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contributor authorZhang, C.;Xie, S.;Tao, C.;Tang, S.;Emmenegger, T.;Neelin, J. D.;Schiro, K. A.;Lin, W.;Shaheen, Z.
date accessioned2022-01-30T17:46:44Z
date available2022-01-30T17:46:44Z
date copyright10/6/2020 12:00:00 AM
date issued2020
identifier issn0003-0007
identifier otherbamsd190282.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4263920
description abstractThe U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program User Facility produces ground-based long-term continuous unique measurements for atmospheric state, precipitation, turbulent fluxes, radiation, aerosol, cloud, and the land surface, which are collected at multiple sites. These comprehensive datasets have been widely used to calibrate climate models and are proven to be invaluable for climate model development and improvement. This article introduces an evaluation package to facilitate the use of ground-based ARM measurements in climate model evaluation. The ARM data-oriented metrics and diagnostics package (ARM-DIAGS) includes both ARM observational datasets and a Python-based analysis toolkit for computation and visualization. The observational datasets are compiled from multiple ARM data products and specifically tailored for use in climate model evaluation. In addition, ARM-DIAGS also includes simulation data from models participating the Coupled Model Intercomparison Project (CMIP), which will allow climate-modeling groups to compare a new, candidate version of their model to existing CMIP models. The analysis toolkit is designed to make the metrics and diagnostics quickly available to the model developers.
publisherAmerican Meteorological Society
titleThe ARM Data-Oriented Metrics and Diagnostics Package for Climate Models: A New Tool for Evaluating Climate Models with Field Data
typeJournal Paper
journal volume101
journal issue10
journal titleBulletin of the American Meteorological Society
identifier doi10.1175/BAMS-D-19-0282.1
journal fristpageE1619
journal lastpageE1627
treeBulletin of the American Meteorological Society:;2020:;volume( 101 ):;issue: 010
contenttypeFulltext


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