1. Improved road weather forecasting by using high resolution satellite data Claus Petersen and Bent H. Sass Danish Meteorological Institute

2. Background It has been realized that prediction of cloud cover and precipitation play a key role in prediction of the road surface temperature and the road conditions. Prediction of cloud cover requires a NWP model which can model clouds and data-assimilation of cloud cover and precipitation observations.

3. Viking project Title –Development of new generation of cloud and precipitation analyses for the automatic Road Weather Model Duration –2003-2005 Goal –Improvement of the forecasts for slippery roads by developing a new prediction model

4. Numerical Weather Prediction (NWP) model Horizontal resolution 0.15x0.15 (degree) Vertical levels40 Rotation of south pole Lon.=80 Lat.=0 (degree) Number of grid points 610x568=346480 Dynamic time step 360 (s) Physical time step 360 (s) Boundary updateEvery 3rd hour Boundary age0-6 hours First guess age3 or 6 hours Forecast frequency Every 4th hour Forecast length60 hours Data-assimilation4 times daily+2 reassimilation cycles

5. Already used observations

6. Model domain of NWP model and network of road stations Horizontal resolution 0.15x0.15 Vertical levels 40 Number of grid points 82x98=8036 Dynamic time step 72 s. Physical time step 360 s. Boundary update 1 hour Boundary age 0-5 hours First guess age 0-1 hour Forecast frequency Every hour Forecast length 5-24 hours Data-assimilation period3 hours Road stations300

7. Data sources

8. Single channels or composite

9. Cloud maskCloud top temperature Precipitation intensityCloud type

10. Application of cloud observations

12. FORECAST 1 hour forecast of cloud mask 1 hour forecast of precipitation, mslp1 hour forecast of wind and temperature Observed cloud mask 1 hour forecast with data-assimilation of satellite data

13. FORECAST 1 hour forecast of cloud mask 1 hour forecast of precipitation, mslp1 hour forecast of wind and temperature Observed cloud mask 1 hour forecast without data-assimilation of satellite data

14. 6 hour forecast of cloud mask 6 hour forecast of precipitation, mslp6 hour forecast of wind and temperature Observed cloud mask 6 hour forecast with data-assimilation of satellite data

15. 6 hour forecast of cloud mask 6 hour forecast of precipitation, mslp6 hour forecast of wind and temperature Observed cloud mask 6 hour forecast without data-assimilation of satellite data

16. 21 hour forecast of cloud mask 21 hour forecast of precipitation, mslp21 hour forecast of wind and temperature Observed cloud mask 21 hour forecast with data-assimilation of satellite data

17. 21 hour forecast of cloud mask 21 hour forecast of precipitation, mslp21 hour forecast of wind and temperature Observed cloud mask 21 hour forecast without data-assimilation of satellite data

18. Road Condition Model G: Ground heat flux S: Direct insolation D: Diffuse insolation R: Infrared radiation H: Sensible heat flux L: Latent heat flux F: Flux correction

19. User interface

20. Verification of cloud forecast First two weeks of March 2005 Danish SYNOP stations Limited MSG1 data Verifcation for model run every hour

21. Best practice A general method has been developed to assimilate cloud observations into a NWP model. Verification and case studies indicate that prediction of cloud cover is improved for short range forecasting but that results can be further improved with more experience. Further verification and investigation of the road surface temperature dependency of cloud cover are needed. Satellite data will be used in the road weather model from this season The potential use of satellite data in other road application is very large.

22. QUESTIONS CONTACT Claus Petersen cp@dmi.dkcp@dmi.dk Danish Meteorological Institute LINKS www.dmi.dk www.eumetsat.int http://nwcsaf.inm.es