Planed treatment recommendations

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Chrystal Philomena Bell
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1 Planed treatment recommendations for the MDSS ASFiNAG up-to-date 18 th September 2007 Albert Mathis, Dipl. Ing FH, Executive MBA, AnyData AG, Switzerland MDSS - MDSS Stakeholder - Stakeholder Meeting, Meeting, Kansas, Kansas, 18th September 18th September

2 Contents Concept of the MDSS ASFiNAG. MDSS Code Example. MDSS Code & Precipitation probability. Precipitation Probability forecast. Road weather scenarios examples 5,7,10 and 14. Road weather scenarios summary. Treatment recommendations. 2/16

Concept of the MDSS ASFiNAG Meteorological

3 Concept of the MDSS ASFiNAG Meteorological office Weather forecast. MDSS-Codes Radara data Satellite data Actual Treatment Maintenance vehicle Data (real-time truck AVL data). Treatment recommendations back to the driver in the truck. Traffic data, real time data. Measured road data. Road weather information systems (RWIS). MDSS Graphical User Interface. Type of slipperiness, black ice, rime ice, compacted snow. Treatment recommendations MDSS Codes & Precipitation probability. Alarms & Warnings. Road weather scenario. 3/16

MDSS Code 066623-80 example Compact high fog, locally icing drizzl. Black ice. XXYYzZ-%. XX Change on the road surface. YY Weather, Precipitation.

4 MDSS Code example Compact high fog, locally icing drizzl. Black ice. XXYYzZ-%. XX Change on the road surface. YY Weather, Precipitation. z Wind Z Temperature characteristics. % Precipitation probability. MDSS Code = freezing rain. 66 freezing rain. 2 Wind: kmh. 3 Temp.: +/- 0 C % Precipitation probability. BlueSky & AnyData 4/15

5 Probability forecast for precipitation. Probability 100% 0% Time 5/15

6 MDSS Codes & Precipitation probability MDSS Codes are generated every hour for 3h, every 6h for 32h and every 12h for 72 hours. The Precipitation probability are generated for the next 3h. MDSS Codes are generated for approximately 220 sections of road (1 600 miles of highway). Communication between road maintenance staff and weather prognosticator with MDSS Codes & Precipitation probability are very efficient. BlueSky & AnyData 6/15

7 Concept of the MDSS ASFiNAG Meteorological office Weather forecast MDSS-Codes Radara data Satellite data Actual Treatment Maintenance vehicle Data (real-time truck AVL data). Treatment recommendations back to the driver in the truck. Traffic data, real time data Type of slipperiness, black ice, rime ice, compacted snow. Alarms & Warnings Measured road data Road weather information systems (RWIS) MDSS Graphical User Interface Treatment recommendations. MDSS Codes & Precipitation probability. Road weather scenario 7/15

8 (1 of 5) 15 Road weather scenarios overview Scenario 1 27% 2 5% 3 5% 4 1% 5 1% 6 7% Description (yellow = 90%) Clarification after precipitation. Cooling after thaw. Warm front after cold period, Föhn. Warm front with precipitation, freezing rain. Super cooled rain. Frost Slipperiness Black ice. Black ice. Black ice. Black ice or frozen slush (extreme). Black ice. Rime ice. 7 9% 8 1% 9 1% 10 2% 11 37% 12 1% 13 0% 14 1% 15 0% Hoar frost, flash frost. Black ice. Radiation fog. Black ice. Each treatment route Drizzle from fog, freezing fog. Black ice. Industrial snow, inversion temperature. will Slipperiness have of compacted unique snow or frozen objectives slush. Cold front with precipitation. Cold snap after cold front with precipitation. Slipperiness of compacted snow or frozen slush. Precipitation according to USA scenario. affect Slipperiness their of compacted snow selection or frozen slush. for Driven snow. Incidence of snow. and priorities that will Slipperiness of compacted snow or frozen slush. the decision. Slipperiness of compacted snow or frozen slush. Slipperiness of compacted snow or frozen slush. AnyData 8/15

Example at the borderline between the cantons of Lucerne and Berne 14

9 Road weather scenarios example 5 (2 of 5) Super cooled rain. Rain, where the temperature of the raindrops is below 0 C (32 F). Example at the borderline between the cantons of Lucerne and Berne 14 hours after the start of the precipitation. Timing is critical. Black ice. AnyData 9/15

10 Road weather scenarios example 7 (3 of 5) Hoar frost, flash frost. The rapid build up of hoar frost on roads around sunrise. Dew point above the surface temperature but below 0 C (32 F). Road surface temperature <0 C, Wind speed <5 km/h, air temperature <-2 C, high air humidity >90%, Thermal Map minimum temperature <0 C, visibility <200 m or <600 m and sinking gradient 50m/15 Minute. Black ice. AnyData 10/15

(4 of 5) Road weather scenarios example 10 Industrial snow, inversions temperature out of compact fog with a 700 m altitude top, with moderate north east wind (cold),

11 (4 of 5) Road weather scenarios example 10 Industrial snow, inversions temperature out of compact fog with a 700 m altitude top, with moderate north east wind (cold), cold lowland (below 3ºC) high fog lasting several days. Check: needs to be controlled local. Slipperiness of compacted snow or frozen slush (only local). AnyData 11/15

12 (5 of 5) Road weather scenarios example 14 Driven snow mostly by uninterrupted, cold wind on wind exposed places after powder snow fall. Snow fall less than 3 days, no sunshine, no wet snow and road surface temperature 0 C (32 F) or minus 0 C. Check: Go if the wind speed is more than 8 km/h. Slipperiness of compacted snow or frozen slush. AnyData 14/15

13 Road weather scenarios summary 07 Hoar frost, flash frost. 9% 11b Cold front with precipitation Nord. 10% 06 Frost. 7% 02 Cooling after thaw. 5% 00 Two Szenarios. 2% 05 Super cooled rain. 1% 04 Warm front with precipitation, freezing rain. 03 Warmfront after cold 1% period, Fhn. 5% 08 Radiation fog. 1% 11a Cold front with precpitation West. 27% Weitere 10% 01 Clarification after Precipitation. 09 Drizzle from fog, freezing fog. 1% 10 Industrial snow, inversion temperature. 2% 12 Cold front after cold front with precipitaiton. 1% AnyData 13/15

14 Treatment recommendations The real-time data from the trucks will not change the forecast but will change the road state in the treatments. Some weather phenomena are very difficult to predict: localized precipitations, combination of snow/rain/snow change line, liquid equivalents, wind drifts, convection and of course the clouds. From my experience in the Canton Lucerne, Switzerland. My recommend / advise to train the road maintenance staff with the road weather scenarios in order to enable them to react fast and accurate to weather changes. AnyData 14/15

15 Thank you Albert Mathis, Dipl. Ing FH, Executive MBA, AnyData AG, Switzerland

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