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Comparing the Lasso Predictor-Selection and Regression Method with Classical Approaches of Precipitation Bias Adjustment in Decadal Climate Predictions

Li, Jingmin and Pollinger, Felix and Paeth, Heiko (2020) Comparing the Lasso Predictor-Selection and Regression Method with Classical Approaches of Precipitation Bias Adjustment in Decadal Climate Predictions. Monthly Weather Review, 148 (10), pp. 4339-4351. American Meteorological Society. doi: 10.1175/MWR-D-19-0302.1. ISSN 0027-0644.

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Official URL: http://dx.doi.org/10.1175/MWR-D-19-0302.1

Abstract

In this study, we investigate the technical application of the regularized regression method Lasso for identifying systematic biases in decadal precipitation predictions from a high-resolution regional climate model (CCLM) for Europe. The Lasso approach is quite novel in climatological research. We apply Lasso to observed precipitation and a large number of predictors related to precipitation derived from a training simulation, and transfer the trained Lasso regression model to a virtual forecast simulation for testing. Derived predictors from the model include local predictors at a given grid box and EOF predictors that describe large-scale patterns of variability for the same simulated variables. A major added value of the Lasso function is the variation of the so-called shrinkage factor and its ability in eliminating irrelevant predictors and avoiding overfitting. Among 18 different settings, an optimal shrinkage factor is identified that indicates a robust relationship between predictand and predictors. It turned out that large-scale patterns as represented by the EOF predictors outperform local predictors. The bias adjustment using the Lasso approach mainly improves the seasonal cycle of the precipitation prediction and, hence, improves the phase relationship and reduces the root-mean-square error between model prediction and observations. Another goal of the study pertains to the comparison of the Lasso performance with classical model output statistics and with a bivariate bias correction approach. In fact, Lasso is characterized by a similar and regionally higher skill than classical approaches of model bias correction. In addition, it is computationally less expensive. Therefore, we see a large potential for the application of the Lasso algorithm in a wider range of climatological applications when it comes to regression-based statistical transfer functions in statistical downscaling and model bias adjustment.

Item URL in elib:https://elib.dlr.de/138043/
Document Type:Article
Title:Comparing the Lasso Predictor-Selection and Regression Method with Classical Approaches of Precipitation Bias Adjustment in Decadal Climate Predictions
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Li, JingminDLR, IPA und Univ. Würzburghttps://orcid.org/0000-0002-7328-5102
Pollinger, FelixUniv. WürzburgUNSPECIFIED
Paeth, HeikoUniv. WürzburgUNSPECIFIED
Date:October 2020
Journal or Publication Title:Monthly Weather Review
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:148
DOI :10.1175/MWR-D-19-0302.1
Page Range:pp. 4339-4351
Editors:
EditorsEmailEditor's ORCID iD
Hacker, JoshUNSPECIFIEDUNSPECIFIED
Publisher:American Meteorological Society
ISSN:0027-0644
Status:Published
Keywords:Bias, Empirical orthogonal functions, Statistical techniques, Hindcasts, Seasonal forecasting, Climate models
HGF - Research field:other
HGF - Program:other
HGF - Program Themes:other
DLR - Research area:no assignment
DLR - Program:no assignment
DLR - Research theme (Project):no assignment
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics > Earth System Modelling
Deposited By: Li, Jingmin
Deposited On:24 Nov 2020 11:28
Last Modified:24 Nov 2020 11:28

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