Articles | Volume 15, issue 20
https://doi.org/10.5194/gmd-15-7641-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-7641-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
20 Oct 2022
Development and technical paper |
| 20 Oct 2022
A fast, single-iteration ensemble Kalman smoother for sequential data assimilation
Center for Western Weather and Water Extremes (CW3E), Scripps Institution of Oceanography, University of California San Diego, San Diego, CA, USA
Department of Mathematics and Statistics, University of Nevada, Reno, Reno, Nevada, USA
Marc Bocquet
CEREA, École des Ponts and EDF R&D, Île-de-France, France
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Colin Grudzien, Marc Bocquet, and Alberto Carrassi
Geosci. Model Dev., 13, 1903–1924, https://doi.org/10.5194/gmd-13-1903-2020, https://doi.org/10.5194/gmd-13-1903-2020, 2020
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All scales of a dynamical physical process cannot be resolved accurately in a multiscale, geophysical model. The behavior of unresolved scales of motion are often parametrized by a random process to emulate their effects on the dynamically resolved variables, and this results in a random–dynamical model. We study how the choice of a numerical discretization of such a system affects the model forecast and estimation statistics, when the random–dynamical model is unbiased in its parametrization.
Colin Grudzien, Alberto Carrassi, and Marc Bocquet
Nonlin. Processes Geophys., 25, 633–648, https://doi.org/10.5194/npg-25-633-2018, https://doi.org/10.5194/npg-25-633-2018, 2018
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Using the framework Lyapunov vectors, we analyze the asymptotic properties of ensemble based Kalman filters and how these are influenced by dynamical chaos, especially in the context of random model errors and small ensemble sizes. Particularly, we show a novel derivation of the evolution of forecast uncertainty for ensemble-based Kalman filters with weakly-nonlinear error growth, and discuss its impact for filter design in geophysical models.
Pierre J. Vanderbecken, Joffrey Dumont Le Brazidec, Alban Farchi, Marc Bocquet, Yelva Roustan, Élise Potier, and Grégoire Broquet
Atmos. Meas. Tech., 16, 1745–1766, https://doi.org/10.5194/amt-16-1745-2023, https://doi.org/10.5194/amt-16-1745-2023, 2023
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Instruments dedicated to monitoring atmospheric gaseous compounds from space will provide images of urban-scale plumes. We discuss here the use of new metrics to compare observed plumes with model predictions that will be less sensitive to meteorology uncertainties. We have evaluated our metrics on diverse plumes and shown that by eliminating some aspects of the discrepancies, they are indeed less sensitive to meteorological variations.
Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan
Geosci. Model Dev., 16, 1039–1052, https://doi.org/10.5194/gmd-16-1039-2023, https://doi.org/10.5194/gmd-16-1039-2023, 2023
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When radionuclides are released into the atmosphere, the assessment of the consequences depends on the evaluation of the magnitude and temporal evolution of the release, which can be highly variable as in the case of Fukushima Daiichi.
Here, we propose Bayesian inverse modelling methods and the reversible-jump Markov chain Monte Carlo technique, which allows one to evaluate the temporal variability of the release and to integrate different types of information in the source reconstruction.
Tobias Sebastian Finn, Charlotte Durand, Alban Farchi, Marc Bocquet, Yumeng Chen, Alberto Carrassi, and Véronique Dansereau
EGUsphere, https://doi.org/10.5194/egusphere-2022-1342, https://doi.org/10.5194/egusphere-2022-1342, 2023
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We combine machine learning with a simplified regional sea-ice model to correct errors in sea-ice dynamics of low-resolution forecasts towards a high-resolution truth. The combined model improves the forecast by up to 75 % and surpasses thereby the performance of persistence. As the error correction can also represent physically-explainable relations, this study highlights the potential of combined modelling for short-term sea-ice forecasting.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Marc Bocquet, Jinghui Lian, Grégoire Broquet, Gerrit Kuhlmann, Alexandre Danjou, and Thomas Lauvaux
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-288, https://doi.org/10.5194/gmd-2022-288, 2022
Preprint under review for GMD
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Monitoring of CO2 emissions is key to the development of reduction policies. Local emissions, from cities or power plants, may be estimated from CO2 plumes detected in satellite images. CO2 plumes generally have a weak signal and are partially concealed by highly variable background concentrations and instrument errors, which hampers their detection. To address this problem, we propose and apply deep learning methods to detect the contour of a plume in simulated CO2 satellite images.
Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan
Atmos. Chem. Phys., 21, 13247–13267, https://doi.org/10.5194/acp-21-13247-2021, https://doi.org/10.5194/acp-21-13247-2021, 2021
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The assessment of the environmental consequences of a radionuclide release depends on the estimation of its source. This paper aims to develop inverse Bayesian methods which combine transport models with measurements, in order to reconstruct the ensemble of possible sources.
Three methods to quantify uncertainties based on the definition of probability distributions and the physical models are proposed and evaluated for the case of 106Ru releases over Europe in 2017.
Colin Grudzien, Marc Bocquet, and Alberto Carrassi
Geosci. Model Dev., 13, 1903–1924, https://doi.org/10.5194/gmd-13-1903-2020, https://doi.org/10.5194/gmd-13-1903-2020, 2020
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All scales of a dynamical physical process cannot be resolved accurately in a multiscale, geophysical model. The behavior of unresolved scales of motion are often parametrized by a random process to emulate their effects on the dynamically resolved variables, and this results in a random–dynamical model. We study how the choice of a numerical discretization of such a system affects the model forecast and estimation statistics, when the random–dynamical model is unbiased in its parametrization.
Thomas Lauvaux, Liza I. Díaz-Isaac, Marc Bocquet, and Nicolas Bousserez
Atmos. Chem. Phys., 19, 12007–12024, https://doi.org/10.5194/acp-19-12007-2019, https://doi.org/10.5194/acp-19-12007-2019, 2019
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A small-size ensemble of mesoscale simulations has been filtered to characterize the spatial structures of transport errors in atmospheric CO2 mixing ratios. The extracted error structures in in situ and column CO2 show similar length scales compared to other meteorological variables, including seasonality, which could be used as proxies in regional inversion systems.
Marc Bocquet, Julien Brajard, Alberto Carrassi, and Laurent Bertino
Nonlin. Processes Geophys., 26, 143–162, https://doi.org/10.5194/npg-26-143-2019, https://doi.org/10.5194/npg-26-143-2019, 2019
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This paper describes an innovative way to use data assimilation to infer the dynamics of a physical system from its observation only. The method can operate with noisy and partial observation of the physical system. It acts as a deep learning technique specialised to dynamical models without the need for machine learning tools. The method is successfully tested on chaotic dynamical systems: the Lorenz-63, Lorenz-96, and Kuramoto–Sivashinski models and a two-scale Lorenz model.
Julien Brajard, Alberto Carrassi, Marc Bocquet, and Laurent Bertino
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-136, https://doi.org/10.5194/gmd-2019-136, 2019
Revised manuscript not accepted
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We explore the possibility of combining data assimilation with machine learning. We introduce a new hybrid method for a two-fold scope: (i) emulating hidden, possibly chaotic, dynamics and (ii) predicting its future states. Numerical experiments have been carried out using the chaotic Lorenz 96 model, proving both the convergence of the hybrid method and its statistical skills including short-term forecasting and emulation of the long-term dynamics.
Liza I. Díaz-Isaac, Thomas Lauvaux, Marc Bocquet, and Kenneth J. Davis
Atmos. Chem. Phys., 19, 5695–5718, https://doi.org/10.5194/acp-19-5695-2019, https://doi.org/10.5194/acp-19-5695-2019, 2019
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We demonstrate that transport model errors, one of the main contributors to the uncertainty in regional CO2 inversions, can be represented by a small-size ensemble carefully calibrated with meteorological data. Our results also confirm transport model errors represent a significant fraction of the model–data mismatch in CO2 mole fractions and hence in regional inverse CO2 fluxes.
Alban Farchi and Marc Bocquet
Nonlin. Processes Geophys., 25, 765–807, https://doi.org/10.5194/npg-25-765-2018, https://doi.org/10.5194/npg-25-765-2018, 2018
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Data assimilation looks for an optimal way to learn from observations of a dynamical system to improve the quality of its predictions. The goal is to filter out the noise (both observation and model noise) to retrieve the true signal. Among all possible methods, particle filters are promising; the method is fast and elegant, and it allows for a Bayesian analysis. In this review paper, we discuss implementation techniques for (local) particle filters in high-dimensional systems.
Colin Grudzien, Alberto Carrassi, and Marc Bocquet
Nonlin. Processes Geophys., 25, 633–648, https://doi.org/10.5194/npg-25-633-2018, https://doi.org/10.5194/npg-25-633-2018, 2018
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Using the framework Lyapunov vectors, we analyze the asymptotic properties of ensemble based Kalman filters and how these are influenced by dynamical chaos, especially in the context of random model errors and small ensemble sizes. Particularly, we show a novel derivation of the evolution of forecast uncertainty for ensemble-based Kalman filters with weakly-nonlinear error growth, and discuss its impact for filter design in geophysical models.
Olivier Pannekoucke, Marc Bocquet, and Richard Ménard
Nonlin. Processes Geophys., 25, 481–495, https://doi.org/10.5194/npg-25-481-2018, https://doi.org/10.5194/npg-25-481-2018, 2018
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The forecast of weather prediction uncertainty is a real challenge and is crucial for risk management. However, uncertainty prediction is beyond the capacity of supercomputers, and improvements of the technology may not solve this issue. A new uncertainty prediction method is introduced which takes advantage of fluid equations to predict simple quantities which approximate real uncertainty but at a low numerical cost. A proof of concept is shown by an academic model derived from fluid dynamics.
Anthony Fillion, Marc Bocquet, and Serge Gratton
Nonlin. Processes Geophys., 25, 315–334, https://doi.org/10.5194/npg-25-315-2018, https://doi.org/10.5194/npg-25-315-2018, 2018
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This study generalizes a paper by Pires et al. (1996) to state-of-the-art data assimilation techniques, such as the iterative ensemble Kalman smoother (IEnKS). We show that the longer the time window over which observations are assimilated, the better the accuracy of the IEnKS. Beyond a critical time length that we estimate, we show that this accuracy finally degrades. We show that the use of the quasi-static minimizations but generalized to the IEnKS yields a significantly improved accuracy.
J.-M. Haussaire and M. Bocquet
Geosci. Model Dev., 9, 393–412, https://doi.org/10.5194/gmd-9-393-2016, https://doi.org/10.5194/gmd-9-393-2016, 2016
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The focus is on the development of low-order models of atmospheric transport and chemistry and their use for data assimilation purposes. A new low-order coupled chemistry meteorology model is developed. It consists of the Lorenz40-variable model used as a wind field coupled with a simple ozone photochemistry module. Advanced ensemble variational methods are applied to this model to obtain insights on the use of data assimilation with coupled models, in an offline mode or in an online mode.
M. Bocquet, P. N. Raanes, and A. Hannart
Nonlin. Processes Geophys., 22, 645–662, https://doi.org/10.5194/npg-22-645-2015, https://doi.org/10.5194/npg-22-645-2015, 2015
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The popular data assimilation technique known as the ensemble Kalman filter (EnKF) suffers from sampling errors due to the limited size of the ensemble. This deficiency is usually cured by inflating the sampled error covariances and by using localization. This paper further develops and discusses the finite-size EnKF, or EnKF-N, a variant of the EnKF that does not require inflation. It expands the use of the EnKF-N to a wider range of dynamical regimes.
M. Bocquet, H. Elbern, H. Eskes, M. Hirtl, R. Žabkar, G. R. Carmichael, J. Flemming, A. Inness, M. Pagowski, J. L. Pérez Camaño, P. E. Saide, R. San Jose, M. Sofiev, J. Vira, A. Baklanov, C. Carnevale, G. Grell, and C. Seigneur
Atmos. Chem. Phys., 15, 5325–5358, https://doi.org/10.5194/acp-15-5325-2015, https://doi.org/10.5194/acp-15-5325-2015, 2015
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Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of concentrations, and perform inverse modeling. Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. We review here the current status of data assimilation in atmospheric chemistry models, with a particular focus on future prospects for data assimilation in CCMM.
Y. Wang, K. N. Sartelet, M. Bocquet, P. Chazette, M. Sicard, G. D'Amico, J. F. Léon, L. Alados-Arboledas, A. Amodeo, P. Augustin, J. Bach, L. Belegante, I. Binietoglou, X. Bush, A. Comerón, H. Delbarre, D. García-Vízcaino, J. L. Guerrero-Rascado, M. Hervo, M. Iarlori, P. Kokkalis, D. Lange, F. Molero, N. Montoux, A. Muñoz, C. Muñoz, D. Nicolae, A. Papayannis, G. Pappalardo, J. Preissler, V. Rizi, F. Rocadenbosch, K. Sellegri, F. Wagner, and F. Dulac
Atmos. Chem. Phys., 14, 12031–12053, https://doi.org/10.5194/acp-14-12031-2014, https://doi.org/10.5194/acp-14-12031-2014, 2014
Y. Wang, K. N. Sartelet, M. Bocquet, and P. Chazette
Atmos. Chem. Phys., 14, 3511–3532, https://doi.org/10.5194/acp-14-3511-2014, https://doi.org/10.5194/acp-14-3511-2014, 2014
O. Saunier, A. Mathieu, D. Didier, M. Tombette, D. Quélo, V. Winiarek, and M. Bocquet
Atmos. Chem. Phys., 13, 11403–11421, https://doi.org/10.5194/acp-13-11403-2013, https://doi.org/10.5194/acp-13-11403-2013, 2013
M. Bocquet and P. Sakov
Nonlin. Processes Geophys., 20, 803–818, https://doi.org/10.5194/npg-20-803-2013, https://doi.org/10.5194/npg-20-803-2013, 2013
M. R. Koohkan, M. Bocquet, Y. Roustan, Y. Kim, and C. Seigneur
Atmos. Chem. Phys., 13, 5887–5905, https://doi.org/10.5194/acp-13-5887-2013, https://doi.org/10.5194/acp-13-5887-2013, 2013
Y. Wang, K. N. Sartelet, M. Bocquet, and P. Chazette
Atmos. Chem. Phys., 13, 269–283, https://doi.org/10.5194/acp-13-269-2013, https://doi.org/10.5194/acp-13-269-2013, 2013
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We develop a multi-dimensional, parallelized domain decomposition strategy for mass-transfer particle tracking methods in two and three dimensions, investigate different procedures for decomposing the domain, and prescribe an optimal tiling based on physical problem parameters and the number of available CPU cores. For an optimally subdivided diffusion problem, the parallelized algorithm achieves nearly perfect linear speedup in comparison with the serial run-up to thousands of cores.
Xiaqiong Zhou and Hann-Ming Henry Juang
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GFS is one of the most important operational global weather forecast systems at NCEP/EMC. The stability of GFS on model integration is as important as its forecast skills to deliver dependable real-time products to its users and downstream forecast systems. The model instability issue of GFSv16 caught our attention when several cases in its real-time parallel runs failed to finish 16-day forecasts. Potential solutions were proposed to fix the model instability issue.
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In this work, we formulate the sequential geoscientific data acquisition problem as a problem that is similar to playing chess against nature, except the pieces are not fully observed. Solutions to these problems are given in AI and rarely used in geoscientific data planning. We illustrate our approach to a simple 2D problem of mineral exploration.
Ziqi Gao, Yifeng Wang, Petros Vasilakos, Cesunica E. Ivey, Khanh Do, and Armistead G. Russell
Geosci. Model Dev., 15, 9015–9029, https://doi.org/10.5194/gmd-15-9015-2022, https://doi.org/10.5194/gmd-15-9015-2022, 2022
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While the national ambient air quality standard of ozone is based on the 3-year average of the fourth highest 8 h maximum (MDA8) ozone concentrations, these predicted extreme values using numerical methods are always biased low. We built four computational models (GAM, MARS, random forest and SVR) to predict the fourth highest MDA8 ozone in Southern California using precursor emissions, meteorology and climatological patterns. All models presented acceptable performance, with GAM being the best.
Zhihao Wang, Jason Goetz, and Alexander Brenning
Geosci. Model Dev., 15, 8765–8784, https://doi.org/10.5194/gmd-15-8765-2022, https://doi.org/10.5194/gmd-15-8765-2022, 2022
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A lack of inventory data can be a limiting factor in developing landslide predictive models, which are crucial for supporting hazard policy and decision-making. We show how case-based reasoning and domain adaptation (transfer-learning techniques) can effectively retrieve similar landslide modeling situations for prediction in new data-scarce areas. Using cases in Italy, Austria, and Ecuador, our findings support the application of transfer learning for areas that require rapid model development.
Till Sachau, Haibin Yang, Justin Lang, Paul D. Bons, and Louis Moresi
Geosci. Model Dev., 15, 8749–8764, https://doi.org/10.5194/gmd-15-8749-2022, https://doi.org/10.5194/gmd-15-8749-2022, 2022
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Knowledge of the internal structures of the major continental ice sheets is improving, thanks to new investigative techniques. These structures are an essential indication of the flow behavior and dynamics of ice transport, which in turn is important for understanding the actual impact of the vast amounts of water trapped in continental ice sheets on global sea-level rise. The software studied here is specifically designed to simulate such structures and their evolution.
Keith J. Roberts, Alexandre Olender, Lucas Franceschini, Robert C. Kirby, Rafael S. Gioria, and Bruno S. Carmo
Geosci. Model Dev., 15, 8639–8667, https://doi.org/10.5194/gmd-15-8639-2022, https://doi.org/10.5194/gmd-15-8639-2022, 2022
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Finite-element methods (FEMs) permit the use of more flexible unstructured meshes but are rarely used in full waveform inversions (FWIs), an iterative process that reconstructs velocity models of earth’s subsurface, due to computational and memory storage costs. To reduce those costs, novel software is presented allowing the use of high-order mass-lumped FEMs on triangular meshes, together with a material-property mesh-adaptation performance-enhancing strategy, enabling its use in FWIs.
Emma Johanne MacKie, Michael Field, Lijing Wang, Zhen Yin, Nathan Schoedl, Matthew Hibbs, and Allan Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2022-1224, https://doi.org/10.5194/egusphere-2022-1224, 2022
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Earth scientists often have to fill in spatial gaps in measurements. This gap-filling or interpolation can be accomplished with geostatistical methods, where the statistical relationships between measurements are used to inform how these gaps should be filled. Despite the broad utility of these methods, there are few freely available geostatistics software. We present GStatSim, a Python package for performing different geostatistical interpolation methods.
Grant Thomas Euen, Shangxin Liu, Rene Gassmöller, Timo Heister, and Scott David King
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-252, https://doi.org/10.5194/gmd-2022-252, 2022
Revised manuscript accepted for GMD
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Due to the increasing availability of high-performance computing over the past decades numerical models have become an important tool for research. Here we test two geodynamic codes that produce such models: ASPECT, a newer code, and CitcomS, an older one. We show that they produce solutions that are extremely close. As methods and codes become more complex over time, showing reproducibility allows us to seamlessly link previously-known information to modern methodologies.
Konstantinos Papadakis, Yann Pfau-Kempf, Urs Ganse, Markus Battarbee, Markku Alho, Maxime Grandin, Maxime Dubart, Lucile Turc, Hongyang Zhou, Konstantinos Horaites, Ivan Zaitsev, Giulia Cozzani, Maarja Bussov, Evgeny Gordeev, Fasil Tesema, Harriet George, Jonas Suni, Vertti Tarvus, and Minna Palmroth
Geosci. Model Dev., 15, 7903–7912, https://doi.org/10.5194/gmd-15-7903-2022, https://doi.org/10.5194/gmd-15-7903-2022, 2022
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Vlasiator is a plasma simulation code that simulates the entire near-Earth space at a global scale. As 6D simulations require enormous amounts of computational resources, Vlasiator uses adaptive mesh refinement (AMR) to lighten the computational burden. However, due to Vlasiator’s grid topology, AMR simulations suffer from grid aliasing artifacts that affect the global results. In this work, we present and evaluate the performance of a mechanism for alleviating those artifacts.
Artur Safin, Damien Bouffard, Firat Ozdemir, Cintia L. Ramón, James Runnalls, Fotis Georgatos, Camille Minaudo, and Jonas Šukys
Geosci. Model Dev., 15, 7715–7730, https://doi.org/10.5194/gmd-15-7715-2022, https://doi.org/10.5194/gmd-15-7715-2022, 2022
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Reconciling the differences between numerical model predictions and observational data is always a challenge. In this paper, we investigate the viability of a novel approach to the calibration of a three-dimensional hydrodynamic model of Lake Geneva, where the target parameters are inferred in terms of distributions. We employ a filtering technique that generates physically consistent model trajectories and implement a neural network to enable bulk-to-skin temperature conversion.
Youtong Rong, Paul Bates, and Jeffrey Neal
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-234, https://doi.org/10.5194/gmd-2022-234, 2022
Revised manuscript accepted for GMD
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A novel subgrid channel model (SGC) is developed for river-floodplain modelling, allowing utilization of sub-grid scale bathymetric information while performing computations on relatively coarse grids. By including an adaptive artificial diffusion, potential numerical instability, which the original SGC solver had, in low friction regions such as the urban areas is addressed. Evaluation of the new SGC model through a structured tests confirmed that the accuracy and stability has been improved.
Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Lixin Wu, Dexun Chen, Yang Gao, Zhiqiang Wei, Dongning Jia, and Xiaopei Lin
Geosci. Model Dev., 15, 6695–6708, https://doi.org/10.5194/gmd-15-6695-2022, https://doi.org/10.5194/gmd-15-6695-2022, 2022
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To understand the scientific consequence of perturbations caused by slave cores in heterogeneous computing environments, we examine the influence of perturbation amplitudes on the determination of the cloud bottom and cloud top and compute the probability density function (PDF) of generated clouds. A series of comparisons of the PDFs between homogeneous and heterogeneous systems show consistently acceptable error tolerances when using slave cores in heterogeneous computing environments.
Vijay S. Mahadevan, Jorge E. Guerra, Xiangmin Jiao, Paul Kuberry, Yipeng Li, Paul Ullrich, David Marsico, Robert Jacob, Pavel Bochev, and Philip Jones
Geosci. Model Dev., 15, 6601–6635, https://doi.org/10.5194/gmd-15-6601-2022, https://doi.org/10.5194/gmd-15-6601-2022, 2022
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Coupled Earth system models require transfer of field data between multiple components with varying spatial resolutions to determine the correct climate behavior. We present the Metrics for Intercomparison of Remapping Algorithms (MIRA) protocol to evaluate the accuracy, conservation properties, monotonicity, and local feature preservation of four different remapper algorithms for various unstructured mesh problems of interest. Future extensions to more practical use cases are also discussed.
Yilin Fang, L. Ruby Leung, Ryan Knox, Charlie Koven, and Ben Bond-Lamberty
Geosci. Model Dev., 15, 6385–6398, https://doi.org/10.5194/gmd-15-6385-2022, https://doi.org/10.5194/gmd-15-6385-2022, 2022
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Accounting for water movement in the soil and water transport within the plant is important for plant growth in Earth system modeling. We implemented different numerical approaches for a plant hydrodynamic model and compared their impacts on the simulated aboveground biomass (AGB) at single points and globally. We found care should be taken when discretizing the number of soil layers for numerical simulations as it can significantly affect AGB if accuracy and computational costs are of concern.
Andrew M. Bradley, Peter A. Bosler, and Oksana Guba
Geosci. Model Dev., 15, 6285–6310, https://doi.org/10.5194/gmd-15-6285-2022, https://doi.org/10.5194/gmd-15-6285-2022, 2022
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Tracer transport in atmosphere models can be computationally expensive. We describe a flexible and efficient interpolation semi-Lagrangian method, the Islet method. It permits using up to three grids that share an element grid: a dynamics grid for computing quantities such as the wind velocity; a physics parameterizations grid; and a tracer grid. The Islet method performs well on a number of verification problems and achieves high performance in the E3SM Atmosphere Model version 2.
Léo Pujol, Pierre-André Garambois, and Jérôme Monnier
Geosci. Model Dev., 15, 6085–6113, https://doi.org/10.5194/gmd-15-6085-2022, https://doi.org/10.5194/gmd-15-6085-2022, 2022
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This contribution presents a new numerical model for representing hydraulic–hydrological quantities at the basin scale. It allows modeling large areas at a low computational cost, with fine zooms where needed. It allows the integration of local and satellite measurements, via data assimilation methods, to improve the model's match to observations. Using this capability, good matches to in situ observations are obtained on a model of the complex Adour river network with fine zooms on floodplains.
Ludovic Räss, Ivan Utkin, Thibault Duretz, Samuel Omlin, and Yuri Y. Podladchikov
Geosci. Model Dev., 15, 5757–5786, https://doi.org/10.5194/gmd-15-5757-2022, https://doi.org/10.5194/gmd-15-5757-2022, 2022
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Continuum mechanics-based modelling of physical processes at large scale requires huge computational resources provided by massively parallel hardware such as graphical processing units. We present a suite of numerical algorithms, implemented using the Julia language, that efficiently leverages the parallelism. We demonstrate that our implementation is efficient, scalable and robust and showcase applications to various geophysical problems.
Meriem Krouma, Pascal Yiou, Céline Déandreis, and Soulivanh Thao
Geosci. Model Dev., 15, 4941–4958, https://doi.org/10.5194/gmd-15-4941-2022, https://doi.org/10.5194/gmd-15-4941-2022, 2022
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We evaluated the skill of a stochastic weather generator (SWG) to forecast precipitation at different time scales and in different areas of western Europe from analogs of Z500 hPa. The SWG has the skill to simulate precipitation for 5 and 10 d. We found that forecast weaknesses can be associated with specific weather patterns. The comparison with ECMWF forecasts confirms the skill of our model. This work is important because it provides information about weather forecasts over specific areas.
Piotr Dziekan and Piotr Zmijewski
Geosci. Model Dev., 15, 4489–4501, https://doi.org/10.5194/gmd-15-4489-2022, https://doi.org/10.5194/gmd-15-4489-2022, 2022
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Detailed computer simulations of clouds are important for understanding Earth's atmosphere and climate. The paper describes how the UWLCM has been adapted to work on supercomputers. A distinctive feature of UWLCM is that air flow is calculated by processors at the same time as cloud droplets are modeled by graphics cards. Thanks to this, use of computing resources is maximized and the time to complete simulations of large domains is not affected by communications between supercomputer nodes.
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 15, 4147–4161, https://doi.org/10.5194/gmd-15-4147-2022, https://doi.org/10.5194/gmd-15-4147-2022, 2022
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A scale-dependent error growth described by a power law or by a quadratic hypothesis is studied in Lorenz’s system with three spatiotemporal levels. The validity of power law is extended by including a saturation effect. The quadratic hypothesis can only serve as a first guess. In addition, we study the initial error growth for the ECMWF forecast system. Fitting the parameters, we conclude that there is an intrinsic limit of predictability after 22 days.
Michael A. Olesik, Jakub Banaśkiewicz, Piotr Bartman, Manuel Baumgartner, Simon Unterstrasser, and Sylwester Arabas
Geosci. Model Dev., 15, 3879–3899, https://doi.org/10.5194/gmd-15-3879-2022, https://doi.org/10.5194/gmd-15-3879-2022, 2022
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In systems such as atmospheric clouds, droplets undergo growth through condensation of vapor. The broadness of the resultant size spectrum of droplets influences precipitation likelihood and the radiative properties of clouds. One of the inherent limitations of simulations of the problem is the so-called numerical diffusion causing overestimation of the spectrum width, hence the term numerical broadening. In the paper, we take a closer look at one of the algorithms used in this context: MPDATA.
Navjot Kukreja, Jan Hückelheim, Mathias Louboutin, John Washbourne, Paul H. J. Kelly, and Gerard J. Gorman
Geosci. Model Dev., 15, 3815–3829, https://doi.org/10.5194/gmd-15-3815-2022, https://doi.org/10.5194/gmd-15-3815-2022, 2022
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Full waveform inversion (FWI) is a partial-differential equation (PDE)-constrained optimization problem that is notorious for its high computational load and memory footprint. In this paper we present a method that combines recomputation with lossy compression to accelerate the computation with minimal loss of precision in the results. We show this using experiments running FWI with a variety of compression settings on a popular academic dataset.
Richard Scalzo, Mark Lindsay, Mark Jessell, Guillaume Pirot, Jeremie Giraud, Edward Cripps, and Sally Cripps
Geosci. Model Dev., 15, 3641–3662, https://doi.org/10.5194/gmd-15-3641-2022, https://doi.org/10.5194/gmd-15-3641-2022, 2022
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This paper addresses numerical challenges in reasoning about geological models constrained by sensor data, especially models that describe the history of an area in terms of a sequence of events. Our method ensures that small changes in simulated geological features, such as the position of a boundary between two rock layers, do not result in unrealistically large changes to resulting sensor measurements, as occur presently using several popular modeling packages.
Romit Maulik, Vishwas Rao, Jiali Wang, Gianmarco Mengaldo, Emil Constantinescu, Bethany Lusch, Prasanna Balaprakash, Ian Foster, and Rao Kotamarthi
Geosci. Model Dev., 15, 3433–3445, https://doi.org/10.5194/gmd-15-3433-2022, https://doi.org/10.5194/gmd-15-3433-2022, 2022
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In numerical weather prediction, data assimilation is frequently utilized to enhance the accuracy of forecasts from equation-based models. In this work we use a machine learning framework that approximates a complex dynamical system given by the geopotential height. Instead of using an equation-based model, we utilize this machine-learned alternative to dramatically accelerate both the forecast and the assimilation of data, thereby reducing need for large computational resources.
Hiromasa Yoshimura
Geosci. Model Dev., 15, 2561–2597, https://doi.org/10.5194/gmd-15-2561-2022, https://doi.org/10.5194/gmd-15-2561-2022, 2022
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This paper proposes a new double Fourier series (DFS) method on a sphere that improves the numerical stability of a model compared with conventional DFS methods. The shallow-water model and the advection model using the new DFS method give stable results without the appearance of high-wavenumber noise near the poles. The model using the new DFS method is faster than the model using spherical harmonics (especially at high resolutions) and gives almost the same results.
Mirko Mälicke
Geosci. Model Dev., 15, 2505–2532, https://doi.org/10.5194/gmd-15-2505-2022, https://doi.org/10.5194/gmd-15-2505-2022, 2022
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I preset SciKit-GStat, a well-documented and tested Python package for variogram estimation. The variogram is the core means of geostatistics, which almost all other methods rely on. Geostatistical interpolation and field generation are widely spread in geoscience, i.e., for data assimilation or modeling.
While SciKit-GStat focuses on effective and intuitive variogram estimation, it can interface with other prominent packages and make its variograms available for a multitude of methods.
Christopher J. L. D'Amboise, Michael Neuhauser, Michaela Teich, Andreas Huber, Andreas Kofler, Frank Perzl, Reinhard Fromm, Karl Kleemayr, and Jan-Thomas Fischer
Geosci. Model Dev., 15, 2423–2439, https://doi.org/10.5194/gmd-15-2423-2022, https://doi.org/10.5194/gmd-15-2423-2022, 2022
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The term gravitational mass flow (GMF) covers various natural hazard processes such as snow avalanches, rockfall, landslides, and debris flows. Here we present the open-source GMF simulation tool Flow-Py. The model equations are based on simple geometrical relations in three-dimensional terrain. We show that Flow-Py is an educational, innovative GMF simulation tool with three computational experiments: 1. validation of implementation, 2. performance, and 3. expandability.
Evan Baker, Anna B. Harper, Daniel Williamson, and Peter Challenor
Geosci. Model Dev., 15, 1913–1929, https://doi.org/10.5194/gmd-15-1913-2022, https://doi.org/10.5194/gmd-15-1913-2022, 2022
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We have adapted machine learning techniques to build a model of the land surface in Great Britain. The model was trained using data from a very complex land surface model called JULES. Our model is faster at producing simulations and predictions and can investigate many different scenarios, which can be used to improve our understanding of the climate and could also be used to help make local decisions.
Daichun Wang, Wei You, Zengliang Zang, Xiaobin Pan, Yiwen Hu, and Yanfei Liang
Geosci. Model Dev., 15, 1821–1840, https://doi.org/10.5194/gmd-15-1821-2022, https://doi.org/10.5194/gmd-15-1821-2022, 2022
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This paper presents a 3D variational data assimilation system for aerosol optical properties, including aerosol optical thickness (AOT) retrievals and lidar-based aerosol profiles, which was developed for a size-resolved sectional model in WRF-Chem. To directly assimilate aerosol optical properties, an observation operator based on the Mie scattering theory was designed. The results show that Himawari-8 AOT assimilation can significantly improve model aerosol analyses and forecasts.
Kevin Bulthuis and Eric Larour
Geosci. Model Dev., 15, 1195–1217, https://doi.org/10.5194/gmd-15-1195-2022, https://doi.org/10.5194/gmd-15-1195-2022, 2022
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We present and implement a stochastic solver to sample spatially and temporal varying uncertain input parameters in the Ice-sheet and Sea-level System Model, such as ice thickness or surface mass balance. We represent these sources of uncertainty using Gaussian random fields with Matérn covariance function. We generate random samples of this random field using an efficient computational approach based on solving a stochastic partial differential equation.
Urmas Raudsepp and Ilja Maljutenko
Geosci. Model Dev., 15, 535–551, https://doi.org/10.5194/gmd-15-535-2022, https://doi.org/10.5194/gmd-15-535-2022, 2022
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A model's ability to reproduce the state of a simulated object is always a subject of discussion. A new method for the multivariate assessment of numerical model skills uses the K-means algorithm for clustering model errors. All available data that fall into the model domain and simulation period are incorporated into the skill assessment. The clustered errors are used for spatial and temporal analysis of the model accuracy. The method can be applied to different types of geoscientific models.
Emmanuel Wyser, Yury Alkhimenkov, Michel Jaboyedoff, and Yury Y. Podladchikov
Geosci. Model Dev., 14, 7749–7774, https://doi.org/10.5194/gmd-14-7749-2021, https://doi.org/10.5194/gmd-14-7749-2021, 2021
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We propose an implementation of the material point method using graphical processing units (GPUs) to solve elastoplastic problems in three-dimensional configurations, such as the granular collapse or the slumping mechanics, i.e., landslide. The computational power of GPUs promotes fast code executions, compared to a traditional implementation using central processing units (CPUs). This allows us to study complex three-dimensional problems tackling high spatial resolution.
Rafael Lago, Thomas Gastine, Tilman Dannert, Markus Rampp, and Johannes Wicht
Geosci. Model Dev., 14, 7477–7495, https://doi.org/10.5194/gmd-14-7477-2021, https://doi.org/10.5194/gmd-14-7477-2021, 2021
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In this work we discuss a two-dimensional distributed parallelization of MagIC, an open-source code for the numerical solution of the magnetohydrodynamics equations. Such a parallelization involves several challenges concerning the distribution of work and data. We detail our algorithm and compare it with the established, optimized, one-dimensional distribution in the context of the dynamo benchmark and discuss the merits of both implementations.
Moritz Lange, Henri Suominen, Mona Kurppa, Leena Järvi, Emilia Oikarinen, Rafael Savvides, and Kai Puolamäki
Geosci. Model Dev., 14, 7411–7424, https://doi.org/10.5194/gmd-14-7411-2021, https://doi.org/10.5194/gmd-14-7411-2021, 2021
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This study aims to replicate computationally expensive high-resolution large-eddy simulations (LESs) with regression models to simulate urban air quality and pollutant dispersion. The model development, including feature selection, model training and cross-validation, and detection of concept drift, has been described in detail. Of the models applied, log-linear regression shows the best performance. A regression model can replace LES unless high accuracy is needed.
Hynek Bednář, Aleš Raidl, and Jiří Mikšovský
Geosci. Model Dev., 14, 7377–7389, https://doi.org/10.5194/gmd-14-7377-2021, https://doi.org/10.5194/gmd-14-7377-2021, 2021
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Forecast errors in numerical weather prediction systems grow in time. To quantify the impacts of this growth, parametric error growth models may be employed. This study recalculates and newly defines parameters for several statistic models approximating error growth in the ECMWF forecasting system. Accurate values of parameters are important because they are used to evaluate improvements of the forecasting systems or to estimate predictability.
Denise Degen, Cameron Spooner, Magdalena Scheck-Wenderoth, and Mauro Cacace
Geosci. Model Dev., 14, 7133–7153, https://doi.org/10.5194/gmd-14-7133-2021, https://doi.org/10.5194/gmd-14-7133-2021, 2021
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In times of worldwide energy transitions, an understanding of the subsurface is increasingly important to provide renewable energy sources such as geothermal energy. To validate our understanding of the subsurface we require data. However, the data are usually not distributed equally and introduce a potential misinterpretation of the subsurface. Therefore, in this study we investigate the influence of measurements on temperature distribution in the European Alps.
Geoffroy Kirstetter, Olivier Delestre, Pierre-Yves Lagrée, Stéphane Popinet, and Christophe Josserand
Geosci. Model Dev., 14, 7117–7132, https://doi.org/10.5194/gmd-14-7117-2021, https://doi.org/10.5194/gmd-14-7117-2021, 2021
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The development of forecasting tools may help to limit the impacts of flash floods. Our purpose here is to demonstrate the possibility of using b-flood, which is a 2D tool based on shallow-water equations and adaptive mesh refinement.
Sojung Park and Seon K. Park
Geosci. Model Dev., 14, 6241–6255, https://doi.org/10.5194/gmd-14-6241-2021, https://doi.org/10.5194/gmd-14-6241-2021, 2021
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One of the biggest uncertainties in numerical weather predictions (NWPs) comes from treating subgrid-scale physical processes. Physical processes, such as cumulus, microphysics, and planetary boundary layer processes, are parameterized in NWP models by empirical and theoretical backgrounds. We developed an interface between a micro-genetic algorithm and the WRF model for a combinatorial optimization of physics for heavy rainfall events in Korea. The system improved precipitation forecasts.
Olivier Pannekoucke and Philippe Arbogast
Geosci. Model Dev., 14, 5957–5976, https://doi.org/10.5194/gmd-14-5957-2021, https://doi.org/10.5194/gmd-14-5957-2021, 2021
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This contributes to research on uncertainty prediction, which is important either for determining the weather today or estimating the risk in prediction. The problem is that uncertainty prediction is numerically very expensive. An alternative has been proposed wherein uncertainty is presented in a simplified form with only the dynamics of certain parameters required. This tool allows for the determination of the symbolic equations of these parameter dynamics and their numerical computation.
Annika Günther, Johannes Gütschow, and Mairi Louise Jeffery
Geosci. Model Dev., 14, 5695–5730, https://doi.org/10.5194/gmd-14-5695-2021, https://doi.org/10.5194/gmd-14-5695-2021, 2021
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The mitigation components of the nationally determined contributions (NDCs) under the Paris Agreement are essential in our fight against climate change. Regular updates with increased ambition are requested to limit global warming to 1.5–2 °C. The new and easy-to-update open-source tool NDCmitiQ can be used to quantify the NDCs' mitigation targets and construct resulting emissions pathways. In use cases, we show target uncertainties from missing clarity, data, and methodological challenges.
Cited articles
Ait-El-Fquih, B. and Hoteit, I.: Filtering with One-Step-Ahead Smoothing for
Efficient Data Assimilation, in: Data Assimilation for Atmospheric, Oceanic
and Hydrologic Applications (Vol. IV), edited by: Park, S. K. and Xu, L.,
Springer, Cham, 69–96, https://doi.org/10.1007/978-3-030-77722-7_1, 2022. a, b
Ait-El-Fquih, B., El Gharamti, M., and Hoteit, I.: A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology, Hydrol. Earth Syst. Sci., 20, 3289–3307, https://doi.org/10.5194/hess-20-3289-2016, 2016. a
Bannister, R. N.: A review of operational methods of variational and
ensemble-variational data assimilation, Q. J. Roy. Meteor. Soc., 143,
607–633, https://doi.org/10.1002/qj.2982, 2017. a
Bezanson, J., Edelman, A., Karpinski, S., and Shah, V.: Julia: A
fresh approach to numerical computing, SIAM Rev., 59, 65–98, https://doi.org/10.1137/141000671, 2017. a
Bocquet, M.: Ensemble Kalman filtering without the intrinsic need for inflation, Nonlin. Processes Geophys., 18, 735–750, https://doi.org/10.5194/npg-18-735-2011, 2011. a
Bocquet, M.: Localization and the iterative ensemble Kalman smoother, Q. J. Roy.
Meteor. Soc., 142, 1075–1089, https://doi.org/10.1002/qj.2711, 2016. a, b
Bocquet, M. and Carrassi, A.: Four-dimensional ensemble variational data
assimilation and the unstable subspace, Tellus A, 69, 1304504, https://doi.org/10.1080/16000870.2017.1304504, 2017. a
Bocquet, M. and Sakov, P.: Combining inflation-free and iterative ensemble Kalman filters for strongly nonlinear systems, Nonlin. Processes Geophys., 19, 383–399, https://doi.org/10.5194/npg-19-383-2012, 2012. a, b
Bocquet, M. and Sakov, P.: Joint state and parameter estimation with an iterative ensemble Kalman smoother, Nonlin. Processes Geophys., 20, 803–818, https://doi.org/10.5194/npg-20-803-2013, 2013. a, b
Bocquet, M., Raanes, P. N., and Hannart, A.: Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation, Nonlin. Processes Geophys., 22, 645–662, https://doi.org/10.5194/npg-22-645-2015, 2015. a, b, c
Bocquet, M., Brajard, J., Carrassi, A., and Bertino, L.: Bayesian inference of
chaotic dynamics by merging data assimilation, machine learning and
expectation-maximization, Foundations of Data Science, 2, 55–80, https://doi.org/10.3934/fods.2020004, 2020. a
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.: Data Assimilation in
the Geosciences-An overview on methods, issues and perspectives, WIREs Clim.
Change, 9, e535, https://doi.org/10.1002/wcc.535, 2018. a
Carrassi, A., Bocquet, M., Demaeyer, J., Grudzien, C., Raanes, P., and
Vannitsem, S.: Data Assimilation for Chaotic Dynamics, in: Data Assimilation
for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV), edited by:
Park, S. K. and Xu, L., Springer, Cham, 1–42,
https://doi.org/10.1007/978-3-030-77722-7_1, 2022. a
Chen, Y. and Oliver, D. S.: Ensemble randomized maximum likelihood method as an
iterative ensemble smoother, Math. Geosci., 44, 1–26, https://doi.org/10.1007/s11004-011-9376-z, 2012. a
Corazza, M., Kalnay, E., Patil, D. J., Yang, S.-C., Morss, R., Cai, M., Szunyogh, I., Hunt, B. R., and Yorke, J. A.: Use of the breeding technique to estimate the structure of the analysis “errors of the day”, Nonlin. Processes Geophys., 10, 233–243, https://doi.org/10.5194/npg-10-233-2003, 2003. a
Cosme, E., Verron, J., Brasseur, P., Blum, J., and Auroux, D.: Smoothing
problems in a Bayesian framework and their linear Gaussian solutions,
Mon. Weather Rev., 140, 683–695, https://doi.org/10.1175/MWR-D-10-05025.1, 2012. a
Desbouvries, F., Petetin, Y., and Ait-El-Fquih, B.: Direct, prediction-and
smoothing-based Kalman and particle filter algorithms, Signal Process.,
91, 2064–2077, https://doi.org/10.1016/j.sigpro.2011.03.013, 2011. a
Emerick, A. A. and Reynolds, A. C.: Ensemble smoother with multiple data
assimilation, Comput. Geosci., 55, 3–15, https://doi.org/10.1016/j.cageo.2012.03.011, 2013. a, b
Evensen, G.: Analysis of iterative ensemble smoothers for solving inverse
problems, Comput. Geosci., 22, 885–908, https://doi.org/10.1007/s10596-018-9731-y, 2018. a
Evensen, G. and Van Leeuwen, P. J.: An ensemble Kalman smoother for nonlinear
dynamics, Mon. Weather Rev., 128, 1852–1867, https://doi.org/10.1175/1520-0493(2000)128<1852:AEKSFN>2.0.CO;2, 2000. a
Fertig, E. J., Harlim, J., and Hunt, B. R.: A comparative study of 4D-VAR and
a 4D ensemble Kalman filter: Perfect model simulations with
Lorenz-96, Tellus A, 59, 96–100, https://doi.org/10.1111/j.1600-0870.2006.00205.x, 2007. a
Fillion, A., Bocquet, M., and Gratton, S.: Quasi-static ensemble variational data assimilation: a theoretical and numerical study with the iterative ensemble Kalman smoother, Nonlin. Processes Geophys., 25, 315–334, https://doi.org/10.5194/npg-25-315-2018, 2018. a
Fillion, A., Bocquet, M., Gratton, S., Görol, S., and Sakov, P.: An
iterative ensemble Kalman smoother in presence of additive model error,
SIAM/ASA J. Uncertainty Quantification, 8, 198–228, 2020. a
Gharamti, M. E., Ait-El-Fquih, B., and Hoteit, I.: An iterative ensemble
Kalman filter with one-step-ahead smoothing for state-parameters estimation
of contaminant transport models, J. Hydrol., 527, 442–457, https://doi.org/10.1016/j.jhydrol.2015.05.004, 2015. a
Grudzien, C. and Bocquet, M.: A Tutorial on Bayesian Data Assimilation, arXiv
[preprint],
https://doi.org/10.48550/arXiv.2112.07704, 2021. a
Grudzien, C., Carrassi, A., and Bocquet, M.: Asymptotic forecast uncertainty
and the unstable subspace in the presence of additive model error, SIAM/ASA
J. Uncertainty Quantification, 6, 1335–1363, https://doi.org/10.1137/17M114073X, 2018. a
Gu, Y. and Oliver, D. S.: An iterative ensemble Kalman filter for multiphase
fluid flow data assimilation, SPE J., 12, 438–446, https://doi.org/10.2118/108438-PA, 2007. a
Harlim, J. and Hunt, B. R.: Four-dimensional local ensemble transform Kalman
filter: numerical experiments with a global circulation model, Tellus A, 59,
731–748, https://doi.org/10.1111/j.1600-0870.2007.00255.x, 2007. a
Hunt, B. R., Kalnay, E., Kostelich, E. J., Ott, E., Patil, D. J., Sauer, T.,
Szunyogh, I., Yorke, J. A., and Zimin, A. V.: Four-dimensional ensemble
Kalman filtering, Tellus A, 56, 273–277, https://doi.org/10.3402/tellusa.v56i4.14424, 2004. a, b
Hunt, B. R., Kostelich, E. J., and Szunyogh, I.: Efficient data assimilation
for spatiotemporal chaos: A local ensemble transform Kalman filter, Phys.
D, 230, 112–126, https://doi.org/10.1016/j.physd.2006.11.008, 2007. a, b, c, d
Iglesias, M. A., Law, K. J. H., and Stuart, A. M.: Ensemble Kalman methods for
inverse problems, Inverse Problems, 29, 045001, https://doi.org/10.1088/0266-5611/29/4/045001, 2013. a
Kalnay, E. and Yang, S. C.: Accelerating the spin-up of ensemble Kalman
filtering, Q. J. Roy. Meteor. Soc., 136, 1644–1651, https://doi.org/10.1002/qj.652, 2010. a
Kalnay, E., Li, H., Miyoshi, T., Yang, S.-C., and Ballabrera-Poy, J.:
4-D-Var or ensemble Kalman filter?, Tellus A, 59, 758–773, https://doi.org/10.1111/j.1600-0870.2007.00261.x, 2007. a
Kovachki, N. B. and Stuart, A. M.: Ensemble Kalman inversion: a derivative-free
technique for machine learning tasks, Inverse Problems, 35, 095005, https://doi.org/10.1088/1361-6420/ab1c3a, 2019. a
Liu, C., Xiao, Q., and Wang, B.: An Ensemble-Based Four-Dimensional Variational
Data Assimilation Scheme. Part I: Technical Formulation and Preliminary
Test, Mon. Weather Rev., 136, 3363–3373, https://doi.org/10.1175/2008MWR2312.1, 2008. a
Lorenc, A. C.: The potential of the ensemble Kalman filter for NWP – A
comparison with 4D-Var, Q. J. Roy. Meteor. Soc., 129, 3183–3203, https://doi.org/10.1256/qj.02.132, 2003. a, b
Lorenz, E. N.: Predictability: A problem partly solved, in: Proc. Seminar on
predictability, vol. 1, https://www.ecmwf.int/node/10829 (last access: 10 October 2022), 1996. a
Lorenz, E. N. and Emanuel, K. A.: Optimal sites for supplementary weather
observations: Simulation with a small model, J. Atmos. Sci., 55, 399–414, https://doi.org/10.1175/1520-0469(1998)055<0399:OSFSWO>2.0.CO;2,
1998. a
Neal, R. M.: Sampling from multimodal distributions using tempered transitions,
Stat. Comput., 6, 353–366, https://doi.org/10.1007/BF00143556, 1996. a
Nerger, L., Schulte, S., and Bunse-Gerstner, A.: On the influence of model
nonlinearity and localization on ensemble Kalman smoothing, Q. J. Roy.
Meteor. Soc., 140, 2249–2259, https://doi.org/10.1002/qj.2293, 2014. a
Nocedal, J. and Wright, S.: Numerical optimization, Springer Science &
Business Media, https://doi.org/10.1007/978-0-387-40065-5, 2006. a
Pulido, M., Tandeo, P., Bocquet, M., Carrassi, A., and Lucini, M.: Stochastic
parameterization identification using ensemble Kalman filtering combined
with maximum likelihood methods, Tellus A, 70, 1442099, https://doi.org/10.1080/16000870.2018.1442099, 2018. a
Raanes, P. N.: On the ensemble Rauch-Tung-Striebel smoother and its
equivalence to the ensemble Kalman smoother, Q. J. Roy. Meteor. Soc., 142,
1259–1264, https://doi.org/10.1002/qj.2728, 2016. a
Raanes, P. N., Bocquet, M., and Carrassi, A.: Adaptive covariance inflation in
the ensemble Kalman filter by Gaussian scale mixtures, Q. J. Roy. Meteor.
Soc., 145, 53–75, https://doi.org/10.1002/qj.3386, 2019a. a, b
Raanes, P. N., Stordal, A. S., and Evensen, G.: Revising the stochastic iterative ensemble smoother, Nonlin. Processes Geophys., 26, 325–338, https://doi.org/10.5194/npg-26-325-2019, 2019b. a
Raanes, P. N., Grudzien, C., and14tondeu: nansencenter/DAPPER: Version 0.8, Zenodo [code],
https://doi.org/10.5281/zenodo.2029296, 2018. a
Raboudi, N. F., Ait-El-Fquih, B., and Hoteit, I.: Ensemble Kalman filtering
with one-step-ahead smoothing, Mon. Weather Rev., 146, 561–581, https://doi.org/10.1175/MWR-D-17-0175.1, 2018. a
Sakov, P. and Bertino, L.: Relation between two common localisation methods for
the EnKF, Comput. Geosci., 15, 225–237, https://doi.org/10.1007/s10596-010-9202-6, 2011. a, b
Sakov, P. and Oke, P. R.: A deterministic formulation of the ensemble Kalman
filter: an alternative to ensemble square root filters, Tellus A, 60,
361–371, https://doi.org/10.1111/j.1600-0870.2007.00299.x, 2008a. a
Sakov, P. and Oke, P. R.: Implications of the form of the ensemble
transformation in the ensemble square root filters, Mon. Weather Rev., 136,
1042–1053, https://doi.org/10.1175/2007MWR2021.1, 2008b. a, b, c
Sakov, P., Evensen, G., and Bertino, L.: Asynchronous data assimilation with
the EnKF, Tellus A, 62, 24–29, https://doi.org/10.1111/j.1600-0870.2009.00417.x, 2010. a
Sakov, P., Oliver, D. S., and Bertino, L.: An iterative EnKF for strongly
nonlinear systems, Mon. Weather Rev., 140, 1988–2004, https://doi.org/10.1175/MWR-D-11-00176.1, 2012. a, b, c
Sakov, P., Haussaire, J. M., and Bocquet, M.: An iterative ensemble Kalman
filter in presence of additive model error, Q. J. Roy. Meteor. Soc., 144, 1297–1309, https://doi.org/10.1002/qj.3213, 2018.
a
Sankhya, A.: Reprint of: Mahalanobis, P.C. (1936) “On the Generalised Distance in Statistics”, 80 (Suppl 1), 1–7, https://doi.org/10.1007/s13171-019-00164-5, 2018. a
Schillings, C. and Stuart, A. M.: Convergence analysis of ensemble Kalman
inversion: the linear, noisy case, Appl. Anal., 97, 107–123, https://doi.org/10.1080/00036811.2017.1386784, 2018. a
Tandeo, P., Ailliot, P., Bocquet, M., Carrassi, A., Miyoshi, T., Pulido, M.,
and Zhen, Y.: A review of innovation-based methods to jointly estimate model
and observation error covariance matrices in ensemble data assimilation, Mon.
Weather Rev., 148, 3973–3994, https://doi.org/10.1175/MWR-D-19-0240.1, 2020. a
Taylor, M. E.: Partial differential equations. 1, Basic theory, Springer, https://doi.org/10.1007/978-1-4419-7055-8, 1996. a
Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M., and Whitaker,
J. S.: Ensemble square root filters, Mon. Weather Rev., 131, 1485–1490, https://doi.org/10.1175/1520-0493(2003)131<1485:ESRF>2.0.CO;2, 2003. a
Whitaker, J. S. and Loughe, A. F.: The relationship between ensemble spread and
ensemble mean skill, Mon. Weather Rev., 126, 3292–3302, https://doi.org/10.1175/1520-0493(1998)126<3292:TRBESA>2.0.CO;2, 1998. a
Yang, S.-C., Lin, K. J., Miyoshi, T., and Kalnay, E.: Improving the spin-up of
regional EnKF for typhoon assimilation and forecasting with Typhoon Sinlaku
(2008), Tellus A, 65, 20804, https://doi.org/10.3402/tellusa.v65i0.20804, 2013. a
Zupanski, M.: Maximum likelihood ensemble filter: Theoretical aspects, Mon.
Weather Rev., 133, 1710–1726, https://doi.org/10.1175/MWR2946.1, 2005. a, b
Zupanski, M., Navon, I. M., and Zupanski, D.: The Maximum Likelihood Ensemble
Filter as a non-differentiable minimization algorithm, Q. J. Roy. Meteor.
Soc., 134, 1039–1050, https://doi.org/10.1002/qj.251, 2008. a
Short summary
Iterative optimization techniques, the state of the art in data assimilation, have largely focused on extending forecast accuracy to moderate- to long-range forecast systems. However, current methodology may not be cost-effective in reducing forecast errors in online, short-range forecast systems. We propose a novel optimization of these techniques for online, short-range forecast cycles, simultaneously providing an improvement in forecast accuracy and a reduction in the computational cost.
Iterative optimization techniques, the state of the art in data assimilation, have largely...
