Automated Weather Reports from Aircraft: TAMDAR and the U.S. AMDAR Fleet

Commercial aircraft now provide more than 130,000 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. To address this weakness, a sensor developed by AirDat, LLC, under sponsorship of the NASA Aviation Safety and Security Program, has been deployed on 61 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.

We are evaluating the extent to which TAMDAR adds value to the preexisting AMDAR data in several ways:

- TAMDAR data, along with all other AMDAR data, are available to operational forecasters via a web-based interactive display operated by the Forecast Systems Laboratory. We are studying how the TAMDAR data--particularly the soundings at regional airports--improve the local forecasting performed by NWS forecast offices. This will be described in more detail in a companion paper by Mamrosh, et al. ("Aviation Applications of TAMDAR Aircraft Data Reports")

- We are assessing how TAMDAR data improve the skill of forecasts by the Rapid Update Cycle (RUC). We are running real-time parallel versions of the RUC 20km model, one with TAMDAR and one without, and are continuously monitoring the skill of each. This will be discussed in more detail in a companion paper by Benjamin et al. ("Impact of TAMDAR humidity, temperature, and wind observations in RUC parallel experiments")

- We are looking at differences between between RUC background fields (one hour forecasts from the previous hour) and all AMDAR fleets. Initial 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.

We will report on current results of these investigations.