On the Impact and Benefits of AMDAR Observations in Operational Forecasting - Part I: A Review of the Impact of Automated Aircraft Wind and Temperature Reports

Petersen, Ralph Alvin

Année de publication
This paper reviews the impact of World Meteorological Organization (WMO) Aircraft Meteorological Data Relay (AMDAR) observations on operational numerical weather prediction (NWP) forecasts at both regional and global scales that support national and local weather forecast offices across the globe. Over the past three decades, data collected from commercial aircraft have helped reduce flight-level wind and temperature forecast errors by nearly 50%. Improvements are largest in 3–48-h forecasts and in regions where the automated reports 1) are most numerous, 2) cover a broad area, and 3) are available at multiple levels (e.g., made during aircraft ascent and descent). Improvements in weather forecasts due to these data have already had major impacts on a variety of aspects of airline operations, ranging from fuel savings from improved wind and temperature forecasts used in flight planning to passenger comfort and safety due to better awareness of en route and near-terminal weather hazards. Aircraft wind and temperature observations now constitute the third most important dataset for global NWP and, in areas of ample reports, have become the single most important dataset for use in shorter-term, regional NWP applications. Automated aircraft reports provide the most cost-effective data source for improving NWP, being more than 5 times more cost effective than any other major-impact observing system. They also present an economical alternative for obtaining tropospheric profiles both in areas of diminishing conventional observation and as a supplement to existing datasets, both in time and space. An evaluation of moisture observations becoming available from an increasing number of AMDAR-equipped aircraft will be presented in Part II of this paper, including examples of the use of the full array of AMDAR observations in a variety of forecasting situations.

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