Optimization of In-situ Aircraft Observations for Various Assimilation Techniques

During 2010, 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 Tropospheric Airborne Meteorological Data Reporting (TAMDAR) sensor measures humidity, pressure, temperature, winds aloft, icing, and turbulence, along with the corresponding location, time, and altitude from built-in GPS. These observations are transmitted in real time to a ground-based network operations center via a global satellite network.

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.

Current optimization testing of the assimilation methods include splitting the ascent, descent, and cruise observations into different matrices because the error statistics are unique for each phase of flight. Additional testing includes developing a multivariate analysis, as well as splitting the vertical profiles into individual observations based on the GPS position. It is crucial to understand the possible impact of the observation position, as the flight paths (especially the descents) have a much larger horizontal displacement compared to RAOBs -- the initial observation operator code template.

Preliminary results suggest that the proper assimilation of the TAMDAR data improves the short-range forecast skill of the WRF-ARW when parameters such as wind, temperature, and relative humidity are verified against observations. The ongoing studies will present these findings, as well as the various degrees of forecast impacts provided by the additional airlines currently being equipped with TAMDAR sensors.