The amount and distribution of moisture in the lower troposphere is critical for many weather forecasts. However, accurate measurements of point observations of moisture above the surface are generally available only twice per day at widely spaced upper air rawinsonde sites (hereafter, RAOBs). Wind and temperature data from aircraft, known as AMDAR (Aircraft Meteorological Data Relay), have been routinely used by forecasters and ingested in numerical models, but until recently, there were no routine aircraft measurements of moisture. This has changed with the development and experimental deployment of an aircraft sensor capable of accurate measurement of moisture, both in the boundary layer and aloft. The NASA Aviation Safety Program recently funded the development of a sensor called TAMDAR (Tropospheric AMDAR) by AirDat, LLC, of Raleigh NC, designed for deployment on aircraft flown by regional airlines (Daniels et al., 2006). This sensor package measures moisture as well as wind and temperature. For the past year (15 January 2005 to 15 January 2006), with the support of NASA and the FAA, these sensors have been deployed on 63 aircraft flying over the U. S. Midwest in an experiment called the Great Lakes Fleet Evaluation (GLFE). In addition to the added measurement of moisture, the aircraft taking part in the GLFE fly out of many smaller airports (in addition to major hubs) that typically do not have coverage from the current aircraft data, adding a considerable number of ascent/descent soundings. Furthermore, many of the flights are at levels well below the jet stream level of typical AMDAR aircraft, adding much data in the level between approximately 14 to 20 kft AGL. Typical coverage for TAMDAR flights is shown in Fig. 1. The purpose of this study is to examine the impact of TAMDAR on numerical weather prediction through the use of the Rapid Update Cycle (RUC, Benjamin et al. 2004) assimilation and model system. Other studies presented at this conference will examine the use of TAMDAR by forecasters (Mamrosh et al. 2006, Brusky et al. 2006), and a statistical evaluation of the impact of TAMDAR on RUC forecasts (Benjamin et al. 2006). This paper focuses on a subjective evaluation of the impact of TAMDAR through case studies evaluating RUC short-term forecasts for runs made with and without TAMDAR. The methodology for choosing cases and evaluating them are discussed in the next section.
This publication was presented at the following: