We present version 3 (V3) of the Cloud
The following new digital identifier has been issued for the Cloud
Clouds play a critical role in the Earth's radiation budget as their response to the changing climate can cool or warm the planet. There is considerable uncertainty in the balance between these cooling and warming effects.
The Fifth Assessment Report of the Intergovernmental Panel on Climate Change summarised the current understanding of climate sensitivity, which measures the temperature change when the amount of carbon dioxide (
A number of satellite cloud records exist to address this question. The longest series of satellite instruments used to measure cloud comes from the Advanced Very High Resolution Radiometers (AVHRRs). Satellite cloud climatologies based on these instruments include the International Satellite Cloud Climatology Project
Spanning a gap in time between AVHRR and MODIS, the second Along-Track Scanning Radiometer (ATSR-2)/Advanced ATSR (AATSR) instrument series has the potential to offer a much more stable cloud property record than AVHRR. The ATSR-2/AATSR are part of a well-characterised series of instruments, using on-board calibration and post hoc vicarious calibration activities. These instruments' orbits are very similar and stable (see Table
This paper documents production of the ATSR-2/AATSR cloud and flux property data set, completed as part of the ESA Cloud
Outline of the key specifications of the ATSR-2 and AATSR instruments compared to the follow-on SLSTR instruments. LTDN stands for local time descending node.
The ATSR series of instruments are a multi-channel (0.55, 0.66, 0.87, 1.6, 3.7, 11 and 12
ATSR channels are specifically designed to have low noise. Furthermore, AATSR measurements are carried out with a high level of accuracy as the instrument includes an on-board thermal blackbody and a visible calibration system designed for high uniformity and stability
Cloud
The same cloud variables are produced in version 3 (V3) as in version 2 (V2), but the flux products are new for V3. The variables, naming abbreviation, units and algorithm type are summarised in Table
In addition to cloud properties, each of the retrieved cloud variables includes pixel-level uncertainties. The propagation of those from Level-2 to Level-3 is described in
The ATSR-2/AATSR cloud products were produced using the Community Cloud retrieval for Climate (CC4CL) algorithm, developed during the ESA CCI programme. The algorithm has been described in detail in
The optimal estimation retrieval within CC4CL, known as the Optimal Retrieval of Aerosol and Cloud (ORAC), is a non-linear statistical inversion method based on Bayes’ theorem
The radiation products are created using BUGSRad
Since V2 was produced, a number of developments have been made regarding the algorithm, resulting in considerable improvement to the ATSR-2/AATSR records as summarised below. Figures
The cloud mask was retrained using a larger data set including 1 km CALIPSO data. This has reduced the number of clouds falsely detected over polar regions (sea, sea ice and land), reduced cloud coverage in the topics and increased the number of clouds detected in stratocumulus cloud banks. The cloud phase selection in V2 used a threshold scheme developed by The surface reflectance model was revised to correct a bug in the application of large solar zenith angles over bright polar surfaces. This resulted, in a significant decrease in the COT and CER, to much more realistic values. Changes outside the polar regions were minimal. The look-up tables (LUTs) are now based on In V2, maintaining consistency with the earlier sections of the AVHRR record required using lower-resolution (and less accurate) auxiliary data sets for ice and snow, such as European Centre for Medium Range Forecasting (ECMWF) reanalysis, and inferior land sea masks. This resulted in poor results over mountainous and snow- or ice-covered regions. These auxiliary data sets were also not consistent with those used by the AATSR ORAC Aerosol In V2 there was a discontinuity between the ATSR-2 and AATSR cloud retrievals, particularly in cloud fraction, COT and CER. This discontinuity was caused by a number of factors:
The use of the 3.7 Differences between the two instruments in the availability of shortwave channels across the swath during the day. In order to create a record which minimised the inconsistency between ATSR-2 and AATSR (and the aerosol record), in V3 cloud properties were retrieved using the 1.6
The key strengths of the Cloud The spectral consistency of derived parameters, which is achieved by an OE approach based on a physically consistent cloud model simultaneously fitting satellite observations from the visible to the mid-infrared. Uncertainty characterisation, which is inferred at pixel level from OE theory, that is physically consistent (1) with the uncertainties of the input data (e.g. measurements, a priori) and (2) among the retrieved variables. These pixel-level uncertainties are further propagated into the monthly products. Comprehensive assessment and documentation of the retrieval schemes and the derived cloud property data sets, including the exploitation of applicability for evaluation of climate models and reanalyses.
Examples from 2008 of Level-3C (yearly average) Cloud
As in Fig.
An evaluation of CC4CL cloud mask and cloud top height Level-2 products has been carried out based on CALIPSO data for five days, covering all seasons, in 2008: 20 March, 13 June, 20 June, 21 September and 20 December.
The cloud fraction and height validation was based on CALIPSO cloud observations which were simultaneously (i.e. within 5 min) observing the same location as the AATSR satellite. For morning satellites, such co-locations only occur at high latitudes, i.e. greater that 70
The AATSR Level-2 cloud fraction products are retrieved at 1 km resolution, and thus the retrievals were co-located with the CALIPSO 1 km cloud products. These are less sensitive to thin clouds than the 5 km products
The results of the comparison are shown in Table
Comparison of co-located AATSR cloud mask and CALIPSO 1 km layer product for V2 (left) and V3 (right). The comparison metrics shown are hit rate, the percentage of pixels identified correctly as either cloudy or clear, the Probability Of Detection (POD) for cloudy and clear pixels separately, the Hanssen-Kuiper skill score (KSS; defined as
The cloud top height product was validated using the CALIPSO 1 km product. In previous studies
Comparison of AATSR cloud top height with co-located CALIPSO measurements for 5 d in 2008. On the left V2 is shown, and V3 is shown on the right . The results are shown for all observations, only opaque clouds (as defined by CALIPSO), the corrected cloud top height product and retrievals with a cost of less than 5. All values are in kilometres.
The liquid water path of the Cloud
This evaluation focuses on regions where liquid clouds are dominant (i.e. fewer than 5 % ice clouds), specifically three stratocumulus regions: the oceanic area west of Africa at 10–20
Comparison of ATSR-2/AATSR Cloud
Multi-annual (2000–2012) liquid water path validation results for ATSR-2/AATSR when compared with MAC-LWP monthly data for three regions of predominantly stratocumulus cloud. The results for V2 (left) and V3 (right) are compared for correlation, bias and standard deviation.
Examples of Level-3C (yearly average for 2008) Cloud
Examples of Level-3C (yearly average for 2008) Cloud
Examples of the Cloud
Multi-annual (2003–2012) zonally averaged broadband shortwave and longwave fluxes (SWF, LWF) at the top-of-atmosphere (TOA) inferred from the Cloud
As in Table
The BOA longwave downwelling fluxes (all-sky and clear) have a minimum in the cold polar regions and a maximum in the tropics. The corresponding shortwave fluxes are lowest in the southern and northern storm tracks and peak in the tropics. The BOA all-sky shortwave downwelling flux shows the largest regional differences. The BOA shortwave downwelling clear-sky fluxes show AATSR to be higher in regions of high aerosol loading. The downward longwave fluxes are also higher for AATSR. The BOA LW fluxes show the largest disagreement with CERES. The global mean BOA comparisons are summarised in Table
A DOI has been issued for the data set Cloud_cci AATSR and ATSR-2v3 described in this paper:
The AATSR-2/AATSR cloud data sets provide a unique data set that straddles the AVHRR and MODIS timelines and maintains a stable orbit between satellite platforms. Version 3 of the Cloud
Cloud fraction and cloud top height have been validated using CALIPSO measurements. While the lidar only finds good co-locations in the polar regions, the comparison demonstrates some of the key changes between V2 and V3. The cloud fraction shows considerable improvement in its ability to discern clear scenes, with the Kuiper skill score improving from 0.49 to 0.66. There were no major developments from V2 to V3 that would significantly affect the cloud top height retrievals, so the cloud top height validation has remained similar.
The MAC-LWP product has been compared with the ATSR-2 and AATSR product in regions of stratocumulus cloud. The V3 data set shows significantly improved consistency between ATSR-2 and AATSR resulting from changes in the channel selection. The ATSR-2/AATSR liquid water path is shown to be highly correlated with the MAC-LWP in these regions (coefficients
The TOA and BOA flux products have been compared with the latest CERES EBAF version 4.1 products and show good agreement, within the estimated uncertainties. The differences are largest and the most uncertain over polar regions.
CP coordinated the generation of the presented data set, which was undertaken by GT and EC; contributed to key developments of the algorithm; evaluated the data; and drafted the manuscript. MS developed the cloud detection and phase determination. GM contributed key developments to the algorithm; AP, SP, RG, and GT contributed to the development of the optimal estimation scheme. MC developed the radiation scheme. All authors assisted in drafting the manuscript.
The authors declare that they have no conflict of interest.
This work was undertaken in the Cloud_cci project as part of the European Space Agency (ESA) CCI programme. The data was generated and archived at the Centre for Environmental Data Analysis (CEDA), which is supported by the Natural Environment Research Council (NERC). The MAC-LWP and CERES data were obtained from the NASA Langley Research Center Atmospheric Science Data Center.
This research has been supported by the ESA CCI (contract no. 4000109870/13/INB) and the NERC National centre for Earth Observation (contract no. PR140015).
This paper was edited by Alexander Kokhanovsky and reviewed by three anonymous referees.