An Analog Approach for Weather Estimation Using Climate Projections and Reanalysis Data


TitleAn Analog Approach for Weather Estimation Using Climate Projections and Reanalysis Data
Publication TypeJournal Article
Year of Publication2019
AuthorsClemins, PJ, Bucini, G, Winter, JM, Beckage, B, Towler, E, Betts, AK, Cummings, R, Queiroz, HC
JournalJournal of Applied Meteorology and Climatology
Volume58
Start Page1763
Issue8
Pagination1763 - 1777
Date Published2019/08
ISSN1558-8424
Abstract

General circulation models (GCMs) are essential for projecting future climate; however, despite the rapid advances in their ability to simulate the climate system at increasing spatial resolution, GCMs cannot capture the local and regional weather dynamics necessary for climate impacts assessments. Temperature and precipitation, for which dense observational records are available, can be bias corrected and downscaled, but many climate impacts models require a larger set of variables such as relative humidity, cloud cover, wind speed and direction, and solar radiation. To address this need, we develop and demonstrate an analog-based approach, which we call a ‘‘weather estimator.’’ The weather estimator employs a highly generalizable structure, utilizing temperature and precipitation from previously downscaled GCMs to select analogs from a reanalysis product, resulting in a complete daily gridded dataset. The resulting dataset, constructed from the selected analogs, contains weather variables needed for impacts modeling that are physically, spatially, and temporally consistent. This approach relies on the weather variables’ correlation with temperature and precipitation, and our correlation analysis indicates that the weather estimator should best estimate evaporation, relative humidity, and cloud cover and do less well in estimating pressure and wind speed and direction. In addition, while the weather estimator has several user-defined parameters, a sensitivity analysis shows that the method is robust to small variations in important model parameters. The weather estimator recreates the historical distributions of relative humidity, pressure, evaporation, shortwave radiation, cloud cover, and wind speed well and outperforms a multiple linear regression estimator across all predictands.

URLhttps://journals.ametsoc.org/doi/pdf/10.1175/JAMC-D-18-0255.1
DOI10.1175/JAMC-D-18-0255.1
Short TitleJ. Appl. Meteor. Climatol.
Refereed DesignationRefereed
Status: 
Published
Attributable Grant: 
BREE
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Year4 StatusChanged
Acknowledged VT EPSCoR: 
Ack-Yes
2nd Attributable Grant: 
RACC
2nd Grant Year: 
2nd_Post_Grant
2nd Acknowledged Grant: 
2nd_Ack-Yes