Articles | Volume 14, issue 9
https://doi.org/10.5194/gmd-14-5583-2021
© Author(s) 2021. 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-14-5583-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Efficient ensemble generation for uncertain correlated parameters in atmospheric chemical models: a case study for biogenic emissions from EURAD-IM version 5
Institute for Energy and Climate Research – Troposphere (IEK-8), Forschungszentrum Jülich, Germany
Rhenish Institute for Environmental Research at the University of Cologne, Cologne, Germany
Institute of Geophysics and Meteorology, University of Cologne, Cologne, Germany
Hendrik Elbern
Institute for Energy and Climate Research – Troposphere (IEK-8), Forschungszentrum Jülich, Germany
Rhenish Institute for Environmental Research at the University of Cologne, Cologne, Germany
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Cited articles
Auger, L. and Tangborn, A.: A wavelet-based reduced rank Kalman filter for
assimilation of stratospheric chemical tracer observations, Mon. Weather Rev., 132, 1220–1237,
https://doi.org/10.1175/1520-0493(2004)132<1220:AWRRKF>2.0.CO;2, 2004. a
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical
weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. a
Bocquet, M., Elbern, H., Eskes, H., Hirtl, M., Žabkar, R., Carmichael, G. R., Flemming, J., Inness, A., Pagowski, M., Pérez Camaño, J. L., Saide, P. E., San Jose, R., Sofiev, M., Vira, J., Baklanov, A., Carnevale, C., Grell, G., and Seigneur, C.: Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models, Atmos. Chem. Phys., 15, 5325–5358, https://doi.org/10.5194/acp-15-5325-2015, 2015. a
Boynard, A., Beekmann, M., Foret, G., Ung, A., Szopa, S., Schmechtig, C., and
Coman, A.: An ensemble assessment of regional ozone model uncertainty with an
explicit error representation, Atmos. Environ., 45, 784–793,
https://doi.org/10.1016/j.atmosenv.2010.08.006, 2011. a
Buizza, R.: Introduction to the special issue on “25 years of ensemble
forecasting”, Q. J. Roy. Meteor. Soc., 145,
1–11, https://doi.org/10.1002/qj.3370, 2019. a
Buizza, R., Tribbia, J., Molteni, F., and Palmer, T.: Computation of optimal
unstable structures for a numerical weather prediction model, Tellus A, 45,
388–407, https://doi.org/10.1034/j.1600-0870.1993.t01-4-00005.x, 1993. a
Buizza, R., Milleer, M., and Palmer, T. N.: Stochastic representation of model uncertainties in the ECMWF ensemble prediction system, Q. J. Roy. Meteor. Soc., 125, 2887–2908,
https://doi.org/10.1002/qj.49712556006, 1999. a
Candiani, G., Carnevale, C., Finzi, G., Pisoni, E., and Volta, M.: A comparison of reanalysis techniques: Applying optimal interpolation and Ensemble Kalman
Filtering to improve air quality monitoring at mesoscale, Sci.
Total Environ., 458-460, 7–14,
https://doi.org/10.1016/j.scitotenv.2013.03.089, 2013. a
Cohn, S. E. and Todling, R.: Approximate Data Assimilation Schemes for Stable
and Unstable Dynamics, J. Met. Soc. Jpn., 74, 63–75, 1996. a
Elbern, H., Strunk, A., Schmidt, H., and Talagrand, O.: Emission rate and chemical state estimation by 4-dimensional variational inversion, Atmos. Chem. Phys., 7, 3749–3769, https://doi.org/10.5194/acp-7-3749-2007, 2007. a, b, c, d
Emili, E., Gürol, S., and Cariolle, D.: Accounting for model error in air quality forecasts: an application of 4DEnVar to the assimilation of atmospheric composition using QG-Chem 1.0, Geosci. Model Dev., 9, 3933–3959, https://doi.org/10.5194/gmd-9-3933-2016, 2016. a, b
Galin, M. B.: Study of the Low-Frequency Variability of the Atmospheric General Circulation with the Use of Time-Dependent Empirical Orthogonal Functions, Atmos. Ocean. Phys., 43, 15–23, https://doi.org/10.1134/S0001433807010021,
2007. a
Gaubert, B., Coman, A., Foret, G., Meleux, F., Ung, A., Rouil, L., Ionescu, A., Candau, Y., and Beekmann, M.: Regional scale ozone data assimilation using an ensemble Kalman filter and the CHIMERE chemical transport model, Geosci. Model Dev., 7, 283–302, https://doi.org/10.5194/gmd-7-283-2014, 2014. a
Geiger, H., Barnes, I., Bejan, I., Benter, T., and Spittler, M.: The
tropospheric degradation of isoprene: An updated module for the regional
atmospheric chemistry mechanism, Atmos. Environ., 37, 1503–1519,
https://doi.org/10.1016/S1352-2310(02)01047-6, 2003. a
Goris, N. and Elbern, H.: Singular vector-based targeted observations of chemical constituents: description and first application of the EURAD-IM-SVA v1.0, Geosci. Model Dev., 8, 3929–3945, https://doi.org/10.5194/gmd-8-3929-2015, 2015. a
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, https://doi.org/10.5194/gmd-5-1471-2012, 2012. a, b, c
Guilloteau, C., Mamalakis, A., Vulis, L., Le, P. V. V., Georgiou, T. T., and
Foufoula-Georgiou, E.: Rotated Spectral Principal Component Analysis (rsPCA)
for Identifying Dynamical Modes of Variability in Climate Systems, J. Cimate, 34, 715–736, https://doi.org/10.1175/JCLI-D-20-0266.1, 2021. a
Hanea, R., Velders, G., and Heemink, A.: Data assimilation of ground-level
ozone in Europe with a Kalman filter and chemistry transport model, J. Geophys. Res.-Atmos., 109, D10302, https://doi.org/10.1029/2003JD004283,
2004. a
Hanea, R. G. and Velders, G. J. M.: A hybrid Kalman filter algorithm for
large-scale atmospheric chemistry data assimilation, Mon. Weather Rev., 135, 140–151, https://doi.org/10.1175/MWR3269.1, 2007. a
Hanna, S. R., Chang, J. C., and Fernau, M. E.: Monte carlo estimates of
uncertainties in predictions by a photochemical grid model (UAM-IV) due to
uncertainties in input variables, Atmos. Environ., 32, 3619–3628,
https://doi.org/10.1016/S1352-2310(97)00419-6, 1998. a, b
Hannachi, A., Jolliffe, I. T., and Stephenson, D. B.: Empirical orthogonal
functions and related techniques in atmospheric science: A review,
Int. J. Climatol., 27, 1119–1152,
https://doi.org/10.1002/joc.1499, 2007. a
Houtekamer, P. L., Lefaivre, L., Derome, J., Ritchie, H., and Mitchell, H. L.: A System Simulation Approach to Ensemble Prediction, Mon. Weather Rev., 124, 1225–1242, https://doi.org/10.1175/1520-0493(1996)124<1225:ASSATE>2.0.CO;2, 1996. a, b
Jülich Supercomputing Centre: JURECA: Modular supercomputer at
Jülich Supercomputing Centre, J. Large-scale Res.
Fac., 4, A132, https://doi.org/10.17815/jlsrf-4-121-1, 2018. a
Karhunen, K.: Über lineare methoden in der Wahrscheinlichkeitsrechnung,
Annales Academiae Scientarum Fennicae, 37, 3–79, 1947. a
Leutbecher, M.: Ensemble size: How suboptimal is less than infinity?, Q. J. Roy. Meteor. Soc., 145, 107–128,
https://doi.org/10.1002/qj.3387, 2019. a
Leutbecher, M., Lock, S.-J., Ollinaho, P., Lang, S. T. K., Balsamo, G.,
Bechtold, P., Bonavita, M., Christensen, H. M., Diamantakis, M., Dutra, E.,
English, S., Fisher, M., Forbes, R. M., Goddard, J., Haiden, T., Hogan,
R. J., Juricke, S., Lawrence, H., MacLeod, D., Magnusson, L., Malardel, S.,
Massart, S., Sandu, I., Smolarkiewicz, P. K., Subramanian, A., Vitart, F.,
Wedi, N., and Weisheimer, A.: Stochastic representations of model
uncertainties at ECMWF: state of the art and future vision, Q. J. Roy. Meteor. Soc., 143, 2315–2339, https://doi.org/10.1002/qj.3094,
2017. a
Liu, Y., Wang, L., Zhou, W., and Chen, W.: Three Eurasian teleconnection
patterns: spatial structures, temporal variability, and associated winter
climate anomalies, Clim. Dynam., 42, 2817–2839,
https://doi.org/10.1007/s00382-014-2163-z, 2014. a
Lock, S.-J., Lang, S. T. K., Leutbecher, M., Hogan, R. J., and Vitart, F.:
Treatment of model uncertainty from radiation by the Stochastically Perturbed
Parametrization Tendencies (SPPT) scheme and associated revisions in the
ECMWF ensembles, Q. J. Roy. Meteor. Soc., 145,
75–89, https://doi.org/10.1002/qj.3570, 2019. a
Loéve, M.: Fonctions aleatoires du second ordre, Processus Stochastiques et
Mouvement Brownien, Gauthier-Villars, Paris, 42th edn., 1948. a
McKeen, S., Chung, S. H., Wilczak, J., Grell, G., Djalalova, I., Peckham, S.,
Gong, W., Bouchet, V., Moffet, R., Tang, Y., Carmichael, G. R., Mathur, R.,
and Yu, S.: Evaluation of several PM2.5 forecast models using data collected
during the ICARTT/NEAQS 2004 field study, J. Geophys. Res.-Atmos., 112, D10S20, https://doi.org/10.1029/2006JD007608, 2007. a
Pöschl, U., Kuhlmann, R., Poisson, N., and Crutzen, P.: Development and
Intercomparison of Condensed Isoprene Oxidation Mechanisms for Global
Atmospheric Modeling, J. Atmos. Chem., 37, 29–52,
https://doi.org/10.1023/A:1006391009798, 2000. a
Schutgens, N. A. J., Miyoshi, T., Takemura, T., and Nakajima, T.: Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model, Atmos. Chem. Phys., 10, 2561–2576, https://doi.org/10.5194/acp-10-2561-2010, 2010. a
Schwab, C. and Todor, R. A.: Karhunen-Loève Approximation of Random Fields by Generalized Fast Multipole Methods, J. Comput. Phys., 217, 100–122, https://doi.org/10.1016/j.jcp.2006.01.048, 2006. a, b
Shutts, G.: A kinetic energy backscatter algorithm for use in ensemble
prediction systems, Q. J. Roy. Meteor. Soc.,
131, 3079–3102, https://doi.org/10.1256/qj.04.106, 2005. a
Siripatana, A., Mayo, T., Knio, O., Dawson, C., Le Maitre, O., and Hoteit, I.: Ensemble Kalman filter inference of spatially-varying Manning's n
coefficients in the coastal ocean, J. Hydrol., 562,
664–684, https://doi.org/10.1016/j.jhydrol.2018.05.021, 2018. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda,
M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A Description of the
Advanced Research WRF Version 3, National Center for Atmospheric Research
Boulder, Colorado, USA, NCAR technical note, 2008. a
Sorensen, D.: Implicitly Restarted Arnoldi/Lanczos Methods for Large Scale
Eigenvalue Calculations, in: Parallel Numerical Algorithms, ICASE/LaRC
Interdisciplinary Series in Science and Engineering, edited by: Keyes, D.,
Sameh, A., and Venkatakrishnan, V., vol 4., Springer, Dordrecht, 1997. a
Toth, Z. and Kalnay, E.: Ensemble forecasting at NMC: The generation of
perturbations, B. Am. Meteorol. Soc., 74,
2317–2330, 1993. a
Vautard, R., Blond, N., Schmidt, H., Derognat, C., and Beekmann, M.:
Multi-model ensemble ozone forecasts over Europe: analysis of uncertainty,
Mesoscale Transport of Air Pollution, OA15. EGS XXXVI General Assembly, Nice, France, European Geophysical Society, 25–30 March 2001, 26 pp.,
2001. a
Verlaan, M. and Heemink, A. M.: Data assimilation schmes for non-linear shallow water flow models, Adv. Fluid Mech., 96, 277–286, 1996. a
Vogel, A. and Elbern, H.: Karhunen-Loéve (KL) Ensemble Routines of the
EURAD-IM modeling system, Zenodo, https://doi.org/10.5281/zenodo.4468571, 2021b. a
Vogel, A. and Elbern, H.: Data of Karhunen-Loéve (KL) ensemble generation
algorithm for biogenic emissions from EURAD-IM, Zenodo,
https://doi.org/10.5281/zenodo.4772909, 2021c. a
Xian, P., Reid, J. S., Hyer, E. J., Sampson, C. R., Rubin, J. I., Ades, M.,
Asencio, N., Basart, S., Benedetti, A., Bhattacharjee, P. S., Brooks, M. E.,
Colarco, P. R., da Silva, A. M., Eck, T. F., Guth, J., Jorba, O., Kouznetsov,
R., Kipling, Z., Sofiev, M., Perez Garcia-Pando, C., Pradhan, Y., Tanaka, T.,
Wang, J., Westphal, D. L., Yumimoto, K., and Zhang, J.: Current state of the
global operational aerosol multi-model ensemble: An update from the
International Cooperative for Aerosol Prediction (ICAP), Q. J. Roy. Meteor. Soc., 145, 176–209, https://doi.org/10.1002/qj.3497, 2019.
a
Xiu, D.: Numerical Methods for Stochastic Computations: A Spectral Method
Approach, Princeton University Press, Princeton, NJ, USA, 2010. a
Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C., and Baklanov, A.: Real-time
air quality forecasting, part I: History, techniques, and current status,
Atmos. Environ., 60, 632 – 655,
https://doi.org/10.1016/j.atmosenv.2012.06.031, 2012a. a
Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C., and Baklanov, A.: Real-time
air quality forecasting, part II: State of the science, current research
needs, and future prospects, Atmos. Environ., 60, 656–676,
https://doi.org/10.1016/j.atmosenv.2012.02.041, 2012b. a
Short summary
While atmospheric chemical forecasts rely on uncertain model parameters, their huge dimensions hamper an efficient uncertainty estimation. This study presents a novel approach to efficiently sample these uncertainties by extracting dominant dependencies and correlations. Applying the algorithm to biogenic emissions, their uncertainties can be estimated from a low number of dominant components. This states the capability of an efficient treatment of parameter uncertainties in atmospheric models.
While atmospheric chemical forecasts rely on uncertain model parameters, their huge dimensions...