• forcing data for urban meteorological models (or land surface models in urban areas), such as near-surface air temperature, humidity, wind, precipitation, solar and longwave radiation;
• observational data to characterize urban areas and determine urban model parameters (e.g., roughness length, impermeable areas) and urban sources/sinks (e.g., anthropogenic heating);
• observational data for urban model validation; and
• long-term observational data for end users.
A variety of data in each category are available from different communities. The best source of forcing data in the urban boundary layer likely would come from previous field experiments in urban areas. The down-scaling from 32 km regional atmospheric reanalysis over North America (Mesinger et al., 2006) and coarser resolution (from 0.5 to 2.5 deg or about 50 to 250 km) global reanalysis (e.g., Decker et al., 2011) provides another possibility.
For the urban characterization and source/sink data, the necessary spatial coverage is provided by satellite and aircraft data, such as the Landsat 30 m land cover data, the MODIS (Moderate Resolution Imaging Spectrometer) suite of land surface data (e.g., land cover, vegetation index, surface skin temperature) at 250 m to 1 km resolution, high spatial resolution aircraft lidar digital elevation data, and survey and field experimental data for urban sources/sinks.
Model validation data are primarily from field experiments and surveys. Polar-orbiting and geostationary satellite data (e.g., surface skin temperature) are also crucial. The geostationary data can be available at 4 km and hourly resolutions. They are available up to about 60 degree of latitude; however, the spatial resolution decreases at higher latitudes.
Much work has been done in attempting to remove the urban effect in the long-term climate data record (such as the 2m air temperature) (e.g., Kalnay and Cai, 2003). To avoid urban contamination, only sites far away from current and expected future urban areas are selected by the U.S. Climate Reference Network. However, for urban studies, urban effects need to be included, and long-term urban data need to be developed from the Global Historical Climate Network (GHCN; Peterson and Vose, 1997).
Short-term need #1: maximum access to observational data in different categories from diverse sources, by
• securing access to existing data sets from previous urban campaigns (e.g., through central archives for existing urban data sets and corresponding metadata),