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gmd.copernicus.org | ||
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research.google
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| | | | | Posted by Jason Hickey, Senior Software Engineer, Google Research The weather can affect a person's daily routine in both mundane and serious way... | |
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progearthplanetsci.springeropen.com
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| | | | | Proposed in 1954, Alisov's climate classification (CC) focuses on climatic changes observed in January-July in large-scale air mass zones and their fronts. Herein, data clustering by machine learning was applied to global reanalysis data to quantitatively and objectively determine air mass zones, which were then used to classify the global climate. The differences in air mass zones between two half-year seasons were used to determine climatic zones, which were then subdivided into continental or maritime climatic regions or according to east-west climatic differences. This study renews Alisov's CC for the first time in almost 70years and employs data-driven machine learning to establish a standard for causal CC based on air masses. | |
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www.geoscientific-model-development.net
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| | | | | [AI summary] The provided text is a list of upcoming and ongoing special issues for the journal *Geoscientific Model Development* (GMD). These special issues cover a wide range of topics related to climate modeling, greenhouse gas monitoring, geological processes, and Earth system simulations. Each entry includes the title, description, and dates for the special issue. The themes span from the development and evaluation of climate forcing agents, to airborne greenhouse gas measurement campaigns, and numerical modeling of geological processes. Additionally, there are entries related to the Coupled Model Intercomparison Project (CMIP), including its seventh phase (CMIP7), and the integration of machine learning with geoscientific models. | |
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hess.copernicus.org
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