Atmospheric Motion Vectors (AMV) are routinely generated from geostationary and polar orbiting satellites and they are incorporated into most global numerical weather prediction models. However, the quality control and thinning strategies applied to the AMVs vary greatly in the modeling community and are usually based on quality flags since, until recently, no error estimate was provided with each observation.

Satellite-derived AMVs are assigned individual quality flags. Most of these quality indicators are normalized scores (e.g., the Quality Indicator, QI), which are not in the units of the wind vector. The Expected Error (EE), developed at the Bureau of Meteorology in Australia, extends the usability of the QI by regressing the QI and other AMV parameters against co-located rawinsondes. This results in coefficients that are applied to individual AMVs to compute an Expected Error in units of speed, which are more amenable to assimilation systems.

We expect that the use of the EE will provide a more quantitative screening of the satellite-derived winds product, resulting in better assimilation statistics and improved global forecasts. Results of initial experiments using polar AMVs in the NCEP Global Data Assimilation System/Global Forecast System (GDAS/GFS) will be discussed.