8.4
An Introduction to the NCAR-AirDat Operational TAMDAR-Enhanced RTFDDA-WRF

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Wednesday, 20 January 2010: 11:15 AM
B207 (GWCC)
Peter Childs, AirDat, Morrisville, NC; and N. A. Jacobs, M. Croke, Y. Liu, W. Wu, G. Roux, and M. Ge

Presentation PDF (267.9 kB)

During the summer of 2009, AirDat and NCAR jointly implemented an operational CONUS-scale cloud-resolvable (4-km grid) Real-Time Four Dimensional Data Assimilation (RTFDDA) and forecasting system, known as the NCAR-AirDat RTFDDA-WRF. RTFDDA was originally developed jointly by NCAR and the US Army Test and Evaluation Command (ATEC). The operational NCAR-AirDat RTFDDA-WRF is built upon the Weather Research and Forecasting (WRF) ARW-core. RTFDDA uses a Newtonian relaxation observational nudging data assimilation engine, which allows the model to more effectively assimilate measurements from fixed-location platforms, as well as continuous, moving platforms such as the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) sensor, than a 3D data assimilation scheme currently used by many operational centers.

The 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 TAMDAR temperature, winds and humidity reports are continuously assimilated into the NCAR-AirDat RTFDDA-WRF modeling system. The operational modeling system cold-starts once a week, and produces 4 forecast cycles a day with each cycle producing a 6 h analysis and 72 h forecast from the dynamically consistent and cloud “spun-up” analysis produced by 4D continuous data assimilation.

Preliminary analyses of the operational forecasts during the late 2009 summer and fall will be presented. Advantages of using high-resolution WRF and FDDA of the TAMDAR data for the short-range forecasting for summer convection over the currently available operational products will be reported. The ongoing studies focus on the various degrees of forecast impacts provided by the additional airlines currently being equipped with TAMDAR sensors, and refine the data assimilation settings for optimal use of the TAMDAR data.