Global climate model tracking using geospatial neighborhoods

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2012, Pages 335-341

  McQuade, Scott ^a,b   Monteleoni, Claire ^a  

^a The George Washington University   (United States)

^b MITRE CORPORATION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLIMATE SCIENCE; GEO-SPATIAL; GLOBAL CLIMATE MODEL; GLOBAL TEMPERATURE ANOMALIES; HISTORICAL DATA; MODEL PREDICTION; MULTI-MODEL ENSEMBLE; ONLINE LEARNING; SPATIAL RESOLUTION; SWITCHING PROBABILITY; TRANSITION DYNAMICS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; FORECASTING; HIDDEN MARKOV MODELS;

CLIMATE MODELS;

EID: 84868275717     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Casanova, S., and Ahrens, B. 2009. On the weighting of multimodel ensembles in seasonal and short-range weather forecasting. Mon. Wea. Rev. 137:3811-3822.
2. CMIP3. 2007. The World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset. http://www-pcmdi.llnl.gov/ipcc/about-ipcc.php.
3. DelSole, T. 2007. A bayesian framework for multimodel regression. J. Climate 20:2810-2826.
4. Greene, A.; Goddard, L.; and Lall, U. 2006. Probabilistic multimodel regional temperature change projections. J. Climate 19(17):4326-4343.
5. Herbster, M., and Warmuth, M. K. 1998. Tracking the best expert. Machine Learning 32:151-178.
6. Monteleoni, C., and Jaakkola, T. 2003. Online learning of non-stationary sequences. In NIPS '03: Advances in Neural Information Processing Systems 16.
7. Monteleoni, C.; Schmidt, G.; Saroha, S.; and Asplund, E. 2011. Tracking climate models. Statistical Analysis and Data Mining: Special Issue on Best of CIDU 4(4):72-392.
8. NASA GISS. GISTEMP. http://data.giss.nasa.gov/gistemp/.
9. Peña, M., and van den Dool, H. 2008. Consolidation of multimodel forecasts by ridge regression: Application to pacific sea surface temperature. J. Climate 21:6521-6538.
10. Raftery, A. E.; Gneiting, T.; Balabdaoui, F.; and Polakowski, M. 2005. Using bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev. 133:1155-1174.
11. Reichler, T., and Kim, J. 2008. How well do coupled models simulate today's climate? Bull. Amer. Meteor. Soc. 89:303-311.
12. Reifen, C., and Toumi, R. 2009. Climate projections: Past performance no guarantee of future skill? Geophys. Res. Lett. 36.
13. Sain, S., and Furrer, R. 2010. Combining climate model output via model correlations. Stochastic Environmental Research and Risk Assessment.
14. Smith, R. L.; Tebaldi, C.; Nychka, D.; and Mearns, L. O. 2009. Bayesian modeling of uncertainty in ensembles of climate models. Journal of the American Statistical Association 104(485):97-116.
15. Tippett, M. K., and Barnston, A. G. 2008. Skill of multimodel ENSO probability forecasts. Mon. Wea. Rev. 136:3933-3946.