Improving the selection of IASI channels for use in numerical weather prediction

Ventress, Lucy J.; Dudhia, A.

doi:10.1002/qj.2280

Cited by 28 publications

(23 citation statements)

References 12 publications

(15 reference statements)

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“…Third , we study the reconstruction of an atmospheric temperature profile from IASI radiance measurements . In principle, the observation operator can be viewed as a discretized version of an integral equation of the form

f (s) = \int_{G} w (s, τ) ϕ (τ) dτ, s \in M

with w ( s , τ ) being the sensitivity of the IASI measurement in channel s with respect to temperature variations at point τ in the column G .…”

Section: Introductionmentioning

confidence: 99%

Transformed and generalized localization for ensemble methods in data assimilation

Nadeem

Potthast

2015

Math Methods in App Sciences

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The task of this paper is to study and analyse transformed localization and generalized localization for ensemble methods in data assimilation. Localization is an important part of ensemble methods such as the ensemble Kalman filter or square root filter. It guarantees a sufficient number of degrees of freedom when a small number of ensembles or particles, respectively, are used. However, when the observation operators under consideration are non-local, the localization that is applicable to the problem can be severly limited, with strong effects on the quality of the assimilation step. Here, we study a transformation approach to change non-local operators to local operators in transformed space, such that localization becomes applicable. We interpret this approach as a generalized localization and study its general algebraic formulation. Examples are provided for a compact integral operator and a non-local Matrix observation operator to demonstrate the feasibility of the approach and study the quality of the assimilation by transformation. In particular, we apply the approach to temperature profile reconstruction from infrared measurements given by the infrared atmospheric sounding interferometer (IASI) infrared sounder and show that the approach is feasible for this important data type in atmospheric analysis and forecasting.

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f (s) = \int_{G} w (s, τ) ϕ (τ) dτ, s \in M

with w ( s , τ ) being the sensitivity of the IASI measurement in channel s with respect to temperature variations at point τ in the column G .…”

Section: Introductionmentioning

confidence: 99%

Transformed and generalized localization for ensemble methods in data assimilation

Nadeem

Potthast

2015

Math Methods in App Sciences

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“…This is because off-diagonal elements in S a represent physical correlations between various pressure levels and may not follow Gaussian statistics (von Clarmann, 2014). More rigorous derivations and discussions of mapping between states can be found in Worden et al (2006), Bowman et al (2006), and Rodgers (2000, ch.…”

Section: Constraint Mappingmentioning

confidence: 99%

“…For an effectively a priori free retrieval, the number of estimates should be at most equal to the DFS on the fine grid rounded down (von Clarmann and Grabowski, 2007), but this comes at the cost of a modest reduction in DFS. However, if it is acceptable for some a priori to appear in the estimate, then how many levels beyond the DFS should be included?…”

Section: Deciding Upon the Number Of Levelsmentioning

confidence: 99%

“…The surface plot shown is also for a temperature retrieval with IASI using the CO 2 region previously mentioned. Pressure levels have been ranked by the iterative method just described and spectral channels have been ranked according to the channel selection method found in Ventress and Dudhia (2013). The vertical and spectral selection methods bear much similarity.…”

Section: Deciding Upon the Number Of Levelsmentioning

confidence: 99%

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Vertical level selection for temperature and trace gas profile retrievals using IASI

2015

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Abstract. This work presents a new iterative method for optimally selecting a vertical retrieval grid based on the location of the information while accounting for inter-level correlations. Sample atmospheres initially created to parametrise the Radiative Transfer Model for the Television Infrared Observation Satellite Operational Vertical Sounder (RTTOV) forward model are used to compare the presented iterative selection method with two other common approaches, which are using levels of equal vertical spacing and selecting levels based on the cumulative trace of the averaging kernel matrix (AKM). This new method is shown to outperform compared methods for simulated profile retrievals of temperature, H 2 O, O 3 , CH 4 , and CO with the Infrared Atmospheric Sounding Interferometer (IASI). However, the benefits of using the more complicated iterative approach compared to the simpler cumulative trace method are slight and may not justify the added effort for the cases studied, but may be useful in other scenarios where temperature and trace gases have strong vertical gradients with significant estimate sensitivity. Furthermore, comparing retrievals using a globally optimised static grid vs. a locally adapted one shows that a static grid performs nearly as well for retrievals of O 3 , CH 4 , and CO. However, developers of temperature and H 2 O retrieval schemes may at least consider using adaptive or location specific vertical retrieval grids.

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“…For example, Li and Huang (1994) developed an approach based on a stepwise regression for AIRS channel selection; Collard (2007) selected channels for assimilation based on information content analysis along with some additional criteria; Rabier et al (2002) studied channel selection based on the Jacobian matrix and iterative method for sequentially selecting channels with the largest information content; Ventress and Dudhia (2014) modified the standard algorithm to allow spectrally correlated errors to be properly modelled, and quantified, * Corresponding author: Jun LI Email: Jun.Li@ssec.wisc.edu within the channel selection process; and Migliorini (ECMWF technical Memo no. 727, 2014) studied the optimal flow-dependent selection of channels in the presence of cloud.…”

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confidence: 99%

A step forward toward effectively using hyperspectral IR sounding information in NWP

Han

2017

Adv. Atmos. Sci.

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Citation: Li, J., and W. Han: A step forward toward effectively using hyperspectral IR sounding information in NWP. Adv. Atmos. Sci., 34(11), 1263-1264, https://doi.org/ 10.1007/s00376-017-7167-2.Satellite-based atmospheric sounding measurements with high spectral resolution or from hyperspectral infrared (IR) sounders are important global observations for improving weather forecasts through assimilating them into operational numerical weather prediction (NWP) systems. Advanced IR sounders, such as the Atmospheric Infrared Sounder (AIRS) (Chahine et al., 2006) onboard Aqua, the Infrared Atmospheric Sounding Interferometer (IASI) (Hilton et al., 2012) onboard Metop-A and -B, and the Crosstrack Infrared Sounder (CrIS) (Gambacorta et al., 2014) onboard SNPP (Suomi National Polar-orbiting Partnership), together with microwave sounders, have become the most important providers of observations for operational centers (Le Marshall et al., 2005, 2006a, 2006bCardinali, 2009). These advanced IR and microwave sounders have provided the largest impact on forecast skill amongst all satellite observations (Joo et al., 2013;Cucurull and Anthes, 2014). Although advanced IR sounders have played a critical role in NWP systems, only a few hundred of the channels are selected in for assimilation, instead of all available channels, due partly to the large amount of data. For example, AIRS has 2372 channels, IASI has 8461 channels, and CrIS has 1305 channels, but in each case only a subset of channels are assimilated in the NWP systems at the major operational centers.Methodologies have been developed for selecting a subset of channels for NWP assimilation. For example, Li and Huang (1994) developed an approach based on a stepwise regression for AIRS channel selection; Collard (2007) selected channels for assimilation based on information content analysis along with some additional criteria; Rabier et al. (2002) studied channel selection based on the Jacobian matrix and iterative method for sequentially selecting channels with the largest information content; Ventress and Dudhia (2014) modified the standard algorithm to allow spectrally correlated errors to be properly modelled, and quantified, * Corresponding author: Jun LI Email: Jun.Li@ssec.wisc.edu within the channel selection process; and Migliorini (ECMWF technical Memo no. 727, 2014) studied the optimal flow-dependent selection of channels in the presence of cloud. These methodologies are very useful for selecting a subset of channels presenting optimal information for radiance assimilation, especially when hyperspectral advanced sounder data are available from geostationary orbit; for example, the Geostationary Interferometric Infrared Sounder onboard the FengYun-4 series (Yang et al., 2017). In general, these methodologies are based on information content analysis, in which the linearization is used in the channel selection process. One limitation of information content analysis is the linear approach for selecting absorption channels that have high nonlinearity (e.g., the r...

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Improving the selection of IASI channels for use in numerical weather prediction

Cited by 28 publications

References 12 publications

Transformed and generalized localization for ensemble methods in data assimilation

Transformed and generalized localization for ensemble methods in data assimilation

Vertical level selection for temperature and trace gas profile retrievals using IASI

A step forward toward effectively using hyperspectral IR sounding information in NWP

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