Building a National Network of Mobile Platforms for Weather Detection

Global Science & Technology, Inc. (GST) developed the United States' first mobile (vehicle-based) environmental observation network for the National Oceanic Atmospheric Administration's (NOAA) National Mesonet program. GST and partner Weather Telematics established a national infrastructure for mobile platforms to conduct their routes, while taking weather observations along the way. The Mobile Platform Environmental Data (MoPED) system, hosted by GST, is an information processing system that acquires and disseminates mobile environmental and vehicle data to the Meteorological Assimilation Data Ingest System (MADIS), in order to improve NOAA's meteorological monitoring and detection of phenomenon at the mesoscale level, and with a temporal frequency greater than what might otherwise be available from fixed observing sites. The benefit to the forecaster and end user is increased situational awareness, which potentially factors into decisions taken regarding travel risk. Moreover, NOAA is able to refine forecasts, watches, and warnings based on the detailed data provided by mobile platforms in areas that otherwise would be un-sampled. NWS further benefits by the possible inclusion of mobile platform observations into the initialization of predictive models.

The development and roll out of the national network focused on highly-traveled national corridors, such as Interstates 5 and 95, where significant population centers are located. Because the commercial vehicles travel major transportation routes, the vehicles provide excellent urban coverage near population centers, as well as coverage in suburban or rural areas in between the origin and destination points of travel. A mobile platform based in Charlotte, North Carolina, for example, will sample rural areas along Interstate 77 as the vehicle heads north into Virginia. The routes of travel are not exclusively Interstate travel: many mobile platforms conduct regional routes on state and local roads near their base hub. GST developed ‘heat maps' (i.e., density maps) that depict the geography sampled by mobile platforms.

Using mobile platforms to acquire environmental data supplements traditional fixed weather stations from airports and road weather information stations (RWIS). Vehicles taking meteorological observations every 10 seconds microscale level of spatial detail, which aids in the detection of small-scale phenomenon (valley fog, overpass icing, etc.) that might be missed by airports or road weather stations that are spaced further apart. Case studies will be presented that show the passage of mobile platforms through thunderstorms, for example, which might be encountered by a mobile platform, but miss a fixed site. In many cases, the mobile platforms can serve as ‘ground truth' to radar imagery for important interpretations, such as whether or not light precipitation is reaching the ground, or for the existence of precipitation that is outside of radar range, especially precipitation from shallow cloud layers in mountainous terrain.

Commercial vehicles provide 10 million or more observations weekly to MADIS. Most of the observations are during vehicle travel, so the geographic locations are random. In some cases, ‘over sampling' occurs when multiple vehicles travel the same road in succession. These observations include traditional meteorological parameters of air temperature, relative humidity, and barometric pressure. Additional sensors are added for ambient light (which aids in knowledge about sky cover), precipitation, and ozone.

In addition to observations taken by instrumentation that is mounted on the vehicle, there is Original Equipment Manufacturer (OEM) instrumentation on the fleet's vehicles that give air temperature and barometric pressure data, but at an accuracy resolution that is more coarse than the third-party instrumentation. This paper will discuss some of the accuracy of vehicle OEM data versus that obtained by a weather instrumentation package.

GST has established and created quality control methodologies, including the comparison of mobile platform data to nearby Automated Surface Observing Systems (ASOS) when the mobile platform passes nearby. This studt will include findings of these comparisons between mobile platform observations and ASOS. Different signatures will be shown for ambient light readings and their correlations to sky condition, as might be reported by ASOS or expressed in satellite imagery.