Combining Analog Method and Ensemble Data Assimilation: Application to the Lorenz-63 Chaotic System

Ailliot, Pierre; Ruiz, Juan; Hannart, Alexis; Chapron, Bertrand; Cuzol, Anne; Monbet, Valérie; Easton, Robert W.; Fablet, Ronan

doi:10.1007/978-3-319-17220-0_1

Cited by 36 publications

(53 citation statements)

References 24 publications

(21 reference statements)

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“…Finally, hot day occurrence and intensity is evaluated using bias-corrected output from the same ensembles (McGinnis et al 2015;Oleson et al this issue). Since we based our previous analyses on changes computed as trends, and the bias-correction preserves the same horizontal resolution and the trends in the original model output by construction, our results would not be different had we used bias-corrected output for the whole study.…”

Section: Methods and Datamentioning

confidence: 99%

Estimated impacts of emission reductions on wheat and maize crops

Tebaldi

Lobell

2015

Climatic Change

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We assess the benefits of climate change mitigation for global maize and wheat production over the 21st century by comparing outcomes under RCP4.5 and RCP8.5 as simulated by two large initial condition ensembles from NCAR's Community Earth System Model. We use models of the relation between climate variables, CO 2 concentrations, and yields built on observations and then project this relation on the basis of simulated future temperature and precipitation and CO 2 trajectories under the two scenarios, for short (2021-2040), medium (2041-2060) and long (2061-2080) time horizons. We focus on projected mean yield impacts, chances of significant slowdowns in yield, and exposure to damaging heat during critical periods of the growing seasons, the last of which is not explicitly considered in yield impacts by most models, including ours. We find that substantial benefits from mitigation would be achieved throughout the 21st century for maize, in terms of reducing (1) the size of average yield impacts, with mean losses for maize under RCP8.5 reduced under RCP4.5 by about 25 %, 40 % and 50 % as the time horizon lengthens over the 21st century; (2) the risk of major slowdowns over a 10 or 20 year period, with maize chances under RCP4.5 being reduced up to~75 % by the end of the century compared to those estimated under RCP8.5; and (3) exposure to critical or Blethal^heat extremes, with the number of extremely hot days under RCP8.5 roughly triple current levels by end of century, compared to a doubling for RCP4.5. For wheat, we project small or occasionally negative effects of mitigation for projected yields, because of stronger CO 2 fertilization effects than in maize, but substantial benefits of mitigation remain in terms of exposure to extremely high temperatures.Climatic Change

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Section: Methods and Datamentioning

confidence: 99%

Estimated impacts of emission reductions on wheat and maize crops

Tebaldi

Lobell

2015

Climatic Change

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“…The same holds for other parametric models of the marginal distribution of the high-resolution detail. The extension to spatio-temporal fields will also be of key interest, including exemplar-based data assimilation issues [34]. Such extension might require manifold learning and kernel learning strategies to define a patch similarity measure adapted to the considered space-time processes [41].…”

Section: Discussionmentioning

confidence: 99%

Joint Interpolation of Multisensor Sea Surface Temperature Fields Using Nonlocal and Statistical Priors

Fablet

Rousseau

2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper addresses the joint analysis of multisource and multiresolution remote sensing data for the interpolation of high-resolution sea surface geophysical fields. As case-study application, we consider the interpolation of sea surface temperature (SST) fields. We propose a novel statistical model that combines two key features: an exemplar-based prior and statistical priors. The exemplar-based prior, referred to as a nonlocal prior, exploits similarities between local patches (small field regions) to interpolate missing data areas from previously observed exemplars. This nonlocal prior also sets an explicit conditioning between the multisensor data. Two complementary statistical priors, namely a prior on the spatial covariance and a prior on the marginal distribution of the high-resolution details, are considered as sea surface geophysical fields that are expected to depict specific spectral and marginal features in relation to the underlying turbulent ocean dynamics. We report the experiments on both synthetic and real SST data. These experiments demonstrate the contributions of the proposed combination of nonlocal and statistical priors to interpolate visually consistent and geophysically sound SST fields from multisource satellite data. We further discuss the key features and parameterizations of this model as well as its relevance with respect to classical interpolation techniques.Index Terms-Exemplar-based texture models, inpainting, missing data interpolation, non-local models, ocean remote sensing, sea surface fields.

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“…We show here that analog strategies are highly flexible to consider such conditioning; • the introduction of an analog forecasting operator embedding physically-sound priors. We further benefit from the flexibility of analog operators to exploit the synergy between SSH (Sea Surface Height) [35,37] and SST. We investigate locally-linear analog forecasting operators where SSH is used as a complementary regressor; • the reduction of the computational complexity of AnDA using a clustering-based analog forecasting operator.…”

Section: Problem Statement and Related Workmentioning

confidence: 99%

Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation

et al. 2018

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The ever increasing geophysical data streams pouring from earth observation satellite missions and numerical simulations along with the development of dedicated big data infrastructure advocate for truly exploiting the potential of these datasets, through novel data-driven strategies, to deliver enhanced satellite-derived gapfilled geophysical products from partial satellite observations. We here demonstrate the relevance of the analog data assimilation (AnDA) for an application to the reconstruction of cloud-free level-4 gridded Sea Surface Temperature (SST). We propose novel AnDA models which exploit auxiliary variables such as sea surface currents and significantly reduce the computational complexity of AnDA. Numerical experiments benchmark the proposed models with respect to state-of-the-art interpolation techniques such as optimal interpolation and EOF-based schemes. We report relative improvement up to 40%/50% in terms of RMSE and also show a good parallelization performance, which supports the feasibility of an upscaling on a global scale.filled and the fine-scale variability of the SST fields. Physically-driven data assimilation models [20] may outperform OI if relevant dynamical priors can be defined [21]. The trade-off to be considered between the complexity and genericity of this physical prior remains however complex, especially when considering the assimilation of a single sea surface tracer as SST.Besides model-driven schemes, the ever increasing availability of satellite-derived data and of simulation data from high-resolution ocean models has paved the way for the development of data-driven methods. EOF-based models were among the early and perhaps most popular data-driven methods applied to the reconstruction of SST fields from cloudy SST data [14,15,22] as well of other sea surface tracers such as ocean colour [22]. EOF-based approaches are particularly appealing for ocean remote sensing as they relate to a model of the covariance structure of the considered fields and may adapt to any type of geometry of missing data and interpolation grid. Their use is also motivated by their ability to decompose the spatiotemporal variability of the sea surface fields according to different modes, which may be interpreted geophysically. A renewed interest can also be noticed for analog schemes and applications to forecasting and assimilation issues [23,24]. Analog schemes, proposed a long time ago in geoscience [25], rely on the idea that the dynamics of a given system may repeat to some extent. Given a set of previously observed or analysed data, one may retrieve examples similar to a current state in this set, such that the future of this current state may be forecasted from the known evolution of these similar situations. The lack of large-scale dataset along with the computational complexity of analog methods has long limited their applicability. In this context, we recently introduced the analog data assimilation (AnDA) and demonstrated its relevance for the reconstruction of complex dynamical systems fo...

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Combining Analog Method and Ensemble Data Assimilation: Application to the Lorenz-63 Chaotic System

Cited by 36 publications

References 24 publications

Estimated impacts of emission reductions on wheat and maize crops

Estimated impacts of emission reductions on wheat and maize crops

Joint Interpolation of Multisensor Sea Surface Temperature Fields Using Nonlocal and Statistical Priors

Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation

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