Weather Support to Deicing Decision Making (WSDDM): A Winter Weather Nowcasting System

Transcription

1 Weather Support to Deicing Decision Making (WSDDM): A Winter Weather Nowcasting System Roy Rasmussen,* Mike Dixon,* Frank Hage,* Jeff Cole,* Chuck Wade,* John Tuttle,* Starr McGettigan, + Thomas Carty, + Lloyd Stevenson, # Warren Shelly Knight,* Eli Karplus,* and Nancy Rehak,* ABSTRACT This paper describes a winter weather nowcasting system called Weather Support to Deicing Decision Making (WSDDM), designed to provide airline, airport, and air traffic users with winter weather information relevant to their operations. The information is provided on an easy to use graphical display and characterizes airport icing conditions for nonmeteorologists. The system has been developed and refined over a series of winter-long airport demonstrations at Denver s Stapleton International Airport, Chicago s O Hare International Airport, and New York s LaGuardia Airport. The WSDDM system utilizes commercially available weather information in the form of Next Generation Weather Radar WSR-88D radar reflectivity data depicted as color coded images on a window of the display and Aviation Routine Weather Report (METAR) surface weather reports from Automated Surface Observating System stations and observers. METAR information includes wind speed and direction, air temperature, and precipitation type/rate, which are routinely updated on an hourly basis or more frequently if conditions are changing. Recent studies have shown that the liquid equivalent snowfall rate is the most important factor in determining the holdover time of a deicing fluid. However, the current operational snowfall intensity reported in METARs is based on visibility, which has been shown to give misleading information on liquid equivalent rates in many cases due to the wide variation in density and shape of snow. The particular hazard has been identified as high visibility high snowfall conditions. The WSDDM system addresses this potentially hazardous condition through the deployment of snow gauges at an airport. These snow gauges report real-time estimates of the liquid equivalent snowfall rate once every minute to WSDDM users. The WSDDM system also provides 30-min nowcasts of liquid equivalent snowfall rate through the use of a real-time calibration of radar reflectivity and snow gauge snowfall rate. This paper discusses the development of the system, including the development of new wind shields for snow gauges to improve catch efficiency, as well as the development of the above mentioned real-time method to convert radar reflectivity to snowfall rate on the ground using snow gauges. In addition, we discuss results from a user evaluation of the system, as well as results from an efficiency and safety benefits study of the system. 1. Introduction *National Center for Atmospheric Research, Boulder, Colorado. + FAA W. J. Hughes Technical Center, Atlantic City International Airport, New Jersey. # Volpe National Transportation Systems Center, Cambridge, System Resources Corporation, Washington, D.C. Corresponding author address: Roy M. Rasmussen, NCAR, Box 3000, Boulder, CO In final form 6 October American Meteorological Society The March 1992 takeoff-icing accident at LaGuardia Airport (NTSB 1993) marked a turning point in winter operations at U.S. air carrier airports. Within nine months of the accident, the Federal Aviation Administration (FAA) had established new U.S. rules for de/anti-icing aircraft prior to takeoff. In that environment, the FAA also expanded its Weather Support to Deicing Decision Making (WSDDM) system efforts at the National Center for Atmospheric Research (NCAR), which had been initiated in Through WSDDM, the FAA has pursued two goals: (a) performing the basic scientific research needed to provide the aviation community with a deeper understanding of the airport icing environment behind takeoff-icing accidents (such as snow, freezing rain or drizzle, frost, or freezing fog), and (b) developing product concepts that characterize airport 579

2 icing conditions for use by those individuals concerned with the safety and efficiency of aircraft operations during airport icing conditions. Typically, these individuals are not meteorologists. Technical terms Four terms are used throughout the paper and are introduced here: deicing fluid, anti-icing fluid, holdover time, and holdover tables. Deicing fluid removes ice and snow from an aircraft and is applied hot, while anti-icing fluid extends the time that a deiced aircraft will remain free of ice and snow contamination prior to takeoff when it is snowing at the airport and is typically applied at the ambient temperature. Holdover time refers to the estimated time the applied de/antiicing fluids will prevent the formation of frost or ice or the accumulation of snow on the protected surfaces of the aircraft, while holdover tables permit pilots to estimate their aircraft s holdover time after being de/anti-iced, given the (a) type of fluid used and its concentration, (b) outside air temperature, (c) type of icing condition (e.g., frost, freezing fog, snow, freezing rain), and (d) icing intensity (i.e., light, moderate, heavy). All U.S. airlines are currently required to have an FAA approved winter operations plan that includes the use of holdover tabes in order to operate at airports impacted by snow, freezing rain, drizzle, or fog. 2. WSDDM product concept and motivation The WSDDM product concept characterizes airport icing conditions for nonmeteorologists. The concept has been developed and refined FIG. 1. Block diagram of the various components of the WSDDM system. See text for details. 580 Vol. 82, No. 4, April 2001

3 FIG. 2. WSDDM screen depicting winter storm conditions during the 10 Dec 1997 snow event. The main panel on the left displays the km horizontal resolution WSR-88D radar reflectivity scan with arbitrary zoom, pan, and movie looping capability. Also shown on the main panel are TREC storm motion vectors. The length of the vector represents 30 min of storm motion. The upper-right panel displays surface METARS from NWS surface stations throught the region. The column data displayed are three letter station ID, GMT time, temperature in C, dewpoint in C, wind direction (true), wind speed (kt), wind gusts (kt), visibility (miles), ceiling (ft), and current weather code ( SN, RA, BR, PL, etc.). The user can click in the main radar window and the closest 10 METAR s to the location of the click will appear in this upper-right panel. The second panel from the top right displays 1-min mesonet text data from the WSDDM snow gauges and weather stations at the various locations. The data displayed in column form are Station ID, GMT time, temperature in C, dewpoint in C, wind speed in knots, wind gusts in knots, liquid equivalent precipitation rate from the snow gauges in millimeters per hour, and an indication of intensity based on the liquid equivalent rate, not visibility. A liquid equivalent snowfall rate between 0 and 1 mm h 1 is light, 1 to 2.5 mm h 1 moderate, and greater than 2.5 mm h 1 is heavy. The third panel from the top right provides a time series graph of the 1-min WSDDM snow gauge and surface weather data from each of the snow gauge locations plotted over the past two hours. All variables at a single site or a single variable at all the sites can be selected for viewing. The available variables are liquid equivalent precipitation rate (mm h 1 ), precipitation accumulation (mm), temperature ( C), wind speed (kt), wind direction ( C), humidity (%), and pressure (mb). The bottom panel on the right displays the radar reflectivity and snow gauge past 1-h accumulation trend, as well as a 30-min accumulation forecast for a specific user site. The user can choose which site and which radar to display. The radar data are plotted as either a solid yellow line (past data) or dotted yellow line (forecast). The snow gauge data are plotted with a color specific to each site, with again the solid line representing past data and dotted the forecast accumulation. The red vertical line represents the current time. Also given as a text message in this panel are 1) the current liquid equivalent snowfall rate from either a WSDDM system snowgauge or the NWS surface station, whichever is greater; and 2) the predicted liquid equivalent snowfall rate over the next 30 min. 581

4 over a series of winter-long airport demonstrations conducted at Denver s Stapleton International Airport, Chicago s O Hare International Airport, and New York s LaGuardia Airport. The product concept integrates a variety of weather information, tailors the information for winter-storm airport operations, and is designed to be a real-time product displayed on a computer monitor. The concept is shown schematically in Fig. 1 in block diagram form. The product utilizes commercially available weather information in the form of Next Generation Weather Radar WSR-88D radar reflectivity data depicted as color-coded images on a window of the display and Aviation Routine Weather Report (METAR) surface weather reports from Automated Surface Observing System (ASOS) stations and observers. METAR information includes wind speed and direction, air temperature, and precipitation type/rate, which are routinely updated on an hourly basis or more frequently if conditions are changing. Terminal Doppler Weather Radar radar data are currently not used by the system but will be added in the near future. The WSDDM system also utilizes a set of specially installed snow gauge/mesonet weather stations located in the vicinity of the airport to obtain surface and snow data updated on a 1 5-min basis. The snow gauges provide real-time estimates of the liquid equivalent snowfall rate once every minute. Recent studies have shown that the liquid equivalent snowfall rate is the most important factor in determining the holdover time of a deicing fluid (Bernadin et al. 1997; Rasmussen et al. 1999a). However, current National Weather Surface (NWS) stations do not provide liquid equivalent snowfall rates, but rather hourly snow intensity estimates based on visibility. Snow intensity estimates made in this fashion have been shown by Rasmussen et al. (1999b, 2000) to be misleading when wet snow, heavily rimed snow (snow that has accreted significant amounts of cloud droplets), and snow containing single crystals of compact shape (nearly spherical) occurs. Under these conditions the visibility can be high due to the relatively small cross-sectional area of these types of crystals, leading to estimates of light snowfall intensity when in fact the actual snowfall rate in liquid equivalent terms is quite high. Rasmussen et al. (1999b, 2000) define the hazard as high visibility high snowfall rate conditions. Rasmussen et al. (2000) examined five of the major ground deicing accidents and showed that high visibility high snowfall rate conditions were present at a number of these accidents. All of the accidents had nearly the same liquid equivalent rate of 2.5 mm h 1 (0.1 in h 1 ), but widely varying visibilities. The LaGuardia accident in particular had light-snow intensity based on visibility during the entire event. Since the failure of a deicing fluid is inversely proportional to the liquid equivalent snowfall rate, the WSDDM system was designed to include a real-time snowfall-rate estimate as one of its core components to help alert users of this potentially hazardous condition and to help make the right decision regarding the appropriate holdover time. 3. Description of the WSDDM system a. General overview The WSDDM product consists of a graphical display that provides real-time nowcasts of snowfall events and other winter weather conditions. This integrated display system requires minimal training to operate, and displays weather information related to aircraft deicing that is easily understood by nonmeteorologists. The various components of the WSDDM system that support this product are shown in Fig. 1 and a sample screen from the WSDDM graphical user interface in Fig. 2. As depicted in Fig. 1, the WSDDM system uses real-time WSR-88D radar data, NWS METAR reports and locally deployed snow gauges and weather stations to provide users with 1) real-time snow gauge data (updated every one minute) of the liquid equivalent snowfall rate at the airport and two to three sites km away from the airport; 2) real-time radar reflectivity from WSR-88D radars depicting current locations of precipitation and snow; 3) meteorological data at the airport and two to three sites km away from the airport updated every 1 min and displayed in text and timeline form, with the timeline going back to 2 h; 4) a 30-min nowcast of radar reflectivity based on the use of a cross-correlation technique on the radar reflectivity data updated every 6 min; 5) a 30-min nowcast of liquid equivalent snowfall rate at the airport and the offsite snow gauge locations by applying a real-time snow gauge radar reflectivity calibration algorithm at each of the snow gauge sites, updated every 6 min; and 6) a depiction of the current weather conditions from NWS METAR hourly or special reports in text format and also depicted graphically on the display. 582 Vol. 82, No. 4, April 2001

5 The WSDDM system display depicts this information graphically on a radar reflectivity display and in text form as decoded METAR reports displayed in an easily readable column format. The user can also bring up the 10 closest METAR reports in a text window by clicking on the desired location on the radar screen. The WSDDM system has the ability to animate the radar data, and to zoom in for detail in the immediate vicinity of the airport or other desired locations. The system also includes geographical overlays showing a detailed layout of the airport s runways, taxiways, and concourses against which to view the weather radar data. The display has multiple windows that depict the above information without the need to bring up additional windows. Note that the system currently does not nowcast precipitation type. This capability is highly desirable since holdover time depends on the type of precipitation occurring. A research and development effort is currently underway to achieve this capability. b. Detailed description of the data and graphics presented in each panel on the display The WSDDM system display consists of five graphical panels organized on one screen on a workstation. The data needed for each panel, and the processing required to produce the graphic shown in Fig. 2, are described below. 1) RADAR DATA (MAIN PANEL ON THE LEFT IN FIG. 2) The WSDDM system displays NEXRAD Information Dissemination Services (NIDS) radar products. These data are collected from WSR-88D radar sites and distributed to the WSDDM system via satellite by a NIDS vendor. An ingest machine collects the data from the satellite and passes it into the radar processing machine. Both the ingest machine and radar processing machine are typically located at a central site. From this central site data are passed to WSDDM remote hosts via 56-kB leased land lines. Each WSR-88D operates independently, and asynchronously, and it typically takes about 2 10 min to move the data from the radar to the vendor to the central processing machines to the WSDDM remote displays. WSDDM uses the km horizontal resolution reflectivity scan, which is one of the currently available NIDS products. NIDS reflectivity products contain only 16 discrete data values whose range changes depending on the scan strategy the WSR-88D operators have chosen. In clear air mode, the data processing is done such that the range of the data covers about 28 to +35 dbz. In storm mode the 16 bins cover a 5 70 dbz range. Values greater than 70 dbz are put into the 70-dBZ bin. When the radar is scanning clear air, all data over 35 dbz are put in the 35-dBZ bin. Conversely, when the radar is scanning in storm mode, only data greater than 5 dbz are displayed. The different scan strategies take different amounts of time to collect. WSR-88D s resample their airspace every 5 or 6 min (storm mode) to 13 min (clear-air mode), depending on their operating mode. If data from a WSR-88D radar are not available, the screen will display the graphic: RADAR LINK DOWN. 2) TREC VECTORS (DISPLAYED IN THE MAIN PANEL ON THE LEFT SIDE IN FIG. 2) Each time the WSDDM system receives a NIDS radar image, it compares the current image with the previous image. Using the Tracking Radar Echoes by Correlation (TREC) cross-correlation technique (Rinehart and Garvey 1978; Tuttle and Foote 1990) described in the next section, the system determines the most likely direction the echoes are moving and at what speed. These motion data are then output as a grid of vectors, centered over the radar. WSDDM is configured to output an approximate grid of vectors at all zoom levels. The length of the vector is equivalent to the distance that the echo will move in 30 min, that is, the head of the vector shows the 30-min forecast position of the echo currently located at the tail of the vector. Tick marks on the vector represent 10 min of equivalent distance. When the WSDDM display is in looping mode (animating the radar images), vectors are set to appear only on the last image of the loop. For the LaGuardia system, we calculate TREC vectors for two radars (Fort Dix and Brookhaven), and display the resulting vectors side by side in regions of overlap in different colors (as depicted in Fig. 2). This allows the user to easily determine the relative performance of TREC from two independent radars. Vectors that are nearly parallel and similar in length indicate a high confidence level in the storm movement, while widely different vectors indicate less confidence. This is also a way to flag spurious vectors that are produced by ground clutter, beam blockage, or anomalous propagation. TREC cannot accurately compute motion if the most recent two NIDS images are more than 20 min apart from each other. Thus, if the system is not ac- 583

6 quiring NIDS products reliably, it will not compute any products. It also will not produce vectors in areas of radar reflectivity less than a preset noise threshold, currently set at 0 dbz. 3) REGIONAL SURFACE REPORTS (UPPER-RIGHT PANEL IN FIG. 2) This text window is generated from surface observations throughout the region. National Weather Service surface data from a variety of automated (ASOS) and manual sensor networks in METAR form are obtained in real time. The central WSDDM system formats the METAR data into easier to read columns, and then distributes the results to the WSDDM displays. The column data displayed are three letter station ID, GMT time, temperature in C, dewpoint in C, wind direction (true), wind speed (kt), wind gusts (kt), visibility (miles), ceiling (ft), and current weather code ( SN, RA, BR, PL, etc.). METAR data are displayed as soon as available, and special reports are included. The data are typically from a few minutes to an hour old. The user can click in the radar window and the closest 10 METARs to the location of the click will appear in the window. 4) MESONET TEXT REPORT (SECOND PANEL FROM THE TOP, RIGHT IN FIG. 2) This text window is generated from data collected from snow gauges and weather stations placed at strategic locations around the airport. These sensors are polled every minute and the data are relayed to the WSDDM system central computers. Both the snow gauges and weather stations provide 1-min data updates. The data displayed in column form are station ID, GMT time, temperature in C, dewpoint in C, wind speed in knots, wind gusts in knots, liquid equivalent precipitation rate from the snow gauges in millimeters per hour, and an indication of intensity based on the liquid equivalent rate, not visibility. A liquid equivalent snowfall rate between 0 and 1 mm h 1 is light, 1 to 2.5 mm h 1 is moderate, and greater than 2.5 mm h 1 is heavy. This definition of liquid equivalent snowfall rate was determined jointly by the Society of Automotive Engineers International Ground Deicing Committee and NCAR. 5) GAUGE STRIP CHART DISPLAY WINDOW (THIRD PANEL FROM THE TOP, RIGHT IN FIG. 2) This window displays the one-minute surface observations in a graphical form, plotted over the past two hours. It is intended to relay trends over time and the timing of frontal passages, in a simple and quick form. It updates every minute or so, with old data scrolling to the left and new data being added to the right-hand edge of the plot. The user can quickly intercompare specific sensor readings at the various snow gauge locations by selecting different variables to view. All variables at a single site can also be intercompared. The available variables are liquid equivalent precipitation rate (mm h 1 ), precipitation accumulation (mm), temperature ( C), wind speed (kt), wind direction (degrees), humidity (%), and pressure (mbars). 6) RADAR, SNOW TREND, AND ACCUMULATION PREDICTION PLOT (BOTTOM RIGHT PANEL IN FIG. 2): This display shows four quantities. The first quantity is past WSR-88D radar reflectivity data, averaged over a 10 km 10 km square above each gauge site (typically the location of the airport) and plotted over time. These data are plotted as a solid yellow line and it is always to the left of the NOW vertical red reference line. The second quantity is generated by the TREC echo tracking system (Tuttle and Foote 1990) and is a reflectivity forecast derived from the computed speed and direction of the radar echoes. This trace shows what the radar reflectivity is predicted to be from 0 to 30 min into the future over the snow gauge or airport site and appears as a dashed yellow line extending from the solid yellow trace to the right of the red NOW vertical reference line. The third trace shows the amount of precipitation in units of liquid equivalent precipitation that has fallen in the recent past (solid line color coded to the gauge location). A fourth trace shows a predicted amount of liquid equivalent precipitation (dashed line the same color as the past gauge accumulation). This trace is derived from the real-time correlation of Z and S discussed in next section. After each successive radar scan, the coefficients of the real-time Z S relationship are analyzed and recomputed, and a new prediction is output and displayed. The relationship considers data up to two hours in the past in order to ensure that the relationship is stable. Also given as text messages in this panel are 1) the current liquid equivalent snowfall rate (light, moderate, or heavy) from either a WSDDM system snow gauge or the NWS surface station, whichever is greater; and 2) the predicted liquid equivalent snowfall rate over the next 30 min. The current snowfall rate is determined from either WSDDM snow gauges (see section 4 for details of the WSDDM snow gauge measurement) or from dialing up the airport ASOS 584 Vol. 82, No. 4, April 2001

7 station and determining the current snow intensity based on 5-min data. The maximum of the NWS rate determined by visibility or the WSDDM liquid equivalent rate determined by the snow gauges is displayed as the current rate. This procedure allows the WSDDM system to alert operators of the hazardous high visibility high snowfall rate condition. Thus, the system always reports the most intense rate, providing the largest safety margin. c. Description of the hardware, software, and networking required to run the WSDDM system 1) HARDWARE AND SOFTWARE The WSDDM system runs on Pentium PCs running the LINUX operating system. Digital phone lines are used to transfer data from the central WSDDM site out to the user sites and to transfer snow gauge data from the user sites back to the central site for processing. The communications hardware includes CSU/DSUs and routers to interface between the digital phone lines and the computers. Each user site has its own Pentium machine for a display, and each machine operates independently of the others. Seven independent displays were operated during the LaGuardia demonstration, as depicted in Fig. 3. products were sent back out to the remote display computers via the 56-kB Frame Relay Network. The process is shown schematically in Fig. 3 and is based on client/server technology. 4. Real-time snowfall measurement with snow gauges An important feature of the WSDDM system is the real-time display of the current liquid equivalent snowfall rate updated every minute. This high update rate is required to support aircraft deicing activities that often have to deal with holdover times as short as 5 min. Current national weather service snow intensities are inadequate for this purpose due to 1) the inaccuracies involved in estimating snowfall rates using visibility (Rasmussen et al. 1999b, 2000), and 2) the slow update rate (as much as an hour between obser- 2) NETWORKING: EXAMPLE FROM LAGUARDIA DEMONSTRATION The Pentium computers ingesting and processing the radar and METAR data, as well as producing the TREC vectors, were all located at NCAR/RAP in Boulder, Colorado, for the LaGuardia demonstration (Fig. 3). The products produced from these machines were then transferred to the remote sites via a 56- kb Frame Relay network (Fig. 3). The snow gauge data were transferred via radio modem to a snow gauge ingest and processing computer located at the LaGuardia Delta Tower. The LaGuardia snow gauge data were transferred via a 56-kB phone network, the Newark snow gauge data via a 56-kB line, and the John F. Kennedy International Airport (JFK) data via a combined radio modem and phone line network. The snow gauge data were then transferred back to Boulder for use in the algorithms, and then the final FIG. 3. Schematic diagram showing the hardware and networking configuration for the 1997 LaGuardia Airport setup of WSDDM. 585

8 FIG. 4. Photograph of double Alter wind shield with a GEONOR snow gauge located at the center. FIG. 5. Photograph of a half-scale Wyoming shield with a GEONOR snow gauge located at the center. vations). In addition, the measurement of liquid equivalent rate using heated tipping-bucket rain gauges is known to significantly underestimate snowfall amounts. For instance, a NWS report (Johnson et al. 1994) documented that catch efficiency for a heated tipping bucket to average only 35% for frozen precipitation. Thus, as part of the WSDDM system development a test site was set up five miles south of Boulder, Colorado, to evaluate snow gauge performance and determine which snow gauge wind shield combination to use with the WSDDM system. Various snow gauge and wind shield combinations were tested from 1994 to 1999 [see Rasmussen et al. 1999b) for details]. The snow gauges tested were manufactured by Belfort, ETI, and GEONOR. The shields tested were Alter shield, Nipher shield, Wyoming shield, and the Double Fence Intercomparison Reference (DFIR) shield. The DFIR shield is the international standard reference wind shield used by the World Meteorological Organization for snow gauge evaluation for climatological measurement purposes. The NCAR testing consisted of comparison of manual snow accumulations using a cm 2 snow pan every 15 min to the snow gauge measurements of snowfall. The testing revealed that the GEONOR snow gauge in the DFIR shield agreed best with the manual snow measurements. On average, the GEONOR in the DFIR was within 5% of the manual measurement. The key factors identified during these tests that limited the accuracy and timeliness of liquid equivalent snowfall measurements were 1) the undercatch of snowfall at higher wind speeds due to airflow distortions around the snow gauge not completely prevented by the wind shields, and 2) the undercatch of snowfall in real time due to sidewall accumulation. Snow collected on the sidewalls of the gauge would often remain on the side of the gauge until solar heating melted the bond between the sidewall and the snow the following day, resulting in a snow dump into the gauge. For storm totals and other climatological purposes this may be adequate; however, this is clearly unacceptable for real-time deicing and other real-time purposes. In order to improve the measurement, two changes were made: 1) the wind shielding around the snow gauge was improved to prevent undercatch of snow due to airflow distortions around the gauge, and 2) a method was developed to prevent the accumulation of snowfall along the inner surface of the snow gauge. These two changes are described below. a. New wind shields Due to its large size (40 ft in diameter), the DFIR shield is not a practical wind shield to deploy at airports. Two new smaller wind shields were developed during this test period a double Alter shield and a half-scale Wyoming shield. The standard Alter shield has a ring of vertically oriented slats surrounding the snow gauge approximately 0.5-m radius from the snow gauge. The double Alter shield has in addition a second ring of vertically oriented slats 0.5-m from the inner ring of slats, as shown in Fig. 4. The half-scale Wyoming shield (Fig. 5) is 10 ft in diameter, half the diameter of a full size Wyoming shield. These two new shields improved the snow catch efficiency over the single Alter shield and are significantly smaller and cheaper to deploy than the DFIR shield. 586 Vol. 82, No. 4, April 2001

9 Catch efficiency as a function of wind speed for a GEONOR gauge within a single Alter shield is shown in Fig. 6a, and the catch efficiency for an identical GEONOR gauge within a double Alter shield is shown in Fig. 6b. In both cases the standard measurement is the GEONOR snow gauge in the DFIR shield. The results are for cases in which no sidewall accumulation occurred. One-hour-averaged wind speeds are compared to 1-h snow accumulations using data from the winter of 1998/99. Equal numbers of data points are plotted for each box plot symbol, with over 100 h of snowfall data represented. As can be seen, the catch efficiency for the GEONOR within the double Alter shield is over 80% for wind speeds as high as 6 m s 1, while the catch efficiency for the GEONOR within the single Alter decreases nearly linearly to approximately 60% by 6 m s 1. Thus, catch efficiency is significantly improved with the double Alter wind shield. Catch efficiency curves developed by Goodison (1978) and Yang et al. (1998) for a single Alter shield and various snow gauge types developed by averaging wind over the time period of a storm, agree well with the current single Alter results obtained on an hourly basis (Fig. 6a). Catch efficiency as a function of wind speed for a GEONOR in a half-size Wyoming shield is shown in Fig. 6c. In this case, the efficiency is over 90% to wind speeds as high as 6 m s 1. This high efficiency shield is relatively simple to manufacture and deploy. The above results for both shield types apply to the median catch efficiency. As can be seen in the figures, the scatter in the data is typically ±30%. This scatter is mainly attributed to turbulent effects on different snow crystal types. Future testing will examine the role of turbulence on snow catch efficiency in order to reduce some of this scatter. b. Method to prevent sidewall snow accumulation The method developed to prevent the accumulation of snowfall on the sidewalls of the snow gauges was to apply temperature-controlled heat tape to the sidewall and maintain the sidewall temperature at +2 C whenever the temperature dropped below +2 C. This prevented snow from freezing on the sidewalls and starting an accumulation that could extend into the center of the gauge. Without heat this accumulation would block the opening of the snow gauge and prevent the real-time measurement of the snow. The heating was maintained at +2 C in order not to overheat the sidewalls and cause a heat plume that would reduce the snow catch of the gauge. Snowflakes hit- a) b) c) FIG. 6. Catch efficiency [as compared to the GEONOR snow gauge in the Double Fence Intercomparison Reference (DFIR) shield] as a function of wind speed for the GEONOR snow gauge in a (a) single Alter shield, (b) a GEONOR snow gauge in a double Alter shield, and (c) a GEONOR snow gauge in a half-scale Wyoming shield. The box tops and bottoms represent the 25th and 75th percentiles of the data, while the whiskers represent the 10th and 90th percentiles of the data. 587

10 temperature drops below +2 C. The real-time WSDDM system corrects for wind undercatch of snow using the curves in Fig. 6 (depending on the shield type). d. Additional considerations During our deployment of snow gauge at JFK and LaGuardia airports, NCAR found it necessary to develop a method to prevent radio frequency noise from affecting the measurement of snow accumulation and rate by the GEONOR. This was accomplished through the use of a passive electronic low-pass filter and proper grounding and shielding. 5. WSDDM algorithms FIG. 7. Photograph of GEONOR snow gauge with sidewall snow accumulation. In this case no sidewall heating was applied. ting the sidewalls would melt into drops, and eventually drip into the bucket. The drops on the sidewalls represent a real-time reduction in snowfall rate of less than 5%. This small reduction is considered acceptable compared to the 30% 40% reduction in snowfall rates occurring when sidewall snow blocks the orifice. An example of a test without a heated sidewall and the subsequent snow blockage of the orifice of a GEONOR snow gauge is shown in Fig. 7. With sidewall heating no accumulation builds up on the sidewalls and snowfall is allowed to freely enter the snow gauge opening. c. Recommended snow gauge and wind shield combination for use with the WSDDM system Based on the above results, it is recommended that a GEONOR snow gauge with a temperature-controlled heat tape in either a double Alter or half-size Wyoming shield be deployed as part of a WSDDM system. The heat tape should maintain the sidewalls of the GEONOR snow gauge to +2 C whenever the sidewall a. 30-min radar reflectivity and storm motion nowcast The real-time radar data ingested into the WSDDM system from a NIDS vendor are used to produce a 30-min nowcast of radar reflectivity and storm motion. This forecast is depicted in the radar-snow trend panel (lower-right panel in Fig. 2). The 30-min nowcast of reflectivity is based on the TREC technique for tracking radar echoes by cross correlation (Tuttle and Foote 1990; Rinehart and Gravey 1978). This technique compares two consecutive radar images (typically 6 or 13 min apart depending on the scan strategy), and determines through a pattern-matching technique the most likely direction and speed of motion of 10 km 10 km blocks throughout the radar domain. This estimate is expressed by regularly spaced vectors on the radar screen, which depicts the direction of motion and the distance that the reflectivity at the tail of the vector would move in 30 min. Thus, the assumption is made that the future 30-min snow echo motion will be very similar to the previous motion during the past 12 to 24 min. An evaluation of TREC performance during WSDDM demonstrations at Chicago and New York by Turner et al. (1999) showed that TREC consistently beat persistence by up to 21% for winter storms in these regions. b. 30-min snowfall rate and accumulation nowcast The 30-min snow echo motion estimated from TREC is also used to produce a 30-min liquid equivalent snowfall rate and accumulation nowcast at airports in the WSDDM system domain. To do this, a conversion between radar reflectivity and liquid equivalent snowfall rate is required. Initial attempts involved the use of climatological relationships be- 588 Vol. 82, No. 4, April 2001

11 tween the radar reflectivity factor Z and liquid equivalent snowfall rate S, with the relationship given in terms of a power law: Z = as b, where a and b are constants to be determined from climatological data. It was found, however, that the large variation in snow density and type both during a storm and from storm to storm produced unacceptable results. Instead, a real-time technique was developed to calibrate the reflectivity data using the liquid equivalent accumulation from the snow gauges and corresponding radar reflectivity estimate of snowfall accumulation (Dixon and Rasmussen 1999). This calibration is then used to convert the 30-min reflectivity forecast to a snowfall forecast. The following section describes this algorithm. 1) ALGORITHM In the Z S equation above, b is assumed to be equal to a constant based on theoretical considerations [see Dixon and Rasmussen (1999) for details]. For pure rain events (T > 5 C) the Marshall Palmer value of 1.6 is used. For snow events (T < 0 C), a value of 1.75 is used. This value is consistent with published Z S relationships and with theoretical considerations. For mixed events, the value of b is interpolated assuming a linear relationship with temperature. At each radar scan time, the storm motion vector V trec at the gauge location is computed using TREC. The average fall time (t f ) for the snow particles from the radar beam height to the gauge is computed. The fall speed used for this calculation is 0.9 m s 1 for dry snow (T < 0 C) based on three years of snow particle fall speed measurements at the NCAR snow measurement site using a vertically pointing Doppler radar called POSS (Sheppard 1990). For rain events (T > 5 C), a fall speed of 10 m s 1 is assumed. For mixed events, the fall speed is assumed to vary between these two values linearly with surface temperature. The radar reflectivity associated with the precipitation falling into the snow gauge was measured t f seconds ago at a distance (t f V trec ) upwind of the radar site. Therefore, a search back in time and upwind in space is executed to locate the relevant reflectivity region associated with the snow gauge measurement. This upwind reflectivity is averaged over a 10 km 10 km square, and then used to calculate a radarestimated liquid equivalent snowfall rate using the assumed Z S parameters. This rate is integrated over the chosen accumulation period (typically min) to yield the radar estimated snow accumulation. The Z S calibration is then simple. The coefficient a in the Z S relationship is suitably adjusted to make the radar-estimated and gauge-estimated accumulations equal. No attempt is made to adjust the exponent b because the data is too sparse and too noisy for calibration with 2 degrees of freedom. Integral quantities were used because it was found that the relationship was much more stable if snowfall rate and reflectivity are integrated over time. A typical integration time to establish the real-time value of the coefficient a is 30 to 120 min. The value of a is constrained to be within climatological limits in order to ensure algorithm stability during startup and low data periods. Once the real-time coefficient a is found, the forecast is made using the following steps. 1) Determine the average TREC storm motion vector V trec for the most current radar scan over the location of the gauge by averaging all the TREC vectors in a km 2 box centered over the gauge. 2) Determine the time for the snow to fall to the ground from the closest radar scan (typically the 0.5 scan) overhead of the gauge (t f ). 3) Based on the fall time, t f, determine the distance and direction upwind of the gauge from which the snow particles that fall at the airport most likely came from (V trec t f ). 4) Based on the TREC storm motion, determine the 0-, 5-, 10-, 15-, 20-, 25-, and 30-min forecast of reflectivity at the upstream location (V trec t f upstream FIG. 8. Times series of radar reflectivity from the 0.5 elevation angle WSR-88D radar scan (averaged over a km 2 ) box over the snow gauge (dashed line) and the liquid equivalent snowfall rate from the GEONOR snow gauge (solid line) from LaGuardia Airport on 15 Mar

12 8) Integrate the ground forecast rates over time to produce an accumulation forecast. FIG. 9. Time series of the forecast (dashed line) and measured (solid line), 30-min liquid equivalent snow accumulation at LaGuardia Airport on 5 Mar The accumulation is plotted at the end of the 30-min accumulation period. of the gauge site) by using the TREC storm motion vectors to advect reflectivity to that location. 5) Centered on the upwind location, average the forecasted reflectivity pattern over a 10 km 10 km area for each of the above forecast times. 6) Convert the forecasted averaged reflectivity into snowfall rate using the calibrated Z S relationship. 7) Add t f to the forecast times above to get the actual forecast times for snowfall rate at the ground. Thus, the upstream forecasts at 0, 5, 10, 15, 20, 25 and 30 min at the upstream radar altitude become the ground forecasts at 0 + t f, 5 + t f, 10 + t f, 15 + t f, 20 + t f, 25 + t f, 30 + t f. Thus, the fall time of the snow adds additional time to the forecast. TABLE 1. Overall median ratings for WSDDM by user type. The snowfall accumulation and rate information is displayed in both graphical form and text form on the lower right panel of the display. The text message states whether the 30-min future snowfall rate is expected to be light, moderate, or heavy based on a liquid equivalent scale, and the graphical form gives the expected liquid equivalent snow accumulation over the next 30 min. A statistical evaluation of the algorithm using a number of cases from New York showed that the above algorithm consistently beat snow gauge persistence in terms of probability of detection (POD) and false alarm rate (FAR; Vassiloff et al. 2000). This was especially true in cases with strong horizontal gradients of reflectivity present, such as storms with snowbands. In these cases, the algorithm beat snow gauge persistence 30-min snow accumulation POD values by up to 25% for similar FAR values. For nearly uniform echo cases, snow gauge persistence and the real-time algorithm performed similarly, as expected. 2) EXAMPLE CASE FROM NEW YORK On 15 March 1999, a significant snow event impacted the New York area. Figure 8 shows the radar reflectivity and liquid equivalent snowfall rate at LaGuardia airport for a two-hour period during this storm. An approximate 25-min lag is noted between the reflectivity peak and the surface liquid equivalent snowfall rate peak. This lag is consistent with a 0.9 m s 1 fall speed for the snow mentioned earlier. The measured and forecast 30-min liquid equivalent snow accumulations for this storm (Fig. 9) show reasonable agreement and demonstrate the ability of the technique to forecast snow. Airline Overall All tower Dispatch TRACON ATCT PANY rating users users users users users users Utility Readability Ease of use Note: 1 = completely acceptable, 2 = slightly acceptable, 3 = borderline, 4 = slightly unacceptable, 5 = completely unacceptable, 9 = not applicable. 6. User evaluation of the WSDDM system The FAA William J. Hughes Technical Center (WJHTC) performed an evaluation of the user response to the system during the 1997/98 demonstration at LaGuardia. This encompassed the overall utility of the system, ease of use, and readability. The evaluation included all 590 Vol. 82, No. 4, April 2001

13 users of the LaGuardia system, which included airline tower personnel, airline dispatchers, New York TRACON Traffic Management Unit Coordinators, LaGuardia Air Traffic Control Tower Supervisors, LaGuardia Air Traffic Control managers, and Port Authority of New York and New Jersey (PANY) personnel. Prior to the start of the evaluation each of the users received two hours of initial training on the system, followed up by refresher training closer to the start of the winter season. Overall, 70 users were trained in this fashion. During the season WJHTC personnel observed how the system was used during storm events. Following the season, interviews were conducted with each of the users by WJHTC personnel, and questionnaires were also filled out by the users. In the following section results based on the interviews and the questionnaires are presented. a. Overall results Questionnaire ranking results regarding the overall utility, readability, and ease of use of the WSDDM system are summarized in Table 1. Results are summarized for all users, airline tower users, dispatch users, TRACON users, ATCT users, and PANY users. All users rated the product features favorably with airline tower users and TRACON users rating the WSDDM most favorably. b. Utility results Questionnaire ranking results regarding the utility scores for each product/feature are summarized in Table 2. Results are categorized according to all users, airline tower users, dis- TABLE 2. Median utility ratings per product by user type. Airline All tower Dispatch TRACON ATCT PANY users users users users users users Products (n = 32) (n = 6) (n = 8) (n = 6) (n = 5) (n = 7) Radar reflectivity Nexrad velocity TREC vectors METAR reports Station models Mesonet temperature Mesonet wind Current precipitation rate Mesonet dewpoint Trend plots Graphical past precipitation rate Graphical forecast precipitation rate Current category precipitation rate Forecast category precipitation rate Graphical past radar reflectivity Graphical forecast radar reflectivity Looping View Field Overlays Note: 1 = completely acceptable, 2 = slight acceptable, 3 = borderline, 4 = slightly unacceptable, 5 = completely unacceptable, 9 = not applicable. 591

14 TABLE 3. Median ease of use ratings per product by user type. Airline All tower Dispatch TRACON ATCT PANY users users users users users users Products (n = 32) (n = 6) (n = 8) (n = 6) (n = 5) (n = 7) METAR reports Station models Trend plots Graphical past precipitation rate Graphical forecast precipitation rate Graphical past radar reflectivity Looping View the user to view the winter storm data in a manner that was meaningful to them. 7. WSDDM potential role regarding efficiency of operations and safety The Volpe National Transportation System Center (VNTSC) performed an efficiency and safety benefits assessment of the WSDDM system and a brief summary of its results are presented below. For further information the reader is referred to two reports, one on efficiency benefits (Stevenson 1998a) and another on safety benefits (Stevenson 1998b). Field Overlays Note: 1 = completely acceptable, 2 = slightly acceptable, 3 = borderline, 4 = slightly unacceptable, 5 = completely unacceptable, 9 = not applicable. patch users, TRACON users, ATCT users, and PANY users. As a whole, users rated the display features (i.e., looping, field, view, and overlays) as having high utility. The mesonet dewpoint, graphical past precipitation rate, graphical past radar reflectivity, and graphical forecast radar reflectivity were rated as having less utility than the other WSDDM products. c. Ease of use ratings Table 3 lists the median ratings for product ease of use. Similar to the utility ratings, medians are listed for each product across all users and per user group. Overall, users found the products easy to use. TRACON and ATCT users assigned borderline ease of use ratings to the graphical past precipitation rate, graphical forecast precipitation rate, and graphical forecast radar reflectivity products. The looping, view, field, and overlay features of the display were rated as being completely acceptable by all user groups. Many users noted these features were easy to use and allowed a. Efficiency benefits of WSDDM A survey of WSDDM system demonstration participants by VNTSC personnel identified the following areas in which the WSDDM system can lead to reductions at major air-carrier airports when they are impacted by snowstorms or forecasts for snowstorms: (a) aircraft delay, (b) flight cancellations, (c) diversions of inbound flights, (d) the amount of deicing and anti-icing fluids used to keep departing aircraft free of ice and snow contamination at takeoff, and (e) the amount of anti-icing chemicals used to prevent ice and snow from bonding to the taxiways. The user survey also identified that the potential user population for WSDDM is large and diverse. To date, the WSDDM system has been beneficial to one or more demonstration participants in the following groups: (a) airline personnel who oversee aircraft deicing operations, (b) airline personnel who staff an airport s Snow Desk, (c) airport management personnel overseeing runway/taxiway snow and ice clearing operations, (d) FAA personnel who staff the Traffic Management Unit, and (e) airline dispatchers. One common theme among the participants has been that the WSDDM system improved their ability 592 Vol. 82, No. 4, April 2001

15 to anticipate storm conditions at the airport: (a) if and when snow will start at the airport, (b) to read storm structure regarding the arrival and extent of snowbands and lulls in the snowstorm conditions approaching the airport, and (c) when the final end of snow at the airport will occur. Consequently, the WSDDM system benefits identified by the participants reflect that they expect to be less vulnerable to faulty forecasts and better able to plan and match their actions to changing snowstorm conditions at the airport. The estimated cost benefit for efficiency from the use of the WSDDM system at LaGuardia airport is $1 million per year (Stevenson 1998a). b. What was the basis for the favorable user reactions to the WSDDM system? It was found in the demonstrations that the sources of weather information available to the demonstration participants prior to the utilization of the WSDDM system varied considerably. At one end of the spectrum were the participants that had little direct access to weather information at their workstations and depended primarily on communications with a meteorology department within their organizations to get their weather picture. The meteorologist is available to give expert advice on storm status and forecasts and to service the needs of the many. The WSDDM system is seen as complementing the meteorologist by allowing the user to monitor evolving storm conditions, both at the airport and approaching the airport (a) on a real-time basis between discussions with the meteorologist and (b) in greater spatial and temporal detail than can be provided by a meteorologist. The ability to monitor approaching storms was noted as particularly useful in providing the individual with a reality check on forecasts in the last hour or two before the storm is forecasted to arrive at the airport. As a group, these participants greatly appreciated having access to WSDDM system information at their workstations. At the other end of the spectrum were the participants that had direct access to commercial weather products at their workstations in addition to the ability to communicate with a meteorologist. These weather products utilized the same commercially available NEXRAD and METAR data as WSDDM. As a group, these participants also tended to value WSDDM over their commercial products. The reasons were summarized by one participant. With WSDDM, he was able to (a) use the product at a glance due to its integration of information, (b) zoom in for detail in the immediate vicinity of the airport, (c) select WSDDM s overlay showing a detailed layout of the airport s runways and taxiways against which to view the weather radar data, (d) select WSDDM s vectors showing estimated storm motion over the next 30 min, and (e) access near-real-time snow gauge and other meteorological data from the WSDDM system snow gauge and mesonet stations on and around the airport. c. WSDDM s potential role regarding safety of operations In the 15-yr period prior to 1993, snow and ice accumulation on a wing prior to takeoff was identified by the National Transportation Safety Board as factors in seven domestic, Part-121 takeoff-icing accidents and one incident. Part-121 operators are the major air carriers. These eight accidents/incidents resulted in 142 fatalities and an estimated economic cost of $458 million (1995 dollars) in terms of fatalities, injuries, aircraft damage, and government investigations. Three veteran members of the airline winter operations community reviewed these accidents/incidents and took part in an assessment of the potential role of WSDDM, as well as other developments, in reducing the likelihood of such accidents in the future. Each of the three experts had been involved in airline winter operations in a number of capacities, and they all had been members of various company, national, and international de/anti-icing working groups. The experts consisted of an airline pilot, aircraft de/anti-icing coordinator, and an airline manager. These experts were of the following opinions: 1) Regarding recent developments, the 1992 Deicing Regulations and the domestic introductions of Type II, III, and IV anti-icing fluids have sharply reduced the likelihood of these accidents in the future. The average of the individual estimates by the experts suggests that the likelihood of these accidents has been reduced about 70% relative to the pre-1993 environment. If near the mark, the 70% statistic suggests that the frequency of occurrence of these accidents/incidents has shifted from eight in 15 years or one every 23 months prior to 1993 to a current rate of something like one such domestic accident/incident every 6 7 years, on average. The accident statistics support the view that 1992 was a turning point. Although domestic Part-121 takeoff-icing accidents have occurred since 1992, none of the accidents have involved the 593