hydrometeor phase product. First, each CIP gridpoint (20 km resolution) is mapped to 16 GOES-LaRC product pixels (5 km resolution). If more than 40% of the phase product pixels contain clouds, then the CIP gridpoint is considered cloudy. At this point in the algorithm, additional calculations are made for subsequent use in relating icing probability to cloud top temperature. Cloud pixels are sorted from cold to warm using the LaRC Cloud Effective Temperature (Te) product; 25th, 50th, and 75th percentiles of Te values are calculated. The fraction of liquid phase pixels within each Te bin is also calculated. CLOUD TOP HEIGHT AND EFFECTIVE CLOUD TEMPERATURE Cloud top height estimates in the current operational version of CIP combine model-derived temperature profiles with GOES brightness temperature measurements at cloud top. The method (hereafter referred to as “CIP CTZ”) assume cloud top height is at the level where model temperature is equal to the coldest GOES cloud pixel. The LaRC cloud top height product (ASAP CTZ) also uses a temperature matching scheme that employs vertical profiles from models. A hybrid of these schemes, referred to as the Combined CIP CTZ (CCZ), method uses the LaRC cloud top height and Te products together with model profiles. The CCZ method employs fuzzy logic based on knowledge that cloud tops are often found in a sounding where: • Relative humidity (RH) decreases to less than 100% • An inversion in equivalent potential temperature (θe) exists
• • •
Vertical velocity changes sign from positive to negative Wind shear is present GOES brightness temperature matches model profile temperature
Based on this knowledge, fuzzy logic membership functions are created for each pertinent variable including: 1) Model temperature – Te(25thpercentile); RH; d(RH)/dz; d(θe)/dz; d(total model condensate)/dz. Following the CIP paradigm, interest maps are then developed to quantify the value of each variable in specific conditions. Figure 3 shows the interest map for [Model temperature – Te] Weighted values derived from each interest map are combined to arrive at the new cloud top height estimate according to: 0.4(Teff_map) + 0.1(RH_map) + 0.15(D(qe)map) + 0.3(Drh_map) + 0.05(D(totC)_map)
Figure 3: Interest map for [Model temperature – Te]. This function quantifies the level of interest for purposes of detecting icing conditions in observations with a given difference in the model temperature and the satellite-derived effective temperature.
The current cloud top temperature (CTT) map can be enhanced using information from the LaRC phase product. The original CTT map was based on the statistical probability that ice or liquid would be associated with a certain cloud top temperature. A new variable (the percent of liquid pixels associated with the cloud top temperature, as described above) is added to the algorithm. Addition of the observed cloud phase to the statistical CTT map refines the algorithm’s level of interest in icing. The new equation for cloud top phase interest is: newCTT_interest = (0.5 * CTTmap) + (0.5 * % liquid phase) Thus if a cold cloud top has liquid according to the LaRC LWP product, the interest in icing is increased. Conversely, if a warm cloud top has ice phase at cloud top, the interest will be reduced.
RESULTS CLOUD TOP HEIGHT Each of the three cloud top height estimate methods (i.e., CIP CTZ, ASAP CTZ, and the new CCZ) are compared with a set of 769 daytime pilot reports giving cloud top height (TOP-REPs). The tendency for CIP algorithms to overestimate cloud top height is reduced significantly by the hybrid CCZ method which combines model and LaRC satellite cloud product data, as illustrated in Figure 4..
Figure 4: The differences (calculated cloud top height minus PIREP measured cloud top) in the three cloud top schemes for the 769 daytime TOP-REPs. The red line denotes the median error while the box encloses the 25th – 75th percentiles of the errors.
ICING PROBABILITY Icing probability estimates from the experimental version of CIP including the new hydrid cloud top height scheme and other modifications described above have been compared with a set of pilot reports from a 6-week winter-time period in 2005. Comparison of icing volume estimated by the New CIP and the Old CIP with these daytime pilot reports shows that, according to Old CIP, 9% of the volume contains icing. New CIP estimates an icing volume of 8.4% for the same data set. Overall, 83% of cases had less icing volume in the New CIP compared to the Old CIP. A 6% reduction in estimated icing volume is achieved using the New CIP. Probability of detection (POD) statistics show higher detection of “No” observations and lower detection of “Yes” observations by New CIP.
SUMMARY The operational version of CIP (Old CIP) is known to overestimate cloud top height, which leads to an overestimation of the volume containing icing. Hence, the system is potentially warning pilots of icing conditions where they likely do not exist. The reduction in cloud top height estimates achieved using the CCZ method results in an overall reduction in the volume of icing, which, if demonstrated to be accurate, could provide benefit to the aviation community. Further studies are in progress to verify the accuracy of New CIP and to investigate the integration of the LaRC liquid water path product to refine estimates of icing severity.
REFERENCES Bernstein, B., F. McDonough, M. Politovich, B. Brown, T. Ratvasky, D. Miller, C.A. Wolff, and G. Cunning, 2005: Current Icing Potential (CIP): Algorithm description and comparison with aircraft observations. J. Appl. Meteorol., 44, 969-986. Black, J., J. Haggerty, S. Landolt, F. McDonough, C. Wolff, St. Mueller, P. Minnis, L. Nguyen, 2008: Comparison of NASA-Langley satellite-derived cloud properties with pilot reports in aircraft icing scenarios. Submitted to the 12th Conference on Aviation, Range, and Aerospace Meteorology, Amer. Meteorol. Soc., New Orleans, 20-24 January. Haggerty, J.A., G. Cunning, B. Bernstein, M. Chapman, D. Johnson, M. Politovich, C.A. Wolff, P. Minnis, R. Palikonda, 2005: Integration of advanced satellite cloud products into an icing nowcasting system. Proc. AMS 14th Conf. on Satellite Oceanography and Meteorology, Amer. Meteorol. Soc., Atlanta, 29 January - 2 February. Minnis, P., L. Nguyen, W.L. Smith, J., M.M. Khaiyer, R. Palikonda, D. Spangenberg, Dr. Doelling, D. Phan, G. Nowicki, P. Heck, and C.A. Wolff, 2004: Rel-time cloud, radiation, and aircraft icing parameters from GOES over the USA. Proc. 13th AMS Conf. Satellite Oceanogr. and Meteorol., Norfolk, VA, Sept. 20-24, CD-ROM, P7.1. Wolff, C.A., B. Bernstein, and F. McDonough, 2005: Nowcasting aircraft icing conditions using GOESderived satellite products. Proc. WWRP Symposium on Nowcasting and Very Short Range Forecasting, Toulouse, France, 5-9 September, CD-ROM.
ACKNOWLEDGMENTS This project is supported by the NASA Applied Sciences Program through the NASA Advanced Satellite Aviation-weather Products (ASAP) initiative. NCAR is sponsored by the National Science Foundation.
