Articles | Volume 12, issue 1
Atmos. Meas. Tech., 12, 83–106, 2019
https://doi.org/10.5194/amt-12-83-2019
Atmos. Meas. Tech., 12, 83–106, 2019
https://doi.org/10.5194/amt-12-83-2019

Research article 07 Jan 2019

Research article | 07 Jan 2019

Using reference radiosondes to characterise NWP model uncertainty for improved satellite calibration and validation

Fabien Carminati et al.

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Cited articles

Agusti-Panareda, A., Vasiljevic, D., Beljaars, A., Bock, O., Guichard, F., Nuret, M., Garcia Mendez, A., Andersson, E., Bechtold, P., Fink, A., Hersbach, H., Lafore, J.-P., Ngamini, J.-B., Parker, D. J., Redelsperger, J.-L., and Tompkins, A. M.: Radiosonde humidity bias correction over the West African region for the special AMMA reanalysis at ECMWF, Q. J. Roy. Meteor. Soc., 135, 595–617, https://doi.org/10.1002/qj.396, 2009. 
Auligné, T., McNally, A. P., and Dee, D. P.: Adaptive bias correction for satellite data in a numerical weather prediction system, Q. J. Roy. Meteor. Soc., 133, 631–642, https://doi.org/10.1002/qj.56, 2007. 
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. 
Bell, W., Candy, B., Atkinson, N., Hilton, F., Baker, N., Bormann, N., Kelly, G., Kazumori, M., Campbell, W. F., and Swadley, S. D.: The assimilation of SSMIS radiances in numerical weather prediction models, IEEE T. Geosci. Remote Sens., 46, 884–900, https://doi.org/10.1109/TGRS.2008.917335, 2008. 
Bojinski, S., Verstraete, M., Peterson, T. C., Richter, C., Simmons, A., and Zemp, M.: The concept of essential climate variables in support of climate research, applications, and policy, B. Am. Meteorol. Soc., 95, 1431–1443, https://doi.org/10.1175/BAMS-D-13-00047.1, 2014. 
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Short summary
The GRUAN processor is a software developed to collocate radiosonde profiles and numerical weather prediction model fields, simulate top-of-atmosphere brightness temperature at frequencies used by space-borne instruments, and propagate the radiosonde uncertainties in that simulation. This work responds to an identified lack of metrologically traceable characterisation of uncertainties in model fields that are increasingly used for the validation and calibration of space-borne instruments.