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Below is the combined list of references from refs_sat.bib and refs_external.bib. It is intended for our group's internal use.

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                1. Bauer, P., A. J. Geer, P. Lopez, and D. Salmond (2010), Direct 4D-Var assimilation of all-sky radiances. Part I: Implementation, Q. J. R. Meteorol. Soc., 136(652), 1868–1885, doi:10.1002/qj.659.
                2. Birkenheuer, D. (1998), P5.27A Radiance Assimilation of Polar and Geostationary Satellite Data in Laps, NOAA Forecasts systems Laboratory.
                3. Cohn, S. E. (1997), An Introduction to Estimation Theory, J. Meteorol. Soc. Jpn., 75(1B), 257–288.
                4. English, S. J., J. R. Eyre, and J. A. Smith (1999), A cloud-detection scheme for use with satellite sounding radiances in the context of data assimilation for numerical weather prediction, Q. J. R. Meteorol. Soc., 125(559), 2359–2378.
                5. Fisher, M. and D. J. Lary (1995), Lagrangian four-dimensional variational data assimilation of chemicel species, Q. J. R. Meteorol. Soc., 121, 1681–1704.
                6. Geer, A. J., P. Bauer, and C. W. O'Dell (2009), A Revised Cloud Overlap Scheme for Fast Microwave Radiative Transfer in Rain and Cloud, J. Appl. Meteorol. Clim., 48(11), 2257–2270, doi:10.1175/2009JAMC2170.1.
                7. Greenwald, T., R. Bennartz, A. Heidinger, and C. O'Dell (xx), Radiative Transfer Model Development for Operational Global Assimilation of Passive Microwave Radiances in Precipitation Clouds, University of Wisconsin-Madison, NOAA/NESDIS.
                8. Hunt, B. R., E. J. Kostelich, and I. Szunyogh (2007), Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica D, 230(1-2), 112–126, doi:10.1016/j.physd.2006.11.008.
                9. Joiner, J., A. M. da Silva, and R. Menard (1111), Efficient Methods to Assimilate Data from Future remote Sounding instruments, NASA/Goddard Data Assimilation Office (DAO).
                10. Kulie, M. S., R. Bennartz, T. J. Greenwald, Y. Chen, and F. Weng (2010), Uncertainties in Microwave Properties of Frozen Precipitation: Implications for Remote Sensing and Data Assimilation, J. Atmos. Sci., 67(11), 3471–3487.
                11. Lary, D. J. (1999), Data Assimilation: A Powerful Tool for Atmospheric Chemistry, , doi:10.1098/rsta.1999.0502.
                12. Liu, C., Q. Xiao, and B. Wang (2008), An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test, Mon. Weather Rev., 136(9), 3363–3373, doi:10.1175/2008MWR2312.1.
                13. Rizvi, S. R. H., R. Kamineni, and U. C. Mohanty (2002), Impact of MSMR data on NCMRWF Global Data Assimilation System, Met. Atm. Phys., 81, 257–272, doi:10.1007/s00703-002-0550-1.
                14. Tian, M., X. Zou, and F. Weng (2015), Use of Allan Deviation for Characterizing Satellite Microwave Sounder Noise Equivalent Differential Temperature (NEDT), IEEE Geosci. Remote Sens. Let., 12(12), 2477–2480, doi:10.1109/LGRS.2015.2485945.