Google Earth Engine——GRACE Tellus月度质量网格提供了相对于2004-2010年时间平均基线的月度引力异常值。该数据集所包含的数据是以 “等水厚度 “为单位,以厘米为单位

简介: Google Earth Engine——GRACE Tellus月度质量网格提供了相对于2004-2010年时间平均基线的月度引力异常值。该数据集所包含的数据是以 “等水厚度 “为单位,以厘米为单位

Dataset Availability

2002-04-01T00:00:00 - 2017-02-03T00:00:00

Dataset Provider

NASA Jet Propulsion Laboratory

Collection Snippet

ee.ImageCollection("NASA/GRACE/MASS_GRIDS/MASCON")

DESCRIPTION

GRACE Tellus Monthly Mass Grids provides monthly gravitational anomalies relative to a 2004-2010 time-mean baseline. The data contained in this dataset are units of "Equivalent Water Thickness" which represent the deviations of mass in terms of vertical extent of water in centimeters. See the provider's Monthly Mass Grids Overview for more details.

The GRACE Tellus (GRCTellus) Global Mascon dataset is based on Level-1 GRACE observations and processed at NASA Jet Propulsion Laboratory (JPL).

This dataset uses a-priori constraints in space and time to estimate global, monthly gravity fields in terms of equal-area 3°x3° spherical cap mass concentration (mascon) functions to minimize the effect of measurement errors. No additional empirical destriping filter has been applied to the data. This results in better S/N ratios of the mascon fields compared to the conventional spherical-harmonic solutions.


Note

  • Mascons which lie on coastlines contain mixed land and ocean signals. A version of this dataset with the Coastline Resolution Improvement (CRI) filter applied to separate the land and ocean portions of mass within each land/ocean mascon in a post-processing step. This dataset is available at NASA/GRACE/MASS_GRIDS/MASCON_CRI.
  • The data are represented on a 1/2 degree lon-lat grid, but they represent the 3x3 degree equal-area caps, which is the current native resolution of JPL-RL05M.
    GRACE Tellus月度质量网格提供了相对于2004-2010年时间平均基线的月度引力异常值。该数据集所包含的数据是以 "等水厚度 "为单位,以厘米为单位表示水的垂直范围的质量偏差。更多细节请参见提供者的月度质量网格概述。
    GRACE Tellus(GRCTellus)全球质量数据集基于一级GRACE观测,由NASA喷气推进实验室(JPL)处理。
    该数据集利用空间和时间上的先验约束,以等面积的3°x3°球盖质量浓度(mascon)函数来估计全球每月的重力场,以尽量减少测量误差的影响。没有对数据进行额外的经验性去分化过滤。这使得mascon场的信噪比比传统的球面谐波解决方案更好。
    备注
    位于海岸线上的马斯克包含陆地和海洋的混合信号。这个数据集的一个版本,在后处理步骤中应用了海岸线分辨率改进(CRI)过滤器,以分离每个陆地/海洋mascon中的陆地和海洋部分的质量。该数据集可在NASA/GRACE/MASS_GRIDS/MASCON_CRI上找到。
    这些数据以1/2度的纬度网格表示,但它们代表了3x3度的等面积上限,这也是JPL-RL05M目前的原始分辨率。

Resolution

55660 meters

Bands Table

Name Description Min* Max* Units
lwe_thickness Equivalent liquid water thickness in centimeters. -370.43 139.04 cm
uncertainty

1-sigma uncertainty for each 3-degree mascon estimate. The provided estimates of uncertainty are regarded to be conservative. See the provider's [Error & Uncertainty Estimates section](https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/)

0.29 31.1


* = Values are estimated使用说明:

All NASA-produced data from the GRACE mission is made freely available for the public to use. When using any of the GRCTellus data, please add an acknowledgment: "GRACE land are available at https://grace.jpl.nasa.gov, supported by the NASA MEaSUREs Program." and cite with the citations provided.

引用:

S.C. Swenson. 2012. GRACE monthly land water mass grids NETCDF RELEASE 5.0. Ver. 5.0. PO.DAAC, CA, USA. Dataset accessed [YYYY-MM-DD] at https://doi.org/10.5067/TELND-NC005.

Landerer F.W. and S. C. Swenson, Accuracy of scaled GRACE terrestrial water storage estimates. Water Resources Research, Vol 48, W04531, 11 PP, doi:10.1029/2011WR011453, 2012.

Swenson, S. C. and J. Wahr, Post-processing removal of correlated errors in GRACE data, Geophys. Res. Lett., 33, L08402, doi:10.1029/2005GL025285, 2006.

代码:

var dataset = ee.ImageCollection('NASA/GRACE/MASS_GRIDS/MASCON')
                  .filter(ee.Filter.date('2016-08-01', '2016-08-30'));
var equivalentWaterThickness = dataset.select('lwe_thickness');
var equivalentWaterThicknessVis = {
  min: -25.0,
  max: 25.0,
};
Map.setCenter(6.746, 46.529, 2);
Map.addLayer(
    equivalentWaterThickness, equivalentWaterThicknessVis,
    'Equivalent Water Thickness');

 

var dataset = ee.ImageCollection('NASA/GRACE/MASS_GRIDS/MASCON_CRI')
                  .filter(ee.Filter.date('2016-08-01', '2016-08-30'));
var equivalentWaterThickness = dataset.select('lwe_thickness');
var equivalentWaterThicknessVis = {
  min: -25.0,
  max: 25.0,
};
Map.setCenter(6.746, 46.529, 2);
Map.addLayer(
    equivalentWaterThickness, equivalentWaterThicknessVis,
    'Equivalent Water Thickness');

 



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