MIdASv0.2.1 – MultI-scale bias AdjuStment

نویسندگان

چکیده

Abstract. Bias adjustment is the practice of statistically transforming climate model data in order to reduce systematic deviations from a reference set, typically some sort observations. There are numerous proposed methodologies perform adjustments – ranging simple scaling approaches advanced multi-variate distribution-based mapping. In practice, actual bias method small step application, and most processing handles reading, writing, linking different sets. These practical steps become especially heavy with increasing domain size resolution both time space. Here, we present new implementation platform for adjustment, which call MIdAS (MultI-scale AdjuStment). modern code that supports features such as Python libraries allow efficient large sets at computing clusters, state-of-the-art methods based on quantile mapping, “day-of-year-based” avoid artificial discontinuities, it also introduces cascade The has been set up will continually support development aimed towards higher-resolution data, explicitly targeting cases where there scale mismatch between paper presents comparison quantile-mapping-based subsequently chosen MIdAS. A current recommended setup presented evaluated pseudo-reference regions around world. Special focus put preservation trends future projections, shown better than standard mapping implementations often similar preserve trends.

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ژورنال

عنوان ژورنال: Geoscientific Model Development

سال: 2022

ISSN: ['1991-9603', '1991-959X']

DOI: https://doi.org/10.5194/gmd-15-6165-2022