Articles | Volume 17, issue 18
https://doi.org/10.5194/amt-17-5637-2024
https://doi.org/10.5194/amt-17-5637-2024
Research article
 | 
26 Sep 2024
Research article |  | 26 Sep 2024

Merging TEMPEST microwave and GOES-16 geostationary IR soundings for improved water vapor profiles

Chia-Pang Kuo and Christian Kummerow

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Cited articles

Aires, F.: Measure and exploitation of multisensor and multiwavelength synergy for remote sensing: 1. Theoretical considerations, J. Geophys. Res., 116, D02301, https://doi.org/10.1029/2010JD014701, 2011. 
Aires, F., Paul, M., Prigent, C., Rommen, B., and Bouvet, M.: Measure and exploitation of multisensor and multiwavelength synergy for remote sensing: 2. Application to the retrieval of atmospheric temperature and water vapor from MetOp, J. Geophys. Res., 116, D02302, https://doi.org/10.1029/2010JD014702, 2011. 
Aires, F., Aznay, O., Prigent, C., Paul, M., and Bernardo, F.: Synergistic multi-wavelength remote sensing versus a posteriori combination of retrieved products: Application for the retrieval of atmospheric profiles using MetOp-A, J. Geophys. Res., 117, D18304, https://doi.org/10.1029/2011JD017188, 2012. 
Berg, W., Brown, S. T., Lim, B. H., Reising, S. C., Goncharenko, Y., Kummerow, C. D., Gaier, T. C., and Padmanabhan, S.: Calibration and validation of the TEMPEST-D CubeSat radiometer, IEEE T. Geosci. Remote, 59, 4904–4914, https://doi.org/10.1109/TGRS.2020.3018999, 2021. 
Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., Kumagai, Y., Miyakawa, T., Murata, H., Ohno, T., Okuyama, A., Oyama, R., Sasaki, Y., Shimazu, Y., Shimoji, K., Sumida, Y., Suzuki, M., Taniguchi, H., Tsuchiyama, H., Uesawa, D., Yokota, H., and Yoshida, R.: An introduction to Himawari-8/9 – Japan's new-generation geostationary meteorological satellites, J. Meteorol. Soc. Jpn., 94, 151–183, https://doi.org/10.2151/jmsj.2016-009, 2016. 
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Short summary
A small satellite about the size of a shoe box, named TEMPEST, carries only a microwave sensor and is designed to measure the water cycle of the Earth from space in an economical way compared with traditional satellites, which have additional infrared sensors. To overcome the limitation, extra infrared signals from GOES-R ABI are combined with TEMPEST microwave measurements. Compared with ground observations, improved humidity information is extracted from the merged TEMPEST and ABI signals.