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TAMDARTropospheric Airborne Meteorological Data Reporting
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We assimilate TAMDAR observations on a 1 km WRFARW domain using observation nudging to investigate the potential value of TAMDAR in high-resolution simulations and explore the issues associated with assimilating single-level above-surface observations.
Observation nudging towards TAMDAR observations is applied to the 1 km domain during the first 6 h of the simulation for certain experiments.
Obsgrid is part of the WRFARW software suite and is used here to provide quality control of both the TAMDAR observations used for observation nudging and all of the other observations used for verification except for the stage IV precipitation data.
Due to the horizontal movement of aircraft, TAMDAR observations were often processed in this study as single-level above-surface observations rather than profiles.
For verification purposes, in addition to TAMDAR data, observations were obtained from the Meteorological Assimilation Data Ingest System (MADIS; https://madis .ncep.noaa.gov).
TAMDAR observations are assimilated during a 6 h preforecast (12-18 UTC) using observation nudging [18, 24, 33].
The innovation for a given TAMDAR observation is calculated starting 1.5 h prior to the observation and continuing until 1.5 h after the observation.
In order to test the potential impacts of assimilating TAMDAR observations and to form a preliminary understanding of the sensitivity of the impacts to the data assimilation configuration, four experiments were used for the 5 case days.
To evaluate the potential value of assimilating TAMDAR observations, model forecasts from the four experiments were compared above the surface against ACARS, TAMDAR, and rawinsonde observations and at the surface against MADIS standard and mesonet surface observations in addition to maritime observations; additionally precipitation analyses were also used for verification.
Temperature MAE (Figure 4(a)) is improved by assimilating TAMDAR observations by 0.1-0.2 K in the lowest 1000 m AGL.
The 0-1000 m AGL layer has the largest number of observations, and for temperature, dewpoint, and wind speed this is a layer where assimilating TAMDAR observations improves the 1-6 h forecast.
The temporal evolution of the 0-1000 m AGL error in this layer demonstrates the length of time over which TAMDAR observations continue to influence the forecast (Figure 6).