SARAL Near-Real-Time Value-added Operational Geophysical Data Record Sea Surface Height Anomaly
SARAL 近实时增值业务地球物理数据记录海面高度异常
简介
2020 年 3 月 18 日至今
ALTIKA_SARAL_L2_OST_XOGDR
这些数据是近实时(NRT)(测量后7-9小时内)海面高度异常(SSHA)数据,来自ARgos和ALtiKa卫星(SARAL)上的AltiKa高度计。SARAL是法国(法国国家空间研究中心)和印度(SARAL)的一项合作任务,利用Ka波段AltiKa高度计测量海面高度,于2013年2月25日发射。这些数据与该项目生成的业务地球物理数据记录(OGDR)数据的主要区别在于,SARAL的轨道在卫星间交叉位置使用SSHA与OSTM/Jason-2 GPS-OGDR-SSHA产品的差异进行了调整。这样,利用 OSTM/Jason-2 GPS-OGDR-SSHA 产品使用的 GPS 轨道的 1 厘米(径向均方根)精度,为 SARAL 生成了精度为 1.5 厘米(均方根)的更精确的 NRT 轨道高度。该数据集还包含项目(缩小版)OGDR 的所有数据,以及改进的测高仪风速和海况偏差校正。有关 SARAL 任务的更多信息,请访问:Home
DOI | 10.5067/AKASA-XOGD1 |
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Measurement | OCEANS > SEA SURFACE TOPOGRAPHY > SEA SURFACE HEIGHT OCEANS > OCEAN WAVES > SIGNIFICANT WAVE HEIGHT |
Swath Width | 11 km |
Platform/Sensor | SARAL / ALTIKA Altimeter |
Project | Satellite with ARgos and ALtiKa (SARAL) |
Data Provider | Publisher: JPL Creator: Shailen Desai Release Place: JPL Release Date: 2013-Dec-03 |
Format | netCDF-4 |
分辨率
空间分辨率: 8000 米 x 8000 米 时间分辨率月 - < 年 覆盖范围 北边界坐标: 82 度 南边界坐标:-82 度 西边界坐标: -180度 东边界坐标: -180度180 度 时间跨度:2020 年 3 月 18 日至今 颗粒时间跨度:2013 年 11 月 18 日至 2024 年 6 月 02 日 扫描带宽:11 千米 投影 投影类型:卫星原生沿轨投影 投影细节每个像素都包含地理位置信息 椭球面WGS 84
表格:数据变量
Name | Long Name | Unit |
---|---|---|
alt | 1 Hz altitude of satellite | m |
alt_dyn | 1 Hz altitude of satellite (Dynamic fit to DORIS-DIODE) | m |
bathymetry | ocean depth/land elevation | m |
ecmwf_meteo_map_avail | ECMWF meteorological map availability | |
hf_fluctuations_corr | high frequency fluctuations of the sea surface topography | m |
ice_flag | ice flag | |
internal_tide | internal tide height | m |
inv_bar_corr | inverted barometer height correction | m |
iono_corr_gim | GIM ionospheric correction | m |
lat | latitude | degrees_north |
lon | longitude | degrees_east |
mean_sea_surface_sol1 | mean sea surface height (solution 1) above reference ellipsoid | m |
mean_topography | mean dynamic topography above geoid | m |
model_dry_tropo_corr | model dry tropospheric correction | m |
ocean_tide_sol2 | geocentric ocean tide height (solution 2) | m |
pole_tide | geocentric pole tide height | m |
rad_liquid_water | radiometer liquid water content | kg/m^2 |
rad_surf_type | radiometer surface type | |
rad_water_vapor | radiometer water vapor content | kg/m^2 |
rad_wet_tropo_corr | radiometer wet tropospheric correction | m |
range | 1 Hz corrected altimeter range | m |
sea_state_bias | sea state bias correction | m |
sig0 | Corrected backscatter coefficient | dB |
solid_earth_tide | solid earth tide height | m |
ssha | sea surface height anomaly | m |
ssha_dyn | sea surface height anomaly | m |
surface_type | surface type | |
swh | Corrected significant waveheight | m |
time | time (sec. since 2000-01-01) | seconds since 2000-01-01 00:00:00.0 |
trailing_edge_variation_flag | 1 Hz trailing edge variation flag | |
wind_speed_alt | altimeter wind speed | m/s |
xover_corr | sea surface height cross over correction | m |
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CLOUD ENABLED
Status:
ACTIVE
Short Name:
ALTIKA_SARAL_L2_OST_XOGDR
Collection Concept ID:
C2251465126-POCLOUD
Spatial Coverage:
N: 82°
S: -82°
E: 180°
W: -180°
Access:
- Search Granules
- Browse Granule Listing
Capabilities:
DownloadSubsetVisualize
Data Recipes:
- Generic Data Readers
代码
代码语言:javascript复制!pip install leafmap
!pip install pandas
!pip install folium
!pip install matplotlib
!pip install mapclassify
import pandas as pd
import leafmap
url = "https://github.com/opengeos/NASA-Earth-Data/raw/main/nasa_earth_data.tsv"
df = pd.read_csv(url, sep="t")
df
leafmap.nasa_data_login()
results, gdf = leafmap.nasa_data_search(
short_name="ALTIKA_SARAL_L2_OST_XOGDR",
cloud_hosted=True,
bounding_box=(-180.0, -82.0, 180.0, 82.0),
temporal=("2020-03-18", "2020-08-08"),
count=-1, # use -1 to return all datasets
return_gdf=True,
)
gdf.explore()
#leafmap.nasa_data_download(results[:5], out_dir="data")
引用
DIRECT ACCESS | |
---|---|
Browse Granule Listing | |
Sort By | |
Search Granules | |
https://search.earthdata.nasa.gov/search/granules?p=C2251465126-POCLOUD |