journal article Sep 01, 2015

The quiet revolution of numerical weather prediction

View at Publisher Save 10.1038/nature14956
Topics

No keywords indexed for this article. Browse by subject →

References
100
[1]
Abbe, C. The physical basis of long-range weather forecasts. Mon. Weath. Rev. 29, 551–561 (1901) 10.1175/1520-0493(1901)29[551c:tpbolw]2.0.co;2
[2]
Bjerknes, V. Das Problem der Wettervorhersage betrachtet vom Standpunkt der Mechanik und Physik. Meteorol. Z. 21, 1–7 (1904)
[3]
Lazo, J. K., Morss, R. E. & Demuth, J. L. 300 billion served: sources, perceptions, uses, and values of weather forecasts. Bull. Am. Meteorol. Soc. 90, 785–798 (2009) 10.1175/2008bams2604.1
[4]
Kalnay, E. Atmospheric Modeling, Data Assimilation and Predictability (Cambridge Univ. Press, 2003) A classic textbook on the fundamentals of modern weather prediction.
[5]
Richardson, L. F. Weather Prediction by Numerical Process (Cambridge Univ. Press, 1922) Already in the early 1920s the author proposed numerical algorithms to solve the NWP partial differential equations using a huge staff of humans as computers.
[6]
Charney, J. G., Fjoertoft, R. & Neumann, J. v. Numerical integration of the barotropic vorticity equation. Tellus 2, 237–254 (1950) The first calculation of a numerical weather prediction on an electronic computer. 10.3402/tellusa.v2i4.8607
[7]
Bolin, B. Numerical forecasting with the barotropic model. Tellus 7, 27–49 (1955) 10.3402/tellusa.v7i1.8770
[8]
Lynch, P. The origins of computer weather prediction and climate modeling. J. Comput. Phys. 227, 3431–3444 (2008) 10.1016/j.jcp.2007.02.034
[9]
Robert, A. J. A semi-Lagrangian and semi-implicit numerical integration scheme for the primitive meteorological equations. J. Meteorol. Soc. Jpn 60, 319–324 (1982) This seminal paper presents a numerical method used worldwide since the 1990s to solve the partial differential equations with a significantly longer time step thus greatly enhancing efficiency. 10.2151/jmsj1965.60.1_319
[10]
Staniforth, A., Wood, N. & Côté, J. A simple comparison of four physics–dynamics coupling schemes. Mon. Weath. Rev. 130, 3129–3135 (2002) 10.1175/1520-0493(2002)130<3129:ascofp>2.0.co;2
[11]
Williamson, D. L. The evolution of dynamical cores for global atmospheric models. J. Meteorol. Soc. Jpn B 85, 241–269 (2007) 10.2151/jmsj.85b.241
[12]
Lorenz, E. N. Reflections on the conception, birth and childhood of numerical weather prediction. Annu. Rev. Earth Planet. Sci. 34, 37–45 (2006) 10.1146/annurev.earth.34.083105.102317
[13]
Flato, G. et al. in Climate Change 2013 (eds Stocker, T. F. et al.) 741–866 (Cambridge Univ. Press, 2013)
[14]
Dudhia, J. A history of mesoscale model development. Asia-Pac. J. Atmos. Sci. 50, 121–131 (2014) Reviews the evolution of limited area modelling. 10.1007/s13143-014-0031-8
[15]
Zhang, Y. Online-coupled meteorology and chemistry models: history, current status, and outlook. Atmos. Chem. Phys. 8, 2895–2932 (2008) 10.5194/acp-8-2895-2008
[16]
Arakawa, A. Adjustment mechanisms in atmospheric motions. J. Meteorol. Soc. Jpn 75, 155–179 (1997) 10.2151/jmsj1965.75.1b_155
[17]
Williams, P. D. Modelling climate change: the role of unresolved processes. Phil. Trans. R. Soc. A 363, 2931–2946 (2005) 10.1098/rsta.2005.1676
[18]
Grenier, H. & Bretherton, C. S. A moist PBL parameterization for large-scale models and its application to subtropical cloud-topped marine boundary layers. Mon. Weath. Rev. 129, 357–377 (2001) 10.1175/1520-0493(2001)129<0357:amppfl>2.0.co;2
[19]
Arakawa, A. The cumulus parameterization problem: past, present, and future. J. Clim. 17, 2493–2525 (2004)Reviews the necessity and challenges of parameterizing sub-grid scale physical processes for cumulus clouds. 10.1175/1520-0442(2004)017<2493:ratcpp>2.0.co;2
[20]
Stensrud, D. J. Parameterization Schemes, Keys to Understanding Numerical Weather Prediction Models (Cambridge Univ. Press, 2007) 10.1017/cbo9780511812590
[21]
Poincare, H. Science and Method ( T. Nelson, London, 1914)
[22]
Thompson, P. D. Uncertainty of initial state as a factor in the predictability of large scale atmospheric flow patterns. Tellus 9, 275–295 (1957) 10.1111/j.2153-3490.1957.tb01885.x
[23]
Deterministic Nonperiodic Flow

Edward N. Lorenz

Journal of the Atmospheric Sciences 1963 10.1175/1520-0469(1963)020<0130:dnf>2.0.co;2
[24]
Slingo, J. & Palmer, T. N. Uncertainty in weather and climate prediction. Phil. Trans. R. Soc. A 369, 4751–4767 (2011) 10.1098/rsta.2011.0161
[25]
Zhang, H. & Pu, Z. Beating the uncertainties: ensemble forecasting and ensemble based data assimilation. Adv. Meteorol. 2010, 432160 (2010) 10.1155/2010/432160
[26]
Epstein, E. S. Stochastic-dynamic prediction. Tellus 21, 739–759 (1969) 10.3402/tellusa.v21i6.10143
[27]
Leith, C. E. Theoretical skill of Monte Carlo forecasts. Mon. Weath. Rev. 102, 409–418 (1974)Discusses the estimation of atmospheric forecast uncertainties using a Monte Carlo approach based on an ensemble of perturbed numerical predictions. 10.1175/1520-0493(1974)102<0409:tsomcf>2.0.co;2
[28]
Ehrendorfer, M. Predicting the uncertainty of numerical weather forecasts: a review. Meteorol. Z. 6, 147–183 (1997) 10.1127/metz/6/1997/147
[29]
Palmer, T. N. Towards the probabilistic Earth-system simulator: a vision for the future of climate and weather prediction. Q. J. R. Meteorol. Soc. 138, 841–861 (2012) 10.1002/qj.1923
[30]
Lions, J. Contrôle Optimal de Systèmes Gouvernés par des Équations aux Dérivées Partielles (Dunod-Gauthier-Villars, Paris, 1968)
[31]
Lorenc, A. C. Analysis methods for numerical weather prediction. Q. J. R. Meteorol. Soc. 112, 1177–1194 (1986) 10.1002/qj.49711247414
[32]
Daley, R. Atmospheric Data Analysis (Cambridge Univ. Press, 1991)A comprehensive textbook on data assimilation and its application in atmospheric sciences.
[33]
Navon, I. M. in Data Assimilation for Atmospheric, Oceanic and Hydrologic Application (eds Park, S. K. & Xu, L. ) 21–65 (Springer, 2009) 10.1007/978-3-540-71056-1_2
[34]
Courtier, P., Thepaut, J.-N. & Hollingsworth, A. A strategy for operational implementation of 4D-Var, using an incremental approach. Q. J. R. Meteorol. Soc. 120, 1367–1387 (1994) 10.1002/qj.49712051912
[35]
Rabier, F., Jaervinen, H., Klinker, E., Mahfouf, J.-F. & Simmons, A. The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics. Q. J. R. Meteorol. Soc. 126, 1143–1170 (2000) 10.1002/qj.49712656415
[36]
Gauthier, P. & Thepaut, J.-N. Impact of the digital filter as a weak constraint in the preoperational 4DVAR assimilation system of Météo-France. Mon. Weath. Rev. 129, 2089–2102 (2001) 10.1175/1520-0493(2001)129<2089:iotdfa>2.0.co;2
[37]
Rawlins, F. et al. The Met Office global four-dimensional variational data assimilation scheme. Q. J. R. Meteorol. Soc. 133, 347–362 (2007) 10.1002/qj.32
[38]
Kadowaki, T. A 4-dimensional variational assimilation system for the JMA Global Spectrum Model. CAS/JSC WGNE Res. Act. Atmos. Ocea. Modell. 34, 117–118 (2005)
[39]
Gauthier, P., Tanguay, M., Laroche, S., Pellerin, S. & Morneau, J. Extension of 3D-Var to 4D-Var: implementation of 4D-Var at the Meteorological Service of Canada. Mon. Weath. Rev. 135, 2339–2354 (2007) 10.1175/mwr3394.1
[40]
Xu, L., Rosmond, T. & Daley, R. Development of NAVDAS-AR: formulation and initial tests of the linear problem. Tellus A 57, 546–559 (2005) 10.1111/j.1600-0870.2005.00123.x
[41]
Saunders, R. W., Matricardi, M. & Brunel, P. An improved fast radiative transfer model for assimilation of satellite radiance observations. Q. J. R. Meteorol. Soc. 125, 1407–1425 (1999) 10.1002/qj.1999.49712555615
[42]
Bauer, P., Moreau, E., Chevallier, F. & O'Keeffe, U. Multiple-scattering microwave radiative transfer for data assimilation applications. Q. J. R. Meteorol. Soc. 132, 1259–1281 (2006) 10.1256/qj.05.153
[43]
Buehner, M. Ensemble-derived stationary and flow-dependent background-error covariances: evaluation in a quasi-operational NWP setting. Q. J. R. Meteorol. Soc. 131, 1013–1043 (2006) 10.1256/qj.04.15
[44]
Chapnik, B., Desroziers, G., Rabier, F. & Talagrand, O. Diagnosis and tuning of observational error in a quasi-operational data assimilation setting. Q. J. R. Meteorol. Soc. 132, 543–565 (2006) 10.1256/qj.04.102
[45]
Janisková, M. & Lopez, P. in Data Assimilation for Atmospheric, Oceanic and Hydrological Applications (eds Park, S. K. & Xu, L. ) 251–286 (Springer, 2013)
[46]
Trenberth, K. E. & Fasullo, J. T. Climate extremes and climate change: the Russian heat wave and other climate extremes of 2010. J. Geophys. Res. 117, D17103 (2012) 10.1029/2012jd018020
[47]
Hoskins, B. The potential for skill across the range of the seamless weather-climate prediction problem: a stimulus for our science. Q. J. R. Meteorol. Soc. 139, 573–584 (2013) 10.1002/qj.1991
[48]
Jung, T., Miller, M. J. & Palmer, T. N. Diagnosing the origin of extended-range forecast errors. Mon. Weath. Rev. 138, 2434–2446 (2010) 10.1175/2010mwr3255.1
[49]
Duc, L., Saito, K. & Seko, H. Spatial-temporal fractions verification for high-resolution ensemble forecasts. Tellus A 65, 18171 (2013) 10.3402/tellusa.v65i0.18171
[50]
Rodwell, M. J., Richardson, D. S., Hewson, T. D. & Haiden, T. A new equitable score suitable for verifying precipitation in numerical weather prediction. Q. J. R. Meteorol. Soc. 136, 1344–1363 (2010) 10.1002/qj.656

Showing 50 of 100 references

Cited By
2,048
Numerical simulations of a heavy rainfall event in the Sahelian region of Zinder in Niger

Abdoul Aziz Saidou Chaibou, Kodjo Gboneh Gratien Edoh · 2026

Atmospheric Research
Opinion: Inferring process from snapshots of cloud systems

Graham Feingold, Franziska Glassmeier · 2025

Atmospheric Chemistry and Physics
Quarterly Journal of the Royal Mete...
Transportation Research Part D: Tra...
Forecast combinations: An over 50-year review

Xiaoqian Wang, Rob J. Hyndman · 2023

International Journal of Forecastin...
Aerospace
Market expectations of a warming climate

Wolfram Schlenker, Charles A. Taylor · 2021

Journal of Financial Economics
Weather and Forecasting
Meteorological Monographs
Metrics
2,048
Citations
100
References
Details
Published
Sep 01, 2015
Vol/Issue
525(7567)
Pages
47-55
License
View
Cite This Article
Peter Bauer, Alan Thorpe, Gilbert Brunet (2015). The quiet revolution of numerical weather prediction. Nature, 525(7567), 47-55. https://doi.org/10.1038/nature14956
Related

You May Also Like

Deep learning

Yann LeCun, Yoshua Bengio · 2015

78,982 citations

Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans · 2021

42,787 citations

Helical microtubules of graphitic carbon

Sumio Iijima · 1991

38,201 citations

Collective dynamics of ‘small-world’ networks

Duncan J. Watts, Steven H. Strogatz · 1998

33,426 citations