Applications of information theory in ensemble data assimilation

Zupanski, D.; Hou, Arthur Y.; Zhang, Sara Q.; Zupanski, Milija; Kummerow, Christian D.; Cheung, Samson

doi:10.1002/qj.123

Cited by 52 publications

(57 citation statements)

References 63 publications

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“…Inversion of fluxes from concentrations to derive surface fluxes using transport models has already proved capable of providing global-scale, and in some instances continentalscale, information on fluxes with uncertainties. Some pilot studies have applied atmospheric inversion models at a finer scale down to ∼ 1 km, constrained by regionally denser atmospheric measurements (Schmitgen et al, 2004;Zupanski et al, 2007;Lauvaux et al, 2012;Göckede et al, 2010;Broquet et al, 2011;Lauvaux et al, 2009). However, the current sparseness of the ground-based network of atmospheric stations cannot constrain the patterns of CO 2 sources and sinks at the scale of nations, states/provinces, or cities (Hungershoefer et al, 2010;Chevallier et al, 2010), although some country-level estimates have been derived for CH 4 in Europe (Bergamaschi et al, 2010).…”

Section: )mentioning

confidence: 99%

Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system

Dolman²,

et al. 2014

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Abstract.A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The paper is addressed to scientists, policymakers, and funding agencies who need to have a global picture of the current state of the (diverse) carbon observations. We identify the current state of carbon observations, and the needs and notional requirements for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion of the ground-based observation networks required to reach the high spatial resolution for CO 2 and CH 4 fluxes, and for carbon stocks for addressing policy-relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over areas such as the southern oceans, tropical forests, and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote-sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with groundbased data. Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO 2 proxy measurements such as radiocarbon in CO 2 and carbon-fuel combustion tracers. Additionally, a policy-relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. In addition, uncertainties for each observation data-stream should be assessed. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases interoperable, and on the calibration of each component of the system to agreed-upon international scales.

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Section: )mentioning

confidence: 99%

Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system

Dolman²,

et al. 2014

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“…Zupanski et al (2007) showed that this formula could also be useful in reduced-rank, ensemble space calculations, in which the summation is performed over the number of ensemble members. Since an eigenvalue decomposition of the observation information matrix is a component of the MLEF algorithm, the additional cost of calculating d s is minimal.…”

Section: Information Content Of Lightning Observationsmentioning

confidence: 99%

Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model

Apodaca

Zupanski

DeMaria

et al. 2014

Nonlin. Processes Geophys.

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Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Geostationary Operational Environmental Satellite -R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), and improving initial conditions during several data assimilation cycles. However, the 6 h forecast after the assimilation did not show a clear improvement in terms of root mean square (RMS) errors.

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“…In the following, we will show the square root of error variance reduction, i.e., diag(C) (referred to as observation impact signal or OIS in the following) for different variables at different levels. Similar quantities, such as information content, relative entropy, degrees of freedom for signal, and mutual information, for quantifying the impacts of observations in a DA system have been introduced and studied by Rodgers (2000), Xu (2007), Zupanski et al (2007) and Fowler and van Leeuwen (2012).…”

Section: Eigenvalues and Eigenvectorsmentioning

confidence: 99%

Estimation and calibration of observation impact signals using the Lanczos method in NOAA/NCEP data assimilation system

Wei

Pondeca

Tóth³

et al. 2012

Nonlin. Processes Geophys.

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Abstract. Despite the tremendous progress that has been made in data assimilation (DA) methodology, observing systems that reduce observation errors, and model improvements that reduce background errors, the analyses produced by the best available DA systems are still different from the truth. Analysis error and error covariance are important since they describe the accuracy of the analyses, and are directly related to the future forecast errors, i.e., the forecast quality. In addition, analysis error covariance is critically important in building an efficient ensemble forecast system (EFS).Estimating analysis error covariance in an ensemble-based Kalman filter DA is straightforward, but it is challenging in variational DA systems, which have been in operation at most NWP (Numerical Weather Prediction) centers. In this study, we use the Lanczos method in the NCEP (the National Centers for Environmental Prediction) Gridpoint Statistical Interpolation (GSI) DA system to look into other important aspects and properties of this method that were not exploited before. We apply this method to estimate the observation impact signals (OIS), which are directly related to the analysis error variances. It is found that the smallest eigenvalue of the transformed Hessian matrix converges to one as the number of minimization iterations increases. When more observations are assimilated, the convergence becomes slower and more eigenvectors are needed to retrieve the observation impacts. It is also found that the OIS over data-rich regions can be represented by the eigenvectors with dominant eigenvalues.Since only a limited number of eigenvectors can be computed due to computational expense, the OIS is severely underestimated, and the analysis error variance is consequently overestimated. It is found that the mean OIS values for temperature and wind components at typical model levels are increased by about 1.5 times when the number of eigenvectors is doubled. We have proposed four different calibration schemes to compensate for the missing trailing eigenvectors. Results show that the method with calibration for a small number of eigenvectors cannot pick up the observation impacts over the regions with fewer observations as well as a benchmark with a large number of eigenvectors, but proper calibrations do enhance and improve the impact signals over regions with more data.When compared with the observation locations, the method generally captures the OIS over regions with more observation data, including satellite data over the southern oceans. Over the tropics, some observation impacts may be missed due to the smaller background errors specified in the GSI, which is not related to the method. It is found that a large number of eigenvectors are needed to retrieve impact signals that resemble the banded structures from satellite observations, particularly over the tropics. Another benefit from the Lanczos method is that the dominant eigenvectors can be used in preconditioning the conjugate gradient algorithm in the GSI to speed up ...

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Applications of information theory in ensemble data assimilation

Cited by 52 publications

References 63 publications

Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system

Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system

Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model

Estimation and calibration of observation impact signals using the Lanczos method in NOAA/NCEP data assimilation system

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