Building an Advanced State-Wide Severe Weather Early Warning System through a Unique Partnership Between UAlbany and State and Federal Disaster Response Agencies
Currently, the National Weather Service (NWS) in New York relies on 27 automated surface observing system (ASOS) stations deployed across the state. As most of these stations are located at airports, they are not representative of the state's complex topography and weather. Furthermore, the ASOS network does not provide the high-resolution data needed to support monitoring and predictive modeling of events responsible for weather-related risks (such as rainfall/floods, heavy snow/ice, and high winds) statewide. Notable and significant gaps exist throughout the state including in such regions like the Adirondacks and Catskills. These topographies are amongst the wettest regions of New York State but currently have very limited hydrological observations. Numerous studies have shown that accuracy of weather forecasts is limited by the lack of meteorological observations within and above the planetary boundary layer (PBL). PBL temperature, humidity, and winds are presently sampled twice daily at just three NWS upper air stations in NY (Upton, Albany and Buffalo). Given these limitations weather forecasts – including the nature and intensity of hazardous and extreme weather events – are compromised.
To help mitigate the vulnerability of New York to severe weather events the New York State Mesonet (NYSM) being led by UAlbany in partnership with the New York State Division of Homeland Security & Emergency Services (DHSES) and the Federal Emergency Management Agency (FEMA) is being developed. NYSM will consist of a network of 125 meteorological stations permanently and strategically deployed across New York State to provide hazardous weather early warning and decision support to weather forecasters, state emergency managers, and the public. With its 17 enhanced sites that will include atmospheric profilers, advanced data processing system and high quality data standards the system will be one of the most advanced and capable for hazardous weather real-time monitoring and prediction. An overview of the system and lessons learned so far will be presented.