Shallow Cumulus in WRF Parameterizations Evaluated against LASSO Large-Eddy Simulations

Wayne M. Angevine Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, and NOAA/Earth System Research Laboratory, Boulder, Colorado

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Joseph Olson Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, and NOAA/Earth System Research Laboratory, Boulder, Colorado

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Jaymes Kenyon Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, and NOAA/Earth System Research Laboratory, Boulder, Colorado

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William I. Gustafson Jr. Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington

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Satoshi Endo Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, New York

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Kay Suselj Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

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David D. Turner NOAA/Earth System Research Laboratory, Boulder, Colorado

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Abstract

Representation of shallow cumulus is a challenge for mesoscale numerical weather prediction models. These cloud fields have important effects on temperature, solar irradiance, convective initiation, and pollutant transport, among other processes. Recent improvements to physics schemes available in the Weather Research and Forecasting (WRF) Model aim to improve representation of shallow cumulus, in particular over land. The DOE LES ARM Symbiotic Simulation and Observation Workflow (LASSO) project provides several cases that we use here to test the new physics improvements. The LASSO cases use multiple large-scale forcings to drive large-eddy simulations (LES), and the LES output is easily compared to output from WRF single-column simulations driven with the same initial conditions and forcings. The new Mellor–Yamada–Nakanishi–Niino (MYNN) eddy diffusivity mass-flux (EDMF) boundary layer and shallow cloud scheme produces clouds with timing, liquid water path (LWP), and cloud fraction that agree well with LES over a wide range of those variables. Here we examine those variables and test the scheme’s sensitivity to perturbations of a few key parameters. We also discuss the challenges and uncertainties of single-column tests. The older, simpler total energy mass-flux (TEMF) scheme is included for comparison, and its tuning is improved. This is the first published use of the LASSO cases for parameterization development, and the first published study to use such a large number of cases with varying cloud amount. This is also the first study to use a more precise combined infrared and microwave retrieval of LWP to evaluate modeled clouds.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wayne M. Angevine, wayne.m.angevine@noaa.gov

Abstract

Representation of shallow cumulus is a challenge for mesoscale numerical weather prediction models. These cloud fields have important effects on temperature, solar irradiance, convective initiation, and pollutant transport, among other processes. Recent improvements to physics schemes available in the Weather Research and Forecasting (WRF) Model aim to improve representation of shallow cumulus, in particular over land. The DOE LES ARM Symbiotic Simulation and Observation Workflow (LASSO) project provides several cases that we use here to test the new physics improvements. The LASSO cases use multiple large-scale forcings to drive large-eddy simulations (LES), and the LES output is easily compared to output from WRF single-column simulations driven with the same initial conditions and forcings. The new Mellor–Yamada–Nakanishi–Niino (MYNN) eddy diffusivity mass-flux (EDMF) boundary layer and shallow cloud scheme produces clouds with timing, liquid water path (LWP), and cloud fraction that agree well with LES over a wide range of those variables. Here we examine those variables and test the scheme’s sensitivity to perturbations of a few key parameters. We also discuss the challenges and uncertainties of single-column tests. The older, simpler total energy mass-flux (TEMF) scheme is included for comparison, and its tuning is improved. This is the first published use of the LASSO cases for parameterization development, and the first published study to use such a large number of cases with varying cloud amount. This is also the first study to use a more precise combined infrared and microwave retrieval of LWP to evaluate modeled clouds.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wayne M. Angevine, wayne.m.angevine@noaa.gov
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  • Angevine, W. M., 2005: An integrated turbulence scheme for boundary layers with shallow cumulus applied to pollutant transport. J. Appl. Meteor., 44, 14361452, https://doi.org/10.1175/JAM2284.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Angevine, W. M., H. Jiang, and T. Mauritsen, 2010: Performance of an eddy diffusivity–mass flux scheme for shallow cumulus boundary layers. Mon. Wea. Rev., 138, 28952912, https://doi.org/10.1175/2010MWR3142.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Angevine, W. M., L. Eddington, K. Durkee, C. Fairall, L. Bianco, and J. Brioude, 2012: Meteorological model evaulation for CalNex 2010. Mon. Wea. Rev., 140, 38853906, https://doi.org/10.1175/MWR-D-12-00042.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Angevine, W. M., and Coauthors, 2013: Pollutant transport among California regions. J. Geophys. Res. Atmos., 118, 67506763, https://doi.org/10.1002/jgrd.50490.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Angevine, W. M., J. Brioude, S. McKeen, and J. S. Holloway, 2014: Uncertainty in Lagrangian pollutant transport simulations due to meteorological uncertainty from a mesoscale WRF ensemble. Geosci. Model Dev., 7, 28172829, https://doi.org/10.5194/gmd-7-2817-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arakawa, A., 2004: The cumulus parameterization problem: Past, present, and future. J. Climate, 17, 24932525, https://doi.org/10.1175/1520-0442(2004)017<2493:RATCPP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baas, P., F. C. Bosveld, G. Lenderink, E. van Meijgaard, and A. A. M. Holtslag, 2010: How to design single-column model experiments for comparison with observed nocturnal low-level jets. Quart. J. Roy. Meteor. Soc., 136, 671684, https://doi.org/10.1002/qj.592.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Basu, S., A. A. M. Holtslag, B. J. H. Van de Weil, A. F. Moene, and G.-J. Steeneveld, 2008: An inconvenient “truth” about using sensible heat flux as a surface boundary condition in models under stably stratified regimes. Acta Geophys., 56, 8899, https://doi.org/10.2478/s11600-007-0038-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beare, R. J., and Coauthors, 2006: An intercomparison of large-eddy simulations of the stable boundary layer. Bound.-Layer Meteor., 118, 247272, https://doi.org/10.1007/s10546-004-2820-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., and Coauthors, 2016: A North American hourly assimilation and model forecast cycle: The Rapid Refresh. Mon. Wea. Rev., 144, 16691694, https://doi.org/10.1175/MWR-D-15-0242.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosveld, F., and Coauthors, 2014: The third GABLS intercomparison case for evaluation studies of boundary-layer models. Part B: Results and process understanding. Bound.-Layer Meteor., 152, 157187, https://doi.org/10.1007/s10546-014-9919-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cadeddu, M. P., J. C. Liljegren, and D. D. Turner, 2013: The Atmospheric Radiation Measurement (ARM) program network of microwave radiometers: Instrumentation, data, and retrievals. Atmos. Meas. Tech., 6, 23592372, https://doi.org/10.5194/amt-6-2359-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cahalan, R. F., and J. H. Joseph, 1989: Fractal statistics of cloud fields. Mon. Wea. Rev., 117, 261272, https://doi.org/10.1175/1520-0493(1989)117<0261:FSOCF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cancelli, D. M., M. Chamecki, and N. L. Dias, 2014: A large-eddy simulation study of scalar dissimilarity in the convective atmospheric boundary layer. J. Atmos. Sci., 71, 315, https://doi.org/10.1175/JAS-D-13-0113.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chaboureau, J.-P., and P. Bechtold, 2002: A simple cloud parameterization derived from cloud resolving model data: Diagnostic and prognostic applications. J. Atmos. Sci., 59, 23622372, https://doi.org/10.1175/1520-0469(2002)059<2362:ASCPDF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chaboureau, J.-P., and P. Bechtold, 2005: Statistical representation of clouds in a regional model and the impact on the diurnal cycle of convection during Tropical Convection, Cirrus and Nitrogen Oxides (TROCCINOX). J. Geophys. Res., 110, D17103, https://doi.org/10.1029/2004JD005645.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chatfield, R. B., and R. A. Brost, 1987: A two-stream model of the vertical transport of trace species in the convective boundary layer. J. Geophys. Res., 92, 13 26313 276, https://doi.org/10.1029/JD092iD11p13263.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clothiaux, E. E., T. P. Ackerman, G. G. Mace, K. P. Moran, R. T. Marchand, M. A. Miller, and B. E. Martner, 2000: Objective determination of cloud heights and radar reflectivities using a combination of active remote sensors at the ARM CART sites. J. Appl. Meteor., 39, 645665, https://doi.org/10.1175/1520-0450(2000)039<0645:ODOCHA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • De Roode, S. R., A. P. Siebesma, H. J. J. Jonker, and Y. de Voogd, 2012: Parameterization of the vertical velocity equation for shallow cumulus clouds. Mon. Wea. Rev., 140, 24242436, https://doi.org/10.1175/MWR-D-11-00277.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gustafson, W. I., A. M. Vogelmann, X. Cheng, S. Endo, B. Krishna, Z. Li, T. Toto, and H. Xiao, 2017a: Recommendations for the implementation of the LASSO workflow. DOE Tech. Rep. DOE/SC-ARM-17-031, DOE Atmospheric Radiation Measurement Climate Research Facility, 62 pp., https://doi.org/10.2172/1406259.

    • Crossref
    • Export Citation
  • Gustafson, W. I., A. M. Vogelmann, X. Cheng, S. Endo, B. Krishna, Z. Li, T. Toto, and H. Xiao, 2017b: LASSO Alpha 1 data bundles: 36°36′18.0″N, 97°29′6.0″ W: Southern Great Plains Central Facility (C1). ARM Data Archive, Oak Ridge, TN, accessed 18 July 2016, https://doi.org/10.5439/1256454.

    • Crossref
    • Export Citation
  • Gustafson, W. I., A. M. Vogelmann, X. Cheng, S. Endo, B. Krishna, Z. Li, T. Toto, and H. Xiao, 2017c: Description of the LASSO Alpha 1 Release. DOE Atmospheric Radiation Measurement Research Facility, DOE/SC-ARM-TR-194, https://doi.org/10.2172/1373564.

    • Crossref
    • Export Citation
  • Hacker, J. P., and W. M. Angevine, 2013: Ensemble data assimilation to characterize surface-layer errors in numerical weather prediction models. Mon. Wea. Rev., 141, 18041821, https://doi.org/10.1175/MWR-D-12-00280.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, J., M. L. Witek, J. Teixeira, R. Sun, H.-L. Pan, J. K. Fletcher, and C. S. Bretherton, 2016: Implementation in the NCEP GFS of a Hybrid Eddy-Diffusivity Mass-Flux (EDMF) boundary layer parameterization with dissipative heating and modified stable boundary layer mixing. Wea. Forecasting, 31, 341352, https://doi.org/10.1175/WAF-D-15-0053.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heinze, R., C. Moseley, L. N. Böske, S. K. Muppa, V. Maurer, S. Raasch, and B. Stevens, 2017: Evaluation of large-eddy simulations forced with mesoscale model output for a multi-week period during a measurement campaign. Atmos. Chem. Phys., 17, 70837109, https://doi.org/10.5194/acp-17-7083-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hourdin, F., F. Couvreux, and L. Menut, 2002: Parameterization of the dry convective boundary layer based on a mass flux representation of thermals. J. Atmos. Sci., 59, 11051123, https://doi.org/10.1175/1520-0469(2002)059<1105:POTDCB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, M., S. G. Benjamin, T. T. Ladwig, D. C. Dowell, S. S. Weygandt, D. C. Alexander, and J. S. Whitaker, 2017: GSI three-dimensional ensemble–variational hybrid data assimilation using a global ensemble for the regional Rapid Refresh model. Mon. Wea. Rev., 145, 42054225, https://doi.org/10.1175/MWR-D-16-0418.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, H.-Y., A. Hall, and J. Teixeira, 2013: Evaluation of the WRF PBL parameterizations for marine boundary layer clouds: Cumulus and stratocumulus. Mon. Wea. Rev., 141, 22652271, https://doi.org/10.1175/MWR-D-12-00292.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knuteson, R. O., and Coauthors, 2004: Atmospheric Emitted Radiance Interferometer. Part I: Instrument design. J. Atmos. Oceanic Technol., 21, 17631776, https://doi.org/10.1175/JTECH-1662.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Köhler, M., M. Ahlgrimm, and A. Beljaars, 2011: Unified treatment of dry convective and stratocumulus-topped boundary layers in the ECMWF model. Quart. J. Roy. Meteor. Soc., 137, 4357, https://doi.org/10.1002/qj.713.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Larsen, X. G., M. Kelly, and A. M. Sempreviva, 2014: On the temperature and humidity dissimilarity in the marine surface layer. Bound.-Layer Meteor., 151, 273291, https://doi.org/10.1007/s10546-013-9896-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Z., J. C. McWilliams, K. Ide, and J. Farrara, 2015a: A multiscale variational data assimilation scheme: Formulation and illustration. Mon. Wea. Rev., 143, 38043822, https://doi.org/10.1175/MWR-D-14-00384.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Z., S. Feng, Y. Liu, W. Lin, M. Zhang, T. Toto, A. M. Vogelmann, and S. Endo, 2015b: Development of fine-resolution analyses and expanded large-scale forcing properties: 1. Methodology and evaluation. J. Geophys. Res. Atmos., 120, 654666, https://doi.org/10.1002/2014JD022245.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, H. Y., and Coauthors, 2018: CAUSES: On the role of surface energy budget errors to the warm surface air temperature error over the central United States. J. Geophys. Res. Atmos., 123, 28882909, https://doi.org/10.1002/2017JD027194.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mielikainen, J., B. Huang, and H. L. A. Huang, 2015: Optimizing Total Energy-Mass Flux (TEMF) planetary boundary layer scheme for Intel’s Many Integrated Core (MIC) architecture. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8, 41064119, https://doi.org/10.1109/JSTARS.2015.2438638.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., and H. Niino, 2009: Development of an improved turbulence closure model for the atmospheric boundary layer. J. Meteor. Soc. JapanSer. II, 87, 895–912, https://doi.org/10.2151/jmsj.87.895.

    • Crossref
    • Export Citation
  • Neggers, R. A. J., 2015: Exploring bin-macrophysics models for moist convective transport and clouds. J. Adv. Model. Earth Syst., 7, 20792104, https://doi.org/10.1002/2015MS000502.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neggers, R. A. J., H. J. J. Jonker, and A. P. Siebesma, 2003: Size statistics of cumulus cloud populations in large-eddy simulations. J. Atmos. Sci., 60, 10601074, https://doi.org/10.1175/1520-0469(2003)60<1060:SSOCCP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neggers, R. A. J., A. P. Siebesma, G. Lenderink, and A. A. Holtslag, 2004: An evaluation of mass flux closures for diurnal cycles of shallow cumulus. Mon. Wea. Rev., 132, 25252538, https://doi.org/10.1175/MWR2776.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neggers, R. A. J., M. Koehler, and A. C. M. Beljaars, 2009: A dual mass flux framework for boundary layer convection. Part I: Transport. J. Atmos. Sci., 66, 14651487, https://doi.org/10.1175/2008JAS2635.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neggers, R. A. J., A. P. Siebesma, and T. Heus, 2012: Continuous single-column model evaluation at a permanent meteorological supersite. Bull. Amer. Meteor. Soc., 93, 13891400, https://doi.org/10.1175/BAMS-D-11-00162.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pergaud, J., V. Masson, S. Malardel, and F. Couvreux, 2009: A parameterization of dry thermals and shallow cumuli for mesoscale numerical weather prediction. Bound.-Layer Meteor., 132, 83106, https://doi.org/10.1007/s10546-009-9388-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rio, C., and F. Hourdin, 2008: A thermal plume model for the convective boundary layer: Representation of cumulus clouds. J. Atmos. Sci., 65, 407425, https://doi.org/10.1175/2007JAS2256.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Siebesma, A. P., and J. Teixeira, 2000: An advection-diffusion scheme for the convective boundary layer: Description and 1D results. 14th Symp. on Boundary Layer and Turbulence, Aspen, CO, Amer. Meteor. Soc., 4.16, https://ams.confex.com/ams/AugAspen/techprogram/paper_14840.htm.

  • Siebesma, A. P., P. M. M. Soares, and J. Teixeira, 2007: A combined eddy-diffusivity mass-flux approach for the convective boundary layer. J. Atmos. Sci., 64, 12301248, https://doi.org/10.1175/JAS3888.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simpson, J., and V. Wiggert, 1969: Models of precipitating cumulus towers. Mon. Wea. Rev., 97, 471489, https://doi.org/10.1175/1520-0493(1969)097<0471:MOPCT>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sisterson, D. L., R. A. Peppler, T. S. Cress, P. J. Lamb, and D. D. Turner, 2016: The ARM Southern Great Plains (SGP) Site. The Atmospheric Radiation Meaurement (ARM) Program: The First 20 Years, Meteor. Monogr., No. 57, 6.1–6.14, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0004.1.

    • Crossref
    • Export Citation
  • Sušelj, K., J. Teixeira, and G. Matheou, 2012: Eddy diffusivity/mass flux and shallow cumulus boundary layer: An updraft PDF multiple mass flux scheme. J. Atmos. Sci., 69, 15131533, https://doi.org/10.1175/JAS-D-11-090.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sušelj, K., J. Teixeira, and D. Chung, 2013: A unified model for moist convective boundary layers based on a stochastic eddy-diffusivity/mass-flux parameterization. J. Atmos. Sci., 70, 19291953, https://doi.org/10.1175/JAS-D-12-0106.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sušelj, K., T. F. Hogan, and J. Teixeira, 2014: Implementation of a stochastic eddy-diffusivity/mass-flux parameterization into the Navy Global Environmental Model. Wea. Forecasting, 29, 13741390, https://doi.org/10.1175/WAF-D-14-00043.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Svensson, G., and Coauthors, 2011: Evaluation of the diurnal cycle in the atmospheric boundary layer over land as represented by a variety of single column models—The second GABLS experiment. Bound.-Layer Meteor., 140, 177206, https://doi.org/10.1007/s10546-011-9611-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, S., M. Zhang, and S. Xie, 2017: Investigating the dependence of SCM simulated precipitation and clouds on the spatial scale of large-scale forcing at SGP. J. Geophys. Res. Atmos., 122, 87248738, https://doi.org/10.1002/2017JD026565.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, Y., and Z. Kuang, 2016: Dependence of entrainment in shallow cumulus convection on vertical velocity and distance to cloud edge. Geophys. Res. Lett., 43, 40564065, https://doi.org/10.1002/2016GL069005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Turner, D. D., 2007: Improved ground-based liquid water path retrievals using a combined infrared and microwave approach. J. Geophys. Res., 112, D15204, https://doi.org/10.1029/2007JD008530.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Turner, D. D., and U. Loehnert, 2014: Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground-based Atmospheric Emitted Radiance Interferometer (AERI). J. Appl. Meteor. Climatol., 53, 752771, https://doi.org/10.1175/JAMC-D-13-0126.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Turner, D. D., and W. G. Blumberg, 2018: Improvements to the AERIoe thermodynamic profile retrieval algorithm. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., https://doi.org/10.1109/JSTARS.2018.2874968, in press.

    • Search Google Scholar
    • Export Citation
  • Van Weverberg, K., and Coauthors, 2018: CAUSES: Attribution of surface radiation biases in NWP and climate models near the U.S. Southern Great Plains. J. Geophys. Res. Atmos., 123, 36123644, https://doi.org/10.1002/2017JD027188.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, B., and Coauthors, 2017: Sensitivity of turbine-height wind speeds to parameters in planetary boundary-layer and surface-layer schemes in the Weather Research and Forecasting Model. Bound.-Layer Meteor., 162, 117142, https://doi.org/10.1007/s10546-016-0185-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
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