Datasets tagged maca in Earth Engine
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[null,null,[],[[["\u003cp\u003eThe 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.\u003c/p\u003e\n"],["\u003cp\u003eMACA 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.\u003c/p\u003e\n"],["\u003cp\u003eThese datasets offer both daily and monthly summaries of climate variables, making them versatile for various climate change research and applications.\u003c/p\u003e\n"]]],["The MACAv2-METDATA dataset, created by the University of Idaho, comprises 20 global climate models focused on the conterminous USA. It employs the Multivariate Adaptive Constructed Analogs (MACA) method for statistical downscaling. This process utilizes a meteorological observation dataset to correct historical biases and align spatial patterns. The dataset, available monthly, is tagged with terms like climate, geophysical, and MACA, providing comprehensive climate data.\n"],null,["# Datasets tagged maca in Earth Engine\n\n-\n\n |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### MACAv2-METDATA Monthly Summaries: University of Idaho, Multivariate Adaptive Constructed Analogs Applied to Global Climate Models](/earth-engine/datasets/catalog/IDAHO_EPSCOR_MACAv2_METDATA_MONTHLY) |\n | 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 ... |\n | [climate](/earth-engine/datasets/tags/climate) [conus](/earth-engine/datasets/tags/conus) [geophysical](/earth-engine/datasets/tags/geophysical) [idaho](/earth-engine/datasets/tags/idaho) [maca](/earth-engine/datasets/tags/maca) [monthly](/earth-engine/datasets/tags/monthly) |\n\n-\n\n |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### MACAv2-METDATA: University of Idaho, Multivariate Adaptive Constructed Analogs Applied to Global Climate Models](/earth-engine/datasets/catalog/IDAHO_EPSCOR_MACAv2_METDATA) |\n | 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 ... |\n | [climate](/earth-engine/datasets/tags/climate) [conus](/earth-engine/datasets/tags/conus) [geophysical](/earth-engine/datasets/tags/geophysical) [idaho](/earth-engine/datasets/tags/idaho) [maca](/earth-engine/datasets/tags/maca) [monthly](/earth-engine/datasets/tags/monthly) |"]]
