The need for improvements in observations of this kind is well documented and compelling. Development of a National Mesonet with comprehensive data collection, quality control and dissemination capabilities will provide the critical information needed to improve short- and medium-term weather forecasting (down to local scales), plume dispersion modeling, climate monitoring, as well as air quality analyses. In this manner, not only will the overall capabilities of the atmospheric community be substantially augmented, but decision making will be vastly improved across a broad spectrum of market sectors and end user constituencies including; energy, agriculture, homeland security, emergency response, transportation, education, recreation and research.
As cited the 2009 National Academy of Sciences report Observing Weather and Climate From the Ground Up: A Nationwide Network of Networks, a key component of a national mesonet strategy is the systematic collection of comprehensive metadata from participating networks. Metadata describe how raw observations are measured, processed and disseminated to users. Additionally metadata provide the context of the observation in which it is collected, enabling users to improve their understanding of observation error characteristics and limitations; surgically select data that is appropriate for a particular service application; and, if selected properly, weight the contribution of the data to the service application in question.
During 2009, the National Weather Service (NWS) undertook the National Mesonet Pilot Project (NMPP), to aggregate in situ atmospheric and soil observations as well as comprehensive metadata from disparate local, regional and national networks within the US. With respect to the collection of associated metadata, use of internationally recognized standards was a top priority for NOAA given the strong push by government and commercial organizations worldwide to promote interoperability through standardization. This further encourages broader and automated use of network data as well as creation of derivative applications which ultimately results in greater value to users and consumers of the observational data. This paper will discuss these efforts and in particular highlight the use of Sensor Model Language (SML), an Open Geospatial Consortium standard, for systematically and routinely collecting and delivering detailed network observing system metadata.