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Effects of data selection and error specification on the assimilation of AIRS...

Joiner, J., E. Brin, R. Treadon, J. Derber, P. Van Delst, A. da Silva, J. Le Marshall, P. Poli, R. Atlas, D. Bungato, and C. Cruz (2007), Effects of data selection and error specification on the assimilation of AIRS data, Q. J. R. Meteorol. Soc., 133, 181-196, doi:10.1002/qj.8.

The Atmospheric InfraRed Sounder (AIRS), flying aboard NASA’s Aqua satellite with the Advanced Microwave Sounding Unit-A (AMSU-A) and four other instruments, has been providing data for use in numerical weather prediction and data assimilation systems for over three years. The full AIRS data set is currently not transmitted in near-realtime to the prediction/assimilation centres. Instead, data sets with reduced spatial and spectral information are produced and made available within three hours of the observation time. In this paper, we evaluate the use of different channel selections and error specifications. We achieve significant positive impact from the Aqua AIRS/AMSU-A combination during our experimental time period of January 2003. The best results are obtained using a set of 156 channels that do not include any in the H2O band between 1080 and 2100 cm−1 . The H2O band channels have a large influence on both temperature and humidity analyses. If observation and background errors are not properly specified, the partitioning of temperature and humidity information from these channels will not be correct, and this can lead to a degradation in forecast skill. Therefore, we suggest that it is important to focus on background error specification in order to maximize the impact from AIRS and similar instruments. In addition, we find that changing the specified channel errors has a significant effect on the amount of data that enters the analysis as a result of quality control thresholds that are related to the errors. However, moderate changes to the channel errors do not significantly impact forecast skill with the 156 channel set. We also examine the effects of different types of spatial data reduction on assimilated data sets and NWP forecast skill. Whether we pick the centre or the warmest AIRS pixel in a 3×3 array affects the amount of data ingested by the analysis but does not have a statistically significant impact on the forecast skill.

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