Breiman, L., 2001: Random Forests. Mach. Learn., 45, 5–32, doi:10.1023/A:1010933404324.
Breiman, L., J. H. Friedman, R. A. Olshen, and C. J. Stone, 1984: Classification and Regression Trees. Chapman and Hall/CRC.
Brock, F. V., K. C. Crawford, R. L. Elliott, G. W. Cuperus, S. J. Stadler, H. L. Johnson, and M. D. Eilts, 1995: The Oklahoma Mesonet: A technical overview. J. Atmos. Oceanic Technol., 12, 5–19, doi:10.1175/1520-0426(1995)012<0005:TOMATO>2.0.CO;2.
Freund, Y., and R. E. Schapire, 1995: A desicion-theoretic generalization of on-line learning and an application to boosting. Computational Learning Theory, Springer, 23–37.
Friedman, J. H., 2001: Greedy function approximation: a gradient boosting machine. Ann. Stat., 29, doi:10.1214/aos/1013203451.
Hamill, T. M., G. T. Bates, J. S. Whitaker, D. R. Murray, M. Fiorino, T. J. Galarneau, Y. Zhu, and W. Lapenta, 2013: NOAA’s second-generation global medium-range ensemble reforecast dataset. Bull. Amer. Meteor. Soc., 94, 1553–1565, doi:10.1175/BAMS-D-12-00014.1.
Lakshmanan, V., and J. S. Kain, 2010: A gaussian mixture model approach to forecast verification. Wea. Forecasting, 25, 908–920, doi:10.1175/2010WAF2222355.1.
Lakshmanan, V., K. L. Elmore, and M. B. Richman, 2010: Reaching scientific consensus through a competition. Bull. Amer. Meteor. Soc., 91, 1423–1427, doi:10.1175/2010BAMS2870.1.
McPherson, R. A., and Coauthors, 2007: Statewide monitoring of the mesoscale environment: A technical update on the Oklahoma Mesonet. J. Atmos. Oceanic Technol., 24, 301–321, doi:10.1175/JTECH1976.1.
Nelder, J. A., and R. Mead, 1965: A simplex algorithm for function minimization. Comput. J., 7, 308–313, doi:10.1093/comjnl/7.4.308.
Pedregosa, F., and Coauthors, 2011: Scikit-learn: Machine learning in Python. J. Mach. Learn. Res., 12, 2825–2830.
Rasmussen, C. E., and C. K. I. Williams, 2005: Gaussian Processes for Machine Learning. The MIT Press, 266 pp.
Saha, S., and Coauthors, 2010: The NCEP climate forecast system reanalysis. Bull. Amer. Meteor. Soc., 91, 1015–1057, doi:10.1175/2010BAMS3001.1.
Shafer, M. A., C. A. Fiebrich, D. S. Arndt, S. E. Fredrickson, and T. W. Hughes, 2000: Quality assurance procedures in the quality assurance procedures in the Oklahoma Mesonetwork. J. Atmos. Oceanic Technol., 17, 474–494, doi:10.1175/1520-0426(2000)017<0474:QAPITO>2.0.CO;2.
Wei, M., Z. Toth, R. Wobus, and Y. Zhu, 2008: Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operation forecast system. Tellus, 60A, 62–79.
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Displayed acceptance dates for articles published prior to 2023 are approximate to within a week. If needed, exact acceptance dates can be obtained by emailing amsjol@ametsoc.org.
Breiman, L., 2001: Random Forests. Mach. Learn., 45, 5–32, doi:10.1023/A:1010933404324.
Breiman, L., J. H. Friedman, R. A. Olshen, and C. J. Stone, 1984: Classification and Regression Trees. Chapman and Hall/CRC.
Brock, F. V., K. C. Crawford, R. L. Elliott, G. W. Cuperus, S. J. Stadler, H. L. Johnson, and M. D. Eilts, 1995: The Oklahoma Mesonet: A technical overview. J. Atmos. Oceanic Technol., 12, 5–19, doi:10.1175/1520-0426(1995)012<0005:TOMATO>2.0.CO;2.
Freund, Y., and R. E. Schapire, 1995: A desicion-theoretic generalization of on-line learning and an application to boosting. Computational Learning Theory, Springer, 23–37.
Friedman, J. H., 2001: Greedy function approximation: a gradient boosting machine. Ann. Stat., 29, doi:10.1214/aos/1013203451.
Hamill, T. M., G. T. Bates, J. S. Whitaker, D. R. Murray, M. Fiorino, T. J. Galarneau, Y. Zhu, and W. Lapenta, 2013: NOAA’s second-generation global medium-range ensemble reforecast dataset. Bull. Amer. Meteor. Soc., 94, 1553–1565, doi:10.1175/BAMS-D-12-00014.1.
Lakshmanan, V., and J. S. Kain, 2010: A gaussian mixture model approach to forecast verification. Wea. Forecasting, 25, 908–920, doi:10.1175/2010WAF2222355.1.
Lakshmanan, V., K. L. Elmore, and M. B. Richman, 2010: Reaching scientific consensus through a competition. Bull. Amer. Meteor. Soc., 91, 1423–1427, doi:10.1175/2010BAMS2870.1.
McPherson, R. A., and Coauthors, 2007: Statewide monitoring of the mesoscale environment: A technical update on the Oklahoma Mesonet. J. Atmos. Oceanic Technol., 24, 301–321, doi:10.1175/JTECH1976.1.
Nelder, J. A., and R. Mead, 1965: A simplex algorithm for function minimization. Comput. J., 7, 308–313, doi:10.1093/comjnl/7.4.308.
Pedregosa, F., and Coauthors, 2011: Scikit-learn: Machine learning in Python. J. Mach. Learn. Res., 12, 2825–2830.
Rasmussen, C. E., and C. K. I. Williams, 2005: Gaussian Processes for Machine Learning. The MIT Press, 266 pp.
Saha, S., and Coauthors, 2010: The NCEP climate forecast system reanalysis. Bull. Amer. Meteor. Soc., 91, 1015–1057, doi:10.1175/2010BAMS3001.1.
Shafer, M. A., C. A. Fiebrich, D. S. Arndt, S. E. Fredrickson, and T. W. Hughes, 2000: Quality assurance procedures in the quality assurance procedures in the Oklahoma Mesonetwork. J. Atmos. Oceanic Technol., 17, 474–494, doi:10.1175/1520-0426(2000)017<0474:QAPITO>2.0.CO;2.
Wei, M., Z. Toth, R. Wobus, and Y. Zhu, 2008: Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operation forecast system. Tellus, 60A, 62–79.
All Time | Past Year | Past 30 Days | |
---|---|---|---|
Abstract Views | 0 | 0 | 0 |
Full Text Views | 8217 | 7303 | 57 |
PDF Downloads | 760 | 106 | 4 |