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Quantifying Monetary Impacts of Forecasts

Quantifying Monetary Impacts of Forecasts. National Weather Service Activities Kevin Stone NWS Aviation Services Branch November 1, 2012. Quantifying Monetary Impacts of Forecasts NWS Activities. Why? Recent Attempts Summary. Why?.

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Quantifying Monetary Impacts of Forecasts

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  1. Quantifying Monetary Impacts of Forecasts National Weather Service Activities Kevin Stone NWS Aviation Services Branch November 1, 2012

  2. Quantifying Monetary Impacts of ForecastsNWS Activities • Why? • Recent Attempts • Summary

  3. Why? • Government expending resources providing products/services • Regulatory reasons for NWS products • Ensure products/services are meeting NAS needs • Many forces driving ever-limited resources • Focus areas for improvement • Need factual/reliable data • Draw accurate conclusions

  4. Recent Attempts – Case 1 • Additional meteorologists to Golden Triangle NWS Weather Forecast Offices in 2010 • Weather-related delays decreased in each area 40-55% from previous years • Due to improved forecasts!

  5. Recent Attempts – Case 1 • Additional meteorologists to Golden Triangle Offices in 2010 • Weather-related delays decreased in each area 40-55% from previous years • Due to improved forecasts! Or was it? Many factors can account for reduction in delays from year to year…prime among them, the weather

  6. Recent Attempts – Case 2 • National Aviation Meteorologists at ATCSCC • GDP request based on CIWS/CoSPA forecasts • NAM recognized CoSPA forecast discrepancies • NAM input to decision not to implement GDP • NTMO noted “GDP would have unnecessarily delayed numerous flights and drawn out the schedule”

  7. Recent Attempts – Case 2 • National Aviation Meteorologists at ATCSCC • GDP request based on CIWS/CoSPA forecasts • NAM recognized CoSPA forecast discrepancies • NAM input to decision not to implement GDP • NTMO noted “GDP would have unnecessarily delayed numerous flights and drawn out the schedule” How do we know what would have happened? • Simulated what would have been incurred by the system given a conservative GDP implementation (21z-00z): • 322 flights delayed – 232 actual flights delayed = 90 flight delays saved • 7565 minutes of delay – 5136 minutes actual delay = 2429 minutes saved • 23.5 average delay per aircraft (actual 22.2 minutes for all arrivals) • Assuming $40 cost/minute of delay, this one instance of an avoided TMI would have saved roughly $100,000

  8. Summary • Need to ensure limited resources focused on improvements that show return on investment • Some simulations show promise in demonstrating benefit of improved products/services • Need factual data to draw accurate conclusions

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