Abstract
Numerical weather prediction has become the most important tool for weather forecasting around the world. This chapter provides an overview of the fundamental principles of numerical weather prediction, including the numerical framework of models, numerical methods, physical parameterization, and data assimilation. Historical revolution, the recent development, and future direction are introduced and discussed.
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References
A. Arakawa, C. S. Konor, Vertical differencing of the primitive equations based on the Charney–Phillips grid in hybrid σ – p vertical co-ordinates. Mon. Wea. Rev. 124, 511–528 (1996)
A. Arakawa, Adjustment mechanisms in atmospheric motions. J. Meteorol. Soc. Jpn. 75, 155–179 (1997)
A. Arakawa, The cumulus parameterization problem: past, present, and future. J. Clim. 17, 2493–2525 (2004)
P. Bauer, A. Thorpe, G. Brunet, The quiet revolution of numerical weather prediction. Nature 525, 47–55 (2015)
V. Bjerknes, Das Problem der Wettervorhersage betrachtet vomStandpunkt der Mechanik und Physik. Meteorol. Z. 21, 1–7 (1904)
F. Bouttier, F. Rabier, The operational implementation of 4D-Var. ECMWF Newsl. 78, 2–5 (1997)
G. Brunet et al., Collaboration of the weather and climate communities to advance subseasonal-to-seasonal prediction. Bull. Am. Meteorol. Soc. 91, 1397–1406 (2010)
J.G. Charney, R. Fjoertoft, J.V. Neumann, Numerical integration of the barotropic vorticity equation. Tellus 2, 237–254 (1950)
F. Chen, J. Dudhia, Coupling an advanced land-surface/hydrology model with the Penn State/NCAR MM5 modeling system. Part I: model description and implementation. Mon. Weather Rev. 129, 569–585 (2001)
P. Courtier, O. Talagrand, Variational assimilation of meteorological observations with the adjoint vorticity equations, Part II, numerical results. Quart. J. Roy. Meteor. Soc. 113, 1329–1347 (1987)
J. Derber, A variational continuous assimilation technique. Mon. Weather Rev. 117, 2437–2446 (1989)
R. Daley, Atmospheric Data Analysis (Cambridge University Press, Cambridge, 1991)
D.R. Durran, Numerical Methods for Wave Equations in Geophysical Fluid Dynamics (Springer, New York, 1999)
M.B. Ek, K.E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, J.D. Tarpley, Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res. 22, 8851 (2003)
G. Evensen, Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99, 10143–10162 (1994)
T.M. Hamill, C. Snyder, A hybrid ensemble Kalman filter-3D variational analysis scheme. Mon. Weather Rev. 128, 2905–2919 (2000)
M. Hamrud, M. Bonavita, L. Isaksen, EnKF and hybrid gain ensemble data assimilation. Part I: EnKF implementation. Mon. Weather Rev. 143, 4847–4864 (2015)
S.Y. Hong, Dudhia, Next-generation numerical weather prediction: bridging parameterization, explicit clouds, and large eddies. Bull. Am. Meteorol. Soc. 93, ES6–ES9 (2012)
R.M. Hodur, The naval research laboratory’s coupled ocean/atmosphere mesoscale prediction system (COAMPS). Mon. Weather Rev. 125, 1414–1430 (1997)
J. Holton, An introduction to dynamic meteorology. Fourth edition. (Elsevier Academic Press, 2004)
P.L. Houtekamer, F. Zhang, Review of the ensemble Kalman filter for atmospheric data assimilation. Mon. Weather Rev. 144, 4489–4452 (2016)
R.A. Houze Jr., Cloud Dynamics (Academic, London, 1993)
P.A.E.M. Janssen, The Interaction of Ocean Waves and Wind (Cambridge University Press, Cambridge, UK, 2004)
H.M. Juang, M. Kanamitsu, The NMC nested regional spectral model. Mon. Weather Rev. 122, 3–26 (1994)
E. Kalnay, Atmospheric Modeling, Data Assimilation, and Predictability (Cambridge University Press, 2003)
M.F. Khairoutdinov, D.A. Randall, C. DeMott, Simulations of the atmospheric general circulation using a cloud-resolving model as a super- parameterization of physical processes. J. Atmos. Sci. 62, 2136–2154 (2005)
D.T. Kleist, K. Ide, An OSSE-based evaluation of hybrid variational-ensemble data assimilation for the NCEP GFS. Part II: 4DEnVar and hybrid variants. Mon. Weather Rev. 143, 452–470 (2015)
S.-J. Lin, A finite-volume integration method for computing pressure gradient forces in general vertical coordinates. Q. J. R. Meteorol. Soc. 13, 1749–1762 (1997)
S.J. Lin, R.B. Rood, Multidimensional flux-form semi-Lagrangian transport scheme. Mon. Weather Rev. 124, 2046–2070 (1996)
K.-N. Liou, An Introduction to Atmospheric Radiation (Academic, London, 1980)
A.C. Lorenc, Analysis methods for numerical weather prediction. Q. J. R. Meteorol. Soc. 112, 1177–1194 (1986)
P. Lynch, The origins of computer weather prediction and climate modeling. J. Comput. Phys. 227, 3431–3444 (2008)
T. Miyoshi et al., “Big Data Assimilation” revolutionizing severe weather prediction. Bull. Am. Meteorol. Soc. 97, 1347–1354 (2016)
E. Ott et al., A local ensemble Kalman filter for atmospheric data assimilation. Tellus 56A, 415–428 (2004)
D.F. Parrish, J.C. Derber, The National Meteorological Center’s spectral statistical interpolation analysis system. Mon. Weather Rev. 120, 1747–1763 (1992)
S.G. Penny, The hybrid local ensemble transform Kalman filter. Mon. Weather Rev. 142, 2139–2149 (2014)
T.N. Palmer, P.D. Williams, Introduction: stochastic physics and climate modelling. Phil. Trans. R. Soc. A 366, 2421–2427 (2008)
R.A. Pielke Sr., Mesoscale meteorological modelling. Second edition (Academic Press, 2002)
L.F. Richardson, Weather Prediction by Numerical Process (Cambridge University Press, Cambridge, UK, 1922)
A.J. Robert, A semi-Lagrangian and semi-implicit numerical integration scheme for the primitive meteorological equations. J. Meteorol. Soc. Jpn. 60, 319–324 (1982)
A.J. Simmons, A. Hollingsworth, Some aspects of the improvement in skill of numerical weather prediction. Q. J. R. Meteorol. Soc. 128, 647–677 (2002)
W.C. Skamarock, J.B. Klemp, J. Dudhia, D.O. Gill, M. Barker, K.G. Duda, X.Y. Huang, W. Wang, J.G. Powers, A description of the advanced research WRF version 3. NCAR Tech. Note, NCAR/TN-475+STR, 113 pp. (2008)
D.J. Stensrud, Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models (Cambridge University Press, Cambridge, UK, 2007)
G.L. Stephens, The parameterization of radiation for numerical weather prediction and climate models. Mon. Weather Rev. 112, 826–867 (1984)
J.M. Straka, Cloud and Precipitation Microphysics: Principles and Parameterization (Cambridge University Press, Cambridge, UK, 2009)
R.B. Stull, An Introduction to Boundary Layer Meteorology (Kluwer Academic Publishers, Dordrecht, 1988)
O. Talagrand, Assimilation of observations, an introduction. J. Met. Soc. Jpn. Spec. Issue 75(1B), 191–209 (1997)
M. Teixeira, The physics of orographic gravity wave drag. Front. Phys. 2, 43 (2014)
M. Tiedtke, The general problem of parameterization. ECMWF Lecture Note (1984), http://www.ecmwf.int/en/learning/education-material/introductory-lectures-nwp
M.K. Tippett, J.L. Anderson, C.H. Bishop, T.M. Hamill, J.S. Whitaker, Ensemble square-root filters. Mon. Weather Rev. 131, 1485–1490 (2003)
H.L. Tolman, User manual and system documentation of WAVEWATCH III version 4.18. NOAA/NWS/NCEP/MMAB Technical Note 316, 194 pp. (2014)
X. Wang, D. Parrish, D. Kleist, J. Whitaker, GSI 3DVarbased ensemble-variational hybrid data assimilation for NCEP global forecast system: single-resolution experiments. Mon. Weather Rev. 141, 4098–4117 (2013)
T. Warner, Numerical Weather and Climate Prediction (Cambridge Press, Cambridge, UK, 2011)
D.L. Williamson, The evolution of dynamical cores for global atmospheric models. J. Meteorol. Soc. Jpn. B 85, 241–269 (2007)
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Pu, Z., Kalnay, E. (2019). Numerical Weather Prediction Basics: Models, Numerical Methods, and Data Assimilation. In: Duan, Q., Pappenberger, F., Wood, A., Cloke, H., Schaake, J. (eds) Handbook of Hydrometeorological Ensemble Forecasting. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39925-1_11
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