Google Earth Engine——NOAA/CDR/PATMOSX/V53提供了高质量的气候数据记录(CDR),以及高级甚高分辨率辐射计(AVHRR)的亮度温度和反射率的多种云特性

简介: Google Earth Engine——NOAA/CDR/PATMOSX/V53提供了高质量的气候数据记录(CDR),以及高级甚高分辨率辐射计(AVHRR)的亮度温度和反射率的多种云特性

This dataset provides high quality Climate Data Record (CDR) of multiple cloud properties along with Advanced Very High Resolution Radiometer (AVHRR) brightness temperatures and reflectances. These data have been fitted to a 0.1 x 0.1 equal angle-grid with both ascending and descending assets generated daily from two to ten NOAA and MetOp satellite passes per day.

This dataset includes 48 bands, 11 of which are deemed CDR quality (marked with "CDR variable" in the band list). The cloud products are derived using the ABI (Advanced Baseline Imager) Cloud Height Algorithm (ACHA), and the Daytime Cloud Optical Properties (DCOMP) algorithm. For more detail on the processing see the algorithm theoretical basis documents for the ACHA products, DCOMP products, reflectance and brightness temperature products.


这个数据集提供了高质量的气候数据记录(CDR),以及高级甚高分辨率辐射计(AVHRR)的亮度温度和反射率的多种云特性。这些数据被拟合为0.1 x 0.1的等角网格,每天从两到十次NOAA和MetOp卫星传递中产生上升和下降的资产。

这个数据集包括48个波段,其中11个被认为是CDR质量(在波段列表中标有 "CDR变量")。云层产品是使用ABI(高级基线成像仪)云高算法(ACHA)和日间云层光学特性(DCOMP)算法得出。关于处理的更多细节,请参见ACHA产品、DCOMP产品、反射率和亮度温度产品的算法理论基础文件。

Dataset Availability

1979-01-01T00:00:00 - 2021-09-22T00:00:00

Dataset Provider

NOAA

Collection Snippet

ee.ImageCollection("NOAA/CDR/PATMOSX/V53")

Resolution

11132 meters

Bands Table

Name Description Min* Max* Units Wavelength Scale Offset
cld_emiss_acha Cloud emissivity at 11μm, determined from ACHA (CDR variable) -127 127 11μm 0.003937008 0.5
cld_height_acha Cloud height computed using ACHA -32767 32767 km 0.0003051851 10
cld_height_uncer_acha Cloud height uncertainty computed using ACHA -127 127 km 0.03937008 5
cld_opd_acha Cloud optical depth at 0.65μm, determined from ACHA -127 127 0.65μm 0.03228346 3.9
cld_opd_dcomp Cloud optical depth at 0.65μm, determined from DCOMP (CDR variable) -32685 32149 0.65μm 0.002444532 79.9
cld_opd_dcomp_unc Uncertainty in the cloud optical depth at 0.65μm, determined from DCOMP -32685 -32276 0.002444532 79.9
cld_press_acha Cloud-top pressure computed using ACHA -32767 32767 hPa 0.01678518 550
cld_reff_acha Effective radius of cloud particles determined from ACHA -127 127 μm 0.6299213 80
cld_reff_dcomp Effective radius of cloud particles determined from DCOMP (CDR variable) -32767 32767 μm 0.002441481 80
cld_reff_dcomp_unc Uncertainty in the effective radius of cloud particle determined from DCOMP -32767 -32357 μm 0.002441481 80
cld_temp_acha Cloud-top temperature computed using ACHA (CDR variable) -32767 32767 K 0.002441481 240
cloud_fraction Cloud fraction computed over a 3x3 pixel array at the native resolution centered on this pixel -127 127 0.003937008 0.5
cloud_fraction_uncertainty Cloud fraction uncertainty computed over a 3x3 array -127 0 0.003937008 0.5
cloud_probability Probability of a pixel being cloudy from the Bayesian cloud mask -127 127 0.003937008 0.5
cloud_transmission_0_65um Cloud transmission at 0.65μm from DCOMP -127 127 0.65μm 0.003937008 0.5
cloud_type Integer classification of the cloud type including clear and aerosol type 0 0
cloud_water_path Integrated total cloud water over whole column -127 127 g/m^2 4.72441 600
land_class Land classes 0 0
refl_0_65um Top of atmosphere reflectance 0.65μm (CDR variable) -32767 32767 0.65μm 0.001861629 59
refl_0_65um_counts Instrument counts for the 0.65μm channel -21 1017 0 0
refl_0_65um_stddev_3x3 Standard deviation of the 0.63μm reflectance computed over a 3x3 pixel array -127 127 0.07874016 10
refl_0_86um Top of atmosphere reflectance at 0.86μm (CDR variable) -32767 32767 0.86μm 0.001861629 59
refl_0_86um_counts Instrument counts for the 0.86μm channel -21 1016 0 0
refl_1_60um Top of atmosphere reflectance at 1.60μm (CDR variable) -32767 32767 1.60μm 0.001861629 59
refl_1_60um_counts Instrument counts for the 1.60μm channel -12 1629 0 0
refl_3_75um Top of atmosphere reflectance at 3.75μm (CDR variable) -32767 32767 3.75μm 0.001525925 30
relative_azimuth_angle Sun-sensor relative azimuth angle; 0 is the principal plane looking towards sun -127 127 Degrees 0.7086614 90
scan_element_number Scan element index of the pixel chosen for inclusion in level-2b -999 409 0 0
scan_line_number Scan line number -999 13835 0 0
scan_line_time Scan line time 0 23.99 Hours 0 0
sensor_zenith_angle Sensor zenith for each pixel measured in degrees from nadir -127 68 Degrees 0.3543307 45
snow_class Snow classes and values 0 0
solar_azimuth_angle Solar azimuth angle in degrees from north, pixel to sun, positive values are clockwise from north -127 127 Degrees 1.417323 0
solar_zenith_angle Solar zenith for each pixel measured in degrees away from the sun (0=looking at sun) -101 101 Degrees 0.7086614 90
surface_temperature_retrieved Surface temperature retrieved using atmospherically corrected 11μm radiance -127 127 Kelvin 0.472441 280
surface_type UMD surface type 0 0
temp_11_0um Top of atmosphere brightness temperature at 11.0μm (CDR variable) -32767 32767 Kelvin 11.0μm 0.002441481 260
temp_11_0um_clear_sky Top of atmosphere brightness temperature modeled assuming clear skies at 11.0μm -30853 32767 Kelvin 0.002441481 260
temp_11_0um_stddev_3x3 Standard deviation of the 11.0μm brightness temperature computed over a 3x3 pixel array -127 127 Kelvin 0.07874016 10.9
temp_12_0um Top of atmosphere brightness temperature 12.0μm (CDR variable) -32767 32767 Kelvin 12.0μm 0.002441481 260
temp_3_75um Top of atmosphere brightness temperature 3.75μm (CDR variable) -32767 32767 Kelvin 3.75μm 0.002441481 260
acha_info ACHA processing information bit flags 0 0
acha_info Bitmask
  • Bit 0: Cloud height attempted
    • 0: No
    • 1: Yes
  • Bit 1: Bias correction employed
    • 0: No
    • 1: Yes
  • Bit 2: Ice cloud retrieval
    • 0: No
    • 1: Yes
  • Bit 3: Local radiative center processing used
    • 0: No
    • 1: Yes
  • Bit 4: Multi-layer retrieval
    • 0: No
    • 1: Yes
  • Bit 5: Lower cloud interpolation used
    • 0: No
    • 1: Yes
  • Bit 6: Boundary layer inversion assumed
    • 0: No
    • 1: Yes
acha_quality ACHA quality flags 0 0
acha_quality Bitmask
  • Bit 0: ACHA products processed
    • 0: No
    • 1: Yes
  • Bit 1: Valid Tc retrieval
    • 0: No
    • 1: Yes
  • Bit 2: Valid ec retrieval
    • 0: No
    • 1: Yes
  • Bit 3: Valid beta retrieval
    • 0: No
    • 1: Yes
  • Bit 4: Degraded Tc retrieval
    • 0: No
    • 1: Yes
  • Bit 5: Degraded ec retrieval
    • 0: No
    • 1: Yes
  • Bit 6: Degraded beta retrieval
    • 0: No
    • 1: Yes
bad_pixel_mask Mask that distinguishes good from bad pixels 0 0
bad_pixel_mask Bitmask
  • Bit 0: Bad pixel mask
    • 0: Good
    • 1: Bad
cloud_mask Integer classification of the cloud mask 0 0
dcomp_info Processing flags for DCOMP 0 0
dcomp_info Bitmask
  • Bit 0: Info flag set
    • 0: No
    • 1: Yes
  • Bit 1: Land/sea mask
    • 0: Land
    • 1: Sea
  • Bit 2: Day/night mask
    • 0: Day
    • 1: Night
  • Bit 3: Twilight (65-82 solar zenith)
    • 0: No
    • 1: Yes
  • Bit 4: Snow
    • 0: No
    • 1: Snow
  • Bit 5: Sea-ice
    • 0: No
    • 1: Sea-ice
  • Bit 6: Phase
    • 0: Liquid
    • 1: Ice
  • Bit 7: Thick cloud
    • 0: No
    • 1: Yes
  • Bit 8: Thin cloud
    • 0: No
    • 1: Yes
dcomp_quality DCOMP processing information bit flags 0 0
dcomp_quality Bitmask
  • Bit 0: DCOMP products processed
    • 0: No
    • 1: Yes
  • Bit 1: Valid COD retrieval
    • 0: No
    • 1: Yes
  • Bit 2: Valid REF retrieval
    • 0: No
    • 1: Yes
  • Bit 3: Degraded COD retrieval
    • 0: No
    • 1: Yes
  • Bit 4: Degraded REF retrieval
    • 0: No
    • 1: Yes
  • Bit 5: Convergency
    • 0: No
    • 1: Yes
  • Bit 6: Glint
    • 0: No
    • 1: Yes
glint_mask Glint mask 0 0
glint_mask Bitmask
  • Bit 0: Glint mask
    • 0: No
    • 1: Yes


* = Values are estimated

Class Table: cloud_type

Value Color Color Value Description
0 #73d8ff Clear
1 #73d8ff Probably clear
2 #b1d8dc Fog
3 #030bff Water
4 #0013a1 Supercooled water
5 #05ffa3 Mixed
6 #d5fff9 Opaque ice
7 #ffffff Cirrus
8 #b2b8ff Overlapping
9 #b2b8ff Overshooting
10 #f8c4ff Unknown
11 #d7e9a1 Dust
12 #adadad Smoke

Class Table: land_class

Value Color Color Value Description
0 #46ffba Shallow ocean
1 #c09968 Land
2 #eddc66 Coastline
3 #32bc76 Shallow inland water
4 #00b5c8 Ephemeral water
5 #338c91 Deep inland water
6 #0109ff Moderate ocean
7 #010583 Deep ocean

Class Table: snow_class

Value Color Color Value Description
1 #000000 No snow/ice
2 #17b0c0 Sea-ice
3 #ffffff Snow

Class Table: surface_type

Value Color Color Value Description
0 #0d00d4 Water
1 #096619 Evergreen needle
2 #096619 Evergreen broad
3 #2ac027 Deciduous needle
4 #2ac027 Deciduous broad
5 #a0c800 Mixed forest
6 #7c6e48 Woodlands
7 #dcca76 Wooded grass
8 #c7ff42 Closed shrubs
9 #c7ff42 Open shrubs
10 #00ff5a Grasses
11 #fff700 Croplands
12 #ffdb77 Bare
13 #9f9f9f Urban

Class Table: cloud_mask

Value Color Color Value Description
0 #73d8ff Clear
1 #b1d8dc Probably clear
2 #d0d0d0 Probably cloudy
3 #9d9d9d Cloudy

影像属性:

Name Type Description
orbit_node String 'ascending' or 'descending'
platform String Name of platform
status String 'provisional' or 'permanent'


数据说明:

The NOAA CDR Program’s official distribution point for CDRs is NOAA’s National Climatic Data Center which provides sustained, open access and active data management of the CDR packages and related information in keeping with the United States’ open data policies and practices as described in the President's Memorandum on "Open Data Policy" and pursuant to the Executive Order of May 9, 2013, "Making Open and Machine Readable the New Default for Government Information". In line with these policies, the CDR data sets are nonproprietary, publicly available, and no restrictions are placed upon their use. For more information, see the Fair Use of NOAA's CDR Data Sets, Algorithms and Documentation pdf.


数据引用:

For the TOA Reflectances and Brightness Temperatures users must cite: Andrew K. Heidinger, Michael J. Foster, Andi Walther, Xuepeng Zhao, and NOAA CDR Program (2014): NOAA Climate Data Record (CDR) of Reflectance and Brightness Temperatures from AVHRR Pathfinder Atmospheres - Extended (PATMOS-x), Version 5.3. [indicate subset used]. NOAA National Centers for Environmental Information. doi:10.7289/V56W982J [access date].

For the cloud properties users must cite: Andrew K. Heidinger, Michael J. Foster, Andi Walther, Xuepeng Zhao, and NOAA CDR Program (2014): NOAA Climate Data Record (CDR) of Cloud Properties from AVHRR Pathfinder Atmospheres - Extended (PATMOS-x), Version 5.3. [indicate subset used]. NOAA National Centers for Environmental Information. doi:10.7289/V5348HCK [access date].

代码:

var dataset = ee.ImageCollection('NOAA/CDR/PATMOSX/V53')
                  .filter(ee.Filter.date('2017-05-01', '2017-05-14'));
var cloudEmissivityAndHeight = dataset.select(
    ['cld_emiss_acha', 'cld_height_acha', 'cld_height_uncer_acha']);
Map.setCenter(71.72, 52.48, 1);
Map.addLayer(cloudEmissivityAndHeight, {}, 'Cloud Emissivity and Height');


相关文章
|
6月前
|
数据可视化 定位技术 Sentinel
如何用Google Earth Engine快速、大量下载遥感影像数据?
【2月更文挑战第9天】本文介绍在谷歌地球引擎(Google Earth Engine,GEE)中,批量下载指定时间范围、空间范围的遥感影像数据(包括Landsat、Sentinel等)的方法~
2344 1
如何用Google Earth Engine快速、大量下载遥感影像数据?
|
6月前
|
机器学习/深度学习 算法 数据可视化
基于Google Earth Engine云平台构建的多源遥感数据森林地上生物量AGB估算模型含生物量模型应用APP
基于Google Earth Engine云平台构建的多源遥感数据森林地上生物量AGB估算模型含生物量模型应用APP
217 0
|
6月前
|
存储 编解码 数据可视化
Google Earth Engine获取随机抽样点并均匀分布在栅格的不同数值区中
【2月更文挑战第14天】本文介绍在谷歌地球引擎(Google Earth Engine,GEE)中,按照给定的地表分类数据,对每一种不同的地物类型,分别加以全球范围内随机抽样点自动批量选取的方法~
524 1
Google Earth Engine获取随机抽样点并均匀分布在栅格的不同数值区中
|
6月前
|
数据可视化 数据挖掘 数据建模
R语言指数平滑法holt-winters分析谷歌Google Analytics博客用户访问时间序列数据
R语言指数平滑法holt-winters分析谷歌Google Analytics博客用户访问时间序列数据
|
6月前
|
编解码 人工智能 算法
Google Earth Engine——促进森林温室气体报告的全球时间序列数据集
Google Earth Engine——促进森林温室气体报告的全球时间序列数据集
84 0
|
6月前
|
编解码 人工智能 数据库
Google Earth Engine(GEE)——全球道路盘查项目全球道路数据库
Google Earth Engine(GEE)——全球道路盘查项目全球道路数据库
126 0
|
6月前
|
编解码
Open Google Earth Engine(OEEL)——matrixUnit(...)中产生常量影像
Open Google Earth Engine(OEEL)——matrixUnit(...)中产生常量影像
74 0
|
6月前
Google Earth Engine(GEE)——导出指定区域的河流和流域范围
Google Earth Engine(GEE)——导出指定区域的河流和流域范围
252 0
|
6月前
|
传感器 编解码 数据处理
Open Google Earth Engine(OEEL)——哨兵1号数据的黑边去除功能附链接和代码
Open Google Earth Engine(OEEL)——哨兵1号数据的黑边去除功能附链接和代码
118 0
|
6月前
Google Earth Engine(GEE)——当加载图表的时候出现错误No features contain non-null values of “system:time_start“.
Google Earth Engine(GEE)——当加载图表的时候出现错误No features contain non-null values of “system:time_start“.
120 0