ERA5-HEAT数据集
该数据集提供了一组代表室外条件下人类热应力和不适指数的完整历史重建。该数据集也被称为ERA5-HEAT(人类热舒适度),代表了当前生物气候学数据记录制作的最先进水平。该数据集围绕两个主要变量展开:1)平均辐射温度(MRT);2)通用热气候指数(UTCI)。这些变量描述了人体如何体验大气条件,特别是气温、湿度、通风和辐射。
该数据集是利用欧洲中期预报中心(ECMWF)的ERA5 再分析计算得出的。ERA5将模型数据与世界各地的观测数据结合在一起,对地球气候及其近几十年的演变提供了全球范围内完整一致的描述。ERA5被认为是观测到的大气条件的良好代表。有关该产品的更多外部信息,请点击此处。
Attribute | Details |
---|---|
Spatial extent | Global |
Spatial resolution | 27.75km (.25 deg) |
Temporal resolution | Daily |
Time span | 1940-01-01 to present |
Update frequency | Updated daily with lag of 2 weeks |
Variable | Units | Scale factor | Description |
---|---|---|---|
Mean Radiant Temperature (‘mrt_mean’, ‘mrt_max’, ‘mrt_min’, ‘mrt_median’) | Degrees Kelvin | 1.0 | Provided in four bands for daily mean, maximum, minimum, and median. |
Universal Thermal Climate Index (‘utci_mean’, ‘utci_max’, ‘utci_min’, ‘utci_median’) | Degrees Kelvin | 1.0 | Provided in four bands for daily mean, maximum, minimum, and median. |
// 读取图像集合并获取第一幅图像
var era5_heat_ic = ee.ImageCollection('projects/climate-engine-pro/assets/ce-era5-heat')
var era5_heat_i = era5_heat_ic.first()
// 打印第一幅图像,查看波段
print(era5_heat_i)
// 可视化第一幅图像中的选定波段 - 其他波段存在于图像集合中
var temp_palette = ["#b2182b", "#ef8a62", "#fddbc7", "#f7f7f7", "#d1e5f0", "#67a9cf", "#2166ac"].reverse()
Map.addLayer(era5_heat_i.select('mrt_mean').selfMask().subtract(273.15), {min: -10, max: 50, palette: temp_palette}, 'Mean Radiant Temperature, Daily Mean')
Map.addLayer(era5_heat_i.select('utci_mean').selfMask().subtract(273.15), {min: -10, max: 50, palette: temp_palette}, 'Universal Thermal Climate Index, Daily Mean')
Di Napoli C., Barnard C., Prudhomme C., Cloke HL and Pappenberger F. (2020): Thermal comfort indices derived from ERA5 reanalysis. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.553b7518 (Accessed on DD-MMM-YYYY)
ECMWF is available under an open license with terms of agreement available here
Keywords: heat exposure, climate, reanalysis, global, era5, thermal, public health, climate engine
Provided by: Copernicus
Curated in GEE by: Climate Engine Org
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