Cookies?
Library Header Image
LSE Research Online LSE Library Services

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

[img] Text (Designing Multi-model applications manuscript) - Accepted Version
Repository staff only until 16 January 2021.

Download (1MB) | Request a copy

Identification Number: 10.1175/MWR-D-19-0061.1

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: 28 Jun 2020 23:29
URI: http://eprints.lse.ac.uk/id/eprint/103275

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics