Abstract
Various perturbation approaches have been proposed for representing model error in convection-permitting ensemble prediction. Their evaluation usually relies on time-averaged ensemble prediction statistics and on complex case studies. In this work, their detailed physical behaviour is studied in order to understand their differences, and to help their optimization. A process-level intercomparison framework is used to investigate the widely used SPPT (stochastic perturbations of physics tendencies), independent SPPT, and random-parameters model-perturbation approaches. Ensemble predictions with the single-column version of the Arome numerical-weather-prediction model are evaluated on three different boundary-layer regimes: cumulus convection, stratocumulus-topped boundary layer, and radiation fog. The independent SPPT approach is found to produce more dispersion than the SPPT approach, particularly when several physics parametrizations are in near equilibrium. It also appears to be more numerically stable near the surface. The random parameters approach perturbations are structurally very different from the other approaches, particularly regarding cloud structure. The independent SPPT and random parameters approaches have very different sensitivities to the atmospheric conditions, which suggests that intercomparisons of ensemble-model-error approaches should carefully account for situation dependency. Substantial forecast biases are produced by random parameters with respect to the unperturbed model. These results suggest that the independent SPPT approach can bring major improvements over the SPPT approach with minimal effort, that there is some complementarity between the independent SPPT and random parameters approaches, but that implementing random-parameters-type approaches in operational applications may require careful tuning to avoid creating forecast biases.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Baker LH, Rudd AC, Migliorini S, Bannister RN (2014) Representation of model error in a convective-scale ensemble prediction system. Nonlinear Process Geophys 21:19–39. https://doi.org/10.5194/npg-21-19-2014
Barthlott C, Kalthoff N (2011) A numerical sensitivity study on the impact of soil moisture on convection-related parameters and convective precipitation over complex terrain. J Atmos Sci 68:2971–2987. https://doi.org/10.1175/JAS-D-11-027.1
Berner J, Fossell KR, Ha SY, Hacker JP, Snyder C (2015) Increasing the skill of probabilistic forecasts: understanding performance improvements from model-error representations. Mon Weather Rev 153:1295–1320. https://doi.org/10.1175/MWR-D-14-00091.1
Boutle I, Price J, Kudzotsa I, Kokkola H, Romakkaniemi S (2018) Aerosol–fog interaction and the transition to well-mixed radiation fog. Atmos Chem Phys 18:7827–7840. https://doi.org/10.5194/acp-18-7827-2018
Boutle I, Angevine W, Bao JW, Bergot T, Bhattacharya R, Bott A, Ducongé L, Forbes R, Goecke T, Grell E, Hill A, Igel A, Kudzotsa I, Lac C, Maronga B, Romakkaniemi S, Schmidli J, Schwenkel J, Steenveld GJ, Vié B (2021) Demistify: an LES and SCM intercomparison of radiation fog. Atmos Chem Phys. https://doi.org/10.5194/acp-2021-832
Bouttier F, Vié B, Nuissier O, Raynaud L (2012) Impact of stochastic physics in a convection-permitting ensemble. Mon Weather Rev 140:3706–3721. https://doi.org/10.1175/MWR-D-12-00031.1
Bouttier F, Raynaud L, Nuissier O, Ménétrier B (2016) Sensitivity of the AROME ensemble to initial and surface perturbations during HyMeX. Q J R Meteorol Soc 142:390–403. https://doi.org/10.1002/qj.2622
Bowler NE, Arribas A, Mylne KR, Robertson KB, Beare SE (2008) The MOGREPS short-range ensemble prediction system. Q J R Meteorol Soc 134:703–722. https://doi.org/10.1002/qj.234
Brown AR, Cederwall RT, Chlond A, Duynkerke PG, Golaz JC, Khairoutdinov M, Lewellen DC, Lock AP, MacVean MK, Moeng CH, Neggers RAJ, Siebesma AP, Stevens B (2002) Large-eddy simulation of the diurnal cycle of shallow cumulus convection over land. Q J R Meteorol Soc 128:1075–1093. https://doi.org/10.1256/003590002320373210
Buizza R, Miller M, Palmer TN (1999) Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Q J R Meteorol Soc 125:2887–2908. https://doi.org/10.1002/qj.49712556006
Christensen HM (2020) Constraining stochastic parametrisation schemes using high-resolution simulations. Q J R Meteorol Soc 146:938–962. https://doi.org/10.1002/qj.3717
Christensen HM, Moroz IM, Palmer TN (2015) Stochastic and perturbed parameter representations of model uncertainty in convection parameterization. J Atmos Sci 72:2525–2544. https://doi.org/10.1175/JAS-D-14-0250.s1
Christensen HM, Lock SJ, Moroz IM, Palmer TN (2017) Introducing independent patterns into the stochastically perturbed parametrization tendencies (SPPT) scheme. Q J R Meteorol Soc 143:2168–2181. https://doi.org/10.1002/qj.3075
Clark AJ, Gallus WA, Chen TC (2008) Contributions of mixed physics versus perturbed initial/lateral boundary conditions to ensemble-based precipitation forecast skill. Mon Weather Rev 136:2140–2156. https://doi.org/10.1175/2007MWR2029.1
Cohard JM, Pinty JP (2000) A comprehensive two-moment warm microphysical bulk scheme. I: Description and tests. Q J R Meteorol Soc 126:1815–1842. https://doi.org/10.1256/smsqj.56613
Cuxart J, Bougeault P, Redelsperger JL (2000) A turbulence scheme allowing for mesoscale and large-eddy simulations. Q J R Meteorol Soc 126:1–30. https://doi.org/10.1002/qj.49712656202
Davini P, von Hardenberg J, Corti S, Christensen H, Juricke S, Subramanian A, Watson P, Weisheimer A, Palmer TN (2017) Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model. Geosci Model Dev 10:1383–1402. https://doi.org/10.5194/gmd-10-1383-2017
De Roode S, Wang Q (2007) Do stratocumulus clouds detrain? FIRE I data revisited. Boundary-Layer Meteorol 122:479–491. https://doi.org/10.1007/s10546-006-9113-1
Duda JD, Wang X, Kong F, Xue M (2014) Using varied microphysics to account for uncertainty in warm-season QPF in a convection-allowing ensemble. Mon Weather Rev 142:2198–2219. https://doi.org/10.1175/MWR-D-13-00297.1
Duynkerke PG, de Roode SR, van Zanten MC, Calvo J, Cuxart J, Cheinet S, Chlond A, Grenier H, Jonker PJ, Köhler M, Lenderink G, Lewellen D, Lappen C, Lock AP, Moeng C, Muller F, Olmeda D, Piriou JM, Sanchez E, Sednev I (2004) Observations and numerical simulations of the diurnal cycle of the EUROCS stratocumulus case. Q J R Meteorol Soc 130:3269–3296. https://doi.org/10.1256/qj.03.139
Fundel VJ, Fleischhut N, Herzog SM, Goeber M, Hagedorn R (2019) Promoting the use of probabilistic weather forecasts through a dialogue between scientists, developers and end-users. Q J R Meteorol Soc 145:210–231. https://doi.org/10.1002/qj.3482
Fouquart Y, Bonnel B (1980) Computations of solar heating of the earth’s atmosphere: a new parameterization. Beitr Phys Atmos 53:35–62
Gallus W Jr, Wolff J, Gotway JH, Harrold M, Blank M, Beck J (2019) The impacts of using mixed physics in the community leveraged unified ensemble. Weather Forecast 34:849–867. https://doi.org/10.1175/WAF-D-18-0197.1
Garcia-Moya JA, Callado A, Escribá P, Santos C, Santos-Munoz D, Simarro J (2011) Predictability of short-range forecasting: a multimodel approach. Tellus A 63:550–563. https://doi.org/10.1111/j.1600-0870.2010.00506.x
Gebhardt C, Theis SE, Paulat M, Ben Bouallègue Z (2011) Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbations and variation of lateral boundaries. Atmos Res 100:168–177. https://doi.org/10.1016/j.atmosres.2010.12.008
Hacker JP, Snyder C, Ha SY, Pocernich M (2011) Linear and non-linear response to parameter variations in a mesoscale model. Tellus A 63:429–444. https://doi.org/10.1111/j.1600-0870.2010.00505.x
Hagedorn R, Doblas-Reyes FJ, Palmer TN (2005) The rationale behind the success of multi-model ensembles in seasonal forecasting. I. Basic concept. Tellus A 57:219–233. https://doi.org/10.3402/tellusa.v57i3.14657
Hou D, Toth Z, Zhu Y (2006) A stochastic parameterization scheme within the NCEP global ensemble forecast system. In: 2006 AMS workshop proceedings, section 4.5, 5pp. https://ams.confex.com/ams/Annual2006/techprogram/paper_101401.htm. Last Accessed 20 July 2021
Kober K, Craig G (2016) Physically based stochastic perturbations (PSP) in the boundary layer to represent uncertainty in convective initiation. J Atmos Sci 73:2893–2911. https://doi.org/10.1175/JAS-D-15-0144.1
Jankov I, Beck J, Wolff J, Harrold M, Olson J, Smirnova T, Alexander C (2019) Stochastically perturbed parameterizations in an HRRR-based ensemble. Mon Weather Rev 147:153–173. https://doi.org/10.1175/MWR-D-18-0092.1
Lang S, Lock SJ, Leutbencher M, Bechtold P, Forbes R (2021) Revision of the stochastically perturbed parametrisations model uncertainty scheme in the integrated forecasting system. Q J R Meteorol Soc 147:1364–1381. https://doi.org/10.1002/qj.3978
Lupo K, Torn R, Yang SC (2020) Evaluation of stochastic perturbed parameterization tendencies on convective-permitting ensemble forecasts of heavy rainfall events in New York and Taiwan. Weather Forecast 35:5–24. https://doi.org/10.1175/WAF-D-19-0064.1
Masson V, Le Moigne P, Martin E, Faroux S, Alias A, Alkama R, Belamari S, Barbu A, Boone A, Bouyssel F, Brousseau P, Brun E, Calvet JC, Carrer D, Decharme B, Delire C, Donier S, Essaouini K, Gibelin AL, Giordani H, Habets F, Jidane M, Kerdraon G, Kourzeneva E, Lafaysse M, Lafont S, Lebeaupin Brossier C, Lemonsu A, Mahfouf JF, Marguinaud P, Mokhtari M, Morin S, Pigeon G, Salgado R, Seity Y, Taillefer F, Tanguy G, Tulet P, Vincendon B, Vionnet V, Voldoire A (2013) The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes. Geosci Model Dev 6:929–960. https://doi.org/10.5194/gmd-6-929-2013
McCabe A, Swinbank R, Tennant W, Lock A (2016) Representing model uncertainty in the Met Office convection-permitting ensemble prediction system and its impact on fog forecasting. Q J R Meteorol Soc 142:2897–2910. https://doi.org/10.1002/qj.2876
Mlawer EJ, Taubman SJ, Brown P, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102:16663–16682. https://doi.org/10.1029/97JD00237
Ollinaho P, Lock SJ, Leutbecher M, Bechtold P, Beljaars A, Bozzo A, Forbes RM, Haiden T, Hogan RJ, Sandu I (2017) Towards process-level representation of model uncertainties: stochastically perturbed parametrizations in the ECMWF ensemble. Q J R Meteorol Soc 143:408–422. https://doi.org/10.1002/qj.2931
Palmer TN (2001) A nonlinear dynamical perspective on model error: a proposal for non-local stochastic-dynamic parametrization in weather and climate prediction models. Q J R Meteorol Soc 127:279–304. https://doi.org/10.1002/qj.49712757202
Palmer TN, Buizza R, Doblas-Reyes F, Jung T, Leutbecher M, Shutts G, Steinheimer M, Weisheimer A (2009) Stochastic parametrization and model uncertainty. ECMWF Res. Dept. Tech Memo no. 598, 44pp. Available from www.ecmwf.int. https://doi.org/10.21957/ps8gbwbdv
Pergaud J, Masson V, Malardel S, Couvreux F (2009) A parameterization of dry thermals and shallow cumuli for mesoscale numerical weather prediction. Boundary-Layer Meteorol 132:83–106. https://doi.org/10.1007/s10546-009-9388-0
Price J, Lane S, Boutle I, Smith D, Bergot T, Lac C, Ducongé L, McGregor J, Kerr-Munslow A, Pickering M, Clark R (2018) LANFEX: a field and modelling study to improve our understanding and forecasting of radiation fog. Bull Am Meteorol Soc 99:2061–2077. https://doi.org/10.1175/BAMS-D-16-0299.1
Romine GS, Schwartz CS, Berner J, Fossell KR, Snyder C, Anderson JL, Weisman ML (2014) Representing forecast error in a convection-permitting ensemble system. Mon Weather Rev 142:4519–4541. https://doi.org/10.1175/MWR-D-14-00100.1
Sakradzija M, Klocke D (2018) Physically constrained stochastic shallow convection in realistic kilometer-scale simulations. J Adv Model Earth Syst 10:2755–2776. https://doi.org/10.1029/2018MS001358
Shutts GJ (2005) A kinetic energy backscatter algorithm for use in ensemble prediction systems. Q J R Meteorol Soc 131:3079–3102. https://doi.org/10.1256/qj.04.106
Shutts GJ, Pallarès AC (2014) Assessing parametrization uncertainty associated with horizontal resolution in numerical weather prediction models. Philos Trans R Soc A 372:20130284. https://doi.org/10.1098/rsta.2013.0284
Seity Y, Brousseau P, Malardel S, Hello G, Bénard P, Bouttier F, Lac C, Masson V (2011) The AROME-France convective scale operational model. Mon Weather Rev 139:976–99. https://doi.org/10.1175/2010MWR3425.1
Termonia P, Fischer C, Bazile E, Bouyssel F, Brožková R, Bénard P, Bochenek B, Degrauwe D, Derková M, El Khatib R, Hamdi R, Mašek J, Pottier P, Pristov N, Seity Y, Smolíková P, Španiel O, Tudor M, Wang Y, Wittmann C, Joly A (2018) The ALADIN System and its canonical model configurations AROME CY41T1 and ALARO CY40T1. Geosci Model Dev 11:257–281. https://doi.org/10.5194/gmd-11-257-2018
Wastl C, Wang Y, Atencia A, Wittmann C (2019a) Independent perturbations for physics parametrization tendencies in a convection-permitting ensemble (pSPPT). Geosci Model Dev 12:261–273. https://doi.org/10.5194/gmd-12-261-2019
Wastl C, Wang Y, Atencia A, Wittmann C (2019b) A hybrid stochastically perturbed parametrization scheme in a convection-permitting ensemble. Mon Weather Rev 147:2217–2230. https://doi.org/10.1175/MWR-D-18-0415.1
Zhang X (2018) Application of a convection-permitting ensemble prediction system to quantitative precipitation forecasts over southern China: Preliminary results during SCMREX. Q J R Meteorol Soc 144:2842–2862. https://doi.org/10.1002/qj.3411
Acknowledgements
This study was funded by the French Government through Météo-France, CNRS and the University of Toulouse. It received the IPSIPE research Grant from the LEFE/MANU programme of CNRS/INSU. The LANFEX case set-up used data that was kindly provided by I. Boutle and J. Price. The manuscript was improved by the comments and suggestions of two reviewers.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Bouttier, F., Fleury, A., Bergot, T. et al. A Single-Column Comparison of Model-Error Representations for Ensemble Prediction. Boundary-Layer Meteorol 183, 167–197 (2022). https://doi.org/10.1007/s10546-021-00682-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10546-021-00682-6