Skip to main content

Environmental science is one of the oldest scientific endeavors. Since the dawn of humanity, along with the ability to reason came the ability to observe and interpret the physical world. It is only natural that people would observe patterns then build basic mental models to predict a future state. For instance, records indicate that there has long been a version of the adage “Red at night, sailor's delight. Red in the morning, sailors take warning.”1 This saying is a simple predictive model based on generations of experience, and it often works. Over time people noted relationships between observations of the sky and subsequent conditions, formed this mental model, and used it to predict future behavior of the weather (Fig. 1.1).

The age of enlightenment following the Renaissance brought a more modern approach to science. Careful experimentation and observation led to uncovering the physics underlying natural phenomena. For instance, a modern understanding of “Red at night, sailor's delight” is based on the theory of Mie scattering. Light rays are scattered by large dry dust particles to the west in the setting sun. According to this theory, large particles tend to scatter the longer wavelength red light forward more than they do the other frequencies of visible light. The long trajectory of the solar beams through the atmosphere when the sun is at a very low zenith angle (such as at sunset or sunrise) compounds this effect. Thus, when light rays from the setting sun are scattered by large dry dust particles associated with a high pressure system to the west, more red light reaches the observer, and the sky appears red. Since prevailing winds in the mid latitudes (where this adage is common) blow from west to east, more Mie scattering at dusk implies that a dry weather pattern is approaching. “Red in the morning, sailors take warning,” refers to a similar process at dawn when the low zenith angle in the east would produce more scattering associated with a high pressure system that has already passed, thus suggesting the possibility that a low pressure system is now approaching and wet weather may follow. This example exemplifies the types of physical explanations of observed phenomena that developed in the environmental sciences.

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

Access this chapter

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 PDF
  • Read on any device
  • Instant download
  • Own it forever
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Baer, F., & Tribbia, J. J. (1977). On complete filtering of gravity modes through nonlinear initialization.Monthly Weather Review 1051536–1539

    Article  Google Scholar 

  • Branstator, G., & Haupt, S. E. (1998). An empirical model of barotropic atmospheric dynamics and its response to tropical forcing.Journal of Climate 112645–2667

    Article  Google Scholar 

  • Charney, J. G., Fjortoft, R., & von Neumann, J. (1950). Numerical integration of the barotropic vorticity equation.Tellus 2237–254

    Article  Google Scholar 

  • Cybenko, G. V. (1989). Approximation by superpositions of a sigmoidal function.Mathematics of Control, Signals and Systems 2303–314

    Article  Google Scholar 

  • Daley, R. (1991).Atmospheric data analysis(457 pp.). Cambridge: Cambridge University Press

    Google Scholar 

  • Einstein, A. (1922).Sidelights on relativity(56 pp.). London: Methuen & Co

    Google Scholar 

  • Glahn, H. R., & Lowry, D. A. (1972). The use of model output statistics (MOS) in objective weather forecasting.Journal of Applied Meteorology 111203–1211

    Article  Google Scholar 

  • Gleick, J. (1987).Chaos: Making a new science(352 pp.). New York: Viking

    Google Scholar 

  • Gneiting, T., & Raftery, A. E. (2005). Weather forecasting with ensemble methods.Science 310(5746), 248–249

    Article  Google Scholar 

  • Kalnay, E. (2005).Atmospheric modeling, data assimilation, and predictability(341 pp.). Cambridge, UK: Cambridge University Press

    Google Scholar 

  • Lorenz, E. N. (1963). Deterministic nonperiodic flow.Journal of the Atmospheric Sciences 20130–141

    Article  Google Scholar 

  • Lorenz, E. N. (1986). On the existence of a slow manifold.Journal of the Atmospheric Sciences 431547–1557

    Article  Google Scholar 

  • Lorenz, E. N. (1992). The slow manifold. What is it?.Journal of the Atmospheric Sciences 492449–2451

    Article  Google Scholar 

  • Lorenz, E. N. (2006). Reflections on the conception, birth, and childhood of numerical weather prediction.Annual Review of Earth and Planetery Sciences 3437–45

    Article  CAS  Google Scholar 

  • Lorenz, E. N., & Krishnamurthy, V. (1987). On the nonexistence of a slow manifold.Journal of the Atmospheric Sciences 442940–2950

    Article  Google Scholar 

  • Machenhauer, B. (1977). On the dynamics of gravity wave oscillations in a shallow water model with application to normal mode initialization.Beitr. Phys. Atmos. 50253– 271

    Google Scholar 

  • Pasini, A. (2005).From observations to simulations. A conceptual introduction to weather and climate modeling(201 pp.). Singapore: World Scientific Publishers

    Google Scholar 

  • Penland, C., & Matrosova, L. (1998). Prediction of tropical Atlantic sea surface temperatures using linear inverse modeling.Journal of Climate 11483–496

    Article  Google Scholar 

  • Reed, R. J. (1977). Bjerknes memorial lecture: The development and status of modern weather prediction.Bulletin American Meteorological Society 8390–399

    Google Scholar 

  • Richardson, L. F. (1922).Weather prediction by numerical process. Cambridge: Cambridge University Press

    Google Scholar 

  • Setiono, R., Leow, W. K., & Zurada, J. M. (2002). Extraction of rules from artificial neural networks for nonlinear regression.IEEE Transactions on Neural Networks 13564– 577

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sue Ellen Haupt .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media B.V

About this chapter

Cite this chapter

Haupt, S.E., Lakshmanan, V., Marzban, C., Pasini, A., Williams, J.K. (2009). Environmental Science Models and Artificial Intelligence. In: Haupt, S.E., Pasini, A., Marzban, C. (eds) Artificial Intelligence Methods in the Environmental Sciences. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-9119-3_1

Download citation

Publish with us

Policies and ethics