ACT-America: L2 In Situ Atmospheric CO2, CO, CH4, and O3 Concentrations, Eastern USA
简介
该数据集提供了大气中二氧化碳 (CO2)、一氧化碳 (CO)、甲烷 (CH4)、水蒸气 (H2O) 和臭氧 (O3) 的浓度，这些浓度是在大气碳和运输-美国 (ACT-America) 项目开展的空中活动期间收集的。ACT-America 的任务持续了 4 年，包括五次为期 6 周的空中活动，覆盖美国中部和东部的所有 4 个季节和 3 个地区。该数据集提供了所有五次活动的结果，包括 2016 年夏季、2017 年冬季、2017 年秋季、2018 年春季和 2019 年夏季。两个仪器飞机平台，即 NASA 兰利比奇 B200 空中国王和 NASA 戈达德太空飞行中心的 C-130H 大力神，用于收集各种大陆表面和大气条件下的高质量现场测量数据。采用红外腔衰荡光谱仪系统（CRDS；Picarro Inc.）收集二氧化碳、一氧化碳、甲烷和水。采用双光束差分紫外吸收臭氧监测仪（型号 205；2B Technologies）收集臭氧数据。两架飞机均安装了相同的原位传感器阵列。此外，还提供了完整的飞机飞行信息，包括但不限于纬度、经度、海拔和气象条件。
ACT-America 的整个任务持续了四年，包括覆盖美国中部和东部地区所有四个季节的空中活动。ACT-America 的目标是研究大气中 CO2 和 CH4 的输送和通量。两个仪器飞机平台，NASA 兰利比奇 B-200 空中国王和 NASA 瓦洛普斯飞行设施的 C-130 大力神，用于收集各种大陆表面和大气条件下的高质量现场测量数据。有时，它们直接飞过轨道碳观测站-2 (OCO-2) 立交桥，以评估 OCO-2 观测高分辨率大气 CO2 变化的能力。C-130 飞机还配备了主动遥感仪器，用于行星边界层高度探测和柱状温室气体测量。
摘要
Table 1. Names and descriptions for variables in respective instrument files.
Instrument Variable name Units Description
PICARRO (Summer 2016 campaign only) CH4_PIC_ppmv, CO_PIC_ppmv, CO2_PIC_ppmv parts per million volume Methane mixing ratio, Carbon monoxide mixing ratio, Carbon dioxide mixing ratio
PICARRO-CH4 CH4_PIC_ppmv parts per million volume Methane mixing ratio
PICARRO-CO CO_PIC_ppmv parts per million volume Carbon monoxide mixing ratio
PICARRO-CO2 CO2_PIC_ppmv parts per million volume Carbon dioxide mixing ratio
PICARRO-H2O H2O_PIC_pct percent Water vapor volume mixing ratio
PICARRO-H2O H2O_PIC_gkg grams per kilogram Water vapor mass mixing ratio
PICARRO-H2O eH2O_PIC_mbar millibars Derived water vapor pressure
PICARRO-H2O RHi_PIC_pct percent Derived relative humidity wrt ice
PICARRO-H2O RHw_PIC_pct percent Derived relative humidity wrt liquid water
PICARRO-H2O DP_PIC_degC Celsius Derived dew point
Ozone O3_ppbv parts per billion volume Ozone mixing ratio
Table 2. Names and descriptions of navigation and meteorological variables. These variables are present in the netCDF files.
Variable name Units Description
time seconds seconds since 2016-01-01 00:00:00.0 UTC
time_bnds boundary (start and end time) of each time step
Start_UTC seconds start UTC time of day for measurement
Stop_UTC seconds stop UTC time of day for measurement interval
Mid_UTC seconds mean UTC time of day of measurement interval
Flight_ID Flight identification (aircraft and flight date)
Aircraft_Sun_Azimuth degree Platform azimuth angle
Aircraft_Sun_Elevation degree Solar elevation angle
Cabin_Pressure millibars Air pressure of cabin
Day_of_Year day Day of year starting Jan 1 UTC
Dew_Point Celsius Dew point temperature
Drift_Angle degree Drift angle
GPS_Altitude meters Global Positioning System altitude
GPS_Time hours since 2016-01-01 00:00:00.0 UTC Time
Ground_Speed meters per second Platform speed with respect to ground
Indicated_Air_Speed knots Indicated air speed
Latitude degree north Latitude, EPSG: 4326
Longitude degree east Longitude, EPSG: 4326
Mach_Number Mach number
Mixing_Ratio grams per kilogram H2O mixing ratio
Part_Press_Water_Vapor millibars Water vapor partial pressure in air
Pitch_Angle degree Platform pitch angle
Potential_Temp Celsius Potential temperature
Pressure_Altitude feet Barometric altitude
Relative_Humidity percent Relative humidity
Roll_Angle degree Platform roll angle
Sat_Vapor_Press_H2O millibars H2O saturation vapor pressure of water
Sat_Vapor_Press_Ice millibars H2O saturation vapor pressure of ice
Solar_Zenith_Angle degree Solar zenith angle
Static_Air_Temp Celsius Static air temperature
Static_Pressure millibars Air pressure
Sun_Azimuth degree Solar azimuth angle
Total_Air_Temp Celsius Total air temperature
Track_Angle degree Track angle
True_Air_Speed knots Platform speed with respect to air
True_Heading degree Platform yaw angle
Vertical_Speed feet per minute Vertical speed
Wind_Direction degree Wind direction
Wind_Speed meters per second Wind speed
Altitude_AGL_m meters Aircraft altitude above ground level
Ground_Elevation_m meters Ground elevation above mean sea level
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/raw"
df = pd.read_csv(url, sep="\t")
df
leafmap.nasa_data_login()
results, gdf = leafmap.nasa_data_search(
short_name="ACTAMERICA_PICARRO_1556",
cloud_hosted=True,
bounding_box=(-110.0, 25.0, -70.0, 50.55),
temporal=("2016-07-11", "2019-07-27"),
count=-1, # use -1 to return all datasets
return_gdf=True,
)
gdf.explore()
leafmap.nasa_data_download(results[:5], out_dir="data")
