AI-generated Key Takeaways
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The MACAv2-METDATA datasets provide data from 20 global climate models, downscaled using the Multivariate Adaptive Constructed Analogs (MACA) method, and cover the conterminous United States.
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MACA is a statistical downscaling approach that leverages a meteorological observation training dataset to correct biases and align spatial patterns from global climate models with observed data.
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These datasets offer valuable insights into climate patterns and projections for the conterminous US, useful for researchers and analysts in climate-related fields.
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MACAv2-METDATA Monthly Summaries: University of Idaho, Multivariate Adaptive Constructed Analogs Applied to Global Climate Models
The MACAv2-METDATA dataset is a collection of 20 global climate models covering the conterminous USA. The Multivariate Adaptive Constructed Analogs (MACA) method is a statistical downscaling method which utilizes a training dataset (i.e. a meteorological observation dataset) to remove historical biases and match spatial patterns … climate conus geophysical idaho maca monthly -
MACAv2-METDATA: University of Idaho, Multivariate Adaptive Constructed Analogs Applied to Global Climate Models
The MACAv2-METDATA dataset is a collection of 20 global climate models covering the conterminous USA. The Multivariate Adaptive Constructed Analogs (MACA) method is a statistical downscaling method which utilizes a training dataset (i.e. a meteorological observation dataset) to remove historical biases and match spatial patterns … climate conus geophysical idaho maca monthly