PURPOSE
RELATED DOCUMENTS
METHODS - MDSS_Tech_Perf_Report_29120010
Fig. 6.1. Map of routes to be supported by the MDSS prototype during the winter of 2003-2004 Iowa field demonstration
Table 6.1. Iowa Maintenance Routes for the MDSS Field Demonstration
MDSS SYSTEM CONFIGURATION
Supplemental Numerical Weather Prediction Models
MDSS Prototype Optimization Period
DATA COLLECTION PROCESS
Supplemental Data Collection
Data Sources: Weather Observations
Verification Data Forms
Road Condition and Treatment Module (RCTM)
Road Weather Forecast System (RWFS)
MDSS Display Application
SELECTED CASE STUDIES
Fig. 10.1. Photo of the weather sensor suite at the Iowa DOT maintenance garage in Ames, Iowa
Case Studies - MDSS_Tech_Perf_Report_29120025
Fig. 10.2. Surface fax maps showing the progression of the synoptic setup
Fig. 10.3. Radar images showing the progression of the precipitation
Fig. 10.4. Air Temperature (C) time-series plot comparing the Ames METAR and Ames RWIS observations to the RWFS forecasts
Fig. 10.5. Same as Fig. 10.4, except for wind speed (m/s)
Fig. 10.6. Same as Fig. 10.4, except for visibility (mi)
Fig. 10.7. Same as Fig. 10.4 except for probability of precipitation (%) and quantitative precipitation forecast (mm/hr)
Fig. 10.8. Same as Fig. 10.4 except the 18 UTC 16 January 2004 run and for probability of precipitation (%) and quantitative precipitation forecast
Fig. 10.9. Road Temperature (C) time-series plot comparing the Ames RWIS observations to the RWFS forecasts
Fig. 10.10. Cloud cover time-series plot comparing the Ames METAR only to the RWFS forecasts
Fig. 10.11. RMSE plots for all sites across Iowa (top four panels) and for Ames only
Fig. 10.12. Same as Fig. 10.11, except bias plots
Light Snow Case 26 January 2004
Light Snow Case 26 January 2004-continue
Fig. 10.14. Surface maps showing the progression of the synoptic setup
Fig. 10.15. Air Temperature (C) time-series plot comparing the Ames METAR and Ames RWIS observations to the RWFS forecasts
Fig. 10.16. Same as Fig. 10.15, except for dew point temperature (C)
Fig. 10.17. Same as Fig. 10.15, except for wind speed (m/s)
Fig. 10.18. Radar images showing the progression of the snowfall
Fig. 10.19. Same as Fig. 10.15 except for probability of precipitation (%) and quantitative precipitation forecast
Fig. 10.20. Same as Fig. 10.15 except for probability of precipitation (%) and quantitative precipitation forecast (mm/hr) for the 18 UTC 26 January RWFS run
LEDWI data
Fig. 10.21. Estimated snow accumulation (inches) from the LEDWI sensor
Fig. 10.22. Same as Fig. 10.15 except for snow depth (inches)
Fig. 10.23. Road Temperature (C) time-series plot comparing the Ames RWIS observations to the RWFS forecasts
Cumulative verification results-continue
Fig. 10.24. Same as Fig. 10.11, except Hr 0 = 18 UTC 25 January 2004
Fig. 10.25. Same as Fig. 10.12, except Hr 0 = 18 UTC 25 January 2004
Blowing Snow Case 9-11 February 2004
Fig. 10.26. Surface fax maps showing the progression of the synoptic setup
Fig. 10.27. Radar images showing the progression of the precipitation
Fig. 10.28. Air Temperature (C) time-series plot comparing the Ames METAR and Ames RWIS observations to the RWFS forecasts
Fig. 10.29. Cloud cover time-series plot comparing the Ames METAR only to the RWFS forecasts
Fig. 10.30. Same as Fig. 10.28 except for probability of precipitation (%) and quantitative precipitation forecast
Fig. 10.31. Estimated snow accumulation (inches) from the LEDWI sensor located at the METAR site
Fig. 10.32. Same as Fig. 10.28, but for 06 UTC, 11 Feb 2004 RWFS run
The blowing snow algorithm
Fig. 10.34. Same as Fig. 10.11, except Hr 0 = 18 UTC 9 February, 2004
Fig. 10.35. Same as Fig. 10.12, except Hr 0 = 18 UTC 9 February, 2004.
Mixed Precipitation Case 20 February 2004
Figs. 10.36. Surface fax maps showing the progression of the synoptic setup
Figs. 10.37. Radar images showing the progression of the precipitation
Fig. 10.38. Air Temperature (C) time-series plot comparing the Ames METAR and Ames RWIS observations to the RWFS forecasts
Fig. 10.39. Same as Fig. 10.38, except for dew point temperature (C)
Fig. 10.40. Cloud cover time-series plot comparing the Ames METAR only to the RWFS forecasts
Fig. 10.42. Same as Fig. 10.38, except for visibility (mi)
Fig. 10.43. Same as Fig. 10.38 except for probability of precipitation (%) and quantitative precipitation forecast (mm/hr)
Fig. 10.44. Same as Fig. 10.43 except the 06 UTC 20 February run
Fig. 10.45. Precipitation type comparison between the Ames METAR observations and the RWFS forecasts of type both with and without FSL
Fig. 10.46. Liquid precipitation (inches) from the tipping bucket gauge located at the METAR site
Fig. 10.47. Snow depth (inches) time-series comparison of the Ames METAR observations versus the RWFS forecast for both the METAR and RWIS sites
Fig. 10.48. Road Temperature (C) time-series plot comparing the Ames RWIS observations to the RWFS forecasts
Fig. 10.49. Same as Fig. 10.11, except Hr 0 = 00 UTC 20 February 2004
Fig. 10.50. Same as Fig. 10.12, except Hr 0 = 00 UTC 20 February 2004
Heavy Snow Case 15 March 2004
Fig. 10.51. Surface fax maps showing the progression of the synoptic setup
Fig. 10.52. Radar images showing the progression of the snowfall
Fig. 10.53. Air Temperature (C) time-series plot comparing the Ames METAR and Ames RWIS observations to the RWFS forecasts
Fig. 10.54. Same as Fig. 10.53, except for dew point temperature (C)
Fig. 10.55. Same as Fig. 10.53, except for visibility (mi)
Fig. 10.57. Cloud cover time-series plot comparing the Ames METAR only to the RWFS forecasts
The QPF forecasts -continue
Fig. 10.58. Same as Fig. 10.45 except for probability of precipitation (%) nd quantitative precipitation forecast (mm/hr)
Fig. 10.59. Estimated snow accumulation (inches) from the LEDWI sensor on the left axis
Fig. 10.60. Same as Fig. 10.45 except for snow depth (inches)
Fig. 10.61. Road Temperature (C) time-series plot comparing the Ames RWIS observations to the RWFS forecasts
Fig. 10.62. Same as Fig. 10.11, except Hr 0 = 06 UTC 15 March 2004
Fig. 10.63. Same as Fig. 10.11, except Hr 0 = 06 UTC 15 March 2004
OVERALL PERFORMANCE RESULTS
Overall Performance Assessment
Fig. 11.1. Seasonal analysis of RMSE for six meteorological parameters
Results and Recommendations - MDSS_Tech_Perf_Report_29120096
Fig. 11.2. Same as Fig. 11.1, except for bias
Fig. 11.3. Same as Fig. 11.1, except for forecast initialization times
Quantitative Precipitation Forecasts (QPF)
Insolation
Eta Model
Results and Recommendations - MDSS_Tech_Perf_Report_29120102
Fig. 11.4. Comparison between MM5, Eta, WRF, and RWFS output (12Z model runs) of short wave radiation and pyranometer data
Fig. 11.5. Comparison between RWFS output (12 Z model runs) of short wave radiation and pyranometer data
Conditional Probability of Precipitation Type
Conditional Probability of Precipitation Type-continue
Fig. 11.7. Conditional probabilities of rain, snow and ice predicted for all cases
Road Temperature Predictions
Fig. 11.8. Seasonal analysis of RMSE for road temperature, broken down by month
Fig 11.10. Same as Fig. 11.8, except for road temperature
Fig. 11.11. Seasonal analysis of RMSE versus MAE for road temperature
Fig. 11.12. Seasonal analysis of RMSE for road temperature less than 40F
Fig. 11.13. Seasonal analysis of bias for road temperatures less than 40F
Fig. 11.14. Season analysis of RMSE for road temperature forecasts for all sky conditions
Results and Recommendations - MDSS_Tech_Perf_Report_29120115
Treatment Recommendations
Fig. 11.17. Legend keys for RCTM verification variables
Verification report for March 15-16, 2004 storm
Fig. 11.19. March 15-16 forecast timeline with freeze-point adjustment
Actual conditions and operations - MDSS_Tech_Perf_Report_29120120
Variability of the treatment recommendations with time - MDSS_Tech_Perf_Report_29120121
Verification report for January 25-26, 2004-continue
Actual conditions and operations - MDSS_Tech_Perf_Report_29120123
Fig. 11.23. Forecasted and actual precipitation, road temperatures and treatment recommendations for I35N (Ames)
Variability of the treatment recommendations with time - MDSS_Tech_Perf_Report_29120125
Verification report for January 16-17 cold rain event
Fig. 11.26. Forecasted precipitation, road temperatures and recommended treatments along with actual treatments for I35N Ames route
Overall treatment verification summary and conclusions
Mesoscale Modeling System Reliability
Mesoscale Model Performance
Fig. 11.29. Example plot of model output, showing the ensemble model domain
Shelter-level (2m) temperature and dewpoint
Tower-level (10m) winds-continue
Fig. 11.30. Twelve-hour wind speed forecast errors from the MDSS ensemble models and the NWS Eta model. Bias (above) and RMS (below)
Fig. 11.31. Time series of temperature forecast errors from the 06 UTC (1 AM CST) model runs and verifying observations
Results and Recommendations - MDSS_Tech_Perf_Report_29120136
Fig 11.32a. 3-h precipitation forecast verification
Fig 11.32b. 6-h precipitation forecast verification
ANALYSIS OF ROAD TEMPERATURE PREDICTION
Fig. 12.1. Infrared images of a roadway temperature sensor
Iowa MDSS Demonstration Background
Analysis Approach
Results - MDSS_Tech_Perf_Report_29120143
Results-continue
Fig. 12.3. Infrared imagery for 03/17/2004 on I-35N near the Ames DOT garage
Fig. 12.4. SNTHERM-RT predicted road surface temperature using the average solar flux
Fig. 12.5. Root Mean Square Error (RMSE) for SNTHERM-RT road surface temperature predictions
Fig. 12.6. SNTHERM-RT predicted road surface temperature using the average solar flux
Fig. 12.7. SNTHERM-RT predicted bridge surface temperature using the instantaneous solar flux
Table 12.3. RMSE (degree centigrade) for the reanalysis of the Iowa MDSS bridge surface temperatures
CHEMICAL CONCENTRATION SAMPLING
Table 13.4. Results of chemical concentration sampling
DISPLAY SOFTWARE
Fig. 14.1. MDSS summary display, indicating colored dots and values for temperature
User Survey
User Survey-continue
IOWA DOT USER FEEDBACK
Paul Durham - Garage Supervisor, Ames, IADOT (Feb 25, 2004)
Gary McDaniel - Garage Assistant, Des Moines North, IADOT (Feb 16 2004)
Rich Hedlund - Garage Supervisor, Des Moines West, IADOT (Feb 25, 2004)
LESSONS LEARNED OR CONFIRMED
Road Condition & Treatment Module RCTM)
General
SUMMARY - MDSS_Tech_Perf_Report_29120164
SUMMARY-continue - MDSS_Tech_Perf_Report_29120165
SUMMARY-continue - MDSS_Tech_Perf_Report_29120166
Appendix A: Technical Points of Contact
Appendix A: Technical Points of Contact-continue
The primary points of contact for the Iowa DOT staff that participated in the 2004 winter field demonstration
The primary points of contact for the CTRE staff that participated in the 2004 winter field demonstration
Appendix B: MDSS Field Demonstration Event Summary
Appendix B: MDSS Field Demonstration Event Summary-continue - MDSS_Tech_Perf_Report_29120172
Appendix B: MDSS Field Demonstration Event Summary-continue - MDSS_Tech_Perf_Report_29120173
Appendix B: MDSS Field Demonstration Event Summary-continue - MDSS_Tech_Perf_Report_29120174
Appendix B: MDSS Field Demonstration Event Summary-continue - MDSS_Tech_Perf_Report_29120175
Appendix B: MDSS Field Demonstration Event Summary-continue - MDSS_Tech_Perf_Report_29120176
Appendix B: MDSS Field Demonstration Event Summary-continue - MDSS_Tech_Perf_Report_29120177
Appendix C: Ames Garage Weather Sensor Suite
MDSS_Tech_Perf_Report_2912
RELATED DOCUMENTS
METHODS - MDSS_Tech_Perf_Report_29120010
Fig. 6.1. Map of routes to be supported by the MDSS prototype during the winter of 2003-2004 Iowa field demonstration
Table 6.1. Iowa Maintenance Routes for the MDSS Field Demonstration
MDSS SYSTEM CONFIGURATION
Supplemental Numerical Weather Prediction Models
MDSS Prototype Optimization Period
DATA COLLECTION PROCESS
Supplemental Data Collection
Data Sources: Weather Observations
Verification Data Forms
Road Condition and Treatment Module (RCTM)
Road Weather Forecast System (RWFS)
MDSS Display Application
SELECTED CASE STUDIES
Fig. 10.1. Photo of the weather sensor suite at the Iowa DOT maintenance garage in Ames, Iowa
Case Studies - MDSS_Tech_Perf_Report_29120025
Fig. 10.2. Surface fax maps showing the progression of the synoptic setup
Fig. 10.3. Radar images showing the progression of the precipitation
Fig. 10.4. Air Temperature (C) time-series plot comparing the Ames METAR and Ames RWIS observations to the RWFS forecasts
Fig. 10.5. Same as Fig. 10.4, except for wind speed (m/s)
Fig. 10.6. Same as Fig. 10.4, except for visibility (mi)
Fig. 10.7. Same as Fig. 10.4 except for probability of precipitation (%) and quantitative precipitation forecast (mm/hr)
Fig. 10.8. Same as Fig. 10.4 except the 18 UTC 16 January 2004 run and for probability of precipitation (%) and quantitative precipitation forecast
Fig. 10.9. Road Temperature (C) time-series plot comparing the Ames RWIS observations to the RWFS forecasts
Fig. 10.10. Cloud cover time-series plot comparing the Ames METAR only to the RWFS forecasts
Fig. 10.11. RMSE plots for all sites across Iowa (top four panels) and for Ames only
Fig. 10.12. Same as Fig. 10.11, except bias plots
Light Snow Case 26 January 2004
Light Snow Case 26 January 2004-continue
Fig. 10.14. Surface maps showing the progression of the synoptic setup
Fig. 10.15. Air Temperature (C) time-series plot comparing the Ames METAR and Ames RWIS observations to the RWFS forecasts
Fig. 10.16. Same as Fig. 10.15, except for dew point temperature (C)
Fig. 10.17. Same as Fig. 10.15, except for wind speed (m/s)
Fig. 10.18. Radar images showing the progression of the snowfall
Fig. 10.19. Same as Fig. 10.15 except for probability of precipitation (%) and quantitative precipitation forecast
Fig. 10.20. Same as Fig. 10.15 except for probability of precipitation (%) and quantitative precipitation forecast (mm/hr) for the 18 UTC 26 January RWFS run
LEDWI data
Fig. 10.21. Estimated snow accumulation (inches) from the LEDWI sensor
Fig. 10.22. Same as Fig. 10.15 except for snow depth (inches)
Fig. 10.23. Road Temperature (C) time-series plot comparing the Ames RWIS observations to the RWFS forecasts
Cumulative verification results-continue
Fig. 10.24. Same as Fig. 10.11, except Hr 0 = 18 UTC 25 January 2004
Fig. 10.25. Same as Fig. 10.12, except Hr 0 = 18 UTC 25 January 2004
Blowing Snow Case 9-11 February 2004
Fig. 10.26. Surface fax maps showing the progression of the synoptic setup
Fig. 10.27. Radar images showing the progression of the precipitation
Fig. 10.28. Air Temperature (C) time-series plot comparing the Ames METAR and Ames RWIS observations to the RWFS forecasts
Fig. 10.29. Cloud cover time-series plot comparing the Ames METAR only to the RWFS forecasts
Fig. 10.30. Same as Fig. 10.28 except for probability of precipitation (%) and quantitative precipitation forecast
Fig. 10.31. Estimated snow accumulation (inches) from the LEDWI sensor located at the METAR site
Fig. 10.32. Same as Fig. 10.28, but for 06 UTC, 11 Feb 2004 RWFS run
The blowing snow algorithm
Fig. 10.34. Same as Fig. 10.11, except Hr 0 = 18 UTC 9 February, 2004
Fig. 10.35. Same as Fig. 10.12, except Hr 0 = 18 UTC 9 February, 2004.
Mixed Precipitation Case 20 February 2004
Figs. 10.36. Surface fax maps showing the progression of the synoptic setup
Figs. 10.37. Radar images showing the progression of the precipitation
Fig. 10.38. Air Temperature (C) time-series plot comparing the Ames METAR and Ames RWIS observations to the RWFS forecasts
Fig. 10.39. Same as Fig. 10.38, except for dew point temperature (C)
Fig. 10.40. Cloud cover time-series plot comparing the Ames METAR only to the RWFS forecasts
Fig. 10.42. Same as Fig. 10.38, except for visibility (mi)
Fig. 10.43. Same as Fig. 10.38 except for probability of precipitation (%) and quantitative precipitation forecast (mm/hr)
Fig. 10.44. Same as Fig. 10.43 except the 06 UTC 20 February run
Fig. 10.45. Precipitation type comparison between the Ames METAR observations and the RWFS forecasts of type both with and without FSL
Fig. 10.46. Liquid precipitation (inches) from the tipping bucket gauge located at the METAR site
Fig. 10.47. Snow depth (inches) time-series comparison of the Ames METAR observations versus the RWFS forecast for both the METAR and RWIS sites
Fig. 10.48. Road Temperature (C) time-series plot comparing the Ames RWIS observations to the RWFS forecasts
Fig. 10.49. Same as Fig. 10.11, except Hr 0 = 00 UTC 20 February 2004
Fig. 10.50. Same as Fig. 10.12, except Hr 0 = 00 UTC 20 February 2004
Heavy Snow Case 15 March 2004
Fig. 10.51. Surface fax maps showing the progression of the synoptic setup
Fig. 10.52. Radar images showing the progression of the snowfall
Fig. 10.53. Air Temperature (C) time-series plot comparing the Ames METAR and Ames RWIS observations to the RWFS forecasts
Fig. 10.54. Same as Fig. 10.53, except for dew point temperature (C)
Fig. 10.55. Same as Fig. 10.53, except for visibility (mi)
Fig. 10.57. Cloud cover time-series plot comparing the Ames METAR only to the RWFS forecasts
The QPF forecasts -continue
Fig. 10.58. Same as Fig. 10.45 except for probability of precipitation (%) nd quantitative precipitation forecast (mm/hr)
Fig. 10.59. Estimated snow accumulation (inches) from the LEDWI sensor on the left axis
Fig. 10.60. Same as Fig. 10.45 except for snow depth (inches)
Fig. 10.61. Road Temperature (C) time-series plot comparing the Ames RWIS observations to the RWFS forecasts
Fig. 10.62. Same as Fig. 10.11, except Hr 0 = 06 UTC 15 March 2004
Fig. 10.63. Same as Fig. 10.11, except Hr 0 = 06 UTC 15 March 2004
OVERALL PERFORMANCE RESULTS
Overall Performance Assessment
Fig. 11.1. Seasonal analysis of RMSE for six meteorological parameters
Results and Recommendations - MDSS_Tech_Perf_Report_29120096
Fig. 11.2. Same as Fig. 11.1, except for bias
Fig. 11.3. Same as Fig. 11.1, except for forecast initialization times
Quantitative Precipitation Forecasts (QPF)
Insolation
Eta Model
Results and Recommendations - MDSS_Tech_Perf_Report_29120102
Fig. 11.4. Comparison between MM5, Eta, WRF, and RWFS output (12Z model runs) of short wave radiation and pyranometer data
Fig. 11.5. Comparison between RWFS output (12 Z model runs) of short wave radiation and pyranometer data
Conditional Probability of Precipitation Type
Conditional Probability of Precipitation Type-continue
Fig. 11.7. Conditional probabilities of rain, snow and ice predicted for all cases
Road Temperature Predictions
Fig. 11.8. Seasonal analysis of RMSE for road temperature, broken down by month
Fig 11.10. Same as Fig. 11.8, except for road temperature
Fig. 11.11. Seasonal analysis of RMSE versus MAE for road temperature
Fig. 11.12. Seasonal analysis of RMSE for road temperature less than 40F
Fig. 11.13. Seasonal analysis of bias for road temperatures less than 40F
Fig. 11.14. Season analysis of RMSE for road temperature forecasts for all sky conditions
Results and Recommendations - MDSS_Tech_Perf_Report_29120115
Treatment Recommendations
Fig. 11.17. Legend keys for RCTM verification variables
Verification report for March 15-16, 2004 storm
Fig. 11.19. March 15-16 forecast timeline with freeze-point adjustment
Actual conditions and operations - MDSS_Tech_Perf_Report_29120120
Variability of the treatment recommendations with time - MDSS_Tech_Perf_Report_29120121
Verification report for January 25-26, 2004-continue
Actual conditions and operations - MDSS_Tech_Perf_Report_29120123
Fig. 11.23. Forecasted and actual precipitation, road temperatures and treatment recommendations for I35N (Ames)
Variability of the treatment recommendations with time - MDSS_Tech_Perf_Report_29120125
Verification report for January 16-17 cold rain event
Fig. 11.26. Forecasted precipitation, road temperatures and recommended treatments along with actual treatments for I35N Ames route
Overall treatment verification summary and conclusions
Mesoscale Modeling System Reliability
Mesoscale Model Performance
Fig. 11.29. Example plot of model output, showing the ensemble model domain
Shelter-level (2m) temperature and dewpoint
Tower-level (10m) winds-continue
Fig. 11.30. Twelve-hour wind speed forecast errors from the MDSS ensemble models and the NWS Eta model. Bias (above) and RMS (below)
Fig. 11.31. Time series of temperature forecast errors from the 06 UTC (1 AM CST) model runs and verifying observations
Results and Recommendations - MDSS_Tech_Perf_Report_29120136
Fig 11.32a. 3-h precipitation forecast verification
Fig 11.32b. 6-h precipitation forecast verification
ANALYSIS OF ROAD TEMPERATURE PREDICTION
Fig. 12.1. Infrared images of a roadway temperature sensor
Iowa MDSS Demonstration Background
Analysis Approach
Results - MDSS_Tech_Perf_Report_29120143
Results-continue
Fig. 12.3. Infrared imagery for 03/17/2004 on I-35N near the Ames DOT garage
Fig. 12.4. SNTHERM-RT predicted road surface temperature using the average solar flux
Fig. 12.5. Root Mean Square Error (RMSE) for SNTHERM-RT road surface temperature predictions
Fig. 12.6. SNTHERM-RT predicted road surface temperature using the average solar flux
Fig. 12.7. SNTHERM-RT predicted bridge surface temperature using the instantaneous solar flux
Table 12.3. RMSE (degree centigrade) for the reanalysis of the Iowa MDSS bridge surface temperatures
CHEMICAL CONCENTRATION SAMPLING
Table 13.4. Results of chemical concentration sampling
DISPLAY SOFTWARE
Fig. 14.1. MDSS summary display, indicating colored dots and values for temperature
User Survey
User Survey-continue
IOWA DOT USER FEEDBACK
Paul Durham - Garage Supervisor, Ames, IADOT (Feb 25, 2004)
Gary McDaniel - Garage Assistant, Des Moines North, IADOT (Feb 16 2004)
Rich Hedlund - Garage Supervisor, Des Moines West, IADOT (Feb 25, 2004)
LESSONS LEARNED OR CONFIRMED
Road Condition & Treatment Module RCTM)
General
SUMMARY - MDSS_Tech_Perf_Report_29120164
SUMMARY-continue - MDSS_Tech_Perf_Report_29120165
SUMMARY-continue - MDSS_Tech_Perf_Report_29120166
Appendix A: Technical Points of Contact
Appendix A: Technical Points of Contact-continue
The primary points of contact for the Iowa DOT staff that participated in the 2004 winter field demonstration
The primary points of contact for the CTRE staff that participated in the 2004 winter field demonstration
Appendix B: MDSS Field Demonstration Event Summary
Appendix B: MDSS Field Demonstration Event Summary-continue - MDSS_Tech_Perf_Report_29120172
Appendix B: MDSS Field Demonstration Event Summary-continue - MDSS_Tech_Perf_Report_29120173
Appendix B: MDSS Field Demonstration Event Summary-continue - MDSS_Tech_Perf_Report_29120174
Appendix B: MDSS Field Demonstration Event Summary-continue - MDSS_Tech_Perf_Report_29120175
Appendix B: MDSS Field Demonstration Event Summary-continue - MDSS_Tech_Perf_Report_29120176
Appendix B: MDSS Field Demonstration Event Summary-continue - MDSS_Tech_Perf_Report_29120177
Appendix C: Ames Garage Weather Sensor Suite
MDSS_Tech_Perf_Report_2912
