Channel selection methods for Infrared Atmospheric Sounding Interferometer radiances

Rabier, Florence; Fourrié, Nadia; Chafäı, Djalil; Prunet, Pascal

doi:10.1256/0035900021643638

Cited by 135 publications

(118 citation statements)

References 19 publications

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“…This has been shown by Rabier et al(2002) to be the best method for a priori determination of an optimal channel set, and has been further evaluated in the context of AIRS by Fourrié and Thépaut (2003).…”

Section: Selection Methodologymentioning

confidence: 99%

“…An alternative is the entropy reduction, − 1 2 ln |AB −1 |, but past experience (e.g. Rabier et al, 2002) has shown that the differences between choosing DFS or entropy reduction are small. This method requires an estimate of the background-error covariance matrix for temperature and all species included in the state vector, an estimate of the observation-error covariance matrix (including forward-model errors), and a forward model to estimate the Jacobians for the atmospheres being considered.…”

Section: Selection Methodologymentioning

confidence: 99%

“…As the precise atmospheric profiles used may have an important effect on the final selection (Rabier et al, 2002), the final channel selection should be tested against the optimal selection for a diverse range of atmospheric profiles.…”

Section: Selection Methodologymentioning

confidence: 99%

“…A number of techniques have been suggested for channel selection from high-spectral-resolution infrared sounders; these have been reviewed and compared by Rabier et al(2002), who conclude that the channelselection method of Rodgers (1996Rodgers ( , 2000 is the most nearly optimal (for fixed channel selections, as opposed to selections that are evaluated for individual observations).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Selection of IASI channels for use in numerical weather prediction

Collard

2007

Quart J Royal Meteoro Soc

121

137

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ABSTRACT:The Infrared Atmospheric Sounding Interferometer (IASI) is an infrared Fourier-transform spectrometer that has been flying on the MetOp satellite series since October 2006. It measures the radiance emitted from the Earth in 8461 channels, covering the spectral interval 645-2760 cm −1 at a resolution of 0.5 cm −1 (apodized). The high volume of data resulting from IASI presents many challenges, particularly in the areas of data transmission, storage and assimilation.The simplest methods for reducing the data volume are spatial sampling and channel selection. In this paper, the issue of channel selection is discussed, and a selection of 300 channels suitable for transmission to numerical-weather-prediction centres is examined; the primary aim is the improvement of the temperature, humidity, ozone and carbon-dioxide state vectors. The channel-selection method takes account of redundancy, the effects of interfering species, and robustness against the choice of assumed background-error covariance and atmospheric state.

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Section: Selection Methodologymentioning

confidence: 99%

Section: Selection Methodologymentioning

confidence: 99%

Section: Selection Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Selection of IASI channels for use in numerical weather prediction

Collard

2007

Quart J Royal Meteoro Soc

121

137

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“…Information theory has also come to the attention of data assimilation, where it has been used to calculate information content of various observations (e.g. Wahba, 1985;Purser and Huang, 1993;Wahba et al, 1995;Rodgers, 2000;Rabier et al, 2002;Fisher, 2003;Johnson, 2003;Engelen and Stephens, 2004;L'Ecuyer et al, 2006). Information content of observations can potentially have many applications, including planning measurement missions, designing observational systems and defining targeted observations and data selection strategies.…”

Section: Introductionmentioning

confidence: 99%

Applications of information theory in ensemble data assimilation

Zupanski

Hou

Zhang

et al. 2007

Quart J Royal Meteoro Soc

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ABSTRACT:We apply information theory within an ensemble-based data assimilation approach and define information matrix in ensemble subspace. The information matrix in ensemble subspace employs a flow-dependent forecast error covariance and it is of relatively small dimensions (equal to the ensemble size). The information matrix in ensemble subspace can be directly linked to the information matrix typically used in non-ensemble-based data assimilation methods, such as the Kalman Filter (KF) and the three-dimensional variational (3D-Var) methods, which provides a framework for consistent comparisons of information measures between different data assimilation methods.We evaluate information measures, such as degrees of freedom for signal, within the Maximum Likelihood Ensemble Filter (MLEF) data assimilation approach and compare them with those obtained using the KF approach and the 3D-Var approach. We assimilate model-simulated observations and use the Goddard Earth Observing System Single Column Model (GEOS-5 SCM) as a dynamical forecast model.The experimental results demonstrate that the proposed framework is useful for comparing information measures obtained in different data assimilation approaches. These comparisons indicate that using a flow-dependent forecast error covariance matrix (e.g. as in the KF and the MLEF experiments) is fundamentally important for adequately describing prior knowledge about the true model state when calculating information measures of assimilated observations. We also demonstrate that data assimilation results obtained using the KF and the MLEF approach (when ensemble size is larger than 10 ensemble members) are superior to the results of the 3D-Var approach.

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Added value of far‐infrared radiometry for remote sensing of ice clouds

Libois

Blanchet

2017

JGR Atmospheres

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Several cloud retrieval algorithms based on satellite observations in the infrared have been developed in the last decades. However, these observations only cover the midinfrared (MIR, λ < 15 μm) part of the spectrum, and none are available in the far‐infrared (FIR, λ≥ 15 μm). Using the optimal estimation method, we show that adding a few FIR channels to existing spaceborne radiometers would significantly improve their ability to retrieve ice cloud radiative properties. For clouds encountered in the polar regions and the upper troposphere, where the atmosphere is sufficiently transparent in the FIR, using FIR channels would reduce by more than 50% the uncertainties on retrieved values of optical thickness, effective particle diameter, and cloud top altitude. Notably, this would extend the range of applicability of current retrieval methods to the polar regions and to clouds with large optical thickness, where MIR algorithms perform poorly. The high performance of solar reflection‐based algorithms would thus be reached in nighttime conditions. Since the sensitivity of ice cloud thermal emission to effective particle diameter is approximately 5 times larger in the FIR than in the MIR, using FIR observations is a promising venue for studying ice cloud microphysics and precipitation processes. This is highly relevant for cirrus clouds and convective towers. This is also essential to study precipitation in the driest regions of the atmosphere, where strong feedbacks are at play between clouds and water vapor. The deployment in the near future of a FIR spaceborne radiometer is technologically feasible and should be strongly supported.

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Channel selection methods for Infrared Atmospheric Sounding Interferometer radiances

Cited by 135 publications

References 19 publications

Selection of IASI channels for use in numerical weather prediction

Selection of IASI channels for use in numerical weather prediction

Applications of information theory in ensemble data assimilation

Added value of far‐infrared radiometry for remote sensing of ice clouds

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