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J1.7 OBSERVATION-QUALITY ESTIMATION AND ITS APPLICATION IN THE NCAR/ATEC REAL-TIME
FDDA AND FORECAST (RTFDDA) SYSTEM
Yubao Liu*, Francois Vandenberghe, Simon Low-Nam, Tom Warner and Scott Swerdlin
National Center for Atmospheric Research/RAP, Boulder, Colorado
1. INTRODUCTION
In the last three years, the National Center for Atmospheric
Research (NCAR) and the Army Test and Evaluation Command
(ATEC) have been developing a multi-scale (with grid sizes of
0.5 - 45 km), rapidly cycling (at time intervals of 1 - 12 hours), real-time four-dimensional data assimilation and forecasting
(RTFDDA) system. By August, 2003, RTFDDA systems were customized and deployed to five Army test ranges, and to seven other regions to support specific missions of three other US government agencies. A Newtonian-relaxation-based "station-nudging" approach, by which all observations that are available in real-time are incorporated into a continuously running MM5 model, is employed to accomplish four-dimensional data assimilation. The nudging-based data assimilation weights each observation uniquely according to the observation time and location, and thus allows ingest of conventional and unconventional observations that are available at regular and irregular times intervals. The data sources incorporated include the traditional hourly surface (METAR, ship, buoy and special) reports and twice-daily upper-air rawinsondes. Also used are high-frequency measurements from various mesonets and special field experiments; wind profiler data from NOAA/FSL NPN profilers and CAP-Cooperative Agency Profilers; NOAA/NESDIS hourly GOES winds derived from IR, visible and water-vapor images; aircraft reports (ACARS/AMDAR) processed and disseminated by NOAA/FSL; and data from other non-conventional sources.
The "station-nudging" approach appears to alleviate some of the problems in mesoscale data assimilation and prediction.
Another remaining problem is that data from different
FDDA AND FORECAST (RTFDDA) SYSTEM
Yubao Liu*, Francois Vandenberghe, Simon Low-Nam, Tom Warner and Scott Swerdlin
National Center for Atmospheric Research/RAP, Boulder, Colorado
1. INTRODUCTION
In the last three years, the National Center for Atmospheric
Research (NCAR) and the Army Test and Evaluation Command
(ATEC) have been developing a multi-scale (with grid sizes of
0.5 - 45 km), rapidly cycling (at time intervals of 1 - 12 hours), real-time four-dimensional data assimilation and forecasting
(RTFDDA) system. By August, 2003, RTFDDA systems were customized and deployed to five Army test ranges, and to seven other regions to support specific missions of three other US government agencies. A Newtonian-relaxation-based "station-nudging" approach, by which all observations that are available in real-time are incorporated into a continuously running MM5 model, is employed to accomplish four-dimensional data assimilation. The nudging-based data assimilation weights each observation uniquely according to the observation time and location, and thus allows ingest of conventional and unconventional observations that are available at regular and irregular times intervals. The data sources incorporated include the traditional hourly surface (METAR, ship, buoy and special) reports and twice-daily upper-air rawinsondes. Also used are high-frequency measurements from various mesonets and special field experiments; wind profiler data from NOAA/FSL NPN profilers and CAP-Cooperative Agency Profilers; NOAA/NESDIS hourly GOES winds derived from IR, visible and water-vapor images; aircraft reports (ACARS/AMDAR) processed and disseminated by NOAA/FSL; and data from other non-conventional sources.
The "station-nudging" approach appears to alleviate some of the problems in mesoscale data assimilation and prediction.
Another remaining problem is that data from different