Skip to main content
SpringerLink
Log in
Book cover

Climate Change Modeling Methodology pp 89–114Cite as

Weather Prediction Models

  • Chapter
  • First Online:

  • 1572 Accesses

Abstract

Awareness of weather and concern about weather in the proximate future certainly must have accompanied the emergence of human self-consciousness. Although weather is a basic idea in human existence, it is difficult to define precisely.

This chapter was originally published as part of the Encyclopedia of Sustainability Science and Technology edited by Robert A. Meyers. DOI:10.1007/978-1-4419-0851-3

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Abbreviations

Assimilation:

The process of combining observations of the atmosphere with a “first guess” (usually a model forecast) to define the atmospheric state on a forecast model grid.

Geostrophic balance:

A possible state of rotating fluids in which flow is directed along pressure gradients rather than across them.

Gravity waves:

Rapidly moving atmospheric disturbances driven by gravity acting on vertical density gradients. Often arise as a consequence of spurious geostrophic imbalance in initial conditions.

Hydrostatic balance:

State in which the vertical pressure gradient force cancels the downward accleration of gravity. Approximately obeyed in atmospheric flows with horizontal scales larger than several km.

Instabilities (or unstable modes):

Spatial patterns in a flow that are able to extract energy from the background flow and grow in amplitude.

Primitive equations:

Complete set of equations describing flow of a thin envelope of fluid or gas surrounding a sphere.

Resolution:

Separation in space of notional points at which quantities are defined in a numerical model.

Bibliography

  1. Tribbia J, Anthes R (1987) Scientific basis of modern weather prediction. Science 237:493–499

    Article  ADS  Google Scholar 

  2. Lynch P (2006) The emergence of numerical weather prediction: Richardson’s dream. Cambridge University Press, Cambridge, MA

    Google Scholar 

  3. Richardson L (1922) Weather prediction by numerical process. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  4. Gill A (1982) Atmosphere–ocean dynamics. Academic, London

    Google Scholar 

  5. Simpson J (1994) Sea breeze and local winds. Cambridge University Press, New York

    Google Scholar 

  6. Lau K, Li M (1984) The monsoon of East Asia and its global associations—survey. Bull Amer Meteor Soc 65:114–125

    Article  Google Scholar 

  7. Webster P, Tomas R (1998) Monsoons- processes, predictability, and the prospects for prediction. J Geophys Res 103(C7):14451–14510

    Article  ADS  Google Scholar 

  8. Douglas M, Maddox R, Howard K, Reyes S (1993) The Mexican monsoon. J Clim 6(8):1665–1677

    Article  ADS  Google Scholar 

  9. Koch S, Handley C (1997) Operational forecasting and detection of mesoscale gravity waves. Weather Forecast 12(2)

    Google Scholar 

  10. Moncrieff M, Liu C (1999) Convection initiation by density currents: role of convergence, shear, and dynamical organization. Mon Weather Rev 127(10):2455–2464

    Article  ADS  Google Scholar 

  11. Lac C, Lafore J, Redelsperger J (2002) Role of gravity waves in triggering deep convection during TOGA COARE. J Atmos Sci 59(8)

    Google Scholar 

  12. Houze R Jr (2004) Mesoscale convective systems. Rev Geophys 42. doi:10.1029/2004RG000150

  13. Grabowski W, Moncrieff M (2006) Large-scale organization of tropical convection in two-dimensional explicit numerical simulations. Q J Roy Meteor Soc 127(572):445–468

    Article  ADS  Google Scholar 

  14. Smith R (1979) The influence of mountains on the atmosphere. Adv Geophys 21:87–230

    Article  ADS  Google Scholar 

  15. Longuet-Higgins M (1964) Planetary waves on a rotating sphere. Proc R Soc Lond A Math Phys Sci 279(1379):446–473

    Article  ADS  Google Scholar 

  16. Andrews D, McIntyre M (1976) Planetary waves in horizontal and vertical shear: the generalized Eliassen-palm relation and the mean zonal acceleration. J Atmos Sci 33:2031–2048

    Article  MathSciNet  ADS  Google Scholar 

  17. Hoskins B, Ambrizzi T (1993) Rossby wave propagation on a realistic longitudinally varying flow. J Atmos Sci 50:1661–1671

    Article  ADS  Google Scholar 

  18. Charney J (1947) The dynamics of long waves in a baroclinic westerly current. J Meteor 4(5):135–162

    Article  MathSciNet  Google Scholar 

  19. Eady E (1949) Long waves and cyclone waves. Tellus 1(3):33–52

    Article  MathSciNet  Google Scholar 

  20. Kiladis G, Wheeler M, Haertel P, Straub K, Roundy P (2009) Convectively coupled equatorial waves. Rev Geophys 47. doi:10.1029/2008RG000266

  21. Burpee R (1972) The origin and structure of easterly waves in the lower troposphere of North Africa. J Atmos Sci 29(1):77–90

    Article  ADS  Google Scholar 

  22. Landsea C et al (1993) A climatology of intense (or major) Atlantic hurricanes. Mon Weather Rev 121(6):1703–1713

    Article  ADS  Google Scholar 

  23. Pytharoulis I, Thorncroft C (1999) The low-level structure of African easterly waves in 1995. Mon Weather Rev 127(10)

    Google Scholar 

  24. Thorncroft C, Hodges K (2001) African easterly wave variability and its relationship to Atlantic tropical cyclone activity. J Clim 14(6):1166–1179

    Article  ADS  Google Scholar 

  25. Madden R, Julian P (1994) Observations of the 40–50-day tropical oscillation: a review. Mon Weather Rev 122(5):814–837

    Article  ADS  Google Scholar 

  26. Zhang C (2005) Madden-Julian oscillation. Rev Geophys 43(2). doi:10.1029/2004RG000158

  27. Liebmann B, Hendon H, Glick J (1994) The relationship between tropical cyclones of the western Pacific and Indian Oceans and the Madden–Julian oscillation. J Meteor Soc Japan 72(41):1–412

    Google Scholar 

  28. Maloney E, Hartmann D (2000) Modulation of hurricane activity in the Gulf of Mexico by the Madden-Julian oscillation. Science 287(5460):2002

    Article  ADS  Google Scholar 

  29. Maloney E, Hartmann D (2000) Modulation of eastern North Pacific hurricanes by the Madden-Julian oscillation. J Clim 13(9)

    Google Scholar 

  30. Elsner J, Jagger T, Niu X et al (2000) Changes in the rates of North Atlantic major hurricane activity during the 20th century. Geophys Res Lett 27(12):1743–1746

    Article  ADS  Google Scholar 

  31. Hall J, Matthews A, Karoly D (2001) The modulation of tropical cyclone activity in the Australian region by the Madden–Julian oscillation. Mon Weather Rev 129:12

    Article  Google Scholar 

  32. Bond N, Vecchi G (2003) The influence of the Madden–Julian oscillation on precipitation in Oregon and Washington. Weather Forecast 18(4):600–613

    Article  ADS  Google Scholar 

  33. Jones C, Waliser D, Lau K, Stern W (2004) The Madden–Julian oscillation and its impact on northern hemisphere weather predictability. Mon Weather Rev 132(6)

    Google Scholar 

  34. Waliser DE, Lau KM, Stern W, Jones C (2003) Potential predictability of the Madden–Julian oscillation. Bull Amer Meteor Soc 84:33–50

    Article  Google Scholar 

  35. Manabe S, Smagorinsky J, Strickler R (1965) Simulated climatology of a general circulation model with a hydrologic cycle. Mon Weather Rev 93(12):769–798

    Article  ADS  Google Scholar 

  36. Namias J (1980) The early influence of the Bergen School on synoptic meteorology in the United States. Pure App Geophys 119(3):491–500

    MathSciNet  ADS  Google Scholar 

  37. Grenas S (2005) Vilhelm Bjerknes’ vision for scientific weather prediction. In: The Nordic seas: an integrated perspective: oceanography, climatology, biogeochemistry, and modeling. American Geophysical Union, Washington, DC, p 357

    Chapter  Google Scholar 

  38. Hinkelmann K (1951) Der Mechanismus des meteorologischen Lärmes. Tellus 3:285–296

    Article  ADS  Google Scholar 

  39. Laprise R (1992) The Euler equations of motion with hydrostatic pressure as an independent variable. Mon Weather Rev 120(1):197–207

    Article  ADS  Google Scholar 

  40. Shuman F, Hovermale J (1968) An operational six-layer primitive equation model. J Appl Meteor 7(4):525–547

    Article  Google Scholar 

  41. Bengtsson L (2001) The development of medium range forecasts. In: 50th anniversary of numerical weather prediction, commemorative symposium, Potsdam

    Google Scholar 

  42. Kasahara A (2000) On the origin of cumulus parameterization for numerical prediction models. Gen Circ Model Dev 70:199–224

    Article  Google Scholar 

  43. Tiedtke M (1993) Representation of clouds in large-scale models. Mon Weather Rev 121:3040–3061

    Article  ADS  Google Scholar 

  44. Del Genio A, Yao M, Kovari W, Lo K (1996) A prognostic cloud water parameterization for global climate models. J Clim 9:270–304

    Article  ADS  Google Scholar 

  45. Haltiner G, Williams R (1983) Numerical prediction and dynamic meteorology, 2nd edn. Wiley, New York

    Google Scholar 

  46. Leith C (1965) Numerical simulation of the earth’s atmosphere. In: Methods in computational physics, vol 4. Academic, New York

    Google Scholar 

  47. Webster S, Brown A, Cameron D, Jones C (2006) Improvements to the representation of orography in the Met Office Unified Model. Q J Roy Meteor Soc 129(591):1989–2010

    Article  ADS  Google Scholar 

  48. Clark A, Gallus W Jr, Xue M, Kong F (2009) A comparison of precipitation forecast skill between small convection-allowing and large convection-parameterizing ensembles. Weather Forecast 24(4):1121–1140

    Article  ADS  Google Scholar 

  49. Bhanu Kumar O, Ramalingeswara Rao S, Muni Krishna K (2009) Role of cumulus parameterization schemes in simulating heavy rainfall episodes off the coast of Maharashtra state during 28 June–4 July 2007. Meteorol Atmos Phys 105(3):167–179

    Article  Google Scholar 

  50. Gleckler P, Taylor K, Doutriaux C (2008) Performance metrics for climate models. J Geophys Res 113:D06104

    Article  Google Scholar 

  51. Teweles S, Wobus H (1954) Verification of prognostic charts. Bull Amer Meteor Soc 35:455–463

    Google Scholar 

  52. Shuman F (1989) History of numerical weather prediction at the National Meteorological Center. Weather Forecast 4:286–296

    Article  ADS  Google Scholar 

  53. Gandin L, Murphy A (1992) Equitable skill scores for categorical forecasts. Mon Weather Rev 120:361–370

    Article  ADS  Google Scholar 

  54. Kalnay E (2003) Atmospheric modeling, data assimilation, and predictability. Cambridge University Press, Cambridge, MA

    Google Scholar 

  55. Machenhauer B (1977) On the dynamics of gravity oscillations in a shallow water model, with applications to normal mode initialization. Beitr Phys Atmos 50:253–271

    MATH  Google Scholar 

  56. Baer F, Tribbia J (1977) On complete filtering of gravity modes through nonlinear initialization. Mon Weather Rev 105(12):1536–1539

    Article  ADS  Google Scholar 

  57. Tribbia J (1984) A simple scheme for high-order nonlinear normal mode initialization. Mon Weather Rev 112:278–284

    Article  ADS  Google Scholar 

  58. Daley R (1993) Atmospheric data analysis. Cambridge University Press, New York

    Google Scholar 

  59. Thiebaux HJ, Pedder MA (1987) Spatial objective analysis with applications in atmospheric science. Academic, London/Orlando, p 308

    Google Scholar 

  60. Parrish D, Derber J (1992) The National Meteorological Center’s spectral statistical-interpolation analysis system. Mon Weather Rev 120(8):1747–1763

    Article  ADS  Google Scholar 

  61. Buehner M, Gauthier P, Liu Z (2005) Evaluation of new estimates of background-and observation-error covariances for variational assimilation. Q J Roy Meteor Soc 131(613):3373–3384

    Article  Google Scholar 

  62. Derber J, Wu W (1998) The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon Weather Rev 126(8):2287–2299

    Article  ADS  Google Scholar 

  63. Rienecker M, Suarez M, Todling R, Bacmeister J, Takacs L, Liu H, Gu W, Sienkiewicz M, Koster R, Gelaro R et al (2008) The GEOS-5 data assimilation system-documentation of versions 5.0.1, 5.1.0. NASA Technical Memorandum, NASA/TM-2007, 104606

    Google Scholar 

  64. Lorenz E (1965) A study of the predictability of a 28-variable atmospheric model. Tellus 17(3):321–333

    Article  ADS  Google Scholar 

  65. Lorenz E (1969) The predictability of a flow which possesses many scales of motion. Tellus 21(3):289–307

    Article  ADS  Google Scholar 

  66. Lorenz E (1969) Atmospheric predictability as revealed by naturally occurring analogues. J Atmos Sci 26(4):636–646

    Article  ADS  Google Scholar 

  67. Lewis J (2005) Roots of ensemble forecasting. Mon Weather Rev 133(7):1865–1885

    Article  ADS  Google Scholar 

  68. Toth Z, Kalnay E (1993) Ensemble forecasting at NMC: the generation of perturbations. Bull Amer Meteor Soc 74:2317–2330

    Article  Google Scholar 

  69. Simmons A, Hollingsworth A (2002) Some aspects of the improvement in skill of numerical weather prediction. Q J Roy Meteor Soc 128(580):647–678

    Article  ADS  Google Scholar 

  70. Ringler T, Heikes R, Randall D (2000) Modeling the atmospheric general circulation using a spherical geodesic grid: a new class of dynamical cores. Mon Weather Rev 128(7):2471–2490

    Article  ADS  Google Scholar 

  71. Skamarock W, Klemp J, Ringler T, Thuburn J (2008) A hexagonal C-grid atmospheric core formulation for multiscale simulation on the sphere. AGU fall meeting abstracts, San Francisco, CA, p 01

    Google Scholar 

  72. Putman W, Lin S (2009) A finite-volume dynamical core on the cubed-sphere grid. Astron Soc Pac Conf Ser 406:268

    ADS  Google Scholar 

  73. Taylor M, Fournier A (2010) A compatible and conservative spectral element method on unstructured grids. J Comput Phys 229:5879–5895

    Article  MathSciNet  ADS  MATH  Google Scholar 

  74. Klemp J, Skamarock W, Dudhia J (2007) Conservative split-explicit time integration methods for the compressible nonhydrostatic equations. Mon Weather Rev 135(8):2897–2913

    Article  ADS  Google Scholar 

  75. Grabowski W, Smolarkiewicz P (2002) A multiscale anelastic model for meteorological research. Mon Weather Rev 130(4)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. NCAR Earth System Laboratory, National Center for Atmospheric Research, 1850 Table Mesa Drive, 80305, Boulder, CO, USA

    Dr. Julio T. Bacmeister

Authors
  1. Dr. Julio T. Bacmeister
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Julio T. Bacmeister .

Editor information

Editors and Affiliations

  1. Pacific Northwest National Laboratory, Richland, 99352, Washington, USA

    Philip J. Rasch

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media New York

About this chapter

Cite this chapter

Bacmeister, J.T. (2012). Weather Prediction Models. In: Rasch, P. (eds) Climate Change Modeling Methodology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5767-1_5

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-5767-1_5

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-5766-4

  • Online ISBN: 978-1-4614-5767-1

  • eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)

Publish with us

Policies and ethics

Search

Navigation