
Additive Model Perturbations Scaled by Physical Tendencies for Use in Ensemble Prediction
References
- Astakhova, E, Montani, A and Alferov, DY. 2015. Ensemble forecasts for the Sochi-2014 Olympic Games. Russian Meteorology and Hydrology, 40(8): 531–539. DOI: 10.3103/S1068373915080051
- Baldauf, M, Seifert, A, Förstner, J, Majewski, D, Raschendorfer, M and Reinhardt, T. 2011. Operational convective-scale numerical weather prediction with the COSMO model: description and sensitivities. Monthly Weather Review, 139(12): 3887–3905. DOI: 10.1175/MWR-D-10-05013.1
- Berner, J, Fossell, K, Ha, S-Y, Hacker, J and Snyder, C. 2015. Increasing the skill of probabilistic forecasts: Understanding performance improvements from model-error representations. Monthly Weather Review, 143(4): 1295–1320. DOI: 10.1175/MWR-D-14-00091.1
- Bouttier, F, Fleury, A, Bergot, T and Riette, S. 2022. A single-column comparison of model-error representations for ensemble prediction. Boundary-Layer Meteorology, 183(2): 167–197. DOI: 10.1007/s10546-021-00682-6
- Bouttier, F, Vié, B, Nuissier, O and Raynaud, L. 2012. Impact of stochastic physics in a convection-permitting ensemble. Monthly Weather Review, 140(11): 3706–3721. DOI: 10.1175/MWR-D-12-00031.1
- Buizza, R, Miller, M and Palmer, T. 1999. Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Quarterly Journal of the Royal Meteorological Society, 125(560): 2887–2908. DOI: 10.1002/qj.49712556006
- Candille, G and Talagrand, O. 2005. Evaluation of probabilistic prediction systems for a scalar variable. Quarterly Journal of the Royal Meteorological Society, 131(609): 2131–2150. DOI: 10.1256/qj.04.71
- Christensen, H, Lock, S-J, Moroz, I and Palmer, T. 2017. Introducing independent patterns into the stochastically perturbed parametrization tendencies (SPPT) scheme. Quarterly Journal of the Royal Meteorological Society, 143(706): 2168–2181. DOI: 10.1002/qj.3075
- Clark, P, Halliwell, C and Flack, D. 2021. A physically based stochastic boundary layer perturbation scheme. Part I: Formulation and evaluation in a convection-permitting model. Journal of the Atmospheric Sciences, 78(3): 727–746. DOI: 10.1175/JAS-D-19-0291.1
- Dorrestijn, J, Crommelin, DT, Siebesma, AP and Jonker, HJ. 2013. Stochastic parameterization of shallow cumulus convection estimated from high-resolution model data. Theoretical and Computational Fluid Dynamics, 27(1): 133–148. DOI: 10.1007/s00162-012-0281-y
- Eckel, FA and Mass, CF. 2005. Aspects of effective mesoscale, short-range ensemble forecasting. Weather and Forecasting, 20(3): 328–350. DOI: 10.1175/WAF843.1
- Efron, B and Tibshirani, RJ. 1994. An introduction to the bootstrap. CRC press. DOI: 10.1201/9780429246593
- Fortin, V, Abaza, M, Anctil, F and Turcotte, R. 2014. Why should ensemble spread match the RMSE of the ensemble mean? Journal of Hydrometeorology, 15(4): 1708–1713. DOI: 10.1175/JHM-D-14-0008.1
- Frogner, I-L, Andrae, U, Ollinaho, P, Hally, A, Hämäläinen, K, Kauhanen, J, Ivarsson, K-I and Yazgi, D. 2022. Model uncertainty representation in a convection-permitting ensemble — SPP and SPPT in HarmonEPS. Monthly Weather Review, 150(4): 775–795. DOI: 10.1175/MWR-D-21-0099.1
- Fuchs, A. 2014. Nonlinear dynamics in complex systems. Springer. DOI: 10.1007/978-3-642-33552-5
- Gal-Chen, T and Somerville, RC. 1975. On the use of a coordinate transformation for the solution of the Navier-Stokes equations. Journal of Computational Physics, 17(2): 209–228. DOI: 10.1016/0021-9991(75)90037-6
- Gofa, F, Raspanti, A and Charantonis, T. 2010. Assessment of the performance of the operational forecast model COSMOGR using a conditional verification tool. In: Proc. 10th COMECAP Conference of Meteorology, Patras, Greece,
Hellenic Meteorological Society . - Hersbach, H. 2000. Decomposition of the continuous ranked probability score for ensemble prediction systems. Weather and Forecasting, 15(5): 559–570. DOI: 10.1175/1520-0434(2000)015<;0559:DOTCRP>2.0.CO;2
- Hirt, M, Rasp, S, Blahak, U and Craig, GC. 2019. Stochastic parameterization of processes leading to convective initiation in kilometer-scale models. Monthly Weather Review, 147(11): 3917–3934. DOI: 10.1175/MWR-D-19-0060.1
- Kiktev, D, Joe, P, Isaac, GA, Montani, A, Frogner, I-L, Nurmi, P, Bica, B, Milbrandt, J, Tsyrulnikov, M, Astakhova, E, Bundel, A, Belair, S, Pyle, M, Muravyev, A, Rivin, G, Rozinkina, I, Paccagnella, T, Wang, Y, Reid, J, Nipen, T and Ahn, K-D. 2017. FROST-2014: The Sochi winter Olympics international project. Bulletin of the American Meteorological Society, 98(9): 1908–1929. DOI: 10.1175/BAMS-D-15-00307.1
- Kober, K and Craig, GC. 2016. Physically based stochastic perturbations (PSP) in the boundary layer to represent uncertainty in convective initiation. Journal of the Atmospheric Sciences, 73(7): 2893–2911. DOI: 10.1175/JAS-D-15-0144.1
- Kwasniok, F. 2012. Data-based stochastic subgrid-scale parametrization: an approach using cluster-weighted modelling. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 370(1962): 1061–1086. DOI: 10.1098/rsta.2011.0384
- Lang, ST, Lock, S-J, Leutbecher, M, Bechtold, P and Forbes, RM. 2021. Revision of the stochastically perturbed parametrisations model uncertainty scheme in the integrated forecasting system. Quarterly Journal of the Royal Meteorological Society, 147(735): 1364–1381. DOI: 10.1002/qj.3978
- Leutbecher, M, Lock, S-J, Ollinaho, P, Lang, ST, Balsamo, G, Bechtold, P, Bonavita, M, Christensen, HM, Diamantakis, M, Dutra, E, et al. 2017. Stochastic representations of model uncertainties at ECMWF: State of the art and future vision. Quarterly Journal of the Royal Meteorological Society, 143(707): 2315–2339. DOI: 10.1002/qj.3094
- Leutbecher, M and Palmer, TN. 2008. Ensemble forecasting. Journal of Computational Physics, 227(7): 3515–3539. DOI: 10.1016/j.jcp.2007.02.014
- Machulskaya, E and Seifert, A. 2019. Stochastic differential equations for the variability of atmospheric convection fluctuating around the equilibrium. Journal of Advances in Modeling Earth Systems, 11(8): 2708–2727. DOI: 10.1029/2019MS001638
- Maurer, D, Walser, A and Arpagaus, M. 2014. First COSMO-E experiments with the stochastically perturbed parametrization tendencies (SPPT) scheme. COSMO Newsletter, 14: 19–27. DOI: 10.1175/MWR-D-21-0316.1
- McTaggart-Cowan, R, Separovic, L, Charron, M, Deng, X, Gagnon, N, Houtekamer, PL and Patoine, A. 2022. Using stochastically perturbed parameterizations to represent model uncertainty. Part II: Comparison with existing techniques in an operational ensemble. Monthly Weather Review, 150(11): 2859–2882.
- Monin, AS and Yaglom, AM. 2013. Statistical fluid mechanics, volume II: Mechanics of turbulence, Vol. 2. Courier Corporation.
- Montani, A, Alferov, D, Astakhova, E, Marsigli, C and Paccagnella, T. 2014. Ensemble forecasting for Sochi-2014 Olympics: The COSMO-based ensemble prediction systems. COSMO Newsletter, 14: 88–94.
- Montani, A, Cesari, D, Marsigli, C and Paccagnella, T. 2011. Seven years of activity in the field of mesoscale ensemble forecasting by the COSMO-LEPS system: main achievements and open challenges. Tellus A: Dynamic Meteorology and Oceanography, 63(3): 605–624. DOI: 10.1111/j.1600-0870.2010.00499.x
- Ollinaho, P, Lock, S-J, Leutbecher, M, Bechtold, P, Beljaars, A, Bozzo, A, Forbes, RM, Haiden, T, Hogan, RJ and Sandu, I. 2017. Towards process-level representation of model uncertainties: Stochastically perturbed parametrisations in the ECMWF ensemble. Quarterly Journal of the Royal Meteorological Society, 143(702): 408–422. DOI: 10.1002/qj.2931
- Orrell, D, Smith, L, Barkmeijer, J and Palmer, TN. 2001. Model error in weather forecasting. Nonlinear processes in geophysics, 8(6): 357–371. DOI: 10.5194/npg-8-357-2001
- Palmer, T. 2012. Towards the probabilistic earth-system simulator: A vision for the future of climate and weather prediction. Quarterly Journal of the Royal Meteorological Society, 138(665): 841–861. DOI: 10.1002/qj.1923
- Palmer, T, Shutts, G, Hagedorn, R, Doblas-Reyes, F, Jung, T and Leutbecher, M. 2005. Representing model uncertainty in weather and climate prediction. Annu. Rev. Earth Planet. Sci., 33: 163–193. DOI: 10.1146/annurev.earth.33.092203.122552
- Plant, R and Craig, GC. 2008. A stochastic parameterization for deep convection based on equilibrium statistics. Journal of the Atmospheric Sciences, 65(1): 87–105. DOI: 10.1175/2007JAS2263.1
- Rieger, D, Milelli, M, Boucouvala, D, Gofa, F, Iriza-Burca, A, Khain, P, Kirsanov, A, Linkowska, J and Marcucci, F. 2021. Verification of ICON in limited area mode at COSMO national meteorological services. Reports on ICON, 6. DOI: 10.5676/DWD_pub/nwv/icon_006
- Rivin, G, Rozinkina, I, Astakhova, E and Coauthors. 2018. The COSMO Priority Project CORSO (Consolidation of Operational and Research Results for the Sochi Olympic Games). Final Report. COSMO Technical report, (35): 249–262. DOI: 10.5676/DWD_pub/nwv/cosmo-tr_35
- Romine, GS, Schwartz, CS, Berner, J, Fossell, KR, Snyder, C, Anderson, JL and Weisman, ML. 2014. Representing forecast error in a convection-permitting ensemble system. Monthly Weather Review, 142(12): 4519–4541. DOI: 10.1175/MWR-D-14-00100.1
- Sakradzija, M, Seifert, A and Heus, T. 2015. Fluctuations in a quasi-stationary shallow cumulus cloud ensemble. Nonlinear Processes in Geophysics, 22(1): 65–85. DOI: 10.5194/npg-22-65-2015
- Schraff, C, Reich, H, Rhodin, A, Schomburg, A, Stephan, K, Perianez, A and Potthast, R. 2016. Kilometre-scale ensemble data assimilation for the COSMO model (KENDA). Quarterly Journal of the Royal Meteorological Society, 142(696): 1453–1472. DOI: 10.1002/qj.2748
- Shutts, G and Pallarès, AC. 2014. Assessing parametrization uncertainty associated with horizontal resolution in numerical weather prediction models. Phil. Trans. R. Soc. A, 372(2018): 20130284. DOI: 10.1098/rsta.2013.0284
- Toth, Z, Talagrand, O, Candille, G and Zhu, Y. 2003.
Probability and ensemble forecasts . In: Jolliffe, IT and Stephenson, DB (eds.), Forecast verification: A practitioner’s guide in atmospheric science. John Wiley and Sons. pp. 137–163. - Touil, H, Hussaini, M, Gotoh, T, Rubinstein, R and Woodruff, S. 2007. Development of stochastic models for turbulence. New Journal of Physics, 9(7): 215. DOI: 10.1088/1367-2630/9/7/215
- Tsyroulnikov, MD. 2001. Proportionality of scales: An isotropy-like property of geophysical fields. Quarterly Journal of the Royal Meteorological Society, 127(578): 2741–2760. DOI: 10.1002/qj.49712757812
- Tsyrulnikov, M and Gayfulin, D. 2017. A limited-area spatio-temporal stochastic pattern generator for simulation of uncertainties in ensemble applications. Meteorol. Zeitschrift, 26(5): 549–566. DOI: 10.1127/metz/2017/0815
- Tsyrulnikov, M and Rakitko, A. 2019. Impact of non-stationarity on hybrid ensemble filters: A study with a doubly stochastic advection-diffusion-decay model. Quarterly Journal of the Royal Meteorological Society, 145(722): 2255–2271. DOI: 10.1002/qj.3556
- Van Ginderachter, M, Degrauwe, D, Vannitsem, S and Termonia, P. 2020. Simulating model uncertainty of subgrid-scale processes by sampling model errors at convective scales. Nonlinear Processes in Geophysics, 27(2): 187–207. DOI: 10.5194/npg-27-187-2020
- Wastl, C, Wang, Y, Atencia, A and Wittmann, C. 2019. A hybrid stochastically perturbed parametrization scheme in a convection-permitting ensemble. Monthly Weather Review, 147(6): 2217–2230. DOI: 10.1175/MWR-D-18-0415.1
- Wilks, DS. 2011. Statistical methods in the atmospheric sciences. Academic press.
DOI: https://doi.org/10.16993/tellusa.3224 | Journal eISSN: 3035-9554
Language: English
Page range: 334 - 357
Submitted on: Nov 22, 2022
Accepted on: Oct 21, 2023
Published on: Nov 13, 2023
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services
© 2023 Michael Tsyrulnikov, Elena Astakhova, Dmitry Gayfulin, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.