Literature DB >> 16224011

Atmospheric science. Weather forecasting with ensemble methods.

Tilmann Gneiting1, Adrian E Raftery.   

Abstract

Traditional weather forecasting has been built on a foundation of deterministic modeling--start with initial conditions, put them into a supercomputer model, and end up with a prediction about future weather. But as Gneiting and Raftery discuss in their Perspective, a new approach--ensemble forecasting--was introduced in the early 1990s. In this method, up to 100 different computer runs, each with slightly different starting conditions or model assumptions, are combined into a weather forecast. In concert with statistical techniques, ensembles can provide accurate statements about the uncertainty in daily and seasonal forecasting. The challenge now is to improve the modeling, statistical analysis, and visualization technologies for disseminating the ensemble results.

Year:  2005        PMID: 16224011     DOI: 10.1126/science.1115255

Source DB:  PubMed          Journal:  Science        ISSN: 0036-8075            Impact factor:   47.728


  31 in total

1.  Improved evolutionary optimization from genetically adaptive multimethod search.

Authors:  Jasper A Vrugt; Bruce A Robinson
Journal:  Proc Natl Acad Sci U S A       Date:  2007-01-10       Impact factor: 11.205

2.  In silico and in vivo approach to elucidate the inflammatory complexity of CD14-deficient mice.

Authors:  Jose M Prince; Ryan M Levy; John Bartels; Arie Baratt; John M Kane; Claudio Lagoa; Jonathan Rubin; Judy Day; Joyce Wei; Mitchell P Fink; Sanna M Goyert; Gilles Clermont; Timothy R Billiar; Yoram Vodovotz
Journal:  Mol Med       Date:  2006 Apr-Jun       Impact factor: 6.354

3.  Use and Communication of Probabilistic Forecasts.

Authors:  Adrian E Raftery
Journal:  Stat Anal Data Min       Date:  2016-02-23       Impact factor: 1.051

4.  Ensemble modeling informs hypoxia management in the northern Gulf of Mexico.

Authors:  Donald Scavia; Isabella Bertani; Daniel R Obenour; R Eugene Turner; David R Forrest; Alexey Katin
Journal:  Proc Natl Acad Sci U S A       Date:  2017-07-31       Impact factor: 11.205

Review 5.  Deciphering the complexity of acute inflammation using mathematical models.

Authors:  Yoram Vodovotz
Journal:  Immunol Res       Date:  2006       Impact factor: 2.829

6.  Adaptively stacking ensembles for influenza forecasting.

Authors:  Thomas McAndrew; Nicholas G Reich
Journal:  Stat Med       Date:  2021-10-14       Impact factor: 2.373

Review 7.  Systems engineering medicine: engineering the inflammation response to infectious and traumatic challenges.

Authors:  Robert S Parker; Gilles Clermont
Journal:  J R Soc Interface       Date:  2010-02-10       Impact factor: 4.118

8.  Improvement of disease prediction and modeling through the use of meteorological ensembles: human plague in Uganda.

Authors:  Sean M Moore; Andrew Monaghan; Kevin S Griffith; Titus Apangu; Paul S Mead; Rebecca J Eisen
Journal:  PLoS One       Date:  2012-09-14       Impact factor: 3.240

9.  Modeling causes of death: an integrated approach using CODEm.

Authors:  Kyle J Foreman; Rafael Lozano; Alan D Lopez; Christopher Jl Murray
Journal:  Popul Health Metr       Date:  2012-01-06

Review 10.  A forecast for large-scale, predictive biology: Lessons from meteorology.

Authors:  Markus W Covert; Taryn E Gillies; Takamasa Kudo; Eran Agmon
Journal:  Cell Syst       Date:  2021-06-16       Impact factor: 11.091

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