Dataset
FIDUCEO: Fundamental Climate Data Record of recalibrated brightness temperatures for the Advanced Very-High-Resolution Radiometer (AVHRR) with ten member ensemble of perturbed level1 data, 2006 - 2016, v1.0
Update Frequency: Not Planned
Latest Data Update: 2020-01-13
Status: Completed
Online Status: ONLINE
Publication State: Published
Publication Date: 2019-11-08
Download Stats: last 12 months
Dataset Size: 163.58K Files | 3TB
Abstract
This Fundamental Climate Data Record (FDCR) ensemble product contains both recalibrated AVHRR/3 MetOp-A Radiance/Brightness Temperature data with associated metrologically traceable uncertainties in the FIDUCEO FCDR format. It also contains files containing an Ensemble dataset consisting of perturbations to the associated FIDUCEO FCDR radiances and brightness temperatures. By applying the 10 perturbations to the baseline FCDR radiances and brightness temperatures a user is able to generate 10 sets of new measurements whose variance capture the associated underlying uncertainty distributions contained in the Easy FCDR itself.
The FIDelity and Uncertainty in Climate data records from Earth Observations (FIDUCEO) project AVHRR FCDR improves on existing AVHRR level-1B: in the infrared the calibration has been improved with a measurement function approach such that the data is of better quality (noise has been reduced, outliers have been filtered) the metrologically traceable uncertainties have been derived together with their associated effects, cross-channel correlations and long-term correlation structures have now been calculated from the processed data and are being understood and used to improve data quality and consistency. For the Ensemble product the sensors have been calibrated against the Advanced Along-Track Scanning Radiometer (AATSR) sensor with additional corrections to calibration parameters which make the data better able to derive sea surface temperature estimates that are consistent with theInternational Comprehensive Ocean-Atmosphere Data Set (ICOADS) drifting buoy network. Because the Ensemble has been tuned for Sea Surface Temperature retrieval it should only be used over ocean scenes.
Citable as: Mittaz, J.; Merchant, C.J.; Holl, G.; Bulgin, C.E.; Taylor, M.; Mollard, J. (2019): FIDUCEO: Fundamental Climate Data Record of recalibrated brightness temperatures for the Advanced Very-High-Resolution Radiometer (AVHRR) with ten member ensemble of perturbed level1 data, 2006 - 2016, v1.0. Centre for Environmental Data Analysis, date of citation. https://catalogue.ceda.ac.uk/uuid/631e1f22d1754b78b5a64a3d66f4ce73
Abbreviation: Not defined
Keywords: FIDUCEO. AVHRR, ensemble, uncertainty
Details
| Previous Info: |
No news update for this record
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| Previously used record identifiers: |
No related previous identifiers.
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| 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 Claire Bulgin of the University of Reading 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
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| File Format: |
The data files are cf compliant NetCDF version 4 . See file format specification in the documentation for further information
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Related Documents
Process overview
This dataset was generated by a combination of instruments deployed on platforms
and computations as detailed below.
Instrument/Platform pairings
| AVHRR series | Deployed on: Metop-A |
Mobile platform operations
| Mobile Platform Operation 1 | Mobile Platform Operation for: Metop-A |
Computation Element: 1
| Title | FIDUCEO: AVHRR Enseble Fundamental Climate Data Record V1.0 |
| Abstract | The ensemble dataset was generated by first perturbing all of the input values (recorded counts, temperatures etc.) as well as model values (calibration coefficients and ICT temperature corrections) using their individual uncertainties as the standard deviation used in the random generation process. For single error sources which are uncorrelated a random generation scheme was used which samples the Gaussian distribution in 10 equal area segments and enables a small number of samples (in this case 10) to give a mean and variance that is closer to the expected values of zero and one than if a purely Gaussian random number generator was used. In the case of those parameters where a covariance matrix exists (the calibration parameters themselves) this was also sampled so as to capture the correct covariance with a small number of samples. The actual values of the Harmonisation/calibration parameters used for the infrared channels were generated in a two stage process. First, estimates of the non-linearity and instrument temperature terms were determined using SNO matchups using the AATSR as a reference. Then in a separate process small corrections to the bias and emissivity correction terms were found using sea surface temperature (SST) retrievals compared to the drifting buoy network to ensure that the Ensemble SST would be accurate. This was needed because the SNO matchups were concentrated at cloud like temperatures (240K-250K) and so had not sampled many SST like temperatures (>270K) leading to a small error in the Harmonised calibration for SST retrievals. When the new FCDR values are combined with the perturbed input values from the Ensemble files it is possible to generate a new set of perturbed Radiances/Brightness Temperatures which capture the underlying sources of uncertainty. These were then used as input into the AVHRR ESA CCI SST/Lake Water Surface Temperature (LWST) algorithms which also included a perturbation to the cloud detection threshold to derive a new baseline SST/LWST data together with perturbed SST/LWST values to create the SST/LWST Ensemble dataset. Relationship between the SST CDR and the ensemble The FCDR ensemble is used to generate an SST ensemble product where the complete SST retrieval process is applied to each of the ensemble members directly generating 10 SST perturbations to the baseline SST. Also included in the SST ensemble is a variation in the cloud detection threshold which adds extra uncertainty to the ensemble in an attempt to capture uncertainties in the cloud detection process. |
| Input Description | None |
| Output Description | None |
| Software Reference | None |
| Output Description | None |
- units: 1
- long_name: Channel 1 Reflectance
- standard_name: toa_reflectance
- var_id: Ch1
- units: 1
- standard_name: toa_reflectance
- long_name: Channel 2 Reflectance
- var_id: Ch2
- units: 1
- standard_name: toa_reflectance
- long_name: Channel 3a Reflectance
- var_id: Ch3a
- units: K
- standard_name: toa_brightness_temperature
- long_name: Channel 3b Brightness Temperature
- var_id: Ch3b
- units: K
- standard_name: toa_brightness_temperature
- long_name: Channel 4 Brightness Temperature
- var_id: Ch4
- units: K
- standard_name: toa_brightness_temperature
- long_name: Channel 5 Brightness Temperature
- var_id: Ch5
- units: 1
- var_id: channel_correlation_matrix_independent
- long_name: Channel_correlation_matrix_independent_effects
- units: 1
- var_id: channel_correlation_matrix_structured
- long_name: Channel_correlation_matrix_structured_effects
- long_name: Indicator of original file
- var_id: scanline_map_to_origl1bfile
- units: K
- long_name: MonteCarlo delta from FCDR
- var_id: Ch5_MC
- long_name: Original_Scan_line_number
- var_id: scanline_origl1b
- units: um
- long_name: Spectral Response Function wavelengths
- var_id: SRF_wavelengths
- long_name: Spectral Response Function weights
- var_id: SRF_weights
- standard_name: status_flag
- long_name: bitmask for quality per channel
- var_id: quality_channel_bitmask
- standard_name: status_flag
- var_id: data_quality_bitmask
- long_name: bitmask for quality per pixel
- standard_name: status_flag
- var_id: quality_scanline_bitmask
- long_name: bitmask for quality per scanline
- var_id: channel
- units: percent
- long_name: common uncertainty per pixel for channel 1
- var_id: u_common_Ch1
- units: percent
- long_name: common uncertainty per pixel for channel 2
- var_id: u_common_Ch2
- units: percent
- long_name: common uncertainty per pixel for channel 3a
- var_id: u_common_Ch3a
- units: K
- long_name: common uncertainty per pixel for channel 3b
- var_id: u_common_Ch3b
- units: K
- long_name: common uncertainty per pixel for channel 4
- var_id: u_common_Ch4
- units: K
- long_name: common uncertainty per pixel for channel 5
- var_id: u_common_Ch5
- long_name: cross_element_correlation_coefficients
- var_id: cross_element_correlation_coefficients
- long_name: cross_line_correlation_coefficients
- var_id: cross_line_correlation_coefficients
- units: 1
- long_name: independent uncertainty per pixel for channel 1
- var_id: u_independent_Ch1
- units: 1
- long_name: independent uncertainty per pixel for channel 2
- var_id: u_independent_Ch2
- units: 1
- long_name: independent uncertainty per pixel for channel 3a
- var_id: u_independent_Ch3a
- units: K
- long_name: independent uncertainty per pixel for channel 3b
- var_id: u_independent_Ch3b
- units: K
- long_name: independent uncertainty per pixel for channel 4
- var_id: u_independent_Ch4
- units: K
- long_name: independent uncertainty per pixel for channel 5
- var_id: u_independent_Ch5
- var_id: lookup_table_BT
- var_id: lookup_table_radiance
- units: degree
- standard_name: relative_azimuth_angle
- var_id: relative_azimuth_angle
- units: degree
- standard_name: sensor_zenith_angle
- var_id: satellite_zenith_angle
- units: degree
- var_id: solar_zenith_angle
- standard_name: solar_zenith_angle
- standard_name: status_flag
- var_id: quality_pixel_bitmask
- units: 1
- long_name: structured uncertainty per pixel for channel 1
- var_id: u_structured_Ch1
- units: 1
- long_name: structured uncertainty per pixel for channel 2
- var_id: u_structured_Ch2
- units: 1
- long_name: structured uncertainty per pixel for channel 3a
- var_id: u_structured_Ch3a
- units: K
- long_name: structured uncertainty per pixel for channel 3b
- var_id: u_structured_Ch3b
- units: K
- long_name: structured uncertainty per pixel for channel 4
- var_id: u_structured_Ch4
- units: K
- long_name: structured uncertainty per pixel for channel 5
- var_id: u_structured_Ch5
- var_id: x
- var_id: y
Co-ordinate Variables
- units: degrees_north
- standard_name: latitude
- var_id: latitude
- units: degrees_east
- standard_name: longitude
- var_id: longitude
- standard_name: time
- var_id: Time
- units: s
Coverage
Temporal Range
Start time:
2006-11-21T00:00:00
End time:
2016-12-31T00:00:00
Geographic Extent
90.0000° |
||
-180.0000° |
180.0000° |
|
-90.0000° |
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