**The U.S. Federal Highway Administration**

**Winter Road Maintenance Decision Support System (MDSS) Project:
Overview and Results**

W. Mahoney III & W. Myers

National Center for Atmospheric Research

Boulder, Colorado USA

E-mail: <Mahoney@ucar.edu>

E-mail: <myers@ucar.edu>

P. Pisano

Federal Highway Administration

Washington, D.C. USA

E-mail: <paul.pisano@fhwa.dot.gov>

R. Hallowell

MIT Lincoln Laboratory

Lexington, Massachusetts USA

E-mail: <bobh@ll.mit.edu>

A. Stern

Mitretek Systems

Falls Church, Virginia USA

<astern@mitretek.org>

The Maintenance Decision Support System (MDSS) project was initiated in
1999 by the U.S. Federal Highway Administration (FHWA) Office of
Transportation Operations Road Weather Management (RWM) Program. The
MDSS capabilities were designed based on feedback received by the FHWA
in 2001 from maintenance managers at a number of State Departments of
Transportation (DOTs) as part of an initiative to capture surface
transportation weather decision support requirements.

The MDSS project goal is to seed the implementation of advanced decision
support services provided by the private sector for state DOTs. This has
been achieved by developing core software capabilities that serve as a
basis for these tailored products.

After the 2001 user needs assessment was completed, the MDSS program was
extended with the objective of developing and demonstrating a functional
prototype MDSS. Field demonstrations of the prototype MDSS were
conducted in Iowa between February and April 2003, and during the winter
of 2004. Additional field tests were conducted during the winter of
2004-2005 in Colorado.

The performance of the prototype MDSS improved during each of the
successive field demonstrations. The weather and road condition
predictions and treatment recommendations generated by the system were
increasingly more accurate as the development effort moved forward. Iowa
garage supervisors actively considered the treatment guidance and on
occasion they successfully used the recommended treatments without
modification.

This paper describes the status of the MDSS project, results and lessons
learned from the Iowa field demonstrations, and future development
efforts.

**KEY WORDS:** WINTER MAINTENANCE, DECISION SUPPORT, SNOW, ICE

1\. Introduction

With tight budgets and the high expectation of the public for keeping
roads clear of snow and ice, today's maintenance snow and ice manager
must be able to handle multiple tasks or risk falling behind the
onslaught of winter weather. While good information leads to effective
practices, all of the regulations concerning chemical applications,
environmental impacts and multiple, often contradictory, weather
forecasts can lead to information overload.

The FHWA recognized this potential problem in the late 1990's as part of
its RWM program. Weather forecasts were plentiful and a few companies
issued road-specific forecasts, but there was a lack of linkage between
the information available and the decisions made by winter maintenance
managers. It was this gap between meteorology and surface transportation
that became the genesis for the winter MDSS (*1*).

Since 1999, the MDSS has evolved into a functional prototype system.
During the winter of 2002−2003, the prototype was deployed at several
maintenance garages in central Iowa for an initial field demonstration.
A second, more comprehensive field demonstration was conducted during
the winter of 2003−2004. The prototype was moved to Colorado in 2005 to
assess its capabilities in complex terrain.

2\. PROJECT Organization

The MDSS is a research project that is funded and administered by the
FHWA RWM program. A consortium of five national laboratories in
coordination with state DOTs, academia, and the private sector have
participated in the development and implementation of the project. These
laboratories include:

-   Cold Regions Research and Engineering Laboratory (CRREL)

-   National Center for Atmospheric Research (NCAR)

-   Massachusetts Institute of Technology/Lincoln Laboratory (MIT/LL)

-   National Oceanic and Atmospheric Administration (NOAA) Forecast
    Systems Laboratory (FSL)

-   NOAA National Severe Storms Laboratory (NSSL)

The MDSS project integrates state-of-the-art weather forecasting, data
fusion, and optimization techniques with computerized winter road
maintenance Rules of Practice (RoP) logic. The result is guidance aimed
at maintenance managers that provides a specific forecast of surface
conditions and treatment recommendations customized for individual plow
routes. The MDSS project has several goals:

-   Demonstrate to the state DOTs that new technologies are available to
    assist maintenance managers with maintaining safety and mobility on
    roadways and provide for more efficient use of chemicals, equipment
    and staff.

-   Show the private sector road weather providers that there is a
    market for these new technologies. To aid this process, the FHWA is
    providing the core MDSS modules to any company or organization using
    an aggressive technology transfer process with the expectation that
    the MDSS technologies will become commercialized.

Success, as defined by the FHWA, will be reached when private sector
companies integrate MDSS components or similar functions into their
products and state DOTs purchase these new services. Both of these
activities have already begun. In the end, the project will serve to
raise the bar on standards for services provided by the private road
weather forecasting industry. This cycle has the potential to make state
DOT operations more efficient and road conditions safer.

3\. Prototype System Overview

Each national laboratory brings unique capabilities and expertise to the
project. CRREL has experience in creating models for predicting road
surface temperature. MIT/LL concentrates on translating the road
maintenance RoP into computer algorithms. FSL provides high resolution
weather models. NSSL contributed algorithms for determining
precipitation type (e.g., rain, ice, snow) in weather models. NCAR is
the lead laboratory providing the core data processing capability, the
graphical user display, and the engineering to integrate all of the
disparate parts into a complete, working prototype.

Figure 1 shows a high-level flow diagram for the MDSS functional
prototype that was used in the winter 2003−2004 demonstration. The top
box in the left column represents data received from the U.S. National
Weather Service (NWS) National Centers for Environmental Prediction
(NCEP). These data include surface observations, statistical guidance
products, daily weather summaries, and numerical weather prediction
model output from national scale numerical weather prediction models
called Eta and Global Forecast System (GFS).

Figure 1 - High-level flow diagram of the prototype Maintenance Decision
Support System (MDSS) as configured for the winter 2003−2004 Iowa field
demonstration.

The NWS models were supplemented by high-resolution models run by NOAA
FSL. These models were the NCAR/Penn State Mesoscale Model 5 (MM5) and
the Weather and Research Forecasting (WRF) model. The fine-scale models
had a grid spacing of 12 km. In order to provide diversity into the data
fusion module, FSL used the NWS Eta model to provide lateral boundary
conditions to initialize each model. For the 2004 field demonstration,
the FSL models were run hourly generating 15 hour forecasts.

Differing from the NWS models, the FSL models used a new initialization
routine to add realistic distributions of moisture and clouds to the
model atmosphere. This method, called "hot-start," allows the models to
begin with a much better representation of clouds and precipitation
(*2*). The anticipated benefit of the hot start process is a more
accurate prediction, particularly in the first six hours of the forecast
cycle.

Forecast output from the models, plus surface observations from state
DOT road weather information systems (RWIS) were forwarded to NCAR's
data fusion engine called the Road Weather Forecast System (RWFS). The
RWIS data were obtained from the Iowa DOT server and ingested into the
NOAA/FSL Meteorological Assimilation Data Ingest System (MADIS) where
they were quality controlled and disseminated. The RWFS module uses a
fuzzy-logic ensembling scheme that has the ability to generate more
accurate forecasts than any one individual model input by optimizing the
blend of predictors based on recent skill (*3,4*).

Specialized components such as the road temperature forecast module and
the road condition and treatment module (RCTM) use the weather model
outputs to generate temperature forecasts for the state and condition of
the road surface as well as guidance for chemical concentration and
dilution rates.

The final module in the system contains the RoP algorithms. The RoP are
customized rules and techniques that are used at DOT maintenance garages
for maintaining mobility during winter conditions. These rules tend to
be different for each state and in many cases are different for each
garage. Hence, this module has the ability to customize many of its
inputs so that it can be portable between garages. Treatment
recommendations include the following information:

-   Recommended treatment plan such as plow only, chemical use, and
    abrasives

-   Recommended chemical amount (e.g., pounds per lane mile)

-   Timing of initial and subsequent treatments

-   Indication of the need to pre-treat or post-treat the roads

Figure 2 is an example from the MDSS prototype route view page. The top
left panel shows an alert summary table with color-coded bars displaying
forecasted weather and road condition alerts for the next 48 hours. The
panel at the left center provides access for displaying predicted
weather parameters and measured road temperatures. The bottom section
controls forecast time selection and animation. Forecasts are generated
for locations that routinely provide observations (e.g., airports, NWS
reporting sites, and road weather observations stations) and along each
plow route using interpolation techniques. Moving a cursor over any
point brings up a time series graph of the selected forecast parameter
plus additional site specific details. Actual treatments selected by the
user are shown in the bottom center along with blowing snow alerts.

The MDSS contains a "what-if" scenario treatment selector. This means
that the operator is able to modify the recommended treatment times,
chemical types or application rates, and see how the road condition
predictions change.

![](media/image1.jpeg){width="4.64375in" height="4.098611111111111in"}

Figure 2 - Des Moines area MDSS plow routes and forecast points. Users
can view the weather and road condition alerts and information for each
route as well as the current treatment plan. In addition, users can
select to view the recommended treatments and treatment history.

An example of the MDSS treatment selector for US 30 in Iowa on 2
February 2004 is shown in Figure 3. This provides the user with an
indication of the mobility, snow depth on the road, road surface
temperature and chemical concentration for various treatment scenarios.
The lower part of the screen shows the recommended treatment plan and
the current plan chosen by the Ames, Iowa garage supervisor. In this
example, the recommended and current plan includes a plowing treatment
followed by applications of salt (NaCl).

![](media/image2.jpeg){width="4.772916666666666in"
height="3.8895833333333334in"}

Figure 3 - Sample image of the MDSS treatment selector screen. The
traces (top window) show the predicted snow depth on the road for
various treatment scenarios (e.g., no treatment, recommended treatment,
and current treatment plan). The lower part of the screen shows the
recommended treatment plan and the current plan chosen by the Ames, Iowa
garage supervisor.

4\. Field Demonstration RESULTS

During the summer of 2002, six states competed to win the opportunity to
host the MDSS project. While there were several very good candidates,
Iowa DOT was selected. Determining factors included the climatological
frequency of snow and ice events, their progressive maintenance
programs, the availability of high-speed communications and computers at
maintenance garages, and a willingness of DOT personnel to participate
in training and verification activities. Iowa was also surrounded by a
dense network of surface observations and did not have complex terrain.
In all, 15 plow routes and 3 maintenance garages around Des Moines and
Ames, Iowa were selected to participate in the demonstration.

The first MDSS demonstration was quite valuable as it gave the
development team first hand observations of winter maintenance
operations. While the demonstration was a success because the prototype
was deployed and utilized by several state DOT maintenance personnel, a
long list of lessons learned was compiled (*5*). Some of the more
important lessons included:

1)  The MDSS requires highly specific forecasts of precipitation type,
    amount, start time, and duration. However, there were numerous
    occurrences of light precipitation that were not captured by the
    numerical models. It was found that numerous, short-lived, light
    precipitation events could produce significant problems on roads
    because drivers and maintenance crews alike were often caught off
    guard. It was determined that such accurate predictions of
    small-scale precipitation events push current limits of
    predictability.

2)  The RoP module needed additional development to handle a wider
    variety of weather and road condition scenarios and treatment
    responses.

3)  The availability and quality of observed real-time precipitation
    rate and accumulation data are very poor for snow and ice. State DOT
    RWIS sites do not have heated precipitation gauges making them
    useless for measuring freezing and frozen precipitation amounts. The
    lack of accurate precipitation ground truth data prevents the use of
    the dynamic weighting schemes and forward correcting algorithms in
    the RWFS.

The second field demonstration that took place in central Iowa from
December 29, 2003 through March 24, 2004 (*6*). Improvements made prior
to the second field demonstration are described below.

4.1 Road Condition and Treatment Module (RCTM) Enhancements

The RCTM is the module that receives weather forecast and observations
as input and generates road condition forecasts and treatment guidance.
The RCTM encompasses several algorithms such as the RoP module and the
road temperature forecast model.

The RoP module was significantly enhanced to recognize the overall storm
situation. This new logic allowed the algorithm to handle changing
weather situations predicted several hours into the future as the storm
evolved.

In the absence of a more sophisticated solution, a blowing snow
potential alert was created for the maintenance managers. This alert was
designed to indicate when snow was likely to blow across roads (from the
surface to approximately 1 meter above the surface) and it was based on
precipitation type, snowfall history, snowfall rate, air temperature and
wind speed. Also taken into account was the occurrence of any other
precipitation types (such as rain) that may have fallen since the last
snowfall.

4.2 Road Weather Forecast System (RWFS) Enhancements

The RWFS is the weather data fusion component of the prototype MDSS. It
ingests several types of weather data including observations, model
output and statistical data and uses ensemble and data optimization
techniques to generate forecast parameters for the RCTM modules.

The RWFS was configured to utilize and integrate ten different forecast
modules for the winter 2003-2004 demonstration. Models that were
ingested into the RWFS included the Eta, GFS, MM5, Rapid Update Cycle
(RUC), and WRF. MM5 and WRF were initialized using the NWS Eta model for
boundary conditions and was run hourly. MM5 and WRF forecasts from the
current run and previous two runs (valid at the same time) were
utilized. Aviation model Model Output Statistics (MAVMOS) were also used
as input, and Dynamic Model Output Statistics (DMOS) were calculated
within the RWFS for each of the model inputs. Other improvements to RWFS
included adding probabilistic forecast information for selected fields,
such as the conditional probability of precipitation type as shown in
Figure 4, and forward error correction to adjust predictions to better
match observations when the forecast time is the current time.

4.3 Mesoscale Model Enhancements

An evaluation was conducted to compare the forecast accuracy of the
mesoscale models used in the first demonstration. The results indicated
that two of the three models (MM5 and WRF) had more skillful
precipitation results. These models were also used in the second
demonstration. However, because the method used to create a diversity of
forecast output was not optimal, the ensemble technique was altered. For
the second demonstration, rather than running six different combinations
of models every three hours, a time-lagged ensemble technique was used.
This method had the mesoscale models running with new data every hour.
Then, every three hours the RWFS would accept forecast data from the
current hour, the previous hour, and the hour before that as input from
the mesoscale model ensemble. This method was also selected to reduce
forecast inconsistencies that would sometimes occur as the models would
alternate from one solution to another.

4.4 MDSS Graphical User Interface (GUI) Enhancements

An event summary page (see Figure 4) was developed to provide a
"quick-look" summary of weather and road variables for each forecast
period and for each plow route. The summary page included maximum and
minimum predicted air and road temperatures, total new snow accumulation
on the roadway, and an indication of the conditional probability of
precipitation type, which is the probability of a certain precipitation
type (e.g., rain, snow or ice) should any precipitation occur.

![](media/image3.png){width="4.38125in" height="3.6319444444444446in"}

Figure 4 - Sample image of the MDSS event summary page. Graphical
probabilistic forecasts were introduced (top set of graphics) for
precipitation type. Each bar in the time line contains the model derived
probability of rain (green), ice (red) or snow (white).

4.5 Results and Recommendations

The second field demonstration began on December 29, 2003. By the
conclusion of the 88-day demonstration, there were 15 weather event days
ranging from mixed rain and snow events to freezing rain and record
heavy snows.

Enhancements made to all aspects of the prototype based on the results
of the 2003 demonstration made a significant difference in the quality
of the forecasts and treatment guidance, and the subsequent confidence
shown by the users. The primary results and recommendations are
described below.

4.5.1 Road Weather Forecast System

The data fusion methods and statistical techniques used in the RWFS
improved the overall weather prediction skill for parameters that are
measured and available in real time. The use of multiple inputs also
made the system more robust as it was not prone to down time with the
loss of individual forecast modules.

The RWFS forecasts had significantly better skill in the first six hours
because of their forward error correcting capability. The performance of
the RWFS was analyzed via comparison of the forecast skill of individual
inputs to the consensus (final) forecast. Seasonal assessments of RMSE
and bias (forecast-observation) were performed for the ten input
forecast modules and the final consensus forecast for the meteorological
state variables (temperature, dewpoint, wind speed, cloud cover, and
wind direction). Results were based on weighted average RMSE and bias
values at every lead time for all forecasts initiated at 18 UTC over the
entire winter season for all sites across the state of Iowa. Note that
the FSL models only covered a 15 hour forecast period, whereas the NCEP
model products covered a 48-hour period.

For all variables except cloud cover, the RWFS forecast has a lower RMSE
than its components for a majority of the lead times (0-48 hours). For
the air temperature forecasts, it is interesting to note that the
individual inputs are clustered between 2.0 and 2.5 C. The MM5 based air
temperature forecasts were slightly more accurate than WRF, and AVN MOS
is slightly better than both. For dew point predictions, the AVN MOS had
the best skill of the individual components, while the mesoscale models
had less skill. The increased skill of the RWFS air and dew point
temperatures is good news as these fields are important for predictions
of road temperature and frost. The RMSE of the RWFS wind speed
predictions was 1.5 meters per second (\~3 mph).

The high resolution models did not add value above the standard NWS
products for wind, temperature and humidity forecasts, but they did have
more skill at predicting precipitation type, rate and timing. The
reduction is skill is partially due to the fact that the fine-scale
models did not include a coupled land surface model, which is very
important for predicting air temperature and humidity in the lowest few
meters.

4.5.2 Winter Precipitation Measurements

The poor quality of winter precipitation measurements did not provide
the same automatic forward correcting benefit for the prediction of
precipitation amount. If a data fusion system that adjusts itself based
on observations similar to the RWFS is utilized, care must be taken to
ensure that the weights given to individual prediction modules are
appropriate. If only low quality verification data are available, the
weights should be fixed based on experience and/or expert opinion.

4.5.3 Short Wave Solar Radiation

The ability of models to predict insolation varies greatly between
models, particularly in partly cloudy conditions. Care must be taken to
ensure the model values compare well with measured values. Changes to
both research and operational models may impact insolation calculations
so routine comparisons should be made. Because insolation measurements
are critical for road temperature prediction, it is recommended that
insolation measurements be added to surface observing stations and be
provided in real time to weather service providers. Real time access to
insolation data would provide an opportunity for systems like the RWFS
to utilize the data and optimize predictions.

4.5.4 Probabilistic Products

Winter maintenance supervisors are in the business of risk management.
Because weather and road conditions will never be perfectly predicted on
the scale of plow routes, probabilistic prediction products should be
considered. The MDSS conditional probability of precipitation type
product was well received by the end users.

4.5.5 Road Temperature Prediction

The MDSS provides three sets of road temperature forecasts for each
segment of road: untreated, recommended treatment, and actual treatment.
The "untreated" forecast is the expected road temperature given that the
weather occurs (snow fall on the road, etc.) and no treatments are made
(the road is left "as is"). "Recommended treatment" is the expected road
temperature, given that the treatment suggested by the MDSS is applied.
"Actual treatment" is the expected road temperature, given the actual
performed, but this was not always known. The most appropriate field to
verify is the "recommended treatment" field, since it represents a
treatment scenario for each event. Only results for that field are
discussed here.

It must be noted that there is uncertainty in establishing ground truth
for road surface temperature. The MDSS is configured to model the road
temperature from the surface to 3 mm deep. The RWIS pavement sensors are
embedded in the pavement and the sensor materials and their thermal
coupling (or lack thereof) to the pavement below and around them
contributes to some of the measurement differences.

For all months there were considerably higher measurement differences
during the heat of the day. In January, daytime Mean Absolute Error
(MAE) values were up to 3.5˚C, while they were nearly 4˚C during March.
During the night, the MAE values were between 1 and 2˚C during all
months. A positive road temperature bias was found during the morning
hours, followed by a weaker negative (cold) bias in the afternoon. This
daytime swing in bias may be attributable to differences in the depth
over which road temperature is forecast (\~ 3 mm) and the depth of
pavement that surrounds the measuring device (several centimeters).
Response time differences may have caused a phase shift in the diurnal
heating/cooling cycle. Cold biases up to 1.5˚C were present during the
night, and were typically strongest during the hours between midnight
and sunrise.

The road temperature predictions were the most accurate during snowy
periods, particularly at night. During nighttime snow events, the MAE
ranged from 0.25 to 1.0 C. The next best skill occurred during cloudy
days and the least skill occurred during clear or partly cloudy days. It
is no surprise that these results strongly suggest that the prediction
of insolation is critical and that the weather models have difficulty
predicting insolation on partly cloudy days. Additional research
focusing on insolation prediction is required.

4.5.6 Rules of Practice

One of the biggest improvements to the MDSS in 2004 was the revision of
the RoP code based on lessons learned and experience gained from the
first MDSS field demonstration. The primary improvement in the RoP was
in considering the entire storm life-cycle for characterizing the
overall storm conditions and for recommending treatment strategies. The
storm life-cycle includes not only the in-storm conditions when
precipitation is falling but also the pre- and post-storm road
conditions. Pre-storm conditions control the application of
pre-treatment chemicals (such as liquid brine) that are used to prevent
icing at precipitation onset. Post-storm conditions (including road
temperature and residual water on the pavement) are utilized to protect
against re-freezing of the roads after the precipitation has stopped.

Interviews with Ames and Des Moines garage supervisors indicated that
there was a general comfort level with the treatment recommendations. On
several occasions, the DOT used the recommended treatment strategy with
little modification. Results in most cases were satisfactory unless
storm conditions varied greatly from the forecast. Unfortunately,
quantifying the verification of treatment strategies is a formidable
task as it is nearly impossible to ascertain whether seemingly different
treatment applications might yield the same ice-free road conditions. It
is onerous, but manageable, to determine if the selected Iowa DOT
strategy did or did not keep the roads at the desired level of service.
But it is almost impossible to take an unimplemented recommended
treatment strategy and determine the exact road conditions.

The 2004 analysis of treatment recommendations highlighted strengths and
weaknesses in the current version of the RCTM. The lack of logic for
explicitly dealing with blowing snow conditions was a noticeable
weakness causing the recommended treatments to be below what might be
needed during blowing snow conditions. Modifications made to the RCTM
and improvements in the RWFS have yielded recommended treatment
strategies that are fairly consistent from run to run. Enhancements made
to suppress pre-treatments under warm road and cold road/ blowing snow
conditions are effective. Finally, the added ability to protect the wet
road surface after the storm precipitation ends was also effective.

4.5.7 User Benefits

There was general agreement that the system showed significant promise
and had proven itself to be a valuable tool both for operations and
training. Iowa DOT estimated that an operational MDSS has the potential
of saving the DOT between 10% and 15% of their annual maintenance costs
(materials and manpower), which equates to approximately \$3.5M per
year.

5\. Technology Transfer

One of the main goals of the MDSS project was to use federal funds and
national laboratory resources to create a base capability for the
private sector to embrace and nurture into new products and services for
the state DOTs. Throughout the life of the MDSS project, all briefing
materials, technical reports, evaluations, and software have been made
available to any interested parties via NCAR's MDSS Web site at:
<http://www.rap.ucar.edu/projects/rdwx_mdss/index.html>.

6\. Summary

The FHWA has been funding and directing a team of national laboratories
to create and refine a decision support system for the winter road
maintenance community. Demonstrations of the MDSS prototype were
conducted in central Iowa during the winters of 2003 and 2004. The
system showed consistent improvement as the demonstration progressed and
a list of lessons learned has been described. Comments from Iowa DOT
personnel found the system to be of growing value and showed increasing
confidence in its use. They also stated that an operational system could
save millions of dollars per year in winter maintenance costs.

7\. ACKNOwledgements

The FHWA's Office of Transportation Operations, Road Weather Management
program sponsors this project. The MDSS development team is grateful for
the support and leadership provided by Regina McElroy, James Pol, and
Ray Murphy. More than twenty state DOTs have been active participants
and their feedback has been critical.

8\. References

1.  Mahoney, W.P. and W. Myers. (2003) Predicting Weather and Road
    Conditions: Integrated Decision Support Tool for Winter Maintenance
    Operations. In *Transportation Research Record*, *Journal of the
    Transportation Research Board, No. 1824*, TRB, National Research
    Council, Washington, D.C., pp. 98−105.

2.  McGinley, J., S. Albers, D. (2000) Birkenheuer, B. Shaw and P.
    Shultz. The LAPS Water in All Phases Analysis: the Approach and
    Impacts on Numerical Prediction. *Preprints, 5^th^ International
    Symposium on Tropospheric Profiling,* Adelaide, Australia.

3.  Myers, W., M. Petty and J, Cowie. (2001) An Automated Road Weather
    Prediction System for Road Maintenance Decision Support*. Preprints,
    18^th^ International Conference on Interactive Information and
    Processing Systems*, Orlando, Florida, American Meteorological
    Society, Boston.

4.  Pisano, P., A. Stern and W. Mahoney. (2004) The Winter Maintenance
    Decision Support System: Demonstration Results and Future Plans.
    *Preprints, 20^th^ Conference on Interactive Information and
    Processing Systems*, Seattle, Wash., American Meteorological
    Society, Boston.

5.  The Maintenance Decision Support System (MDSS) Project, Preliminary
    Technical Performance Report, Second Iowa Field Demonstration Winter
    2003-2004. Version 1.0, National Center for Atmospheric Research,
    Boulder, Colo., April 29, 2004.

6.  The Maintenance Decision Support System (MDSS) Project, Technical
    Performance Report, Iowa Field Demonstration Winter 2003. Version
    1.1, National Center for Atmospheric Research, Boulder, Colo.,
    September 12, 2003.