92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Wednesday, 25 January 2012: 4:45 PM
An Update on the TAMDAR Sensor Network Expansion and Data Utility
Room 335/336 (New Orleans Convention Center )
Neil A. Jacobs, AirDat LLC, Morrisville, NC; and P. Childs, M. Croke, A. Huffman, D. J. Mulally, A. K. Anderson, Y. Liu, and X. Y. Huang

Lower and middle-tropospheric observations are disproportionately sparse, both temporally and geographically, when compared to surface observations. The limited density of observations is likely one of the largest constraints in numerical weather prediction. Atmospheric observations collected by a multi-function in-situ atmospheric sensor on aircraft, called the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) sensor, contain measurements of humidity, pressure, temperature, winds aloft, icing, and turbulence, along with the corresponding location, time, and altitude from built-in GPS are relayed via satellite in real-time to a ground-based network operations center.

The TAMDAR sensor was originally deployed in December 2004 on a fleet of 63 Saab 340s operated by Mesaba Airlines in the Great Lakes region as a part of the NASA-sponsored Great Lakes Fleet Experiment (GLFE).

Over the last seven years, the equipage of the sensors has expanded beyond CONUS to include Alaska and Mexico on Horizon, Republic, Chautauqua, Shuttle America, PenAir, Piedmont, ERA Alaska, AeroMexico Connect, ACE Air Cargo, and Mesaba Airlines, as well as a few research aircraft. Upon completion of the 2011 installations, more than 6000 daily sounding will be produced in North America.

During 2011, AirDat and NCAR have been optimizing the operational suite of CONUS-Scale Advanced Research WRF (ARW) models known as the NCAR-AirDat RTFDDA-WRF and the NCAR-AirDat 3D- and 4DVAR-WRF. These systems employ various assimilation frameworks and techniques. The temporal and spatial weighting of the various observations can be adjusted to allow optimal TAMDAR data characteristics, and dynamically evolve according to present weather flow regimes.

An update will be provided on the status of the TAMDAR sensor network deployment and data availability, as well as an update on data quality, error statistics, and operational forecasting utility, both from soundings and various data assimilation techniques.

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