Thursday, 24 January 2008
Development of a lead-time metric for assessing skill in forecasting the onset of IFR events: poster and laptop presentation of a prototype web tool and database
Exhibit Hall B (Ernest N. Morial Convention Center)
Andrew Loughe, NOAA/ESRL/GSD/CIRES/Univ. of Colorado, Boulder, CO; and S. Madine and J. Mahoney
Poster PDF
(554.8 kB)
In a companion oral presentation at this conference, we have discussed the use of a lead-time metric for assessing skill in forecasting the onset of Instrument Flight Rules events (IFR events). The method utilized by the Real-time Verification System (RTVS) of NOAA-ESRL/GSD's Aviation Branch, compares the onset from observed IFR events with the onset from NWS-issued Terminal Aerodrome Forecasts (TAFs). This study is an example of how RTVS is increasingly being utilized to supply information to individual users of aviation-related weather forecasts. An essential part of the current development is a web-based verification tool that allows users to interrogate a large database of lead-time metrics from over 3.2 million TAFs. The database covers a 3-year time period and comprises over 600 individual stations.
During this poster session, we will highlight in greater detail the methods used to build this extensive database of IFR events, and will demonstrate the power of the web-based interrogation tool. We plan to demonstrate this functionality during the poster session, in part by accessing the internet at the convention center.
The web tool that we will demonstrate provides a straightforward mechanism for comparing TAF weather elements, and observed weather conditions obtained from routine and special aviation weather reports (METARs). The main purpose in gathering statistics on such a large number of events-- over 1 million TAF IFR events are analyzed-- is to highlight the capability of forecasts that have such a profound impact on strategic planning of air travel throughout the United Sates. The approach used to associate forecast and observed IFR events is highly event focused, and yields results that are stratified by station, region, weather scenario, forecast issuance hour, and additional TAF attributes.
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