![](media/image1.jpeg){width="6.45in" height="9.54861111111111in"}

**The Maintenance Decision Support System (MDSS) Project**

**PRELIMINARY**

**TECHNICAL PERFORMANCE**

**ASSESSMENT REPORT**

**SECOND**

**IOWA FIELD DEMONSTRATION**

**WINTER 2003-2004**

**\
Version 1.1**

**11 May 2004**

**Prepared for:**

**Federal Highway Administration\
Office of Transportation Operations**

**Road Weather Management Program\
**

**Prepared by:**

**National Center for Atmospheric Research (NCAR)**

**P.O. Box 3000**

**Boulder, Colorado 80301**

REPORT CONTRIBUTORS

[National Center for Atmospheric Research (NCAR)]{.underline}:

Ben C. Bernstein Jim Cowie

Jamie K. Wolff Arnaud Dumont

Seth Linden Paddy McCarthy

Bill Myers Bill Mahoney

Jaimi Yee

[Lincoln Laboratory (LL):]{.underline}

Robert Hallowell

[NOAA/Forecast Systems Laboratory:]{.underline}

Paul Schultz

Brent Shaw

[Cold Regions Research and Engineering Laboratory (CRREL):]{.underline}

Gary Phetteplace

George Koenig

[Center for Transportation Research and Education (CTRE)]{.underline}

Dennis Kroeger

[Iowa Department of Transportation (IADOT)]{.underline}

Dennis Burkheimer

Paul Durham

Edward Mahoney

Richard Hedlund

Jim Dowd

**RELEASE NOTES**

  ---------------- ----------------- -------------------------------------
  **Version        **Date**          **Notes**
  Number**                           

                                     

  Version 1.0      29 April 2004     Submitted to the FHWA

  Version 1.1      11 May 2004       Revised based on FHWA comments
  ---------------- ----------------- -------------------------------------

ACRONYM GLOSSARY

AMW -- Ames, Iowa airport identifier

AOTR -- Agreement Officer's Technical Representative

ASOS -- Automates Surface Observing System (NWS)

AVN -- National Weather Service model

AWOS -- Automated Weather Observing System (FAA)

CRREL -- U.S. Army Cold Regions Research and Engineering Laboratory

CTRE -- Center for Transportation Research and Education (Iowa State
University)

DMOS -- Dynamic Model Output Statistics

DOT - Department of Transportation

DSM -- Des Moines, Iowa airport identifier

DSS -- Decision Support System

EMC -- Environmental Modeling Center

EMFP - Ensemble Model Forecast Provider

ESRI -- Environmental Systems Research Institute

ESS - Equitable Skill Statistic

Eta -- National Weather Service Model

ETL -- NOAA, Environmental Technology Laboratory

FAA -- Federal Aviation Administration

FHWA -- Federal Highway Administration

FSL -- NOAA, Forecast Systems Laboratory

FTP -- File Transfer Protocol

GFS -- Global Forecast System

GRIB -- Gridded Binary

HADS - Hydrometeorological Automated Data System of the NWS

HRDO -- Office of Operations Research and Development (FHWA)

HOTO - Office of Transportation Operations

IADOT -- Iowa Department of Transportation

IKV -- Ankeny, Iowa airport identifier

ITS -- Intelligent Transportation System

JPO -- Joint Program Office (FHWA)

LAPS -- Local Analysis and Prediction System

LEDWI -- Light emitting diode weather indicator (ASOS sensor)

LDADS -- Local Data Acquisition and Dissemination System

LDM -- Local Data Manager

MADIS -- Meteorological Assimilation Data Ingest System (NOAA/FSL)

MDSS - Maintenance Decision Support System

MDSS FP -Maintenance Decision Support System -- Functional Prototype

METAR -- Meteorological Surface Observation

MIT/LL - Massachusetts Institute of Technology - Lincoln Laboratory

MM5 -- Mesoscale Model -- Version 5 (NCAR & Penn State)

MOS -- Model Output Statistics

NCEP - National Centers for Environmental Prediction

NSF -- National Science Foundation

NSSL -- NOAA, National Severe Storms Laboratory

NOAA -- National Oceanic and Atmospheric Administration

NCAR - National Center for Atmospheric Research

NVD - Non-Verifiable Data

NWP -- Numerical Weather Prediction

NWS -- National Weather Service

OCD -- Operational Concepts Description

QPF -- Quantitative Precipitation Forecast

RAMS -- Regional Atmospheric Modeling System (Colorado State University)

RCTM -- Road Condition & Treatment Module

RMSE -- Root mean squared error

RTVS -- Real Time Verification System (RTVS)

RUC -- Rapid Update Cycle (NWS weather prediction model)

RWFS -- Road Weather Forecast System

RWIS -- Road Weather Information System

RWMP - Road Weather Management Program

STWDSR - Surface Transportation Weather Decision Support Requirements

UTC -- Universal Time Coordinated (same as Greenwich Mean Time)

VAMS - Value Added Meteorological Services

WIST-DSS - Weather Information for Surface Transportation Decision
Support System

WMO -- World Meteorological Organization

WRF -- Weather Research & Forecasting Model

Z -- Zulu time (same as Greenwich Mean Time)

ACKNOWLEDGEMENTS

The development of the MDSS concepts and functional prototype is a team
effort involving several U.S. national laboratories, several State DOTs,
the University of Iowa Center for Transportation Research and Education
(CTRE), and Professor Wilf Nixon of the University of Iowa. The authors
are particularly grateful to Gary Petteplace and George Koenig (CRREL),
Robert Hallowell and Darin Meyer (MIT/LL), and Paul Schultz and Brent
Shaw (NOAA/FSL). The MDSS development team at NCAR consists of several
scientists and software engineers including Paddy McCarthy, Arnaud
Dumont, Jim Cowie, Ben Bernstein, Jamie Wolff, Jaimi Yee, Seth Linden,
Sue Dettling, and Bill Mahoney. The development team appreciates the
support provided by CTRE, particularly Dennis Kroeger.

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, Paul Pisano,
Rudy Persaud, Randall VanGorder, Andy Stern and Ray Murphy. More than
twenty State DOTs have been active participants and their feedback has
been critical.

The MDSS Iowa demonstration could not have been conducted without the
hard work of staff from the Iowa Department of Transportation (IADOT).
We want to give special thanks to Dennis Burkheimer, Paul Durham, Edward
Mahoney, and Richard Hedlund.

TABLE OF CONTENTS

1 PURPOSE [8](#purpose)

2 INTENDED AUDIENCE [8](#intended-audience)

3 BACKGROUND [8](#background)

4 RELATED DOCUMENTS [9](#related-documents)

5 METHODS [10](#methods)

6 MDSS VERIFICATION ROUTES [10](#mdss-verification-routes)

7 MDSS SYSTEM CONFIGURATION [13](#mdss-system-configuration)

7.1 Supplemental Numerical Weather Prediction Models
[14](#supplemental-numerical-weather-prediction-models)

7.2 MDSS Prototype Optimization Period
[15](#mdss-prototype-optimization-period)

8 DATA COLLECTION PROCESS [16](#data-collection-process)

8.1 Supplemental Data Collection [17](#supplemental-data-collection)

8.2 Data Sources: Weather Observations
[18](#data-sources-weather-observations)

8.3 Data Sources: Road Condition Observations
[18](#data-sources-road-condition-observations)

8.4 Data Sources: Actual Treatment Performed
[18](#data-sources-actual-treatment-performed)

8.5 Verification Data Forms [19](#verification-data-forms)

9 MDSS ENHANCEMENTS FOR 2004 [20](#mdss-enhancements-for-2004)

9.1 Road Condition and Treatment Module (RCTM)
[20](#road-condition-and-treatment-module-rctm)

9.2 Road Weather Forecast System (RWFS)
[21](#road-weather-forecast-system-rwfs)

9.3 Mesoscale Model Ensemble [22](#mesoscale-model-ensemble)

9.4 MDSS Display Application [22](#mdss-display-application)

10 SELECTED CASE STUDIES [23](#selected-case-studies)

11 PRELIMINARY PERFORMANCE RESULTS
[23](#preliminary-performance-results)

11.1 Road Weather Forecast System (RWFS)
[23](#road-weather-forecast-system-rwfs-1)

11.1.1 Configuration [23](#configuration)

11.1.2 Performance Assessment [24](#performance-assessment)

11.1.3 Quantitative Precipitation Forecasts (QPF)
[29](#quantitative-precipitation-forecasts-qpf)

11.1.4 Insolation [30](#insolation)

11.1.5 Conditional Probability of Precipitation Type
[35](#conditional-probability-of-precipitation-type)

11.2 Road Temperature Predictions [36](#road-temperature-predictions)

11.3 Treatment Recommendations [41](#treatment-recommendations)

11.4 Mesoscale Modeling System Reliability
[44](#mesoscale-modeling-system-reliability)

11.5 Mesoscale Model Performance [46](#mesoscale-model-performance)

12 IOWA DOT USER FEEDBACK [50](#iowa-dot-user-feedback)

13 LESSONS LEARNED OR CONFIRMED [54](#lessons-learned-or-confirmed)

14 SUMMARY [56](#summary)

15 ANALYSIS PLANS FOR THE FINAL REPORT
[58](#analysis-plans-for-the-final-report)

APPENDIX A: POINTS OF CONTACT
.............................................60

APPENDIX B: DEMONSTRATION EVENT SUMMARY............................64

APPENDIX C: PHOTO OF AMES GARAGE WEATHER SENSORS.........\......71

# PURPOSE

This document describes the [preliminary]{.underline} technical
performance results of the prototype MDSS weather and road condition
prediction products and treatment recommendations based on the winter
2003-2004 Iowa field demonstration. The field demonstration ended on 24
March 2004. Data analysis began shortly after the end of the field
demonstration and will take 2-3 months to complete; therefore, only a
fraction of the results are presented herein. In addition, the findings
and recommendations must be considered preliminary and may change based
on additional analyses. The final Technical Performance Report will be
available in September 2004. Additional performance results to be
discussed in the final report are listed in Section 15.

For detailed information on the winter 2003-2004 Iowa field
demonstration, the reader is directed to the document titled "Winter
2004 Field Demonstration & Evaluation Plan" dated 6 January 2004
(reference provided in Table 4.1). For reference, technical points of
contact for the prototype MDSS Project are provided in Appendix A.

# INTENDED AUDIENCE

The intended audiences of this document are persons directly involved in
the MDSS field demonstration (e.g., FHWA, Iowa State DOT, National
Laboratories), stakeholders with an interest in the MDSS project
(private sector meteorological service providers and State DOTs), and
casual observers who wish to follow developments related to winter road
maintenance technologies. It is recommended that the reader have a
working knowledge of meteorological verification methods and statistical
analysis metrics.

# BACKGROUND

This MDSS Project is part of a federal procurement for research projects
and deployment advocacy, which is funded through the Intelligent
Transportation System (ITS) Joint Project Office (JPO) of the FHWA.

It is anticipated that components of the prototype MDSS system developed
by this project will be further developed, integrated with other
operational components, and deployed by road operating agencies,
including State Departments of Transportation (DOTs), and generally
supplied by commercial weather service providers.

Four national research centers have participated in the development of
the prototype MDSS in 2003-2004. The participating national labs
include:

-   Army Cold Regions Research and Engineering Laboratory (CRREL)

-   National Center for Atmospheric Research (NCAR)

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

-   NOAA Forecast Systems Laboratory (FSL)

The Center for Transportation Research and Education (CTRE) of Iowa
State University provided valuable support to the field demonstration.
Professor Wilf Nixon of University of Iowa provided additional support
and recommendations to the development team.

# RELATED DOCUMENTS

For additional information on the MDSS Project, the reader is directed
to the related project documents and web sites listed in Table 4.1.

Table 4.1. MDSS Project Related Documents

+--------------------------------------------------+-------------------+
| **Document and/or Web Sites**                    | **Primary         |
|                                                  | Source**          |
+--------------------------------------------------+-------------------+
|                                                  |                   |
+--------------------------------------------------+-------------------+
| STWDSR-- Version 1.0 (Needs Analysis)            | Federal Highway   |
|                                                  | Administration    |
| <http://www.i                                    |                   |
| tsdocs.fhwa.dot.gov/jpodocs/repts_te/9dc01!.pdf> |                   |
+--------------------------------------------------+-------------------+
| STWDSR-- Operational Concept Description (OCD)   | Federal Highway   |
|                                                  | Administration    |
| Version 2.0                                      |                   |
|                                                  |                   |
| <http://ww                                       |                   |
| w.itsdocs.fhwa.dot.gov/jpodocs/EDLBrow/401!.pdf> |                   |
+--------------------------------------------------+-------------------+
| Maintenance Decision Support System (MDSS)       | National Center   |
| Release-2 Technical Description, Version 1.0.    | for Atmospheric   |
| Dated 5 January 2004.                            | Research          |
|                                                  |                   |
| <http://www.rap.ucar.edu/projects/rdwx_          |                   |
| mdss/documents/MDSS_Tech_Description_5Jan04.pdf> |                   |
+--------------------------------------------------+-------------------+
| Maintenance Decision Support System (MDSS)       | Federal Highway   |
| Project Web Site at FHWA:                        | Administration    |
|                                                  |                   |
| <http://ops.fhwa.dot.gov/weather/index.asp>      |                   |
+--------------------------------------------------+-------------------+
| Maintenance Decision Support System (MDSS)       | National Center   |
| Project Web Site at NCAR:                        | for Atmospheric   |
|                                                  | Research          |
| <http:/                                          |                   |
| /www.rap.ucar.edu/projects/rdwx_mdss/index.html> |                   |
+--------------------------------------------------+-------------------+

# METHODS

This document provides objective and subjective verification of winter
2003-2004 MDSS weather forecasts and road treatment recommendations,
respectively. In addition, analyses of the road temperature model and
rules of practice components are provided. Where possible, results and
recommendations are summarized to provide guidance to those planning to
implement MDSS components or similar technologies.

Obtaining sufficient high quality verification data continues to be a
challenge for this project and similar efforts. Issues related to
verification data limitations are described throughout this document.

Surface weather observation quality was assessed in 2003 via coincident
observations of state and road parameters. Differences apparent in the
observations, themselves, set a lower bound for the accuracy one can
expect from the MDSS forecasts, for if the observations can only be
measured within a certain tolerance, then differences between such
observations and the MDSS forecasts are attributable to the uncertainty
in the observations themselves.

Objective verification is achieved via direct comparisons of MDSS
forecasts and observations from National Weather Service and RWIS
weather stations, including diagrams of root-mean squared error (RMSE)
and bias for state parameter fields (e.g. air temperature, wind speed)
as well as road temperature. The complexity and subjective nature of the
verification of road treatment recommendations lends itself well to a
case-study approach. This approach places the recommendations into the
necessary context of the forecast itself, as well as the actual
conditions that occurred. Contrast between the forecast and reality are
often at the root of differences between the recommended and actual
treatments. Several case studies are presented here, including light,
heavy, and mixed precipitation events.

# MDSS VERIFICATION ROUTES

A total of 16 routes were configured in the MDSS for the 2003-2004 field
demonstration. The selected routes are shown in Figure 6.1 and a
corresponding description of the routes is provided in Table 6.1.
Separate treatment plans were generated by the MDSS prototype for each
of the routes.

![](media/image2.jpeg){width="6.179921259842519in"
height="6.21259842519685in"}![](media/image2.jpeg){width="6.179921259842519in"
height="6.21259842519685in"}![](media/image3.jpeg){width="0.17913385826771652in"
height="0.19173228346456692in"}Figure 6.1 Map of routes to be supported
by the MDSS prototype during the winter of 2003-2004 Iowa field
demonstration. Key observation sites are annotated.

Table 6.1. Iowa Maintenance Routes for the MDSS Field Demonstration

  --------------- ----------- ----------- ------- ------------ ------------ ----------------- -----------
  **Garage**      **Segment   **Route**           **Start Mile **End Mile   **ADT Range**     **Service
                  Number**                        Post**       Post**                         Level**

  Ames            1           US 65               98.38        112.09       1500-2000         C

  Ames            1           US 30               164.93       172.30       5000-6000         B

  Ames            2           US 65               112.09       132.59       1000-2000         C

  **\*Ames**      **3**       **I-35**            **111.60**   **128.46**   **20000-23000**   **A**

  Ames            4           I-35                96.60        111.60       23000-26000       A

  **\*Ames**      **5**       **US 30**           **142.88**   **164.93**   **6000-27000**    **B**

  Ames            6           IA 210              13.79        34.43        1000-3000         D

  Ames            \-          I-35        **B**   Bridge       Bridge       **20000-23000**   A

  **\*Des Moines  **7**       **I-35**            **93.20**    **96.60**    **26000-53000**   **A**
  North**                                                                                     

  Des Moines      8           I-35                86.94        93.20        53000-59000       A
  North                                                                                       

  Des Moines      9           I-80                137.82       142.10       50000-61000       A
  North                                                                                       

  Des Moines      10          I-35/I-80           131.50       137.82       59000-63000       A
  North                                                                                       

  Des Moines West 11          I-35/I-80           123.53       131.50       32000-72000       A

  Des Moines West 12          I-35                67.89        72.70        22000-33000       A

  Des Moines West 13          I-235               0.00         8.80         42000-125000      A

  Des Moines      14          I-235               8.80         14.26        46000-125000      A
  North                                                                                       

  Des Moines      15          IA 415              0.00         21.93        1000-21000        B-D
  North                                                                                       
  --------------- ----------- ----------- ------- ------------ ------------ ----------------- -----------

Service Levels:

> A Interstates C 2,500 -- 5,000 vehicles per day
>
> B 5,000+ vehicles per day D less than 2,500 vehicles per day

Road condition data were only collected for a subset of the total routes
supported by the prototype MDSS in 2004. This decision was based on the
need to collect quality data and the fact that obtaining accurate road
condition data throughout a storm is difficult, manually intensive and
time consuming. Road condition data were collected for the following
routes (see Figure 6.1):

-   Ames Garage:

> Routes #3 (I-35) and #5 (US 30)

-   Des Moines Garage:

> Route #7 (I-35)

The three verification routes are highlighted in bold the Table 6.1 and
annotated with an asterisk. The selection of routes was based on several
factors including proximity to an RWIS, service level (traffic count),
diversity of expected treatment plans, and proximity to Lab, Iowa DOT
Headquarters and CTRE support staff who were responsible for collecting
verification data and were staying in Ames.

# MDSS SYSTEM CONFIGURATION

The MDSS core components (e.g., Road Weather Forecast System, Road
Condition and Treatment Module and data server) were operated centrally
at NCAR in Boulder, Colorado. A server at NCAR communicated (via the
Internet) with local PCs running the display application at the Iowa DOT
maintenance garages. Supplemental weather forecast models were run at
FSL in Boulder and the data forwarded to NCAR for inclusion in the Road
Weather Forecast System (RWFS). Iowa DOT RWIS data were provided to NCAR
via FSL as part of the MADIS program.

The MDSS displays were located in three maintenance garages. Each garage
had an instance of the MDSS display running at the supervisor's desk.
Data were obtained over the Internet (client-server approach). A
simplified illustration of the system configuration is provided in
Figure 7.1.

Figure 7.1. Depiction of the prototype MDSS configuration for the winter
2003-2004 Iowa field demonstration. All network MDSS connections to the
sites were via the Internet.

## Supplemental Numerical Weather Prediction Models

For the 2003-2004 MDSS demonstration FSL configured multiple,
high-resolution models to run over the identical area, all centered on
Iowa, with identical grid configurations and execution schedules. FSL
elected to use two different local-scale models, each of which used
different physics packages for parameterizing clouds and precipitation.
The configuration consisted of running MM5 and an improved version of
WRF every hour, and using "time-lagged" ensembling techniques. For
example, a 6-hr ensemble forecast used the current 6-hr forecast, the
previous 7-hr forecast, and the 8-hr forecast from the previous model
cycles before that.

There were no obvious alternatives to the FSL method for initializing
the local models (other methods for diabatic initialization are too
computationally intensive to use in real time), so the Local Analysis
and Prediction System (LAPS) *hot start* initialization was used for all
models. A *hot start* initialization is a method to generate cloud water
and precipitation processes at the start of the model run. This is a new
and promising technique that improves the ability of models to predict
clouds and precipitation, particularly in the 0-6 hr forecast period.

The local-scale models used for the MDSS field demonstration were:

MM5: <http://www.mmm.ucar.edu/mm5/mm5-home.html>

WRF: <http://wrf-model.org/>

The lateral boundary model was the Eta model provided by the NWS
National Centers for Environmental Prediction (NCEP). The Eta model was
delivered to NWS field offices and FSL four times daily.

The supplemental modeling configuration used for the Iowa MDSS field
demonstration is summarized in Table 7.1.

Table 7.1. Configuration of the MDSS Supplemental Models

  --------------- --------------------- -------------- ----------- ----------
  **Forecast      **Boundary Condition  **Forecast     **Grid      **Update
  Model**         Source**              Period**       Spacing**   Rate**

  WRF             NWS: Eta              15 hours       12-km       1-hr

  MM5             NWS: Eta              15 hours       12-km       1-hr
  --------------- --------------------- -------------- ----------- ----------

The mesoscale models (WRF and MM5) were run out to 15 hours ensuring
that, with a 3 hour MDSS update cycle, there were forecasts available
from the fine scale models out to 12 hours at all times. The fine scale
models ran every hour providing a new 15 hour forecast to the RWFS. The
grid spacing used in the fine scale models was 12-km (\~7.5 miles). The
MM5 and WRF supplemental models run by FSL had a domain centered on Iowa
as shown in Figure 7.2.

![](media/image4.jpeg){width="2.875in" height="2.515972222222222in"}

Figure 7.2. Domain of MM5 and WRF supplemental models provided by FSL.

FSL's Experimental Rapid Update Cycle (RUC) Model was also added in
FY2004 and those data were acquired by NCAR from FSL. No special runs
were generated to provide the RUC model as it runs daily as part of
ongoing NOAA and FAA research programs. The domain of the RUC model is
the continental U.S. For information on the RUC model, please see:

RUC: <http://maps.fsl.noaa.gov/>

## MDSS Prototype Optimization Period

The RWFS is tasked with ingesting reformatted meteorological data
(observations, models, statistical data, climate data, etc.) and
producing meteorological forecasts at user defined forecast sites and
forecast lead times. The forecast variables output by the RWFS are used
by the RCTM to calculate the road surface temperature and to determine a
suggested treatment plan. In order to achieve this goal, the RWFS
generates independent forecasts from each of the data sources using a
variety of forecasting techniques.

A single consensus forecast from the set of individual forecasts is
provided for each user defined forecast site based on a processing
method that takes into account the recent skill of each forecast module.
This consensus forecast is nearly always more skillful than any
component forecast. The RWFS is designed to optimize itself using
available observations near the routes (e.g., RWIS, METARS). The
forecast modules that perform the best are given more weight over time.
In addition, Dynamic Model Output Statistics (DMOS) are calculated
weekly using observations and model output. The DMOS process is used to
remove model biases. The optimization period of the RWFS is
approximately 90-100 days and because the revised version of the RWFS
was not fully operational until October 2003, the MDSS route specific
forecasts were not fully 'tuned' until late December 2003. For more
information on the RWFS, the reader is directed to Appendix A of the
MDSS Prototype Release-2 Technical Description (see reference in Table
4.1).

# DATA COLLECTION PROCESS

The national labs were responsible for analyzing the MDSS technical
performance. A critical component of the verification process was the
collection of weather, road condition, and actual treatment data. During
the winter 2003 field demonstration, Iowa DOT personnel were primarily
responsible for collecting the road condition and treatment data using
special data collection forms. This process put a large burden on the
DOT staff during winter storms. The data collection process during the
winter of 2003-2004 was changed to reduce the impact on DOT staff. In
addition, road condition data were only collected for a subset of the
total routes supported by the prototype MDSS as described earlier in
this document. This was designed to allow for a more intensive data
collection process so that a more thorough analysis of the road
conditions before and after treatment could be performed.

The Center for Transportation Research and Education (CTRE) provided
support for the demonstration effort, particularly in the area of
verification data collection and processing. In addition, the University
of Iowa, Dr. Wilfred Nixon, provided feedback to the development team on
the MDSS, particularly the road condition and treatment module, which
contains the rules of practice for anti-icing and deicing.

The MDSS field demonstration began following a demonstration readiness
telecom on 30 December 2003. A daily summary of the weather, DOT
actions, system issues and software updates is provided in Appendix B.

The first weather event occurred on 7 January 2004 and the last weather
event occurred on 16 March 2004. There was excellent diversity in the
type of cases this year. Central Iowa experienced heavy and light snow
events, mixed rain and snow, freezing rain, refreezing water on the
road, and several blowing and drifting snow events. There were 15 winter
weather event days during the course of the 88 day demonstration.

The MDSS operated quite well throughout the period; however, there were
several periods when the supplemental mesoscale models from FSL were
unavailable, primarily due to hardware problems associated with the
computer cluster. An evaluation of the impact of these models on the
MDSS performance will be provided in the final report.

To facilitate the collection of weather and road condition data during
the field program several MDSS project members traveled to Iowa to
participate in the field program. A list of the personnel that
participated in the field program is provided in Table 8.1.

Table 8.1. MDSS Project Visitors to Iowa for the MDSS Demonstration.

  ---------- ---------------- ------------------------- ------------------
  **Week     **Date**         **Name**                  **Organization**
  \#**                                                  

                                                        

  1          28 December 2003 Open (Holidays)           N/A

  2          4 January 2004   Robert Hallowell          LL

  3          11 January 2004  Gary Phetteplace          CRREL

  4          18 January 2004  Bill Myers                NCAR

  5          25 January 2004  Brent Shaw                NOAA/FSL

  6          1 February 2004  Darin Meyer               LL

  7          8 February 2004  Arnaud Dumont             NCAR

  8          15 February 2004 Andy Stern & Ray Murphy   Mitretek & FHWA

  9          22 February 2004 Bill Mahoney              NCAR

  10         29 February 2004 Open                      N/A

  11         7 March 2004     Paul Schultz              NOAA/FSL

  12         14 March 2004    Goeff Koenig              CRREL

  13         22 March 2004    Open                      N/A
  ---------- ---------------- ------------------------- ------------------

## Supplemental Data Collection

To augment the weather data collection activities, NCAR installed a
suite of weather sensors at the Ames garage on 17 December 2003. The
weather sensors includes a Geonor snow gauge with an Alter shield, air
temperature and dew point sensor, R.M. Young anemometer, sonic snow
depth sensor, and pyranometer. A photo of the Ames garage site is
provided in Appendix C. Several technical problems delayed the
availability of the data. For example, about 7 days after the site was
installed, the power failed to the site. In addition, access to the Iowa
DOT network was delayed due to computer system security concerns. The
Geonor gauge also suffered a rare power conditioner failure. All of the
problems were resolved by 9 February 2004.

To reduce the workload of the Iowa DOT staff, GPS/AVL data recording
systems were installed on the 8 snowplows covering the three
verification routes. These data were downloaded after the events and
processed by CTRE.

The goal of the data collection process was to capture the evolution of
the weather and road conditions during the life cycle of the winter
storm events near the MDSS demonstration routes. Verification data were
obtained and archived from several sources near the selected MDSS routes

Several data collection methods were used this year including:

a)  Automated collection of actual treatments using Iowa DOT maintenance
    vehicles recording systems (e.g., GPS/AVL systems)

b)  Manual data collection of actual treatments using standard Iowa DOT
    post-treatment (shift) forms

c)  Automated collection of weather data (local ASOS, AWOS, and RWIS)

d)  Manual collection of weather and road condition data using
    specialized forms filled out by CTRE, Iowa DOT and Lab staff either
    riding in private cars or in DOT maintenance vehicles along selected
    routes

## Data Sources: Weather Observations

The following [weather observation]{.underline} data sources were used
for verification and analysis:

a)  Iowa RWIS (primarily sites in Des Moines, Ames, and Ankeny)

b)  NWS ASOS/AWOS (DSM, IKV, AMW)

c)  Local observer surface data

d)  Weather satellite

e)  Weather radar

f)  NWS storm summaries

g)  Iowa DOT observations

h)  CTRE, Iowa DOT and Lab staff observations using forms along
    verification routes

i)  Geonor snow gauge (Ames garage)

j)  Other sources as available

## Data Sources: Road Condition Observations

The following [road condition]{.underline} data sources were used for
verification and analysis:

a)  Iowa RWIS road temperature (primarily sites in Des Moines, Ames, and
    Ankeny)

b)  CTRE, Lab and Iowa DOT staff on-site data collection using forms
    along selected verification routes

c)  Iowa DOT operator and supervisor observations

d)  Iowa DOT maintenance vehicle data

    i.  Pavement temperature

    ii. Freezing point

    iii. Truck location and time

## Data Sources: Actual Treatment Performed

The following data sources were used to obtain data describing the
actual winter maintenance treatments:

a)  CTRE, Lab and Iowa DOT staff on-site data collection using forms
    along selected verification routes

b)  Iowa DOT standard shift forms

c)  Iowa DOT Maintenance Vehicle data

    i.  Material distribution setting

    ii. Treatment type

    iii. Treatment rate

    iv. Plow position

    v.  Date, time and location

## Verification Data Forms

In order to fully estimate the weather and road conditions performed
during each event, it was necessary for Lab and CTRE personnel to fill
out data collection forms. The weather and road condition forms used by
CTRE, Iowa DOT and Lab staff were designed to capture the following
information about weather and road conditions:

-   Date

-   Time

-   Route

-   Treatment performed (if riding in maintenance vehicle)

    -   Treatment start and stop times

    -   Chemicals used (NaCl, solids, liquids, etc.)

    -   Chemical amount (tonnage and/or pounds per lane mile or gallons
        per lane mile)

    -   Plowing performed

-   Estimated road condition per route

    -   Wet, dry, icy, snowpacked, blowing snow, snow depth, slush,
        rain, freezing rain, frost, etc.

    -   Road temperature (if available)

-   Weather conditions along route

    -   Precipitation type, precipitation rate, precipitation start and
        stop times, blowing snow, snow depth, frost, air temperature,
        wind speed, etc.

-   Any other pertinent observations such as chemical dispersion rate,
    condition of road before and after treatment, precipitation start
    and stop times.

A digital camera was also used by CTRE and Lab staff to capture images
of the road conditions throughout the life of a storm.

# MDSS ENHANCEMENTS FOR 2004

Using the 2003 post-demonstration evaluations, technical performance
results, and the lessons learned from meetings with Iowa DOT and support
personnel, a list of system development activities was compiled that was
considered important for the success of the winter 2004 field
demonstration. The list below includes enhancements for 2004 to the Road
Condition and Treatment Module (RCTM), the Road Weather Forecast System
(RWFS), the mesoscale model ensemble and the client display application.

RCTM module modifications focused on the preparation of better road
treatment recommendations. The RWFS updates were centered on improved
handling of the quantitative precipitation forecast data, increasing the
temporal resolution of the output, and better quality control and use of
observations in the preparation of weather forecasts. The mesoscale
model ensemble configuration was changed to use a better selection of
models with a higher temporal resolution in an attempt to improve the
detection of weak weather events. And finally, several refinements were
made to the display application based on Iowa DOT feedback.

## Road Condition and Treatment Module (RCTM)

The following enhancements were made to the road condition and treatment
module including the road temperature model (SNTHERM-RT) and coded rules
of practice:

1)  [Road Temperature Model Initialization:]{.underline} Revised the
    road temperature model (SNTHERM-RT) so that it could be initialized
    using road temperature surface and subsurface observations for
    routes with RWIS stations that had both surface and subsurface data.
    For evaluation purposes, this method was applied to two RWIS
    stations (Ames RWIS, and I-235 RWIS).

2)  [Plowing Rules]{.underline}: Fixed a software bug that was
    introduced in the middle of the winter 2003 demonstration that did
    not allow 'plow only' treatments to be recommended.

3)  [System Reset]{.underline}: Added ability for the users to reset the
    road conditions (road snow depth = 0 and chemical concentration = 0)
    for each route or a group of routes.

4)  [Blowing Snow Potential:]{.underline} In the absence of a more
    sophisticated solution, created a 'blowing snow' alert when blowing
    snow conditions were likely. The blowing snow potential algorithm is
    based on precipitation type, snowfall rate, air temperature, wind
    speed, number of diurnal cycles, and occurrence of other
    precipitation types since the last snowfall.

5)  [Rules Of Practice:]{.underline} Refined the coded rules of practice
    using Iowa weather and treatment case data. Several significant
    improvements were made to the code for 2004. Logic was added within
    the MDSS system to recognize the overall storm situation. During the
    winter of 2003, the treatment module essentially determined the next
    treatment recommendation based on the weather that was forecasted
    from the current trigger to the time it would take to traverse a
    given route. This approach was too short sighted, as it was unable
    to handle changing weather situations predicted to occur several
    hours into the future.

6)  [Dry Road Time]{.underline}: A critical factor in after storm
    maintenance is the time at which the road surface becomes "dry". If
    chemicals fail before a road is completely dry then subsequent
    treatments will be needed to mitigate refreezing. An algorithm was
    developed to determine the factors affecting dry road time and the
    treatment recommendations sensitive to this factor.

7)  [Bridge Segment Support:]{.underline} Configured and tuned MDSS to
    support bridges as weather and road condition forecast points. For
    the winter of 2004, a bridge on I-35 near Ames was added for
    evaluation purposes.

## Road Weather Forecast System (RWFS)

The following enhancements were made to the Road Weather Forecast
system:

1)  [Forward Error Correction:]{.underline} Implemented a technique to
    adjust the RWFS predictions to better match observations when the
    forecast time is the current time (t=0). This technique was applied
    to verifiable parameters.

2)  [Spatial Consistency]{.underline}: Added 'neighborhood DMOS'
    (Dynamic Model Output Statistics) to the system so there would be
    more consistency between nearby prediction points.

3)  [Quality Control Flags]{.underline}: Developed software to take
    advantage of the quality control flags provided with the MADIS data
    stream.

4)  [Hourly Forecasts:]{.underline} Revised the RWFS to accept, process
    and output hourly data out to at least 15 hours into the forecast
    period. This provided an opportunity to take advantage of the higher
    temporal resolution of the mesoscale models. The hourly output was
    merged with the 3-hour NWS model data (interpolated to hourly) for
    the first 15 hours, while only NWS models were used for the 16-48
    hour time period.

5)  [Probabilistic Information]{.underline}: Using the data contained in
    the RWFS, generated probabilistic information for select data fields
    (e.g., precipitation, and precipitation type).

6)  [Bridge Support:]{.underline} Added the capability in the RWFS to
    predict weather and road temperatures for bridge segments.

## Mesoscale Model Ensemble

The following changes or enhancements were made to the mesoscale
ensemble component of the prototype MDSS:

1)  [Ensemble Configuration]{.underline}: Updated the selection of
    models, the model output frequency, the run schedule, and the output
    products. The MM5 and WRF models were included in 2004. Also, FSL\'s
    Rapid Update Cycle (RUC) experimental model runs (which are separate
    from those RUC runs conducted by NOAA's National Centers for
    Environmental Prediction (NCEP)) were included.

2)  [System Stability]{.underline}: Made changes to the scripts for the
    ensemble modeling system in order to make the software system more
    robust.

## MDSS Display Application

The following enhancements were made to the MDSS display application:

1)  [Digital Values]{.underline}: Provided digital values to the state
    and route view graphics.

2)  [System Reset]{.underline}: Added ability of the users to reset the
    road conditions (road snow depth = 0 and chemical concentration = 0)
    for each route or all routes via the user interface.

3)  [RWIS Observations:]{.underline} Added ability to view the most
    current road temperature data from RWIS stations via the display
    interface.

4)  [Dynamic Range]{.underline}: Added dynamic range code for the
    time-series parameters to reduce the chance of having data go out of
    range.

5)  [Historical View:]{.underline} Designed a way to view recent history
    (\~6 hrs) on the display so that more than just the latest 48-hrs is
    viewable.

6)  [Display Summary Page]{.underline}: Users indicated that they needed
    a quick-look summary page of weather and road variables for each
    forecast period for each plow route. Summary page products include
    maximum and minimum predicted air and road temperatures, total new
    snow accumulation, and an indication of conditional probabilities of
    precipitation type.

7)  [Blowing Snow Potential:]{.underline} Created a treatment
    recommendation alert graphic on treatment page when blowing snow
    conditions were likely.

# SELECTED CASE STUDIES

![](media/image5.jpeg){width="1.9805555555555556in"
height="2.8152777777777778in"}Several case studies were chosen for
analysis. The cases were chosen to include several weather and treatment
scenarios. The cases listed below will be analyzed and the results
presented in the final technical performance report.

The case studies described in the final report will include the
following:

-   Cold Rain Case 16 January 2004

-   Light Snow Case 26 January 2004

-   Blowing Snow Case 9 February 2004

-   Mixed Precip Case 20 February 2004

-   Heavy Snow Case 15 March 2004

# PRELIMINARY PERFORMANCE RESULTS

In this section preliminary snapshots of performance results are
described that provide an early indication of how components of the MDSS
performed during the winter 2003-2004 Iowa field demonstration. All
aspects of the system improved over the first field demonstration. A
more in-depth discussion of performance and implementation
recommendations will be provided in the final report.

## Road Weather Forecast System (RWFS)

### Configuration

The RWFS was configured to utilize and integrate ten different forecast
modules this year. Models that were ingested into the RWFS included the
Eta, GFS, MM5, RUC and WRF. MM5 and WRF were initialized using the NWS
Eta model for boundary conditions and 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 use
as input, and Dynamic Model Output Statistics (DMOS) were calculated
within the RWFS for each of the model inputs. The ten weather forecast
modules that were used to predict the weather parameters for each MDSS
forecast point were:

1)  NWS Aviation MOS (MAVMOS)

2)  Eta DMOS

3)  GFS DMOS

4)  MM5 Eta4 (run from 4 hours previous)

5)  MM5 Eta3 (run from 3 hours previous)

6)  MM5 Eta (run from 2 hours previous, the most current run)

7)  WRF Eta4 (run from 4 hours previous)

8)  WRF Eta3 (run from 3 hours previous)

9)  WRF Eta (run from 2 hours previous, the most current run)

10) RUC DMOS

The RWFS integration process optimized the forecasts based on recent
skill. The forecasts were optimized independently at each prediction
site for each parameter and forecast lead-time. The forecast modules
with the most skill were weighted more heavily.

A final consensus forecast was the ultimate output of the RWFS.

### Performance Assessment

The performance of the RWFS was analyzed by comparing the forecast skill
of the individual inputs (forecast modules) with the consensus forecast.
The system is designed to optimize forecast skill using recent skill and
statistical techniques. The design goal of the system is for it to
produce better forecasts than the individual inputs most of the time.

Analyses of RWFS skill for air temperature, dew point, wind speed and
cloud cover were performed and the results are shown in Figures 11.1
a-d, respectively. The plots show the root mean square error (RMSE) of
each of the ten input forecast modules and the final consensus forecast
generated by the RWFS. Data cover 29 December 2003 to 24 March 2004.
Note that the mesoscale models only covered a 15 hour forecast period,
whereas the NCEP model products covered a 48 hour period.

![](media/image6.jpeg){width="5.935433070866142in"
height="4.451574803149606in"}

For air temperature forecasts, the consensus forecast was more accurate
than any of the individual prediction modules for the entire 24 hour
comparison period. The improvement was particularly evident in the first
six hours of the forecast period where the influence of the forward
error correction is most pronounced. The RMSE for the RWFS predictions
at 12 hours is 1.7 C. It is interesting to note that the individual
inputs are clustered between 2.0 and 2.5 C. The MM5 based air
temperature forecasts are more accurate than WRF and AVN MOS is slightly
better than both.

![](media/image7.jpeg){width="6.064566929133858in"
height="4.53740157480315in"}

For dew point predictions, the best skill is with the RWFS followed by
AVN MOS. The mesoscale model based predictions have less skill. The RMSE
for the RWFS at 12 hours is 2.2 C. The increased skill of the RWFS air
and dew point temperatures over the first demonstration is good news as
these data are important for road temperature and frost predictions.

![](media/image8.jpeg){width="6.064566929133858in"
height="4.53740157480315in"}

The wind speed predictions of the RWFS are also better than any of the
individual forecasts. The RMSE of the RWFS wind speed predictions is 1.5
meters per second (\~3 mph).

![](media/image9.jpeg){width="5.440944881889764in"
height="4.086220472440945in"}

For cloud cover, none of the predictors stands about. The improvement in
RWFS skill during the first 6 hours is primarily due to forward error
correction. From this analysis, it appears that all of the models have
nearly the same chance of getting the cloud cover correct on average.
The outlier is the AVN MOS, which has less skill.

Additional analyses of the RWFS skill will be presented in the final
performance report.

### Quantitative Precipitation Forecasts (QPF)

The RWFS is designed to optimize forecasts based on feedback it receives
from observations collected at or nearby the RWFS forecast points. One
of the most difficult weather prediction problems is forecasting
precipitation amounts. Part of the problem is in the forecasting process
(model physics and knowing the state of the atmosphere) and the other
part is related to the poor observation network and the inability to
verify actual precipitation amounts, particularly for winter
precipitation (e.g., snow, sleet, ice, etc.).

The lack of quality winter precipitation observations hindered the RWFS
in that it was unable to tune itself for the QPF parameter. When the
RWFS was initialized in the fall of 2003, each of the ten forecast
modules had a 10% weight. A few fall rain cases were enough to move the
weights from the initial values, but they never had a chance to adjust
fully before the winter precipitation season began.

Without the tuning process, all the QPF modules would essentially be
averaged (10-member average) and the MDSS would not be able to take
advantage of the skill of any particular model or dataset. This could
result in a worse precipitation prediction than directly using the more
skilled models. It could also result in the inability for the system to
declare a light precipitation event that may have been well predicted by
one or more of the forecast modules. This of course is opposite of what
the RWFS was designed to do. The project staff monitored the weights
during the first 45 days of the demonstration and, based on the lack of
adjustment, decided to intercede.

Because of the poor quality of the winter precipitation measurements, it
was necessary to modify the RWFS to fix the QPF weights across the ten
modules based on expert opinion. The weights for the QPF parameter were
fixed on 10 February 2004 as shown in Table 11.1.

Table 11.1. Weights given to each of the ten RWFS quantitative
precipitation forecast modules.

  ---------- --------- ----- ----- ----- ----- ------- ----- ----- ----- ------- ----- ------ -------- --------------
  Des Moines RWFS QPF                                                                                  
  & Ames     Weights                                                                                   
  Area                                                                                                 

  Forecast   GFS       Eta   MM5   MM5   MM5   Total   WRF 2 WRF   WRF   Total   RUC   MAV-   Total %  TOTAL MM5+WRF
  Period                     2hr   3hr   4hr   MM5     hr    3hr   4hr   WRF           MOS             Contribution

                                                                                                       

  0 hr       9         11    15    12    10    37      17    14    12    43      0     0      100.00   80
  Forecast                                                                                             

                                                                                                       

  3 hr       9         11    15    12    10    37      17    14    12    43      0     0      100.00   80
  Forecast                                                                                             

                                                                                                       

  6 hr       9         11    15    12    10    37      17    14    12    43      0     0      100.00   80
  Forecast                                                                                             
  ---------- --------- ----- ----- ----- ----- ------- ----- ----- ----- ------- ----- ------ -------- --------------

The subjective opinion was that the time-lagged ensemble of mesoscale
models (WRF and MM5), which were being run hourly out to 15 hours, had a
better handle on precipitation timing and amount. In addition, because
of the "hot start" process, it was felt that the models did a better job
of identifying the prediction of very light precipitation cases. As can
be seen above, the WRF and MM5 data were given 80% of the total weight
for QPF in the RWFS.

An objective analysis of the performance of the models in predicting QPF
will be conducted. Due to the poor observation quality for winter
precipitation, it is not clear if the analysis will provide meaningful
results. A more thorough discussion of this issue will be in the final
performance report.

### Insolation

It is well recognized that incoming solar radiation (insolation) is a
critical parameter for predicting road temperature. During the winter of
2003, the insolation value was calculated in SNTHERM-RT from weather
model cloud layer data. The calculation of insolation from a routine in
SNTHERM-RT is mainly due to the fact that until recently, operational
weather models did not explicitly output insolation data. It was
generally believed that the explicit calculation of insolation provided
by the mesoscale models would be better than estimating it in SNTHERM-RT
from cloud layer data; therefore, the MDSS was configured to utilize the
insolation values from the models this year. This was the right approach
as some test cases indicated that the road temperature error was reduced
by about 50%, but several data quality issues related to modeled
insolation had to be resolved during the demonstration period.

As part of the performance assessment task in 2003, CRREL analyzed
SNTHERM-RT using actual weather measurements in Iowa to determine its
skill given "perfect" weather inputs. The goal was to assess the skill
of the model itself and this could only be done if the model was
provided highly accurate weather inputs. Without insolation data near
the Iowa routes, it was difficult to fully assess model skill as the
insolation values had to be estimated from cloud cover observations. As
part of the MDSS project in 2004, a pyranometer was installed at the
Ames garage. The primary objective was to measure short wave radiation
so that a more thorough analysis of SNTHERM-RT could be performed after
the field demonstration. The results of this analysis will be presented
in the final report.

The insolation data did provide an opportunity to evaluate the skill of
the individual weather models in predicting incoming short wave
radiation. The data proved very valuable as it identified several issues
and problems related to modeled insolation data. Short wave radiation
data are available from the MM5, WRF, Eta, and GFS, but there are
significant differences in the way they are calculated and provided. For
example, the GFS short wave radiation field is a 3-hour time-averaged
value, while the Eta provides an instantaneous value. Listed below are
descriptions of the issues that arose with the short wave radiation
fields used in the MDSS.

[Eta Model]{.underline}: The short wave radiation data from Eta model is
an instantaneous value, but it had a time lag problem. On 17 March 2004,
an NCEP bulletin identified that the Eta model insolation data were
offset by one hour. For example, the data values given for 06:00 hrs
were really valid at 05:00 hrs. In addition, NCEP corrected a problem
with long wave radiation where low cloud and fog were not accounted for
properly. These problems were observed during the MDSS field program.
NCEP corrected these problems on or about 17 March 2004[^1]. Although
this was too late to help the MDSS this year, these changes should
result in better forecast skill in the future for those using the Eta
model.

[GFS Model]{.underline}: The short wave radiation data from GFS model is
a time averaged value over a 3 hr period. Because of the time averaging
characteristic, it is not practical to use it as an input to a road
temperature model.

[WRF Model]{.underline}: The radiation data from WRF model is an
instantaneous field, but early in the field program, the WRF provided
the net radiation, which was not separated into long and short wave
fields. Later on, an underestimation of the insolation was discovered on
clear days and it was determined that WRF was retaining too many
hydrometeor species, which attenuated short wave radiation. On 17
February, the WRF model was corrected and the insolation data matched
the pyranometer data on clear and cloudy days.

[MM5 Model]{.underline}: The MM5 radiation data is an instantaneous
field and matched the field data more closely than any of the other
models, at least until the WRF was corrected. After that time, the two
models values were very close on clear days.

The RWFS was configured to use the MM5 and WRF insolation data for the
part of the forecast period where those data were available (0-15 hrs)
and the Eta insolation data for the 15-48 hour forecast period. The
real-time insolation data from Ames were not used in the RWFS;
therefore, the weight blending values for insolation had to be fixed. In
the future, it may be desirable to utilize real time insolation data to
forward correct model data, but additional research is required to
assess this approach.

At the start of the MDSS demonstration, when it was believed that the
WRF and MM5 had equally valid values, the RWFS was configured to weight
each of them 50%. On 27 January 2004, an analysis showed that the WRF
was underestimating the insolation values on clear days so the weights
were changed to 75% to 25% in favor of the MM5 model. On 16 February
2004, the WRF model insolation calculation was corrected and the weight
blending was returned to 50% for each model. The purpose for changing
the values was to provide the best input available to the road
temperature model (SNTHERM-RT) and to get a feel for the response.

A comparison of the modeled insolation (short wave radiation) values
with the pyranometer measurements for a 7 day period after the WRF was
corrected is provided in Figure 11.2 and a separate figure of the same
except for just the RWFS is provide in Figure 11.3. During this period
there were two clear days (18 and 22 February 2004) where the insolation
values were near the theoretical maximum (\~630 watts per square meter),
and several partly cloudy days.

The comparison illustrates the differences between models and their
respective physics for determining cloud characteristics. For example,
MM5 had a tendency to overestimate insolation (underestimate clouds) on
partly cloudy days while WRF tended to underestimate insolation
(overestimate clouds). Eta was closer to MM5 than to WRF. The blending
used by the RWFS provided the best overall results even though the
weights were fixed. Because the dynamic data blending process used in
the RWFS works well for verifiable parameters, it is believed that this
would hold true for insolation. Real time insolation data would need to
be available over a broad region to prove this concept.

![](media/image10.wmf)

Figure 11.2. Comparison between MM5, Eta, WRF, and RWFS output (12Z
model runs) of short wave radiation and pyranometer data.

![](media/image11.wmf)

Figure 11.3. Comparison between RWFS output (12 Z model runs) of short
wave radiation and pyranometer data.

### Conditional Probability of Precipitation Type

Feedback received after the 2003 field demonstration indicated that the
end users had a strong desire for information related to the evolution
of precipitation phase during winter weather events. Given the
complexity of the physics and the difficulties that arise in providing a
deterministic output, the MDSS team chose to develop a probabilistic
precipitation type product and present it in an intuitive graphical
form. Because it was very well received by the end users, the graphical
product is described here so that future implementers can use this
example to guide their own product design process.

The conditional probability of precipitation type describes the
probability that the precipitation will be rain, snow or ice if any
precipitation occurs. The MDSS graphical product combines the
probability of precipitation and the conditional probability of
precipitation type. The conditional probability of precipitation type is
normalized to the total probability of precipitation so the user can get
a quick sense of the likelihood of any precipitation and the probability
that it will be a specific precipitation type or mix of types. An
example of the product is shown in Figure 11.4.

![](media/image12.jpeg){width="6.0in" height="1.5562992125984252in"}

In this case, the total probability of precipitation rises to a peak of
75% between 6 and 7 AM on 19 February 2004. The event starts as rain and
then is predicted to slowly transition to snow at around 9 AM. The MDSS
is configured to declare a specific precipitation type based on criteria
that includes a combination of total probability of precipitation,
conditional probability of precipitation type, and precipitation rate.
To declare a precipitation event, the MDSS was configured as follows
(for the winter 2003-2004 demonstration):

a.  Probability of precipitation was \>= 25%

b.  Precipitation rate was \>= 0.05 mm/hour

These values were chosen based on subjective analyses prior to and
during the early part of the field season. The precipitation type was
declared when the following criteria were met:

a)  Ice declared, if the conditional probability of ice was \>= 25%

b)  If the conditional probability of ice was \< 25%, the declared
    precipitation type is the maximum conditional probability between
    rain and snow.

The users indicated and reiterated several times that ice is a large
winter maintenance problem and they wanted to have a conservative
heads-up if there was any chance of freezing rain; therefore, the 25%
threshold was chosen. In the graphic example above, the declared
precipitation type is shown above the precipitation probability graph.

## Road Temperature Predictions

Road temperature predictions were made for each of the 16 road segments
(including one bridge) and for each RWIS site in the Des Moines and Ames
area (see Figure 6.1). 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 treatment
that was entered into the system by IADOT personnel during the event.
The "untreated" forecast is the most unrealistic, since treatment will
nearly always take place. 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.

The preliminary road temperature data that follows are for January and
February 2004. This corresponds to the period when the modeled
insolation values were problematic (see Section 11.1.4); therefore the
results should not be considered indicative of the final performance
potential. Road temperature results for mid February to late March will
be provided in the final report to represent the performance after the
insolation data were corrected. Road temperature prediction data for
cloudy and precipitating periods, when insolation is minimized, are
broken out to provide an indication of the skill when insolation was
low.

The final report will also include an analysis of the road temperature
model SNTHERM-RT run using actual weather data including insolation
measurement from the Ames garage. This analysis will provide an
indication of the overall skill of SNTHERM-RT given accurate weather
input data.

![](media/image13.png){width="5.988194444444445in"
height="4.495833333333334in"}

Figure 11.5 illustrates the RMSE for road temperature predictions for
the Des Moines RWIS from the 18Z runs and shows that the average evening
to morning error ranged from 1.5 to 2.2 C for the first 24 hours and
rose to 2.5 to 3.5 C for the 24-48 hour forecast period. The daytime
error (evident during hours 18-27 and 42+) was significantly higher.

Figure 11.6 illustrates the RMSE for road temperature predictions for
the Ames RWIS and shows that the average evening to morning error was
slightly better than Des Moines and ranged from 1.2 to 2.0 C. Like Des
Moines, the daytime error rose sharply higher to approximately 4.5 C. An
analysis of bias (not shown) for all sites indicated that the MDSS road
temperature forecasts were too cold during daytime heating period.
Further analysis indicated that the insolation values output by the WRF
were about 30% low as described in Section 11.1.4. The error was
resolved in mid February 2004.

![](media/image14.png){width="5.988194444444445in"
height="4.495833333333334in"}

![](media/image15.png){width="5.988194444444445in"
height="4.495833333333334in"}

Figure 11.7 illustrates the RMSE for road temperature predictions for
all the RWIS sites and shows that the average evening to morning error
was ranged from 1.5 to 2.0 C in January and 1.5 to 3.2 C in February.
The error rose to approximately 4 C at midday.

An analysis was performed to determine the road temperature prediction
skill during cloudy and precipitation periods to isolate the errors that
may have been caused by poor insolation predictions.

![](media/image16.png){width="5.988194444444445in"
height="4.495833333333334in"}

Figure 11.8 shows the RMSE for road temperature predictions during
January 2004 for clear periods (cloud cover \< 10%), mostly cloudy
periods (cloud cover \> 90%) and during periods when snow was predicted
(conditional probability of snow \> 90%). The actual road temperature
prediction generated by the MDSS for the recommended treatment is also
shown.

The road temperature predictions were the most accurate during snowy
periods, particularly at night. During nighttime snow events, the RMSE
ranged from 0.5 to 1.5 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 focused
on insolation prediction is required. If real time insolation data
become available, perhaps some statistical correction techniques may
helpful. Model physics and data fusion techniques may also provide
opportunities for improvement.

## Treatment Recommendations

One of the biggest improvements to the MDSS in 2004 was the total
revision of the Rules of Practice (RoP) code based on lessons learned
and experience gained from the first MDSS field demonstration. The
largest improvement was the addition of code that characterizes the
entire storm environment. The logic that was developed more closely
matched the decision process of the DOT supervisors. The new code
handles additional weather and road condition scenarios including the
refreezing of water and black ice formation.

A detailed analysis of the treatment recommendations will be provided in
the final performance report.

In this report, two of examples of the recommended treatments vs. the
actual treatments are provided. Figure 11.8 provides a comparison of the
recommended treatments and actual treatments for plow route #1
(US-30/US-65) east of Ames, Iowa on 2 February 2004. This was a snow
event where approximately 3-5 inches of snow fell. The air and road
temperatures were in the low 20s F during the snow period.

![](media/image17.jpeg){width="5.125196850393701in"
height="4.186220472440945in"}

Figure 11.9 illustrates that there was reasonable similarity between the
recommended treatments and actual treatments. The Iowa DOT chose to
cluster the actual treatments within the normal shift period; something
not considered in the prototype MDSS. The total amount of chemicals used
during this period was 650 pounds per lane mile and the total amount
that was recommended was 600 pounds per lane mile. A plow only treatment
was performed within an hour of the recommended time.

Figure 11.10 provides a comparison of the recommended treatments and
actual treatments for plow route #5 (US-30) for the same event.

![](media/image18.jpeg){width="5.066535433070866in"
height="4.141732283464567in"}

In this example, a single plow only operation was recommended, but two
were performed. The actual chemical application matched the recommended
treatment. Both of the examples are from plow routes that are the
responsibility of the Ames garage. This example illustrates that the
MDSS had a good handle on the conditions during this event and the
garage supervisors were confident in the recommendations.

The Des Moines North garage also expressed confidence in the MDSS
treatment recommendations, particularly later in the demonstration
period after additional forecast system tuning (e.g., QPF weight fixing)
was performed. Garage personnel indicated that on several occasions,
they performed the recommended treatments with excellent results.

It should be noted that the treatment recommendations are only valid
when the weather forecasts are good. There were several cases where the
precipitation timing was off by a few hours and the total storm amounts
were heavier or lighter than predicted, which impacted the treatment
recommendation. In most of these cases, the forecasts improved with
every update (the system provided a new 48 hour forecast every three
hours) and the users were able to adjust to the changing information.
Improving the quality of the weather forecasts that drive road condition
and treatment prediction systems is critical.

A detailed discussion of the treatment recommendations will be provided
in the final performance report. Preliminary findings indicate that the
Road Condition and Treatment Module, particularly the Rules of Practice
were significantly improved. Even with this improved skill, there are
still several areas that need to be addressed including road frost,
drifting snow, and the need to handle additional chemicals (e.g.,
MgCl~2~).

One ongoing request of the users is that operational versions of the
MDSS include a capability to automatically ingest actual treatments
performed so that the system would properly be initialized at each
update. An automated data input scheme would reduce the workload
required to input actual treatment data during an event.

#  {#section .unnumbered}

## Mesoscale Modeling System Reliability

FSL configured the computing cluster to run a time-lagged ensemble of
the MM5 and WRF models every hour for 24 hrs per day during the field
demonstration. System reliability statistics were calculated between 29
December 2003 and 19 March 2004 (the original end date of the
demonstration). Approximately 90% of the runs were successful. Most of
the system down time occurred between 28 January and 4 February 2004
when a processing node on the computer cluster failed. The MDSS also
lost model data due to transmission losses between FSL and NCAR
associated with a failed disk on the NCAR LDM server. These losses only
accounted for 0.55% and 0.44% for MM5 and WRF, respectively.

**Reliability Report for MDSS Ensemble Member: MM5**

> First cycle logged: 200312290000
>
> Last cycle logged: 200403192300
>
> Total cycles found: 1830
>
> Total expected: 1968
>
> Number complete: 1769
>
> Number partial: 1
>
> Number failed: 60
>
> Number missing: 138
>
> Total possible local GRIB files: 31488
>
> Total possible public GRIB files: 31488
>
> Number of local GRIB found: 28397
>
> Number of public GRIB found: 28397
>
> **MM5 Reliability Percentages**
>
> Completely successful: 89.89 %
>
> Percent missing: 7.01 %
>
> Percent failed: 3.05 %
>
> Percent partial: 0.05 %
>
> Percentage using hot start: 99.94 %
>
> Local file creation: 90.18 %
>
> Public file delivery: 90.18 %

**Reliability Report for MDSS Ensemble Member: WRF**

> First cycle logged: 200312290000
>
> Last cycle logged: 200403192300
>
> Total cycles found: 1830
>
> Total expected: 1968
>
> Number complete: 1769
>
> Number partial: 32
>
> Number failed: 29
>
> Number missing: 138
>
> Total possible local GRIB files: 31488
>
> Total possible public GRIB files: 31488
>
> Number of local GRIB found: 28769
>
> Number of public GRIB found: 28767
>
> **WRF Reliability Percentages**
>
> Completely successful: 89.89 %
>
> Percent missing: 7.01 %
>
> Percent failed: 1.47 %
>
> Percent partial: 1.63 %
>
> Percentage using hot start: 99.94 %
>
> Local file creation: 91.36 %
>
> Public file delivery: 91.36 %

A day-by-day breakdown is shown in Figure 11.11. The down time in late
January 2004 corresponds to a crashed head node on the modeling cluster.
The down period in late February 2004 is the result of damage caused by
a hacker.

![](media/image19.wmf){width="5.833333333333333in"
height="4.270833333333333in"}

## Mesoscale Model Performance

For this preliminary report, a comparison is made in the precipitation
forecast skill between the NCEP Eta, and the MM5, and WRF models run by
FSL for the MDSS demonstration. The comparison is for cases over the
entire demonstration period (29 December 2003 to 19 March 2004). The 1
hour precipitation forecasts generated by the MDSS models were combined
into 3 and 6 hour quantitative (liquid) precipitation forecasts. This
was done to match the processing capabilities of the FSL Real Time
Verification System.

The precipitation observations used for the comparison are from the
Hydrometeorological Automated Data System (HADS)[^2], a real-time data
acquisition and data distribution system operated by the Office of
Hydrologic Development of the National Weather Service. A HADS site in
Des Moines, Iowa was used for this analysis. The HADS data is quality
controlled after the data are collected at the National Climate Data
Center to ensure that there are no significant format or bounds errors
(values out of normal range).

The results are provided in Figure 11.12a and 11.12b.

![](media/image20.png){width="5.999305555555556in"
height="5.928472222222222in"}

![](media/image21.png){width="5.999305555555556in"
height="5.928472222222222in"}

These figures illustrate statistical agreement between model forecasts
of precipitation and actual (observed) precipitation over the area
included in all three of the models being evaluated.  The blue lines
represent the Eta model, the other two are the supplemental models run
by FSL specifically for the MDSS demonstration.  In each figure, the top
panel is the Equitable Skill Statistic (ESS), in which 1.0 is the best
possible score and 0 indicates performance similar to random guessing.
For practically all thresholds of precipitation, the models run by FSL
for the MDSS project (MM5 and WRF) show greater skill statistics.  The
bottom panel in each figure is the Bias score, in which a value of 1.0
is perfect, indicating no over forecasting or under forecasting. 

All three models over forecasted the light precipitation amounts (\<
\~0.65 inches liquid for 6 hour totals and \~0.5 inches for 3 hour
totals) and under forecasted the heavier amounts (\~ 1.0 inch for the 6
hour totals and 0.5 inches for the 3 hour totals), but the supplemental
models provided by FSL are closer to the 1.0 target for all thresholds.

Caution should be used when considering these statistics because of the
tendency of precipitation gauges to under catch winter precipitation,
particularly light precipitation. Similar comparisons made during warm
season rain events for other research projects show similar performance
tendencies supporting the conclusion that the mesoscale models
outperform the NWS models for quantitative precipitation forecasting.

Other features of the MDSS modeling system that had positive impacts on
the MDSS (based on subjective analyses) were its rapid update cycle
(hourly), *hot start* capability and time-lagged ensemble approach. The
rapid update cycle provided an opportunity for the model to respond to
changing conditions. The hot start capability utilized radar and
satellite data, which provided better short term forecasts. On several
occasions the models identified very weak snow showers that were missed
by the NWS national scale models. The time-lagged ensemble method help
filter the rate of change of the forecasts providing a more consistent
forecast.

Comparisons of other parameters will be provided in the final report.

# IOWA DOT USER FEEDBACK

During the latter part of the field demonstration, several Lab staff met
with Iowa DOT personnel to obtain feedback on the prototype system. A
sample of the feedback is provided in this section. Following the
demonstration the FHWA visited Iowa and participated in a post
demonstration meeting to gather additional feedback. The results of that
debriefing are not reported here.

**Dennis Burkheimer - Winter Operations, IADOT HQ, Ames (Feb 24, 2004)**

-   There were noticeable improvements made to the MDSS for the winter
    2003-2004 demonstration.

-   The garage PC\'s only have 196 MB of memory, which causes some slow
    loading and response of the MDSS display application software.

-   There was some \"disappointment\" this year that the high resolution
    models only went out 12-15 hours. The most important forecast of the
    day occurs at noon so that overnight and morning plans can be made
    before personnel leave by 3:30 PM.

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-   There is an ongoing desire to have frost prediction and alerts on
    the prototype MDSS.

-   Adjustments to the blowing snow algorithm to make it more sensitive
    were welcomed.

-   The MDSS products and GUI were well received. There is a desire to
    have several other data sets integrated with an MDSS including radar
    and satellite products, alert notification (cell, pager, e-mail
    etc.). There was a discussion of how this is the path that the
    commercial sector should certainly move along.

-   Observed that the MDSS \"nails the bigger storms\". It also did
    pretty well on precipitation timing (when it knows about an event).
    Some weaker events were missed, particularly early in the
    demonstration period and when the mesoscale models were unavailable.

-   Discussed how the MDSS is an integrated test bed of methods,
    techniques, and algorithms that are designed to compliment and not
    compete with commercial sector offerings. Commercial entities should
    combine their best technologies with those demonstrated in the MDSS
    to improve the overall services provided to the DOTs.

**Paul Durham - Garage Supervisor, Ames, IADOT (Feb 25, 2004)**

-   Indicated that significant progress was seen between last year and
    this year. His staff very much likes the MDSS capabilities and it is
    viewed by staff as becoming very credible. The staff sees great
    potential for the MDSS technology. He is concerned that the program
    may fade away just when it is getting its feet.

-   The staff would very much like to see the program continue as "you
    are on the edge of having something very special that will
    significantly support our decision making". Reiterated that the
    \"golden goose\" would be for MDSS to incorporate actual treatments
    directly into the system. This capability should be included in
    commercial offerings.

-   The MDSS is doing much better overall than the first winter.

-   Discussed the 15-hr period for the high-resolution models. Paul
    reiterated that the most critical forecast of the day occurs between
    12 PM and 3 PM when they make their plans for that night and the
    following morning.

-   Paul liked the blowing snow alert product and was supportive of the
    adjustments made to the sensitivity of the blowing snow alert.

-   They believe that a \"human in the loop\" should be included in an
    operational service as it would improve the chances of catching some
    otherwise missed events and provide for some analysis.

-   The garage has an increasing \"mass\" of weather information to sift
    through. In the old days, you looked out the window. Then, they
    waited for the local news to see the radar. Now, with cable TV and
    the Internet, all of this information rushes at you. At times the
    amount of information is a detriment. They need one system that they
    can build confidence in. The MDSS is rapidly developing a following
    and more credibility.

-   Beside road/bridge frost, they are all satisfied that the prototype
    system contains all of the tools to meet their needs.

-   Paul would like to see his favorite features and functions of the
    MDSS and commercial services combined into a single system. They are
    becoming more reliant on weather system technologies such as MDSS.

**Gary McDaniel - Garage Assistant, Des Moines North, IADOT (Feb 16
2004)**

-   The MDSS was generally conservative on alerts, wind speed and snow
    amounts. However, it appeared to do a good job on precipitation
    timing. For the most part, it worked quite well.

-   The system needs to be able to \"stack treatments\". For example,
    when temperatures become marginal (10 - 15F), they will
    simultaneously treat with both salt brine and salt either by using 2
    trucks or by towing a trailer. The current system does not allow for
    multiple, simultaneous treatments to be entered.

-   For some small events (\<1-2 inches), the MDSS showed no
    precipitation at all. \[This issue was addressed later in the period
    as described in Section 11.1.3\]

-   When first logging on to the system, he immediately zooms to the DSM
    route region. He looks at recommended treatments. He finds the
    graphical display easier to digest than the tabular form.

-   He has found that the treatments are pretty close especially in
    melting (not blowing snow) situations.

-   He absolutely supports continued development of MDSS and technology
    transfer to the private sector.

-   The system was \"head and shoulders above\" last year\'s prototype.

**Ed Mahoney - Garage Supervisor, Des Moines North, IADOT (Feb 25,
2004)**

-   Indicated that the MDSS was very useful this year and the staff
    members were more comfortable using it.

-   Found that the application rate for the total storm period to be
    very close to what was actually used. Some treatment timing
    sometimes differed during the storm because of small scale features
    like snow bands and rush hour issues. He described a case where the
    MDSS had a total forecast of 700 tons of salt predicted. He
    estimated that they used around 725 tons during the storm. He
    indicated that he was impressed with its recommendations this year.

-   During the January 29-30, 2004 storm, they wanted to experiment on
    one route by applying exactly what the MDSS forecast. For the 17
    miles on IA route 415, they followed the guidance and in 3 (or 4)
    passes dropped 24 tons of salt at the times recommended by MDSS.
    Upon inspection of the road after the treatment, it was deemed to be
    "perfect".

-   Overall, the system is somewhat conservative on early snow
    projections and on material rates. He has found the precipitation
    ending time to be very good.

-   The rules of practice need to be modified to take into account the
    time of day and direction (inbound vs outbound) during rush hours. A
    prediction of 200 lbs/lane mile will be increased to 350 lbs/lane
    mile during rush hours because it will take the trucks longer to
    complete a route.

-   Was comfortable with the changes recently made to make the blowing
    snow algorithm more sensitive.

-   He and his staff trusted the system much more for reference this
    year. It appears that the system is better within 12 hours of a
    storm than in the 12-24 hour planning stage. This may be due to the
    loss of the mesoscale models after that period.

-   Indicated that the road temperature predictions were much better
    this year.

-   The most important forecast of the day is from noon to 3 PM for crew
    scheduling.

-   Successfully used the "what if" scenario generation function to
    create treatment plans.

**Rich Hedlund - Garage Supervisor, Des Moines West, IADOT (Feb 25,
2004)**

-   Because Rich was not involved in the verification program this year,
    he did not routinely use the system, but his sense was that it was
    more complete and accurate. It was mainly used to plan for upcoming
    storms.

-   The lack of consistent bandwidth at this garage (due to line
    sharing) made using the MDSS more difficult.

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-   Was comfortable with the changes recently made to make the blowing
    snow algorithm more sensitive and wished the MDSS had a road/bridge
    frost product.

-   Stated that he would like to see the research program continue given
    the rapid progress being made. He indicated that he appreciates the
    outreach that is being done on road weather issues.

-   The interface is intuitive and easy to navigate.

-   They never just \"plow\". If they are out there, they will always
    drop something. The only time that they just plow is if the snow is
    falling too fast.

-   He really likes the addition of the blowing snow alert. Bridge frost
    forecasts would be helpful. He would like to have a program running
    in the background that would pop up an alert window if an element
    crosses a threshold (e.g. blowing snow).

-   The MDSS would best be used in an organization with a dedicated
    \"snow desk\" where someone could help in coordinating the different
    garage operations. Currently, the garages use a \"waterfall effect\"
    for coordination. As the weather affects one route area, the
    supervisor calls the next downstream office that passes the
    information on.

# LESSONS LEARNED OR CONFIRMED

This second MDSS field season provided a wealth of information to the
development team. Preliminary lessons learned or confirmed are listed
below. Note, these may change based on additional analysis.

[Road Weather Forecast System (RWFS)]{.underline}

-   There were many cases were the human could see light snow in the raw
    model data, but the RWFS filtered it out. The weights used for QPF
    drifted due to poor observational data and the regression process
    added negative biases to the data, which filtered weak cases.
    Corrective actions included:

    a.  Fixing the weights using expert opinion until good winter
        precipitation observations become available

    b.  Don't attempt automatic bias correction on QPF data due to poor
        winter precipitation observations.

-   The data fusion used in the RWFS works very well for verifiable
    parameters.

-   The forward error correction scheme works very well.

-   The blowing snow potential product helped. A more sophisticated
    blowing snow product is desired. The algorithm should also include a
    road direction factor to account for cross-winds vs. along road
    winds. The users continue to request information on areas that may
    be prone to drifting snow. Snow drift models should be investigated
    for this application.

-   Probabilistic/Confidence Products: Work should continue to provide
    probabilistic information for key parameters (e.g., road
    temperature, precipitation amount, etc.). The conditional
    probability of precipitation type graphical product was well
    received by the users.

[Road Condition & Treatment Module RCTM)]{.underline}

> Road Temperature Model

-   SNTHERM Initialization: Using surface and subsurface data from RWIS
    helps predictions, particularly when the sun angles are high and the
    temperatures are not driven as much by air temperatures.

-   Use of insolation data from the mesoscale models (instead of
    calculated it from modeled cloud layer data) improves the road
    temperature predictions.

Rules of Practice

-   Addition of storm characterization logic significantly improved the
    product.

-   Adjustment of chemical amounts upward to be less idealistic about
    amount of scatter and splatter was correct approach.

-   Rules need to account for multiple treatments being performed at the
    same time (e.g., more than one truck on a route).

[Weather Modeling]{.underline}

-   The "hot start" model approach improved the prediction of
    precipitation.

-   Forecast Period: Because winter maintenance decisions are often made
    24 hrs in advance, it would be best to run the "best" models out to
    at least 24 hrs.

[Display]{.underline}

-   Snow Accumulation on Road Product: Due to the various factors that
    influence the snow depth on the road, the users would rather see
    estimated snow fall accumulation on the event summary page and not
    the estimated snow accumulation on the road.

-   System Status: The operational versions of the MDSS needs to show
    the users when critical parts or inputs to the system are
    unavailable and the forecasts may be impaired.

[General]{.underline}

-   [Tactical Support]{.underline}: The DOTs require guidance once a
    storm has begun. A tactical DSS solution will need to include radar,
    snow gauge data and nowcasting algorithms.

-   [Real-time Precipitation Observations:]{.underline} Developing a
    capability that provides precipitation rate information in real-time
    would help tactical decision making and will provide verification
    data to help tune predictions systems. A new sensor or the
    application of an existing technology reapplied to this problem
    could be very beneficial. If proven, the real time precipitation
    rate sensor could become an enhancement to automatic weather
    stations.

-   [Weak Events:]{.underline} Weak snow events turn out to be more
    important than strong events for two reasons: (1) they occur more
    frequently and (2) they usually catch the operators by surprise.
    Unfortunately, forecasting these events 12 hours in advance is as
    challenging as trying to determine where/when small thunderstorms
    will develop. The hot start modeling approach, which takes advantage
    of radar data appears to help. \[This will be analyzed further and
    reported as part of the final report\].

-   [Start/Stop Time]{.underline}: The end-users may want to see a
    dynamic range of likely start and stop times. For example, snow will
    start between 6AM and 9AM and end between 4PM and 5PM. This will
    give them some feel for the confidence we have in the forecast. The
    range could be based on the spread of the individual forecast
    modules.

-   [Snow drifting]{.underline}: Drifting snow creates dangerous driving
    conditions, particularly when the drifts are intermittent. The users
    continue to seek solutions that provide guidance on drifting snow.
    The feasibility of using snow drift models in real time should be
    analyzed.

# SUMMARY

The MDSS prototype was improved significantly between the first and
second field demonstration periods. The end users, primarily Iowa DOT
maintenance supervisors, indicated that they felt much more comfortable
with the system and that the treatment recommendations were much closer
to the actual treatments. The 2003-2004 winter field demonstration
included a broad range of weather and road conditions and provided an
excellent dataset for analysis.

Weather and road condition prediction on road scales is complex and
poses significant challenges for decision support systems for winter
maintenance operations. The MDSS project has been very useful in
highlighting areas that need attention as well as capabilities
(techniques, methods, features, and functions) that are ready or nearly
ready for transition to operational use.

There were several successes this year. They include:

a)  An improved understanding of numerical weather prediction,
    particularly mesoscale modeling, and its current capabilities and
    limits for decision support applications such as the MDSS.

b)  The success of the Road Weather Forecast System's data fusion
    methods (e.g., model output statistics, dynamic weighting, forward
    error correction, etc.) in providing additional weather forecast
    skill above and beyond the individual inputs.

c)  The use of direct insolation (short wave solar radiation) data from
    the mesoscale models instead of calculating the insolation from
    cloud field data improved the road temperature predictions.

d)  The use of "hot start" models improved precipitation forecasts.
    Maintenance personnel have indicated on several occasions that a
    better prediction of precipitation start and end times would be a
    major benefit.

e)  The revised Rules of Practice code that included logic to
    characterize the pre-storm, during storm and post storm conditions
    and covered more weather and road condition scenarios, was a
    significant success.

f)  The provision of probabilistic information on precipitation type
    allowed users to get a much better feel for not only the probability
    of precipitation, but the timing and evolution of the transition of
    precipitation type (e.g., rain to snow, snow to rain, etc.).

g)  The addition of a blowing snow potential product allowed users to
    get a better feel for the duration of an event.

There were also some issues that arose during the field season that were
problematic. These issues and lessons learned also provide useful
information for future researchers and developers of MDSS like
capabilities. Some of the outstanding issues are listed below.

a)  Forecast Period: The decision to shorten the forecast period of the
    mesoscale models from a 24 to 15 hour period in favor of hourly runs
    was flawed because storm staffing decisions were made about 15 hours
    beforehand, typically 12 to 3 PM the day before the event. Many of
    the decisions were based on forecast data from 15-24 hours, which
    was the period not covered by the mesoscale models. There were cases
    where the mesocale models were correctly predicting rain up to 15
    hours and then the MDSS precipitation type switched to snow at hour
    16 because of the data source changed to the older runs of the NWS
    models (Eta or GFS).

b)  Road Frost: The lack of guidance on road frost remains a hole in the
    prototype MDSS. Users have frequently asked for improved road frost
    guidance. There are several road frost research efforts that could
    be leveraged toward the development of a more mature product.

c)  Winter Precipitation Measurements: The lack of quality winter
    precipitation measurements impacts the ability to identify snowy
    regions, determine where blowing snow may be occurring, and impacts
    the skill of predicting quantitative precipitation amount.

d)  Weather Prediction Models: Weather prediction models are evolving
    over time and improving. Care must be taken to ensure that the data
    used is what is expected. The MDSS team discovered several issues
    related to short wave radiation fields from GFS, Eta, and WRF that
    were not resolved or addressed until mid way through the
    demonstration. NCEP corrected some of the Eta problems in late
    March, too late for the field demonstration. Those using data from
    both the research and operational models are cautioned to monitor
    the output and track updates to ensure the data are actually what
    was expected.

Based on the analyses presented herein, we can conclude that the second
demonstration of the MDSS was successful as the performance of the
prototype was significantly improved. Several outstanding issues were
resolved between the first and second year. Several key capabilities
(e.g., methods, techniques, code, algorithms, etc.) of the system have
matured and provide a solid template and framework for an operational
capability.

Additional research and development are still needed to improve several
aspects of the system as it is still somewhat ideal in its calculations.
Additional research is required in the areas of fine scale weather
modeling, road frost, blowing and drifting snow, and road condition
modeling. The Rules of Practice were improved, but are still limited.

Additional research and development are required to handle an expanded
number of chemical types (e.g., MgCl~2,~ and specialized formulations),
treatment scenarios (e.g., multiple concurrent treatments), water and
chemical solution runoff, and traffic effects on anti- and deicing
chemicals.

# ANALYSIS PLANS FOR THE FINAL REPORT

This report only presents a snapshot of results from the winter
2003-2004 MDSS field demonstration. Analysis will continue through the
summer of 2004. Additional analyses will be conducted that will focus on
answering the questions below.

1)  How well did the RWFS predict the weather at each forecast lead-time
    (0-24 hours)?

2)  How did the MDSS generated treatment recommendations compare to the
    actual treatments performed for specific cases?

3)  What was the overall impact of the supplemental models on the MDSS
    predicted weather conditions? Analysis to compare:

    a.  RWFS weather prediction performance using only NWS models.

    b.  RWFS weather prediction performance using both NWS and FSL
        models.

4)  What was the skill of the individual supplemental model members
    provided by FSL? Analyses will also include comparisons between the
    WRF, MM5, RUC, and standard NCEP models (e.g. Eta and GFS).

5)  How accurate were the road and bridge temperature forecasts
    generated by the MDSS?

6)  What were the differences in road temperature prediction performance
    between segments initialized with RWIS surface and subsurface and
    those without this initialization?

7)  What kind of skill can be expected from SNTHERM-RT when provided
    observed weather input data? And are the RWIS stations a reasonable
    standard to compare SNTHERM-RT results to?

8)  Were the data collected by the GPS/AVL systems useful for
    verification? What can be learned from these data?

**Appendix A:**

**Technical Points of Contact**

The primary points of contact for the MDSS project are listed below.

+-----------+---------+------------------------------------------------+
| **MDSS**  | *       | **Technical Point of Contact**                 |
|           | *Source |                                                |
| **        | Lab**   |                                                |
| Technical |         |                                                |
| Co        |         |                                                |
| mponent** |         |                                                |
+-----------+---------+------------------------------------------------+
| Road      | CRREL   | Gary Phetteplace                               |
| Te        |         |                                                |
| mperature |         | CRREL                                          |
| Model     |         |                                                |
| S         |         | 72 Lyme Road                                   |
| NTHERM-RT |         |                                                |
|           |         | Hanover, NH 03755-1290                         |
|           |         |                                                |
|           |         | Ph: 603-646-4248                               |
|           |         |                                                |
|           |         | Email:                                         |
|           |         | <Gary.E.Phetteplace@erdc.usace.army.mil>       |
|           |         |                                                |
|           |         | George Koenig                                  |
|           |         |                                                |
|           |         | CRREL                                          |
|           |         |                                                |
|           |         | 72 Lyme Road                                   |
|           |         |                                                |
|           |         | Hanover, NH 03755-1290                         |
|           |         |                                                |
|           |         | Ph: 603-646-4556                               |
|           |         |                                                |
|           |         | Fax: 603-646-4730                              |
|           |         |                                                |
|           |         | Email:                                         |
|           |         |                                                |
|           |         | <gkoenig@crrel.usace.army.mil>                 |
+-----------+---------+------------------------------------------------+
| Chemical  | LL      | Robert G. Hallowell                            |
|           |         |                                                |
| Conc      |         | MIT Lincoln Laboratory                         |
| entration |         |                                                |
| A         |         | 244 Wood Street                                |
| lgorithms |         |                                                |
|           |         | Lexington MA 02420-9180                        |
| Coded     |         |                                                |
| Rules of  |         | Ph: 781-981-3645                               |
| Practice  |         |                                                |
|           |         | Fax: 781-981-0632                              |
|           |         |                                                |
|           |         | Email: <bobh@ll.mit.edu>                       |
+-----------+---------+------------------------------------------------+
| Road      | NCAR    | Bill Myers                                     |
| Weather   |         |                                                |
| Forecast  |         | NCAR                                           |
| System    |         |                                                |
| (based on |         | 3450 Mitchell Lane                             |
| DICAST©)  |         |                                                |
|           |         | Boulder CO 80301                               |
| Road      |         |                                                |
| Condition |         | Ph: 303-497-8412                               |
| and       |         |                                                |
| Treatment |         | Fax: 303-497-8401                              |
| Module    |         |                                                |
|           |         | Email: <myers@ucar.edu>                        |
| **Lead    |         |                                                |
| Software  |         |                                                |
| E         |         |                                                |
| ngineer** |         |                                                |
|           |         |                                                |
| System    |         |                                                |
| In        |         |                                                |
| tegration |         |                                                |
+-----------+---------+------------------------------------------------+
| MDSS-FP   | NCAR    | Paddy McCarthy                                 |
| Display   |         |                                                |
| Ap        |         | NCAR                                           |
| plication |         |                                                |
|           |         | 3450 Mitchell Lane                             |
|           |         |                                                |
|           |         | Boulder CO 80301                               |
|           |         |                                                |
|           |         | Ph: 303-497-8461                               |
|           |         |                                                |
|           |         | Fax: 303-497-8401                              |
|           |         |                                                |
|           |         | Email: <paddy@ucar.edu>                        |
|           |         |                                                |
|           |         | Arnaud Dumont                                  |
|           |         |                                                |
|           |         | NCAR                                           |
|           |         |                                                |
|           |         | 3450 Mitchell Lane                             |
|           |         |                                                |
|           |         | Boulder CO 80301                               |
|           |         |                                                |
|           |         | Ph: 303-497-8434                               |
|           |         |                                                |
|           |         | Fax: 303-497-8401                              |
|           |         |                                                |
|           |         | Email: <dumont@ucar.edu>                       |
+-----------+---------+------------------------------------------------+
| Meteo     | FSL     | Patty Miller                                   |
| rological |         |                                                |
| Ass       |         | NOAA/Forecast Systems Lab                      |
| imilation |         |                                                |
| Data      |         | 325 Broadway, R/FS1                            |
| Ingest    |         |                                                |
| System    |         | Boulder CO 80303                               |
| (MADIS)   |         |                                                |
|           |         | Ph: 303-497-6365                               |
| RWIS Data |         |                                                |
| Ingest    |         | Fax: 303-497-7256                              |
|           |         |                                                |
|           |         | Email: <miller@fsl.noaa.gov>                   |
+-----------+---------+------------------------------------------------+
| Ensemble  | FSL     | Paul Schultz                                   |
| Modeling  |         |                                                |
| System    |         | NOAA/Forecast Systems Lab                      |
|           |         |                                                |
|           |         | 325 Broadway, R/FS1                            |
|           |         |                                                |
|           |         | Boulder CO 80303                               |
|           |         |                                                |
|           |         | Ph: 303-497-6997                               |
|           |         |                                                |
|           |         | Fax: 303-497-7262                              |
|           |         |                                                |
|           |         | Email:                                         |
|           |         | [paul.j.]{.underline}[                         |
|           |         | schultz@noaa.gov](mailto:schultz@fsl.noaa.gov) |
+-----------+---------+------------------------------------------------+
| MDSS Lab  | NCAR    | Bill Mahoney                                   |
| Lead      |         |                                                |
|           |         | NCAR                                           |
| Pro       |         |                                                |
| grammatic |         | 3450 Mitchell Lane                             |
| Issues    |         |                                                |
| and       |         | Boulder CO 80301                               |
| Questions |         |                                                |
|           |         | Ph: 303-497-8426                               |
|           |         |                                                |
|           |         | Fax: 303-497-8401                              |
|           |         |                                                |
|           |         | Cell: 303-817-7975                             |
|           |         |                                                |
|           |         | Email: <mahoney@ucar.edu>                      |
+-----------+---------+------------------------------------------------+
| MDSS      | FHWA    | Paul Pisano                                    |
| Program   |         |                                                |
| Leader    |         | FHWA                                           |
| Road      |         |                                                |
| Weather   |         | HOTO-1 Room 3408                               |
| M         |         |                                                |
| anagement |         | 400 Seventh St SW                              |
| Program   |         |                                                |
|           |         | Washington, D.C. 20590                         |
| FHWA      |         |                                                |
|           |         | Ph: 202-366-1301                               |
|           |         |                                                |
|           |         | Email: <paul.pisano@fhwa.dot.gov>              |
+-----------+---------+------------------------------------------------+
| MDSS      | FHWA    | Rudy Persaud                                   |
| Project   |         |                                                |
| COTR      |         | FHWA/HRDO                                      |
|           |         |                                                |
|           |         | Turner Fairbanks Research Center               |
|           |         |                                                |
|           |         | 6300 Georgetown Pike                           |
|           |         |                                                |
|           |         | McLean, VA 22101                               |
|           |         |                                                |
|           |         | Ph: 202-493-3391                               |
|           |         |                                                |
|           |         | Email: <rudy.persaud@fhwa.dot.gov>             |
+-----------+---------+------------------------------------------------+
| MDSS      | M       | Andy Stern                                     |
| Program   | itretek |                                                |
| Support   |         | Mitretek                                       |
| for the   |         |                                                |
| FHWA      |         | 3150 Fairview Park Drive South                 |
|           |         |                                                |
|           |         | MS-530                                         |
|           |         |                                                |
|           |         | Falls Church, VA 22042                         |
|           |         |                                                |
|           |         | Ph: 703-610-1754                               |
|           |         |                                                |
|           |         | Email: <astern@mitretek.org>                   |
+-----------+---------+------------------------------------------------+

The primary points of contact for the Iowa DOT staff that participated
in the 2004 winter field demonstration are provided below.

+---------+----------+--------+-------+------+-----------------------+
| *       | **       | **Add  | **Ph  | **F  | **E-mail**            |
| *Name** | Agency** | ress** | one** | ax** |                       |
+---------+----------+--------+-------+------+-----------------------+
|         |          |        |       |      |                       |
+---------+----------+--------+-------+------+-----------------------+
| Dennis  | Iowa     | 800    | 51    | 515- | <dennis.burkhe        |
| Bur     | DOT,     | L      | 5-239 | 239- | imer@dot.state.ia.us> |
| kheimer | Winter   | incoln | -1355 | 1005 |                       |
|         | Op       | Way,   |       |      |                       |
|         | erations | Ames   |       |      |                       |
|         | Admin    | Iowa   |       |      |                       |
|         | istrator | 50010  |       |      |                       |
+---------+----------+--------+-------+------+-----------------------+
| Richard | Iowa     | 12493  | 51    | 515- | <richard.hed          |
| Hedlund | DOT,     | Univ   | 5-986 | 225- | lund@dot.state.ia.us> |
|         | Sup      | ersity | -5726 | 0762 |                       |
|         | ervisor, | Ave.   |       |      |                       |
|         | Des      | Clive, |       |      |                       |
|         | Moines   | IA     |       |      |                       |
|         | West     | 50325  |       |      |                       |
|         | Garage   |        |       |      |                       |
+---------+----------+--------+-------+------+-----------------------+
| Edward  | Iowa     | 1530   | 51    | 515- | <edward.mah           |
| Mahoney | DOT,     | N.E.   | 5-265 | 265- | oney@dot.state.ia.us> |
|         | Sup      | 53^rd^ | -1614 | 4122 |                       |
|         | ervisor, | Ave.   |       |      |                       |
|         | Des      | Des    |       |      |                       |
|         | Moines   | M      |       |      |                       |
|         | North    | oines, |       |      |                       |
|         | Garage   | IA     |       |      |                       |
|         |          | 50313  |       |      |                       |
+---------+----------+--------+-------+------+-----------------------+
| Gary    | Iowa     | 1530   | 51    | 515- | <gary.mcda            |
| M       | DOT, DSM | N.E.   | 5-265 | 265- | niel@dot.state.ia.us> |
| cDaniel | North    | 53^rd^ | -1614 | 4122 |                       |
|         | Garage   | Ave.   |       |      |                       |
|         | Office,  | Des    |       |      |                       |
|         | Asst.    | M      |       |      |                       |
|         |          | oines, |       |      |                       |
|         |          | IA     |       |      |                       |
|         |          | 50313  |       |      |                       |
+---------+----------+--------+-------+------+-----------------------+
| Claude  | Iowa     | 12493  | 51    | 515- | <claude.frazie        |
| Frazier | DOT,     | Univ   | 5-986 | 225- | riii@dot.state.ia.us> |
| III     |          | ersity | -5726 | 0762 |                       |
|         | Des      | Ave.   |       |      |                       |
|         | Moines   |        |       |      |                       |
|         | West     | Clive, |       |      |                       |
|         | Garage   | IA     |       |      |                       |
|         | Office   | 50325  |       |      |                       |
|         | Assist.  |        |       |      |                       |
+---------+----------+--------+-------+------+-----------------------+
| Paul    | Iowa     | U.S.   | 51    | 515- | <paul.du              |
| Durham  | DOT,     | 30     | 5-232 | 232- | rham@dot.state.ia.us> |
|         | Sup      | East   | -8226 | 8227 |                       |
|         | ervisor, |        |       |      |                       |
|         | Ames     | Ames,  |       |      |                       |
|         | Garage   | IA     |       |      |                       |
|         |          | 50010  |       |      |                       |
+---------+----------+--------+-------+------+-----------------------+
| Jim Van | Iowa     | U.S.   | 51    | 515- | <jim.vansi            |
| Sickle  | DOT,     | 30     | 5-232 | 232- | ckle@dot.state.ia.us> |
|         | Garage   | East   | -8226 | 8227 |                       |
|         | Office   |        |       |      |                       |
|         | Asst.,   | Ames,  |       |      |                       |
|         | Ames     | IA     |       |      |                       |
|         | Garage   | 50010  |       |      |                       |
+---------+----------+--------+-------+------+-----------------------+
| Jim     | Iowa     | 800    | 51    | 515- | <jim.                 |
| Dowd    | DOT,     | L      | 5-239 | 239- | dowd@dot.state.ia.us> |
|         | MDSS HQ  | incoln | -1724 | 1005 |                       |
|         | Liaison  | Way,   |       |      |                       |
|         |          | Ames   |       |      |                       |
|         |          | Iowa   |       |      |                       |
|         |          | 50010  |       |      |                       |
+---------+----------+--------+-------+------+-----------------------+

##   {#section-1 .unnumbered}

The primary points of contact for the CTRE staff that participated in
the 2004 winter field demonstration are provided below.

  ------------ ------------ ---------------- -------------- -------------- ------------------------
  **Name**     **Agency**   **Address**      **Phone**      **Fax**        **E-mail**

                                                                           

  Dennis       CTRE         ISU Research     515-296-0910   515-294-0467   <kroeger@iastate.edu>
  Kroeger                   Park, 2901 S.                                  
                            Loop Dr., Suite                                
                            3100, Ames, IA                                 
                            50010-8632                                     

  Jill         CTRE         ISU Research     515-296-0910   515-294-0467   <jrmascar@iastate.edu>
  Mascarello                Park, 2901 S.                                  
                            Loop Dr., Suite                                
                            3100, Ames, IA                                 
                            50010-8632                                     
  ------------ ------------ ---------------- -------------- -------------- ------------------------

**APPENDIX B**

**MDSS Field Demonstration Event Summary**

\*Winter weather cases are highlighted

+----+-----+-----------------+-----------------+------------+--------+
| ** | **  | **Weather**     | **System        | **System   | **Lab  |
| De | Dat |                 | Notes**         | Upgrades,  | Member |
| mo | e** |                 |                 | Fixes and  | in     |
| D  |     |                 |                 | Ref        | Iowa** |
| ay |     |                 |                 | inements** |        |
| ** |     |                 |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 1  | 29  | No significant  | Demonstration   |            | None   |
|    | Dec | Weather         | began           |            |        |
|    | 2   |                 |                 |            |        |
|    | 003 |                 |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 2  | 30  | No significant  |                 |            | None   |
|    | Dec | Weather         |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 3  | 31  | No significant  |                 |            | None   |
|    | Dec | Weather         |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 4  | 1   | No significant  |                 |            | None   |
|    | Jan | Weather         |                 |            |        |
|    | 2   |                 |                 |            |        |
|    | 004 |                 |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 5  | 2   | No significant  |                 |            | None   |
|    | Jan | Weather         |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 6  | 3   | Approaching     |                 |            | None   |
|    | Jan | event           |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| [7 | [4  | [Cold (teens),  | [MDSS started   | []{.mark}  | None   |
| ]{ | Ja  | snow            | the event       |            |        |
| .m | n]{ | event]{.mark}   | several hrs too |            |        |
| ar | .ma |                 | early. Highly   |            |        |
| k} | rk} | [Ames \~1       | variable        |            |        |
|    |     | inch]{.mark}    | snowfall totals |            |        |
|    |     |                 | across demo     |            |        |
|    |     | [Des Moines \~  | routes.]{.mark} |            |        |
|    |     | 7               |                 |            |        |
|    |     | inches]{.mark}  |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 8  | 5   | No significant  |                 | Display:   | Robert |
|    | Jan | Weather         |                 | Corrected  | Hal    |
|    |     |                 |                 | snow total | lowell |
|    |     |                 |                 | label on   |        |
|    |     |                 |                 | event      |        |
|    |     |                 |                 | summary    |        |
|    |     |                 |                 | page and   |        |
|    |     |                 |                 | brine      |        |
|    |     |                 |                 | label on   |        |
|    |     |                 |                 | history    |        |
|    |     |                 |                 | page.      |        |
+----+-----+-----------------+-----------------+------------+--------+
| 9  | 6   | No significant  |                 | RCTM:      | Robert |
|    | Jan | weather         |                 | Installed  | Hal    |
|    |     |                 |                 | temporary  | lowell |
|    |     |                 |                 | fix to     |        |
|    |     |                 |                 | short wave |        |
|    |     |                 |                 | radiation  |        |
|    |     |                 |                 | field by   |        |
|    |     |                 |                 | using only |        |
|    |     |                 |                 | MM5        |        |
|    |     |                 |                 | radiation  |        |
|    |     |                 |                 | data       |        |
|    |     |                 |                 | starting   |        |
|    |     |                 |                 | with 3:15  |        |
|    |     |                 |                 | pm update. |        |
+----+-----+-----------------+-----------------+------------+--------+
| 10 | 7   | No significant  |                 | Display:   | Robert |
|    | Jan | weather         |                 | Added      | Hal    |
|    |     |                 |                 | blowing    | lowell |
|    |     |                 |                 | snow       |        |
|    |     |                 |                 | button to  |        |
|    |     |                 |                 | replace    |        |
|    |     |                 |                 | dew point  |        |
|    |     |                 |                 | selection  |        |
|    |     |                 |                 | on radio   |        |
|    |     |                 |                 | buttons.   |        |
+----+-----+-----------------+-----------------+------------+--------+
| [  | [8  | [Light snow     | [**Lost FSL     | []{.mark}  | [      |
| 11 | Ja  | began at \~4 pm | model data**    |            | Robert |
| ]{ | n]{ | CST. Air        | starting at     |            | Hallo  |
| .m | .ma | temperatures in | 6UTC to 17UTC.  |            | well]{ |
| ar | rk} | mid twenties.   | This may have   |            | .mark} |
| k} |     | Based on the 6  | impacted the    |            |        |
|    |     | pm CST update,  | ability of the  |            |        |
|    |     | 100 lb          | MDSS to fully   |            |        |
|    |     | treatments were | capture the     |            |        |
|    |     | recommended at  | light snow      |            |        |
|    |     | 7 pm CST for a  | event.]{.mark}  |            |        |
|    |     | small number of |                 |            |        |
|    |     | Ames routes.    | []{.mark}       |            |        |
|    |     | Some Ames sites |                 |            |        |
|    |     | got 0.4 inches  | **[MDSS Missed  |            |        |
|    |     | of              | Event]{.mark}** |            |        |
|    |     | snow.]{.mark}   |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 12 |     | No significant  |                 |            | Robert |
|    |     | weather         |                 |            | Hal    |
|    |     |                 |                 |            | lowell |
+----+-----+-----------------+-----------------+------------+--------+
| 13 | 9   | No significant  |                 |            | Gary   |
|    | Jan | weather         |                 |            | Phett  |
|    |     |                 |                 |            | eplace |
+----+-----+-----------------+-----------------+------------+--------+
| 14 | 10  | No significant  |                 |            | Gary   |
|    | Jan | weather         |                 |            | Phett  |
|    |     |                 |                 |            | eplace |
+----+-----+-----------------+-----------------+------------+--------+
| 15 | 11  | No significant  |                 |            | Gary   |
|    | Jan | weather         |                 |            | Phett  |
|    |     |                 |                 |            | eplace |
+----+-----+-----------------+-----------------+------------+--------+
| 16 | 12  | No significant  |                 |            | Gary   |
|    | Jan | weather         |                 |            | Phett  |
|    |     |                 |                 |            | eplace |
+----+-----+-----------------+-----------------+------------+--------+
| 17 | 13  | No significant  |                 |            | Gary   |
|    | Jan | weather         |                 |            | Phett  |
|    |     |                 |                 |            | eplace |
+----+-----+-----------------+-----------------+------------+--------+
| 18 | 14  | No significant  |                 |            | Gary   |
|    | Jan | weather         |                 |            | Phett  |
|    |     |                 |                 |            | eplace |
+----+-----+-----------------+-----------------+------------+--------+
| 19 | 15  | No significant  |                 |            | Gary   |
|    | Jan | weather         |                 |            | Phett  |
|    |     |                 |                 |            | eplace |
+----+-----+-----------------+-----------------+------------+--------+
| [  | [16 | [Rain event     | [200-300 lb     | []{.mark}  | [Gary  |
| 20 | Ja  | moving in with  | treatments      |            | P      |
| ]{ | n]{ | a slight period | recommended     |            | hettep |
| .m | .ma | of freezing     | early due to    |            | lace]{ |
| ar | rk} | rain for NE     | wetness with    |            | .mark} |
| k} |     | Iowa. Chance of | road            |            |        |
|    |     | black ice       | temperatures    |            |        |
|    |     | conditions      | predicted to    |            |        |
|    |     | after           | fall to 33-35F  |            |        |
|    |     | sunset.]{.mark} | and air         |            |        |
|    |     |                 | temperatures at |            |        |
|    |     |                 | 32F. Ames       |            |        |
|    |     |                 | garage did      |            |        |
|    |     |                 | treat this      |            |        |
|    |     |                 | event.]{.mark}  |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 21 | 17  | No significant  |                 |            | Gary   |
|    | Jan | weather         |                 |            | Phett  |
|    |     |                 |                 |            | eplace |
+----+-----+-----------------+-----------------+------------+--------+
| 22 | 18  | No significant  |                 |            | No one |
|    | Jan | weather         |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 23 | 19  | No significant  |                 |            | Bill   |
|    | Jan | weather         |                 |            | Myers  |
+----+-----+-----------------+-----------------+------------+--------+
| 24 | 20  | Very light snow | MDSS not        |            | Bill   |
|    | Jan | mid morning for | showing the     |            | Myers  |
|    |     | brief period.   | light snow      |            |        |
|    |     | Air             | flurries. No    |            |        |
|    |     | temperatures in | models showing  |            |        |
|    |     | low 20s. road   | flurries        |            |        |
|    |     | temps in mid    | either. Very    |            |        |
|    |     | 20s.            | light snow      |            |        |
|    |     |                 | blowing across  |            |        |
|    |     |                 | roads.          |            |        |
|    |     |                 | Treatments were |            |        |
|    |     |                 | not required    |            |        |
|    |     |                 | since snow      |            |        |
|    |     |                 | totals minimal. |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 25 | 21  | No significant  |                 | Insolation | Bill   |
|    | Jan | weather         |                 | field      | Myers  |
|    |     |                 |                 | (short     |        |
|    |     |                 |                 | wave) from |        |
|    |     |                 |                 | WRF now    |        |
|    |     |                 |                 | corrected  |        |
|    |     |                 |                 | by FSL and |        |
|    |     |                 |                 | the MDSS   |        |
|    |     |                 |                 | is now     |        |
|    |     |                 |                 | blending   |        |
|    |     |                 |                 | based on a |        |
|    |     |                 |                 | subjective |        |
|    |     |                 |                 | analysis   |        |
|    |     |                 |                 | of skill.  |        |
+----+-----+-----------------+-----------------+------------+--------+
| 26 | 22  | Light event     |                 | Display:   | Bill   |
|    | Jan | predicted in    |                 | Now, the   | Myers  |
|    |     | the evening     |                 | number of  |        |
|    |     | after 10 pm     |                 | s          |        |
|    |     | mainly east of  |                 | ignificant |        |
|    |     | the routes      |                 | digits     |        |
|    |     |                 |                 | shown      |        |
|    |     |                 |                 | depends on |        |
|    |     |                 |                 | range of   |        |
|    |     |                 |                 | values     |        |
|    |     |                 |                 | over 48 hr |        |
|    |     |                 |                 | period.    |        |
+----+-----+-----------------+-----------------+------------+--------+
| 27 | 23  | No significant  |                 | Event      | Bill   |
|    | Jan | weather         |                 | Trigger:   | Myers  |
|    |     |                 |                 | The        |        |
|    |     |                 |                 | pre        |        |
|    |     |                 |                 | cipitation |        |
|    |     |                 |                 | rate       |        |
|    |     |                 |                 | threshold  |        |
|    |     |                 |                 | for        |        |
|    |     |                 |                 | declaring  |        |
|    |     |                 |                 | an event   |        |
|    |     |                 |                 | was        |        |
|    |     |                 |                 | changed    |        |
|    |     |                 |                 | from 0.1   |        |
|    |     |                 |                 | mm/hr to   |        |
|    |     |                 |                 | 0.05 mm/hr |        |
|    |     |                 |                 | to be more |        |
|    |     |                 |                 | sensitive  |        |
|    |     |                 |                 | to light   |        |
|    |     |                 |                 | snow. The  |        |
|    |     |                 |                 | PoP        |        |
|    |     |                 |                 | threshold  |        |
|    |     |                 |                 | is still   |        |
|    |     |                 |                 | 25%.       |        |
+----+-----+-----------------+-----------------+------------+--------+
| 28 | 24  | No significant  |                 |            | Bill   |
|    | Jan | weather         |                 |            | Myers  |
+----+-----+-----------------+-----------------+------------+--------+
| 29 | 25  | Weather event   |                 |            | Brent  |
|    | Jan | approaching     |                 |            | Shaw   |
|    |     | from southwest  |                 |            |        |
|    |     | and west.       |                 |            |        |
|    |     | Snowfall of 1-3 |                 |            |        |
|    |     | inches          |                 |            |        |
|    |     | predicted       |                 |            |        |
|    |     | across routes.  |                 |            |        |
|    |     | Snow start time |                 |            |        |
|    |     | approximately   |                 |            |        |
|    |     | 01:00 on        |                 |            |        |
|    |     | 26^th^.         |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| [  | [26 | [Snow event.    | [Chemical       | []{.mark}  | [Brent |
| 30 | Ja  | Snow (4-5       | treatments and  |            | Shaw]{ |
| ]{ | n]{ | inches) falling | plowing         |            | .mark} |
| .m | .ma | over routes for | recommended.    |            |        |
| ar | rk} | long period     | Some actual     |            |        |
| k} |     | (\~12 hrs).     | treatments were |            |        |
|    |     | Road            | e               |            |        |
|    |     | temperatures    | ntered.]{.mark} |            |        |
|    |     | cold and        |                 |            |        |
|    |     | treatments were |                 |            |        |
|    |     | recom           |                 |            |        |
|    |     | mended.]{.mark} |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 31 | 27  | Clear and cold  |                 | Insolation | Brent  |
|    | Jan |                 |                 | field      | Shaw   |
|    |     |                 |                 | (short     |        |
|    |     |                 |                 | wave) from |        |
|    |     |                 |                 | WRF seems  |        |
|    |     |                 |                 | to be      |        |
|    |     |                 |                 | under      |        |
|    |     |                 |                 | estimating |        |
|    |     |                 |                 | insolation |        |
|    |     |                 |                 | on clear   |        |
|    |     |                 |                 | days.      |        |
|    |     |                 |                 | Changed    |        |
|    |     |                 |                 | weight     |        |
|    |     |                 |                 | blend to   |        |
|    |     |                 |                 | be 75% MM5 |        |
|    |     |                 |                 | and 25%    |        |
|    |     |                 |                 | WRF.       |        |
+----+-----+-----------------+-----------------+------------+--------+
| 32 | 28  | Cloudy with     | **Lost FSL      |            | Brent  |
|    | Jan | light snow      | model data      |            | Shaw   |
|    |     | north and east  | after 05Z due   |            |        |
|    |     | of Ames routes, | to H/W          |            |        |
|    |     | but not over    | problem**       |            |        |
|    |     | the Ames routes |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| [  | [29 | [Light snow     | **[FSL model    | [Display   | [Brent |
| 33 | Ja  | early in the    | data            | (version   | Shaw]{ |
| ]{ | n]{ | morning. Not    | unavai          | 3.03):     | .mark} |
| .m | .ma | picked up by    | lable]{.mark}** | Weather    |        |
| ar | rk} | MDSS (sans FSL  |                 | alert      |        |
| k} |     | m               | []{.mark}       | criteria   |        |
|    |     | odels).]{.mark} |                 | were       |        |
|    |     |                 | **[MDSS Missed  | modified   |        |
|    |     | [Treatments     | Event]{.mark}** | to remove  |        |
|    |     | required around |                 | the        |        |
|    |     | morning rush    |                 | original   |        |
|    |     | hour. Some      |                 | simple     |        |
|    |     | sand/salt mix   |                 | blowing    |        |
|    |     | used.]{.mark}   |                 | snow       |        |
|    |     |                 |                 | alerts.    |        |
|    |     |                 |                 | Blowing    |        |
|    |     |                 |                 | snow       |        |
|    |     |                 |                 | alerts are |        |
|    |     |                 |                 | still      |        |
|    |     |                 |                 | being      |        |
|    |     |                 |                 | provided   |        |
|    |     |                 |                 | by the     |        |
|    |     |                 |                 | new, more  |        |
|    |     |                 |                 | sop        |        |
|    |     |                 |                 | histicated |        |
|    |     |                 |                 | algorith   |        |
|    |     |                 |                 | m.]{.mark} |        |
+----+-----+-----------------+-----------------+------------+--------+
| 34 | 30  | [Light snow     | **[FSL model    | []{.mark}  | [Brent |
|    | Jan | event of \~3    | data            |            | Shaw]{ |
|    |     | inches.         | unavai          |            | .mark} |
|    |     | Temperatures    | lable]{.mark}** |            |        |
|    |     | near            |                 |            | [Paul  |
|    |     | zero.]{.mark}   | []{.mark}       |            | Pi     |
|    |     |                 |                 |            | sano]{ |
|    |     |                 | **[MDSS Missed  |            | .mark} |
|    |     |                 | Event]{.mark}** |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 35 | 31  | Light snow in   | **FSL model     |            | Brent  |
|    | Jan | morning.        | data            |            | Shaw   |
|    |     | Temperature     | unavailable**   |            |        |
|    |     | -2F. Not clear  |                 |            |        |
|    |     | if treatments   |                 |            |        |
|    |     | needed.         |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
|    |     |                 |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| [  | [1  | [Snow event.    | **[FSL model    | []{.mark}  | [Darin |
| 36 | Fe  | Temps in 20s.   | data            |            | Me     |
| ]{ | b]{ | MDSS predicted  | unavai          |            | yers]{ |
| .m | .ma | 4-6 for Ames    | lable]{.mark}** |            | .mark} |
| ar | rk} | and 3-4 for     |                 |            |        |
| k} |     | DSM. Long event | []{.mark}       |            |        |
|    |     | with light      |                 |            |        |
|    |     | rates.]{.mark}  | [Iowa DOT input |            |        |
|    |     |                 | several         |            |        |
|    |     |                 | 'actual'        |            |        |
|    |     |                 | treatments.     |            |        |
|    |     |                 | Good case for   |            |        |
|    |     |                 | matching        |            |        |
|    |     |                 | treatment       |            |        |
|    |     |                 | recommen        |            |        |
|    |     |                 | dations]{.mark} |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| [  | [2  | [Snow event     | **[FSL model    | []{.mark}  | [Darin |
| 37 | Fe  | continues in    | data            |            | Me     |
| ]{ | b]{ | early           | unavai          |            | yers]{ |
| .m | .ma | m               | lable]{.mark}** |            | .mark} |
| ar | rk} | orning.]{.mark} |                 |            |        |
| k} |     |                 |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 38 | 3   | Clear and cold. | **FSL model     |            | Darin  |
|    | Feb |                 | data partially  |            | Meyers |
|    |     |                 | back** with     |            |        |
|    |     |                 | temporary       |            |        |
|    |     |                 | reconfiguration |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 39 | 4   | Cloudy and      | **FSL model     |            | Darin  |
|    | Feb | cold. Temps in  | data back       |            | Meyers |
|    |     | the 20s. Storm  | on-line...**all |            |        |
|    |     | approaching     | systems normal. |            |        |
|    |     | from southwest. |                 |            |        |
|    |     | Snow predicted  |                 |            |        |
|    |     | to start around |                 |            |        |
|    |     | midnight. Snow  |                 |            |        |
|    |     | started near    |                 |            |        |
|    |     | midnight in DSM |                 |            |        |
|    |     | and later in    |                 |            |        |
|    |     | Ames.           |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| [  | [5  | [Snow all day.  | [Actual         | []{.mark}  | [Darin |
| 40 | Fe  | MDSS showed 5"  | treatments      |            | Me     |
| ]{ | b]{ | for Ames and 4" | entered. Good   |            | yers]{ |
| .m | .ma | for DSM.        | agreement early |            | .mark} |
| ar | rk} | Actuals were    | in              |            |        |
| k} |     | \~5 for Ames    | event.]{.mark}  |            |        |
|    |     | and 7" for      |                 |            |        |
|    |     | DSM.]{.mark}    |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| [  | [6  | [Light snow     | [**MDSS missed  | [Display   | [Darin |
| 41 | Fe  | fell in         | light event**   | (Version   | Me     |
| ]{ | b]{ | morning. Not    | even though     | 3.1.1):    | yers]{ |
| .m | .ma | shown on MDSS,  | models showed   | Fixed bug  | .mark} |
| ar | rk} | but shown in    | light           | that       |        |
| k} |     | FSL             | snow.]{.mark}   | sometimes  |        |
|    |     | models.]{.mark} |                 | caused     |        |
|    |     |                 |                 | routes not |        |
|    |     |                 |                 | to change  |        |
|    |     |                 |                 | when       |        |
|    |     |                 |                 | selecte    |        |
|    |     |                 |                 | d.]{.mark} |        |
+----+-----+-----------------+-----------------+------------+--------+
| 42 | 7   | No significant  |                 |            |        |
|    | Feb | Weather         |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 43 | 8   | No Significant  |                 |            | Arnaud |
|    | Feb | Weather         |                 |            | Dumont |
+----+-----+-----------------+-----------------+------------+--------+
| [  | [9  | [Scattered snow | [MDSS showed    | [Geonor    | [      |
| 44 | Fe  | flurries for    | some very light | snow gauge | Arnaud |
| ]{ | b]{ | short time at   | snow early in   | voltage    | Du     |
| .m | .ma | some locations  | the forecast    | regulator  | mont]{ |
| ar | rk} | followed by     | cycle, but      | replaced.  | .mark} |
| k} |     | wind causing    | dropped snow as | Data       |        |
|    |     | some blowing    | time            | norm       |        |
|    |     | snow. Snow      | prog            | al]{.mark} |        |
|    |     | accumulation of | ressed.]{.mark} |            |        |
|    |     | \~0.3 inches in |                 |            |        |
|    |     | some places.    |                 |            |        |
|    |     | Plowing of      |                 |            |        |
|    |     | drifts occurred |                 |            |        |
|    |     | for some        |                 |            |        |
|    |     | northern        |                 |            |        |
|    |     | routes.]{.mark} |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 45 | 10  | Clear. No       | MDSS not        | RWFS:      | Arnaud |
|    | Feb | significant     | showing any     | Added new  | Dumont |
|    |     | weather. Weak   | precipitation   | code to    |        |
|    |     | front to move   | from front. No  | fix the    |        |
|    |     | through in      | models show     | weights    |        |
|    |     | evening with    | precipitation   | given to   |        |
|    |     | NWS calling for | in central      | predicted  |        |
|    |     | chance of light | Iowa.           | pre        |        |
|    |     | scattered       |                 | cipitation |        |
|    |     | flurries.       |                 | (QPF).     |        |
|    |     |                 |                 | Also added |        |
|    |     |                 |                 | code to    |        |
|    |     |                 |                 | remove all |        |
|    |     |                 |                 | bias       |        |
|    |     |                 |                 | c          |        |
|    |     |                 |                 | orrections |        |
|    |     |                 |                 | on the     |        |
|    |     |                 |                 | final      |        |
|    |     |                 |                 | pre        |        |
|    |     |                 |                 | cipitation |        |
|    |     |                 |                 | forecasts. |        |
+----+-----+-----------------+-----------------+------------+--------+
| 46 | 11  | Partly cloudy.  | At noon, MDSS   |            | Arnaud |
|    | Feb | No snow fell    | not showing any |            | Dumont |
|    |     | overnight, but  | accumulating    |            |        |
|    |     | winds did kick  | precipitation   |            |        |
|    |     | up and blow     | today or        |            |        |
|    |     | older snow      | tonight.        |            |        |
|    |     | around. Light   |                 |            |        |
|    |     | snow flurries   |                 |            |        |
|    |     | and blowing     |                 |            |        |
|    |     | snow predicted  |                 |            |        |
|    |     | by NWS.         |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| [  | [12 | [Brief period   | [MDSS missed    | []{.mark}  | [      |
| 47 | Fe  | of snow         | weak event      |            | Arnaud |
| ]{ | b]{ | flurries, not   | (\~0.3          |            | Du     |
| .m | .ma | predicted by    | inches)]{.mark} |            | mont]{ |
| ar | rk} | MDSS models.    |                 |            | .mark} |
| k} |     | One treatment   |                 |            |        |
|    |     | round (near     |                 |            |        |
|    |     | rush hour       |                 |            |        |
|    |     | required). Snow |                 |            |        |
|    |     | \~0.3           |                 |            |        |
|    |     | inches.]{.mark} |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 48 | 13  | No significant  |                 |            |        |
|    | Feb | weather         |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 49 | 14  | No significant  |                 | Blowing    |        |
|    | Feb | weather         |                 | Snow:      |        |
|    |     |                 |                 | Based on   |        |
|    |     |                 |                 | feedback   |        |
|    |     |                 |                 | from the   |        |
|    |     |                 |                 | mid-demo   |        |
|    |     |                 |                 | telecom,   |        |
|    |     |                 |                 | the        |        |
|    |     |                 |                 | blowing    |        |
|    |     |                 |                 | snow       |        |
|    |     |                 |                 | algorithm  |        |
|    |     |                 |                 | was        |        |
|    |     |                 |                 | refined to |        |
|    |     |                 |                 | be more    |        |
|    |     |                 |                 | sensitive  |        |
|    |     |                 |                 | to wind    |        |
|    |     |                 |                 | and snow   |        |
|    |     |                 |                 | age.       |        |
+----+-----+-----------------+-----------------+------------+--------+
| 50 | 15  | No significant  |                 |            | Andy   |
|    | Feb | weather         |                 |            | Stern  |
|    |     |                 |                 |            | & Ray  |
|    |     |                 |                 |            | Murphy |
+----+-----+-----------------+-----------------+------------+--------+
| 51 | 16  | No significant  |                 |            | Andy   |
|    | Feb | weather         |                 |            | Stern  |
|    |     |                 |                 |            | & Ray  |
|    |     |                 |                 |            | Murphy |
+----+-----+-----------------+-----------------+------------+--------+
| 52 | 17  | Foggy in the    |                 | I          | Andy   |
|    | Feb | morning         |                 | nsolation: | Stern  |
|    |     | followed by     |                 | WRF model  | & Ray  |
|    |     | partly cloudy   |                 | revision   | Murphy |
|    |     | skies. A very   |                 | to remove  |        |
|    |     | brief flurry    |                 | mic        |        |
|    |     | was reported at |                 | rophysical |        |
|    |     | Ames. Not       |                 | species    |        |
|    |     | action          |                 | from grid  |        |
|    |     | required.       |                 | boxes with |        |
|    |     |                 |                 | low        |        |
|    |     |                 |                 | conce      |        |
|    |     |                 |                 | ntrations. |        |
|    |     |                 |                 | This       |        |
|    |     |                 |                 | improved   |        |
|    |     |                 |                 | the WRF    |        |
|    |     |                 |                 | insolation |        |
|    |     |                 |                 | values on  |        |
|    |     |                 |                 | clear      |        |
|    |     |                 |                 | days.      |        |
+----+-----+-----------------+-----------------+------------+--------+
| 53 | 18  | Clear and mild  | Road            |            | Andy   |
|    | Feb | (temps in mid   | temperatures    |            | Stern  |
|    |     | 30s to low      | well above      |            |        |
|    |     | 40s). Rain even | freezing during |            |        |
|    |     | predicted by    | daytime.        |            |        |
|    |     | MDSS starting   |                 |            |        |
|    |     | near midnight   |                 |            |        |
|    |     | on the 20^th^.  |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 54 | 19  | Cloudy. Storm   | MDSS shows rain |            | Andy   |
|    | Feb | approaching     | to snow case.   |            | Stern  |
|    |     | from southwest. | Treatments      |            |        |
|    |     | Rain predicted  | recommended for |            |        |
|    |     | to start late   | rain and cold   |            |        |
|    |     | in the evening  | roads, then for |            |        |
|    |     | then change to  | snow.           |            |        |
|    |     | snow at sun     |                 |            |        |
|    |     | rise on the     |                 |            |        |
|    |     | 20th. About 2-3 |                 |            |        |
|    |     | inches          |                 |            |        |
|    |     | predicted for   |                 |            |        |
|    |     | Ames and 1 inch |                 |            |        |
|    |     | for DSM.        |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| [  | [20 | [Rain to light  | [MDSS had rain  | []{.mark}  | [Andy  |
| 55 | Fe  | snow event.     | start well      |            | S      |
| ]{ | b]{ | Rain to begin   | predicted. Late |            | tern]{ |
| .m | .ma | near midnight   | evening         |            | .mark} |
| ar | rk} | and briefly     | (19^th^)        |            |        |
| k} |     | change to snow  | updates         |            |        |
|    |     | near dawn, then | downplayed snow |            |        |
|    |     | to rain         | accum. Meso     |            |        |
|    |     | again.]{.mark}  | models did well |            |        |
|    |     |                 | with type. NWS  |            |        |
|    |     |                 | models          |            |        |
|    |     |                 | predicted too   |            |        |
|    |     |                 | much snow the   |            |        |
|    |     |                 | day             |            |        |
|    |     |                 | before.]{.mark} |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 56 | 21  | No significant  |                 |            |        |
|    | Feb | Weather         |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 57 | 22  | Rain showers in | **MDSS lost FSL |            | Bill   |
|    | Feb | morning through | models** from   |            | M      |
|    |     | early afternoon | 11-22Z          |            | ahoney |
|    |     | in DSM. No snow | (inclusive).    |            |        |
|    |     | in DSM.         | The first       |            |        |
|    |     |                 | forecast with   |            |        |
|    |     |                 | the restored    |            |        |
|    |     |                 | data would have |            |        |
|    |     |                 | been            |            |        |
|    |     |                 |                 |            |        |
|    |     |                 | the 9 pm        |            |        |
|    |     |                 | update.         |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 58 | 23  | Low clouds.     |                 |            | Bill   |
|    | Feb | Temps near 35F. |                 |            | M      |
|    |     | Road temps near |                 |            | ahoney |
|    |     | 40F.            |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 59 | 24  | Low clouds.     |                 |            | Bill   |
|    | Feb | Temps near 37F. |                 |            | M      |
|    |     | Road temps in   |                 |            | ahoney |
|    |     | 40s.            |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 60 | 25  | Clear and mild. |                 |            | Bill   |
|    | Feb | No significant  |                 |            | M      |
|    |     | weather.        |                 |            | ahoney |
+----+-----+-----------------+-----------------+------------+--------+
| 61 | 26  | Clear ands      |                 |            | Bill   |
|    | Feb | mild. No        |                 |            | M      |
|    |     | significant     |                 |            | ahoney |
|    |     | weather.        |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 62 | 27  | Mild. No        |                 |            |        |
|    | Feb | significant     |                 |            |        |
|    |     | weather.        |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 63 | 28  | Mild. No        |                 |            |        |
|    | Feb | significant     |                 |            |        |
|    |     | weather.        |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 64 | 29  | Cloudy and      | Missed some FSL |            |        |
|    | Feb | mild. No        | model runs.     |            |        |
|    |     | significant     |                 |            |        |
|    |     | weather.        |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 65 | 1   | Rain showers    |                 |            |        |
|    | Ma  | developing      |                 |            |        |
|    | rch | during day.     |                 |            |        |
|    |     | Mild, temps in  |                 |            |        |
|    |     | mid 40s to low  |                 |            |        |
|    |     | 50s.            |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 66 | 2   | Cloudy and      | Lost FSL data   |            |        |
|    | Ma  | cold. Temps in  | from 13-18Z due |            |        |
|    | rch | mid to upper    | to failed NCAR  |            |        |
|    |     | 30s.            | disk.           |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 67 | 3   | Cloudy and      | NWS servers     |            |        |
|    | Ma  | mild. Air       | down in         |            |        |
|    | rch | temperatures in | Washington.     |            |        |
|    |     | 40s. Rain and   | National data   |            |        |
|    |     | some snow       | feed lost since |            |        |
|    |     | showers         | 00z runs. Also, |            |        |
|    |     | becoming all    | 04-08Z runs of  |            |        |
|    |     | rain by mid     | FSL models lost |            |        |
|    |     | day. Road temps | due to FSL      |            |        |
|    |     | in mid 40s.     | problem. NWS    |            |        |
|    |     |                 | servers back up |            |        |
|    |     | Rain with       | for 6 pm MDSS   |            |        |
|    |     | period of mixed | update.         |            |        |
|    |     | snow verified.  |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 68 | 4   | Cloudy. Steady  |                 |            |        |
|    | Ma  | rain period     |                 |            |        |
|    | rch | after mid       |                 |            |        |
|    |     | afternoon. Air  |                 |            |        |
|    |     | temps in low    |                 |            |        |
|    |     | 40s. Road temps |                 |            |        |
|    |     | in mid 40s.     |                 |            |        |
|    |     | Rain overnight  |                 |            |        |
|    |     | also.           |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 69 | 5   | Cold rain fell  |                 |            |        |
|    | Ma  | overnight.      |                 |            |        |
|    | rch | About 1 inch    |                 |            |        |
|    |     | was recorded at |                 |            |        |
|    |     | the Ames site.  |                 |            |        |
|    |     | Some rain/snow  |                 |            |        |
|    |     | fell north of   |                 |            |        |
|    |     | MDSS routes.    |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 70 | 6   | Cool. No        |                 |            |        |
|    | Ma  | significant     |                 |            |        |
|    | rch | weather.        |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 71 | 7   | Cool. No        |                 |            |        |
|    | Ma  | significant     |                 |            |        |
|    | rch | weather.        |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 72 | 8   | Cool and partly |                 |            |        |
|    | Ma  | cloudy. No      |                 |            |        |
|    | rch | significant     |                 |            |        |
|    |     | weather. Temps  |                 |            |        |
|    |     | in 50s. Road    |                 |            |        |
|    |     | Temps to hit    |                 |            |        |
|    |     | 60s.            |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 73 | 9   | Mild. No        |                 |            |        |
|    | Ma  | Significant     |                 |            |        |
|    | rch | Weather         |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 74 | 10  | Mostly cloudy.  |                 |            |        |
|    | Mar | Temps in 50s.   |                 |            |        |
|    |     | Front may       |                 |            |        |
|    |     | result in a few |                 |            |        |
|    |     | light isolated  |                 |            |        |
|    |     | rain showers.   |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 75 | 11  | Partly cloudy   |                 |            |        |
|    | Mar | and cold. Front |                 |            |        |
|    |     | passage         |                 |            |        |
|    |     | resulted in     |                 |            |        |
|    |     | isolated snow   |                 |            |        |
|    |     | flurries, but   |                 |            |        |
|    |     | no              |                 |            |        |
|    |     | accumulation.   |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 76 | 12  | Clear and mild. |                 |            |        |
|    | Mar |                 |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 77 | 13  | Cloudy with     |                 |            |        |
|    | Mar | some rain       |                 |            |        |
|    |     | showers. Mild.  |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 78 | 14  | Partly cloudy.  | At 6 pm, MDSS   |            |        |
|    | Mar | Mild in 40s.    | calling for 7-8 |            |        |
|    |     | Snow event      | inches of snow  |            |        |
|    |     | expected on     | in Ames and \~9 |            |        |
|    |     | 15^th^. NWS     | inches in DSM.  |            |        |
|    |     | calling for 1-3 | Snow to start   |            |        |
|    |     | in Ames.        | at 6 am on the  |            |        |
|    |     |                 | 15^th^.         |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| [  | [15 | [Snow started   | [MDSS did well  | []{.mark}  | []{    |
| 79 | Ma  | at \~6 am. Air  | on snow totals. |            | .mark} |
| ]{ | r]{ | temps in low    | Treatment       |            |        |
| .m | .ma | 30s. Road temps | recommendations |            |        |
| ar | rk} | in low 30s. By  | given all day   |            |        |
| k} |     | 7 pm, Ames had  | -- both plowing |            |        |
|    |     | \~7 inches and  | and             |            |        |
|    |     | DSM \~ 9        | che             |            |        |
|    |     | inches.]{.mark} | micals.]{.mark} |            |        |
|    |     |                 |                 |            |        |
|    |     |                 | [According to   |            |        |
|    |     |                 | NWS:]{.mark}    |            |        |
|    |     |                 |                 |            |        |
|    |     |                 | [Ames -- 10     |            |        |
|    |     |                 | inches]{.mark}  |            |        |
|    |     |                 |                 |            |        |
|    |     |                 | [DSM - 13.2     |            |        |
|    |     |                 | inches]{.mark}  |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| [  | [16 | [Cold. Period   | [MDSS predicted | []{.mark}  | []{    |
| 80 | Ma  | of light snow.  | this event      |            | .mark} |
| ]{ | r]{ | About 1 inch    | pretty well,    |            |        |
| .m | .ma | fell.]{.mark}   | both timing and |            |        |
| ar | rk} |                 | amount.]{.mark} |            |        |
| k} |     |                 |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 81 | 17  | Partly Cloudy.  |                 |            |        |
|    | Mar | Temps in upper  |                 |            |        |
|    |     | 30s to low 40s. |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 82 | 18  | Cool. No        |                 |            |        |
|    | Mar | significant     |                 |            |        |
|    |     | weather.        |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 83 | 19  | No significant  |                 |            |        |
|    | Mar | weather.        |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 84 | 20  | No significant  |                 |            |        |
|    | Mar | weather.        |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 85 | 21  | No significant  |                 |            |        |
|    | Mar | weather.        |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 86 | 22  | No significant  |                 |            |        |
|    | Mar | weather.        |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 87 | 23  | No significant  |                 |            |        |
|    | Mar | weather.        |                 |            |        |
+----+-----+-----------------+-----------------+------------+--------+
| 88 | 24  | No significant  |                 | Dem        |        |
|    | Mar | weather.        |                 | onstration |        |
|    |     |                 |                 | Ends       |        |
+----+-----+-----------------+-----------------+------------+--------+

**APPENDIX C**

Ames Garage Weather Sensor Suite

![](media/image22.jpeg){width="4.5625984251968505in"
height="6.864566929133859in"}

[^1]: Note that the NCEP fixes were not due to MDSS project feedback,
    but were being addressed independently.

[^2]: Information on HADS can be found at:
    http://www.nws.noaa.gov/oh/hads/