• AAAI, 2017: A brief history of AI. Association for the Advancement of Artificial Intelligence, accessed 8 December 2017, https://aitopics.org/misc/brief-history.

  • Abatzoglou, J. T., C. A. Kolden, J. K. Balch, and B. A. Bradley, 2016: Controls on interannual variability in lightning-caused fire activity in the western US. Environ. Res. Lett., 11, 045005, https://doi.org/10.1088/1748-9326/11/4/045005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adegoke, J. O., R. A. Pielke, J. Eastman, R. Mahmood, and K. G. Hubbard, 2003: Impact of irrigation on midsummer surface fluxes and temperature under dry synoptic conditions: A regional atmospheric model study of the U.S. High Plains. Mon. Wea. Rev., 131, 556564, https://doi.org/10.1175/1520-0493(2003)131<0556:IOIOMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Albini, F. A., 1976: Estimating wildfire behavior and effects. USDA Forest Service Intermountain Forest and Range Experiment Station General Tech. Rep. INT-30, 93 pp., https://www.fs.fed.us/rm/pubs_int/int_gtr030.pdf.

  • Albini, F. A., 1983: Potential spotting distance from wind-driven surface fires. USDA Forest Service Intermountain Forest and Range Experiment Station Res. Paper INT-309, 30 pp., https://www.frames.gov/documents/behaveplus/publications/Albini_1983_INT-RP-309_ocr.pdf.

  • Alessandrini, S., L. Delle Monache, S. Sperati, and J. Nissen, 2015a: A novel application of an analog ensemble for short-term wind power forecasting. Renew. Energy, 76, 768781, https://doi.org/10.1016/j.renene.2014.11.061.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alessandrini, S., L. Delle Monache, S. Sperati, and G. Cervone, 2015b: An analog ensemble for short-term probabilistic solar power forecast. Appl. Energy, 157, 95110, https://doi.org/10.1016/j.apenergy.2015.08.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Allen, C. T., S. E. Haupt, and G. S. Young, 2007a: Source characterization with a receptor/dispersion model coupled with a genetic algorithm. J. Appl. Meteor. Climatol., 46, 273287, https://doi.org/10.1175/JAM2459.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Allen, C. T., G. S. Young, and S. E. Haupt, 2007b: Improving pollutant source characterization by optimizing meteorological data with a genetic algorithm. Atmos. Environ., 41, 22832289, https://doi.org/10.1016/j.atmosenv.2006.11.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alter, R. E., E.-S. Im, and E. A. B. Eltahir, 2015a: Rainfall consistently enhanced around the Gezira scheme in East Africa due to irrigation. Nat. Geosci., 8, 763767, https://doi.org/10.1038/ngeo2514.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alter, R. E., Y. Fan, B. R. Lintner, and C. P. Weaver, 2015b: Observational evidence that Great Plains irrigation has enhanced summer precipitation intensity and totals in the midwestern United States. J. Hydrometeor., 16, 17171735, https://doi.org/10.1175/JHM-D-14-0115.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alter, R. E., H. C. Douglas, J. M. Winter, and E. A. B. Eltahir, 2018: Twentieth century regional climate change during the summer in the central United States attributed to agricultural intensification. Geophys. Res. Lett., 45, 15861594, https://doi.org/10.1002/2017GL075604.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Altieri, M., and C. Nicholls, 2017: The adaptation and mitigation potential of traditional agriculture in a changing climate. Climatic Change, 140, 3345, https://doi.org/10.1007/s10584-013-0909-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aly, A. H., and R. C. Peralta, 1999a: Comparison of a genetic algorithm and mathematical programming to the design of groundwater cleanup systems. Water Resour. Res., 35, 24152425, https://doi.org/10.1029/1998WR900128.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aly, A. H., and R. C. Peralta, 1999b: Optimal design of aquifer cleanup systems under uncertainty using a neural network and a genetic algorithm. Water Resour. Res., 35, 25232532, https://doi.org/10.1029/98WR02368.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, H. E., 1982: Aids to determining fuel models for estimating fire behavior. USDA Forest Service General Tech. Rep. INT-122, 22 pp., https://www.fs.fed.us/rm/pubs_int/int_gtr122.pdf.

    • Crossref
    • Export Citation
  • Andrews, P. L., 1986: BEHAVE: Fire behavior prediction and modeling system – BURN subsystem part 1. USDA Forest Service Intermountain Forest and Range Experiment Station General Tech. Rep. INT-194, 130 pp., https://www.fs.fed.us/rm/pubs_int/int_gtr194.pdf.

    • Crossref
    • Export Citation
  • Andrews, P. L., 2014: Current status and future needs of the BehavePlus fire modeling system. Int. J. Wildland Fire, 23, 2133, https://doi.org/10.1071/WF12167.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anthes, R., and T. T. Warner, 1974: Prediction of mesoscale flows over complex terrain. U.S Army Research Development Tech. Rep. ECOM-5532, White Sands Missile Range, 101 pp.

  • Anthes, R., and T. T. Warner, 1978: Development of hydrodynamic models suitable for air pollution and other mesometerological studies. Mon. Wea. Rev., 106, 10451078, https://doi.org/10.1175/1520-0493(1978)106<1045:DOHMSF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Averyt, K., J. Fisher, A. Huber-Lee, A. Lewis, J. Macknick, N. Madden, J. Rogers, and S. Tellinghuisen, 2011. Freshwater use by U.S. power plants: Electricity’s thirst for a precious resource—A report of the Energy and Water in a Warming World initiative. Union of Concerned Scientists Rep., 62 pp., http://www.ucsusa.org/assets/documents/clean_energy/ew3/ew3-freshwater-use-by-us-power-plants.pdf.

  • Ball, J. T., I. E. Woodrow, and J. A. Berry, 1987: A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions. Progress in Photosynthesis Research, Vol. 4, J. Biggins, Ed., Springer, 221–224, https://link.springer.com/chapter/10.1007/978-94-017-0519-6_48#citeas.

  • Barnston, A. G., and P. T. Schickedanz, 1984: The effect of irrigation on warm season precipitation in the southern Great Plains. J. Climate Appl. Meteor., 23, 865888, https://doi.org/10.1175/1520-0450(1984)023<0865:TEOIOW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bastiaanssen, W. G. M., and P. Steduto, 2017: The water productivity score (WPS) at global and regional level: Methodology and first results from remote sensing measurements of wheat, rice and maize. Sci. Total Environ., 575, 595611, https://doi.org/10.1016/j.scitotenv.2016.09.032.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G, J. M. Brown, G. Brunet, P. Lynch, K. Saito, and T. W. Schlatter, 2019: 100 years of progress in forecasting and NWP applications. A Century of Progress in Atmospheric and Related Sciences: Celebrating the American Meteorological Society Centennial, Meteor. Monogr., No. 59, Amer. Meteor. Soc., https://doi.org/10.1175/AMSMONOGRAPHS- D-18-0020.1.

    • Crossref
    • Export Citation
  • Bieringer, P. E., G. S. Young, L. M. Rodriguez, A. J. Annunzio, F. Vandenberghe, and S. E. Haupt, 2017: Paradigms and commonalities in atmospheric source term estimation methods. Atmos. Environ., 156, 102112, https://doi.org/10.1016/j.atmosenv.2017.02.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Birkeland, K., 1914: A possible connection between magnetic and meteorologic phenomena. Mon. Wea. Rev., 42, 211, https://doi.org/10.1175/1520-0493(1914)42<211a:APCBMA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boteler, D. H., 2001: Space weather effects on power systems. Space Weather, Geophys. Monogr., Vol. 125, Amer. Geophys. Union, 347–352, https://doi.org/10.1029/GM125p0347.

    • Crossref
    • Export Citation
  • Boucher, O., G. Myhre, and A. Myhre, 2004: Direct human influence of irrigation on atmospheric water vapour and climate. Climate Dyn., 22, 597603, https://doi.org/10.1007/s00382-004-0402-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bova, A. S., W. E. Mell, and C. M. Hoffman, 2016: A comparison of level set and marker methods for the simulation of wildland fire front propagation. Int. J. Wildland Fire, 25, 229241, https://doi.org/10.1071/WF13178.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Braneon, C., 2014: Agricultural water demand assessment in the Southeast U.S. under climate change. Ph.D. dissertation, Georgia Institute of Technology, 240 pp., https://smartech.gatech.edu/handle/1853/53409.

  • Branstator, G., and S. E. Haupt, 1998: An empirical model of barotropic atmospheric dynamics and its response to tropical forcing. J. Climate, 11, 26452667, https://doi.org/10.1175/1520-0442(1998)011<2645:AEMOBA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, M. E., E. R. Carr, K. L. Grace, K. Wiebe, C. C. Funk, W. Attavanich, P. Backlund, and L. Buja, 2017: Do markets and trade help or hurt the global food system adapt to climate change? Food Policy, 68, 154159, https://doi.org/10.1016/j.foodpol.2017.02.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Burke, M., and K. Emerick, 2016: Adaptation to climate change: Evidence from US agriculture. Amer. Econ. J. Econ. Policy, 8, 106140, https://doi.org/10.1257/pol.20130025.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Calkin, D. E., M. P. Thompson, M. A. Finney, and K. D. Hyde, 2011: A Real-Time Risk Assessment Tool Supporting Wildland Fire Decisionmaking. USDA Forest Service/UNL Faculty Publications, 359 pp.

  • Campbell, S. D., and S. H. Olson, 1987: Recognizing low-altitude wind shear hazards from Doppler weather radar: An artificial intelligence approach. J. Atmos. Oceanic Technol., 4, 518, https://doi.org/10.1175/1520-0426(1987)004<0005:RLAWSH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cannon, A. J., 2006: Nonlinear principal predictor analysis: Applications to the Lorenz system. J. Climate, 19, 579589, https://doi.org/10.1175/JCLI3634.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carpenter, F. A., 1919: Convectional clouds induced by forest fire. Mon. Wea. Rev., 47, 143144, https://doi.org/10.1175/1520-0493(1919)47<143:CCIBFF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cervone, G., and P. Franzese, 2011: Non-Darwinian evolution for the source detection of atmospheric releases. Atmos. Environ., 45, 44974506, https://doi.org/10.1016/j.atmosenv.2011.04.054.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cervone, G., L. Clemente-Harding, S. Alessandrini, and L. Delle Monache, 2017: Short-term photovoltaic power forecasts using artificial neural networks and an analog ensemble. Renew. Energy, 108, 274286, https://doi.org/10.1016/j.renene.2017.02.052.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan Hilton, A. B., and T. B. Culver, 2000: Constraint handling for genetic algorithms in optimal remediation design. J. Water Resour. Plann. Manage., 126, 128137, https://doi.org/10.1061/(ASCE)0733-9496(2000)126:3(128).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Changnon, D., M. Sandstrom, and C. Schaffer, 2003: Relating changes in agricultural practices to increasing dew points in extreme Chicago heat waves. Climate Res., 24, 243254, https://doi.org/10.3354/cr024243.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Changnon, S. A., 2001: Thunderstorm rainfall in the conterminous United States. Bull. Amer. Meteor. Soc., 82, 19251940, https://doi.org/10.1175/1520-0477(2001)082<1925:TRITCU>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chapman, S., 1957: The aurora in middle and low latitudes. Nature, 179, 711, https://doi.org/10.1038/179007a0.

  • Charbonneau, P., 2010: Dynamo models of the solar cycle. Living Rev. Sol. Phys., 7, 3, https://doi.org/10.12942/lrsp-2010-3.

  • Charney, J. G., R. Fjörtoft, and J. von Neumann, 1950: Numerical integration of the barotropic vorticity equation. Tellus, 2, 237254, https://doi.org/10.3402/tellusa.v2i4.8607.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and Coauthors, 2007: Description and evaluation of the characteristics of the NCAR high-resolution land data assimilation system. J. Appl. Meteor. Climatol., 46, 694713, https://doi.org/10.1175/JAM2463.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., X. Xu, M. Barlage, R. Rasmussen, S. Shen, S. Miao, and G. Zhou, 2018: Memory of irrigation effects on hydroclimate and its modeling challenge. Environ. Res. Lett., 13, 064009, https://doi.org/10.1088/1748-9326/aab9df.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, S., X. Chen, and J. Xu, 2016: Impacts of climate change on agriculture: Evidence from China. J. Environ. Econ. Manage., 76, 105124, https://doi.org/10.1016/j.jeem.2015.01.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, T. L., and W. D. Hall, 1991: Multi-domain simulations of the time dependent Navier–Stokes equation: Benchmark error analyses of nesting procedures. J. Comput. Phys., 92, 456481, https://doi.org/10.1016/0021-9991(91)90218-A.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, T. L., M. A. Jenkins, J. Coen, and D. Packham, 1996: A coupled atmosphere–fire model: Convective feedback on fire-line dynamics. J. Appl. Meteor., 35, 875901, https://doi.org/10.1175/1520-0450(1996)035<0875:ACAMCF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, T. L., J. Coen, and D. Latham, 2004: Description of a coupled atmosphere–fire model. Int. J. Wildland Fire, 13, 4963, https://doi.org/10.1071/WF03043.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clements, C. B., and Coauthors, 2016: Fire weather conditions and fire–atmosphere interactions observed during low-intensity prescribed fires—RxCADRE 2012. Int. J. Wildland Fire, 25, 90101, https://doi.org/10.1071/WF14173.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coen, J., M. Cameron, J. Michalakes, E. G. Patton, P. J. Riggan, and K. J. Yedinak, 2013: WRF-Fire: Coupled weather–wildland fire modeling with the Weather Research and Forecasting Model. J. Appl. Meteor. Climatol., 52, 1638, https://doi.org/10.1175/JAMC-D-12-023.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collatz, G. J., J. T. Ball, C. Grivet, and J. A. Berry, 1991: Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: A model that includes a laminar boundary layer. Agric. For. Meteor., 54, 107136, https://doi.org/10.1016/0168-1923(91)90002-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collins, W., and P. Tissot, 2015: An artificial neural network model to predict thunderstorms within 400 km2 south Texas domains. Meteor. Appl., 22, 650655, https://doi.org/10.1002/met.1499.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cook, B. I., R. L. Miller, and R. Seager, 2009: Amplification of the North American “Dust Bowl” drought through human-induced land degradation. Proc. Natl. Acad. Sci. USA, 106, 49975001, https://doi.org/10.1073/pnas.0810200106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cook, B. I., S. P. Shukla, M. J. Puma, and L. S. Nazarenko, 2015: Irrigation as an historical climate forcing. Climate Dyn., 44, 17151730, https://doi.org/10.1007/s00382-014-2204-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cooley, H., J. Fulton, and P. H. Gleick, 2011: Water for energy: Future water needs for electricity in the Intermountain West. Pacific Institute Rep., 64 pp., https://pacinst.org/publication/water-for-energy-future-water-needs-for-electricity-in-the-intermountain-west/.

  • Cox, D. T., P. Tissot, and P. Michaud, 2002: Water level observations and short-term predictions including meteorological events for entrance of Galveston Bay, Texas. J. Waterway Port Coastal Ocean Eng., 128, 2129, https://doi.org/10.1061/(ASCE)0733-950X(2002)128:1(21).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cummins, K. L., M. J. Murphy, E. A. Bardo, W. L. Hiscox, R. B. Pyle, and A. E. Pifer, 1998: A combined TOA/MDF technology upgrade of the U.S. National Lightning Detection Network. J. Geophys. Res., 103, 90359044, https://doi.org/10.1029/98JD00153.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davin, E. L., S. I. Seneviratne, P. Ciais, A. Olioso, and T. Wang, 2014: Preferential cooling of hot extremes from cropland albedo management. Proc. Natl. Acad. Sci. USA, 111, 97579761, https://doi.org/10.1073/pnas.1317323111.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeAngelis, A., F. Dominguez, Y. Fan, A. Robock, M. D. Kustu, and D. Robinson, 2010: Evidence of enhanced precipitation due to irrigation over the Great Plains of the United States. J. Geophys. Res., 115, D15115, https://doi.org/10.1029/2010JD013892.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Jager, C., 2002: Early Solar Space Research. Kluwer Academic, 203 pp.

    • Crossref
    • Export Citation
  • Delle Monache, L., T. Nipen, Y. Liu, G. Roux, and R. Stull, 2011: Kalman filter and analog schemes to post-process numerical weather predictions. Mon. Wea. Rev., 139, 35543570, https://doi.org/10.1175/2011MWR3653.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Delle Monache, L., F. A. Eckel, D. L. Rife, B. Nagarajan, and K. Searight, 2013: Probabilistic weather prediction with an analog ensemble. Mon. Wea. Rev., 141, 34983516, https://doi.org/10.1175/MWR-D-12-00281.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • del Toro Iniesta, J.-C., and B. Ruiz Cobo, 2016: Inversion of the radiative transfer equation for polarized light. Living Rev. Sol. Phys., 13, 4, https://doi.org/10.1007/s41116-016-0005-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Djalalova, I., L. Delle Monache, and J. Wilczak, 2015: PM2.5 analog forecast and Kalman filtering post-processing for the Community Multiscale Air Quality (CMAQ) model. Atmos. Environ., 119, 431442, https://doi.org/10.1016/j.atmosenv.2015.05.057.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DOE, 2015: Wind vision: A new era for wind power in United States. U.S. Department of Energy Rep. DOE/GO-102015-4557, 291 pp., http://www.energy.gov/windvision.

  • Drewniak, B., J. Song, J. Prell, V. R. Kotamarthi, and R. Jacob, 2013: Modeling agriculture in the Community Land Model. Geosci. Model Dev., 6, 495515, https://doi.org/10.5194/gmd-6-495-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eastes, R. W., and Coauthors, 2017: The Global-Scale Observations of the Limb and Disk (GOLD) mission. Space Sci. Rev., 212, 383408, https://doi.org/10.1007/s11214-017-0392-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • EcoWest, 2013: Wildfire ignition trends: Humans vs. lightning. Accessed 1 August 2018, http://ecowest.org/2013/06/04/wildfire-ignition-trends-humans-versus-lightning/.

  • Eddy, J. A., C. K. Stidd, W. B. Fowler, and J. D. Helvey, 1975: Irrigation increases rainfall? Science, 188, 279281, https://doi.org/10.1126/science.188.4185.279.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Edlen, B., 1945: The identification of the coronal lines. Mon. Not. Roy. Astron. Soc., 105, 323333, https://doi.org/10.1093/mnras/105.6.323.

  • EIA, 2015: Electric power monthly with data for December 2014. U.S. Energy Information Association, accessed February 2015, https://www.eia.gov/electricity/monthly/archive/february2015.pdf.

  • Elio, R., J. de Haan, and G. S. Strong, 1987: METEOR: An artificial intelligence system for convective storm forecasting. J. Atmos. Oceanic Technol., 4, 1928, https://doi.org/10.1175/1520-0426(1987)004<0019:MAAISF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elliott, J., and Coauthors, 2015: The Global Gridded Crop Model Intercomparison: Data and modeling protocols for phase 1 (v1.0). Geosci. Model Dev., 8, 261277, https://doi.org/10.5194/gmd-8-261-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elmore, K. L., and H. Grams, 2016: Using mPING data to generate random forests for precipitation type forecasts. 14th Conf. on Artificial and Computational Intelligence and Its Applications to the Environmental Sciences, New Orleans, LA, Amer. Meteor. Soc., 4.2, https://ams.confex.com/ams/96Annual/webprogram/Paper289684.html.

  • Elmore, K. L., Z. L. Flamig, V. Lakshmanan, B. T. Kaney, V. Farmer, H. D. Reeves, and L. P. Rothfusz, 2014: MPING: Crowd-sourcing weather reports for research. Bull. Amer. Meteor. Soc., 95, 13351342, https://doi.org/10.1175/BAMS-D-13-00014.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elmore, K. L., H. M. Grams, D. Apps, and H. D. Reeves, 2015: Verifying forecast precipitation type with mPING. Wea. Forecasting, 30, 656657, https://doi.org/10.1175/WAF-D-14-00068.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Entekhabi, D., and Coauthors, 2010: The Soil Moisture Active Passive (SMAP). Mission. Proc. IEEE, 98, 704716, https://doi.org/10.1109/JPROC.2010.2043918.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Espy, J. P., 1919: Rain from cumulus clouds over fires. Mon. Wea. Rev., 47, 145–147, https://doi.org/10.1175/1520-0493(1919)47<145:RFCCOF>2.0.CO;2. (Originally published in 1857 in the Fourth Meteorological Report of Prof. James P. Espy., 34th Cong. 3rd Sess. Senate Ex. Doc. 65, 29–36.)

  • Fayad, H., 2001: Application of neural networks and genetic algorithms for solving conjunctive water use problems. Ph.D. dissertation, Utah State University, 152 pp.

  • Filippi, J. B., and Coauthors, 2009: Coupled atmosphere–wildland fire modelling. J. Adv. Model. Earth Syst., 1 (11), https://doi.org/10.3894/JAMES.2009.1.11.

    • Search Google Scholar
    • Export Citation
  • Finney, M. A., 1998: FARSITE: Fire area simulator-model development and evaluation. USDA Forest Service Rocky Mountain Research Station Res. Pap. RMRS-RP-4, 47 pp., https://www.frames.gov/documents/behaveplus/publications/Finney_1998_RMRS-RP-4.pdf.

    • Crossref
    • Export Citation
  • Finney, M. A., J. D. Cohen, S. S. McAllister, and W. M. Jolly, 2013: On the need for a theory of wildland fire spread. Int. J. Wildland Fire, 22, 2536, https://doi.org/10.1071/WF11117.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Finney, M. A., and Coauthors, 2015: Role of buoyant flame dynamics in wildfire spread. Proc. Natl. Acad. Sci. USA, 112, 98339838, https://doi.org/10.1073/pnas.1504498112.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fisher, J., and F. Ackerman, 2011. The water-energy nexus in the western states: Projections to 2100. Stockholm Environment Institute Rep., 39 pp., https://www.sei.org/publications/the-water-energy-nexus-in-the-western-states-projections-to-2100/.

  • Forthofer, J. M., B. W. Butler, and N. S. Wagenbrenner, 2014a: A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. Part I. Model formulation and comparison against measurements. Int. J. Wildland Fire, 23, 969981, https://doi.org/10.1071/WF12089.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Forthofer, J. M., B. W. Butler, C. W. McHugh, M. A. Finney, L. S. Bradshaw, R. D. Stratton, K. S. Shannon, and N. S. Wagenbrenner, 2014b: A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. Part II. An exploratory study of the effect of simulated winds on fire growth simulations. Int. J. Wildland Fire, 23, 982994, https://doi.org/10.1071/WF12090.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fuglie, K., and S. L. Wang, 2012: New evidence points to robust but uneven productivity growth in global agriculture. Amber Waves, 20 September 2012, U.S. Department of Agriculture, https://www.ers.usda.gov/amber-waves/2012/september/global-agriculture/.

  • Gagne, D. J., II, A. McGovern, S. E. Haupt, R. Sobash, J. K. Williams, and M. Xue, 2017: Storm-based probabilistic hail forecasting with machine learning applied to convection-allowing ensembles. Wea. Forecasting, 32, 18191840, https://doi.org/10.1175/WAF-D-17-0010.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gagne, D. J., II, S. E. Haupt, and D. Nychka, 2018: Spatial structure evaluation of unsupervised deep learning for atmospheric data. 17th Conf. on Artificial Intelligence and its Application to the Environmental Sciences, Austin, TX, Amer. Meteor. Soc., TJ4.5, https://ams.confex.com/ams/98Annual/webprogram/Paper334256.html.

  • Gardner, M. W., and S. R. Dorling, 1998: Artificial neural networks (the multilayer perceptron)—A review of applications in the atmospheric sciences. Atmos. Environ., 32, 26272636, https://doi.org/10.1016/S1352-2310(97)00447-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gardner, M. W., and S. R. Dorling, 2000: Statistical surface ozone models: An improved methodology to account for non-linear behavior. Atmos. Environ., 34, 2134, https://doi.org/10.1016/S1352-2310(99)00359-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Geerts, B., 2002: On the effects of irrigation and urbanisation on the annual range of monthly-mean temperatures. Theor. Appl. Climatol., 72, 157163, https://doi.org/10.1007/s00704-002-0683-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Georgescu, M., D. B. Lobell, and C. B. Field, 2011: Direct climate effects of perennial bioenergy crops in the United States. Proc. Natl. Acad. Sci. USA, 108, 43074312, https://doi.org/10.1073/pnas.1008779108.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gilleland, E., 2017: A new characterization in the spatial verification framework for false alarms, misses, and overall patterns. Wea. Forecasting, 32, 187198, https://doi.org/10.1175/WAF-D-16-0134.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gitelson, A. A., 2016: Remote sensing estimation of crop biophysical characteristics at various scales. Hyperspectral Remote Sensing of Vegetation, P. S. Thenkabail, J. G. Lyons, and A. Huete, Eds., CRC Press, 329–358.

    • Crossref
    • Export Citation
  • Glahn, H. R., and D. A. Lowry, 1972: The use of model output statistics (MOS) in objective weather forecasting. J. Appl. Meteor., 11, 12031211, https://doi.org/10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gleick, P. H., 2015. Impacts of California’s ongoing drought: Hydroelectricity generation. Pacific Institute Rep., 13 pp., https://pacinst.org/wp-content/uploads/2015/03/California-Drought-and-Energy-Final1.pdf.

  • Goldberg, D. E., 1989: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 412 pp.

  • Greybush, S. J., S. E. Haupt, and G. S. Young, 2008: The regime dependence of optimally weighted ensemble model consensus forecasts of surface temperature. Wea. Forecasting, 23, 11461161, https://doi.org/10.1175/2008WAF2007078.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grotrian, W., 1939: Zur Frage der Deutung der Linien im Spektrum der Sonnenkorona (On the question of the interpretation of the lines in the solar corona spectrum). Naturwissenschaften, 27, 214, https://doi.org/10.1007/BF01488890.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guan, K., and Coauthors, 2015: Photosynthetic seasonality of global tropical forests constrained by hydroclimate. Nat. Geosci., 8, 284289, https://doi.org/10.1038/ngeo2382.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haines, D. A., 1988: A lower atmospheric severity index for wildland fire. Natl. Wea. Dig., 13 (2), 2327.

  • Hansen, R. T., C. J. Garcia, R. J.-M. Grognard, and K. V. Sheridan, 1971: A coronal disturbance observed simultaneously with a white-light coronameter and the 80 MHz Culgoora radioheliograph. Proc. Astron. Soc. Aust., 2, 5760, https://doi.org/10.1017/S1323358000012856.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harding, K. J., and P. K. Snyder, 2012: Modeling the atmospheric response to irrigation in the Great Plains. Part I: General impacts on precipitation and the energy budget. J. Hydrometeor., 13, 16671686, https://doi.org/10.1175/JHM-D-11-098.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harris Geospatial Solutions, 2017: Vegetation indices. Accessed 9 March 2017, http://www.harrisgeospatial.com/docs/VegetationIndices.html.

  • Harvey, J. W., and N. R. Sheeley Jr., 1979: Coronal holes and solar magnetic fields. Space Sci. Rev., 23, 139158, https://doi.org/10.1007/BF00173808.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hasselmann, K., 1988: PIPs and POPs: The reduction of complex dynamical systems using principal interaction and oscillation patterns. J. Geophys. Res., 93, 11 01511 021, https://doi.org/10.1029/JD093iD09p11015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hatfield, J. L., A. A. Gitelson, J. S. Schepers, and C. L. Walthall, 2008: Application of spectral remote sensing for agronomic decisions. Agron. J., 100 (Suppl. 3), S-117S-131, https://doi.org/10.2134/agronj2006.0370c.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hathaway, D. H., 2015: The solar cycle. Living Rev. Sol. Phys., 12, 4, https://doi.org/10.1007/lrsp-2015-4.

  • Haugland, M. J., and K. C. Crawford, 2005: The diurnal cycle of land–atmosphere interactions across Oklahoma’s winter wheat belt. Mon. Wea. Rev., 133, 120130, https://doi.org/10.1175/MWR-2842.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haupt, R. L., and S. E. Haupt, 2004: Practical Genetic Algorithms. 2nd ed. with CD. John Wiley and Sons, 255 pp.

  • Haupt, S. E., 1996: Eigenvalue matching, a traveling salesman, and a genetic algorithm. NCAR Climate and Global Dynamics research report briefing, 29 April 1996, 18 pp., https://opensky.ucar.edu/islandora/object/conference%3A3392/datastream/PDF/download/citation.pdf.

  • Haupt, S. E., 2005: A demonstration of coupled receptor/dispersion modeling with a genetic algorithm. Atmos. Environ., 39, 71817189, https://doi.org/10.1016/j.atmosenv.2005.08.027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haupt, S. E., 2006: Nonlinear empirical models of dynamical systems. Comput. Math. Appl., 51, 431440, https://doi.org/10.1016/j.camwa.2005.10.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haupt, S. E., 2007: Genetic algorithms and their applications in environmental sciences. Advanced Methods for Decision Making and Risk Management in Sustainability Science, J. Kropp and J. Scheffran, Eds., Nova Science Publishers, 205–220.

  • Haupt, S. E., and L. Delle Monache, 2014: Understanding ensemble prediction: How probabilistic wind power prediction can help in optimising operations. WindTech, 10, 2729, https://www.windtech-international.com/editorial-features/understanding-ensemble-prediction.

    • Search Google Scholar
    • Export Citation
  • Haupt, S. E., G. S. Young, and C. T. Allen, 2006: Validation of a receptor–dispersion model coupled with a genetic algorithm using synthetic data. J. Appl. Meteor. Climatol., 45, 476490, https://doi.org/10.1175/JAM2359.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haupt, S. E., C. T. Allen, and G. S. Young, 2009a: Addressing air quality problems with genetic algorithms. Artificial Intelligence Methods in the Environmental Sciences, S. E. Haupt, A. Pasini, and C. Marzban, Eds., Springer, 269–296.

  • Haupt, S. E., A. Beyer-Lout, K. J. Long, and G. S. Young, 2009b: Assimilating concentration observations for transport and dispersion modeling in a meandering wind field. Atmos. Environ., 43, 13291338, https://doi.org/10.1016/j.atmosenv.2008.11.043.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haupt, S. E., V. Lakshmanan, A. Pasini, C. Marzban, and J. Williams, 2009c: Environmental science models and artificial intelligence. Artificial Intelligence Methods in the Environmental Sciences, S. E. Haupt, A. Pasini, and C. Marzban, Eds., Springer, 1–14.

    • Crossref
    • Export Citation
  • Haupt, S. E., A. Pasini, and C. Marzban, Eds., 2009d: Artificial Intelligence Methods in the Environmental Sciences. Springer, 424 pp.

    • Crossref
    • Export Citation
  • Haupt, S. E., R. L. Haupt, and G. S. Young, 2011: A mixed integer genetic algorithm used in biological and chemical defense applications. J. Soft Computing, 15, 5159, https://doi.org/10.1007/s00500-009-0516-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haupt, S. E., A. J. Annunzio, and K. J. Schmehl, 2013: Evolving dispersion realizations of atmospheric flow. Bound.-Layer Meteor., 149, 197217, https://doi.org/10.1007/s10546-013-9845-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haupt, S. E., J. Copeland, W. Y. Y. Cheng, Y. Zhang, C. Amman, and P. Sullivan, 2016: Quantifying the wind and solar power resource and their inter-annual variability over the United States under current and projected future climate. J. Appl. Meteor. Climatol., 55, 345363, https://doi.org/10.1175/JAMC-D-15-0011.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haupt, S. E., R. M. Rauber, B. Carmichael, J. C. Knievel, and J. L. Cogan, 2019a: 100 years of progress in applied meteorology. Part I: Basic applications. A Century of Progress in Atmospheric and Related Sciences: Celebrating the American Meteorological Society Centennial, Meteor. Monogr., No. 59, Amer. Meteor. Soc., 22.1–22.33, https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0004.1.

    • Crossref
    • Export Citation
  • Haupt, S. E., S. Hanna, M. Askelson, M. Shepherd, M. A. Fragoment, N. Debbage, and B. Johnson, 2019b: 100 years of progress in applied meteorology. Part II: Applications that address growing populations. A Century of Progress in Atmospheric and Related Sciences: Celebrating the American Meteorological Society Centennial, Meteor. Monogr., No. 59, Amer. Meteor. Soc., https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0007.1.

    • Crossref
    • Export Citation
  • Holland, J. H., 1975: Adaptation in Natural and Artificial Systems. The University of Michigan Press, 183 pp.

  • Howitt, R., J. Medellín-Azuara, D. MacEwan, J. Lund, and D. Sumner, 2014: Economic analysis of the 2014 drought for California agriculture. UC Davis Center for Watershed Sciences Rep., 27 pp., https://watershed.ucdavis.edu/files/content/news/Economic_Impact_of_the_2014_California_Water_Drought.pdf.

  • Hsieh, W. W., 2009: Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels. Cambridge University Press, 349 pp.

    • Crossref
    • Export Citation
  • Hsieh, W. W., and B. Tang, 1998: Applying neural network models to prediction and data analysis in meteorology and oceanography. Bull. Amer. Meteor. Soc., 79, 18551870, https://doi.org/10.1175/1520-0477(1998)079<1855:ANNMTP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hufbauer, K., 1991: Exploring the Sun: Solar Science since Galileo. Johns Hopkins University Press, 370 pp.

  • Hundhausen, A. J., 1970: Composition and dynamics of the solar wind plasma. Rev. Geophys. Space Phys., 8, 729811, https://doi.org/10.1029/RG008i004p00729.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huygens, C., 1690: Traité de la Lumière (Treatise on Light), Van der Aa, 128 pp. (Translated by Silvanus P. Thompson as Treatise on Light and published by Macmillan in 1912, 128 pp.)

  • IEA, 2014: The Power of Transformation: Wind, Sun and the Economics of Flexible Power Systems. International Energy Agency, 238 pp., https://webstore.iea.org/the-power-of-transformation.

  • Immel, T. J., and Coauthors, 2018: The Ionospheric Connection Explorer Mission: Mission goals and design. Space Sci. Rev., 214, 13, https://doi.org/10.1007/s11214-017-0449-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IPCC, 2014: Climate Change 2014: Synthesis Report. R. K. Pachauri and L. A. Meyer, Eds., IPCC, 151 pp.

  • Jin, L., C. Yao, and X.-Y. Huang, 2008: A nonlinear artificial intelligence ensemble prediction model for typhoon intensity. Mon. Wea. Rev., 136, 45414554, https://doi.org/10.1175/2008MWR2269.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jolly, W. M., and P. H. Freeborn, 2017: Towards improving wildland firefighter situational awareness through daily fire behaviour risk assessments in the US Northern Rockies and Northern Great Basin. Int. J. Wildland Fire, 26, 574586, https://doi.org/10.1071/WF16153.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Keane, R. E., 2015: Wildland Fuel Fundamentals and Applications. Springer, 191 pp., https://doi.org/10.1007/978-3-319-09015-3.

    • Crossref
    • Export Citation
  • Keetch, J. J., and G. Byram, 1968: A drought index for forest fire control. USDA Forest Service Southeastern Forest Experiment Station Res. Paper SE-38, 32 pp., https://www.srs.fs.usda.gov/pubs/rp/rp_se038.pdf.

  • Kenney, D. S., and R. Wilkinson, Eds., 2011: The Water-Energy Nexus in the American West. Edward Elgar Publishing, 253 pp.

    • Crossref
    • Export Citation
  • Kim, S., H. Lee, J. Kim, C. Kim, J. Ko, H. Woo, and S. Kim, 2002: Genetic algorithms for the application of Activated Sludge Model No. 1. Water Sci. Technol., 45, 405411, https://doi.org/10.2166/wst.2002.0636.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krasnopolsky, V. M., 2009: Neural network applications to solve forward and inverse problems in atmospheric and oceanic satellite remote sensing. Artificial Intelligence Methods in the Environmental Sciences, S. E. Haupt, A. Pasini, and C. Marzban, Eds., Springer, 191–205.

    • Crossref
    • Export Citation
  • Krasnopolsky, V. M., 2013: The Application of Neural Networks in the Earth System Sciences. Springer, 189 pp.

    • Crossref
    • Export Citation
  • Krasnopolsky, V. M., L. C. Breaker, and W. H. Gemmill, 1995: A neural network as a nonlinear transfer function model for retrieving surface wind speeds from the special sensor microwave imager. J. Geophys. Res., 100, 11 03311 045, https://doi.org/10.1029/95JC00857.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krasnopolsky, V. M., M. S. Fox-Rabinovitz, and A. A. Belochitski, 2013: Using ensemble of neural networks to learn stochastic convection parameterization for climate and numerical weather prediction models from data simulated by a cloud resolving model. Adv. Artif. Neural Syst., 2013, 485913, https://doi.org/10.1155/2013/485913.

    • Search Google Scholar
    • Export Citation
  • Krieger, A. S., G. S. Vaiana, and L. P. van Speybroeck, 1971: The X-ray corona and the photospheric magnetic field. Solar Magnetic Fields, R. Howard, Ed., Springer, 397–412, https://doi.org/10.1007/978-94-010-3117-2_52.

    • Crossref
    • Export Citation
  • Krieger, A. S., A. F. Timothy, and E. C. Roelof, 1973: A coronal hole and its identification as the source of a high velocity solar wind stream. Sol. Phys., 29, 505525, https://doi.org/10.1007/BF00150828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kueppers, L. M., M. A. Snyder, and L. C. Sloan, 2007: Irrigation cooling effect: Regional climate forcing by land-use change. Geophys. Res. Lett., 34, L03703, https://doi.org/10.1029/2006GL028679.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuroki, Y., G. S. Young, and S. E. Haupt, 2010: UAV navigation by an expert system for contaminant mapping with a genetic algorithm. Expert Syst. Appl., 37, 46874697, https://doi.org/10.1016/j.eswa.2009.12.039.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lagerquist, R., 2016: Using machine learning to predict damaging straight-line convective winds. M.S. thesis, School of Meteorology, University of Oklahoma, 251 pp., http://hdl.handle.net/11244/44921.

  • Lakshmanan, V., 2009: Automated analysis of spatial grids. Artificial Intelligence Methods in the Environmental Sciences, S. E. Haupt, A. Pasini, and C. Marzban, Eds., Springer, 329–346.

    • Crossref
    • Export Citation
  • Lee, Y. J., C. Bonfanti, L. Trailovic, B. J. Etherton, M. W. Govett, and J. Q. Stewart, 2018: Using deep learning for targeted data selection: Improving satellite observation utilization for model initialization. 17th Conf on Artificial Intelligence and its Application to the Environmental Sciences, Austin, TX, Amer. Meteor. Soc., J38.3, https://ams.confex.com/ams/98Annual/webprogram/Paper333024.html.

  • Leng, G., M. Huang, Q. Tang, W. J. Sacks, H. Lei, and L. R. Leung, 2013: Modeling the effects of irrigation on land surface fluxes and states over the conterminous United States: Sensitivity to input data and model parameters. J. Geophys. Res., 118, 97899803, https://doi.org/10.1002/jgrd.50792.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leng, G., L. R. Leung, and M. Huang, 2017: Significant impacts of irrigation water sources and methods on modeling irrigation effects in the ACME Land Model. J. Adv. Model. Earth Syst., 9, 16651683, https://doi.org/10.1002/2016MS000885.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Levis, S., B. B. Gordon, E. Kluzek, P. E. Thornton, A. Jones, W. J. Sacks, and C. J. Kucharik, 2012: Interactive crop management in the Community Earth System Model (CESM1): Seasonal influences on land–atmosphere fluxes. J. Climate, 25, 48394859, https://doi.org/10.1175/JCLI-D-11-00446.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Linn, R., 1997: A transport model for prediction of wildfire behavior. Los Alamos National Laboratory Sci. Rep. LA-13334-T, 195 pp., https://www.osti.gov/servlets/purl/505313.

    • Crossref
    • Export Citation
  • Linn, R., J. Reisner, J. J. Colman, and J. Winterkamp, 2002: Studying wildfire behavior using FIRETEC. Int. J. Wildland Fire, 11, 233246, https://doi.org/10.1071/WF02007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, X., F. Chen, M. Barlage, G. Zhou, and D. Niyogi, 2016: Noah-MP-Crop: Introducing dynamic crop growth in the Noah-MP land surface model. J. Geophys. Res., 121, 13 95313 972, https://doi.org/10.1002/2016JD025597.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lobell, D., and C. Bonfils, 2008: The effect of irrigation on regional temperatures: A spatial and temporal analysis of trends in California, 1934–2002. J. Climate, 21, 20632071, https://doi.org/10.1175/2007JCLI1755.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lokupitiya, E., and Coauthors, 2009: Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land–atmosphere carbon exchanges from croplands. Biogeosciences, 6, 969986, https://doi.org/10.5194/bg-6-969-2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1956: Empirical orthogonal functions and statistical weather prediction. Massachusetts Institute of Technology Dept. of Meteorology Statistical Forecasting Project Scientific Rep. 1, 49 pp., https://eapsweb.mit.edu/sites/default/files/Empirical_Orthogonal_Functions_1956.pdf.

  • Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20, 130141, https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1973: On the existence of extended range predictability. J. Appl. Meteor., 12, 543546, https://doi.org/10.1175/1520-0450(1973)012<0543:OTEOER>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lyot, M. B., 1939: The study of the solar corona and prominences without eclipses. Mon. Not. Roy. Astron. Soc., 99, 580594, https://doi.org/10.1093/mnras/99.8.580.

    • Search Google Scholar
    • Export Citation
  • Mahesh, A., C. A. Cupertino, T. A. O’Brien, M. Prabhat, and W. Collins, 2018: Assessing uncertainty in deep learning techniques that identify atmospheric rivers in climate simulations. 17th Conf on Artificial Intelligence and its Application to the Environmental Sciences, Austin, TX, Amer. Meteor. Soc., 2.5, https://ams.confex.com/ams/98Annual/webprogram/Paper335955.html.

  • Mahlein, A.-K., 2016: Plant disease detection by imaging sensors—Parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis., 100, 241251, https://doi.org/10.1094/PDIS-03-15-0340-FE.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahmood, R., K. G. Hubbard, R. D. Leeper, and S. A. Foster, 2008: Increase in near-surface atmospheric moisture content due to land use changes: Evidence from the observed dewpoint temperature data. Mon. Wea. Rev., 136, 15541561, https://doi.org/10.1175/2007MWR2040.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahoney, W. P., and Coauthors, 2012: A wind power forecasting system to optimize grid integration. IEEE Trans. Sustainable Energy, 3, 670682, https://doi.org/10.1109/TSTE.2012.2201758.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mandel, J., J. D. Beezley, J. L. Coen, and M. Kim, 2009: Data assimilation for wildland fires—Ensemble Kalman filters in coupled atmosphere–surface models. IEEE Contr. Syst. Mag., 29, 4765, https://doi.org/10.1109/MCS.2009.932224.

    • Search Google Scholar
    • Export Citation
  • Martin, J., and T. Hillen, 2016: The spotting distribution of wildfires. Appl. Sci., 6, 177; https://doi.org/10.3390/app6060177.

  • Marzban, C., and G. J. Stumpf, 1996: A neural network for tornado prediction based on Doppler radar-derived attributes. J. Appl. Meteor., 35, 617626, https://doi.org/10.1175/1520-0450(1996)035<0617:ANNFTP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maupin, M. A., J. F. Kenny, S. S. Hutson, J. K. Lovelace, N. L. Barber, and K. S. Linsey, 2014, Estimated use of water in the United States in 2010. U.S. Geological Survey Circular 1405, 56 pp., https://doi.org/10.3133/cir1405.

    • Crossref
    • Export Citation
  • McArthur, R. C., J. R. Davis, and D. Reynolds, 1987: Scenario-driven automatic pattern recognition in nowcasting. J. Atmos. Oceanic Technol., 4, 2935, https://doi.org/10.1175/1520-0426(1987)004<0029:SDAPRI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCandless, T. M., S. E. Haupt, and G. S. Young, 2011: Statistical guidance methods for predicting snowfall accumulation in the northeast United States. Natl. Wea. Dig., 35, 149162, http://nwafiles.nwas.org/digest/papers/2011/Vol35No2/Pg149-McCandless_etal.pdf.

    • Search Google Scholar
    • Export Citation
  • McCandless, T. C., S. E. Haupt, and G. S. Young, 2016a: A regime-dependent artificial neural network technique for short-range solar irradiance forecasting. Appl. Energy, 89, 351359, https://doi.org/10.1016/j.renene.2015.12.030.

    • Search Google Scholar
    • Export Citation
  • McCandless, T. C., G. S. Young, S. E. Haupt, and L. M. Hinkelman, 2016b: Regime-dependent short-range solar irradiance forecasting. J. Appl. Meteor. Climatol., 55, 15991613, https://doi.org/10.1175/JAMC-D-15-0354.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McDermid, S. S., L. O. Mearns, and A. C. Ruane, 2017: Representing agriculture in Earth system models: Approaches and priorities for development. J. Adv. Model. Earth Syst., 9, 22302265, https://doi.org/10.1002/2016MS000749.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McGovern, A., D. J. Gagne II, J. K. Williams, R. A. Brown, and J. B. Basara, 2014: Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning. Mach. Learn., 95, 2750, https://doi.org/10.1007/s10994-013-5343-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McGovern, A., K. Elmore, D. J. Gagne II, S. E. Haupt, C. D. Karstens, R. Lagerquist, T. Smith, and J. K. Williams, 2017: Using artificial intelligence to improve real-time decision making for high-impact weather. Bull. Amer. Meteor. Soc., 98, 20732090, https://doi.org/10.1175/BAMS-D-16-0123.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McGrattan, K. B., R. McDermott, S. Hostikka, M. Vanella, C. Weinschenk, and K. Overholt, 2018: Fire Dynamics Simulator technical reference guide—Volume 1: Mathematical model. National Institute of Standards and Technology Special Pub. 1018-1 (Sixth Edition), 163 pp., https://github.com/firemodels/fds/releases/download/FDS6.7.0/FDS_Technical_Reference_Guide.pdf

  • McIntosh, S. W., W. J. Cramer, M. Pichardo Marcano, and R. J. Leamon, 2017: The detection of Rossby-like waves on the Sun. Nat. Astron., 1, 0086, https://doi.org/10.1038/s41550-017-0086.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McKinney, D. C., and M.-D. Lin, 1993: Genetic algorithm solution of ground water management models. Water Resour. Res., 30, 18971906, https://doi.org/10.1029/94WR00554.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mell, W., M. A. Jenkins, J. Gould, and P. Cheney, 2007: A physics-based approach to modelling grassland fires. Int. J. Wildland Fire, 16, 122, https://doi.org/10.1071/WF06002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Minchenkov, A., 2009: USDA study finds 54.9 million acres of U.S. farmland now irrigated. USDA News Release, U.S. Department of Agriculture, accessed 1 November 2015, http://www.usda.gov/wps/portal/usda/usdahome?contentid=2009/12/0596.xml.

  • Mitsopoulos, I., P. Trapatsas, and G. Xanthopoulos, 2016: SYPYDA: A software tool for fire management in Mediterranean pine forests of Greece. Comput. Electron. Agric., 121, 195199, https://doi.org/10.1016/j.compag.2015.12.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mohan, S., and D. P. Loucks, 1995: Genetic algorithms for estimating model parameters. 22nd Ann. Conf. on Integrated Water Resource Planning for the 21st Century, Cambridge, MA, ASCE, 460–463, https://www.tib.eu/en/search/id/BLCP%3ACN014115169/Genetic-Alogrithm-for-Estimating-Model-Parameters/.

  • Monahan, A. H., 2000: Nonlinear principal component analysis by neural networks: Theory and application to the Lorenz system. J. Climate, 13, 821835, https://doi.org/10.1175/1520-0442(2000)013<0821:NPCABN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mulligan, A. E., and L. C. Brown, 1998: Genetic algorithms for calibrating water quality models. J. Environ. Eng., 124, 202211, https://doi.org/10.1061/(ASCE)0733-9372(1998)124:3(202).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muñoz-Esparza, D., B. Kosović, P. A. Jiménez, and J. L. Coen, 2018: An accurate fire-spread algorithm in the Weather Research and Forecasting model using the level-set method. J. Adv. Model. Earth Syst., 10, 908926, https://doi.org/10.1002/2017MS001108.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Myers, W., F. Chen, and J. Block, 2008: Application of atmospheric and land data assimilation systems to an agricultural decision support system. Conf. on Agriculture and Forestry, Orlando, FL, Amer. Meteor. Soc., 9.1, https://ams.confex.com/ams/28Hurricanes/techprogram/paper_138947.htm.

  • Myers, W., G. Wiener, S. Linden, and S. E. Haupt, 2011: A consensus forecasting approach for improved turbine hub height wind speed predictions. Proc. WindPower 2011, Anaheim, CA, http://opensky.ucar.edu/islandora/object/conference:3296.

  • National Research Council, 2009: Severe Space Weather Events–Understanding Societal and Economic Impacts: A Workshop Report: Extended Summary. The National Academies Press, 32 pp., https://doi.org/10.17226/12643.

    • Crossref
    • Export Citation
  • NCAR, 2018: Who we are. National Center for Atmospheric Research, accessed 29 January 2018, https://rap.ucar.edu/who-we-are.

  • Neugebauer, M., and C. W. Snyder, 1962: Solar Plasma Experiment. Science, 138, 10951097, https://doi.org/10.1126/science.138.3545.1095-a.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nigam, R., R. Kot, S. S. Sandhu, B. K. Bhattacharya, R. S. Chandi, M. Singh, J. Singh, and K. R. Manjunath, 2016: Ground-based hyperspectral remote sensing to discriminate biotic stress in cotton crop. Proc. SPIE, 9880, 98800H, https://doi.org/10.1117/12.2228122.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niu, G.-Y., and Coauthors, 2011: The community Noah land surface model with multi-physics options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niyogi, D., K. Alapaty, S. Raman, and F. Chen, 2009: Development and evaluation of a coupled photosynthesis-based gas exchange evapotranspiration model (GEM) for mesoscale weather forecasting applications. J. Appl. Meteor. Climatol., 48, 349368, https://doi.org/10.1175/2008JAMC1662.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Osher, S., and J. A. Sethian, 1988: Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton–Jacobi formulations. J. Comput. Phys., 79, 1249, https://doi.org/10.1016/0021-9991(88)90002-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Palmer, W. C., 1965: Meteorological drought. U.S. Department of Commerce Weather Bureau Res. Paper 45, 58 pp., https://www.ncdc.noaa.gov/temp-and-precip/drought/docs/palmer.pdf.

  • Parker, E. N., 1958: Dynamics of the interplanetary gas and magnetic fields. Astrophys. J., 128, 664, https://doi.org/10.1086/146579.

  • Pasini, A., 2009: Neural network modeling in climate change studies. Artificial Intelligence Methods in the Environmental Sciences, S. E. Haupt, A. Pasini, and C. Marzban, Eds., Springer, 235–254.

    • Crossref
    • Export Citation
  • Pasini, A., P. Racca, S. Amendola, G. Cartocci, and C. Cassardo, 2017: Attribution of recent temperature behavior reassessed by a neural-network method. Nat. Sci. Rep., 7, 17681, https://doi.org/10.1038/s41598-017-18011-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pei, L., N. Moore, S. Zhong, A. D. Kendall, Z. Gao, and D. W. Hyndman, 2016: Effects of irrigation on summer precipitation over the United States. J. Climate, 29, 35413558, https://doi.org/10.1175/JCLI-D-15-0337.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pelliccioni, A., and T. Tirabassi, 2006: Air dispersion model and neural network: A new perspective for integrated models in the simulation of complex situations. Environ. Modell. Software, 21, 539546, https://doi.org/10.1016/j.envsoft.2004.07.015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pelliccioni, A., U. Poli, P. Agnello, and A. Coni, 1999: Application of neural networks to model the Monin–Obukhov length and the mixed-layer height from ground-based meteorological data. Trans. Ecol. Environ., 37, 10551064.

    • Search Google Scholar
    • Export Citation
  • Pelliccioni, A., C. Gariazzo, and T. Tirabassi, 2003: Coupling of neural network and dispersion models: A novel methodology for air pollution models. Int. J. Environ. Pollut., 20, 136146, https://doi.org/10.1504/IJEP.2003.004262.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Penland, C., 1989: Random forcing and forecasting using principal oscillation pattern analysis. Mon. Wea. Rev., 117, 21652185, https://doi.org/10.1175/1520-0493(1989)117<2165:RFAFUP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Penland, C., and M. Ghil, 1993: Forecasting Northern Hemisphere 700-mb geopotential height anomalies using empirical normal modes. Mon. Wea. Rev., 121, 23552372, https://doi.org/10.1175/1520-0493(1993)121<2355:FNHMGH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Penland, C., and T. Magorian, 1993: Prediction of Niño 3 sea surface temperatures using linear inverse modeling. J. Climate, 6, 10671076, https://doi.org/10.1175/1520-0442(1993)006<1067:PONSST>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Penland, C., and L. Matrosova, 1998: Prediction of tropical Atlantic sea surface temperatures using linear inverse modeling. J. Climate, 11, 483496, https://doi.org/10.1175/1520-0442(1998)011<0483:POTASS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petrozziello, A., G. Cervone, P. Franzese, S. E. Haupt, and R. Cerulli, 2016: Source reconstruction of atmospheric releases with limited meteorological observations using genetic algorithms. Appl. Artif. Intell., 31, 119133, https://doi.org/10.1080/08839514.2017.1300005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pixalytics, 2017: Earth observation satellites in space in 2016. Accessed 10 March 2017, http://www.pixalytics.com/eo-sat-2016/.

  • Plucinski, M. P., A. L. Sullivan, C. J. Rucinski, and M. Prakash, 2017: Improving the reliability and utility of operational bushfire behavior predictions in Australian vegetation. Environ. Modell. Software, 91, 112, https://doi.org/10.1016/j.envsoft.2017.01.019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Poole, D. L., and A. K. Mackworth, 2017: Artificial Intelligence: Foundations of Computational Agents. 2nd ed., Cambridge University Press, 773 pp., http://artint.info/2e/html/ArtInt2e.html.

    • Crossref
    • Export Citation
  • Pyne, S. J., 1982: Fire in America: A Cultural History of Wildland and Rural Fire. University of Washington Press, 680 pp.

  • Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski, 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133, 11551174, https://doi.org/10.1175/MWR2906.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ramankutty, N., A. Evan, C. Monfreda, and J. Foley, 2008: Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochem. Cycles, 22, GB1003, https://doi.org/10.1029/2007GB002952.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reichelt, C. A., 1919: Notes on a cumulus cloud formed over a fire. Mon. Wea. Rev., 47, 144145, https://doi.org/10.1175/1520-0493(1919)47<144:NOACCF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ritzel, B. J., J. W. Eheart, and S. Rajithan, 1994: Using genetic algorithms to solve a multiple objective groundwater pollution containment problem. Water Resour. Res., 30, 15891603, https://doi.org/10.1029/93WR03511.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roebber, P. J., 2015a: Adaptive evolutionary programming. Mon. Wea. Rev., 143, 14971505, https://doi.org/10.1175/MWR-D-14-00095.1.

  • Roebber, P. J., 2015b: Ensemble MOS and evolutionary program minimum temperature forecast skill. Mon. Wea. Rev., 143, 15061516, https://doi.org/10.1175/MWR-D-14-00096.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roebber, P. J., 2015c: Evolving ensembles. Mon. Wea. Rev., 143, 471490, https://doi.org/10.1175/MWR-D-14-00058.1.

  • Roebber, P. J., S. L. Bruening, D. M. Schultz, and J. V. Cortinas Jr., 2003: Improving snowfall forecasting by diagnosing snow density. Wea. Forecasting, 18, 264287, https://doi.org/10.1175/1520-0434(2003)018<0264:ISFBDS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rogers, L. L. and F. U. Dowla, 1994: Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resour. Res., 30, 457481, https://doi.org/10.1029/93WR01494.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rorig, M. L., and S. A. Ferguson, 1999: Characteristics of lightning and wildland fire ignition in the Pacific Northwest. J. Appl. Meteor., 38, 15651575, https://doi.org/10.1175/1520-0450(1999)038<1565:COLAWF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rosenzweig, C., and Coauthors, 2014: Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl. Acad. Sci. USA, 111, 32683273, https://doi.org/10.1073/pnas.1222463110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rothermel, R. C., 1972: A mathematical model for predicting fire spread in wildland fires. USDA Forest Service Res. Paper INT-115, 40 pp., https://www.fs.fed.us/rm/pubs_int/int_rp115.pdf.

  • Rothermel, R. C., 1991: Predicting behavior and size of crown fires in the northern Rocky Mountains. USDA Forest Service Intermountain Research Station Res. Paper INT-438, 52 pp., https://www.fs.fed.us/rm/pubs_int/int_rp438.pdf.

    • Crossref
    • Export Citation
  • Roujean, J.-L., and F.-M. Breon, 1995: Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens. Environ., 51, 375384, https://doi.org/10.1016/0034-4257(94)00114-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Running, S. W., P. E. Thornton, R. Nemani, and J. M. Glassy, 2000: Global terrestrial gross and net primary productivity from the Earth Observing System. Methods in Ecosystem Science, O. E. Sala et al., Eds., Springer-Verlag, 44–57.

    • Crossref
    • Export Citation
  • Sacks, W. J., B. I. Cook, N. Buenning, S. Levis, and J. H. Helkowski, 2009: Effects of global irrigation on the near-surface climate. Climate Dyn., 33, 159175, https://doi.org/10.1007/s00382-008-0445-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sanderlin, J., and J. Sunderson, 1975: A simulation for wildland fire management planning support (FIREMAN): Volume II. Prototype models for FIREMAN (Part II): Campaign fire evaluation. Mission Research Corp., Contract 231–343, Spec. 222, 249 pp.

  • Sanderlin, J., and R. Van Gelder, 1977: A simulation of fire behavior and suppression effectiveness for operational support. Wildland Fire Management: Proceedings of the First International Conference on Mathematical Modeling, Vol. 2, X. J. R. Avula, Ed., University of Missouri Press, 619–630.

  • Schaible, G. D., and M. P. Aillery, 2012: Water conservation in irrigated agriculture: Trends and challenges in the face of emerging demands. USDA Economic Research Service Economic Information Bull. EIB-99, 67 pp., https://www.ers.usda.gov/webdocs/publications/44696/30956_eib99.pdf?v=0.

  • Schlatter, T. W., G. W. Branstator, and L. G. Thiel, 1976: Testing a global multivariate statistical objective analysis scheme with observed data. Mon. Wea. Rev., 104, 765783, https://doi.org/10.1175/1520-0493(1976)104<0765:TAGMSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schmehl, K. J., S. E. Haupt, and M. Pavolonis, 2012: A genetic algorithm variational approach to data assimilation and application to volcanic emissions. Pure Appl. Geophys., 169, 519537, https://doi.org/10.1007/s00024-011-0385-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schmidt, K. M., J. P. Menakis, C. C. Hardy, W. J. Hann, and D. L. Bunnell, 2002: Development of coarse-scale spatial data for wildland fire and fuel management. USDA Forest Service Rocky Mountain Research Station General Tech. Rep. RMRS-GTR-87, 41 pp. (+ CD), https://www.fs.fed.us/rm/pubs/rmrs_gtr087.pdf.

    • Crossref
    • Export Citation
  • Schrijver, C. J., R. Dobbins, W. Murtagh, and S. M. Petrinec, 2014: Assessing the impact of space weather on the electric power grid based on insurance claims for industrial electrical equipment. Space Wea., 12, 487498, https://doi.org/10.1002/2014SW001066.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schrijver, C. J., and Coauthors, 2015: Understanding space weather to shield society: A global road map for 2015–2025 commissioned by COSPAR and ILWS. Adv. Space Res., 55, 27452807, https://doi.org/10.1016/j.asr.2015.03.023.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwenn, R., 2006: Space weather: The solar perspective. Living Rev. Sol. Phys., 3, 2, https://doi.org/10.12942/lrsp-2006-2.

  • Scott, J. H., and R. E. Burgan, 2005: Standard fire behavior fuel models: A comprehensive set for use with Rothermel’s surface fire spread model. USDA Forest Service General Tech. Rep. RMRS-GTR-153, 72 pp., https://www.fs.fed.us/rm/pubs/rmrs_gtr153.pdf.

    • Crossref
    • Export Citation
  • Selten, F. M., 1997: Baroclinic empirical orthogonal functions as basis functions in an atmospheric model. J. Atmos. Sci., 54, 20992114, https://doi.org/10.1175/1520-0469(1997)054<2099:BEOFAB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sen Roy, S., R. Mahmoud, A. Quintanar, and A. Gonzalez, 2010: Impacts of irrigation on dry season precipitation in India. Theor. Appl. Climatol., 104, 193207, https://doi.org/10.1007/s00704-010-0338-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shepherd, J. M., and P. Knox, 2016: The Paris COP21 Climate Conference: What does it mean for the Southeast? Southeast. Geogr., 56, 147151, https://doi.org/10.1353/sgo.2016.0023.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shepherd, J. M., S. Burian, C. Liu, and S. Bernardes, 2016: Satellite precipitation metrics to study the energy-water-food nexus within the backdrop of an urbanized globe. Earthzine, 31 March 2016, https://atmos.tamucc.edu/liu/reprints/2016_ieee_shepherd_etal.pdf.

  • Shieh, H.-J., and R. C. Peralta, 1997: Optimal system design of in-situ bioremediation using genetic annealing algorithm. Ground Water: An Endangered Resource—Proceedings of Theme C, Water for a Changing Global Community, 27th Annual Congress of the International Association of Hydrologic Research, A. N. Findikakis and F. Stauffer, Eds., ASCE, 95–100.

  • Silver, D., and Coauthors, 2016: Mastering the game of Go with deep neural networks and tree search. Nature, 529, 484489, https://doi.org/10.1038/nature16961.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Silver, D., and Coauthors, 2017: Mastering the game of Go without human knowledge. Nature, 550, 354359, https://doi.org/10.1038/nature24270.

  • Simpson, A. R., G. C. Dandy, and L. J. Murphy, 1994: Genetic algorithms compared to other techniques for pipe optimization. J. Water Resour. Plann. Manage., 120, 423443, https://doi.org/10.1061/(ASCE)0733-9496(1994)120:4(423).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and J. B. Klemp, 2008: A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys., 227, 34653485, https://doi.org/10.1016/j.jcp.2007.01.037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, C., B. McGuire, T. Huang, and G. Yang, 2006: The history of artificial intelligence. U. Washington Course Doc., 27 pp., https://courses.cs.washington.edu/courses/csep590/06au/projects/history-ai.pdf.

  • Sperati, S., S. Alessandrini, and L. Delle Monache, 2017: Gridded probabilistic forecasts with an analog ensemble. Quart. J. Roy. Meteor. Soc., 143, 28742885, https://doi.org/10.1002/qj.3137.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sullivan, A. L., 2009a: Wildland surface fire spread modelling, 1990–2007. 1: Physical and quasi-physical models. Int. J. Wildland Fire, 18, 349368, https://doi.org/10.1071/WF06143.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sullivan, A. L., 2009b: Wildland surface fire spread modelling, 1990–2007. 2: Empirical and quasi-empirical models. Int. J. Wildland Fire, 18, 369386, https://doi.org/10.1071/WF06142.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sullivan, A. L., 2009c: Wildland surface fire spread modelling, 1990–2007. 3: Simulation and mathematical analogue models. Int. J. Wildland Fire, 18, 387403, https://doi.org/10.1071/WF06144.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, R., S. K. Krueger, M. A. Jenkins, and J. J. Charney, 2009: The importance of fire–atmosphere coupling and boundary layer turbulence to wildfire spread. Int. J. Wildland Fire, 18, 5060, https://doi.org/10.1071/WF07072.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Timothy, A. F., A. S. Krieger, and G. S. Vaiana, 1975: The structure and evolution of coronal holes. Sol. Phys., 42, 135156, https://doi.org/10.1007/BF00153291.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tissot, P. E., and D. T. Cox, and P. Michaud, 2002: Neural network forecasting of storm surges along the Gulf of Mexico. Proc. Fourth Int. Symp. on Ocean Wave Measurement and Analysis (Waves ’01), San Francisco, CA, ASCE, 15351544, https://doi.org/10.1061/40604(273)155.

    • Crossref
    • Export Citation