Designing multi-model applications with surrogate forecast systems

Smith, Leonard A., Du, Hailiang and Higgins, Sarah (2020) Designing multi-model applications with surrogate forecast systems. Monthly Weather Review, 148 (6). 2233 - 2249. ISSN 0027-0644

Text ([15200493 - Monthly Weather Review] Designing Multimodel Applications with Surrogate Forecast Systems (1)) - Published Version Download (2MB)

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Abstract

Probabilistic forecasting is common in a wide variety of fields including geoscience, social science, and finance. It is sometimes the case that one has multiple probability forecasts for the same target.How is the information in these multiple nonlinear forecast systems best "combined"? Assuming stationarity, in the limit of a very large forecast-outcome archive, each model-based probability density function can be weighted to form a "multimodel forecast" that will, in expectation, provide at least as much information as the most informative single model forecast system. If one of the forecast systems yields a probability distribution that reflects the distribution from which the outcome will be drawn, Bayesian model averaging will identify this forecast system as the preferred system in the limit as the number of forecast-outcome pairs goes to infinity. In many applications, like those of seasonal weather forecasting, data are precious; the archive is often limited to fewer than 26 entries. In addition, no perfect model is in hand. It is shown that in this case forming a single "multimodel probabilistic forecast" can be expected to provemisleading. These issues are investigated in the surrogatemodel (here a forecast system) regime,where using probabilistic forecasts of a simplemathematical systemallowsmany limiting behaviors of forecast systems to be quantified and compared with those undermore realistic conditions.

Item Type:	Article
Official URL:	https://journals.ametsoc.org/toc/mwre/current
Additional Information:	© 2020 American Meteorological Society
Divisions:	Statistics Centre for Analysis of Time Series
Subjects:	H Social Sciences > HA Statistics G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics
Date Deposited:	03 Feb 2020 15:12
Last Modified:	12 Dec 2024 02:03
URI:	http://eprints.lse.ac.uk/id/eprint/103275

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