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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2018-379
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2018-379
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 16 Nov 2018

Research article | 16 Nov 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Correlated observation error models for assimilating all-sky infrared radiances

Alan J. Geer Alan J. Geer
  • ECMWF, Shinfield Park, Reading RG2 9AX

Abstract. The benefit of hyperspectral infrared sounders to weather forecasting has been improved with the representation of interchannel correlations in the observation error model. The aim is now to assimilate these observations in all-sky conditions. However, in cloudy skies, observation errors exhibit much stronger interchannel correlations, as well as much larger variances, compared to clear sky conditions. An observation error model is developed to represent these effects, building from the symmetric error models developed for all-sky microwave assimilation. The combination of variational quality control with correlated errors is also introduced. The new error model is tested in all-sky assimilation of 7 water vapour sounding channels from the Infrared Atmospheric Sounding Interferometer (IASI). However, its initial formulation degrades both tropospheric and stratospheric analyses. To explain this the eigendeparture and eigenjacobian are introduced as a way of understanding the effect of correlated observation errors in data assimilation. The trailing eigenvalues can be problematic because they strongly amplify high-order harmonic combinations of the water vapour channels, which could have at least three consequences: first, if there are small inter-channel biases, these can be greatly amplified; second, the trailing eigenjacobians map onto features resembling gravity waves that the data assimilation may not be able to handle; finally, these harmonic combinations can amplify trace sensitivities, for example revealing a strong upper stratospheric sensitivity over high cloud in what are usually mid- to upper-tropospheric water vapour channels. The most likely explanation is the sensitivity to gravity wave features that are present in the observations but hard for the data assimilation to handle. After reducing the sensitivity to the trailing eigenjacobians, the new error covariance matrix gives good results in all-sky infrared assimilation.

Alan J. Geer
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Status: open (until 11 Jan 2019)
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Alan J. Geer
Alan J. Geer
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Latest update: 18 Dec 2018
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Short summary
Using more satellite data in cloudy areas will help improve weather forecasts. All-sky assimilation of microwave radiances is common but of infrared radiances less so. To allow the all-sky use of hyperspectral infrared sounders, an error model is developed to represent the effect of cloud in broadening the correlations between channels as well as increasing error variances. After fixing problems of gravity wave and bias amplification, the results of all-sky assimilation trials were promising.
Using more satellite data in cloudy areas will help improve weather forecasts. All-sky...
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