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: 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: 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: 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: Added `neighborhood DMOS' (Dynamic Model Output
Statistics) to the system so there would be more consistency between nearby
prediction points that have similar characteristics.
3) Quality Control Flags: Developed software to take advantage of the quality
control flags provided with the MADIS data stream.
4) Hourly Forecasts: 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: Using the data contained in the RWFS, generated
probabilistic information for select data fields (e.g., precipitation, and
6) Bridge Support: Added the capability in the RWFS to predict weather and road
temperatures for bridge segments.
Mesoscale Model Ensemble