Comparison of co-located DCNet and AWS/Weatherbug urban temperature observations

The urban forecasting problem we face is complex. The meteorology within a city sometimes bears little resemblance to that of the surrounding countryside. The weather forecasting community tends to rely on wind information collected at airports, typically well outside the urban areas that are now of main interest. To assume that the meteorological fields experienced at airports are the same as those appropriate for downtown areas would clearly be inappropriate. The presence of buildings and street canyons causes behaviors that are almost random, exceedingly difficult to predict or even describe. However the flow above the “urban canopy” is far more describable in terms of larger scale meteorology. It is convenient to think in terms of two regimes – the street canyon flows beneath the urban canopy and the “skimming flow” above it. Washington, D.C., presents an excellent testbed for studies, because the urban canopy is well defined by the height constraint on the buildings.

In 2004, the Air Resources Laboratory (ARL) of the National Oceanic and Atmospheric Administration initiated a research program involving the private sector, to explore the utility of using local meteorological data from private as well as government sources in forecasting for urban areas. The program is referred to as UrbaNet. The first studies have focused on the National Capital Region using ARL's DCNet system of urban observations as the core public observation network. The work has been in collaboration with AWS Convergence Technologies, Inc. (AWS/Weatherbug), which operates a private/commercial meteorological network with a large array of meteorological measurement sites within the United States known as the Weatherbug Network.

While the focus of the UrbaNet program continues to be on the forecasting of personal exposures to hazardous materials, the suite of observations can be used to address a wide range of issues, including temperature measurements of the urban climate. In this study, aspirated temperature systems as currently deployed within NOAA's Climate Reference Network were installed at three DCNet stations collocated with AWS. Before installation, for quality assurance the DCNet CRN temperature systems were calibrated against NOAA's CRN standard. The data considered within this study represent one year of observations from both monitoring networks. While there is considerable scatter between individual 15-minute average temperatures, long term means yield correlation coefficients above 0.98.