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 in climate model output.
The MACA method was used to downscale the model output from 20 global climate models (GCMs) of the Coupled Model Inter-Comparison Project 5 (CMIP5) for the historical GCM forcings (1950-2005) and the future Representative Concentration Pathways (RCPs) RCP 4.5 and RCP 8.5 scenarios (2006-2100) from the native resolution of the GCMS to 4km.
The full list of models can be found at: https://climate.northwestknowledge.net/MACA/GCMs.php
MACAv2-METDATA 数据集是涵盖美国本土的 20 个全球气候模型的集合。 Multivariate Adaptive Constructed Analogs (MACA) 方法是一种统计降尺度方法，它利用训练数据集（即气象观测数据集）来消除历史偏差并匹配气候模型输出中的空间模式。
MACA 方法用于缩小来自耦合模型相互比较项目 5 (CMIP5) 的 20 个全球气候模型 (GCM) 的模型输出，用于历史 GCM 强迫 (1950-2005) 和未来的代表性浓度路径 (RCP) RCP 4.5 和 RCP 8.5 情景 (2006-2100) 从 GCMS 的原始分辨率到 4km。
完整的模型列表可以在：https://climate.northwestknowledge.net/MACA/GCMs.php
Dataset Availability
1900-01-01T00:00:00 - 2100-12-31T00:00:00
Dataset Provider
University of California Merced
Collection Snippet
ee.ImageCollection("IDAHO_EPSCOR/MACAv2_METDATA")
Resolution
2.5 arc minutes
Bands Table
Name | Description | Min* | Max* | Units |
|---|---|---|---|---|
tasmax | Maximum daily temperature near surface | 222.4 | 334.92 | K |
tasmin | Minimum daily temperature near surface | 215.33 | 315.61 | K |
rhsmax | Maximum daily relative humidity near surface, not present in models CCSM4 or NorESM1-M | 1 | 100 | % |
rhsmin | Minimum daily relative humidity near surface, not present in models CCSM4 or NorESM1-M | 1 | 100 | % |
huss | Average daily specific humidity near surface | 0 | 0.05 | kg/kg |
pr | Average daily precipitation amount at surface | 0 | 1609.77 | mm |
rsds | Average daily downward shortwave radiation at surface | 9.06 | 455.61 | W/m^2 |
uas | Average daily eastward component of wind near surface | -29.74 | 25.96 | m/s |
vas | Average daily northward component of wind near surface | -29.26 | 33.06 | m/s |
* = Values are estimated
影像属性：
Name | Type | Description |
|---|---|---|
scenario | String | Name of the CMIP5 scenario, one of 'rcp85', 'rcp45', or 'historical' |
model | String | Name of the CMIP5 model, eg 'inmcm4' |
ensemble | String | Either 'r1i1p1' or 'r6i1p1' |
使用说明：
The MACA datasets were created with funding from the US government and are in the public domain in the United States. For further clarity, unless otherwise noted, the MACA datasets are made available with a Creative Commons CC0 1.0 Universal dedication. In short, John Abatzoglou waives all rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law. You can copy, modify, distribute, and perform the work, even for commercial purposes, all without asking permission. John Abatzoglou makes no warranties about the work, and disclaims liability for all uses of the work, to the fullest extent permitted by applicable law. Users should properly cite the source used in the creation of any reports and publications resulting from the use of this dataset and note the date when the data was acquired. For more information refer to the MACA References and License page.
代码语言：javascript复制MACA 数据集是在美国政府的资助下创建的，在美国属于公共领域。为进一步明确起见，除非另有说明，否则 MACA 数据集以 Creative Commons CC0 1.0 通用免费提供。简而言之，John Abatzoglou 在法律允许的范围内放弃版权法规定的全球范围内作品的所有权利，包括所有相关和邻接权。您可以复制、修改、分发和执行作品，即使是出于商业目的，也无需征得许可。在适用法律允许的最大范围内，John Abatzoglou 对作品不作任何保证，并对作品的所有使用不承担任何责任。用户应正确引用因使用此数据集而产生的任何报告和出版物的创建所使用的来源，并注意获取数据的日期。有关更多信息，请参阅 MACA 参考和许可页面。数据引用：
Abatzoglou J.T. and Brown T.J., A comparison of statistical downscaling methods suited for wildfire applications, International Journal of Climatology(2012) doi:10.1002/joc.2312.
代码：
代码语言：javascript复制var dataset = ee.ImageCollection('IDAHO_EPSCOR/MACAv2_METDATA')
.filter(ee.Filter.date('2018-08-01', '2018-08-15'));
var maximumTemperature = dataset.select('tasmax');
var maximumTemperatureVis = {
min: 290.0,
max: 314.0,
palette: ['d8d8d8', '4addff', '5affa3', 'f2ff89', 'ff725c'],
};
Map.setCenter(-84.37, 33.5, 5);
Map.addLayer(maximumTemperature, maximumTemperatureVis, 'Maximum Temperature');最大气温值：
