Multivariate Postprocessing Methods for High-Dimensional Seasonal Weather Forecasts-Reference-Cited by-同舟云学术

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Multivariate Postprocessing Methods for High-Dimensional Seasonal Weather Forecasts

Published:2020-06-30 Issue:535 Volume:116 Page:1048-1059
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Heinrich Claudio¹,Hellton Kristoffer H.¹,Lenkoski Alex¹,Thorarinsdottir Thordis L.¹

Affiliation:

1. Norwegian Computing Center , Oslo , Norway

Funder

Volkswagen Foundation

Research Council of Norway

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2020.1769634

Reference39 articles.

1. Climate trends in a seasonal forecasting system

2. Impact of Ensemble Size on Ensemble Prediction

3. Geostatistics

4. The Schaake Shuffle: A Method for Reconstructing Space–Time Variability in Forecasted Precipitation and Temperature Fields

5. Seasonal-to-decadal predictions with the ensemble Kalman filter and the Norwegian Earth System Model: a twin experiment

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1. Efficient and effective calibration of numerical model outputs using hierarchical dynamic models;The Annals of Applied Statistics;2024-06-01

2. Additive dynamic models for correcting numerical model outputs;Computational Statistics & Data Analysis;2023-11

3. Subselection of seasonal ensemble precipitation predictions for East Africa;Quarterly Journal of the Royal Meteorological Society;2023-07

4. Comparison of multivariate post‐processing methods using global ECMWF ensemble forecasts;Quarterly Journal of the Royal Meteorological Society;2023-02-21

5. A deep learning approach for spatial error correction of numerical seasonal weather prediction simulation data;Big Earth Data;2023-02-13

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