• Vladimir M. Krasnopolsky
Part of the Atmospheric and Oceanographic Sciences Library book series (ATSL, volume 46)


In this chapter, a notion of Earth System (ES) as a complex dynamical system of interacting components (subsystems) is presented and discussed. Weather and climate systems are introduced as subsystems of the ES. It is shown that any subsystem of ES can be considered as a multidimensional relationship or mapping, which is usually complex and nonlinear. Evolution of approaches to ES and its subsystems is discussed, and the neural network (NN) technique as a powerful nonlinear tool for emulating subsystems of ES is introduced. Multiple NN applications, which have been developed in ES sciences, are categorized and briefly reviewed. The chapter contains an extensive list of references giving extended background and further detail to the interested reader on each examined topic.


Earth System Neural Network Approach Partial Differential Equation Numerical Weather Prediction Model Neural Network Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Ahl V, Allen TFH (1996) Hierarchy theory: a vision, vocabulary, and epistemology. Columbia University Press, New YorkGoogle Scholar
  2. Aires F, Rossow WB, Scott NA, Chedin A (2002) Remote sensing from the infrared atmospheric sounding interferometer instrument: 2 Simultaneous retrieval of temperature, water vapor, and ozone atmospheric profiles. J Geophys Res. doi: 10.1029/2001JD001591 Google Scholar
  3. Atkinson PM, Tatnall ARL (1997) Neural networks in remote sensing – introduction. Int J Remote Sens 18(699):709Google Scholar
  4. Badran F, Mejia C, Thiria S, Crépon M (1995) Remote sensing operations. Int J Neural Syst 6:447–453CrossRefGoogle Scholar
  5. Bankert RL (1994) Cloud pattern identification as part of an automated image analysis. In: Proceedings of the seventh conference on satellite meteorology and oceanography, Monterey, CA, 6–10 June, pp 441–443Google Scholar
  6. Beale R, Jackson T (1990) Neural computing: an introduction. Adam Hilger, Bristol/Philadelphia/New YorkCrossRefGoogle Scholar
  7. Belochitski AP, Binev P, DeVore R, Fox-Rabinovitz M, Krasnopolsky V, Lamby P (2011) Tree approximation of the long wave radiation parameterization in the NCAR CAM global climate model. J Comput Appl Math 236:447–460CrossRefGoogle Scholar
  8. Benestad RE, Hanssen-Bauer I, Chen D (2008) Empirical-statistical downscaling. World Scientific Publishing Company, SingaporeCrossRefGoogle Scholar
  9. Bhattacharya B, Solomatine DP (2006) Machine learning in soil classification. Neural Netw 19:186–195CrossRefGoogle Scholar
  10. Bhattacharya B, Price RK, Solomatine DP (2005) Data-driven modelling in the context of sediment transport. Phys Chem Earth 30:297–302CrossRefGoogle Scholar
  11. Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, OxfordGoogle Scholar
  12. Bollivier M, Eifler W, Thiria S (2000) Sea surface temperature forecasts using on-line local learning algorithm in upwelling regions. Neurocomputing 30:59–63CrossRefGoogle Scholar
  13. Brown TJ, Mielke PW (2000) Statistical mining and data visualization in atmospheric sciences. Kluwer Academic Publishers, BostonCrossRefGoogle Scholar
  14. Cherkassky V, Mulier F (1998) Learning from data. Wiley, HobokenGoogle Scholar
  15. Chevallier F, Chéruy F, Scott NA, Chedin A (1998) A neural network approach for a fast and accurate computation of longwave radiative budget. J Appl Meteorol 37:1385–1397CrossRefGoogle Scholar
  16. DeVore RA (1998) Nonlinear approximation. Acta Numerica 8:51–150CrossRefGoogle Scholar
  17. Dibike YB, Coulibaly P (2006) Temporal neural networks for downscaling climate variability and extremes. Neural Netw 19:135–144CrossRefGoogle Scholar
  18. Elsner JB, Tsonis AA (1992) Nonlinear prediction, chaos, and noise. Bull Am Meteorol Soc 73:49–60CrossRefGoogle Scholar
  19. Fisher RA (1922) On the mathematical foundations of theoretical statistics. Philos Trans R Soc A222:309–368Google Scholar
  20. Gallinari P, Thiria S, Badran F, Fogelman-Soulie F (1991) On the relations between discriminant analysis and multilayer perceptrons. Neural Netw 4:349–360CrossRefGoogle Scholar
  21. Gardner MW, Dorling SR (1998) Artificial neural networks (the multilayer perceptron) – a review of applications in the atmospheric sciences. Atmos Environ 32:2627–2636CrossRefGoogle Scholar
  22. Haupt SE, Pasini A, Marzban C (eds) (2009) Artificial intelligence methods in environmental sciences. Springer, New YorkGoogle Scholar
  23. Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan College Publishing Company, New YorkGoogle Scholar
  24. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational ability. Proc Natl Acad Sci USA 79:2554–2558CrossRefGoogle Scholar
  25. Hsieh WW (2004) Nonlinear multivariate and time series analysis by neural network methods. Rev Geophys. doi: 10.1029/2002RG000112 Google Scholar
  26. Hsieh WW (2009) Machine learning methods in the environmental sciences. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  27. Hsieh WW, Tang B (1998) Applying neural network models to prediction and data analysis in meteorology and oceanography. Bull Am Meteorol Soc 79:1855–1870CrossRefGoogle Scholar
  28. Kohonen T (1982) Self-organizing formation of topologically correct feature maps. Biol Cybern 43:59–69CrossRefGoogle Scholar
  29. Krasnopolsky V (1996) A neural network forward model for direct assimilation of SSM/I brightness temperatures into atmospheric models. Working group on numerical experimentation blue book. 1.29–1.30.
  30. Krasnopolsky V (1997) A neural network based forward model for direct assimilation of SSM/I brightness temperatures. Tech note, OMB contribution No 140, NCEP/NOAA.
  31. Krasnopolsky VM (2007) Neural network emulations for complex multidimensional geophysical mappings: applications of neural network techniques to atmospheric and oceanic satellite retrievals and numerical modeling. Rev Geophys 45(3):RG3009. doi: 10.1029/2006RG000200 CrossRefGoogle Scholar
  32. Krasnopolsky VM, Chevallier F (2003) Some neural network applications in environmental sciences Part II: Advancing computational efficiency of environmental numerical models. Neural Netw 16:335–348CrossRefGoogle Scholar
  33. Krasnopolsky VM, Fox-Rabinovitz MS (2006a) Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction. Neural Netw 19:122–134CrossRefGoogle Scholar
  34. Krasnopolsky VM, Fox-Rabinovitz MS (2006b) A new synergetic paradigm in environmental numerical modeling: hybrid models combining deterministic and machine learning components. Ecol Model 191:5–18CrossRefGoogle Scholar
  35. Krasnopolsky VM, Lin Y (2012) A neural network nonlinear multimodel ensemble to improve precipitation forecasts over continental US. Adv Meteorol 2012:11 pp. Article ID 649450, doi: 10.1155/2012/649450.
  36. Krasnopolsky VM, Schiller H (2003) Some neural network applications in environmental sciences Part I: Forward and inverse problems in satellite remote sensing. Neural Netw 16:321–334CrossRefGoogle Scholar
  37. Krasnopolsky VM, Chalikov DV, Tolman HL (2002) A neural network technique to improve computational efficiency of numerical oceanic models. Ocean Model 4:363–383CrossRefGoogle Scholar
  38. Krasnopolsky VM, Lozano CJ, Spindler D, Rivin I, Rao DB (2006) A new NN approach to extract explicitly functional dependencies and mappings from numerical outputs of numerical environmental models. In: Proceedings of the IJCNN2006, Vancouver, BC, Canada, 16–21 July, pp 8732–8734Google Scholar
  39. Krasnopolsky VM, Fox-Rabinovitz MS, Hou YT, Lord SJ, Belochitski A (2010) Accurate and fast neural network emulations of model radiation for the NCEP coupled climate forecast system: climate simulations and seasonal predictions. Mon Weather Rev 138:1822–1842. doi: 10.1175/2009MWR3149.1 CrossRefGoogle Scholar
  40. Krasnopolsky V, Fox-Rabinovitz M, Belochitski A, Rasch P, Blossey P, Kogan Y (2011) Development of neural network convection parameterizations for climate and NWP models using cloud resolving model simulations. NCEP office note 469.
  41. Lawton J (2001) Earth system science. Science. doi: 10.1126/science.292.5524.1965 Google Scholar
  42. Lippmann RP (1989) Pattern classification using neural networks. IEEE Commun Mag 27:47–64CrossRefGoogle Scholar
  43. Loyola D, Ruppert T (1998) A new PMD cloud-recognition algorithm for GOME. ESA Earth Obs Q 58:45–47Google Scholar
  44. Marzban C (2003) Neural networks for postprocessing model output: ARPS. Mon Weather Rev 131:1103–1111CrossRefGoogle Scholar
  45. Meadows DH (2008) Thinking in systems: a primer. Chelsea Green Publishing Company, VermontGoogle Scholar
  46. Mueller MD et al (2003) Ozone profile retrieval from global ozone monitoring experiment data using a neural network approach (Neural Network Ozone Retrieval System (NNORSY)). J Geophys Res 108:4497. doi: 10.1029/2002JD002784 CrossRefGoogle Scholar
  47. Nabney IT (2002) Netlab: algorithms for pattern recognition. Springer, New YorkGoogle Scholar
  48. Pasini A, Lorè M, Ameli F (2006) Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system. Ecol Model 191:58–67CrossRefGoogle Scholar
  49. Richardson LF (1922) Weather prediction by numerical process. Cambridge University Press, CambridgeGoogle Scholar
  50. Ripley BD (1996) Pattern recognition and neural networks. Cambridge University Press, CambridgeGoogle Scholar
  51. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL, Group PR (eds) Parallel distributed processing, vol 1. MIT Press, Cambridge, MAGoogle Scholar
  52. Salthe SN (1985) Evolving hierarchical systems their structure and representation. Columbia University Press, New YorkGoogle Scholar
  53. Solomatine D, Ostfeld A (2008) Data-driven modeling: some past experiences and new approaches. J Hydroinform 10(1):3–22CrossRefGoogle Scholar
  54. Stogryn AP, Butler CT, Bartolac TJ (1994) Ocean surface wind retrievals from special sensor microwave imager data with neural networks. J Geophys Res 90:981–984CrossRefGoogle Scholar
  55. Tang Y, Hsieh WW (2003) ENSO simulation and prediction in a hybrid coupled model with data assimilation. J Meteorol Soc Jpn 81:1–19CrossRefGoogle Scholar
  56. Tulunay Y, Tulunay E, Senalp ET (2004) The neural network technique – 2: an ionospheric example illustrating its application. Adv Space Res 33:988–992CrossRefGoogle Scholar
  57. Valdés JJ, Bonham-Carter G (2006) Time dependent neural network models for detecting changes of state in complex processes: applications in earth sciences and astronomy. Neural Netw 19:196–207CrossRefGoogle Scholar
  58. Vapnik VN (1995) The nature of statistical learning theory. Springer, New YorkCrossRefGoogle Scholar
  59. von Bertalanffy L (1950) An outline of general system theory. Br J Philos Sci 1:139–164Google Scholar
  60. Vörös Z, Jankovičová D (2002) Neural network prediction of geomagnetic activity: a method using Hölder exponents. Nonlinear Processes Geophys 9:425–433CrossRefGoogle Scholar
  61. Wilber K (1995) Sex, ecology, spirituality: the spirit of evolution. Shambhala Publication, BostonGoogle Scholar
  62. Wu A, Hsieh WW, Tang B (2006) Neural network forecasts of the tropical Pacific sea surface temperatures. Neural Netw 19:145–154CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht(outside the USA.) 2013

Authors and Affiliations

  • Vladimir M. Krasnopolsky
    • 1
  1. 1.NOAA Center for Weather and Climate PredictionCamp SpringUSA

Personalised recommendations