Optimization of In-situ Aircraft Observations for Various Assimilation Techniques

During 2014, Panasonic Avionics and NCAR have been optimizing the operational suite of CONUS-Scale Advanced Research WRF (ARW) models known as the NCAR-PAC 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 since the error statistics are unique for each phase of flight.

Additionally, we explored a new methodology for assimilating wind observations in their observed form of speed and direction, while taking into account both speed and direction error. This ensures the analyzed speed and direction will be consistent with their background and observed values. The new formulation is implemented in the WRFDA system and feeds back to the quality control systems employed to monitor the real-time aircraft data.

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. 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.