Lessons in weather data interoperability: the National Mesonet Program

Abstract:

The National Mesonet Program (NMP) links local, state, and regional surface weather observation networks (a.k.a. mesonets) to enhance the prediction of high-impact, local-scale weather events. A consortium of 23 (and counting) private firms, state agencies, and universities provides near-real-time observations from over 7,000 fixed weather stations, and over 1,000 vehicle-mounted sensors, every 15 minutes or less, together with the detailed sensor and station metadata required for effective forecasts and decision-making.

In order to integrate these weather observations across the United States, and to provide full details about sensors, stations, and observations, the NMP has defined a set of conventions for observational data and sensor metadata. These conventions address the needs of users with limited bandwidth and computing resources, while also anticipating a growing variety of sensors and observations.

For disseminating weather observation data, the NMP currently employs a simple ASCII format derived from the Integrated Ocean Observing System. This simplifies data ingest into common desktop software, and parsing by simple scripts; and it directly supports basic readings of temperature, pressure, etc. By extending the format to vector-valued observations, it can also convey readings taken at different altitudes (e.g. windspeed) or depths (e.g., soil moisture). Extending beyond these observations to fit a greater variety of sensors (solar irradiation, sodar, radar, lidar) may require further extensions, or a move to more complex formats (e.g., based on XML or JSON). We will discuss the tradeoffs of various conventions for different users and use cases.

To convey sensor and station metadata, the NMP uses a convention known as Starfish Fungus Language (*FL), derived from the Open Geospatial Consortium’s SensorML standard. *FL separates static and dynamic elements of a sensor description, allowing for relatively compact expressions that reference a library of shared definitions (e.g., sensor manufacturer’s specifications) alongside time-varying and site-specific details (slope / aspect, calibration, etc.) We will discuss the tradeoffs of *FL, SensorML, and alternatives for conveying sensor details to various users and uses.