GRIDMET: 爱达荷大学网格化地表气象数据集
格雷德地表气象数据集提供了1979年以来美国毗连地区的温度、降水、风、湿度和辐射的高空间分辨率（约4公里）的每日地表场。该数据集将来自PRISM的高分辨率空间数据与来自国家土地数据同化系统（NLDAS）的高时间分辨率数据相融合，以产生空间和时间上的连续场，用于额外的陆地表面建模。
该数据集包含临时产品，当完整的源数据可用时，会被更新的版本所取代。产品可以通过 "状态 "属性的值来区分。起初，资产是以status='early'的方式摄入的。几天后，它们会被状态='临时'的资产所取代。大约2个月后，它们会被状态='永久'的最终资产所取代。
Dataset Availability
1979-01-01T00:00:00 -
Dataset Provider
University of California Merced
Collection Snippet
ee.ImageCollection("IDAHO_EPSCOR/GRIDMET")
Resolution
4638.3 meters
Bands Table
| Name | Description | Min* | Max* | Units |
| pr | Precipitation amount | 0 | 690.44 | mm, daily total |
| rmax | Maximum relative humidity | 1.05 | 100 | % |
| rmin | Minimum relative humidity | 0 | 100 | % |
| sph | Specific humididy | 0 | 0.02 | kg/kg |
| srad | Surface downward shortwave radiation | 0 | 455.61 | W/m^2 |
| th | Wind direction | Degrees clockwise from North | ||
| tmmn | Minimum temperature | 225.54 | 314.88 | K |
| tmmx | Maximum temperature | 233.08 | 327.14 | K |
| vs | Wind velocity at 10m | 0.14 | 29.13 | m/s |
| erc | Energy release component | 0 | 131.85 | NFDRS fire danger index |
| eto | Daily reference evapotranspiration | 0 | 17.27 | grass, mm |
| bi | Burning index | 0 | 214.2 | NFDRS fire danger index |
| fm100 | 100-hour dead fuel moisture | 0.28 | 33.2 | % |
| fm1000 | 1000-hour dead fuel moisture | 0.36 | 47.52 | % |
| etr | Daily reference evapotranspiration | 0 | 27.02 | Alfalfa, mm |
| vpd | Mean vapor pressure deficit | 0 | 9.83 | kPa |
* = Values are estimated
| Name | Type | Description |
| status | String | 'early', 'provisional', or 'permanent' |
This work (METDATA, by John Abatzoglou) is in the public domain and is free of known copyright restrictions. 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.
数据引用：
Abatzoglou J. T., Development of gridded surface meteorological data for ecological applications and modelling, International Journal of Climatology. (2012) doi:10.1002/joc.3413
下载代码：
var table = ee.Geometry.Polygon([ [-111.28567358070336,37.88479180709053],[-110.73635717445336,37.88479180709053],[-111.28567358070336,38.265323220490416],[-111.28567358070336,37.88479180709053] ]) Map.centerObject(table) var date1 = ee.Date('1999-01-01') var date2 = ee.Date('1999-01-03') var dataset = ee.ImageCollection('IDAHO_EPSCOR/GRIDMET') .filter(ee.Filter.date(date1, date2)) .filterBounds(table); var tmax_p97 = dataset.select('tmmx').reduce(ee.Reducer.percentile([97])).clip(table); var tmax_p97Vis = { min: 300.0, max: 314.0, palette: ['blue', 'green', 'yellow', 'orange', 'red'], }; Map.addLayer(tmax_p97, tmax_p97Vis, 'Maximum Temperature p97'); var image = dataset.select('tmmx').mean().clip(table); var projection = image.projection().getInfo() Export.image.toDrive({ image: tmax_p97, description: 'Max_temp_p97_', scale: 4638.3, maxPixels: 1e13, crs: 'EPSG:3857', crsTransform: projection.transform, region: table });
