The Joint Calibration Model in probabilistic weather forecasting: some preliminary issues
DOI:
https://doi.org/10.6092/issn.1973-2201/3524Abstract
Ensemble Prediction Systems play today a fundamental role in weather forecasting. They can represent and measure uncertainty, thereby allowing distributional forecasting as well as deterministic-style forecasts. In this context, we show how the Joint Calibration Model (Agati et al., 2007) – based on a modelization of the Probability Integral Transform distribution – can provide a solution to the problem of information combining in probabilistic forecasting of continuous variables. A case study is presented, where the potentialities of the method are explored and the accuracy of deterministic-style forecasts from JCM is compared with that from Bayesian Model Averaging (Raftery et al., 2005).References
P. AGATI, D.G. CALÒ, L. STRACQUALURSI (2007), A Joint Calibration Model for combining predictive distributions, “Statistica”, 2007, pp. 167-182.
J.D. ANNAN, J.C. HARGREAVES (2007), Efficient estimation and ensemble generation in climate modelling, “Philosophical transactions of the Royal Statistical Society” A, 365, 2077-2088.
R. BUIZZA, M. MILLER, T.N. PALMER (1999), Stochastic representation of model uncertainties in the ECMWF ensemble prediction system, “Quarterly Journal of the Royal Meteorological Society, 125, pp. 2887-2908.
R.H. BYRD, P. LU, J. NOCEDAL, C. ZHU (1995), A limited memory algorithm for bound constrained optimization, “SIAM Journal of Scientific Computing”, 16, pp. 1190-1208.
M. COLLINS (2007), Ensemble and probabilities: a new era in the prediction of climate change, “Philosophical Transactions of the Royal Society”, A, 365, pp. 1957-1970.
M. COLLINS, B.B.B. BOOTH, G.R. HARRIS, J. M. MURPHY, D.M.H. SEXTON, M.J. WEBB (2006), Towards quantifying uncertainty in transient climate change, “Climate Dynamics”, 27, pp. 127-147.
M. COLLINS, S. KNIGHT eds (2007), Theme issue Ensembles and probabilities: a new era in the prediction of climate change, “Philosophical Transactions of the Royal Society”, A, 365, 1957-2191.
R. M. COOKE (1991), Experts in uncertainty. Opinion and subjective probability in science, Oxford University Press, Oxford.
A. P. DAWID (1984) Statistical theory: the prequential approach, “Journal of the Royal Statistical Society”, A, 147, pp. 278-290.
F. X. DIEBOLD, T. A. GUNTHER, A.S. TAY (1998), Evaluating density forecasts with applications to financial risk management, “International Economic Review”, 39, pp. 863-883.
T. GNEITING, F. BALABDAOUI, A.E. RAFTERY (2007), Probabilistic forecasts, calibration and sharpness, “Journal of the Royal Statistical Society” B, 69, pp. 243-268.
T. GNEITING, A. RAFTERY (2005), Weather forecasting with ensemble methods, “Science”, 310, pp. 248-249.
T. GNEITING, A.H. WESTVELD, A.E. RAFTERY, T. GOLDMAN (2004), Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Technical report n. 449, Department of Statistics, University of Washington.
R.J. GRAHAM (2001), The ensemble of ensembles: multiple-model forecasting for seasonal timescales, Seasonal Modelling and Prediction Group, Met. Office, United Kingdom, available at www.wmo.ch/pages/prog/wcp/wcasp/clips/modules/documents/mod3_ensembleofensemble.ppt
T.N. KRISHNAMURTI, C.M. KISHTAWAL, T. LAROW, D. BACHIOCHI, E. WILLIFORD, S. GADGIL, S. SURENDRAN (2000), Improved weather and seasonal climate forecasts from multimodel superensemble, “Science”, 285, pp. 1548-1550.
T.N. KRISHNAMURTI, C.M. KISHTAWAL, Z. ZHANG, T. LAROW, D. BACHIOCHI, E. WILLIFORD, S. GADGIL, S. SURENDRAN (2000), Multimodel Ensemble Forecasts for Weather and Seasonal Climate, “Journal of Climate”, 13, pp. 4196-4216.
J.W.B. LIN, J.D. NEELIN (2002), Considerations for stochastic convective parameterization, “Journal of the Atmospheric Sciences”, 59, pp. 959-975.
P. A. MORRIS (1977), Combining expert judgements: a Bayesian approach, “Management Science”, 23, pp. 679-693.
NATIONAL RESEARCH COUNCIL, Committee on Estimating and Communicating Uncertainty in Weather and Climate Forecasts (2006) Completing the forecast: characterizing and communicating uncertainty for better decisions using weather and climate forecasts, National Academies Press, Washington, available at http://www.nap.edu/catalog. php?record_id=11699 #toc
K. PEARSON (1933), On a method of determining weather a sample of size n supposed to have been drawn from a parent population having a known probability integral has probably been drawn at random, “Biometrika”, 25, pp. 379-410.
A.E. RAFTERY, T. GNEITING, F. BALABDAOUI, M. POLAKOWSKI (2005), Using Bayesian model averaging to calibrate forecast ensembles, “Monthly Weather Review”, 133, 1155-1174.
M. ROSENBLATT (1952), Remarks on a multivariate transformation, “Annals of Mathematical Statistics”, 23, pp. 470-472.
J. ROUGIER, D.S. SEXTON (2007), Inference in ensemble experiments, “Philosophical Transactions of the Royal Society”, A, 365, pp. 2133-2143.
F. VITART (2006), Seasonal forecasting of tropical storm frequency using a multi-model ensemble, “Quarterly Journal of Meteorological Society”, 132, pp. 647-666.
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