References

Abdul-Wahab, S. A. and Al-Alawi, S. M. (2002). Assessment and prediction of
tropospheric ozone concentration levels using artificial neural networks.
Environmental Modelling & Software, 17(3):219–28.
Agirre-Basurko, E., Ibarra-Berastegi, G. and Madariaga, I. (2006). Regression and
multilayer perceptron-based models to forecast hourly O3 and NO2 levels in the
Bilbao area. Environmental Modelling & Software, 21(4):430–46.
Aguilar-Martinez, S. (2008). Forecasts of Tropical Pacific Sea Surface Temperatures by
Neural Networks and Support Vector Regression. M.Sc. thesis, University of British
Columbia.
Aires, F. (2004). Neural network uncertainty assessment using Bayesian statistics with
application to remote sensing: 1. Network weights. Journal of Geophysical
Research, 109. D10303, doi:10.1029/2003JD004173.
Aires, F., Chedin, A. and Nadal, J. P. (2000). Independent component analysis of
multivariate time series: Application to the tropical SST variability. Journal of
Geophysical Research, 105(D13):17437–55.
Aires, F., Prigent, C. and Rossow, W. B. (2004a). Neural network uncertainty assessment
using Bayesian statistics with application to remote sensing: 2. Output errors.
Journal of Geophysical Research, 109. D10304, doi:10.1029/2003JD004174.
Aires, F., Prigent, C. and Rossow, W. B. (2004b). Neural network uncertainty assessment
using Bayesian statistics with application to remote sensing: 3. Network Jacobians.
Journal of Geophysical Research, 109. D10305, doi:10.1029/2003JD004175.
Amari, S., Murata, N., Müller, K.-R., Finke, M. and Yang, H. (1996). Statistical theory of
overtraining – is cross validation asymptotically effective? Advances in Neural
Information Processing Systems, 8:176–182.
An, S. I., Hsieh, W. W. and Jin, F. F. (2005). A nonlinear analysis of the ENSO cycle and
its interdecadal changes. Journal of Climate, 18(16):3229–39.
An, S. I., Ye, Z. Q. and Hsieh, W. W. (2006). Changes in the leading ENSO modes
associated with the late 1970s climate shift: Role of surface zonal curren; t.
Geophysical Research Letters, 33(14). L14609, doi:10.1029/2006GL026604.
Bakir, G. H., Weston, J. and Schölkopf, B. (2004). Learning to find pre-images. Advances
in Neural Information Processing Systems, 16:449–56.
Balakrishnan, P. V., Cooper, M. C., Jacob, V. S. and Lewis, P. A. (1994). A study of the
classification capabilities of neural networks using unsupervised learning – a
comparison with k-means clustering. Psychometrika, 59(4):509–25.

322
Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

References

323

Baldwin, M., Gray, L., Dunkerton, T. et al. (2001). The Quasi-Biennial Oscillation.
Reviews of Geophysics, 39:179–229.
Bankert, R. L. (1994). Cloud classification of AVHRR imagery in maritime regions using
a probabilistic neural network. Journal of Applied Meteorology, 33(8):909–18.
Barnett, T. P. (1983). Interaction of the monsoon and Pacific trade wind system at
interannual time scales Part I: The equatorial zone. Monthly Weather Review,
111(4):756–73.
Barnett, T. P. and Preisendorfer, R. (1987). Origins and levels of monthly and seasonal
forecast skill for United States surface air temperatures determined by canonical
correlation analysis. Monthly Weather Review, 115(9):1825–50.
Barnston, A. G. and Ropelewski, C. F. (1992). Prediction of ENSO episodes using
canonical correlation analysis. Journal of Climate, 5:1316–45.
Barnston, A. G., van den Dool, H. M., Zebiak, S. E. et al. (1994). Long-lead seasonal
forecasts – where do we stand? Bulletin of the American Meteorological Society,
75:2097–114.
Barron, A. R. (1993). Universal approximation bounds for superposition of a sigmoidal
function. IEEE Transactions on Information Theory, 39(3):930–45.
Benediktsson, J. A., Swain, P. H. and Ersoy, O. K. (1990). Neural network approaches
versus statistical-methods in classification of multisource remote-sensing data. IEEE
Transactions on Geoscience and Remote Sensing, 28(4):540–52.
Bhattacharya, B., Price, R. K. and Solomatine, D. P. (2007). Machine learning approach
to modeling sediment transport. Journal of Hydraulic Engineering, 133(4):440–50.
Bhattacharya, B. and Solomatine, D. P. (2005). Neural networks and M5 model trees in
modelling water level – discharge relationship. Neurocomputing, 63:381–96.
Bickel, P. J. and Doksum, K. A. (1977). Mathematical Statistics: Basic Ideas and
Selected Topics. Oakland, CA: Holden-Day.
Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford: Clarendon
Press.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. New York: Springer.
Boser, B. E., Guyon, I. M. and Vapnik, V. N. (1992). A training algorithm for optimal
margin classifiers. In Haussler, D., ed., Proceedings of the 5th Annual ACM
Workshop on Computational Learning Theory, pp. 144–52. New York: ACM Press.
Box, G. P. E. and Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control.
Oakland, CA: Holden-Day.
Boyle, P. and Frean, M. (2005). Dependent Gaussian processes. In Saul, L., Weiss, Y. and
Bottou, L., eds., Advances in Neural Information Processing Systems, volume 17,
pp. 217–24. Cambridge, MA: MIT Press.
Boznar, M., Lesjak, M. and Mlakar, P. (1993). A neural-network-based method for
short-term predictions of ambient SO2 concentrations in highly polluted
industrial-areas of complex terrain. Atmospheric Environment Part B-Urban
Atmosphere, 27(2):221–30.
Brajard, J., Jamet, C., Moulin, C. and Thiria, S. (2006). Use of a neuro-variational
inversion for retrieving oceanic and atmospheric constituents from satellite ocean
colour sensor: Application to absorbing aerosols. Neural Networks, 19:178–85.
Breiman, L. (1996). Bagging predictions. Machine Learning, 24:123–40.
Breiman, L. (2001). Random forests. Machine Learning, 45:5–32.
Breiman, L. and Friedman, J. H. (1985). Estimating optimal transformations for multiple
regression and correlation. Journal of the American Statistical Association,
80:580–98.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

324

References

Breiman, L., Friedman, J., Olshen, R. A. and Stone, C. (1984). Classification and
Regression Trees. New York: Chapman and Hall.
Brent, R. P. (1973). Algorithms for Minimization without Derivatives. Englewood Cliffs,
New Jersey: Prentice-Hall.
Bretherton, C. S., Smith, C. and Wallace, J. M. (1992). An intercomparison of methods
for finding coupled patterns in climate data. Journal of Climate, 5:541–60.
Brier, W. G. (1950). Verification of forecasts expressed in terms of probabilities. Monthly
Weather Review, 78:1–3.
Brooks, H. E. and Doswell, C. A. (2002). Deaths in the 3 May 1999 Oklahoma City
tornado from a historical perspective. Weather and Forecasting, 17(3):354–61.
Broyden, C. G. (1970). The convergence of a class of double-rank minimization
algorithms. Journal of the Institute of Mathematics and Its Applications, 6:76–90.
Bürger, C. M., Kolditz, O., Fowler, H. J. and Blenkinsop, S. (2007). Future climate
scenarios and rainfall-runoff modelling in the Upper Gallego catchment (Spain).
Environmental Pollution, 148(3):842–54.
Bürger, G. (1996). Expanded downscaling for generating local weather scenarios.
Climate Research, 7(2):111–28.
Bürger, G. (2002). Selected precipitation scenarios across Europe. Journal of Hydrology,
262(1-4):99–110.
Burrows, W. R. (1991). Objective guidance for 0–24 hour and 24–48 hour mesoscale
forecasts of lake-effect snow using CART. Weather and Forecasting, 6:357–78.
Burrows, W. R. (1997). CART regression models for predicting UV radiation at the
ground in the presence of cloud and other environmental factors. Journal of Applied
Meteorology, 36:531–44.
Burrows, W. R. (1999). Combining classification and regression trees and the neuro-fuzzy
inference system for environmental data modeling. In 18th International Conference
of the North American Fuzzy Information Processing Society - NAFIPS, pp. 695–99.
New York, NY: NAFIPS.
Burrows, W. R., Benjamin, M., Beauchamp, S. et al. CART decision-tree statistical
analysis and prediction of summer season maximum surface ozone for the
Vancouver, Montreal, and Atlantic regions of Canada. Journal of Applied
Meteorology, 34:1848–62.
Burrows, W. R., Price, C. and Wilson, L. J. (2005). Warm season lightning probability
prediction for Canada and the northern United States. Weather and Forecasting,
20:971–88.
Cai, S., Hsieh, W. W. and Cannon, A. J. (2008). A comparison of Bayesian and
conditional density models in probabilistic ozone forecasting. In Proceedings of the
2008 IEEE World Congress in Computational Intelligence, Hong Kong. (See:
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=
4634117)
Camps-Valls, G., Bruzzone, L., Rojo-Alvarez, J. L. and Melgani, F. (2006). Robust
support vector regression for biophysical variable estimation from remotely sensed
images. IEEE Geoscience and Remote Sensing Letters, 3(3):339–43.
Cannon, A. J. (2006). Nonlinear principal predictor analysis: Application to the Lorenz
system. Journal of Climate, 19:579–89.
Cannon, A. J. (2007). Nonlinear analog predictor analysis: A coupled neural
network/analog model for climate downscaling. Neural Networks, 20:444–53.
doi:10.1016/j.neunet.2007.04.002.
Cannon, A. J. (2008). Probabilistic multi-site precipitation downscaling by an expanded
Bernoulli-gamma density network. Journal of Hydrometeorology, 9:1284–300.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

References

325

Cannon, A. J. and Hsieh, W. W. (2008). Robust nonlinear canonical correlation analysis:
application to seasonal climate forecasting. Nonlinear Processes in Geophysics,
12:221–32.
Cannon, A. J. and Lord, E. R. (2000). Forecasting summertime surface-level ozone
concentrations in the Lower Fraser Valley of British Columbia: An ensemble neural
network approach. Journal of the Air and Waste Management Association,
50:322–39.
Cannon, A. J. and McKendry, I. G. (1999). Forecasting all-India summer monsoon
rainfall using regional circulation principal components: A comparison between
neural network and multiple regression models. International Journal of
Climatology, 19(14):1561–78.
Cannon, A. J. and McKendry, I. G. (2002). A graphical sensitivity analysis for statistical
climate models: application to Indian monsoon rainfall prediction by artificial neural
networks and multiple linear regression models. International Journal of
Climatology, 22:1687–708.
Cannon, A. J. and Whitfield, P. H. (2002). Downscaling recent streamflow conditions in
British Columbia, Canada using ensemble neural network models. Journal of
Hydrology, 259(1-4):136–51.
Cavazos, T. (1997). Downscaling large-scale circulation to local winter rainfall in
northeastern Mexico. International Journal of Climatology, 17(10):1069–82.
Cavazos, T. (1999). Large-scale circulation anomalies conducive to extreme precipitation
events and derivation of daily rainfall in northeastern Mexico and southeastern
Texas. Journal of Climate, 12:1506–23.
Cawley, G. C., Janacek, G. J., Haylock, M. R. and Dorling, S. R. (2007). Predictive
uncertainty in environmental modelling. Neural Networks, 20:537–49.
Chang, C.-C. and Lin, C.-J. (2001). LIBSVM: A library for support vector machines.
Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
Chen, K. S., Tzeng, Y. C. and Chen, P. C. (1999). Retrieval of ocean winds from satellite
scatterometer by a neural network. IEEE Transactions on Geoscience and Remote
Sensing, 37(1):247–56.
Chen, X. L., Li, Y. S., Liu, Z. G. et al. (2004). Integration of multi-source data for water
quality classification in the Pearl River estuary and its adjacent coastal waters of
Hong Kong. Continental Shelf Research, 24(16):1827–43.
Cherkassky, V. and Mulier, F. (1998). Learning from Data. New York: Wiley.
Chevallier, F. (2005). Comments on ‘New approach to calculation of atmospheric model
physics: Accurate and fast neural network emulation of longwave radiation in a
climate model’. Monthly Weather Review, 133(12):3721–3.
Chevallier, F., Cheruy, F., Scott, N. A. and Chedin, A. (1998). A neural network approach
for a fast and accurate computation of a longwave radiative budget. Journal of
Applied Meteorology, 37(11):1385–97.
Chevallier, F., Morcrette, J. J., Cheruy, F. and Scott, N. A. (2000). Use of a
neural-network-based long-wave radiative-transfer scheme in the ECMWF
atmospheric model. Quarterly Journal of the Royal Meteorological Society, 126(563
Part B):761–76.
Christiansen, B. (2005). The shortcomings of nonlinear principal component analysis in
identifying circulation regimes. Journal of Climate, 18(22):4814–23.
Christiansen, B. (2007). Reply. Journal of Climate, 20:378–9.
Chu, W., Keerthi, S. S. and Ong, C. J. (2004). Bayesian support vector regression using a
unified loss function. IEEE Transactions on Neural Networks, 15(1):29–44.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

326

References

Clarke, T. (1990). Generalization of neural network to the complex plane. Proceedings of
International Joint Conference on Neural Networks, 2:435–40.
Collins, D. C., Reason, C. J. C. and Tangang, F. (2004). Predictability of Indian Ocean sea
surface temperature using canonical correlation analysis. Climate Dynamics,
22(5):481–97.
Comon, P. (1994). Independent component analysis – a new concept? Signal Processing,
36:287–314.
Comrie, A. C. (1997). Comparing neural networks and regression models for ozone
forecasting. Journal of the Air and Waste Management Association, 47(6):
653–63.
Cordisco, E., Prigent, C. and Aires, F. (2006). Snow characterization at a global scale
with passive microwave satellite observations. Journal of Geophysical Research,
111(D19). D19102, doi:10.1029/2005JD006773.
Cornford, D., Nabney, I. T. and Bishop, C. M. (1999). Neural network-based wind vector
retrieval from satellite scatterometer data. Neural Computing and Applications,
8:206–17. doi:10.1007/s005210050023.
Cortes, C. and Vapnik, V. (1995). Support vector networks. Machine Learning,
20:273–97.
Coulibaly, P. and Evora, N. D. (2007). Comparison of neural network methods for
infilling missing daily weather records. Journal of Hydrology, 341(1-2):27–41.
Cox, D. T., Tissot, P. and Michaud, P. (2002). Water level observations and short-term
predictions including meteorological events for entrance of Galveston Bay, Texas.
Journal of Waterway, Port, Coastal and Ocean Engineering, 128(1):21–9.
Cressie, N. (1993). Statistics for Spatial Data. New York: Wiley.
Cristianini, N. and Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines
and Other Kernel-based Methods. Cambridge, UK: Cambridge University Press.
Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function.
Mathematics of Control, Signals, and Systems, 2:303–14.
Dash, J., Mathur, A., Foody, G. M. et al. (2007). Land cover classification using
multi-temporal MERIS vegetation indices. International Journal of Remote Sensing,
28(6):1137–59.
Davidon, W. C. (1959). Variable metric methods for minimization. A.E.C.Res. and
Develop. Report ANL-5990, Argonne National Lab.
Dawson, C. W. and Wilby, R. L. (2001). Hydrological modelling using artificial neural
networks. Progress in Physical Geography, 25(1):80–108.
De’ath, G. and Fabricius, K. E. (2000). Classification and regression trees: A powerful yet
simple technique for ecological data analysis. Ecology, 81(11):3178–92.
Del Frate, F., Ferrazzoli, P., Guerriero, L. et al. (2004). Wheat cycle monitoring using
radar data and a neural network trained by a model. IEEE Transactions on
Geoscience and Remote Sensing, 42(1):35–44.
Del Frate, F., Pacifici, F., Schiavon, G. and Solimini, C. (2007). Use of neural networks
for automatic classification from high-resolution images. IEEE Transactions on
Geoscience and Remote Sensing, 45(4):800–9.
Del Frate, F., Petrocchi, A., Lichtenegger, J. and Calabresi, G. (2000). Neural networks
for oil spill detection using ERS-SAR data. IEEE Transactions on Geoscience and
Remote Sensing, 38(5):2282–7.
Del Frate, F. and Schiavon, G. (1999). Nonlinear principal component analysis for the
radiometric inversion of atmospheric profiles by using neural networks. IEEE
Transactions on Geoscience and Remote Sensing, 37(5):2335–42.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

References

327

Del Frate, F. and Solimini, D. (2004). On neural network algorithms for retrieving forest
biomass from SAR data. IEEE Transactions on Geoscience and Remote Sensing,
42(1):24–34.
Deser, C. and Blackmon, M. L. (1995). On the relationship between tropical and North
Pacific sea surface temperature variations. Journal of Climate, 8(6):1677–80.
Diaz, H. F. and Markgraf, V., eds. (2000). El Niño and the Southern Oscillation:
Multiscale Variability and Global and Regional Impacts. Cambridge, UK:
Cambridge University Press.
Dibike, Y. B. and Coulibaly, P. (2006). Temporal neural networks for downscaling climate
variability and extremes. Neural Networks, 19(2):135–44.
Dibike, Y. B. and Solomatine, D. P. (2001). River flow forecasting using artificial neural
networks. Physics and Chemistry of the Earth, Part B - Hydrology, Oceans and
Atmosphere, 26(1):1–7.
Dibike, Y. B., Velickov, S., Solomatine, D. and Abbott, M. B. (2001). Model induction
with support vector machines: Introduction and applications. Journal of Computing
In Civil Engineering, 15(3):208–16.
Dong, D. and McAvoy, T. J. (1996). Nonlinear principal component analysis based on
principal curves and neural networks. Computers and Chemical Engineering,
20:65–78.
Dorling, S. R., Foxall, R. J., Mandic, D. P. and Cawley, G. C. (2003). Maximum
likelihood cost functions for neural network models of air quality data. Atmospheric
Environment, 37:3435–43. doi:10.1016/S1352-2310(03)00323-6.
Draper, N. R. and Smith, H. (1981). Applied Regression Analysis, 2nd edn. New York:
Wiley.
Duda, R. O., Hart, P. E. and Stork, D. G. (2001). Pattern Classification, 2nd edn.
New York: Wiley.
Dutot, A. L., Rynkiewicz, J., Steiner, F. E. and Rude, J. (2007). A 24 hr forecast of ozone
peaks and exceedance levels using neural classifiers and weather predictions.
Environmental Modelling & Software, 22(9):1261–9.
Efron, B. (1979). Bootstrap methods: another look at the jackknife. Annals of Statistics,
7:1–26.
Efron, B. and Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Boca Raton,
Florida: CRC Press.
Elachi, C. and van Zyl, J. (2006). Introduction To The Physics and Techniques of Remote
Sensing, 2nd edn. Hoboken, NJ: Wiley-Interscience.
Elsner, J. B. and Tsonis, A. A. (1996). Singular Spectrum Analysis. New York: Plenum.
Emery, W. J. and Thomson, R. E. (1997). Data Analysis Methods in Physical
Oceanography. Oxford: Pergamon.
Essenreiter, R., Karrenbach, M. and Treitel, S. (2001). Identification and classification of
multiple reflections with self-organizing maps. Geophysical Prospecting,
49(3):341–52.
Fablet, R. and Le Josse, N. (2005). Automated fish age estimation from otolith images
using statistical learning. Fisheries Research, 72(2-3):279–90.
Fang, W. and Hsieh, W. W. (1993). Summer sea surface temperature variability off
Vancouver Island from satellite data. Journal of Geophysical Research,
98(C8):14391–400.
Faucher, M., Burrows, W. R. and Pandolfo, L. (1999). Empirical-statistical reconstruction
of surface marine winds along the western coast of Canada. Climate Research,
11(3):173–90.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

328

References

Fletcher, R. (1970). A new approach to variable metric algorithms. Computer Journal,
13:317–22.
Fletcher, R. and Powell, M. J. D. (1963). A rapidly convergent descent method for
minization. Computer Journal, 6:163–8.
Fletcher, R. and Reeves, C. M. (1964). Function minimization by conjugate gradients.
Computer Journal, 7:149–54.
Fogel, D. (2005). Evolutionary Computation: Toward a New Philosophy of Machine
Intelligence, 3rd edn. Hoboken, NJ: Wiley-IEEE.
Foody, G. M. and Mathur, A. (2004). A relative evaluation of multiclass image
classification by support vector machines. IEEE Transactions on Geoscience and
Remote Sensing, 42(6):1335–43.
Foresee, F. D. and Hagan, M. T. (1997). Gauss–Newton approximation to Bayesian
regularization. In Proceedings of the 1997 International Joint Conference on Neural
Networks. (See: http://ieeexplore.ieee.org/xpls/abs_all.
jsp?arnumber=614194)
Fraser, R. H. and Li, Z. (2002). Estimating fire-related parameters in boreal forest using
spot vegetation. Remote Sensing of Environment, 82(1):95–110.
Freund, Y. and Schapire, R. E. (1997). A decision-theoretical generalization of on-line
learning and an application to boosting. Journal of Computer System Sciences,
55:119–39.
Galton, F. J. (1885). Regression towards mediocrity in hereditary stature. Journal of the
Anthropological Institute, 15:246–63.
Garcia-Gorriz, E. and Garcia-Sanchez, J. (2007). Prediction of sea surface temperatures
in the western Mediterranean Sea by neural networks using satellite observations.
Geophysical Research Letters, 34. L11603, doi:10.1029/2007GL029888.
Gardner, M. W. and Dorling, S. R. (1999). Neural network modelling and prediction of
hourly NOx and NO2 concentrations in urban air in London. Atmospheric
Environment, 33:709–19.
Gardner, M. W. and Dorling, S. R. (2000). Statistical surface ozone models: an improved
methodology to account for non-linear behaviour. Atmospheric Environment,
34:21–34.
Georgiou, G. and Koutsougeras, C. (1992). Complex domain backpropagation. IEEE
Trans. Circuits and Systems II, 39:330–4.
Ghil, M., Allen, M. R., Dettinger, M. D. et al. (2002). Advanced spectral methods for
climatic time series. Reviews of Geophysics, 40. 1003, DOI: 10.1029/
2000RG000092.
Ghosh, S. and Mujumdar, P. P. (2008). Statistical downscaling of GCM simulations to
streamflow using relevance vector machine. Advances in Water Resources,
31(1):132–46.
Gill, A. E. (1982). Atmosphere-Ocean Dynamics. Orlando Florida: Academic Press.
Gill, P. E., Murray, W. and Wright, M. H. (1981). Practical Optimization. London:
Academic Press.
Gneiting, T., Raftery, A. E., Westveld, A. H. I. and Goldman, T. (2005). Calibrated
probabilistic forecasting using ensemble model output statistics and minimum CRPS
estimation. Monthly Weather Review, 133:1098–118.
Goddard, L., Mason, S. J., Zebiak, S. E. et al. (2001). Current approaches to
seasonal-to-interannual climate predictions. International Journal of Climatology,
21(9):1111–52.
Goldfarb, F. (1970). A family of variable metric methods derived by variational means.
Mathematics of Computation, 24:23–6.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

References

329

Golub, G. H., Heath, M. and Wahba, G. (1979). Generalized cross-validation as a method
for choosing a good ridge parameter. Technometrics, 21:215–23.
Gopal, S. and Woodcock, C. (1996). Remote sensing of forest change using artificial
neural networks. IEEE Transactions on Geoscience and Remote Sensing,
34:398–404.
Gourrion, J., Vandemark, D., Bailey, S. et al. (2002). A two-parameter wind speed
algorithm for Ku-band altimeters. Journal of Atmospheric and Oceanic Technology,
19(12):2030–48.
Grieger, B. and Latif, M. (1994). Reconstruction of the El Niño attractor with neural
networks. Climate Dynamics, 10(6):267–76.
Gross, L., Thiria, S., Frouin, R. and Greg, M. B. (2000). Artificial neural networks for
modeling the transfer function between marine reflectance and phytoplankton
pigment concentration. Journal of Geophysical Research, 105(C2):3483–3496. doi:
10.1029/1999JC900278.
Guégan, J. F., Lek, S. and Oberdorff, T. (1998). Energy availability and habitat
heterogeneity predict global riverine fish diversity. Nature, 391:382–4.
Gull, S. F. (1989). Developments in maximum entropy data analysis. In Skilling, J., ed.
Maximum Entropy and Bayesian Methods, pp. 53–71. Dordrecht: Kluwer.
Haber, E. and Oldenburg, D. (2000). A GCV based method for nonlinear ill-posed
problems. Computational Geoscience, 4:41–63.
Hamilton, K. (1988). A detailed examination of the extratropical response to tropical El
Niño/Southern Oscillation events. Journal of Climatology, 8:67–86.
Hamilton, K. (1998). Dynamics of the tropical middle atmosphere: A tutorial review.
Atmosphere-Ocean, 36(4):319–54.
Hamilton, K. and Hsieh, W. W. (2002). Representation of the QBO in the tropical
stratospheric wind by nonlinear principal component analysis. Journal of
Geophysical Research, 107(D15). 4232, doi: 10.1029/2001JD001250.
Han, G. and Shi, Y. (2008). Development of an Atlantic Canadian coastal water level
neural network model (ACCSLENNT). Journal of Atmospheric and Oceanic
Technology, 25:2117–32.
Hardman-Mountford, N. J., Richardson, A. J., Boyer, D. C., Kreiner, A. and Boyer, H. J.
(2003). Relating sardine recruitment in the Northern Benguela to satellite-derived
sea surface height using a neural network pattern recognition approach. Progress in
Oceanography, 59:241–55.
Hardoon, D. R., Szedmak, S. and Shawe-Taylor, J. (2004). Canonical correlation
analysis: An overview with application to learning methods. Neural Computation,
16:2639–64.
Hardy, D. M. (1977). Empirical eigenvector analysis of vector wind measurements.
Geophysical Research Letters, 4:319–20.
Hardy, D. M. and Walton, J. J. (1978). Principal component analysis of vector wind
measurements. Journal of Applied Meteorology, 17:1153–62.
Hasselmann, K. (1988). PIPs and POPs – a general formalism for the reduction of
dynamical systems in terms of Principal Interaction Patterns and Principal
Oscillation Patterns. Journal of Geophysical Research, 93:11015–20.
Hasselmann, S. and Hasselmann, K. (1985). Computations and parameterizations of the
nonlinear energy-transfer in a gravity-wave spectrum. Part I: A new method for
efficient computations of the exact nonlinear transfer integral. Journal of Physical
Oceanography, 15(11):1369–77.
Hasselmann, S., Hasselmann, K., Allender, J. H. and Barnett, T. P. (1985). Computations
and parameterizations of the nonlinear energy-transfer in a gravity-wave spectrum.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

330

References

Part II: Parameterizations of the nonlinear energy-transfer for application in wave
models. Journal of Physical Oceanography, 15(11):1378–91.
Hastie, T. and Stuetzle, W. (1989). Principal curves. Journal of the American Statistical
Association, 84:502–16.
Hastie, T., Tibshirani, R. and Friedman, J. (2001). Elements of Statistical Learning: Data
Mining, Inference and Prediction. New York: Springer-Verlag.
Haupt, R. L. and Haupt, S. E. (2004). Practical Genetic Algorithms. New York: Wiley.
Haupt, S. E., Pasini, A. and Marzban, C., eds. (2009). Artificial Intelligence Methods in
the Environmental Sciences. Springer.
Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. New York:
Prentice Hall.
Haylock, M. R., Cawley, G. C., Harpham, C., Wilby, R. L. and Goodess, C. M. (2006).
Downscaling heavy precipitation over the United Kingdom: A comparison of
dynamical and statistical methods and their future scenarios. International Journal
of Climatology, 26(10):1397–415. doi: 10.1002/joc.1318.
Heidke, P. (1926). Berechnung des Erfolges und der Güte der Windstärkevorhersagen in
Sturmwarnungsdienst. Geografiska Annaler, 8:310–49.
Hennon, C. C., Marzban, C. and Hobgood, J. S. (2005). Improving tropical cyclogenesis
statistical model forecasts through the application of a neural network classifier.
Weather and Forecasting, 20:1073–1083. doi: 10.1175/WAF890.1.
Herman, A. (2007). Nonlinear principal component analysis of the tidal dynamics in a
shallow sea. Geophysical Research Letters, 34(2).
Hestenes, M. R. and Stiefel, E. (1952). Methods of conjugate gradients for solving linear
systems. Journal of Research of the National Bureau of Standards, 49(6):409–36.
Hewitson, B. C. and Crane, R. G. (1996). Climate downscaling: Techniques and
application. Climate Research, 7(2):85–95.
Hewitson, B. C. and Crane, R. G. (2002). Self-organizing maps: applications to synoptic
climatology. Climate Research, 22(1):13–26.
Hirose, A. (1992). Continuous complex-valued backpropagation learning. Electronic
Letters, 28:1854–5.
Hoerling, M. P., Kumar, A. and Zhong, M. (1997). El Niño, La Niña and the nonlinearity
of their teleconnections. Journal of Climate, 10:1769–86.
Holton, J. R. and Tan, H.-C. (1980). The influence of the equatorial quasi-biennial
oscillation on the global circulation at 50 mb. Journal of the Atmospheric Sciences,
37:2200–8.
Hong, Y., Hsu, K. L., Sorooshian, S. and Gao, X. G. (2004). Precipitation estimation from
remotely sensed imagery using an artificial neural network cloud classification
system. Journal of Applied Meteorology, 43(12):1834–52.
Horel, J. D. (1981). A rotated principal component analysis of the interannual variability
of the Northern Hemisphere 500 mb height field. Monthly Weather Review,
109:2080–92.
Horel, J. D. (1984). Complex principal component analysis: Theory and examples.
Journal of Climate and Applied Meteorology, 23:1660–73.
Horel, J. D. and Wallace, J. M. (1981). Planetary-scale atmospheric phenomena
associated with the Southern Oscillation. Monthly Weather Review, 109:813–29.
Hornik, K. (1991). Approximation capabilities of multilayer feedforward networks.
Neural Networks, 4:252–7.
Hornik, K., Stinchcombe, M. and White, H. (1989). Multilayer feedforward networks are
universal approximators. Neural Networks, 2:359–66.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

References

331

Horstmann, J., Schiller, H., Schulz-Stellenfleth, J. and Lehner, S. (2003). Global wind
speed retrieval from SAR. IEEE Transactions on Geoscience and Remote Sensing,
41(10):2277–86.
Hotelling, H. (1933). Analysis of a complex of statistical variables into principal
components. Journal of Educational Psychology, 24:417–41.
Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28:321–77.
Hou, Z. and Koh, T. S. (2004). Image denoising using robust regression. IEEE Signal
Processing Letters, 11:234–46.
Hsieh, W. W. (2000). Nonlinear canonical correlation analysis by neural networks.
Neural Networks, 13:1095–105.
Hsieh, W. W. (2001a). Nonlinear canonical correlation analysis of the tropical Pacific
climate variability using a neural network approach. Journal of Climate,
14:2528–39.
Hsieh, W. W. (2001b). Nonlinear principal component analysis by neural networks.
Tellus, 53A:599–615.
Hsieh, W. W. (2004). Nonlinear multivariate and time series analysis by neural network
methods. Reviews of Geophysics, 42. RG1003, doi:10.1029/2002RG000112.
Hsieh, W. W. (2007). Nonlinear principal component analysis of noisy data. Neural
Networks, 20:434–43.
Hsieh, W. W. and Cannon, A. J. (2008). Towards robust nonlinear multivariate analysis by
neural network methods. In Donner, R. and Barbosa, S., eds., Nonlinear Time Series
Analysis in the Geosciences – Applications in Climatology, Geodynamics, and
Solar-Terrestrial Physics, pp. 97–124. Berlin: Springer.
Hsieh, W. W. and Hamilton, K. (2003). Nonlinear singular spectrum analysis of the
tropical stratospheric wind. Quarterly Journal of the Royal Meteorological Society,
129:2367–82.
Hsieh, W. W. and Tang, B. (1998). Applying neural network models to prediction and
data analysis in meteorology and oceanography. Bulletin of the American
Meteorological Society, 79:1855–70.
Hsieh, W. W., Tang, B. and Garnett, E. R. (1999). Teleconnections between Pacific sea
surface temperatures and Canadian prairie wheat yield. Agricultural and Forest
Meteorology, 96:209–17.
Hsieh, W. W. and Wu, A. (2002). Nonlinear multichannel singular spectrum analysis of
the tropical Pacific climate variability using a neural network approach. Journal of
Geophysical Research, 107(C7). doi: 10.1029/2001JC000957.
Hsieh, W. W., Wu, A. and Shabbar, A. (2006). Nonlinear atmospheric teleconnections.
Geophysical Research Letters, 33. L07714, doi:10.1029/2005GL025471.
Hsu, K. L., Gao, X. G., Sorooshian, S. and Gupta, H. V. (1997). Precipitation estimation
from remotely sensed information using artificial neural networks. Journal of
Applied Meteorology, 36(9):1176–90.
Hsu, K. L., Gupta, H. V. and Sorooshian, S. (1995). Artificial neural-network modeling of
the rainfall-runoff process. Water Resources Research, 31(10):2517–30.
Huang, C., Davis, L. S. and Townshend, J. R. G. (2002). An assessment of support vector
machines for land cover classification. International Journal of Remote Sensing,
23(4):725–49.
Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of
Mathematical Statistics, 35:73–101.
Hyvärinen, A., Karhunen, J. and Oja, E. (2001). Independent Component Analysis.
New York: Wiley.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

332

References

IPCC (2007). Climate Change 2007: The Physical Science Basis. Contribution of
Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on
Climate Change. Cambridge, UK: Cambridge University Press.
Jamet, C. and Hsieh, W. W. (2005). The nonlinear atmospheric variability in the winter
northeast Pacific associated with the Madden–Julian Oscillation. Geophysical
Research Letters, 32(13). L13820, doi: 10.1029/2005GL023533.
Jamet, C., Thiria, S., Moulin, C. and Crepon, M. (2005). Use of a neurovariational
inversion for retrieving oceanic and atmospheric constituents from ocean color
imagery: A feasibility study. Journal of Atmospheric and Oceanic Technology,
22(4):460–75.
Jaynes, E. T. (2003). Probability theory: the logic of science. Cambridge, UK: Cambridge
University Press.
Jenkins, G. M. and Watts, D. G. (1968). Spectral Analysis and Its Applications. San
Francisco: Holden-Day.
Jin, F. F. (1997a). An equatorial ocean recharge paradigm for ENSO. Part I: Conceptual
model. Journal of the Atmospheric Sciences, 54(7):811–29.
Jin, F. F. (1997b). An equatorial ocean recharge paradigm for ENSO. Part II: A
stripped-down coupled model. Journal of the Atmospheric Sciences, 54(7):830–47.
Jolliffe, I. T. (2002). Principal Component Analysis. New York: Springer.
Jolliffe, I. T. and Stephenson, D. B., eds., (2003). Forecast Verification: A Practitioner’s
Guide in Atmospheric Science. Chichester: Wiley.
Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis.
Psychometrika, 23:187–200.
Kalnay, E., Kanamitsu, M., Kistler, R. et al. (1996). The NCEP/NCAR 40 year reanalysis
project. Bulletin of the American Meteorological Society, 77(3):437–71.
Kaplan, A., Kushnir, Y. and Cane, M. A. (2000). Reduced space optimal interpolation of
historical marine sea level pressure: 1854-1992. Journal of Climate,
13(16):2987–3002.
Karl, T. R., Wang, W. C., Schlesinger, M. E., Knight, R. W. and Portman, D. (1990). A
method of relating general-circulation model simulated climate to the observed local
climate. 1. Seasonal statistics. Journal of Climate, 3(10):1053–79.
Karush, W. (1939). Minima of functions of several variables with inequalities as side
constraints. M.Sc. thesis, University of Chicago.
Keiner, L. E. and Yan, X.-H. (1998). A neural network model for estimating sea surface
chlorophyll and sediments from Thematic Mapper imagery. Remote Sensing of
Environment, 66:153–65.
Kelly, K. (1988). Comment on ‘Empirical orthogonal function analysis of advanced very
high resolution radiometer surface temperature patterns in Santa Barbara Channel’
by G.S.E. Lagerloef and R.L. Bernstein. Journal of Geophysical Research,
93(C12):15743–54.
Khan, M. S. and Coulibaly, P. (2006). Application of support vector machine in lake
water level prediction. Journal of Hydrologic Engineering, 11(3):199–205.
Kharin, V. V. and Zwiers, F. W. (2003). On the ROC score of probability forecasts.
Journal of Climate, 16(24):4145–50.
Kim, T. and Adali, T. (2002). Fully complex multi-layer perceptron network for nonlinear
signal processing. Journal of VLSI Signal Processing, 32:29–43.
Kirby, M. J. and Miranda, R. (1996). Circular nodes in neural networks. Neural
Computation, 8:390–402.
Kohonen, T. (1982). Self-organizing formation of topologically correct feature maps.
Biological Cybernetics, 43:59–69.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

References

333

Kohonen, T. (2001). Self-Organizing Maps, 3rd edn. Berlin: Springer.
Kolehmainen, M., Martikainen, H. and Ruuskanen, J. (2001). Neural networks and
periodic components used in air quality forecasting. Atmospheric Environment,
35(5):815–25.
Kramer, M. A. (1991). Nonlinear principal component analysis using autoassociative
neural networks. AIChE Journal, 37:233–43.
Krasnopolsky, V. M. (2007). Neural network emulations for complex multidimensional
geophysical mappings: Applications of neural network techniques to atmospheric
and oceanic satellite retrievals and numerical modeling. Reviews of Geophysics,
45(3). RG3009, doi:10.1029/2006RG000200.
Krasnopolsky, V. M., Breaker, L. C. and Gemmill, W. H. (1995). A neural network as a
nonlinear transfer function model for retrieving surface wind speeds from the special
sensor microwave imager. Journal of Geophysical Research, 100(C6):11033–45.
Krasnopolsky, V. M., Chalikov, D. V. and Tolman, H. L. (2002). A neural network
technique to improve computational efficiency of numerical oceanic models. Ocean
Modelling, 4:363–83.
Krasnopolsky, V. M. and Chevallier, F. (2003). Some neural network applications in
environmental sciences. Part II: advancing computational efficiency in
environmental numerical models. Neural Networks, 16(3-4):335–48.
Krasnopolsky, V. M. and Fox-Rabinovitz, M. S. (2006). Complex hybrid models
combining deterministic and machine learning components for numerical climate
modeling and weather prediction. Neural Networks, 19:122–34.
Krasnopolsky, V. M., Fox-Rabinovitz, M. S. and Chalikov, D. V. (2005a). Comments on
‘New approach to calculation of atmospheric model physics: Accurate and fast
neural network emulation of longwave radiation in a climate model’ - Reply.
Monthly Weather Review, 133(12):3724–8.
Krasnopolsky, V. M., Fox-Rabinovitz, M. S. and Chalikov, D. V. (2005b). New approach
to calculation of atmospheric model physics: Accurate and fast neural network
emulation of longwave radiation in a climate model. Monthly Weather Review,
133(5):1370–83.
Krasnopolsky, V. M., Gemmill, W. H. and Breaker, L. C. (1999). A multiparameter
empirical ocean algorithm for SSM/I retrievals. Canadian Journal of Remote
Sensing, 25:486–503.
Kuhn, H. W. and Tucker, A. W. (1951). Nonlinear programming. In Proceedings of the
2nd Berkeley Symposium on Mathematical Statistics and Probabilities, pp. 481–92.
University of California Press.
Kukkonen, J., Partanen, L., Karppinen, A. et al. (2003). Extensive evaluation of neural
network models for the prediction of NO2 and PM10 concentrations, compared with
a deterministic modelling system and measurements in central Helsinki.
Atmospheric Environment, 37(32):4539–50.
Kwiatkowska, E. J. and Fargion, G. S. (2003). Application of machine-learning
techniques toward the creation of a consistent and calibrated global chlorophyll
concentration baseline dataset using remotely sensed ocean color data. IEEE
Transactions on Geoscience and Remote Sensing, 41(12):2844–60.
Kwok, J. T.-Y. and Tsang, I. W.-H. (2004). The pre-image problem in kernel methods.
IEEE Transactions on Neural Networks, 15:1517–25.
Lai, P. L. and Fyfe, C. (1999). A neural implementation of canonical correlation analysis.
Neural Networks, 12:1391–7.
Lai, P. L. and Fyfe, F. (2000). Kernel and non-linear canonical correlation analysis.
International Journal of Neural Systems, 10:365–77.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

334

References

Lambert, S. J. and Fyfe, J. C. (2006). Changes in winter cyclone frequencies and strengths
simulated in enhanced greenhouse warming experiments: results from the models
participating in the IPCC diagnostic exercise. Climate Dynamics, 26(7-8):713–28.
Lax, D. A. (1985). Robust estimators of scale: Finite-sample performance in long-tailed
symmetric distributions. Journal of the American Statistical Association, 80:736–41.
Le, N. D. and Zidek, J. V. (2006). Statistical Analysis of Environmental Space-Time
Processes. New York: Springer.
LeBlond, P. H. and Mysak, L. A. (1978). Waves in the Ocean. Amsterdam: Elsevier.
LeCun, Y., Kanter, I. and Solla, S. A. (1991). Second order properties of error surfaces:
Learning time and generalization. In Advances in Neural Information Processing
Systems, volume 3, pp. 918–24. Cambridge, MA: MIT Press.
Lee, J., Weger, R. C., Sengupta, S. K. and Welch, R. M. (1990). A neural network
approach to cloud classification. IEEE Transactions on Geoscience and Remote
Sensing, 28(5):846–55.
Lee, Y., Wahba, G. and Ackerman, S. A. (2004). Cloud classification of satellite radiance
data by multicategory support vector machines. Journal of Atmospheric and Oceanic
Technology, 21(2):159–69.
Legler, D. M. (1983). Empirical orthogonal function analysis of wind vectors over the
tropical Pacific region. Bulletin of the American Meteorological Society,
64(3):234–41.
Levenberg, K. (1944). A method for the solution of certain non-linear problems in least
squares. Quarterly Journal of Applied Mathematics, 2:164–8.
Li, S., Hsieh, W. W. and Wu, A. (2005). Hybrid coupled modeling of the tropical Pacific
using neural networks. Journal of Geophysical Research, 110(C09024). doi:
10.1029/2004JC002595.
Liu, Y. G. and Weisberg, R. H. (2005). Patterns of ocean current variability on the West
Florida Shelf using the self-organizing map. Journal Of Geophysical Research –
Oceans, 110(C6). C06003, doi:10.1029/2004JC002786.
Liu, Y., Weisberg, R. H. and Mooers, C. N. K. (2006). Performance evaluation of the
self-organizing map for feature extraction. Journal of Geophysical Research, 111
(C05018). doi:10.1029/2005JC003117.
Liu, Y. G., Weisberg, R. H. and Shay, L. K. (2007). Current patterns on the West Florida
Shelf from joint self-organizing map analyses of HF radar and ADCP data. Journal
of Atmospheric and Oceanic Technology, 24(4):702–12.
Liu, Z. and Jin, L. (2008). LATTICESVM – A new method for multi-class support vector
machines. In Proceedings of the 2008 IEEE World Congress in Computational
Intelligence, Hong Kong. (See http://ieeexplore.ieee.org/xpls/
abs_all.jsp?arnumber=463387)
Lorenz, E. N. (1956). Empirical orthogonal functions and statistical weather prediction.
Sci. rep. no. 1, Statistical Forecasting Project, Dept. of Meteorology, MIT.
Lorenz, E. N. (1963). Deterministic nonperiodic flow. Journal of the Atmospheric
Sciences, 20:130–41.
Lu, W. Z. and Wang, W. J. (2005). Potential assessment of the ‘support vector machine’
method in forecasting ambient air pollutant trends. Chemosphere, 59(5):693–701.
Luenberger, D. G. (1984). Linear and Nonlinear Programming, 2nd edn. Reading, MA:
Addison-Wesley.
MacKay, D. J. C. (1992a). A practical Bayesian framework for backpropagation
networks. Neural Computation, 4(3):448–72.
MacKay, D. J. C. (1992b). Bayesian interpolation. Neural Computation, 4(3):415–47.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

References

335

MacKay, D. J. C. (1995). Probable networks and plausible predictions – a review of
practical Bayesian methods for supervised neural networks. Network: Computation
in Neural Systems, 6:469–505.
MacKay, D. J. C. (2003). Information Theory, Inference and Learning Algorithms.
Cambridge, UK: Cambridge University Press.
Maier, H. R. and Dandy, G. C. (2000). Neural networks for the prediction and forecasting
of water resources variables: a review of modelling issues and applications.
Environmental Modelling and Software, 15:101–24.
Mantua, N. J. and Hare, S. R. (2002). The Pacific decadal oscillation. Journal of
Oceanography, 58(1):35–44.
Mardia, K. V., Kent, J. T. and Bibby, J. M. (1979). Multivariate Analysis. London:
Academic Press.
Marquardt, D. (1963). An algorithm for least-squares estimation of nonlinear parameters.
SIAM Journal on Applied Mathematics, 11:431–41.
Marzban, C. (2004). The ROC curve and the area under it as performance measures.
Weather and Forecasting, 19(6):1106–14.
Marzban, C. and Stumpf, G. J. (1996). A neural network for tornado prediction based on
doppler radar-derived attributes. Journal of Applied Meteorology, 35(5):617–26.
Marzban, C. and Stumpf, G. J. (1998). A neural network for damaging wind prediction.
Weather and Forecasting, 13:151–63.
Marzban, C. and Witt, A. (2001). A Bayesian neural network for severe-hail size
prediction. Weather and Forecasting, 16(5):600–10.
Masters, T. (1995). Advanced Algorithms for Neural Networks – A C++ Sourcebook.
New York: Wiley.
McCulloch, W. S. and Pitts, W. (1943). A logical calculus of the ideas immanent in neural
nets. Bulletin of Mathematical Biophysics, 5:115–37.
McGinnis, D. L. (1997). Estimating climate-change impacts on Colorado Plateau
snowpack using downscaling methods. Professional Geographer, 49(1):117–25.
McIntire, T. J. and Simpson, J. J. (2002). Arctic sea ice, cloud, water, and lead
classification using neural networks and 1.61μm data. IEEE Transactions on
Geoscience and Remote Sensing, 40(9):1956–72.
Meinen, C. S. and McPhaden, M. J. (2000). Observations of warm water volume changes
in the equatorial Pacific and their relationship to El Niño and La Niña. Journal of
Climate, 13:3551–9.
Mejia, C., Thiria, S., Tran, N., Crepon, M. and Badran, F. (1998). Determination of the
geophysical model function of the ERS-1 scatterometer by the use of neural
networks. Journal of Geophysical Research, 103(C6):12853–68.
Miikkulainen, R., Bryant, B. D., Cornelius, R. et al. (2006). Computational intelligence
in games. In Yen, G. and Fogel, D., eds., Computational Intelligence: Principles and
Practice, pp. 155–191. IEEE Computational Intelligence Soc.
Mika, S., Schölkopf, B., Smola, A. J. et al. (1999). Kernel PCA and de-noising in feature
spaces. In Kearns, M., Solla, S., and Cohn, D., eds., Advances in Neural Information
Processing Systems, volume 11, pp. 536–42. Cambridge, MA: MIT Press.
Miller, S. W. and Emery, W. J. (1997). An automated neural network cloud classifier for
use over land and ocean surfaces. Journal of Applied Meteorology, 36(10):
1346–62.
Mingoti, S. A. and Lima, J. O. (2006). Comparing SOM neural network with Fuzzy
c-means, K-means and traditional hierarchical clustering algorithms. European
Journal of Operational Research, 174(3):1742–59.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

336

References

Minns, A. W. and Hall, M. J. (1996). Artificial neural networks as rainfall-runoff models.
Hydrological Sciences Journal – Journal Des Sciences Hydrologiques,
41(3):399–417.
Minsky, M. and Papert, S. (1969). Perceptrons. Cambridge, MA: MIT Press.
Monahan, A. H. (2000). Nonlinear principal component analysis by neural networks:
theory and application to the Lorenz system. Journal of Climate, 13:821–35.
Monahan, A. H. (2001). Nonlinear principal component analysis: Tropical Indo-Pacific
sea surface temperature and sea level pressure. Journal of Climate, 14:219–33.
Monahan, A. H. and Fyfe, J. C. (2007). Comment on ‘The shortcomings of nonlinear
principal component analysis in identifying circulation regimes’. Journal of Climate,
20:375–77.
Monahan, A. H., Fyfe, J. C. and Flato, G. M. (2000). A regime view of northern
hemisphere atmospheric variability and change under global warming. Geophysical
Research Letters, 27:1139–42.
Monahan, A. H., Fyfe, J. C. and Pandolfo, L. (2003). The vertical structure of wintertime
climate regimes of the northern hemisphere extratropical atmosphere. Journal of
Climate, 16:2005–21.
Monahan, A. H., Pandolfo, L. and Fyfe, J. C. (2001). The preferred structure of
variability of the northern hemisphere atmospheric circulation. Geophysical
Research Letters, 28:1019–22.
Monahan, A. H., Tangang, F. T. and Hsieh, W. W. (1999). A potential problem with
extended EOF analysis of standing wave fields. Atmosphere-Ocean, 37:241–54.
Moody, J. and Darken, C. J. (1989). Fast learning in networks of locally-tuned processing
units. Neural Computation, 1:281–94.
Morel, A. (1988). Optical modeling of the upper ocean in relation to its biogenous matter
content (case 1 waters). Journal of Geophysical Research, 93:10749–68.
Mosteller, F. and Tukey, J. W. (1977). Data Analysis and Regression: A Second Course in
Statistics. Addison-Wesley.
Nabney, I. T. (2002). Netlab: Algorithms for Pattern Recognition. London: Springer.
Naujokat, B. (1986). An update of the observed quasi-biennial oscillation of the
stratospheric winds over the tropics. Journal of the Atmospheric Sciences,
43:1873–7.
Neal, R. M. (1996). Bayesian Learning for Neural Networks, volume 118 of Lecture
Notes in Statistics. New York: Springer.
Newbigging, S. C., Mysak, L. A. and Hsieh, W. W. (2003). Improvements to the
non-linear principal component analysis method, with applications to ENSO and
QBO. Atmosphere-Ocean, 41(4):290–98.
Nguyen, D. and Widrow, B. (1990). Improving the learning speed of 2-layer neural
networks by choosing initial values of the adaptive weights. In International
Joint Conference on Neural Networks, volume 3, pp. 21–6. (See
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=
137819)
Niang, A., Badran, A., Moulin, C., Crepon, M., and Thiria, S. (2006). Retrieval of aerosol
type and optical thickness over the Mediterranean from SeaWiFS images using an
automatic neural classification method. Remote Sensing of Environment, 100:82–94.
Niermann, S. (2006). Evolutionary estimation of parameters of Johnson distributions.
Journal of Statistical Computation and Simulation, 76:185–93.
Nitta, T. (1997). An extension of the back-propagation algorithm to complex numbers.
Neural Networks, 10:1391–415.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

References

337

North, G. R., Bell, T. L., Cahalan, R. F. and Moeng, F. J. (1982). Sampling errors in the
estimation of empirical orthogonal functions. Monthly Weather Review,
110:699–706.
Nunnari, G., Dorling, S., Schlink, U. et al. (2004). Modelling SO2 concentration at a point
with statistical approaches. Environmental Modelling & Software, 19(10):887–905.
Oja, E. (1982). A simplified neuron model as a principal component analyzer. Journal of
Mathematical Biology, 15:267–73.
Olden, J. D. and Jackson, D. A. (2002). A comparison of statistical approaches for
modelling fish species distributions. Freshwater Biology, 47(10):1976–95.
Olsson, J., Uvo, C. B., Jinno, K. et al. (2004). Neural networks for rainfall forecasting by
atmospheric downscaling. Journal of Hydrologic Engineering, 9(1):1–12.
Pacifici, F., Del Frate, F., Solimini, C. and Emery, W. J. (2007). An innovative neural-net
method to detect temporal changes in high-resolution optical satellite imagery. IEEE
Transactions on Geoscience and Remote Sensing, 45(9):2940–52.
Park, Y. S., Cereghino, R., Compin, A. and Lek, S. (2003). Applications of artificial
neural networks for patterning and predicting aquatic insect species richness in
running waters. Ecological Modelling, 160(3):265–80.
Pasini, A., Lore, M. and Ameli, F. (2006). Neural network modelling for the analysis of
forcings/temperatures relationships at different scales in the climate system.
Ecological Modelling, 191(1):58–67.
Pearson, K. (1901). On lines and planes of closest fit to systems of points in space.
Philosophical Magazine, Ser. 6, 2:559–72.
Peirce, C. S. (1884). The numerical measure of the success of predictions. Science,
4:453–4.
Penland, C. and Magorian, T. (1993). Prediction of Nino-3 sea surface temperatures using
linear inverse modeling. Journal of Climate, 6(6):1067–76.
Philander, S. G. (1990). El Niño, La Niña, and the Southern Oscillation. San Diego:
Academic Press.
Polak, E. (1971). Computational Methods in Optimization: A Unified Approach.
New York: Academic Press.
Polak, E. and Ribiere, G. (1969). Note sur la convergence de methods de directions
conjures. Revue Francaise d’Informat. et de Recherche Operationnelle, 16:35–43.
Powell, M. J. D. (1987). Radial basis functions for multivariate interpolation: a review. In
Mason, J. and Cox, M., eds., Algorithms for Approximation, pp. 143–67. Oxford:
Clarendon Press.
Pozo-Vazquez, D., Esteban-Parra, M. J., Rodrigo, F. S. and Castro-Diez, Y. (2001).
A study of NAO variability and its possible nonlinear influences on European surface
temperature. Climate Dynamics, 17:701–15.
Preisendorfer, R. W. (1988). Principal Component Analysis in Meteorology and
Oceanography. New York: Elsevier.
Press, W. H., Flannery, B. P., Teukolsky, S. A. and Vetterling, W. T. (1986). Numerical
Recipes. Cambridge, UK: Cambridge University Press.
Price, K. V., Storn, R. M. and Lampinen, J. A. (2005). Differential Evolution: A Practical
Approach to Global Optimization. Berlin: Springer.
Pyper, B. J. and Peterman, R. M. (1998). Comparison of methods to account for
autocorrelation in correlation analyses of fish data. Canadian Journal of Fisheries
and Aquatic Sciences, 55:2127–40.
Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. San Mateo: Morgan
Kaufmann.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

338

References

Rasmussen, C. E. and Williams, C. K. I. (2006). Gaussian Processes for Machine
Learning. Cambridge, MA: MIT Press.
Rattan, S. S. P. and Hsieh, W. W. (2004). Nonlinear complex principal component
analysis of the tropical Pacific interannual wind variability. Geophysical Research
Letters, 31(21). L21201, doi:10.1029/2004GL020446.
Rattan, S. S. P. and Hsieh, W. W. (2005). Complex-valued neural networks for nonlinear
complex principal component analysis. Neural Networks, 18(1):61–9.
Rattan, S. S. P., Ruessink, B. G. and Hsieh, W. W. (2005). Non-linear complex principal
component analysis of nearshore bathymetry. Nonlinear Processes in Geophysics,
12(5):661–70.
Recknagel, F., French, M., Harkonen, P. and Yabunaka, K. (1997). Artificial neural
network approach for modelling and prediction of algal blooms. Ecological
Modelling, 96(1-3):11–28.
Reynolds, R. W. and Smith, T. M. (1994). Improved global sea surface temperature
analyses using optimum interpolation. Journal of Climate, 7(6):929–48.
Richardson, A. J., Risien, C. and Shillington, F. A. (2003). Using self-organizing maps to
identify patterns in satellite imagery. Progress in Oceanography, 59(2-3):223–39.
Richaume, P., Badran, F., Crepon, M. et al. (2000). Neural network wind retrieval from
ERS-1 scatterometer data. Journal of Geophysical Research, 105(C4):8737–51.
Richman, M. B. (1986). Rotation of principal components. Journal of Climatology,
6:293–335.
Rojas, R. (1996). Neural Networks– A Systematic Introduction. New York: Springer.
Rojo-Alvarez, J. L., Martinez-Ramon, M., Figueiras-Vidal, A. R., Garcia-Armada, A. and
Artes-Rodriguez, A. (2003). A robust support vector algorithm for nonparametric
spectral analysis. IEEE Signal Processing Letters, 10(11):320–23.
Ropelewski, C. F. and Jones, P. D. (1987). An extension of the Tahiti-Darwin Southern
Oscillation Index. Monthly Weather Review, 115:2161–5.
Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and
organization in the brain. Psychological Review, 65:386–408.
Rosenblatt, F. (1962). Principles of Neurodynamics. New York: Spartan.
Roweis, S. T. and Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear
embedding. Science, 290:2323–6.
Ruessink, B. G., van Enckevort, I. M. J. and Kuriyama, Y. (2004). Non-linear principal
component analysis of nearshore bathymetry. Marine Geology, 203:185–97.
Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986). Learning internal
representations by error propagation. In Rumelhart, D., McClelland, J. and Group,
P. R., eds., Parallel Distributed Processing, volume 1, pp. 318–62. Cambridge, MA:
MIT Press.
Saff, E. B. and Snider, A. D. (2003). Fundamentals of Complex Analysis with
Applications to Engineering and Science. Englewood Cliffs, NJ: Prentice-Hall.
Sajikumar, N. and Thandaveswara, B. S. (1999). A non-linear rainfall-runoff model using
an artificial neural network. Journal of Hydrology, 216(1-2):32–55.
Sanger, T. D. (1989). Optimal unsupervised learning in a single-layer linear feedforward
neural network. Neural Networks, 2:459–73.
Schiller, H. (2007). Model inversion by parameter fit using NN emulating the forward
model - Evaluation of indirect measurements. Neural Networks, 20(4):
479–83.
Schiller, H. and Doerffer, R. (1999). Neural network for emulation of an inverse model –
operational derivation of Case II water properties from MERIS data. International
Journal of Remote Sensing, 20:1735–46.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

References

339

Schiller, H. and Doerffer, R. (2005). Improved determination of coastal water constituent
concentrations from MERIS data. IEEE Transactions on Geoscience and Remote
Sensing, 43(7):1585–91.
Schlink, U., Dorling, S., Pelikan, E. et al. (2003). A rigorous inter-comparison of
ground-level ozone predictions. Atmospheric Environment, 37(23):3237–53.
Schölkopf, B., Smola, A. and Muller, K.-R. (1998). Nonlinear component analysis as a
kernel eigenvalue problem. Neural Computation, 10:1299–319.
Schölkopf, B., Smola, A., Williamson, R. and Bartlett, P. L. (2000). New support vector
algorithms. Neural Computation, 12:1207–45.
Schölkopf, B. and Smola, A. J. (2002). Learning with Kernels: Support Vector Machines,
Regularization, Optimization, and Beyond (Adaptive Computation and Machine
Learning). Cambridge, MA: MIT Press.
Schölkopf, B., Sung, K. K., Burges, C. J. C. et al. (1997). Comparing support vector
machines with Gaussian kernels to radial basis function classifiers. IEEE
Transactions on Signal Processing, 45(11):2758–65.
Schoof, J. T. and Pryor, S. C. (2001). Downscaling temperature and precipitation:
A comparison of regression-based methods and artificial neural networks.
International Journal of Climatology, 21(7):773–90.
Shabbar, A. and Barnston, A. G. (1996). Skill of seasonal climate forecasts in Canada
using canonical correlation analysis. Monthly Weather Review, 124:2370–85.
Shanno, D. F. (1970). Conditioning of quasi-Newton methods for function minimization.
Mathematics of Computation, 24:647–57.
Shanno, D. F. (1978). Conjugate-gradient methods with inexact searches. Mathematics of
Operations Research, 3:244–56.
Shawe-Taylor, J. and Cristianini, N. (2004). Kernel Methods for Pattern Analysis.
Cambridge, UK: Cambridge University Press.
Simpson, J. J. and McIntire, T. J. (2001). A recurrent neural network classifier for
improved retrievals of areal extent of snow cover. IEEE Transactions on Geoscience
and Remote Sensing, 39(10):2135–47.
Simpson, J. J., Tsou, Y. L., Schmidt, A. and Harris, A. (2005). Analysis of along track
scanning radiometer-2 (ATSR-2) data for clouds, glint and sea surface temperature
using neural networks. Remote Sensing of Environment, 98:152–81.
Smith, T. M., Reynolds, R. W., Livezey, R. E. and Stokes, D. C. (1996). Reconstruction of
historical sea surface temperatures using empirical orthogonal functions. Journal of
Climate, 9(6):1403–20.
Solomatine, D. P. and Dulal, K. N. (2003). Model trees as an alternative to neural
networks in rainfall-runoff modelling. Hydrological Sciences Journal, 48:
399–411.
Solomatine, D. P. and Xue, Y. P. (2004). M5 model trees and neural networks:
Application to flood forecasting in the upper reach of the Huai River in China.
Journal of Hydrologic Engineering, 9(6):491–501.
Sorooshian, S., Hsu, K. L., Gao, X. et al. (2000). Evaluation of PERSIANN system
satellite-based estimates of tropical rainfall. Bulletin of the American Meteorological
Society, 81(9):2035–46.
Srivastava, A. N., Oza, N. C. and Stroeve, J. (2005). Virtual sensors: Using data mining
techniques to efficiently estimate remote sensing spectra. IEEE Transactions on
Geoscience and Remote Sensing, 43(3):590–600.
Stacey, M. W., Pond, S. and LeBlond, P. H. (1986). A wind-forced Ekman spiral as a
good statistical fit to low-frequency currents in a coastal strait. Science, 233:470–2.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

340

References

Stogryn, A. P., Butler, C. T. and Bartolac, T. J. (1994). Ocean surface wind retrievals from
Special Sensor Microwave Imager data with neural networks. Journal of
Geophysical Research, 99(C1):981–4.
Storn, R. and Price, K. (1997). Differential evolution – A simple and efficient heuristic for
global optimization over continuous spaces. Journal of Global Optimization,
11:341–59. doi 10.1023/A:1008202821328.
Strang, G. (2005). Linear Algebra and Its Applications. Pacific Grove, CA: Brooks Cole.
Stroeve, J., Holland, M. M., Meier, W., Scambos, T. and Serreze, M. (2007). Arctic sea
ice decline: Faster than forecast. Geophysical Research Letters, 34. L09501,
doi:10.1029/2007GL029703.
Suykens, J. A. K., Van Gestel, T., De Brabanter, J., De Moor, B. and Vandewalle, J.
(2002). Least Squares Support Vector Machines. New Jersey: World Scientific.
Tag, P. M., Bankert, R. L. and Brody, L. R. (2000). An AVHRR multiple cloud-type
classification package. Journal of Applied Meteorology, 39(2):125–34.
Taner, M. T., Berge, T., Walls, J. A. et al. (2001). Well log calibration of Kohonenclassified seismic attributes using Bayesian logic. Journal of Petroleum Geology,
24(4):405–16.
Tang, B. (1995). Periods of linear development of the ENSO cycle and POP forecast
experiments. Journal of Climate, 8:682–91.
Tang, B., Flato, G. M. and Holloway, G. (1994). A study of Arctic sea ice and sea-level
pressure using POP and neural network methods. Atmosphere-Ocean, 32:507–29.
Tang, B. and Mazzoni, D. (2006). Multiclass reduced-set support vector machines. In
Proceedings of the 23rd International Conference on Machine Learning (ICML
2006), Pittsburgh, PA. New York: ACM.
Tang, B. Y., Hsieh, W. W., Monahan, A. H. and Tangang, F. T. (2000). Skill comparisons
between neural networks and canonical correlation analysis in predicting the
equatorial Pacific sea surface temperatures. Journal of Climate, 13(1):287–93.
Tang, Y. (2002). Hybrid coupled models of the tropical Pacific: I. Interannual variability.
Climate Dynamics, 19:331–42.
Tang, Y. and Hsieh, W. W. (2002). Hybrid coupled models of the tropical Pacific: II.
ENSO prediction. Climate Dynamics, 19:343–53.
Tang, Y. and Hsieh, W. W. (2003a). ENSO simulation and prediction in a hybrid coupled
model with data assimilation. Journal of the Meteorological Society of Japan,
81:1–19.
Tang, Y. and Hsieh, W. W. (2003b). Nonlinear modes of decadal and interannual
variability of the subsurface thermal structure in the Pacific Ocean. Journal of
Geophysical Research, 108(C3). 3084, doi: 10.1029/2001JC001236.
Tang, Y., Hsieh, W. W., Tang, B. and Haines, K. (2001). A neural network atmospheric
model for hybrid coupled modelling. Climate Dynamics, 17:445–55.
Tangang, F. T., Hsieh, W. W. and Tang, B. (1997). Forecasting the equatorial Pacific sea
surface temperatures by neural network models. Climate Dynamics, 13:135–47.
Tangang, F. T., Hsieh, W. W. and Tang, B. (1998a). Forecasting the regional sea surface
temperatures of the tropical Pacific by neural network models, with wind stress and
sea level pressure as predictors. Journal of Geophysical Research, 103(C4):
7511–22.
Tangang, F. T., Tang, B., Monahan, A. H. and Hsieh, W. W. (1998b). Forecasting ENSO
events – a neural network-extended EOF approach. Journal of Climate, 11:29–41.
Tenenbaum, J. B., de Silva, V. and Langford, J. C. (2000). A global geometric framework
for nonlinear dimensionality reduction. Science, 290:2319–23.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

References

341

Teng, Q. B., Monahan, A. H., and Fyfe, J. C. (2004). Effects of time averaging on climate
regimes. Geophysical Research Letters, 31(22). L22203, doi:10.1029/
2004GL020840.
Teschl, R. and Randeu, W. L. (2006). A neural network model for short term river flow
prediction. Natural Hazards and Earth System Sciences, 6:629–35.
Teschl, R., Randeu, W. L. and Teschl, F. (2007). Improving weather radar estimates of
rainfall using feed-forward neural networks. Neural Networks, 20:519–27.
Thiria, S., Mejia, C., Badran, F. and Crepon, M. (1993). A neural network approach for
modeling nonlinear transfer functions – application for wind retrieval from
spaceborne scatterometer data. Journal of Geophysical Research,
98(C12):22827–41.
Thompson, D. W. J. and Wallace, J. M. (1998). The Arctic Oscillation signature in the
wintertime geopotential height and temperature fields. Geophysical Research
Letters, 25:1297–300.
Thompson, D. W. J. and Wallace, J. M. (2000). Annular modes in the extratropical
circulation. Part I: Month-to-month variability. Journal of Climate,
13(5):1000–16.
Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine.
Journal of Machine Learning Research, 1:211–44.
Tolman, H. L., Krasnopolsky, V. M. and Chalikov, D. V. (2005). Neural network
approximations for nonlinear interactions in wind wave spectra: direct mapping for
wind seas in deep water. Ocean Modelling, 8:252–78.
Trigo, R. M. and Palutikof, J. P. (1999). Simulation of daily temperatures for climate
change scenarios over Portugal: a neural network model approach. Climate
Research, 13(1):45–59.
Trigo, R. M. and Palutikof, J. P. (2001). Precipitation scenarios over Iberia: A comparison
between direct GCM output and different downscaling techniques. Journal of
Climate, 14(23):4422–46.
Tripathi, S., Srinivas, V. V. and Nanjundiah, R. S. (2006). Downscaling of precipitation
for climate change scenarios: A support vector machine approach. Journal of
Hydrology, 330(3-4):621–40.
Troup, A. J. (1965). The ‘southern oscillation’. Quarterly Journal of the Royal
Meteorological Society, 91:490–506.
UNESCO (1981). The Practical Salinity Scale 1978 and the International Equation of
State for Seawater 1980. Tenth Report of the Joint Panel on Oceanographic Tables
and Standards. Technical Report 36, UNESCO.
van den Boogaard, H. and Mynett, A. (2004). Dynamic neural networks with data
assimilation. Hydrological Processes, 18:1959–66.
van Laarhoven, P. J. and Aarts, E. H. (1987). Simulated Annealing: Theory and
Applications. Dordrecht: Reidel.
Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Berlin: Springer Verlag.
Vapnik, V. N. (1998). Statistical Learning Theory. New York: Wiley.
Vecchi, G. A. and Bond, N. A. (2004). The Madden–Julian Oscillation (MJO) and
northern high latitude wintertime surface air temperatures. Geophysical Research
Letters, 31. L04104, doi: 10.1029/2003GL018645.
Villmann, T., Merenyi, E. and Hammer, B. (2003). Neural maps in remote sensing image
analysis. Neural Networks, 16(3-4):389–403.
von Storch, H. (1999). On the use of ‘inflation’ in statistical downscaling. Journal of
Climate, 12(12):3505–6.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

342

References

von Storch, H., Bruns, T., Fischer-Bruns, I. and Hasselman, K. (1988). Principal
Oscillation Pattern Analysis of the 30 to 60 day oscillation in general circulation
model equatorial troposphere. Journal of Geophysical Research, 93(D9):
11021–36.
von Storch, H., Burger, G., Schnur, R. and von Storch, J.-S. (1995). Principal oscillation
patterns: A review. Journal of Climate, 8(3):377–400.
von Storch, H. and Zwiers, F. W. (1999). Statistical Analysis in Climate Research.
Cambridge, UK: Cambridge University Press.
Wallace, J. M. (1972). Empirical orthogonal representation of time series in the frequency
domain. Part II: Application to the study of tropical wave disturbances. Journal of
Applied Meteorology, 11:893–990.
Wallace, J. M. and Dickinson, R. E. (1972). Empirical orthogonal representation of time
series in the frequency domain. Part I: Theoretical considerations. Journal of
Applied Meteorology, 11(6):887–92.
Wallace, J. M. and Gutzler, D. S. (1981). Teleconnections in the geopotential height fields
during the northern hemisphere winter. Monthly Weather Review, 109:784–812.
Wallace, J. M., Smith, C. and Bretherton, C. S. (1992). Singular value decomposition of
wintertime sea surface temperature and 500 mb height anomalies. Journal of
Climate, 5(6):561–76.
Walter, A., Denhard, M. and Schonwiese, C.-D. (1998). Simulation of global and
hemispheric temperature variations and signal detection studies using neural
networks. Meteorologische Zeitschrift, N.F.7:171–80.
Walter, A. and Schonwiese, C. D. (2002). Attribution and detection of anthropogenic
climate change using a backpropagation neural network. Meteorologische
Zeitschrift, 11(5):335–43.
Wang, W. J., Lu, W. Z., Wang, X. K. and Leung, A. Y. T. (2003). Prediction of maximum
daily ozone level using combined neural network and statistical characteristics.
Environment International, 29(5):555–62.
Webb, A. R. (1999). A loss function approach to model selection in nonlinear principal
components. Neural Networks, 12:339–45.
Weichert, A. and Bürger, G. (1998). Linear versus nonlinear techniques in downscaling.
Climate Research, 10(2):83–93.
Weigend, A. S. and Gershenfeld, N. A., eds., (1994). Time Series Prediction: Forecasting
the Future and Understanding the Past. Santa Fe Institute Studies in the Sciences of
Complexity, Proceedings vol. XV. Addison-Wesley.
Werbos, P. J. (1974). Beyond regression: new tools for prediction and analysis in the
behavioural sciences. Ph.D. thesis, Harvard University.
Widrow, B. and Hoff, M. E. (1960). Adaptive switching circuits. In IRE WESCON
Convention Record, volume 4, pp. 96–104, New York.
Wilby, R. L., Dawson, C. W. and Barrow, E. M. (2002). SDSM – a decision support tool
for the assessment of regional climate change impacts. Environmental Modelling &
Software, 17(2):147–59.
Wilcox, R. R. (2004). Robust Estimation and Hypothesis Testing. Amsterdam: Elsevier.
Wilks, D. S. (1995). Statistical Methods in the Atmospheric Sciences. San Diego:
Academic Press.
Willmott, C. J. (1982). Some comments on the evaluation of model performance. Bulletin
of the American Meteorological Society, 63(11):1309–13.
Wilson, L. J. and Vallée, M. (2002). The Canadian Updateable Model Output Statistics
(UMOS) system: Design and development tests. Weather and Forecasting, 17(2):
206–22.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

References

343

Woodruff, S. D., Slutz, R. J., Jenne, R. L. and Steurer, P. M. (1987). A comprehensive
ocean-atmosphere data set. Bulletin of the American Meteorological Society, 68:
1239–50.
Wu, A. and Hsieh, W. W. (2002). Nonlinear canonical correlation analysis of the tropical
Pacific wind stress and sea surface temperature. Climate Dynamics, 19:713–22. doi
10.1007/s00382-002-0262-8.
Wu, A. and Hsieh, W. W. (2003). Nonlinear interdecadal changes of the El Nino-Southern
Oscillation. Climate Dynamics, 21:719–30.
Wu, A. and Hsieh, W. W. (2004a). The nonlinear association between ENSO and the
Euro-Atlantic winter sea level pressure. Climate Dynamics, 23:859–68. doi:
10.1007/s00382-004-0470-5.
Wu, A. and Hsieh, W. W. (2004b). The nonlinear Northern Hemisphere atmospheric
response to ENSO. Geophysical Research Letters, 31. L02203,
doi:10.1029/2003GL018885.
Wu, A., Hsieh, W. W. and Shabbar, A. (2005). The nonlinear patterns of North American
winter temperature and precipitation associated with ENSO. Journal of Climate,
18:1736–52.
Wu, A., Hsieh, W. W., Shabbar, A., Boer, G. J. and Zwiers, F. W. (2006a). The nonlinear
association between the Arctic Oscillation and North American winter climate.
Climate Dynamics, 26:865–79.
Wu, A., Hsieh, W. W. and Tang, B. (2006b). Neural network forecasts of the tropical
Pacific sea surface temperatures. Neural Networks, 19:145–54.
Wu, A., Hsieh, W. W. and Zwiers, F. W. (2003). Nonlinear modes of North American
winter climate variability detected from a general circulation model. Journal of
Climate, 16:2325–39.
Xu, J. S. (1992). On the relationship between the stratospheric Quasi-Biennial Oscillation
and the tropospheric Southern Oscillation. Journal of the Atmospheric Sciences,
49(9):725–34.
Xu, J.-S. and von Storch, H. (1990). Predicting the state of the Southern Oscillation using
principal oscillation pattern analysis. Journal of Climate, 3:1316–29.
Yacoub, M., Badran, F. and Thiria, S. (2001). A topological hierarchical clustering:
Application to ocean color classification. In Artificial Neural Networks-ICANN
2001, Proceedings. Lecture Notes in Computer Science., volume 2130, pp. 492–499.
Berlin: Springer.
Ye, Z. and Hsieh, W. W. (2006). The influence of climate regime shift on ENSO. Climate
Dynamics, 26:823–33.
Yhann, S. R. and Simpson, J. J. (1995). Application of neural networks to AVHRR cloud
segmentation. IEEE Transactions on Geoscience and Remote Sensing,
33(3):590–604.
Yi, J. S. and Prybutok, V. R. (1996). A neural network model forecasting for prediction of
daily maximum ozone concentration in an industrialized urban area. Environmental
Pollution, 92(3):349–57.
Yu, P. S., Chen, S. T. and Chang, I. F. (2006). Support vector regression for real-time
flood stage forecasting. Journal of Hydrology, 328(3-4):704–16.
Yu, X. Y. and Liong, S. Y. (2007). Forecasting of hydrologic time series with ridge
regression in feature space. Journal of Hydrology, 332(3-4):290–302.
Yuval (2000). Neural network training for prediction of climatological time series;
regularized by minimization of the Generalized Cross Validation function. Monthly
Weather Review, 128:1456–73.

Downloaded from https://www.cambridge.org/core. University of Warwick, on 20 May 2018 at 23:30:30, subject to the Cambridge Core terms of use,
available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/CBO9780511627217.015

344

References

Yuval (2001). Enhancement and error estimation of neural network prediction of Niño 3.4
SST anomalies. Journal of Climate, 14:2150–63.
Yuval and Hsieh, W. W. (2002). The impact of time-averaging on the detectability of
nonlinear empirical relations. Quarterly Journal of the Royal Meteorological
Society, 128:1609–22.
Yuval and Hsieh, W. W. (2003). An adaptive nonlinear MOS scheme for precipitation
forecasts using neural networks. Weather and Forecasting, 18(2):303–10.
Zebiak, S. E. and Cane, M. A. (1987). A model El Niño – Southern Oscillation. Monthly
Weather Review, 115(10):2262–78.
Zhang, C. (2005). Madden–Julian Oscillation. Reviews of Geophysics, 43. RG2003,
doi:10.1029/2004RG000158.
Zhang, X., Hogg, W. D. and Mekis, E. (2001). Spatial and temporal characteristics of
heavy precipitation events over Canada. Journal of Climate, 14:1923–36.
Zorita, E. and von Storch, H. (1999). The analog method as a simple statistical
downscaling technique: Comparison with more complicated methods. Journal of
Climate, 12(8):2474–89.

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