Weather Support to Deicing Decision Making (WSDDM): A

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 Fellner,@ 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
The March 1992 takeoff-icing accident at LaGuardia
Airport (NTSB 1993) marked a turning point in win-
*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,
Massachusetts.
@
System Resources Corporation, Washington, D.C.
Corresponding author address: Roy M. Rasmussen, NCAR, Box
3000, Boulder, CO 80307.
In final form 6 October 2000.
©2001 American Meteorological Society
Bulletin of the American Meteorological Society
ter 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 1991. 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
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
FIG. 2. WSDDM screen depicting winter storm conditions during the 10 Dec 1997 snow event. The main panel on the left displays the 0.5° 1-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.
Bulletin of the American Meteorological Society
581
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 liq582
uid 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 10–20 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 10–20 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.
Vol. 82, No. 4, April 2001
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 0.5° 1-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
Bulletin of the American Meteorological Society
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 25 × 25 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 ac583
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
584
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
Vol. 82, No. 4, April 2001
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 56kB 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
Bulletin of the American Meteorological Society
FIG. 3. Schematic diagram showing the hardware and networking configuration for the 1997 LaGuardia Airport setup of WSDDM.
585
F IG . 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 30 × 50 cm2 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.
586
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.
Vol. 82, No. 4, April 2001
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 hitBulletin of the American Meteorological Society
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
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
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.
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.
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
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-
FIG. 7. Photograph of GEONOR snow gauge with sidewall
snow accumulation. In this case no sidewall heating was applied.
588
Vol. 82, No. 4, April 2001
tween the radar reflectivity factor Z and liquid equivalent snowfall rate S, with the relationship given in
terms of a power law:
Z = aSb,
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
Vtrec at the gauge location is computed using TREC.
The average fall time (tf ) 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 tf seconds ago at a distance (tf • Vtrec) 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
Bulletin of the American Meteorological Society
chosen accumulation period (typically 30–120 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 Vtrec for the most current radar scan over the location of the gauge by averaging all the TREC vectors in a 20 × 20 km2 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 (tf ).
3) Based on the fall time, tf , determine the distance
and direction upwind of the gauge from which the
snow particles that fall at the airport most likely
came from (Vtrec • tf ).
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 (Vtrec • tf upstream
FIG. 8. Times series of radar reflectivity from the 0.5° elevation angle WSR-88D radar scan (averaged over a 10 × 10 km2)
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 1999.
589
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 1999. 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 tf 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 + tf , 5 + tf , 10 + tf , 15 + tf , 20
+ tf , 25 + tf , 30 + tf . Thus, the fall time of the snow
adds additional time to the forecast.
TABLE 1. Overall median ratings for WSDDM by user type.
Overall
rating
All
users
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
tower Dispatch TRACON ATCT
users
users
users
users
PANY
users
Utility
2
1
2
1.5
2
2
Readability
2
1
2
1.5
2
2
Ease of use
2
1
2
1.5
2
2
Note: 1 = completely acceptable, 2 = slightly acceptable, 3 = borderline,
4 = slightly unacceptable, 5 = completely unacceptable, 9 = not applicable.
590
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
Vol. 82, No. 4, April 2001
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.
Products
All
users
(n = 32)
Airline
tower
users
(n = 6)
Radar reflectivity
2
2
2
Nexrad velocity
2
2.5
TREC vectors
2
METAR reports
ATCT
users
(n = 5)
PANY
users
(n = 7)
2.5
3
1
2
2.5
3
2
1
2
2
3
1
2
1.5
2
2.5
3
2
Station models
2
2.5
2
2.5
3
2
Mesonet
temperature
2
1.5
2
2.5
3
2
Mesonet wind
2
1.5
2
3
3
2
Current
precipitation rate
2
1
2
2.5
3
1
Mesonet dewpoint
3
2.5
3
3
3
1
Trend plots
2
2
3
2.5
3
2
Graphical past
precipitation rate
3
2
2.5
3
3
2
Graphical forecast
precipitation rate
2
1.5
2
3
3
2
Current category
precipitation rate
2
1
2
3
3
1
Forecast category
precipitation rate
2
2
2
3
3
2
Graphical past
radar reflectivity
3
2
3
3
3
2
2.5
2
3
3
3
2
Looping
1
1
1
1
1
1
View
1
1
1.5
1
1
1
Field
1
1
2
1
1
1
Overlays
1
1
1.5
1.5
1
1
Graphical forecast
radar reflectivity
Dispatch TRACON
users
users
(n = 8)
(n = 6)
Note: 1 = completely acceptable, 2 = slight acceptable, 3 = borderline,
4 = slightly unacceptable, 5 = completely unacceptable, 9 = not applicable.
Bulletin of the American Meteorological Society
591
the user to view the winter storm
data in a manner that was meaningful to them.
TABLE 3. Median ease of use ratings per product by user type.
All
users
(n = 32)
Airline
tower
users
(n = 6)
METAR reports
1
1
1
Station models
1
1
Trend plots
2
Graphical past
precipitation rate
ATCT
users
(n = 5)
PANY
users
(n = 7)
1
1
1
1.5
1
2
1
1
2
2
2
2
2
1
2
3
2
1
Graphical forecast
precipitation rate
2
1
2
2
3
1
Graphical past
radar reflectivity
2
1
2
1.5
3
2
Looping
1
1
1.5
1
1
1
View
1
1
1
1
1
1
Field
1
1
2
1.5
1
1
Products
Dispatch TRACON
users
users
(n = 8)
(n = 6)
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).
a. Efficiency benefits of
WSDDM
Overlays
1
1
1.5
1
1
1
A survey of WSDDM system
demonstration participants
Note: 1 = completely acceptable, 2 = slightly acceptable, 3 = borderline,
by VNTSC personnel identified
4 = slightly unacceptable, 5 = completely unacceptable, 9 = not applicable.
the following areas in which the
WSDDM system can lead to repatch users, TRACON users, ATCT users, and PANY ductions at major air-carrier airports when they are
users. As a whole, users rated the display features (i.e., impacted by snowstorms or forecasts for snowstorms:
looping, field, view, and overlays) as having high util- (a) aircraft delay, (b) flight cancellations, (c) diverity. The mesonet dewpoint, graphical past precipita- sions of inbound flights, (d) the amount of deicing and
tion rate, graphical past radar reflectivity, and anti-icing fluids used to keep departing aircraft free
graphical forecast radar reflectivity were rated as hav- of ice and snow contamination at takeoff, and (e) the
ing less utility than the other WSDDM products.
amount of anti-icing chemicals used to prevent ice and
snow from bonding to the taxiways.
c. Ease of use ratings
The user survey also identified that the potential
Table 3 lists the median ratings for product ease user population for WSDDM is large and diverse. To
of use. Similar to the utility ratings, medians are listed date, the WSDDM system has been beneficial to one
for each product across all users and per user group. or more demonstration participants in the following
Overall, users found the products easy to use. groups: (a) airline personnel who oversee aircraft deTRACON and ATCT users assigned borderline ease icing operations, (b) airline personnel who staff an
of use ratings to the graphical past precipitation rate, airport’s Snow Desk, (c) airport management persongraphical forecast precipitation rate, and graphical nel overseeing runway/taxiway snow and ice clearing
forecast radar reflectivity products. The looping, view, operations, (d) FAA personnel who staff the Traffic
field, and overlay features of the display were rated as Management Unit, and (e) airline dispatchers.
being completely acceptable by all user groups. Many
One common theme among the participants has
users noted these features were easy to use and allowed been that the WSDDM system improved their ability
592
Vol. 82, No. 4, April 2001
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
Bulletin of the American Meteorological Society
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
icing category under examination “wing ice due to
encountering precipitation at the airport prior to
takeoff.”
2) Regarding emerging developments, two developments have the potential to further reduce the likelihood of these accidents if viable commercial
products are eventually deployed. One is WSDDM.
The other is the development of wing-ice sensing
devices/systems used by aircraft deicing crews
and/or flight crews to check for wing-ice contamination prior to takeoff. The individual impact of
these two emerging developments on the safety of
operations will depend on the order they go to market and become widely deployed. The first to be
widely deployed at the large-hub, commercialservice airports impacted by snow is expected to
have the larger impact.
3) Regarding WSDDM, several aspects have a potential safety role regarding these takeoff-icing accidents. These are (a) utilizing WSDDM’s scientific
insights into the aviation hazards underlying these
takeoff-icing accidents in training Part-121 pilots,
(b) utilizing WSDDM and its scientific findings to
put the current holdover tables used by pilots on a
more scientific basis and to further refine the broad
ranges in the holdover times provided to pilots by
the tables, and (c) provide pilots with cockpit access to WSDDM-based information on airport icing conditions prior to takeoff.
d. A derivative WSDDM product concept for pilots
The assessment of WSDDM’s potential safety role
by the three experts involved in a newWSDDM product concept: a text message for pilots on airport icing
conditions derived from WSDDM-based information.
A common thread running through this type of
Part-121 takeoff-icing accident is that the flight crew
either did not recognize the initial need to deice the
aircraft before takeoff or did not recognize the need
to re-deice the aircraft once it had been deiced. At the
conclusion of a recent WSDDM demonstration, an airline pilot suggested that flight crews need information
in the cockpit that would help them better assess their
need to deice and/or re-deice. The pilot also proposed
a short WSDDM-based text message that would provide the needed information on airport icing conditions. To complete the concept, pilot access to the
proposed text message could be by means of the digital Automated Terminal Information Service (ATIS)
and/or the Transcribed Weather Information for Pilots
Service (TWIPS).
594
The airline pilot’s idea for a derivative WSDDM
product concept resulted in the following text message
concept being assessed in the safety analysis.
1) First line of message: airport’s precipitation type/
rate and a 30-min forecast; e.g., “Moderate snow
increasing to heavy, wet snow next 30 min.”
2) Second line of message: airport’s air temperature
and a 30-min forecast; e.g., “Temperature −1.3°C
increasing to −0.5°C next 30 min.”
3) Remaining lines of message: advisories on factors
that may cause snow/ice accumulation on the aircraft sooner than pilots might expect; e.g.,
“Advisory: high-visibility, high-snowfall condition [1-mile visibility],” or “Advisory: high-wind,
driven-snow condition [13 knots].”
The text message concept was well received by the
three experts during the safety analysis. Note that one
of the experts was the airline pilot who proposed the
original concept. The text message concept remains to
be evaluated by pilots in an operational setting, such
as during a demonstration.
8. Conclusions
A real-time user friendly winter nowcasting capability called the WSDDM system has been developed
for nonmeteorologist aviation users and demonstrated
at three different airports during the past four years.
User feedback from these demonstrations has been used
to improve the product and to help direct ongoing FAA
funded research related to WSDDM at NCAR. A detailed user evaluation conducted by the WJHTC and
a safety and efficiency benefits study conducted by the
VNTSC have both shown that users of the system view
the product favorably in terms of overall utility and
ease of use and also in improving the safety and efficiency of winter operations at an airport. The Volpe
Transportation study estimated over $1 million per
year savings at LaGuardia through the use of WSDDM
and $2 million per year at Chicago’s O’Hare Airport.
A significant result from the demonstrations was the
value of the shared situational awareness of winter
storms that WSDDM facilitated, allowing all users the
same, easy-to-interpret information on winter weather
conditions affecting the airport. This facilitated better
and more timely decision making regarding the start
and stop of winter operations by snow desks, deicing
operators, and slot allocation coordinators, and imVol. 82, No. 4, April 2001
proved real-time decisions regarding deicing operations, runway clearing, aircraft dispatch, and aircraft
control during winter storms. Another important capability of the WSDDM system is to alert users to the
potentially hazardous high snowfall–high visibility
condition, which was a factor in a number of ground
deicing accidents. The above-described WSDDM system technology developed under FAA funding at
NCAR has recently been transferred to a private company, ARINC,1 and is currently available from them
for implementation at airports.
Acknowledgments. This research is in response to requirements and funding by the Federal Aviation Administration (FAA).
The views expressed are those of the authors and do not necessarily represent the official policy of the FAA.
References
Bernadin, S., C. Bubuisson, J.-L. Laforte, 1997: Development of
laboratory test procedures to replace field anti-icing fluid tests
(snow equivalence tests). Transportation Development Centre Rep. TP13141E, Transport Canada, 109 pp.
Dixon, M., and R. M. Rasmussen, 1999: Real-time short-term
snowfall prediction for aviation using storm tracking and
gauge-calibrated radar data. 29th Int. AMS Radar Conf.,
Montreal, Quebec, Canada, Amer. Meteor. Soc., 868–871.
Goodison, B. E., 1978: Accuracy of Canadian snow gage measurements. J. Appl. Meteor., 17, 1542–1548.
Johnson, E. R., T. E. Adler, J. W. Schiesl, R. Dombrowsky,
L. Larson, G. Goodge, and J. Bradley, 1994: Frozen precipitation technical working group recommendations for the automated surface observing system precipitation gauge.
NOAA issue paper, 17 pp.
1
Aeronautical Radio Incorporated, 2551 Riva Rd., Annapolis, MD
21401-7465.
Bulletin of the American Meteorological Society
NTSB, 1993: Takeoff stall in icing conditions. U.S. Air Flight 405,
LaGuardia Airport, Flushing, New York, 22 March 1992.
NTSB Aircraft Accident Rep. AAR-93102, 123 pp.
Rasmussen, R. M., A. Hills, S. Landolt, and C. Knight, 1999a:
Results from holdover time testing of Type IV anti-icing fluids with the improved NCAR artificial snow generation
system. Hughes FAA Technical Center Rep. DOT/FAA/AR99/10, 36 pp.
——, J. Vivekanandan, J. Cole, B. Myers, and C. Masters, 1999b:
The estimation of snowfall rate using visibility. J. Appl. Meteor., 38, 1542–1563.
——, J. Cole, K. R. K. Moore, and M. Kuperman, 2000: Common snowfall conditions associated with aircraft takeoff accidents. J. Aircraft, 37, 110–116.
Rinehart, R. E., and E. T. Garvey, 1978: Three-dimensional storm
motion detection by conventional weather radar. Nature, 273,
287–289.
Sheppard, B. E., 1990: Measurement of raindrop size distributions
using a small Doppler radar. J. Atmos. Oceanic Technol., 7,
255–268.
Stevenson, L., 1998a: “Emerging picture” of WSDDM’s potential efficiency benefits based on field demonstrations and FY98
supplement. U.S. Dept. of Transportation, Volpe National
Transportation Systems Center Rep., 35 pp.
——, 1998b: WSDDM’s potential safety role in takeoff-icing accidents. Dept. of Transportation, Volpe National Transportation System Center Rep., 32 pp.
Turner, C. M., D. Simms, and R. M. Rasmussen, 1999: Meteorological evaluation of the Weather Support to Deicing Decision
Making (WSDDM) system. Eighth Conf. on Aviation, Range,
and Aerospace Meteorology, Dallas, TX, Amer. Meteor. Soc.,
438–442.
Tuttle, J., and B. Foote, 1990: Determination of the boundary layer
airflow from a single Doppler radar. J. Atmos. Oceanic
Technol., 7, 218–232.
Vasiloff, S., R. M. Rasmussen, M. Dixon, and F. Hage, 2000:
Evaluation of snow forecasts provided by the Weather Support to Deicing Decision Making (WSDDM) system. Ninth
Conf. on Aviation, Range, and Aerospace Meteorology, Orlando, FL, Amer. Meteor. Soc., 547–550.
Yang, D., B. E. Goodison, J. R. Metcalfe, V. S. Golubev, R. Bates,
T. Pangburn, and C. L. Hanson, 1998: Accuracy of NWS 8″
standard nonrecording precipitation gauge: Results and application of WMO intercomparison. J. Atmos. Oceanic Technol.,
15, 54–67.
595