My research in this area aims to develop new statistical methods for environmental problems. One aspect of many of these problems is that scientists working on them tend to use deterministic simulation models, in place of or in addition to statistical models. In the past, these two approaches have tended to be pursued in isolation from one another. Both have a lot to contribute, however, and one of my goals has been to develop ways of combining the results and information from deterministic simulation models with statistical approaches.
Probabilistic Climate and Weather Forecasting
This work is part of what started as the larger interdisciplinary
MURI project on
"Integration and Visualization of Multi-source Information for Mesoscale Meteorology:
Statistical and Cognitive Approaches to Visualizing Uncertainty", 2001-2008,
which continued with support from the National Science Foundation.
This project involved atmospheric scientists and pyschologists as well as statisticians.
Our goal in the statistics group is to develop methods for producing sharp and calibrated
probabilistic weather forecasts. This project produced Probcast,
the first real-time probabilistic weather forecasting website in the world,
set up in 2005.
More recently we have been working on climate science, specifically probabilistic climate change forecasting with support from a NICHD grant, and Arctic sea ice forecasting, with support from a NOAA grant. We have also been working on integrated climate assessment, including applications to assessing the social cost of carbon.
Zeppetello, Z.V., Raftery, A.E. and Battisti, D.S. (2022). A Dangerous Future: Probabilistic Projections of Increased Heat Stress Driven By Global Climate Change. Communications Earth and Environment 3: article 183.
Chen, X., Raftery, A.E., Battisti, D.S. and Liu, P.R. (2022). Long-Term Probabilistic Temperature Projections for All Locations. Climate Dynamics 60: 2303--2314.
Director, H.M. and Raftery, A.E. (2022). Contour Models for Physical Boundaries Enclosing Star-Shaped and Approximately Star-Shaped Polygons. Journal of the Royal Statistical Society, Series C-Applied Statistics 71: 1688--1720. Preprint.
Rennert, K., Prest, B.C., Pizer, W.A., Newell, R.G., Anthoff, D., Kington, C., Rennels, L., Cooke, R., Raftery, A.E., ,Ševčíková , H. and Errickson, F. (2022). The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates. Brookings Papers on Economic Activity 2021: 223--305.
Gao, P.A., Director, H.M., Bitz, C.M. and Raftery, A.E. (2021). Probabilistic Forecasts of Arctic Sea Ice Thickness. Journal of Agricultural, Biological and Environmental Statistics, https://doi.org/10.1007/s13253-021-00480-0
Director, H., Raftery, A.E. and Bitz, C. (2021). Probabilistic forecasting of the Arctic sea ice edge with contour modeling. Annals of Applied Statistics, 15:711-726. (Preprint.)
Liu, P. and Raftery, A.E. (2021). Country-based emissions reductions should increase by 80% beyond nationally determined contributions to meet the 2 C target. Communications Earth and Environment 2:Article 29. UW News article. Washington Post article. Spring Nature Sustainability Community post.
Raftery, A.E., Zimmer, A., Frierson, D.M.W., Startz, R., and Liu, P. (2017). Less Than 2 degrees C Warming by 2100 Unlikely. Nature Climate Change 7:637--641.
Director, H.M., Raftery, A.E. and Bitz, C.M. (2017). Improved Sea Ice Forecasting Through Spatiotemporal Bias Correction. Journal of Climate 30:9493--9510.
Sloughter, J.M., Gneiting, T. and Raftery, A.E. (2013) Probabilistic Wind Vector Forecasting using Ensembles and Bayesian Model Averaging. Monthly Weather Review, 141:2107-2119.
Kleiber, W., Raftery, A.E. and Gneiting, T. (2011). Geostatistical model averaging for locally calibrated probabilistic quantitative precipitation forecasting. Journal of the American Statistical Association 106:1291-1303.
Kleiber, W., Raftery, A.E., Baars, J., Gneiting, T., Mass, C.F. and Grimit, E.P. (2011). Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging. Monthly Weather Review 139:2630-2649.
Fraley, C., Raftery, A.E., Gneiting, T., Sloughter, M. and Berrocal, V.J. (2011). Probabilistic Weather Forecasting in R. R Journal, 3:55-63.
Chmielecki, R.M. and A.E. Raftery (2011). Probabilistic Visibility Forecasting Using Bayesian Model Averaging. Monthly Weather Review 139:1626--1636.
Berrocal, V.J., Raftery, A.E. and Gneiting, T. (2010). Probabilistic Weather Forecasting for Winter Road Maintenance. Journal of the American Statistical Association 105:522-537.
Bao, L., Gneiting, T., Grimit, E.P., Guttorp, P. and Raftery, A.E. (2010). Bias correction and Bayesian Model Averaging for ensemble forecasts of surface wind direction. Monthly Weather Review 138:1811-1821.
Sloughter, J.M., Gneiting, T. and Raftery, A.E. (2010). Probabilistic Wind Speed Forecasting using Ensembles and Bayesian Model Averaging. Journal of the American Statistical Association 105:25-35.
Fraley, C., Raftery, A.E. and Gneiting, T. (2010). Calibrating Multi-Model Forecast Ensembles with Exchangeable and Missing Members using Bayesian Model Averaging. Monthly Weather Review 138:190-202.
Mass, C.F., Joslyn, S., Pyle, J., Tewson, P., Gneiting, T., Raftery, A.E., Baars, J., Sloughter, J.M., Jones, D. and Fraley, C. (2009). PROBCAST: A Web-Based Portal to Mesoscale Probabilistic Forecasts. Bulletin of the American Meteorological Society 90:1009-1014.
Berrocal, V.J., Raftery, A.E. and Gneiting, T. (2008). Probabilistic quantitative precipitation field forecasting using a two-stage spatial model. Annals of Applied Statistics 2: 1170-1193.
Fraley, C., Raftery, A.E., Sloughter, J.M. and Gneiting, T. (2007). ``ensembleBMA: An R Package for Probabilistic Forecasting using Ensembles and Bayesian Model Averaging.'' Technical Report no. 516, Department of Statistics, University of Washington.
Gneiting, T., Balabdaoui, F. and Raftery, A.E. (2007). Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society, Series B, 69, 243-268.
Gneiting, T. and Raftery, A.E. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association, 102, 359-378.
Berrocal, V., Raftery, A.E. and Gneiting, T. (2007). Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts. Monthly Weather Review, 135, 1386-1402.
Wilson, L.J., Beauregard, S., Raftery, A.E. and Verret, R. (2007). Calibrated Surface Temperature Forecasts from the Canadian Ensemble Prediction y stem Using Bayesian Model Averaging (with Discussion). Monthly Weather Review, 135, 1364-1385. Discussion pages 4226-4236.
Sloughter, J.M., Raftery, A.E. and Gneiting, T. (2007). Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging. Monthly Weather Review, 135, 3209-3220.
Tewson, P. and Raftery, A.E. (2006). Real-Time Calibrated Probabilistic Forecasting Website. Bulletin of the American Meteorological Society, 7, 880-882.
Gneiting, T. and Raftery, A.E. (2005). Weather forecasting with ensemble methods. Science, 310, 248-249.
Raftery, A.E., Gneiting, T., Balabdaoui, F. and Polakowski, M. (2005). Using Bayesian Model Averaging to Calibrate Forecast Ensembles. Monthly Weather Review, 133, 1155-1174.
Gneiting, T., Raftery, A.E., Westveld, A. and Goldman, T. (2005). Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation. Monthly Weather Review, 133, 1098-1118.
Gel, Y., Raftery, A.E. and Gneiting, T. (2004).
Calibrated probabilistic
mesoscale weather field forecasting: The Geostatistical Output Perturbation
(GOP) method (with Discussion).
Journal of the American Statistical Association, 99, 575-590.
Earlier technical report version with color figures.
Land Use and Transportation
Ševčíková , H., Raftery, A.E., and Waddell, P.A. (2011).
Assessing Uncertainty About the Benefits of Transportation
Infrastructure Projects Using Bayesian Melding:
Application to Seattle's Alaskan Way Viaduct.
Transportation Research Part A - Methodological 45:540-553.
Sevcikova, H., Raftery, A.E. and Waddell, P. (2007). Assessing Uncertainty in Urban Simulations Using Bayesian Melding. Transportation Research B, 41, 652-669.
Air Quality
Fuentes, M. and Raftery, A.E. (2005).
Model evaluation and spatial interpolation by Bayesian
combination of observations with outputs from numerical models.
Biometrics, 66, 36--45.
Stephen, E., Raftery, A.E. and Dowding, P. (1990). Forecasting spore concentrations: A time series approach. International Journal of Biometeorology, 34, 87-89.
Raftery, A.E. (1989). Are ozone exceedance rates decreasing? Statistical Science, 4, 378-381.
Environmental Risk Assessment
Bates, S., Raftery, A.E. and Cullen, A.C. (2003).
Bayesian Uncertainty Assessment in Deterministic
Models for Environmental Risk Assessment. Environmetrics,
14, 355-371.
Raftery, A.E. and Thompson, E.A. (1990). What is the probability of a serious nuclear reactor accident? Journal of Statistical Computation and Simulation, 36, 31-34.
Raftery, A.E. and Thompson, E.A. (1988). How many nuclear reactor accidents? Journal of Statistical Computation and Simulation, 29, 347-350.
Whales
This was a long project, pursued during my membership of the International Whaling
Commission's Scientific Committee, 1988-2000, in collaboration with Judy Zeh, Given Givens
and David Poole. The goal was to assess the Bering-Chukchi-Beaufort
Seas stock of bowhead whales, as a basis for fixing the quota for aboriginal subsistence whaling.
There were two main strands: estimating the population of this elusive animal on
the basis of the annual visual and acoustic "census" (which really missed about three-quarters
of the stock), and estimating the rate of increase of the population.
The latter involved using deterministic population dynamics models, which added
important useful information, and this led us to develop the Bayesian melding
method for the statistical analysis of deterministic simulation models.
Papers:
Poole, D.J. and Raftery, A.E. (2000). Inference for deterministic simulation models: The Bayesian melding approach. Journal of the American Statistical Association, 95, 1244-1255. Earlier, more complete technical report version (ps).
Poole, D., Givens, G.H. and Raftery, A.E. (1999). A proposed stock assessment method and its application to bowhead whales, Balaena mysticetus. Fishery Bulletin, 97, 144-152. Earlier technical report version.
Raftery, A.E. and Zeh, J.E. (1998). Estimating bowhead whale, Balaena mysticetus, population size and rate of increase from the 1993 census. Journal of the American Statistical Association, 93, 451-463.
Givens, G.H., Zeh, J.E. and Raftery, A.E. (1996). Implementing the current management regime for aboriginal subsistence whaling to establish a catch limit for the Bering--Chukchi--Beaufort Seas stock of bowhead whales. Report of the International Whaling Commission, 46, 493--501.
Givens, G.H. and Raftery, A.E. (1996). Local adaptive importance sampling for multivariate densities with strong nonlinear relationships. Journal of the American Statistical Association, 91, 132-141.
Givens, G.H., Zeh, J.E. and Raftery, A.E. (1995). Assessment of the Bering-Chukchi-Beaufort Seas stock of bowhead whales using the BALEEN II model in a Bayesian synthesis framework. Report of the International Whaling Commission, 45, 345-364.
Givens, G.H., Raftery, A.E. and Zeh, J.E. (1995). Response to comments by Butterworth and Punt in SC/46/AS2 on the Bayesian synthesis approach. Report of the International Whaling Commission, 45, 325-330.
Raftery, A.E., Givens, G.H. and Zeh, J.E. (1995). Inference from a deterministic population dynamics model for bowhead whales (with Discussion). Journal of the American Statistical Association, 90, 402-430. Rejoinder. [The 1995 JASA-Applications and Case Studies Invited Paper.]
Givens, G.H., Raftery, A.E. and Zeh, J.E. (1994). A reweighting approach for sensitivity analysis within the Bayesian synthesis framework for population assessment modeling. Report of the International Whaling Commission, 44, 377-384.
Givens, G.H., Raftery, A.E. and Zeh, J.E. (1993). Benefits of a Bayesian approach for synthesizing multiple sources of evidence and uncertainty linked by a deterministic model. Report of the International Whaling Commission, 43, 495-500.
Raftery, A.E. and Schweder, T. (1993). Inference about the ratio of two parameters, with application to whale censusing. The American Statistician, 47, 259-264.
Raftery, A.E. and Zeh, J.E. (1993). Estimation of Bowhead Whale, Balaena mysticetus, population size (with Discussion). In Bayesian Statistics in Science and Technology: Case Studies (C. Gatsonis et al., eds.), New York: Springer-Verlag, pp. 163-240.
Zeh, J.E., George, J.C., Raftery, A.E. and Carroll, G.M. (1990). Rate of increase, 1978-1988, in the Bering Sea stock of bowhead whales, Balaena mysticetus, estimated from ice-based census data. Marine Mammal Science, 7, 105-122.
Zeh, J.E., Raftery, A.E. and Yang, Q. (1990). Assessment of tracking algorithm performance and its effect on population estimates using bowhead whales, Balaena mysticetus, identified visually and acoustically in 1986 off Point Barrow, Alaska. Report of the International Whaling Commission, 40, 411-421.
Raftery, A.E., Zeh, J.E., Yang, Q. and Styer, P.E. (1990). Bayes empirical Bayes interval estimation of bowhead whale, Balaena mysticetus, population size based upon the 1986 combined visual and acoustic census off Point Barrow, Alaska. Report of the International Whaling Commission, 40, 393-409.
Zeh, J.E., Turet, P., Gentleman, R. and Raftery, A.E. (1988). Population size estimation for the bowhead whale, Balaena mysticetus, based on 1985 and 1986 visual and acoustic data. Report of the International Whaling Commission, 38, 349-364.
Raftery, A.E., Turet, P. and Zeh, J.E. (1988). A parametric empirical Bayes approach to interval estimation of bowhead whales, Balaena mysticetus, population size. Report of the International Whaling Commission, 38, 377-388.
Wind and Solar Energy
This was a long project, about 1981-1989, aimed at assessing Ireland's wind power resource.
The overall project was a collaboration between the Trinity College Dublin Department of
Statistics (mostly John Haslett and myself), the Irish Meteorological Office,
the Irish Electricity Supply Board (ESB), and the Irish Department of Energy.
The results were positive, and the project led to statistical innovations in
spatial statistics and time series analysis, but the Irish government did not
respond immediately to the conclusions. More recently, however,
the Irish government has decided to make a major investment in wind power.
Papers:
Haslett, J. and Raftery, A.E. (1989). Space-time modelling with long-memory dependence: Assessing Ireland's wind power resource (with Discussion). Journal of the Royal Statistical Society, series C - Applied Statistics, 38, 1-50.
Raftery, A.E., Haslett, J. and McColl, E. (1982). Wind power: a space-time process? In Time series analysis: theory and practice 2 (O.D. Anderson, ed.), North-Holland, pp. 191-202.
Raftery, A.E.., Shier, P. and Obilade, T. (1980). Domestic space heating and solar energy in Ireland. International Journal of Energy Research, 4, 31-39.
These papers are being made available here to facilitate the timely dissemination of scholarly work; copyright and all related rights are retained by the copyright holders.
Updated May 10, 2023.
Copyright 2005-2023 by Adrian E. Raftery; all rights reserved.