MDSS Challenges, Research, and Managing User Expectations - Weather Issues - Bill Mahoney & Kevin Petty National Center for Atmospheric Research (NCAR) MDSS Stakeholder Meeting Vienna, VA August 2006 Swiss icing event near Interlaken

2 Issue Statement The MDSS is a sophisticated technology that requires accurate weather information on very small scales. Almost all MDSS products (road and weather) are dependent on accurate weather data input. Given this dependency and the knowledge that weather prediction is still imprecise at these scales: 1) How much utility does the system have? 2) What research is required to improve its skill? 3) How do we manage user expectations?

3 Primary MDSS Limitations Inconsistent weather prediction skill Lack of knowledge of actual road conditions Inconsistent pavement condition data quality Lack of knowledge of actual treatments Lack of universal methodology to incorporate actual treatment data Actual Treatments

4 MDSS Weather Challenges For optimal performance, weather forecasts need to be very precise on city block spacial scales and on a time scale of minutes! While weather forecast precision is improving, it will be many years before the skill will match the need. OK – Now What? Washington, D.C. mall region

5 The Weather Forecast Enterprise Worldwide Forecasting Issues Why are weather forecasts often inaccurate? There is a lack of global weather observations taken at high spatial and temporal resolution and a lack of an ability to fully utilize current observational data. We don’t know the state of the atmosphere very well!

6 The Atmosphere is a Fluid The Earth System is well connected! Water vapor false color imagery

7 Primary Weather Prediction Limitations Lack of consistently accurate weather predictions –Precipitation start and stop times –Precipitation amounts (particularly for light events) –Cloud cover (solar radiation) –Water vapor (fog, frost, dew, etc.) Lack of accurate observational weather data –Snow amounts (depth and liquid water equivalent) –Freezing drizzle –Insolation –Radar data quality Lack of knowledge and utilization of land surface data –Soil moisture –Soil temperature –Terrain features (e.g., road cuts, small lakes, clearings, forest boundaries) –Snow cover & albedo

8 MDSS Research Needs - Weather Weather & Land Surface Modeling Boundary layer meteorology (friction, turbulence, water vapor flux, heat exchange, etc.) and land surface observations, data assimilation, and modeling Need to assess performance of surface weather forecasts using models that utilize new data assimilation methods and coupled land surface models. Need techniques to assess utility of solar radiation observations Feedback from snowfall needs to be factored into next model run!

9 Sample General Errors in Air Temperature Prediction Raw models surface temperature errors ~2.5 to 3.0 o C Lowest 10 meters of boundary layer hard to predict! Radiational cooling layer dominates errors. MDSS Road Weather Forecast System Forecast 10 o C Inversion

10 Sample Large Variation in Predicted Precipitation 15 UTC 18 January 2006 RWFS run showing a liquid equivalent accumulation. Measured precipitation 06 UTC 09 February 2006 RWFS run showing a liquid equivalent.

11 Sample Direct Solar Radiation Prediction Clear Day Partly Cloudy Day Model Output Differences

12 Deterministic vs. Probabilistic Forecasts NAS Report – July 2006 “The chaotic character of the atmosphere coupled with inevitable inadequacies in observations and computer models, results in forecasts that always contain uncertainties.” The MDSS and its users, who are risk managers, need to deal with these uncertainties.

13 Communication of Uncertainty Given the weather and road condition prediction is not precise, we need to develop methods to convey uncertainty to decision makers. Need user working group to help define product concepts (text and graphics) for multiple variables. MDSS conditional probability of precipitation type graphic Sample product from Iowa DOT Specification