Cloud cci doc · Doc: Cloud_cci_FINAL_REPORT_Phase2_v1.1.doc Date: 10 February 2020 Issue: 1 Revision: 1 Page 4 Purpose The purpose of this document is to report a complete description

ESA Cloud_cci

Final Report – Phase 2 (2014-2019)

Issue 1 Revision 1

10 February 2020

Deliverable No.: N/A

ESRIN/Contract No.: 4000109870/13/I-NB Project Coordinator: Dr. Rainer Hollmann Deutscher Wetterdienst [email protected] Technical Officer: Dr. Simon Pinnock

European Space Agency [email protected]

Doc: Cloud_cci_FINAL_REPORT_Phase2_v1.1.doc

Date: 10 February 2020

Issue: 1 Revision: 1 Page 2

Document Change Record

Document, Version

Date Changes Originator

Final Report - initially submitted version

14/10/2019 Initial version Martin Stengel, Rainer Hollmann

Final Report – slightly revised version

08/01/2020 Very minor modifications in Sections 7, 8 and 10

Martin Stengel

Reviewed version 05/02/2020 Revision after ESA review Martin Stengel

Final Report v1.1 10/20/2020 Final and issued version Martin Stengel




Table of Contents

Executive Summary .............................................................................................................................................5

1. Introduction .............................................................................................................................................6

1.1 General objectives of Cloud_cci ...................................................................................................6

1.2 Cloud_cci phase 1 heritage ............................................................................................................6

1.3 Strategy followed in Cloud_cci phase 2 ......................................................................................7

2. Cloud_cci Phase 2 products .................................................................................................................9

2.1 Baseline datasets - version 2........................................................................................................ 13

2.2 Baseline datasets - version 3........................................................................................................ 14

2.3 Demonstrator datasets .................................................................................................................. 15

3. Uncertainties and product evaluation ............................................................................................. 16

3.1 Uncertainties ................................................................................................................................... 16

3.2 Validation and intercomparisons ................................................................................................. 17

3.3 GEWEX assessment ......................................................................................................................... 19

4. Accompanying activities ...................................................................................................................... 20

4.1 Level-1 work .................................................................................................................................... 20

4.1.1 Intercalibration ........................................................................................................................ 20

4.1.2 AVHRR Level-1 processing ..................................................................................................... 20

4.2 Diurnal cycle / drift correction .................................................................................................... 22

5. Applications ............................................................................................................................................ 25

5.1 Facilitating model comparisons by satellite simulators ......................................................... 25

5.1.1 Simulator-like data collection in COSMO ............................................................................ 25

5.1.2 SIMFERA ...................................................................................................................................... 26

5.1.3 Enhanced Cloud_cci simulator ............................................................................................. 28

5.2 Cloud climate indices ..................................................................................................................... 31

6. Summary of the overall achievements of Cloud_cci phase II ..................................................... 32

7. List of main Cloud_cci documents .................................................................................................... 34

8. Peer-reviewed Cloud_cci publications ............................................................................................ 35

9. Glossary ................................................................................................................................................... 38

10. References .............................................................................................................................................. 40




Purpose

The purpose of this document is to report a complete description of all the work done during the Cloud_cci phase 2. It is meant to be self-standing, not requiring to be read in conjunction with reports previously issued. This report summarizes all major activities performed and the main results achieved.




Executive Summary

The ESA Cloud_cci project furnishes the Cloud ECV component in the ESA Climate Change Initiative Programme with its Phase II covering 2013-2019. Within this Phase II developments of Phase I have been elevated to a next level delivering cloud retrieval algorithms and long-term cloud ECV datasets that set new standards in many aspects. Prototype processing systems were enhanced to complete end-to-end processing systems facilitating their use in operational framework in the future. The community cloud retrieval schemes CC4CL and FAME-C were further developed and corresponding datasets expanded to cover the full time series of available measurements records of ATSR2, AATSR, MERIS; MODIS and AVHRR (version 2 datasets). A subset of these datasets were further improved (yielding version 3) in terms of algorithms, data coverage and product portfolio, with the later referring to adding radiative broadband fluxes at top and bottom of atmosphere. Prototype datasets based on IASI, SLSTR and OLCI measurements were generated and analysed demonstrating the enhanced potential of some of the latest European satellite sensors with respect to providing cloud information. All baseline datasets were comprehensively evaluated and documented. DOIs were established and data access was installed. In addition to these core activities, several accompanying tasks were accomplished dealing among others with intercalibration of decadal (AVHRR) measurement records, diurnal cycle and drift correction procedures, implementation of a multi-layer cloud scheme, and development of satellite simulators and their application for evaluation of local, regional and global models and reanalyses. To further facilitate the utilization of Cloud_cci data in CMIP frameworks, CMORized versions of the version 3 datasets were generated and provided as part of Obs4Mips. Cloud_cci put as well effort into user consultation for the definition of new climate indices derivable from its datasets.

Cloud_cci established and maintained strong links to other ECV projects in the CCI programme, e.g. Aerosol_cci and SST_cci, while cooperation with projects of the CCI+ programme were also already fostered, e.g. Snow_cci

Furthermore, Cloud_cci contributed to multiple international efforts, e.g. GCOS: helping defining climate research requirements for ECV clouds, collaborated with the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), and providing data and knowledge to the European Union Copernicus Climate Change Service (C3S) activities. Cloud_cci also contributed to international assessments of cloud data in the framework of the WCRP GEWEX Cloud Assessment and CGMS’s International Cloud Working Group (ICWG).

Cloud_cci led and contributed to numerous scientific publications in high-impact journals. Overall, the Cloud_cci project has in its phase II met and actually exceeded corresponding expectations fostering the international acknowledgment of Cloud_cci as very important activity delivering internationally recognized datasets and scientific output and thus supporting climate science.




1. Introduction

1.1 General objectives of Cloud_cci

In ESA Cloud_cci comprehensive efforts were undertaken to develop, validate and apply heritage and novel cloud property climate data sets derived from various space-based sensors maximising the use of European and non-European satellite mission data. The ultimate goal of the ESA Cloud_cci was the preparation of distinguished data sets that fulfil the GCOS requirements and implement successful algorithm and processing concepts that can be used to ensure a sustainable provision of such data from operational entities as for instance the EUMETSAT SAF network after the ESA CCI program has ended. The key objectives of the project were:

Development and application of intercalibrated radiance data sets, so called FCDRs, for ESA and non ESA instruments in an international collaboration by adding complementary methodologies where necessary enhancing GSICS capabilities;

Improvement of the Community Cloud OE retrieval for CLimate (CC4CL), a coherent physical retrieval framework for cloud properties which is an open community retrieval framework and publicly available and usable by all interested scientists;

Integrating the improved European capacities on cloud properties monitoring from

Sentinel-3 into the coherent physical retrieval framework;

Development of two multi-annual global data sets for the GCOS cloud property ECVs including uncertainty estimates. A multi-decadal multi-instrument product from (A)ATSR – AVHRR – MODIS and a second decadal product that uses complimentary information from AATSR and MERIS on-board ENVISAT;

Validation of the multi-annual cloud property products against ground based and other satellite based measurements taking into account the individual error structures of the individual observations as far as possible;

Development of a cloud-simulator package to strengthen the application of Cloud_cci products for global and regional climate model analysis.

Providing a common data base and the necessary assessment of cloud data sets as in the framework of GEWEX;

Extension and advancement of the prototype processing towards a complete processing system distributed over Europe that can further strengthen operational production of cloud property data sets after the ESA CCI program is finished;

Intensify the link with climate modelling community;

1.2 Cloud_cci phase 1 heritage

A key success from Phase 1 of the Cloud_cci was the development of a community code development framework (CC4CL) in which partners from different institutes participated in code development in a structured manner. This code is now being able to ingest several instruments to produce high-quality long time cloud property data set. The aforementioned development was based on a round-robin framework during Cloud_cci Phase 1 which has been developed and implemented to select an appropriate algorithm for climate quality satellite based cloud properties (Stengel et al., 2015). In the course of Cloud_cci Phase 1 this framework has then been extended to a test-bed system to allow for an evaluation of algorithm improvements. In a second development effort, for the first time, a synergetic retrieval for the ENVISAT instruments AATSR and MERIS has been developed. This retrieval system, called FAME-C, takes full advantage of the superior capacities of both instruments.




Both developed algorithms were then integrated into a demonstration system capable to be developed into a sustained system and ready for integration of new satellite instruments and components.

As demonstrator data set, Cloud_cci generated two three year cloud property data sets as follows:

(a) The heritage data set based on MODIS-AATSR-AVHRR for 2007-2009 and

(b) The AATSR-MERIS cloud property data sets.

Both data sets were validated extensively with ground based measurements and external satellite products. Cloud_cci climate research and CMUG explored the data sets in various ways in comparison vs. ERA-Interim and EC-Earth data. Also it was possible to integrate the results into the GEWEX cloud assessment framework.

Summarizing the results of the three-year heritage data set, the data record provided cloud cover and relative cloud amounts similar to other cloud data records using multi-spectral imagers, such as ISCCP. However, at the end of Cloud_cci Phase 1 thin cirrus remained difficult to identify. This led to an underestimation of global cloud cover by about 0.1 and to an overestimation of relative mid-level cloud amount of about 0.15. It is also reflected in distributions of cloud pressure and temperature. However, latitudinal and seasonal variations are similar.

Summarizing the results of the newly developed FAME-C dataset, the cloud cover showed similar spatial patterns as well as seasonal variability compared to cloud cover from other satellite cloud climatologies and ground-based observations. This is also true for the other cloud products. The determined biases generally remained within reasonable ranges. Due to the small swath of AATSR, sampling is relatively low and this is reflected in relatively high variability (rmse/std). Challenging issues remained the identification of thin cirrus clouds, cloud phase discrimination, and cloud optical retrievals above surfaces with high albedo (snow/ice surfaces).

On the international scale and coordination, Cloud_cci helped with its knowledge to update the GCOS-107 document. Cloud_cci published its results in peer-reviewed publications and presented the project with its results on the occasion of several international conference in Europe and elsewhere. Young scientists have been attracted by the Cloud_cci consortium thus gaining and introducing latest scientific knowledge.

1.3 Strategy followed in Cloud_cci phase 2

The GCOS requirements on the ECV clouds underwent major changes since the GCOS 107 report. For instance GCOS-IP (2016) is stating that requirements for a fundamental Climate Data Record (FCDR) stability need to be established and also states that accuracy requirements for cloud liquid and ice water profile and path are not established, but in the 2011 update the community included them for the first time. Still it gives clear indications that the longest satellites records in polar orbit, namely the AVHRR and HIRS should be reprocessed and better exploited.

As in phase 1 where the project started with a consequent analysis of user requirements, for Cloud_cci phase 2 the project gathered and implemented updated user requirements for clouds. However, it remained difficult to establish sound requirements on the stability of the datasets.

To achieve the scientific objectives for the envisaged combined (A)ATSR, AVHRR and MODIS product and the combined AATSR – MERIS product in Cloud_cci phase 2, the following cyclic strategy was followed:

1. Update and extend the validation strategy based on the requirements set and product specifications to be able to evaluate the performance of the Cloud_cci retrieval systems for the cloud ECVs but also to provide assessments of the ESA Cloud_cci products against existing products in the GEWEX framework;




2. Based on the validation results of the 3-year demonstration data sets, further improvements of CC4CL were developed and integrated. Main areas for improvements were e.g. day/night consistency, multi-layer clouds.

3. Extension and adaptation of both optimal estimation techniques to include new variables to allow for radiative-cloud studies.

4. Update the Cloud_cci system in integration of latest algorithm version;

5. Application of improved community retrieval processors and level-3 processor to the full amount of data to obtain a three decade cloud property data record of level-2 and level-3 products;

6. Development of validation tools that allow a consistent validation versus other correlative measurements and a consistent comparisons of the ESA Cloud_cci products to other products;

7. Validation of the products using the validation data base and integrate the products into the GEWEX type assessment;

8. Usage of the Cloud_cci products in the validation of climate models and other envisaged applications. In particular use them for trend analysis or interannual variability studies; provide Cloud_cci data in Obs4Mips data format

9. Assess and investigate the temporal stability of the records.

The different validation and application activities fed back a list of problems that was converted into a list of improvements and consolidation activities of scientific processors at all levels. Then the next improvement cycle started. In parallel to this cyclic approach, the following tasks were conducted

an additional data sets based on IASI sounder instrument data was prepared to evaluate and assess the uncertainties of the VIS/IR retrieval results.

Development of cloud simulators to ease the usage of Cloud_cci datasets in the climate modelling community;

assessment how inclusion in a sustained environment can be performed;

Full dissemination of products to the public




2. Cloud_cci Phase 2 products

In Cloud_cci Phase 2 the two previously developed cloud retrieval schemes were further developed and optimized. They are state-of-the-art physical retrieval systems that use the optimal estimation technique for a simultaneous, spectrally consistent retrieval of cloud properties including pixel-based uncertainty measures.

The first retrieval framework is the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) which is applied to AVHRR and AVHRR-heritage channels (i.e. channels which are available from all sensors) of MODIS and ATSR-2/AATSR/SLSTR.

The second retrieval framework is the Freie Universität Berlin AATSR MERIS Cloud retrieval (FAME-C; Carbajal Henken et al., 2014) and is applied to synergistic MERIS and AATSR measurements on-board of ENVISAT, and OLCI on Sentinel-3.

The cloud properties derived on pixel level of each utilized sensor are listed in Table 2-1. It is important to note that the cloud properties cloud albedo (CLA), Liquid water path (LWP), ice water path (IWP) are not directly retrieved, but rather determined from retrieved cloud optical thickness (COT) and cloud effective radius at the top of the cloud (CER) in a post-processing step. The same applies to cloud top height (CTH) and cloud top temperature (CTT) which are inferred from the retrieved cloud top pressure (CTP). Using the retrieved cloud properties in conjunction with surface properties (e.g. albedo, emissivity) and reanalysis data (e.g. profiles of temperature and moisture) broadband radiative fluxes were determined for the version 3 datasets. The fluxes comprise shortwave and longwave, upwelling and downwelling components at top (TOA) and bottom of atmosphere (BOA).

Based on the pixel level retrievals the data is further processed into different processing levels as summarized in Table 2-2. Level-3U denotes a global composite on a global Latitude-Longitude grid (of 0.05° resolution) onto which the Level-2 data is sampled. Level-3C products are also defined on Latitude-Longitude grid (here 0.5° resolution) with each cell containing averages of the properties and histograms containing their frequency distribution. Further separation of cloud properties in Level-3C in e.g. day/night, liquid/ice, were made wherever suitable (see Table 2-3).

The following figures present snapshots/examples of derived Level-2 cloud properties for CPH (Figure 2-1), CTP (Figure 2-2), COT (Figure 2-3), and CER (Figure 2-4) when CC4CL was applied to AVHRR, MODIS and AATSR.

Figure 2-1 Example scenes for CPH retrieval values with data from AVHRR (a), MODIS (b), and AATSR (c). Figure taken from Sus et al. (2018).




Figure 2-2 Example scenes for CTP retrieval values with data from AVHRR (a), MODIS (b), and AATSR (c). Figure taken from Sus et al. (2018).

Figure 2-3 Example scenes for COT retrieval values with data from AVHRR (a), MODIS (b), and AATSR (c). Figure taken from Sus et al. (2018).

Figure 2-4 Example scenes for CER retrieval values with data from AVHRR (a), MODIS (b), and AATSR (c). Figure taken from Sus et al. (2018).




Table 2-1 List of generated cloud properties. CMA/CFC and CPH are derived in a pre-processing step. In the next step, COT, CER and CTP are retrieved simultaneously by fitting a physically consistent cloud/atmosphere/surface model to the satellite observations using optimal estimation (OE). Moreover, LWP and IWP are obtained from COT and CER. In addition, spectral cloud albedo (CLA) for two visible channels are derived. New properties introduced in versions 3 are broadband fluxes at top of atmosphere (TOA) and surface (bottom of atmosphere-BOA) for upwelling and downwelling radiation, and for all-sky and clear-sky conditions. The property Photosynthetically active radiation (PAR) is a property included in AVHRR-AM/PM datasets only.

Variable Abbrev. Definition

Cloud mask / Cloud fraction

CMA/ CFC

A binary cloud mask per pixel (L2, L3U) and therefrom derived monthly total cloud fractional coverage (L3C, L3S) and separation into 3 vertical classes (high, mid-level, low clouds) following ISCCP classification.

Cloud phase CPH The thermodynamic phase of the retrieved cloud (binary: liquid or ice; in L2, L3U) and the therefrom derived monthly liquid cloud fraction (L3C, L3S).

Cloud optical thickness COT The line integral of the absorption coefficient and the scattering coefficient (at 0.55μm wavelength) along the vertical in cloudy pixels.

Cloud effective radius CER The area weighted radius of the cloud drop and crystal particles, respectively.

Cloud top pressure/ height/ temperature

CTP/ CTH/ CTT

The air pressure [hPa] /height [m] /temperature [K] of the uppermost cloud layer that could be identified by the retrieval system.

Liquid water path/ Ice water path

LWP/ IWP

The vertical integrated liquid/ice water content of existing cloud layers; derived from CER and COT. LWP and IWP together represent the cloud water path (CWP)

Joint cloud property histogram JCH This product is a spatially resolved two-dimensional histogram of combinations of COT and CTP for each spatial grid box.

Spectral cloud albedo CLA The blacksky albedo derived for channel 1 (0.67 µm) and 2 (0.87 µm), respectively (experimental product)

Top of atmosphere flux TOA SW and LW all sky fluxes at the Top of the atmosphere

Top of atmosphere flux clear sky

TOAclear SW and LW clear sky flux.

Bottom of atmosphere flux BOA SW and LW all sky fluxes at the bottom of the atmosphere

Bottom of atmosphere flux clear sky

BOAclear SW and LW clear sky flux.

Photosynthetically active radiation

PAR Bottom of atmosphere incoming shortwave radiation in the spectral range between 400 and 700nm




Table 2-2 Processing levels of Cloud_cci products. Level-3U and Level-3C are each directly derived from Level-2.

Processing level

Spatial resolution

Description

Level 2 (L2)

SLSTR,MODIS,MERIS RR, OLCI RR,ATSR2,AATSR: 1km AVHRR: 5 km

OLCI

Retrieved cloud variables at satellite sensor pixel level, thus with the same resolution and location as the sensor measurements (Level-1)

Level-3U (L3U)

Latitude-Longitude grid at 0.05° resolution

Cloud properties of Level-2 orbits projected onto a global space grid without combining any observations of overlapping orbits. Only subsampling is done. Common notation for this processing level is also L2b. Temporal coverage is 24 hours (0-23:59 UTC).

Level-3C (L3C)

Latitude-Longitude grid at 0.5° resolution

Cloud properties of Level-2 orbits of one single sensor combined (averaged / sampled for histograms) on a global space grid. Temporal coverage of this product is 1 month.

Table 2-3 Cloud_cci product features incl. day and night separation, liquid water and ice separation as well as histogram representation. Level-3U refers to the un-averaged, pixel-based cloud retrievals sampled onto a global Latitude-Longitude (lat/lon) grid. ¹CMA in Level-2 and Level-3U is a binary cloud mask. All products listed exist in each individual dataset.

Level 2 swath based

1km/5km

Level-3U daily sampled

global 0.05° lat/lon grid

Level-3C monthly averages


Level-3C monthly histograms


CMA/CFC as CMA¹ as CMA¹ day/night/high/mid/low -

CTP, CTH, CTT liquid/ice

CPH day/night -

COT liquid/ice liquid/ice

CER liquid/ice liquid/ice

LWP

as CWP as CWP

as CWP

IWP

CLA 0.6/0.8µm 0.6/0.8µm 0.6/0.8µm 0.6/0.8µm/liquid/ice

JCH - - - liquid/ice

TOAup,dn,sw,lw -

BOAup,dn,sw,lw, PAR -




2.1 Baseline datasets - version 2

Six multi-annual, global datasets of cloud properties were generated using the passive imager satellite sensors AVHRR, MODIS, (A)ATSR and MERIS (see Table 2-4). These datasets were published as version 2 and Digital Object Identifiers issued: DOI:10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V002 DOI:10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V002 DOI:10.5676/DWD/ESA_Cloud_cci/MODIS-Terra/V002 DOI:10.5676/DWD/ESA_Cloud_cci/MODIS-Aqua/V002 DOI:10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V002 DOI:10.5676/DWD/ESA_Cloud_cci/MERIS+AATSR/V002

These datasets were comprehensively evaluated: (1) by using accurate reference observations of ground stations and space-based Lidar measurements and (2) by comparisons to existing and well-established global cloud property datasets. Corresponding documentation can be found in Stengel et al. (2017), the Product Validation and Intercomparisons Report (PVIR; PVIRv4.1), the Product User Guide (PUGv3.1) and the overarching Algorithm Theoretical Baseline Document (ATBD, ATBDv5) together with specific ATBDs for FAME-C (ATBD-FAME-Cv5) and CC4CL (ATBD-CC4CLv5).

To facilitate a suitable application of Cloud_cci datasets for model evaluation, satellite simulators have been developed, tested and published (Eliasson et al., 2018 and Stengel et al., 2018).

Table 2-4 Summary of Cloud_cci baseline datasets version 2.

Dataset name Sensor(s) Satellites Time period

Algorithm Properties included

Cloud_cci AVHRR-AM AVHRR-2, AVHRR-3

NOAA-7,-9,-11, -14,-16,-18,-19

1982-2014 CC4CL CMA/CFC,CPH, COT,CER,CTP, CTH,CTT,LWP, IWP,JCH,CLA

Cloud_cci AVHRR-PM AVHRR-2, AVHRR-3

NOAA-12,-15, -17, Metop-A

1991-2014 CC4CL CMA/CFC,CPH, COT,CER,CTP, CTH,CTT,LWP, IWP,JCH,CLA

Cloud_cci MODIS-Terra MODIS Terra 2000-2014 CC4CL CMA/CFC,CPH, COT,CER,CTP, CTH,CTT,LWP, IWP,JCH,CLA

Cloud_cci MODIS-Aqua MODIS Aqua 2002-2014 CC4CL CMA/CFC,CPH, COT,CER,CTP, CTH,CTT,LWP, IWP,JCH,CLA

Cloud_cci ATSR2-AATSR

ATSR2, AATSR

ERS2, ENVISAT 1995-2012 CC4CL CMA/CFC,CPH, COT,CER,CTP, CTH,CTT,LWP, IWP,JCH,CLA

Cloud_cci MERIS+AATSR

MERIS, AATSR

ENVISAT 2003-2011 FAME-C CMA/CFC,CPH, COT,CER,CTP, CTH,CTT,LWP, IWP,JCH,CLA

http://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V002

http://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V002

http://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Terra/V002

http://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Aqua/V002

http://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V002

http://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/MERIS+AATSR/V002




2.2 Baseline datasets - version 3

In addition to the datasets mentioned above, three Cloud_cci version 3 cloud property datasets were generated, based on AVHRR and ATSR2+AATSR, continuing to utilize the AVHRR-heritage channels (0.6, 0.8, 1.6/3.7, 10.8, 12.0 μm) only. The version 3 datasets are based on an updated CC4CL (see ATBD-CC4CLv6.2 and ATBDv6.2), partly covering longer periods (AVHRR) and including an extended product portfolio by including shortwave and longwave, all-sky and clear-sky radiative flux properties at TOA and BOA. Table 2-5 lists details of the version 3 datasets.

The version 3.0 datasets were comprehensively documented in ATBDv6.2, PVIRv6.1, PUGv5.1 and Stengel et al. (2020).

The official versions of the datasets, as released under the issued DOIs do not contain any diurnal cycle or satellite drift correction. Potential methods for such a drift correction were investigated for AVHRR and were documented in RODCv1.0. This information is often essential for properly characterizing time series of cloud properties derived from the satellite-based climate datasets. Other important aspects are the imaging properties. The sensors differ in terms of native footprint resolution (1x1km² for ATSR2, AATSR; 5x1km² for AVHRR GAC). This, together with the sensor swath width, leads to very different observation frequency and spatial coverage. While AVHRRs have a complete global coverage within a day, the AATSR sensor needs about 3 days to accomplish this, however, with a higher spatial resolution compared to AVHRR.

DOI: 10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003

DOI: 10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V003

DOI: 10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003

Table 2-5 Summary of Cloud_cci baseline datasets version 3.

Data Sensor(s) Satellite(s) Time period

Algorithm Processing levels

Properties included

Cloud_cci ATSR2-AATSR v3

ATSR2, AATSR

ERS-2 ENVISAT

1995-2012

CC4CL L2, L3U, L3C CMA/CFC,CPH, COT,CER,CTP, CTH,CTT,LWP, IWP,JCH,CLA, TOA, BOA

Cloud_cci AVHRR-AM v3

AVHRR-2, AVHRR-3

NOAA-12, 15,17, Metop-A

1991-2016

CC4CL L2, L3U, L3C CMA/CFC,CPH, COT,CER,CTP, CTH,CTT,LWP, IWP,JCH,CLA, TOA, BOA, PAR

Cloud_cci AVHRR-PM v3

AVHRR-2, AVHRR-3

NOAA-7,9, 11,14,16,19

1982-2016

CC4CL L2, L3U, L3C CMA/CFC,CPH, COT,CER,CTP, CTH,CTT,LWP, IWP,JCH,CLA, TOA, BOA, PAR

https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003

https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V003

https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003




2.3 Demonstrator datasets

In addition to the baseline datasets, three demonstrator datasets were generated for SLSTR (processed with CC4CL), OLCI (processed with FAME-C incl. modifications) and IASI (the Clouds from Infrared Sounders - CIRS, Stubenrauch et al., 2017). The product portfolio for SLSTR and OCLI is identical to the cloud properties of the Cloud_cci baseline datasets (see previous section), while the portfolio for IASI deviates due to the absence of visible and near-infrared channel information (ses Table 6-2 for details). Figure 2-5 presents IASI example data.

Table 2-6 Details and processing status of Cloud_cci demonstrator datasets.

Data Sensor(s) Satellite(s) Time period

Algorithm Processing levels

Properties included

CC4CL SLSTR SLSTR Sentinel-3 07-09 2017

CC4CL v2.0

L2, L3U, L3C CMA/CFC,CPH, COT,CER,CTP, CTH,CTT,LWP, IWP,JCH,CLA

FAME-C OLCI-only

OLCI Sentinel-3 201611-201612

FAME-C v2.0

L2, L3U, L3C CMA/CFC,CPH, COT,CER,CTP, CTH,CTT,LWP, IWP,JCH,CLA

CIRS IASI IASI Metop-A 2009 CIRSvX L3C GEWEX format

CFC,CTP,CTT, CTH, cloud emissivity, effective CFC, COT

Figure 2-5 Examples for CIRS IASI 0930AMPM: monthly mean cloud amount (left) and cloud top pressure (right) for January 2009.




3. Uncertainties and product evaluation

3.1 Uncertainties

Based on the statistical interpretation of the output of the ANNs and based on the implemented formulations of the optimal estimation theory, pixel-based cloud properties are accompanied with corresponding uncertainty estimates. Figure 3-1 shows pixel-based uncertainties for CTP, COT, CER and CMA.

Figure 3-1 Absolute uncertainties of MODIS AQUA retrieval data for CTP [hPa], COT, CER [μm], and Cloud mask [%]. Figure taken from Sus et al. (2018).

The uncertainty estimates were validated as far as possible, depending on the availability of suitable reference measurements and associated reference uncertainties. It could be shown that the CC4CL uncertainties for LWP and IWP have some skill to reflect the true product uncertainty. The CC4CL uncertainties for CTP were found to be an insufficient reflection of the true product uncertainty. See PVIRv5.0 and PVIRv6.1 for more details.




3.2 Validation and intercomparisons

As part of a thorough characterization of all Cloud_cci generated baseline datasets, comprehensive validation efforts were undertaken, which included:

Validation of cloud mask, phase and height against the active, space-based Lidar CALIOP

Validation of liquid water path against passive microwave LWP products

Validation of ice water path against active, space-based Lidar-Radar products of DARDAR.

with Table 3-1 and Table 3-2 presenting example validation scores for cloud mask and liquid water path. All individual validation scores are details in PVIRv5.0 (version 2) and PVIRv6.1 (version 3).

Furthermore, comparisons of Cloud_cci version 2 and version 3 datasets with well-established cloud climatologies were conducted using:

CLARA-A2, PATMOS-x, ISCCP, MODIS Collection 5, MODIS Collection 6,

covering basically all cloud properties included in Cloud_cci. Figure 3-2 shows corresponding examples by means of multi-annual COT mean maps.

For Cloud_cci v3 datasets these evaluations additionally included comparisons against well-established radiative flux climatologies of

CERES, HIRS OLR, CLARA-A2 and ERA-Interim,

covering TOA and BOA, upwelling and downwelling, shortwave and longwave components of the radiative broadband fluxes.

Ground based in-situ observations of SYNOP stations and of BSRN stations were used to validate monthly cloud cover and monthly mean downwelling radiative fluxes at BOA (see examples in Figure 3-3).

Table 3-1 Validation scores for cloud mask using CALIOP as reference with and without applying an optical thickness filtering on CALIOP data. Collocation for AVHRR-AMv3 and ATSR2-AATSRv3 comprise high latitudes only. Collocations for AVHRR-PMv3 are global. (POD: Probability of detection)

AVHRR-AMv3 (i.e. NOAA-17,METOP-A)

AVHRR-PMv3 (i.e. NOAA-18,NOAA-19)

ATSR2-AATSRv3 (i.e. Envisat)

COT=0.0 COT=0.15 COT=0.0 COT=0.15 COT=0.0 COT=0.15

Matched FOVs 140 805 140 805 16 139 764 16 139 764 1 831 105 1 831 105

Bias -18.3 % -6.2 % -17.4 % -5.4 % -16.5 % -5.1 %

POD cloudy 69.7 % 78.6 % 75.8 % 83.3 % 72.6 % 80.8 %

POD clear 86.3 % 82.7 % 92.0 % 82.5 % 85.6 % 81.5 %

Heidke Skill score 0.56 0.61 0.68 0.66 0.58 0.62

Hit Rate 74.2 % 80.2 % 79.23 % 83.0 % 76.0 % 81.1 %

Table 3-2 Validation scores for liquid water path using AMSR-E as reference. Collocation for AVHRR-AMv3 and ATSR2-AATSRv3 comprise high latitudes only. Collocations for AVHRR-PMv3 are global. (Std: Standard deviation).

AVHRR-AMv3 (i.e. METOP-A)

AVHRR-PMv3 (i.e. NOAA-18)

ATSR2-AATSRv3 (i.e. Envisat)

FOVs 21 837 254 029 13 742

Bias [g m-2] 28.0 26.9 25.1

Std [g m-2] 2.8 -4.0 -1.4

Correlation 0.68 0.62 0.77




Figure 3-2 Globally gridded means liquid cloud optical thickness for all morning satellite retrievals averaged over the years 2003 to 2011. Reference data named in grey.

AVHRR-AMv3 AVHRR-PMv3 ATSR2-AATSRv3

Figure 3-3 Validation results for Cloud_cci monthly mean BOA downwelling shortwave fluxes at BSRN sites in the period 2000 through 2016.




3.3 GEWEX assessment

Cloud_cci datasets have been prepared according to the GEWEX cloud assessment guidelines to allow for a participation in the follow-up rounds of the assessment Following Stubenrauch et al., 2013 independent intercomparisons of the Cloud_cci data sets with the following satellite instruments and missions were conducted: ISCCP (International Satellite Cloud Climatology Project), AVHRR (Advanced Very High Resolution Radiometer) multi-spectral imager aboard NOAA, ATSR (Along-Track Scanning Radiometer) aboard the European Space Agency (ESA) platform ERS-2, HIRS (High resolution Infrared Radiation Sounder) multi-channel radiometer aboard NOAA, AIRS (Atmospheric Infrared Sounder) aboard Aqua, TOVS (TIROS Operational Vertical Sounder) aboard NOAA, MODIS (MODerate resolution Imaging Spectroradiometer) aboard Aqua and Terra, POLDER (POLarization and Directionality of the Earth’s Reflectances) multi-angle multi-spectral imager aboard PARASOL, MISR (Multiangle Imaging SpectroRadiometer) aboard Terra, and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations). A detailed description of the GEWEX Cloud Assessment products can be found in (Stubenrauch et al., 2012).

As a snapshot Figure 3-4 presents global averages of total cloud amount (CA) and for low-level, mid-level and high-level clouds, stratified by illumination conditions (day: left, night: right).Figure 3-4 clearly demonstrates the good performance of the generated Cloud_cci data sets (light blue colours) as they are very close to similar cloud climatologies.

Figure 3-4 Top: Global averages of total cloud amount (CA), as well as of fraction of high-level, mid-level and low-level cloud amount relative to total cloud amount (CAHR + CAMR + CALR = 1). Comparisons of Cloud_cci Data for 2007 with L3 data from the GEWEX Cloud Assessment data base, separately for observations mostly during day (left), corresponding to 1:30 PM (10:30 AM for MERIS, AATSR), and mostly during night (right), corresponding to 1:30 AM (10:30 PM for AATSR). In addition, results from IASI-CIRS are shown for 2009 (at 9:30 AM and 9:30 PM, respectively). Bottom: Averages of ocean-land differences for the same parameters.




4. Accompanying activities

Several activities that focus on better datasets or a better applicability of Cloud_cci datasets were conducted, with two of which being summarized in this section.

4.1 Level-1 work

4.1.1 Intercalibration

An important aspect for any product-based climate dataset (formally denoted Thematic Climate Data Records – TCDRs) is that retrieved products have been derived from accurately calibrated and homogenized radiances (formally denoted Fundamental Climate Data Records – FCDRs). In the ESA Cloud_cci project studies were made to evaluate currently available Level 1 datasets. This activity included collocating all used sensors with MODIS data and calculating radiance quotas as MODIS is considered a well-calibrated instrument that can be used as reference. As an example, Table 4-1 reports the quota calculated for AATSR.

Table 4-1 Reflectance factor quota (AATSR/MODIS) or brightness temperature quota with respect to MODIS-Aqua deduced from SNO inter-comparisons in the period 2007-2009. Results are from Karlsson and Johansson (2014).

AATSR channel Wavelength [nm] Reflectance factor quota or Brightness temperature quota

1 550 n.a.

2 665 1.050

3 865 1.029

4 1610 0.965

5 3740 1.000

6 10850 1.000

7 12000 0.999

4.1.2 AVHRR Level-1 processing

The intercalibration exercises for AVHRR confirmed the calibration coefficients (for VIS and NIR) determined by the University of Wisconsin. One objective of this project phase was to developed a carefully inter-calibrated and rigorously quality checked radiance data sets for AVHRR, so called Fundamental Climate Data Record (FCDR), based on the intercalibration studies conducted. The AVHRR GAC raw data was received from NOAA CLASS (Comprehensive Large Array-data Stewardship System). More information on the AVHRR FCDR produced is available in RAFCDRv1.0. Table 4-2 summarizes the AVHRR sensors processed in this context.




Table 4-2 Summary on satellites/sensors processed for AVHRR in the framework Cloud_cci phase 2. See RAFCDRv1.0 for details.

AVHRR revision

Platform Operational Start Date

Operational End Date

Cloud_cci processing from

Cloud_cci processing until

1

NOAA-5 1978-10-19 1980-01-30 - -

NOAA-6 1979-07-17 1986-07-09 - -

NOAA-8 1983-06-20 1985-10-17 - -

NOAA-10 1986-11-17 1991-09-16 - -

2

NOAA-7 1981-08-24 1985-02-01 1982-01-01 1985-02-28

NOAA-9 1985-02-25 1988-11-07 1985-02-01 1988-10-31

NOAA-11 1988-11-08 1994-10-16 1988-11-01 1994-08-31

NOAA-12 1991-09-17 1998-12-14 1994-09-01 1998-12-31

NOAA-14 1995-04-10 2002-10-07 1995-02-01 2001-03-31

3

NOAA-15 1998-12-15 2015-12-31 1999-01-01 2002-10-31

NOAA-16 2001-03-20 2011-12-31 2001-04-01 2005-07-31

NOAA-17 2002-10-15 2011-12-31 2002-11-01 2007-06-30

NOAA-18 2005-08-30 2015-12-31 2005-08-01 2009-05-31

NOAA-19 2009-06-02 2015-12-31 2009-06-01 2016-31-12

MetOp-1 2013-04-24 2015-12-31 - -

MetOp-2 (A) 2007-05-21 2015-12-31 2007-07-01 2016-12-31

The following pillars summarize the steps undertaken in the processing of the AVHRR GAC Level-1c data.

Quick Level 1b screening

Geo-location and inter-calibration of Level 1b data

Level 1c re-calibration

GAC overlap computation

Monitoring and documenting issues, e.g. with scanline Timestamps and temporary scan motor issues

The output of this processing did not only yield an AVHRR FCDR, but also a comprehensive database documenting the inventory including for example quality control, information overlap, black-/white-listing. Table 4-3 summarized the black-listing statistics (and the reasons) as a subset of the collected information for all orbits, stratified by satellite.




Table 4-3 Number of AVHRR GAC level 1 orbits per satellite, which have been blacklisted due to a specific reason.

Blacklisting reason NOAA

7 NOAA

9 NOAA

11 NOAA

12 NOAA

14 NOAA

15 NOAA

16 NOAA

17 NOAA

18 NOAA

19 Metop

A

PR

E-P

RO

C old 2 1 16 10 3 9 1 1 6 0 18

too_small 156 20 580 687 623 416 135 163 156 121 1036

too_long 1 13 4 3 4 0 0 0 0 0 6

gr_station_duplicate 3 0 0 1 5 0 0 5 12 32 25

redundant 133 241 1050 1560 771 564 265 302 417 124 118

PR

OC

orbit_length_too_long 9 14 8 24 47 776 682 361 7 8 3

negative_orbit_length 1 0 1 0 9 3 2 0 2 0 2

PO

ST

-PR

OC

wrong_l1c_timestamp 2 0 1 2 19 2 0 0 0 0 0

no_valid_l1c_data 191 0 26 95 223 1928 0 239 217 215 5

bad_l1c_quality 0 0 0 0 0 0 0 9512 0 0 0

along_track_too_long 1 0 0 0 0 4 3 0 3 1 0

pygac_indexerror 3 5 0 0 0 0 0 0 0 0 0

ch3a_zero_reflectance 0 0 0 0 0 646 0 0 618 804 0

temporary_scan_motor_issue

0 0 0 0 1914 592 451 0 0 0 0

TOTAL 502 294 1686 2382 3618 4940 1539 10583 1438 1305 1213

4.2 Diurnal cycle / drift correction

The Cloud_cci AVHRR-AM and AVHRR-PM datasets are based on NOAA AVHRR missions. Basically all of these NOAA mission are characterized by a drift from their stipulated sun-synchronous orbits during their life span (Ignatov et al. 2004). The direct impact of orbital drift of satellites is the gradual change in the time of observation. This creates many challenges, for example but not limited to, deducing physical trends from spurious ones. In addition, the rate of drift is different not only among satellites, but it can also change rapidly toward the end of lifespan of a particular satellite. This has implications for assessing the trends in the time-series of geophysical variables retrieved from AVHRRs. Various studies have previously investigated the impact of orbital drift on geophysical variables such as outgoing longwave radiation, surface temperatures, vegetation indices, and clouds (e.g. Waliser and Zhou, 1997; Lucas et al. 2001; Jin and Treadon, 2003; Devasthale and Grassl, 2007; Devasthale et al. 2012; Foster and Heidinger, 2014; Nagol et al. 2014).

In Cloud_cci a detailed feasibility study was carried out to determine how the orbital drift signal can be accounted for in the Cloud_cci dataset using the example of cloud fraction. Two major approaches were considered and investigated for this purpose: one using empirical orthogonal functions and the other using diurnal cycles. While both of these approaches are suitable, each one has its own disadvantage. Considering the fact that the major improvements are seen in cloud masking procedures in recent years (a key requirement for obtaining accurate diurnal cycles) and the lessons learned during investigations of both approaches, the methodology employing diurnal cycles is recommended to be pursued further as it is more physically anchored.

The methodology consists of three steps:

1) Computing the climatological diurnal cycles at each grid point based on morning and afternoon satellites which themselves drifted in their orbits. Figure 4-1 shows an example of a climatological diurnal cycle.




2) The climatological diurnal cycles could be “bumpy” in nature, due to a number of reasons, e.g. poor temporal sampling. Thus, it is necessary to fit a sinusoidal curve to the observed diurnal cycle (Figure 4-1).

3) For fitting polynomial to equator crossing times, the rate of drift needs to be calculated for each satellite. This is done by investigating the equator crossing times for each satellite and fitting a second degree polynomial as a function of days since launch as shown in Figure 4-1. These fitting coefficients are later used to estimate the magnitude of drift in time.

4) The global gridded maps of launch times are needed to estimate the starting cloud fraction expected from the diurnal cycles derived at each grid points. These maps are prepared for each satellite separately.

5) Computing and applying the correction. For each grid point, the rate of drift is estimated based on the polynomials mentioned above, considering difference between the UTC time at present and UTC time at launch. This information is mapped on the climatological diurnal cycles to estimate the percentage change in cloud fraction due to drifting between two points of time in question. The resulting delta change could be positive or negative depending on which segment of the diurnal cycle is required to be considered. This delta amount is then added to the uncorrected cloud fraction, which will result in slight increase or decrease in original cloud fraction. Figure 4-2 shows a time corrected and uncorrected monthly mean time series of total cloud fraction in the tropics (30N-30S), together with the difference. It can be clearly seen that the orbital drift signal is visible in the difference time series.

More information on this study including recommendation for future analyses are given in RODCv1.1.

Figure 4-1 Left: An arbitrary example of original and fitted diurnal cycle. Right: Fitting the second degree polynomial to the equator crossing times of various satellites. Figure taken from RODCv1.1.




Figure 4-2 Corrected and uncorrected time series’ of total cloud fraction in the tropics (30N-30S). Figure taken from RODCv1.1.




5. Applications

5.1 Facilitating model comparisons by satellite simulators

5.1.1 Simulator-like data collection in COSMO

Cloud_cci data (i.e. MODIS-AQUAv2) was used to conduct cloud-type evaluations for the COSMO model (Ritter and Geleyn, 1992). For this a local installations of the COSMO code at ETH were modified to generate COT-CTP histograms, to be used in comparisons with corresponding Cloud_cci histograms.

The calculation of COT was implemented in the radiation code of the COSMO model (calculated from direct solar radiation at ToA (I0) and at certain atmospheric layers (IL) with COTL = − ln(IL/I0). The COT over the full atmospheric column was calculated as COT = COT lowest level. Between ToA and the atmospheric layers, direct solar radiation is reduced considering absorption and scattering due to cloud water and cloud ice at 0.24 μm to 0.7 μm. Water vapor, aerosols, or Rayleigh scattering are not considered, which differs from the standard radiation calculations in the code. Radiation through subgrid clouds was calculated with the maximum random overlap assumption (Geleyn and Hollingsworth, 1979). Compared to COSP, one major difference is that for this cloud simulator no sub-columns are used. CTP is calculated from applying a COT threshold (COTL) and using the corresponding pressure profile. CTP is defined at the pressure level where COTL =0.3. From COT and CTP, COT-CTP histograms were generated and compared to Cloud_cci data. The results of the COSMO evaluations were published in two paper (Keller et al., 2016 and Keller et al., 2018). Examples are shown in Figure 5-1.

Figure 5-1 Histograms of cloud frequency as a function of COT and CTP arranged as in Fig. 3 but showing averages at 13:00UTC (for each model) over the period of 3–13 June 2007. For the observations, the average is taken the time of the Aqua satellite passage (approximately 13:30 UTC). Fractional cloud cover is indicated in the right upper corner of all panels. Cloud-free conditions are assumed for a cloud optical thickness below 0.3. Figure and caption taken from Keller et al. (2018).




5.1.2 SIMFERA

The SIMplified satellite simulator For ERA-interim (SIMFERA, Stengel et al., 2018) translates NWP or reanalysis fields of clouds into pseudo-satellite-retrieved datasets (Figure 5-2). The general features of SIMFERA are:

SIMFERA uses the three-dimensional (3D) model fields as input (see details below). The simplistic approach in offline mode has the advantage of short computation time (e.g. 33 years of reanalysis data processed in less than 2 days on a HPC system).

Unlike sophisticated simulators, which are using modelled radiances and brightness temperatures to retrieve cloud optical parameters based on radiative transfer calculations (e.g., COT and CER following Nakajima-King method), SIMFERA stays very close to the original model fields. For instance, it uses the ERA-Interim CER parameterization (Martin et al. 1994, Sun and Rikus 1999, Sun 2001) along with the original 3D variables to convert the model state into comparable synthetic observations. Details are given in Stengel et al. (2017c).

No satellite overpass is taken into account as ERA-Interim is only available in discrete temporal resolution of several hours. However, day and night conditions are considered for the calculation of cloud optical parameters (i.e. COT, CER, CWP) that are only available during daytime observations since they are based on visible measurements.

SIMFERA provides 2 options about how liquid and ice clouds occurring in the same model grid box are treated during the simulations (in the sub-column procedure):

o mixed phase (i.e. mixed phase clouds if both water/ice contents exists) or

o no-mixed phase (i.e. considering liquid and ice clouds separately).

SIMFERA can be used for other model output evaluation after small modifications since there are no instrument/algorithm specifications implemented.

Stengel et al. (2018) applied SIMFERA to 33 years of ERA-Interim data (1982-2014) and carried out comparisons to the Cloud_cci AVHRR-PMv2 dataset (see example Figure 5-3). Key findings were among others that the global patterns of ERA-Interim total cloud fraction (CFC) agree very well with the observations ERA-Interim. However, ERA-Interim has too few clouds nearly everywhere on the globe except in the polar regions. The underestimation of CFC in ERA-Interim is mainly due to lacking low and mid-level clouds. Furthermore, the cloud top phase is significantly ice-biased in ERA-Interim, which the study links to a suppression of super-cooled liquid clouds below -23°C in ERA-Interim.




Figure 5-2 Example case for converting ERA-Interim grid box profiles to SIMFERA sub-columns and to pseudo-retrievals. The left column shows ERA-Interim-based profiles of cloud fraction (a), cloud phase (b), layer water path (c) and layer cloud optical thickness (d). The middle column gives the same data after columnizing into 20 sub-columns that represent the sub-grid variability and after removing the uppermost cloud layers with layer optical thicknesses below a certain threshold (here 0.15): cloud mask (e), cloud phase (f), layer water path (g, layer liquid and ice water path in liquid and ice cells) and layer optical thickness (h, layer liquid and ice optical thickness in liquid and ice cells). The right panels show vertically summarized values per sub-column (also called pseudo-retrieval): cloud mask (i), cloud-top phase (j), vertically integrated water path (k), vertically integrated optical thickness (l) and cloud-top pressure (m). Each of these sub-column representative values can be seen as an individual pseudo satellite retrieval. Figure and caption taken from Stengel et al. (2018).




5.1.3 Enhanced Cloud_cci simulator

The advanced Cloud_cci simulator has been developed to enable fair comparisons of clouds between climate models and the Cloud_cci climate data record. The simulator takes into account the geometry and cloud detection capabilities of the Cloud_cci CDR as well as ensuring that the model is sampled at the satellite overpass time. The simulator is demonstrated on two climate models, EC Earth and RACMO, and we find the impact of time sampling has a large effect on simulated cloud water amount and that the simulator reduces the cloud cover by about 10% globally resulting in a better agreement with the observations. An overview of the simulator and these results are presented in Eliasson et. al. (2019). Figure 5-4 gives example results showing joint cloud optical thickness – cloud top pressure histograms, converted to cloud fraction, for selected regions (global, polar, midlatitude, tropical land/ocean, stratocumulus). Except for the polar regions and for high clouds in the midlatitudes, there is a good agreement between EC-Earth cloud occurrence and Cloud_cci observational data. In addition to EC-Earth, the enhanced Cloud_cci simulator has been applied to RACMO – a regional climate model (Figure 5-5).

Figure 5-3 Multi-annual mean cloud fraction (CFC) from ERA-Interim(a–c) and Cloud_cci (d), where the ERA-Interim cloud fraction was produced by SIMFERA for three optical thickness thresholds (COTth=0.0, 0.15, 1.0). Panel (e) is the zonal mean plot of CFC for all four sets. The grey shaded area corresponds to the estimated systematic uncertainty in Cloud_cci CFC based on comparisons to CALIOP. The uncertainty in Cloud_cci is mainly due to missing optically very thin clouds. Figure and caption taken from Stengel et al. (2018).




Figure 5-4 CTP–�c histograms of simulated Cloud_cci based on EC-Earth (left column) and the Cloud_cci CDR (right column) for selected regions (see Fig. 6). Total cloud fractions for each region are indicated in the panels. Figure taken from Eliasson et al. (2019).




Figure 5-5 Maps of 2011–2014 mean modelled and retrieved cloud properties: (a–d) total cloud fraction, (e–h) cloud top pressure (hPa), (i–l) LWP (kgm-2), (m–p) liquid COT, and (q–t) liquid CER (μm). The columns show from left to right the native model, the simulator, the Cloud_cci data, and the difference between simulator and retrieval. All properties are all-sky averages, except CTP and CER, which are in-cloud averages. In addition, CTP has been logarithmically averaged, while the other properties have been linearly averaged. Panel (a) shows the areas defined for the time series analysis in Fig. 10. Figure taken from Eliasson et al. (2019).




5.2 Cloud climate indices

New, user-adapted cloud climate indices were developed by the help of a successful user’s consultation. These new indices give information on total cloud cover. The index CCge4 (daily mean cloud cover lower than a certain threshold found in four or more consecutive days) was focused on. It should for example help the Solar Energy Sector with choosing battery systems for specific locations. Analysed thresholds were 2 octas and 6 octas, called CCge4.2 and CCge4.6 respectively. Comparing the Cloud_cci-derived index to the equivalents from SYNOP (ground) observations showed that at the majority (60-70%) of station locations, the mean absolute error (MAE) between Cloud_cci and SYNOP is less than 5% (see example Figure 5-6). The error correlates with the size of the values, so CCge4.2 has higher errors in summer and CCge4.6 in winter.

Figure 5-6 Mean error of CCge4.2 averaged over the seasons between 1982-1999 (left column) and 2000-2014 (right column) at each station (in percent).

Since CCge4.2 and CCge4.6 do not give any information on the actual length of consecutive periods, a third and fourth index was developed that should complement the previous two. These indices are measures that should robustly indicate the maximum lengths of consecutive mostly sunny and cloudy days. Analyses however have indicated that the maximum itself is a too volatile measure when comparing Cloud_cci to SYNOP observations serving as reference. The analysis suggest to use the 98th quantile instead. The 98th quantile balances robustness (not too large errors both in precision as well as in accuracy) and meaningfulness (spatial patterns of maxima remain visible). Figure 5-7 shows climatologies of this index CC98.2.

More information on this study can be found in RCCv2.0.

Figure 5-7 Cloud index CC98.2 (days) in winter (top right), spring (bottom right), summer (top left) and autumn (bottom left)




6. Summary of the overall achievements of Cloud_cci phase II

This section lists the key achievements of Cloud_cci and indicates further documentation.

Development and application of intercalibrated radiance data set - so called FCDRs, for

AVHRR in an international collaboration (→ RAFCDRv1.0)

Improvement of the Community Cloud retrieval for CLimate (CC4CL), a coherent

physical retrieval framework for cloud properties (flanked by threshold-test and neural net based schemes for cloud mask and phase detection) which is an open community retrieval framework and publicly available and usable by all interested scientists; (→ Sus et al.(2018); McGarragh et al. (2018) ATBDv6.2; ATBD-CC4CLv6.2)

Improvement of the Freie Universität Berlin AATSR MERIS Cloud retrieval (FAME-C) optimal estimation retrieval system that is applied to synergistic MERIS and AATSR measurements on-board ENVISAT (→ Carbajal Henken et al.(2014); Hollstein et al. (2015); ATBD-FAME-Cv5)

Extension and advancement of the prototype processing towards complete processing systems distributed over Europe that has the capabilities to be used for operational production of cloud property data sets after the ESA CCI program is finished (→ SSDv4.1; IODDv3.0)

Generation of six multi-mission, multi-decadal global data sets for the GCOS cloud property ECVs including uncertainty estimates. Five of which belong to a AVHRR-heritage family: AVHRR-AM, AVHRR-PM, MODIS-Terra, MODIS-Aqua, ATSR2-AATSR; and one synergetic data set (MERIS+AATSR) making use of the synergistic capabilities of AATSR and MERIS sensors being mounted on the same platform (Envisat). (→ Stengel et al., (2017); CRDPv3; PUGv4)

Implementing a radiation scheme (BUGSRAD) as post-processor in CC4CL to determining top- and bottom-of-atmosphere broadband fluxes for upwelling and downwelling, shortwave and longwave radiation. (→ ATBD-CC4CL-TOA_FLUXv1.1)

Generation of three multi-decadal global data sets for the GCOS cloud property ECVs including uncertainty estimates: AVHRR-AMv3, AVHRR-PMv3, ATSR2-AATSRv3, based on updates of the CC4CL retrieval system and including a suite of broadband flux properties from BUGSRAD (→ CRDPv5; PUGv5)

Comprehensive analysis of uncertainties entering and leaving the retrieval systems and

corresponding sources (→ CECRv3). Validation of Level-2 uncertainties that accompany the retrieval results (→ PVIRv5.x). Development of a framework for propagating Level-2 uncertainties to Level-3 (→ Stengel et al., 2017), and reflecting the applicability of the same framework for uncertainty propagation into even higher level products (→ PUGv5)

Assessment of the temporal stability of the records, (1) by comparison to other data sets (→ PVIRv6.1), (2) by validation against ground-based SYNOP observations (→ PVIRv5), and (3) by assessing the AVHRR diurnal drift impact on cloud products and assessment of potential strategies for correction (→ RODCv1)

Integrating the improved European capacities on cloud properties monitoring from Sentinel-3, i.e. by SLSTR and OLCI sensors; into CC4CL and FAME-C and generate demonstrator data sets. (→ CRDPv4)

Generation of a cloud property data set based on Infrared Sounder Measurements (IASI) (→ CRDPv4; Stubenrauch et al. (2017))

Development and application of multi-layer cloud scheme for improved cloud property

retrievals in multi-layer cloud situations (→ ATBD-CC4CL_MLEVv1.1)




Development of cloud climate indices and application for Europe (→ RCCv2.0)

Development and application of two satellite simulator packages to facilitate a better

evaluation of global and regional climate models. (→ Stengel et al. (2018); Eliasson et al. (2019); CARv3.1)

Contributing to cloud retrieval/dataset assessments conducted in the framework of CGMS’s International Cloud Working Group (ICWG) and WCRP’s GEWEX cloud assessment initiative (→ https://climserv.ipsl.polytechnique.fr/gewexca/index.html)

The Cloud_cci project team published numerous publications with lead authorship as well as contributed to many external publications as co-authors (See Section 7)




7. List of main Cloud_cci documents

(only major issues listed)

User Requirements Document (URD) – ESA Cloud_cci, Issue: 3, Revision: 0, data of issue: 12 February 2018; Available from ESA and the PI.

Algorithm Theoretical Baseline Document (ATBD) – ESA Cloud_cci, Issue 6, Revision: 2, date of Issue: 14/09/2019, Available at: http://www.esa-cloud-cci.org/?q=documentation_v3

Algorithm Theoretical Baseline Document (ATBD) FAME-C – ESA Cloud_cci, Issue 5, Revision: 0, date of Issue: 12/09/2017, Available at: http://www.esa-cloud-cci.org/?q=documentation

Algorithm Theoretical Baseline Document (ATBD) CC4CL – ESA Cloud_cci, Issue 6, Revision: 2, date of Issue: 28/10/2019, Available at: http://www.esa-cloud-cci.org/?q=documentation_v3

Input Output Data Definition Document (IODD) – ESA Cloud_cci, Issue 3, Revision: 0, date of Issue: 20/06/2013. Available from ESA and the PI.

System Specification Document (SSD) – ESA Cloud_cci, Issue 3, Revision: 0, date of Issue: 07/06/2019. Available from ESA and the PI.

Climate Research Data Package (CRDP) – ESA Cloud_cci - version 2 datasets, Issue 3, Revision: 1, date of Issue: 18/04/2017. Available from ESA and the PI.

Climate Research Data Package (CRDP) – ESA Cloud_cci - version 3 of AVHRR-AM, AVHRR-PM and ATSR2-AATSR (75%) datasets, Issue 5a, Revision: 0, date of Issue: 22/05/2019. Available from ESA and the PI.

Climate Research Data Package (CRDP) – ESA Cloud_cci - version 3 of ATSR2-AATSR (100%) dataset, Issue 5a, Revision: 1, data of Issue: 08/01/2020. Available from ESA and the PI.

Climate Assessment Report (CAR) – ESA Cloud_cci, Issue 3, Revision: 1, date of issue: 18/09/2017, Available at http://www.esa-cloud-cci.org/?q=documentation and from PI and ESA

Product Validation and Intercomparison Report (PVIR) – ESA Cloud_cci – version 2 datasets, Issue 5, Revision: 1, Date of Issue: 06/03/2018, Available at: http://www.esa-cloud-cci.org/?q=documentation

Product User Guide (PUG) – ESA Cloud_cci – version 2 datasets, Issue 4, Revision: 0, Date of Issue: 06/03/2018, Available at: http://www.esa-cloud-cci.org/?q=documentation

Product Validation and Intercomparison Report (PVIR) – ESA Cloud_cci – version 2 datasets, Issue 6, Revision: 1, date of Issue: DD/MM/YYYY, Available at: http://www.esa-cloud-cci.org/?q=documentation_v3

Product User Guide (PUG) – ESA Cloud_cci – version 3 datasets, Issue 5, Revision: 1, date of Issue: 09/01/2020, Available at: http://www.esa-cloud-cci.org/?q=documentation_v3

http://www.esa-cloud-cci.org/?q=documentation








http://www.esa-cloud-cci.org/?q=documentation_v3




8. Peer-reviewed Cloud_cci publications

This section lists all publications with traceable Cloud_cci contribution (in reversed order of publication date).

Stengel, M., Stapelberg, S., Sus, O., Finkensieper, S., Würzler, B., Philipp, D., Hollmann, R., Poulsen, C., Christensen, M., and McGarragh, G.: Cloud_cci Advanced Very High Resolution Radiometer post meridiem (AVHRR-PM) dataset version 3: 35-year climatology of global cloud and radiation properties, Earth Syst. Sci. Data, 12, 41–60, https://doi.org/10.5194/essd-12-41-2020, 2020. Feofilov, A. G. and Stubenrauch, C. J.: Diurnal variation of high-level clouds from the synergy of AIRS and IASI space-borne infrared sounders, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-166, in review, 2019.

Eliasson, S., Karlsson, K. G., van Meijgaard, E., Meirink, J. F., Stengel, M., and Willén, U.: The Cloud_cci simulator v1.0 for the Cloud_cci climate data record and its application to a global and a regional climate model, Geosci. Model Dev., 12, 829-847, https://doi.org/10.5194/gmd-12-829-2019, 2019.

Stengel, M., Schlundt, C., Stapelberg, S., Sus, O., Eliasson, S., Willén, U., and Meirink, J. F.: Comparing ERA-Interim clouds with satellite observations using a simplified satellite simulator, Atmos. Chem. Phys., 18, 17601-17614, https://doi.org/10.5194/acp-18-17601-2018, 2018. Baró, R., Jiménez-Guerrero, P., Stengel, M., Brunner, D., Curci, G., Forkel, R., Neal, L., Palacios-Peña, L., Savage, N., Schaap, M., Tuccella, P., Denier van der Gon, H., and Galmarini, S.: Evaluating cloud properties in an ensemble of regional online coupled models against satellite observations, Atmos. Chem. Phys., 18, 15183-15199, https://doi.org/10.5194/acp-18-15183-2018, 2018. Stengel, M., Stapelberg, S., Sus, O., Schlundt, C., Poulsen, C., Thomas, G., Christensen, M., Carbajal Henken, C., Preusker, R., Fischer, J., Devasthale, A., Willén, U., Karlsson, K.-G., McGarragh, G. R., Proud, S., Povey, A. C., Grainger, R. G., Meirink, J. F., Feofilov, A., Bennartz, R., Bojanowski, J. S., and Hollmann, R.: Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project, Earth Syst. Sci. Data, 9, 881-904, https://doi.org/10.5194/essd-9-881-2017, 2017. Sus, O., Stengel, M., Stapelberg, S., McGarragh, G., Poulsen, C., Povey, A. C., Schlundt, C., Thomas, G., Christensen, M., Proud, S., Jerg, M., Grainger, R., and Hollmann, R.: The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors, Atmos. Meas. Tech., 11, 3373-3396, https://doi.org/10.5194/amt-11-3373-2018, 2018. McGarragh, G. R., Poulsen, C. A., Thomas, G. E., Povey, A. C., Sus, O., Stapelberg, S., Schlundt, C., Proud, S., Christensen, M. W., Stengel, M., Hollmann, R., and Grainger, R. G.: The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach, Atmos. Meas. Tech., 11, 3397-3431, https://doi.org/10.5194/amt-11-3397-2018, 2018. Keller, M., Kröner, N., Fuhrer, O., Lüthi, D., Schmidli, J., Stengel, M., Stöckli, R., and Schär, C.: The sensitivity of Alpine summer convection to surrogate climate change: an intercomparison between convection-parameterizing and convection-resolving models, Atmos. Chem. Phys., 18, 5253-5264, https://doi.org/10.5194/acp-18-5253-2018, 2018.




Merchant, C. J., Paul, F., Popp, T., Ablain, M., Bontemps, S., Defourny, P., Hollmann, R., Lavergne, T., Laeng, A., de Leeuw, G., Mittaz, J., Poulsen, C., Povey, A. C., Reuter, M., Sathyendranath, S., Sandven, S., Sofieva, V. F., and Wagner, W.: Uncertainty information in climate data records from Earth observation, Earth Syst. Sci. Data, 9, 511-527, https://doi.org/10.5194/essd-9-511-2017, 2017. Christensen, M. W., Neubauer, D., Poulsen, C. A., Thomas, G. E., McGarragh, G. R., Povey, A. C., Proud, S. R., and Grainger, R. G.: Unveiling aerosol–cloud interactions – Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate, Atmos. Chem. Phys., 17, 13151-13164, https://doi.org/10.5194/acp-17-13151-2017, 2017. Neubauer, D., Christensen, M. W., Poulsen, C. A., and Lohmann, U.: Unveiling aerosol–cloud interactions – Part 2: Minimising the effects of aerosol swelling and wet scavenging in ECHAM6-HAM2 for comparison to satellite data, Atmos. Chem. Phys., 17, 13165-13185, https://doi.org/10.5194/acp-17-13165-2017, 2017. Stubenrauch, C. J., Feofilov, A. G., Protopapadaki, S. E., & Armante, R., Cloud climatologies from the infrared sounders AIRS and IASI: strengths and applications. Atmospheric Chemistry and Physics, 17(22), 13625, 2017. Protopapadaki, S. E., Stubenrauch, C. J., and Feofilov, A. G.: Upper tropospheric cloud systems derived from IR sounders: properties of cirrus anvils in the tropics, Atmos. Chem. Phys., 17, 3845-3859, doi:10.5194/acp-17-3845-2017, 2017. Lauer, A., Eyring, V., Righi, M., Buchwitz, M., Defourny, P., Evaldsson, M., Friedlingstein, P., de Jeu, R., de Leeuw, G., Loew, A., Merchant, C. J., Müller, B., Popp, T., Reuter, M., Sandven, S., Senftleben, D., Stengel, M., van Roozendael, M., Wenzel, S. and Willen, U.,Benchmarking CMIP5 models with a subset of ESA CCI Phase 2 data using the ESMValTool. Remote Sensing of Environment, 203,9-39, 2017 Povey, A. C. and Grainger, R. G.: Known and unknown unknowns: uncertainty estimation in satellite remote sensing, Atmos. Meas. Tech., 8, 4699-4718, doi:10.5194/amt-8-4699-2015, 2015. Bojanowski, J.S., Stöckli, R., Tetzlaff, A., Kunz, H., The Impact of Time Difference between Satellite Overpass and Ground Observation on Cloud Cover Performance Statistics. Remote Sensing 6(12), 12866-12884. 2014. Carbajal Henken, C. K., Doppler, L., Lindstrot, R., Preusker, R., and Fischer, J.: Exploiting the sensitivity of two satellite cloud height retrievals to cloud vertical distribution, Atmos. Meas. Tech., 8, 3419-3431, doi:10.5194/amt-8-3419-2015, 2015. Feofilov, A.G., C. J. Stubenrauch, and J. Delanoë, Ice water content vertical profiles of high-level clouds: classification and impact on radiative fluxes, Atmos. Chem. Phys., 15, 12327–12344, doi:10.5194/acp-15-12327-2015, 2015 Hollstein, A., Fischer, J., Carbajal Henken, C., and Preusker, R.: Bayesian cloud detection for MERIS, AATSR, and their combination, Atmos. Meas. Tech., 8, 1757-1771, doi:10.5194/amt-8-1757-2015, 2015 Keller, M., O. Fuhrer, J. Schmidli, M. Stengel, R. Stöckli, and C. Schär Evaluation of convection-resolving models using satellite data: The diurnal cycle of summer convection over the Alps. Meteorol. Z., doi:10.1127/metz/2015/0715, 2015




Keller, M., Kröner, N., Fuhrer, O., Lüthi, D., Schmidli, J., Stengel, M., Stöckli, R., and Schär, C.: The sensitivity of Alpine summer convection to surrogate climate change: an intercomparison between convection-parameterizing and convection-resolving models, Atmos. Chem. Phys., 18, 5253–5264, https://doi.org/10.5194/acp-18-5253-2018, 2018. Stengel, M., S. Mieruch, M. Jerg, K.-G. Karlsson, R. Scheirer, B. Maddux, J.F. Meirink, C. Poulsen, R. Siddans, A. Walther, R. Hollmann., The Clouds Climate Change Initiative: Assessment of state-of-the-art cloud property retrieval schemes applied to AVHRR heritage measurements. Remote Sensing of Environment, 162, 363-379, 2015 Carbajal Henken, C. K., Lindstrot, R., Preusker, R., and Fischer, J.: FAME-C: cloud property retrieval using synergistic AATSR and MERIS observations, Atmos. Meas. Tech., 7, 3873-3890, doi:10.5194/amt-7-3873-2014, 2014 Karlsson, K.G., and Johansson,E., "Multi-Sensor calibration studies of AVHRR-heritage channel radiances using the simultaneous nadir observation approach." Remote Sensing 6.3 (2014): 1845-1862, 2014 Hollmann, R., Merchant, C., Saunders, R., Downy, C., Buchwitz, M., Cazenave, A., Chuvieco, E., Defourny, P., Leeuw, G. de, Forsberg, R., Holzer-Popp, T., Paul, F., Sandven, S., Sathyendranath, S., Roozendael, M. van, & Wagner W., The ESA Climate Change Initiative: satellite data records for essential climate variables, Bulletin of the American Meteorological Society, doi: 10.1175/BAMS-D-11-00254.1, 2013 Meirink, J. F., Roebeling, R. A., and Stammes, P.: Inter-calibration of polar imager solar channels using SEVIRI, Atmos. Meas. Tech., 6, 2495-2508, doi:10.5194/amt-6-2495-2013, 2013.




9. Glossary

AATSR Advanced Along Track Scanning Radiometer

AIRS Atmospheric InfraRed sounder

AVHRR Advanced Very High Resolution Radiometer

CC4CL Community Cloud retrieval for Climate

CIRS Clouds from IR Sounders

CM SAF Climate Monitoring Satellite Application Facility

CMUG Climate Modelling User Group

ECV Essential Climate Variable

ENVISAT Environmental Satellite

ERS2 European Remote-sensing Satellite - 2

ESA European Space Agency

ETH Eidgenössische Technische Hochschule Zürich

FAME-C Freie Universität Berlin AATSR MERIS Cloud

FCDR Fundamental Climate Data Record

GEWEX Global Energy and Water Exchanges

GCOS Global Climate Observing System

IASI Infrared Atmospheric Sounding Interferometer

ICWG International Cloud Working Group

MERIS Medium Resolution Imaging Spectrometer

MISR Multi-angle Imaging SpectroRadiometer

MODIS Moderate Resolution Imaging Spectroradiometer

NOAA National Oceanic and Atmospheric Administration




OE Optimal Estimation

OLCI Ocean and Land Colour Instrument

PARASOL Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar

RACMO Regional Atmospheric Climate Model

SAF Satellite Application Facility

SEVIRI Spinning Enhanced Visible and Infrared Imager

SLSTR Sea and Land Surface Temperature Radiometer

SST Sea Surface Temperature

WCRP World Climate Research Programme




10.References

ATBDv6.2, Algorithm Theoretical Baseline Document (ATBD) – ESA Cloud_cci, Issue 6, Revision: 2, date of Issue: 14/09/2019, Available at: http://www.esa-cloud-cci.org/?q=documentation_v3

ATBD-FAME-Cv5, Algorithm Theoretical Baseline Document (ATBD) FAME-C – ESA Cloud_cci, Issue 5, Revision: 0, date of Issue: 12/09/2017, Available at: http://www.esa-cloud-cci.org/?q=documentation

ATBD-CC4CLv6.2, Algorithm Theoretical Baseline Document (ATBD) CC4CL – ESA Cloud_cci, Issue 6, Revision: 2, date of Issue: 28/10/2019, Available at: http://www.esa-cloud-cci.org/?q=documentation_v3

ATBD-CC4CL_TOA_FLUXv1.1, Algorithm Theoretical Basis Document (ATBD) of the Community Code for CLimate (CC4CL) Broadband Radiative Flux Retrieval (CC4CL-TOAFLUX) – ESA Cloud_cci, Issue 1, Revision: 1, date of Issue: 14/10/2019, Available at: http://www.esa-cloud-cci.org/?q=documentation

ATBD-CC4CL_MLEVv1.1, Algorithm Theoretical Basis Document (ATBD) of the Community Code for CLimate (CC4CL) Multi Layer Cloud module (CC4CL-MLEV) – ESA Cloud_cci, Issue 1, Revision: 1, date of issue: 31/05/2016, Available at: http://www.esa-cloud-cci.org/?q=documentation

CARv3.1, Climate Assessment Report (CAR) – ESA Cloud_cci, Issue 3, Revision: 1, date of issue: 18/09/2017, Available at: http://www.esa-cloud-cci.org/?q=documentation

Carbajal Henken, C.K., Lindstrot, R., Preusker, R. and Fischer, J.: FAME-C: cloud property retrieval using synergistic AATSR and MERIS observations. Atmos. Meas. Tech., 7, 3873–3890, doi:10.5194/amt-7-3873-2014, 2014

Devasthale A., and H. Grassl, 2007: Dependence of frequency of convective cloud occurrence on the orbital drift of satellites, Int. J. Remote Sens., 28, doi:10.1080/01431160701294646.

Devasthale, A., Karlsson, K.-G., Quaas, J., and Grassl, H., 2012: Correcting orbital drift signal in the time series of AVHRR derived convective cloud fraction using rotated empirical orthogonal function, Atmos. Meas. Tech., 5, 267-273, doi:10.5194/amt-5-267-2012.

Foster, M.J.; Heidinger, A., 2013: PATMOS-x: Results from a diurnally corrected 30-yr satellite cloud climatology. J. Clim., 26, 414–425.

GCOS-107, Systematic Observation Requirements for Satellite-based Products for Climate – Supplemental Details to the Satellite-based Component of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC, Publication No. GCOS-107; available from https://library.wmo.int

GCOS-IP, GCOS 2016 Implementation Plan, 2016, available from https://unfccc.int

Hollstein, A., Fischer, J., Carbajal Henken, C., and Preusker, R.: Bayesian cloud detection for MERIS, AATSR, and their combination, Atmos. Meas. Tech., 8, 1757-1771, doi:10.5194/amt-8-1757-2015, 2015.

Ignatov, A., I. Laszlo, E. D. Harrod, K. B. Kidwell, and G. P. Goodrum, 2004: Equator crossing times for NOAA, ERS and EOS sun-synchronous satellites, Int. J. Remote Sens., 25, 5255–5266.

IODDv3.0 Input Output Data Definition Document – ESA Cloud_cci, Issue 3, Revision: 0, date of Issue: 20 June 2013. Available from PI and ESA.

Jin, M. and R. E. Treadon, 2013: Correcting the orbit drift effect on AVHRR land surface skin temperature measurements, Int. J. Remote Sens., 24, 4543–4558.













Lucas, L. E., D. E. Waliser, P. Xie, J. E. Janowiak, and B. Liebman, 2001: Estimating the satellite equatorial crossing time biases in the daily, global outgoing longwave radiation dataset, J. Climate, 14, 2583-2605.

McGarragh, G. R., Poulsen, C. A., Thomas, G. E., Povey, A. C., Sus, O., Stapelberg, S., Schlundt, C., Proud, S., Christensen, M. W., Stengel, M., Hollmann, R., and Grainger, R. G.: The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach, Atmos. Meas. Tech., 11, 3397-3431, https://doi.org/10.5194/amt-11-3397-2018, 2018.

Nagol, J.R.; Vermote, E.F.; Prince, S.D., 2014: Quantification of Impact of Orbital Drift on Inter-Annual Trends in AVHRR NDVI Data. Remote Sens., 6, 6680-6687.

RAFCDRv1.0, Technical Report on AVHRR GAC FCDR generation – ESA Cloud_cci, Issue 1, Revision: 0, planned date of Issue: 10/05/2017. Available at: http://www.esa-cloud-cci.org/?q=documentation

RCCv2.0, Cloud climate indices: User consultation and new indices, Issue 2, Revision: 0, date of Issue: 27 May 2019. Available from PI and ESA.

RODCv1.1, Report on Orbital Drift Correction for AVHRR – ESA Cloud_cci, Issue 1, Revision: 1, planned date of Issue: 28/08/2017. Available at: http://www.esa-cloud-cci.org/?q=documentation

PUGv5.1, Product User Guide (PUG) – ESA Cloud_cci – version 3 datasets, Issue 5, Revision: 1, date of Issue: 09/01/2020, Available at: http://www.esa-cloud-cci.org/?q=documentation_v3

PVIRv5.1, Product Validation and Intercomparison Report (PVIR) – ESA Cloud_cci, Issue 5, Revision: 1, Date of Issue: 18/04/2017, Available at: http://www.esa-cloud-cci.org/?q=documentation

PVIRv6.1, Product Validation and Intercomparison Report (PVIR) – ESA Cloud_cci, Issue 6, Revision: 1, Date of Issue: 18/04/2017, Available at: http://www.esa-cloud-cci.org/?q=documentation

Sus, O., Stengel, M., Stapelberg, S., McGarragh, G., Poulsen, C., Povey, A. C., Schlundt, C., Thomas, G., Christensen, M., Proud, S., Jerg, M., Grainger, R., and Hollmann, R.: The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors, Atmos. Meas. Tech., 11, 3373-3396, https://doi.org/10.5194/amt-11-3373-2018, 2018.

Stengel, M., Stapelberg, S., Sus, O., Schlundt, C., Poulsen, C., Thomas, G., Christensen, M., Carbajal Henken, C., Preusker, R., Fischer, J., Devasthale, A., Willén, U., Karlsson, K.-G., McGarragh, G. R., Proud, S., Povey, A. C., Grainger, R. G., Meirink, J. F., Feofilov, A., Bennartz, R., Bojanowski, J. S., and Hollmann, R.: Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project, Earth Syst. Sci. Data, 9, 881-904, https://doi.org/10.5194/essd-9-881-2017, 2017.

Stengel, M., Stapelberg, S., Sus, O., Finkensieper, S., Würzler, B., Philipp, D., Hollmann, R., Poulsen, C., Christensen, M., and McGarragh, G.: Cloud_cci Advanced Very High Resolution Radiometer post meridiem (AVHRR-PM) dataset version 3: 35-year climatology of global cloud and radiation properties, Earth Syst. Sci. Data, 12, 41–60, https://doi.org/10.5194/essd-12-41-2020, 2020.

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Stubenrauch, C. J., Feofilov, A. G., Protopapadaki, S. E., and Armante, R.: Cloud climatologies from the infrared sounders AIRS and IASI: strengths and applications, Atmos. Chem. Phys., 17, 13625-13644, https://doi.org/10.5194/acp-17-13625-2017, 2017.

Waliser, D. E., and W. Zhou, 1997: Removing satellite equatorial crossing time biases from the OLR and HRC datasets, J. Climate, 10, 2125-2146.




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Cloud cci doc · Doc: Cloud_cci_FINAL_REPORT_Phase2_v1.1.doc Date: 10 February 2020 Issue: 1 Revision: 1 Page 4 Purpose The purpose of this document is to report a complete description