- Currently such data is not being delivered to government agencies nor will the data be delivered in the next 10 to 15 years. Tens of thousands of lives will be lost because of insufficient warning of severe weather
- The benefit to U.S. alone is in the range of $40 billion annually
- The cost to American taxpayers for evacuation is currently about $1 million per mile of coastline to prepare for a hurricane landfall. The average error associated with a 24-hr forecast of hurricane landfall position is currently about 65 miles
- STORM™ can result in tens of millions of dollars in cost savings for each land-falling storm
- Economic losses associated with hurricanes have been estimated to be in the billions of dollars. Improved hurricane forecasts cannot prevent the hurricane from hitting the coastline, but they can reduce the forecast errors in predicting where the storm will make landfall and the intensity of the hurricane if it were to hit the coast
- Improved tropical cyclone forecasts imply improved accuracy, which can result in direct economic benefits
- Increased warning lead-time—a more accurate assessment of when storm will hit land can improve evacuation procedures and decrease evacuation costs
STORM™ will provide higher spatial resolution and higher frequency data, improving forecast accuracy. The resulting improved forecast accuracy is expected to allow U.S. air traffic to fly more efficiently by avoiding a number of preventable weather-related delays
The average number of flight delays in a given year is 542,584
Percentage of delays due to weather is 69%
The average of annual flight delays due to weather is 373,036
Percentage of weather delays due to convective activity is 50%
The average number of weather delays due to convective activity is 186,519 yearly
Elimination of 10% or 18,652 would result in a savings over 12 years of $276,252,811
The energy industry, identified here strictly as electricity and natural gas, accounts for a significant part of the U.S. economy and over 2% of Gross Domestic Product (GDP). Small inefficiencies in the energy industry can be reduced as a result of the STORM™ sensor and produce large savings that can be passed on directly to consumers.
One large cost of providing energy relates to the ability to forecast demand and then to supply the necessary energy on time. Energy providers rely on demand models to forecast electricity production and natural gas requirements. Demand forecasts for energy production are largely based on temperature forecasts.
The STORM™ sounder data provides the capability to improve temperature forecasts, thereby improving demand forecasts, which lead to energy industry savings.