Extended Abstract (164K)2.8
Assimilation of Diverse Meteorological Datasets with a Four-Dimensional Mesoscale Analysis and Forecast System
Yubao Liu, NCAR, Boulder, CO; and F. Vandenberghe, A. N. Hahmann, W. Yu, T. T. Warner, and S. P. Swerdlin
The NCAR/ATEC (National Center for Atmospheric Research/Army Test and Evaluation Command) Real-Time Four-Dimensional Data Assimilation and forecast (RTFDDA) system is a MM5/WRF-based multi-scale (grid-sizes varying from 0.5 km to 45 km), rapid cycling (at intervals of 1-12 hours), global-relocatable mesoscale analysis and forecast system. The goal of the system is to effectively combine all observations available up to the current time with a full-physics mesoscale model to generate the best possible current, dynamically and adiabatically "spun-up", meso- and small scale analyses, which are used to produce nowcasts and short-term forecasts, and 4-D synthetic retrospective analyses for deriving micro-climatologies. This system is currently operational at ATEC Army test ranges, and it has also been deployed to support a number of DoD, public, and private applications.
The RTFDDA employs a Newtonian-relaxation-based continuous data assimilation technique, termed "observation-nudging", which allows the sequential insertion of observations into a continuously running mesoscale model with carefully-tuned temporal and spatial weights according to observation times and locations. This data assimilation approach incorporates observations that are available at irregular times and locations, and weights each platform discriminatively. The data incorporated in the current system include: conventional twice-daily radiosondes; hourly surface, ship and buoy observations; and special observations such as GTS/WMO; NOAA/NESDIS satellite winds derived from cloud, water vapor and IR imageries; NOAA/FSL aircraft reports of ACARS, AMDAR and MDCR; NOAA/FSL NPN (NOAA Profiler Network) and CAP (Corporative Agencies Profilers) profilers; the 3-hourly cloud-drifting winds and water-vapor-derived winds from NOAA/NESDIS; high-density, high-frequency observations from various mesonets; Radiometric profiles; NEXRAD VAD winds; NASA/JPL QuikScat sea surface winds; and with a special effort, TAMDAR aircraft reports from the Great Lakes Field Experiments.
In this paper, the data quality, usage and impact of different observation platforms are evaluated and compared with numerical studies using the RTFDDA system. Sensitivity experiments are also carried out with sub-groups of observations to study the relative roles of different observations, such as surface data versus upper-air data, temperature versus winds observations, and in-situ observations versus remote-sensing volume measurements. It is found that: 1) Adding new data sources generally reduces the forecast errors, but the benefits vary among platforms; 2) Temporally and spatially well-distributed observations reduce the errors more than those clustered; 3) Upper-air wind observations are more effective in reducing forecast errors than temperature observations. This suggests the important value of accurate wind data sources such as satellite winds, wind profilers, and aircraft reports; 4) Surface observations (of temperature, moisture and winds) are critical in maintaining the correct evolution of the near-surface and lower-tropospheric properties; 5) TAMDAR, a new platform that provides high space and time density of research quality observations in the lower troposphere, display a significant positive impacts in the forecast error; and 6) Finally, and most importantly, the simulations employing a complete set of observations always produce the most accurate analyses and forecasts.
Session 2, Experiments Involving Observations, Real or Hypothetical: Data Impact Tests (Sensitivity of Forecasts to a Particular Source of Observations); Observing System Simulation Experiments (OSSEs) Part 2
Monday, 30 January 2006, 1:30 PM-5:30 PM, A405
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