Many observing systems produce voluminous information. For most applications, at least near real-time communication is essential. Ground-based remote sensors such as radars and lidars have intrinsically high data rates, necessitating an adequate communications bandwidth, which has increasingly become available and affordable. Rapid and flexible access to stored data is often a point of failure, requiring efficient data structures and applications software that are well matched to a wide range of user needs. A communications architecture that permits selective access to full resolution or general access to lower resolution data and analyses should be devised. Larger market forces governing the evolution of the data communications and data storage industries over the coming decade should easily accommodate these requirements.
In order to screen mesoscale observations for specific applications, a sophisticated user interface is required. Behind this interface is a relational database that contains comprehensive metadata from each observing source, a pointer to the repository of each source, and high-bandwidth communication to each repository. These attributes will make it possible to retrieve information based upon highly selective criteria. In the future, given sufficiently detailed metadata for each observing source and specific enough criteria for the intended application, it should be possible to extract from geographically distributed repositories just the information that directly serves the application, no more and no less. Specific examples will more effectively make this point than generalizations:
Search by application: “Show me highway pavement, temperature, and visibility conditions in north central Illinois.”
Search by application: “Show me regional chemical weather fields east of 85°W.”
Get information in the vertical: “Get me the best estimate of the current vertical profiles of temperature, moisture, and wind over Chicago’s O’Hare Airport.”
Search for information that is sensor-, time-, and location-specific: “Show me precipitation data from tipping bucket rain gauges in Missouri between 1800 UTC 6 Aug and 0600 UTC 07 Aug 2007.”
Search for information from a specific network: “Show me all temperature data from the AWS (WeatherBug) surface network at 1200 UTC 06 Apr 2007.”