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 Weather Forecasting
This work is part of 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 is continuing with support from the National Science Foundation.
This project involves 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.
Papers:
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.
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.
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.
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.
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.
Updated May 22, 2008
Earlier technical report version with color figures. 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. 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. 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. 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, 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. Recently, however, the Irish government has
decided to make a major investment in wind power.