A federated partnership for urban meteorological and air quality modeling

Advances in commercial airborne and space-based remote sensing have led to a large collection of high resolution imagery and topographical data of building and vegetation features for most urban areas in the USA. This type of data has become essential for determining the morphological parameter in urban meteorological and air quality modeling. Government agencies and programs such as the National Homeland Security Infrastructure Program run by the US Geological Survey and National Geospatial-Intelligence Agency (NGA), acquire and archive this data for specific uses. The potential exists to move from site specific air modeling (i.e. Houston study) to a nationwide network utilizing these datasets. A data federation is proposed to make possible and accessible this type of data and derivative parameters of urban morphology for urban meteorology and air quality modeling, for homeland security issues and other research activities. First developed by NASA Earth Science Information Partners (ESIP), a federation is an association of autonomous partners that agree to abide by certain standards and practices to achieve a common goal. A data federation is needed to efficiently handle the large amounts of imagery and data needed for air modeling applications. The federation will allow for ease of distribution by creating a common database infrastructure, lower data procurement costs by creating shared funding opportunities, and provide data quality assurance by establishing archive curation procedures. Moreover, the scope of implementation activities could be set to include determining, preparing and or updating the derivative urban canopy parameterizations products needed in models. Criteria for membership in this Federation would consist of agencies that have acquired and processed such databases, as well as additional Federal and other public agencies, and research organizations and private groups that have real stakeholder interest in these activities.