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Dataset

 

FIDUCEO: Advanced Very-High-Resolution Radiometer (AVHRR ) Climate Data Record for Aerosol Optical Depth, V1.0, 2003 -2012

Update Frequency: Not Planned
Status: Completed
Online Status: ONLINE
Publication State: Published
Publication Date: 2019-11-18
Download Stats: last 12 months

Abstract

The Fidelity and uncertainty in climate data records from Earth Observations (FIDUCEO) projcet Advanced Very-High-Resolution Radiometer (AVHRR ) Climate Data Record for Aerosol Optical Depth (AOD) dataset covers Europe and North Africa over land. It was inferred from AVHRR/3 instruments on board the NOAA-16 and NOAA-18 satellites.

The dataset is provided on 3 processing levels: superpixels (L2B: 12x12 km2at nadir), gridded (1°x 1°) daily (L3 daily) and monthly (L3 monthly). The original lowest processing level (on selected dark field pixels) is not provided to users, but can be made available on request. The product contains the best AOD estimate but also a more detailed information on different aerosol types (most likely AOD value based on a multi-model ensemble climatology of the aerosol type and a 36 member ensemble of AOD values for a wide range of aerosol types spanning a realistic range in the atmosphere). A user can also process an application with all 36 ensemble members and then calculate the spread of the application results. Note that AOD values on the lowest processing level can be (slightly) negative reflecting radiometric calibration uncertaintiesand keeping un-cut AOD distributions.

The products contain on all levels sophisticated and detailed estimates of total AOD uncertainties propagated from the input L1B products and the retrieval algorithm through all levels of the processing chain. These total uncertainties can be directly used for data assimilation or to constrain a confidence interval around the AOD solutions. AOD uncertainties are also kept separated into the (relevant) different parts with different correlation structures, so that a user can conduct averaging and uncertainty propagation as suitable for the intended applications. Uncertainties also include separate values for the dominant effects (reflectance inversion, albedo estimation, aerosol type, cloud masking); also estimates of a sampling uncertainty (due to missing pixels from the cloud masking or from failed inversions) are contained.

More information including a report on the datset and scientific background is availible in the documentation section.

Citable as:  Popp, T. (2019): FIDUCEO: Advanced Very-High-Resolution Radiometer (AVHRR ) Climate Data Record for Aerosol Optical Depth, V1.0, 2003 -2012. Centre for Environmental Data Analysis, date of citation. https://catalogue.ceda.ac.uk/uuid/1326447659e34bc3ba8042041ca0546b
Abbreviation: Not defined
Keywords: FIDUCEO, Uncertainty, AVHRRR, Climate data record, Aerosol Optical Depth

Details

Previous Info:
No news update for this record
Previously used record identifiers:
No related previous identifiers.
Access rules:
Public data: access to these data is available to both registered and non-registered users.
Use of these data is covered by the following licence: http://creativecommons.org/licenses/by/4.0/. When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data lineage:

Supplied to CEDA for archival by Thomas Popp of DLR on behalf of the H2020 FIDUCEO project

Data Quality:
Data was dowloaded from the NOAA class archive and contains quality metricsdescribed in the documentation. It was then provide to CEDA byby fiduceo project team
File Format:
The files are CF compliant netCDF version4 . For full information please see the format specification in the documentation section.

Process overview

This dataset was generated by a combination of instruments deployed on platforms and computations as detailed below.

Mobile platform operations

Mobile Platform Operation 1 Mobile Platform Operation for: NOAA-18
Mobile Platform Operation 2 Mobile Platform Operation for: NOAA-16

Computation Element: 1

Title FIDUCEO AVHRR AOD computation V1.0
Abstract The algorithm used to produce the AVHRRAerosol demonstration dataset is described inthe doumentation along with links to the processing software. The AVHRR instrument, which was designed for cloud and land observations,is a weak instrument if it is used for the retrieval of a Climate Data Record (CDR) of Aerosol Optical Depth (AOD) over land, because of its small information content with practically only one useful channel with uncertain calibration. However, AVHRR offers a long historic record back to 1978 from the series of instruments flown on NOAA and METOP platforms.AOD can be inverted from top-of-atmosphere (TOA) reflectance measurements in the red band, (directional) surface albedo estimated from the mid-infraredchannel at 3.7 μm and a vegetation index, and byassuming optical properties of atmospheric aerosol (aerosol type) from a multi-model based climatology.
Input Description None
Output Description None
Software Reference None
Output Description None
  • standard_name: atmosphere_optical_thickness_due_to_ambient_aerosol
  • long_name: AOD at 550 nm for clim mix / strict cloud filter (5%)
  • var_id: AOD1
  • names: atmosphere_optical_thickness_due_to_ambient_aerosol, AOD at 550 nm for clim mix / strict cloud filter (5%)
  • standard_name: atmosphere_optical_thickness_due_to_ambient_aerosol
  • long_name: AOD at 550 nm for clim mix / weak cloud filter (50%)
  • var_id: AOD2
  • names: atmosphere_optical_thickness_due_to_ambient_aerosol, AOD at 550 nm for clim mix / weak cloud filter (50%)
  • long_name: AOD550 uncertainty
  • var_id: AOD_UNCERTAINTY
  • names: AOD550 uncertainty
  • long_name: AOD550 uncertainty assuming all components random
  • var_id: AOD_UNCERTAINTY_random
  • names: AOD550 uncertainty assuming all components random
  • var_id: NUMBER
  • var_id: NUMBER0
  • var_id: NUMBER2
  • var_id: NUMBERc
  • var_id: NUMBERl
  • var_id: NUMBERm
  • var_id: TIME
  • units: 1
  • standard_name: atmosphere_optical_thickness_due_to_ambient_aerosol
  • long_name: aerosol optical depth at 550 nm
  • var_id: AOD
  • names: atmosphere_optical_thickness_due_to_ambient_aerosol, aerosol optical depth at 550 nm
  • standard_name: atmosphere_optical_thickness_due_to_ambient_aerosol
  • long_name: aerosol optical depth at 550 nm for climatology mix
  • var_id: AOD_CLIM
  • names: atmosphere_optical_thickness_due_to_ambient_aerosol, aerosol optical depth at 550 nm for climatology mix
  • standard_name: atmosphere_optical_thickness_due_to_ambient_aerosol
  • long_name: aerosol optical depth at 550 nm for ensemble mean
  • var_id: AOD_ENS
  • names: atmosphere_optical_thickness_due_to_ambient_aerosol, aerosol optical depth at 550 nm for ensemble mean
  • standard_name: atmosphere_optical_thickness_due_to_ambient_aerosol
  • long_name: aerosol optical depth at 550 nm for ensemble weighted mean
  • var_id: AOD_ENSB
  • names: atmosphere_optical_thickness_due_to_ambient_aerosol, aerosol optical depth at 550 nm for ensemble weighted mean
  • units: #
  • var_id: NUMBER0
  • long_name: available pixel number in grid cell
  • names: available pixel number in grid cell
  • long_name: common AOD550 uncertainty due to aerosol type ensemble
  • var_id: AOD_UNCERTAINTY3
  • names: common AOD550 uncertainty due to aerosol type ensemble
  • long_name: common AOD550 uncertainty due to albedo+reflectance effect
  • var_id: AOD_UNCERTAINTY_common
  • names: common AOD550 uncertainty due to albedo+reflectance effect
  • long_name: common AOD550 uncertainty due to type ensemble weightsum norm.A
  • var_id: AOD_UNCERTAINTY3B
  • names: common AOD550 uncertainty due to type ensemble weightsum norm.A
  • units: 1
  • standard_name: atmosphere_optical_thickness_due_to_ambient_aerosol
  • long_name: gridded AOD550 for uncertainty < 0.10 OR 20% of AOD550
  • var_id: AODBEST2
  • names: atmosphere_optical_thickness_due_to_ambient_aerosol, gridded AOD550 for uncertainty < 0.10 OR 20% of AOD550
  • units: 1
  • var_id: AOD_UNCERTAINTY
  • long_name: gridded AOD550 uncertainty
  • names: gridded AOD550 uncertainty
  • units: 1
  • var_id: AOD_UNCERTAINTY_correlated
  • long_name: gridded AOD550 uncertainty assuming all components fully correlated
  • names: gridded AOD550 uncertainty assuming all components fully correlated
  • units: 1
  • var_id: AOD_UNCERTAINTY_random
  • long_name: gridded AOD550 uncertainty assuming all components fully random
  • names: gridded AOD550 uncertainty assuming all components fully random
  • units: 1
  • long_name: gridded AOD550 uncertainty due to sampling gaps
  • var_id: AOD_UNCERTAINTY_sampling
  • names: gridded AOD550 uncertainty due to sampling gaps
  • units: 1
  • standard_name: atmosphere_optical_thickness_due_to_ambient_aerosol
  • long_name: gridded aerosol optical depth at 550 nm for climatology mix
  • var_id: AODCLIM
  • names: atmosphere_optical_thickness_due_to_ambient_aerosol, gridded aerosol optical depth at 550 nm for climatology mix
  • units: 1
  • standard_name: atmosphere_optical_thickness_due_to_ambient_aerosol
  • long_name: gridded aerosol optical depth at 550 nm for ensemble weighted mean
  • var_id: AODENSB
  • names: atmosphere_optical_thickness_due_to_ambient_aerosol, gridded aerosol optical depth at 550 nm for ensemble weighted mean
  • units: 1
  • standard_name: atmosphere_optical_thickness_due_to_ambient_aerosol
  • long_name: gridded aerosol optical depth for AOD550_uncertainty < 0.15
  • var_id: AODBEST
  • names: atmosphere_optical_thickness_due_to_ambient_aerosol, gridded aerosol optical depth for AOD550_uncertainty < 0.15
  • units: 1
  • var_id: AOD_UNCERTAINTY_common
  • long_name: gridded common AOD550 uncertainty
  • names: gridded common AOD550 uncertainty
  • units: 1
  • long_name: gridded common AOD550 uncertainty due to aerosol type ensemble
  • var_id: AOD_UNCERTAINTY_common_3b
  • names: gridded common AOD550 uncertainty due to aerosol type ensemble
  • units: 1
  • long_name: gridded common AOD550 uncertainty due to reflectance and albedo effect
  • var_id: AOD_UNCERTAINTY_common_12
  • names: gridded common AOD550 uncertainty due to reflectance and albedo effect
  • units: 1
  • var_id: AOD_UNCERTAINTY_indep
  • long_name: gridded independent AOD550 uncertainty
  • names: gridded independent AOD550 uncertainty
  • units: 1
  • var_id: AOD_UNCERTAINTY_struct
  • long_name: gridded structured AOD550 uncertainty
  • names: gridded structured AOD550 uncertainty
  • units: 1
  • var_id: albedo
  • long_name: gridded surface directional albedo at 670 nm
  • names: gridded surface directional albedo at 670 nm
  • long_name: indep AOD550 uncertainty due to cloud mask (probability 5% -50%)
  • var_id: AOD_UNCERTAINTY4
  • names: indep AOD550 uncertainty due to cloud mask (probability 5% -50%)
  • long_name: independent AOD550 uncertainty due to albedo+reflectance effect
  • var_id: AOD_UNCERTAINTY_indep
  • names: independent AOD550 uncertainty due to albedo+reflectance effect
  • var_id: lat_bnds
  • var_id: lon_bnds
  • units: #
  • var_id: NUMBER
  • long_name: orbit number in grid cell
  • names: orbit number in grid cell
  • long_name: structured AOD550 uncertainty due to albedo+reflectance effect
  • var_id: AOD_UNCERTAINTY_struct
  • names: structured AOD550 uncertainty due to albedo+reflectance effect
  • var_id: albedo
  • long_name: surface directional albedo at 670 nm
  • names: surface directional albedo at 670 nm
  • units: #
  • long_name: valid pixel number in grid cell
  • var_id: NUMBER1
  • names: valid pixel number in grid cell

Co-ordinate Variables

  • standard_name: time
  • long_name: AOD550 uncertainty assuming all components fully correlated
  • var_id: AOD_UNCERTAINTY_correlated
  • names: time, AOD550 uncertainty assuming all components fully correlated
  • standard_name: latitude
  • long_name: latitude
  • var_id: lat
  • units: degree north
  • names: latitude
  • standard_name: longitude
  • long_name: longitude
  • var_id: lon
  • units: degree east
  • names: longitude
Coverage
Temporal Range
Start time:
2003-01-01T00:00:00
End time:
2012-12-31T00:00:00
Geographic Extent

 
60.0000°
 
-10.0000°
 
50.0000°
 
35.0000°
 
Related parties
Principal Investigators (1)
Authors (1)