New York is a state prone to high-impact weather. Hurricanes, blizzards, ice storms, tornadoes, severe wind storms, severe cold and heat, and drought all impact New York, and these can take a financial and human toll. Between 1996 and 2018, New York averaged over $120 million in damages from flooding annually. More recently between 2008 and 2017, New York State suffered 236 fatalities, 227 injuries, and $3.26 billion in damage from severe and hazardous weather. Indeed, one study by the National Center for Atmospheric Research found that New York was the most economically sensitive state to weather and climate variability.

Two particular events in 2011 – Hurricane Irene and Tropical Storm Lee - spurred the idea to establish a mesonet. On August 27-28, 2011, Hurricane Irene caused 10 fatalities and over $1 billion in flooding damage across the Catskill and Adirondack Mountains. A few weeks later Tropical Storm Lee caused another $500M in damage from flooding along the Susquehanna River valley. In 2012, New York suffered 53 fatalities and around $32 billion in damage from Superstorm Sandy. Following this disaster, FEMA provided New York State with a block grant for recovery and resiliency efforts; utilizing these funds, the Early Warning Severe Weather Detection network, now known as the New York State Mesonet, was established in April 2014. In collaboration with the Division of Homeland Security and Emergency Services, the Mesonet was designed and deployed by atmospheric scientists at the University at Albany. The network was completed on schedule and under budget by March 2018. The University at Albany continues to operate the Mesonet, collecting, processing, and disseminating weather information to users nationwide.

Data from the New York State (NYS) Mesonet now helps mitigate the harmful effects from these high-impact events and helps prepare New Yorkers with greater lead times and more accurate predictions. The NYS Mesonet provides real-time data to operational forecasters from across the state with updates every five minutes and an average station spacing of about 17 miles. These data are combined with data from other surface networks, weather radar, and satellite to improve numerical weather prediction models for even greater accuracy and precision than ever before, giving forecasters much greater confidence in their warning products.