Evaluation of the Impacts of Assimilating the TAMDAR Data on 12/4 km Grid WRF-Based RTFDDA Simulations over the CONUS

Abstract

An analysis of the impacts of assimilating the Tropospheric Airborne Meteorological Data Report (TAMDAR) data with the Weather Research and Forecasting-(WRF-) real-time four-dimensional data assimilation (RTFDDA) and forecasting system over the Contiguous US (CONUS) is presented. The impacts of the horizontal resolution increase from 12 km to 4 km on the WRF-RTFDDA simulations are also examined in conjunction with the TAMDAR data impacts. The assimilation of the TAMDAR data reduces the root mean squared error of the moisture field predictions and increases the correlation between the predictions and the observations for both domains with 12 km and 4 km grid spacings. The TAMDAR data reduce the model dry biases in the middle and lower levels by adding moisture at those levels. Assimilating the TAMDAR data improves temperature predictions at middle to high levels and wind speed predictions at all levels especially for the 12 km domain. Increasing the horizontal resolution from 12 km to 4 km results in significantly larger impacts on surface variables than assimilating the TAMDAR data.

The Status and Future of Small Uncrewed Aircraft Systems (UAS) in Operational Meteorology

“…In particular, the potential for serving as a means of filling observation gaps in less developed regions of the world should be explore in future demonstration testbeds. Commercial UAS operators can use testbeds to develop business models to determine the market value for weather observations through the demonstration of the impact of these observations on forecast skill (e.g., Zhang et al 2016). The approach used here could be similar to that which unfolded with commercial transport aircraft observations via the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) program (Daniels et al 2006).…”

Assimilation of Aircraft Observations in High-Resolution Mesoscale Modeling

“…Various studies have demonstrated the value of assimilating aircraft observations (e.g., [8][9][10][11]) and more recently studies have demonstrated the value of assimilating TAMDAR aircraft observations in particular (e.g., [4,[12][13][14]). However, investigation of the value of assimilating TAMDAR observations in high-resolution (∼1 km horizontal grid spacing) simulations has been limited.…”

“…Observation nudging generally centers the application of an observation on the time at which the observation was taken; this allows the effects of observations to be based on how the model compares to the observation at the time of the observation. Zhang et al [14] assimilated TAMDAR observations during a 6 h preforecast to a 24 h forecast using 12 and 4 km nests. Evaluations over a 6-day period indicated that within the layer where TAMDAR shows the most impact (400-600 hPa) the greatest improvements are in temperature and moisture forecasts, whereas wind speed was improved on the 12 km domain but degraded on the 4 km domain.…”