ABoVE:2016-2019 年环极接收器的 4 级 WRF-STILT 足迹文件

举报
此星光明 发表于 2025/07/07 16:34:33 2025/07/07
【摘要】 ​ABoVE: Level-4 WRF-STILT Footprint Files for Circumpolar Receptors, 2016-2019简介该数据集提供了 2016-2019 年期间天气研究与预报 (WRF) 随机时间反演拉格朗日传输 (STILT) 足迹数据产品,适用于位于飞行路径沿线位置以及北纬极地位置各个固定观测点的接收器(观测点)。在 WRF-STILT 模型中,...

ABoVE: Level-4 WRF-STILT Footprint Files for Circumpolar Receptors, 2016-2019

简介

该数据集提供了 2016-2019 年期间天气研究与预报 (WRF) 随机时间反演拉格朗日传输 (STILT) 足迹数据产品,适用于位于飞行路径沿线位置以及北纬极地位置各个固定观测点的接收器(观测点)。在 WRF-STILT 模型中,每个飞机和站点位置均被视为一个独立的接收器,以模拟陆地表面对观测到的大气成分的影响。这些足迹与化学物质种类无关,可应用于不同的通量模型,并纳入正式的反演框架。决定足迹场的粒子轨迹仅受 WRF 建模域外边缘的限制。该数据集中包含的测量数据对于理解北极碳循环的变化以及北极多年冻土融化带来的潜在威胁至关重要。


摘要

File Name Number of netCDF Files Spatial Resolution OCO Receptor Platform & Date
ACG_2017_insitu-footprints.tar.gz 14,320 low no Alaska Coast Guard, in situ measurements, 2017
ACG_2017_PFP-footprints.tar.gz 99 low no Alaska Coast Guard, PFP measurements, 2017
ArcticCAP_2017_insitu-footprints.tar.gz 45,450 low no Arctic Carbon Aircraft Profiles, in situ measurements, 2017
ArcticCAP_2017_PFP-footprints.tar.gz 331 low no Arctic Carbon Aircraft Profiles, PFP measurements, 2017
ASCENDS_2017_insitu-footprints.tar.gz 12,845 high no Ascends/ABoVE 2017 Airborne Campaign, PFP measurements, 2017
ATom2_2017_insitu-footprints.tar.gz 5667 high no Atmospheric Tomography Mission (ATom), in situ measurements, January-February 2017
ATom2_2017-2019-PFP-footprints.tar.gz 59 high no Atmospheric Tomography Mission (ATom), PFP measurements, January- February 2017
ATom3_2017_insitu-footprints.tar.gz 5598 low no Atmospheric Tomography Mission (ATom), in situ measurements, September-October 2017
ATom3_2018_PFP-footprints.tar.gz 31 low no Atmospheric Tomography Mission (ATom), PFP measurements, September-October 2018
ATom4_2017-2019_PFP-footprints.tar.gz 43 high no Atmospheric Tomography Mission (ATom), PFP measurements, 2017-2019
ATom4_2018_insitu-footprints.tar.gz 6011 high no Atmospheric Tomography Mission (ATom), in situ measurements, April-May 2018
BRW_2017-2019_PFP-footprints.tar.gz 349 high no Barrow Atmospheric Baseline Observatory, PFP measurements, 2017-2019
CBA_2017-2019_PFP-footprints.tar.gz 306 high no Cold Bay Alaska, PFP measurements, 2017-2019
EC-BRW-CRV_insitu-footprints.tar.gz 9844 high no Environment Canada + Barrow Atmospheric Baseline Observatory + Carbon in Arctic Reservoirs Vulnerability Experiment, 2019
ECCC_2019-footprints.tar.gz 2000 high no Environment and Climate Change Canada, 2017-2019
ESP_2017-2019_PFP-footprints.tar.gz 765 high no Estevan Point British Columbia, PFP measurements, 2017-2019
ETL_2017-2019_PFP-footprints.tar.gz 420 high no East Trout Lake Saskatchewan, PFP measurements, 2017-2019
LEF_2017-2019_PFP-footprints.tar.gz 717 high no Park Falls Wisconsin, PFP measurements, 2017-2019
NSA-7800_2016-footprints.tar.gz 7800 low no Modeled using v391 terrain heights, North Slope of Alaska-7800, 2016
NSA-7802_2016-footprints.tar.gz 7802 low no Modeled using v351 terrain heights, North Slope of Alaska-7802, 2016
OCO2-201700-d01-footprints.tar.gz 22,061 high yes WRF model domain d01, January-April and August-December 2017
OCO2-201700-d02-footprints.tar.gz 23,075 high yes WRF model domain d02, January-May and August-December 2017
OCO2-201700-d03-footprints.tar.gz 10,153 high yes WRF model domain d03, January-May and August-December 2017
OCO2-201705-d01-footprints.tar.gz 22,230 high yes WRF model domain d01, May 2017
OCO2-201706-d01-footprints.tar.gz 25,675 high yes WRF model domain d01, June 2017
OCO2-201706-d02-footprints.tar.gz 35,217 high yes WRF model domain d02, June 2017
OCO2-201706-d03-footprints.tar.gz 12,675 high yes WRF model domain d03, June 2017
OCO2-201707-d01-footprints.tar.gz 29,926 high yes WRF model domain d01, July 2017
OCO2-201707-d02-footprints.tar.gz 35,061 high yes WRF model domain d02, July 2017
OCO2-201707-d03-footprints.tar.gz 12,428 high yes WRF model domain d03, July 2017
OCO2-2018-particles.tar.gz 572 high yes OCO-2, 2018
PFA_2017-2019_PFP-footprints.tar.gz 498 high no Poker Flat Alaska, PFP measurements, 2017-2019


代码

!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"
df = pd.read_csv(url, sep="\t")
df
 
leafmap.nasa_data_login()
 
 
results, gdf = leafmap.nasa_data_search(
    short_name="ABoVE_Footprints_WRF_AK_NWCa_1896",
    cloud_hosted=True,
    bounding_box=(-180.0, 30.0, 180.0, 90.0),
    temporal=("2016-07-24", "2019-12-31"),
    count=-1,  # use -1 to return all datasets
    return_gdf=True,
)
 
 
gdf.explore()
 
#leafmap.nasa_data_download(results[:5], out_dir="data")

【声明】本内容来自华为云开发者社区博主,不代表华为云及华为云开发者社区的观点和立场。转载时必须标注文章的来源(华为云社区)、文章链接、文章作者等基本信息,否则作者和本社区有权追究责任。如果您发现本社区中有涉嫌抄袭的内容,欢迎发送邮件进行举报,并提供相关证据,一经查实,本社区将立刻删除涉嫌侵权内容,举报邮箱: cloudbbs@huaweicloud.com
  • 点赞
  • 收藏
  • 关注作者

评论(0

0/1000
抱歉,系统识别当前为高风险访问,暂不支持该操作

全部回复

上滑加载中

设置昵称

在此一键设置昵称,即可参与社区互动!

*长度不超过10个汉字或20个英文字符,设置后3个月内不可修改。

*长度不超过10个汉字或20个英文字符,设置后3个月内不可修改。