Commercial aircraft now provide more than 150,000 observations per day of winds and temperature aloft over the contiguous United States. The general term for these data is AMDAR (Aircraft Meteorological Data Reports). These data have been shown to improve both short-term and long-term weather forecasts.

One weakness of the current AMDAR data set is the absence of data below 25,000 ft between major airline hubs and the almost complete absence of water vapor data. To address this weakness, a sensor called TAMDAR, developed by AirDat, LLC, under sponsorship of the NASA Aviation Safety and Security Program, has been deployed on approximately 60 regional turboprop aircraft operated by Mesaba airlines flying over the middle U. S. Like the rest of the AMDAR fleet, TAMDAR measures winds and temperature. But unlike most of the rest of the fleet, TAMDAR measures humidity, turbulence, and icing. By mid-2007, AirDat expects to have more than 400 aircraft operating in the U.S.

GSD has built an extensive system for evaluating the quality of TAMDAR and AMDAR data, and has applied this system for the two years that TAMDAR has been in operation. Our evaluation system relies on the Rapid Update Cycle (RUC) numerical model and data assimilation system. The RUC provides a common background against which AMDAR and TAMDAR data are compared.

In particular, we look at differences between RUC background fields (one hour forecasts from the previous hour) and aircraft data. Results suggest that TAMDAR data have different error characteristics than those of traditional AMDAR fleets, which consist of long-haul jet aircraft, and that it may be useful and important to treat TAMDAR differently than data from other fleets when assimilating the data into models. TAMDAR data characteristics are, however, somewhat similar to other data from turboprop aircraft, such as those provided by the Canadian AMDAR program.

Moreover, we now maintain detailed statistics of which observations fail RUC data ingest criteria, and how such observations fail. We are therefore able to evaluate how effective RUC quality control standards are at rejecting data that would not enhance model performance, and to tune RUC QC limits accordingly.

This extends our presentation given at the IOAS-AOLS Conference last year: we will include results from 2006—a period during which TAMDAR data processing, data resolution, quality control, and assimilation into the RUC, all changed.

We believe these studies are particularly important as the U.S. government considers paying a larger portion of the costs associated with aircraft-measured meteorological data. In this new era, the government will have to more carefully monitor the quality of data from a variety of aircraft fleets, and provide detailed data quality information to both data providers and data users. We will show how our approaches can help facilitate this function.