84th AMS Annual Meeting

Monday, 12 January 2004: 9:30 AM
Forecasting Adverse Weather in the Baltimore MD -Washington DC Urban Zone: Detection and Predictability Issues
Room 611
Steven M. Zubrick, NOAA/NWSFO, Sterling, VA
Poster PDF (99.7 kB)
The combined Baltimore, Maryland and Washington, DC urban region is home to nearly 7 million people. Adverse weather can have a large impact on the lives of many in this region particularly regarding transportation. This combined region ranks near the top in having the second longest commute times of any urban region in the United States. Any amount of precipitation falling in any form can adversely impact commuting on the major roadways in this region. In particular, accumulation of precipitation in the form of snow, freezing rain/drizzle, and/or sleet is commonly viewed as the primary adverse weather condition that most negatively impacts all facets of the transportation sector.

One can regard most significant wintertime precipitation events (e.g., snowstorms, blizzards, ice storms) as being generally well-forecast for this region. These forecasts provide sufficient lead time to alert the public and transportation interests so that appropriate actions can be taken. But other adverse weather occurs (both wintertime and non-wintertime) that is more subtle, smaller in areal and temporal scales, and a challenge to detect and forecast. Some examples include very light (less than 2 mm) accumulation of wintertime precipitation (e.g., freezing rain or drizzle, snowfall), ice deposition directly from the air onto sub-freezing roadways like bridges and overpasses, local dense fog, and the freezing of residual moisture on wet pavement.

This paper elaborates on the forecast challenges in dealing with these subtle adverse weather conditions mentioned above. A few brief case studies will be presented to highlight these adverse conditions, their forecast predictability, and their associated societal impacts on transportation in this urban region. Urban transportation planners need to be aware of limitations of both existing observing networks to detect these conditions and the limited skill to forecast their occurrence reliably and accurately to better prepare and respond when these conditions occur.

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