Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies
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| Publicado en: | Geoscientific Model Development vol. 11, no. 11 (2018), p. 4435 |
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| Autor Principal: | |
| Outros autores: | , , , , , , |
| Publicado: |
Copernicus GmbH
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| Acceso en liña: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.5194/gmd-11-4435-2018 |2 doi | |
| 035 | |a 2129410069 | ||
| 045 | 2 | |b d20180101 |b d20181231 | |
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| 100 | 1 | |a Lee, Huikyo |u Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA | |
| 245 | 1 | |a Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies | |
| 260 | |b Copernicus GmbH |c 2018 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here, we present a user-oriented document describing the latest version of RCMES, its development process, and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in situ observations, as well as satellite and reanalysis data products. The main components of RCMES are (1) a database of observations widely used for climate model evaluation, (2) various data loaders to import climate models and observations on local file systems and Earth System Grid Federation (ESGF) nodes, (3) a versatile processor to subset and regrid the loaded datasets, (4) performance metrics designed to assess and quantify model skill, (5) plotting routines to visualize the performance metrics, (6) a toolkit for statistically downscaling climate model simulations, and (7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with a brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES. | |
| 651 | 4 | |a United States--US | |
| 653 | |a Evaluation | ||
| 653 | |a Environmental assessment | ||
| 653 | |a Source code | ||
| 653 | |a Microprocessors | ||
| 653 | |a Open source software | ||
| 653 | |a Aeronautics | ||
| 653 | |a Interfaces | ||
| 653 | |a Climate change | ||
| 653 | |a Computer simulation | ||
| 653 | |a Regional development | ||
| 653 | |a Data analysis | ||
| 653 | |a Toolkits | ||
| 653 | |a Climatic data | ||
| 653 | |a Regional climates | ||
| 653 | |a Sustainable development | ||
| 653 | |a Software | ||
| 653 | |a Loaders | ||
| 653 | |a Sustainability | ||
| 653 | |a Regional climate models | ||
| 653 | |a Climatic analysis | ||
| 653 | |a Satellites | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Satellite observation | ||
| 653 | |a Data processing | ||
| 653 | |a Computer programs | ||
| 653 | |a Performance measurement | ||
| 653 | |a Climate models | ||
| 653 | |a Earth | ||
| 653 | |a Components | ||
| 653 | |a Regional analysis | ||
| 653 | |a Business metrics | ||
| 653 | |a Climate studies | ||
| 653 | |a Frameworks | ||
| 653 | |a Datasets | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Goodman, Alexander |u Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA | |
| 700 | 1 | |a Lewis McGibbney |u Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA | |
| 700 | 1 | |a Waliser, Duane E |u Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA | |
| 700 | 1 | |a Kim, Jinwon |u Joint Institute for Regional Earth System Science & Engineering, University of California, Los Angeles, CA, USA; National Institute of Meteorological Sciences/Korean Meteorological Administration, Seogwipo, South Korea | |
| 700 | 1 | |a Loikith, Paul C |u Department of Geography, Portland State University, Portland, OR, USA | |
| 700 | 1 | |a Gibson, Peter B |u Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA | |
| 700 | 1 | |a Massoud, Elias C |u Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA | |
| 773 | 0 | |t Geoscientific Model Development |g vol. 11, no. 11 (2018), p. 4435 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2129410069/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/2129410069/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2129410069/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |