Image Added

Image Added

Table 3-5 shows that AVHRR-heritage information from one morning and one afternoon orbit will be provided for at least 20 more years from present time (i.e., nominally until 2045). Consequently, it means that CLARA-type CDRs will be possible to compile over a time period exceeding 60 years from the initial launch year of 1978 for the first AVHRR instrument.

It is finally worth mentioning that the Chinese Feng-Yun 3 satellites FY-3D to FY-3H carry the 25-channel VIS-IR spectrometer MERSI-2, which also includes all AVHRR heritage channels (see https://space.oscar.wmo.int/satelliteprogrammes/view/fy_3. Partly, these satellites fly in similar orbits to the JPSS and EPS-SG series. Thus, in case problems are encountered with near future satellite coverage of the satellites in the US/European Joint Polar Satellite System, data from the Feng-Yun satellites can be used as a backup solution provided that calibration homogenization can be achieved. However, it should be noted that the upcoming FY-3E and FY-3H satellites will be operated in an early-morning orbit with an equatorial overpass time of 6:00 am.

3.1.2 Cloud Properties TCDR ESA AATSR v3.0 + ICDR ESA SLSTR v3.1.1+v4.0

The Cloud_cci v3.0 is based on radiances provided by the ATSR series of sensors. These instruments flew on sun-synchronous polar orbiting satellites with daytime equatorial crossing times in the mid-morning: 10:30 Local Time on Descending Node (LTDN) for ATSR-2 and 10:00 LTDN for ENVISAT, with both satellites sharing the same ground track. There were 14.3 orbits per day, meaning 28 equatorial overpasses per-day, with measurements covering a total of 18% of equatorial circumference of the Earth (with equally spaced 512 km swaths). The observation frequency increased at higher latitudes (with a maximum of 14 observations per day at the poles) due to increasing overlaps between the satellite swaths. Both sensors provided the same seven channels (and used the same conical dual-viewing geometry), but not all channels were provided at all times, or at full digitization rate, from ATSR-2, due to limitations of the data bandwidth provided by the ERS-2 platform. Over ocean regions, ATRS visible channels were often only provided in a 256 pixel “narrow-swath” mode. The channels provided by both instruments were centered at 0.55, 0.67, 0.87, 1.6, 3.7, 10.8, 12.0 

\mu m

and the filter band passes were very similar between instruments. Despite the low-data rate modes of ATSR-2, the combination of the very similar instrument specifications, very close orbital parameters and the lack of any significant orbital drift in the ERS-2 and ENVISAT satellites mean that ATSR-2 and AATSR provide a highly consistent data record, especially when compared to that provided by the AVHRR record used by the CLARA-A3 TCDR (although AVHRR provides a much longer data record).

The current TCDR from Cloud_cci v3.0 begins with the launch of ERS-2 in mid-1995 and continues until the failure of ENVISAT in April 2012. Due to instrument problems, there is a six-month data gap in the ATSR-2 record from January to June 1996.

There is an overlap of 1 year of data between the two platforms, between mid-2002 (when ENVISAT was launched) and mid-2003 (when the onboard data storage on ERS-2 failed). There is additional ATSR-2 data available up-to 2009, but this is not global as data could only be collected when the satellite was within line-of-sight with a ground receiving station, and has not been included in the TCDR. There is some scope to push the coverage of the ATSR cloud record back to 1991, by using the ATSR-1 instrument (onboard ERS-1), which also flew in a similar orbit to its successors. However, ATSR-1 lacked the shortwave channels (apart from the 1.6 

\mu m

channel), which would reduce the information available to daylight retrievals and would represent a significant inhomogeneity in the TCDR.

The extension of the ATSR TCDR relies on the SLSTR sensors onboard the Sentinel-3 platform. SLSTR represents a significant upgrade over (A)ATSR, providing a wider swath, two satellites with interleaved orbit swaths, additional channels and the data availability security of an operational system. The Sentinel-3s have a very similar orbit to ENVISAT and the ERS satellites, with a sun-synchronous orbit with an LTDN of 10:00, and 14.3 orbits per-day. However, there is slightly longer than 4-year gap between the end of the (A)ATSR record and the first SLSTR data. There are several options available to fill this gap, as ORAC can be applied to most radiometers with similar channels to those provided by ATSR. Indeed, cloud CDRs of ORAC applied to both MODIS and AVHRR already exist, having been produced in the Cloud_cci program. Furthermore, the CLARA-A3 product also discussed in this document also provides a comparable product which fills the (A)ATSR-SLSTR data gap.

It should also be noted that ORAC could be applied to the VIIRS and METimage instruments described above, which could compliment the SLSTR ICDR.

Anchor
section3_2 section3_2

3.2 Development of processing algorithms

3.2.1 Cloud Properties TCDR AVHRR CLARA v2.0 + ICDR v3.x

3.2.1.1 General aspects common to all selected CLARA products

A fundamental aspect is that TCDRs based on AVHRR data must be based on an accurate AVHRR Fundamental Climate Data Record (FCDR), i.e., quality checked radiances corrected for degradations and with proper inter-calibration between individual satellites. The work with FCDRs is an ever-ongoing task (e.g., new data added to the series must be analysed, inter-calibrated and appropriately corrected for possible degradations).

Another factor that affects all CLARA products (as well as all other available AVHRR-based CDRs) is the inhomogeneity of the observational record, i.e., mainly the different observation frequency in the beginning and in the end of the observational record. This is further enhanced by the fact that some satellites exhibit large orbital drift during their lifetime. In the worst case, these two factors may lead to artificial trends in the CDR as discussed by Karlsson et al. (2018). A way to mitigate these problems is to add AVHRR-heritage data from other sensors to the used observational record (as mentioned in section 3.1.1). For the extension of the CDR beyond the AVHRR era, adaptations have also to be done to cope with the AVHRR-heritage channels on post-AVHRR sensors.

The two mentioned aspects are by far the most important ones for assuring a high quality of an AVHRR-based TCDR. The following sub-sections will provide some more details on these aspects. There are also issues (limitations) with the current retrieval algorithms but these will be discussed more in detail later in sections 3.4 and 3.5.

3.2.1.1.1 Maintaining development of the AVHRR Fundamental Climate Data Record

The AVHRR FCDR used as a basis for CLARA-A3 processing was only inter-calibrated and corrected for sensor degradation for the visible channel 1, the near-infrared channel 2 and the short-wave infrared channel 3a (see Table 3-1). This was based on an extension of the method (Heidinger, 2018) originally proposed by Heidinger et al. (2010). Infrared radiances were still based on the nominal operational NOAA calibration method utilising onboard black body reference sensors. Note that the calibrated AVHRR reflectances and brightness temperatures, used as a basis for the CLARA-A3 TCDR processing, were also compiled as a stand-alone dataset in a joint CM SAF/EUMETSAT effort. This dataset, provided by EUMETSAT is denoted the AVHRR FDR (Fundamental Data Record) with the following dataset doi number: 10.15770/EUM_SEC_CLM_0060 (EUMETSAT,2023a, 2023b).

Continued coordination of AVHRR FCDR developments

...

CLARA-A3 will most likely be the last TCDR based exclusively on data from the AVHRR instrument. The very last satellite carrying the AVHRR sensor is Metop-C which was launched on 7^th of November 2018. With a nominal expected lifetime of approximately 5 years it means that AVHRR data beyond 2025 (or maybe 2030 considering the extended life of most recent AVHRRs) will most likely not be available anymore. Of importance here is also that AVHRR observations from a satellite in afternoon orbit will cease in 2022 (according to NOAA). The last satellite carrying AVHRR in an afternoon orbit is NOAA-19. This satellite has now been operational for more than 12 years and it is thus approaching the end of its lifetime. A particular problem with the still available NOAA satellites in afternoon orbit carrying the AVHRR sensor is that orbital drift has already affected them seriously. In fact, for the last few years of the planned CLARA-A3 TCDR (at least years 2019 and 2020) these observations are better characterized as morning observations than afternoon observations as a consequence of the orbital drift. This is another reason for putting a definite end of exclusively AVHRR-based TCDRs to the year 2020.

The AVHRR-based CDRs covering more than four decades are unique and precious datasets from the climate monitoring point of view in that they represent the longest available multispectral imaging observation record from space. Possibilities to extend the observation series even further by use of data from other sensors would therefore be extremely valuable. The prospects here are good considering that the next generation polar-orbiting meteorological satellites will carry sensors with AVHRR-heritage spectral bands as a subset of their full range of spectral bands. Some of these satellites are already in orbit. The most important sensors in this respect are the Visible/Infrared Imager Radiometer Suite (VIIRS) and the Meteorological Imager (METimage) sensors. Both sensors’ spectral channels are described in Table 3-3 and Table 3-4, respectively. We note in Table 3-3 that heritage AVHRR channels are channels M5, M7, M10, M12, M15 and M16 for VIIRS. For METimage in Table 3-4, AVHRR-heritage channels are available at wavelengths 670 nm, 865 nm, 1630 nm, 3740 nm, 10790 nm and 12020 nm. Similar information about METimage is given by the following link: https://www.eumetsat.int/eps-sg-metimage.

Table 3-5 lists the current and planned satellites (including their expected lifetimes) carrying the VIIRS and METimage sensors. The VIIRS sensor is carried on the next generation US polar orbiting meteorological satellites following the NOAA-19 satellite. The first satellite with VIIRS was Suomi-NPP which was launched in 2011. Suomi-NPP was a precursor to the US part of the operational Joint Polar Satellite System program (JPSS). The first JPSS satellite (JPSS-1) was launched in 2017 and it was later renamed to NOAA-20. The US part of JPPS deals with satellites in the afternoon orbit. The second leg, i.e. the morning satellites, will be provided by EUMETSAT with the upcoming EPS-SG satellites. These satellites will carry the METimage sensor.

It should be noted that VIIRS observations in a stable afternoon orbit have been available since 2011. This means that VIIRS data can not only be used to extend CLARA-A3 beyond 2020 but it can also be used to replace data from NOAA-18 and NOAA-19 in the period 2011-2020 when the latter satellites were exposed to serious orbital drift. In other words, VIIRS data can be used to restore the nominal two orbit constellation of observations from AVHRR during the last decade of CLARA-A3 coverage. It means that VIIRS data can not only extend CLARA-A3 results but also improve CLARA-A3 results for the last decade of its temporal coverage. This possibility has been taken up in the planning of a further extension, named CLARA-A3.5, of the CLARA data record in the next CM SAF project phase covering the years 2022-2027. CLARA-A3.5 will then add results derived from the AVHRR-heritage channels of VIIRS from Suomi-NPP and NOAA-20. CLARA-A3.5 is scheduled for release in 2026.

...

Image Removed

...

Image Removed

...

Satellite

...

Provider

...

Start

...

Expected

end of life

...

Orbit
(daytime eq. crossing time)

...

Imaging sensor

...

NOAA-20

...

NOAA

...

2017

...

2024

...

1:30 pm

...

VIIRS

...

JPSS-2

...

NOAA

...

2022

...

2029

...

1:30 pm

...

VIIRS

...

Metop-SG-A1

...

EUMETSAT

...

2024

...

2031

...

9:30 am

...

MetImage

...

JPSS-3

...

NOAA

...

2027

...

2034

...

1:30 pm

...

VIIRS

...

Metop-SG-A2

...

EUMETSAT

...

2031

...

2038

...

9:30 am

...

MetImage

...

JPSS-4

...

NOAA

...

2032

...

2039

...

1:30 pm

...

VIIRS

...

Metop-SG-A3

...

EUMETSAT

...

2038

...

2045

...

9:30 am

...

MetImage

Table 3-5 shows that AVHRR-heritage information from one morning and one afternoon orbit will be provided for at least 20 more years from present time (i.e., nominally until 2045. Consequently, it means that CLARA-type CDRs will be possible to compile over a time period exceeding 60 years from the initial launch year of 1978 for the first AVHRR instrument.

It is finally worth mentioning that the Chinese Feng-Yun 3 satellites FY-3D to FY-3H carry the 25-channel VIS-IR spectrometer MERSI-2, which also includes all AVHRR heritage channels (see https://www.wmo-sat.info/oscar/instruments/view/279). Partly, these satellites fly in similar orbits to the JPSS and EPS-SG series. Thus, in case problems are encountered with near future satellite coverage of the satellites in the US/European Joint Polar Satellite System, data from the Feng-Yun satellites can be used as a backup solution provided that calibration homogenization can be achieved. However, it should be noted that the upcoming FY-3E and FY-3H satellites will be operated in an early-morning orbit with an equatorial overpass time of 6:00 am.

3.1.2 Cloud Properties TCDR ESA AATSR v3.0 + ICDR ESA SLSTR v3.1.1+v4.0

The Cloud_cci v3.0 is based on radiances provided by the ATSR series of sensors. These instruments flew on sun-synchronous polar orbiting satellites with daytime equatorial crossing times in the mid-morning: 10:30 Local Time on Descending Node (LTDN) for ATSR-2 and 10:00 LTDN for ENVISAT, with both satellites sharing the same ground track. There were 14.3 orbits per day, meaning 28 equatorial overpasses per-day, with measurements covering a total of 18% of equatorial circumference of the Earth (with equally spaced 512 km swaths). The observation frequency increased at higher latitudes (with a maximum of 14 observations per day at the poles) due to increasing overlaps between the satellite swaths. Both sensors provided the same seven channels (and used the same conical dual-viewing geometry), but not all channels were provided at all times, or at full digitization rate, from ATSR-2, due to limitations of the data bandwidth provided by the ERS-2 platform. Over ocean regions, ATRS visible channels were often only provided in a 256 pixel “narrow-swath” mode. The channels provided by both instruments were centred at 0.55, 0.67, 0.87, 1.6, 3.7, 10.8, 12.0

\mu m

and the filter band passes were very similar between instruments. Despite the low-data rate modes of ATSR-2, the combination of the very similar instrument specifications, very close orbital parameters and the lack of any significant orbital drift in the ERS-2 and ENVISAT satellites mean that ATSR-2 and AATSR provide a highly consistent data record, especially when compared to that provided by the AVHRR record used by the CLARA-A2.1 TCDR (although AVHRR provides a much longer data record).

The current TCDR from Cloud_cci v3.0 begins with the launch of ERS-2 in mid-1995 and continues until the failure of ENVISAT in April 2012. Due to instrument problems, there is a six-month data gap in the ATSR-2 record from January to June 1996.

There is an overlap of 1 year of data between the two platforms, between mid-2002 (when ENVISAT was launched) and mid-2003 (when the onboard data storage on ERS-2 failed). There is additional ATSR-2 data available up-to 2009, but this is not global as data could only be collected when the satellite was within line-of-sight with a ground receiving station, and has not been included in the TCDR. There is some scope to push the coverage of the ATSR cloud record back to 1991, by using the ATSR-1 instrument (onboard ERS-1), which also flew in a similar orbit to its successors. However, ATSR-1 lacked the shortwave channels (apart from the 1.6

\mu

m channel), which would reduce the information available to daylight retrievals and would represent a significant inhomogeneity in the TCDR.

The extension of the ATSR TCDR relies on the SLSTR sensors onboard the Sentinel-3 platform. SLSTR represents a significant upgrade over (A)ATSR, providing a wider swath, two satellites with interleaved orbit swaths, additional channels and the data availability security of an operational system. The Sentinel-3s have a very similar orbit to ENVISAT and the ERS satellites, with a sun-synchronous orbit with an LTDN of 10:00, and 14.3 orbits per-day. However, there is slightly longer than 4-year gap between the end of the (A)ATSR record and the first SLSTR data. There are several options available to fill this gap, as ORAC can be applied to most radiometers with similar channels to those provided by ATSR. Indeed, cloud CDRs of ORAC applied to both MODIS and AVHRR already exist, having been produced in the Cloud_cci program. Furthermore, the CLARA-A2.1 product also discussed in this document also provides a comparable product which fills the (A)ATSR-SLSTR data gap.

It should also be noted that ORAC could be applied to the VIIRS and METimage instruments described above, which could compliment the SLSTR ICDR.

...

3.2.1 Cloud Properties TCDR AVHRR CLARA v2.0 + ICDR v2.x

3.2.1.1 General aspects common to all selected CLARA products

A fundamental aspect is that TCDRs based on AVHRR data must be based on an accurate AVHRR Fundamental Climate Data Record (FCDR), i.e., quality checked radiances corrected for degradations and with proper inter-calibration between individual satellites. The work with FCDRs is an ever-ongoing task (e.g., new data added to the series must be analysed, inter-calibrated and appropriately corrected for possible degradations).

Another factor that affects all CLARA products (as well as all other available AVHRR-based CDRs) is the inhomogeneity of the observational record, i.e., mainly the different observation frequency in the beginning and in the end of the observational record. Orbital drift effects also contribute but the main problem is the lack of morning observations in the current CLARA-A2.1 CDR in the initial part of the covered period. In the worst case, this may lead to artificial trends in the CDR as discussed by Karlsson et al. (2018). A way to mitigate these problems is to add AVHRR/1 observations to the used observational record (as mentioned in the previous section). For the extension of the CDR beyond the AVHRR era, adaptations have also to be done to cope with the AVHRR-heritage channels on post-AVHRR sensors.

The two mentioned aspects are by far the most important ones for assuring a high quality of an AVHRR-based TCDR. The following sub-sections will provide some more details on these aspects. There are also issues (limitations) with the current retrieval algorithms but these will be discussed more in detail later in sections 3.4 and 3.5.

3.2.1.1.1 Maintaining development of the AVHRR Fundamental Climate Data Record

The AVHRR FCDR used as a basis for CLARA-A2.1 processing was only inter-calibrated and corrected for sensor degradation for the visible channel 1, the near-infrared channel 2 and the short-wave infrared channel 3a (see Table 3-1). This was based on an extension of the method originally proposed by Heidinger et al. (2010). Infrared radiances were still based on the nominal operational NOAA calibration method utilizing onboard black body reference sensors.

In order to coordinate development and to improve progress in calibration methodologies, a special project (SCM-5: Advancing the AVHRR FCDR) was defined in 2014 within the framework of the SCOPE-CM (Sustained and COordinated Processing of Environmental satellite data for Climate Monitoring) activity - https://www.scope-cm.org/). SCOPE-CM is a high-level project network with links to entities like WMO, WCRP, GCOS, CGMS, CEOS and GEO. SCM-5 involved scientists from NOAA and NASA, and also from the CM SAF and ESA Cloud_cci projects. It was also linked to the EU-project FIDUCEO (Fidelity and uncertainty in climate data records from Earth Observations - https://research.reading.ac.uk/fiduceo/) where an improved infrared calibration for AVHRR data was under development. SCM-5 formally ended in February 2019 when SCOPE-CM entered a new project phase (Phase III).

The planning of the next CLARA-A3 CDR will utilize the outcome of SCM-5 which so far mainly consists of an upgraded calibration method for visible channels as a result of a joint NOAA/NASA effort. The infrared calibration upgrade from the FIDUCEO project could unfortunately not be finalized and only a preliminary but incomplete (i.e., only covering about 20 years of the record) version became available in August 2018 for testing. The preliminary FIDUCEO FCDR has recently been evaluated in the CM SAF project (CM SAF, 2022). It was concluded that the FCDR quality was acceptable for the latest AVHRR/3 generation of the sensor but that large problems were found related to the harmonization of results from the previous AVHRR/2 generation.

Continued coordination of AVHRR FCDR developments beyond SCM-5 is needed to ensure the success of future reprocessing events based on the full AVHRR data record. This concerns monitoring and correction of radiances from current and future satellites still equipped with the AVHRR sensor (mainly the NOAA-19 satellite and the Metop satellites) and complementing and extending the FIDUCEO infrared calibration work for the earliest satellites (not included in FIDUCEO coverage). A first step in this direction has been taken by EUMETSAT by forming a joint EUMETSAT/CM SAF project (named the AVHRR GAC FDR project) to build upon previous experiences of the work in CM SAF, ESA Cloud_cci and FIDUCEO for improving the AVHRR GAC data record. The outcome of this project will be an official release of the AVHRR GAC FDR which also will be the basis for the next edition of CLARA (CLARA-A3). This FDR will be released in 2023, almost simultaneously with the CLARA-A3 release. A continuation of the AVHRR GAC FDR project is foreseen by EUMETSAT and the goal is to try to release a full FCDR version by 2027.

3.2.1.1.2 Adaptations to AVHRR/1.

Adding AVHRR/1 data to the AVHRR record processed by CLARA-A3 or similar CDRs means that most retrieval methods, previously used to process AVHRR/2 and AVHRR/3 data, have to be updated to cope with an AVHRR sensor with reduced capability (i.e., not including a split-window channel at 12 µm). Such adaptations have now been completed in the CM SAF project in the preparation of the CLARA-A3 CDRImportant here is to continue and finalize the stricter AVHRR FCDR work that was started within the framework of the FIDUCEO (Fidelity and uncertainty in climate data records from Earth Observations) project. This concerns monitoring and correction of radiances from current and future satellites still equipped with the AVHRR sensor (mainly the NOAA-19 satellite and the Metop satellites) and complementing and extending the FIDUCEO infrared calibration work for the earliest satellites (not included in FIDUCEO coverage). The continued work with the AVHRR FCDR is carried out by EUMETSAT and the goal is to try to release a full FCDR version by 2027. It means that it will not be available for CLARA-A3.5 but for its tentative successor (CLARA-A4).

3.2.1.1.

...

2 Adaptations to AVHRR-heritage channels on post-AVHRR sensors VIIRS and METimage

A challenge for future CDRs (beyond CLARA-A3.5 and beyond) will be the inclusion of radiances from AVHRR-heritage channels from VIIRS and METimage. This requires that appropriate measures are taken to remove effects of differences in spectral response functions between original AVHRR channels and the AVHRR-heritage channels on the new sensors. The largest differences here are found for AVHRR channel 2 with central wavelength at 0.9 µm but with a very wide spectral response (see Table 3-1) compared to corresponding channels on the new sensors. However, data from this channel is only used marginally by the CLARA-A2.1 A3 cloud masking method (affecting CFC) and not at all for the other cloud products. Consequently, adaptations and corrections here appear not to be crucial for securing the quality of the CLARA products in the future. A The first step towards the additional use of non-AVHRR sensors in the CLARA dataset here will be taken shortly to form the CLARA-A3.5 data record where AVHRR-heritage channels in VIIRS will be used to improve the coverage of observations from the afternoon orbit for the period 2012-2024. CLARA-A3.5 is planned for release in 2026. The notation 3.5 indicates that this is not a completely new CLARA edition but, at the same time, it is not just an extension since it involves some development work to define adapt methods to using the AVHRR-heritage channels from VIIRS.

Another necessary adaptation concerns the improved horizontal resolution of the original dataset from VIIRS and METimage sensors. Both sensors provide finer resolutions (better than 1 km) than the AVHRR instrument. In addition, the VIIRS instrument is using a quite advanced across-track scanning mechanism to maintain a high horizontal resolution over the full swath width. In conclusion, efforts are required to resample VIIRS and METimage data to be compatible with the original 5 km AVHRR GAC dataset. Responsible agencies (NOAA and EUMETSAT) have to be aware of the need for such a resampled radiance dataset in order to allow a proper extension of the AVHRR-based CDRs forward in time. To extend the CDR with original VIIRS and METimage data with native spectral and spatial resolution would risk creating unwanted discontinuities in the results when shifting from AVHRR to AVHRR-heritage sensors.

Initial efforts of After some initial efforts made by the ISCCP team of NOAA/NCEI has led to the definition of , a global reduced-resolution version of the VIIRS dataset with all 16 M-Bands (see Table 3-3) and with a horizontal resolution similar to GAC have been compiled. This dataset is denoted VGAC (VIIRS data resampled to AVHRR GAC resolution, Knapp et al., 2019). A commitment to produce a complete VGAC dataset (covering historic, present and future VIIRS data) for ISCCP and for other purposes has been announced by NOAA/NCEI (Andy Heidinger, NOAA, personal communication)NOAA, personal communication). However, EUMETSAT has also announced that they will also contribute in the further development and production of VGAC data, as well as developing a similar reduced resolution dataset for the METimage sensor. The VGAC dataset will be used for the previously mentioned CLARA-A3.5 data record.

...

As with the CLARA CDRs, the stability and quality of the input data is the key parameter which influences the reliability of the Cloud_cci v3 CDRs. The current AATSR TCDR is based on version 3 of the “AATSR multimission archive” maintained by CEDA and the UK National Earth Observation Data Centre (NEODC). This record incorporates the latest calibration corrections (including long-term drift corrections from vicarious calibration) and represents the most consistent and accurate record of radiances from the (A)ATSR record. A future update to this record would make a reprocessing of the Cloud_cci TCDR possible.

In  In the case of the SLSTR ICDR, the status of the level 1 radiances is considerably less stable. As of yet, there is no consistently processed version of the SLSTR radiance record, as the level 1 processing has been under development while the data has been in production. Thus, data from early in the SLSTR record has considerably worse calibration and geolocation than more recent data.

...

m channel, which, due to its location in a water-vapour absorption feature, is particularly sensitive to the presence of high-altitude clouds. Utilizing Utilising this channel in the retrieval scheme itself is unlikely to be beneficial, as accurate knowledge of the water vapour profile is needed to accurately model the radiances. However, the use of this channel in prior cloud-detection and characterization has been studied under the Cloud CCI+ project (which is a follow-on project to the original ESA-CLOUD-CCI project) and an assessment of its impact on the quality of ORAC cloud retrievals is underway.

...

Anchor
section3_3 section3_3

3.3 Methods for estimating uncertainties

3.3.1 Cloud Properties TCDR AVHRR

...

CLARA v3.0 + ICDR v3.x

In the following sub-sections, the uncertainty estimations are described for the different cloud products of the CLARA-A3 TCDR

In the following sub-sections, the uncertainty estimations are described product by product. However, generally CLARA-A3 uncertainty estimates will be expanded by considering uncertainty in ancillary data used in the retrieval process, incl. surface albedo, emissivity and temperature and atmospheric (trace) gas amounts. Furthermore, the aggregation to Level 3 will be refined according to methods described in Stengel et al. (2017).

3.3.1.1 Cloud Fractional Cover (CFC)

The current CLARA-A2.1 CFC products are not associated with any uncertainty estimates. Thus, uncertainty information is only available as results achieved by associated validation activities. Some quality flagging is applied in the original PPS cloud masking product but this information is not propagated or compiled for the Level 3 products.

For CLARA-A3, Bayesian cloud masking will be used which enables estimation of the uncertainty of cloud masking based on the used spectral information. However, this uncertainty will not consider uncertainty in input data (AVHRR radiances or auxiliary data). Further work is needed here (e.g., Monte Carlo simulations) for potential CLARA editions beyond CLARA-A3.

3.3.1.

...

1 Cloud

...

Fractional Cover (

...

CFC)

CFC uncertainties can be interpreted directly from the CMAPROB product which is provided together with the binary cloud mask. Maximum uncertainty is found at the 50% cloud probability level. For the level-3 product, a simple estimation based on the averaging of the probability distance from the 50% threshold for clear and cloudy pixels is provided.

3.3.1.2 Cloud Top Pressure (CTP)

For estimating the CTP uncertainty, the absolute CTP difference from the 16th and the 84th percentile is provided. These CTP 1-sigma uncertainties from Level-2b files are then propagated into level-3 products, following Stengel et al. (2017)

The current CLARA-A2.1 CTP product is not associated with any uncertainty estimates. Thus, uncertainty information is only provided as results achieved by associated validation activities. One aspect leading to additional uncertainty and an inconsistency with the CFC product is the fact that not all pixels declared cloudy in the basic PPS cloud mask product will be given a valid CTP result. This concerns very thin high clouds and some fractional (sub-pixel) clouds at all altitudes. Pixels with lacking CTP results amount to about 2-3 % of all pixels declared cloudy.

For CLARA-A3 limited uncertainty information (i.e., percentile distributions) will be provided in original Level 2 products and this can be utilized to estimate the uncertainty of Level 3 products. Also, all cloudy pixels will be associated with a valid CTP result in CLARA-A3.

3.3.1.3 Liquid Water Path (LWP)

...

Estimated uncertainties in reflectance measurements and various input variables (e.g., surface albedo, total ozone column) are propagated to yield uncertainty estimates in retrieved COT and CER parameters. These are, in turn, propagated to uncertainty estimates in LWP and IWP. Further details are given in NWC SAF (2021). Note, however, that uncertainties do not include deviations from the assumption of horizontal and vertical homogeneity of the clouds as a source of error. Error estimates are propagated into level-3 products, following Stengel et al. (2017). Uncertainties are aggregated to Level 3 by linear averaging.

3.3.1.4 Ice Water Path (IWP)

Uncertainty estimates for IWP in CLARA-A2.1 and CLARA-A3 are /will be derived in the same way as for LWP.

...

C3S products from Cloud_cci data and the SLSTR ICDR follow the methodology described by Stengel et al. (2018), with four separate uncertainty parameters being included for each mean parameter, x:

\sigma_{std}^{2} = \frac{1}{N}\sum\limits_{i=1}^{N}(x_{i} - \langle x \rangle)^{2} \ \ (Eq 2)

\sigma_i

\langle \sigma \rangle

\langle \sigma \rangle = \frac{1}{N}\sum\limits_{i=1}^{N}(\sigma) \ \ (Eq 3)

\sigma_{prop}

\sigma_{prop}^{2} = \frac{1}{N} \langle \sigma_{i}^{2} \rangle \ \ (Eq 4)

\sigma_{corr}

\sigma_{corr}^{2} = \sigma_{std}^{2} - (1-c) \sqrt{\langle \sigma_{i}^{2} \rangle - \langle \sigma_{i} \rangle^{2}} \ \ (Eq 5)

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section3_4 section3_4

3.4 Opportunities to improve quality and fitness-for-purpose of the CDRs

3.4.1 Cloud Properties TCDR AVHRR CLARA

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v3.0 + ICDR

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v3.x

Sections 3.2 and 3.3 have already indicated some of the improvements of CLARA cloud CDRs which will be included in the upcoming introduced in CLARA-A3 CDR. The most prominent updates here are the introduction of Bayesian (probabilistic) cloud masking (Karlsson et al., 2015, 2020) and CTP retrievals based on artificial neural networks (Håkansson et al., 2018). These changes are largely made possible by the access to high-quality cloud information from the CALIPSO-CALIOP sensor since 2006 (Winker et al., 2009).

The high potential of using artificial neural network methods for improving the cloud product quality is not only demonstrated for the planned CLARA-A3 CTP product but also for products like cloud masking and cloud phase retrievals (Stengel et al., 2017, 2020 and Sus et al., 2018). Also, this development is a consequence of the availability of the CALIPSO-CALIOP observations. Continuing development efforts here (e.g. within the ESA CCI+ project) efforts here will most likely impact future compilation of AVHRR-based CDRs. A central question here will be to find out if ANN-based methods are able to match physical retrieval methods (e.g., as described by McGarragh et al., 2018).

An interesting extension of the an AVHRR FCDR was proposed implemented by NOAA in the SCM-5 project. NOAA proposed to add selected information from the PATMOS-x v6 CDR, released in 2023 (Foster et al., 2023). Here, information was added to the AVHRR FCDR from High-resolution Infrared Radiation Sounder (HIRS) instrument to the AVHRR radiance dataset in order to improve in particular the cloud top pressure retrieval. HIRS is another instrument which has been flying on the same satellite platforms as the AVHRR instrument historically but for the purpose of providing coarse resolution vertical sounding information. Current AVHRR-based cloud top retrieval methods have been shown to largely underestimate cloud top heights of thin cirrus clouds. At the same time, it is well-known that the HIRS measurements are very sensitive to the existence of thin Cirrus clouds. Thus, to merge selected HIRS information (e.g., channels near 7 µm and 13 µm) with AVHRR data would potentially improve both thin Cirrus detection and the retrieval of cloud top heights for thin Cirrus clouds. A fusion method applicable for sensors like HIRS and AVHRR has been proposed by Weisz et al. (2017) and the method will be applied was used in the next PATMOS-X v6 dataset (as mentioned by Heidinger et al. , 2014). However, the proposed fusion technique is not the only possible approach here so further research is needed to identify the most efficient method to merge information from the two sensors. In addition, just as for AVHRR-heritage applications, methods must be developed to cope with the transfer from the HIRS sounding instrument to corresponding HIRS-heritage instruments on the JPSS and EPS-SG satellites to enable a consistent use over the full time period of possible AVHRR and AVHRR-heritage measurements.

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section3_5 section3_5

3.5 Scientific Research needs

3.5.1 Cloud Properties TCDR AVHRR CLARA

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v3.0 + ICDR

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v3.x

Regarding the development of processing algorithms, further improvement in this field will be linked to the availability of high-quality reference measurements from active sensors, like the CALIPSO-CALIOP sensor. As the The access to such reference data is not guaranteed (e.g., by further delay or problems with reference data from CALIPSO ceased in summer 2022 and similar data from the upcoming EarthCARE mission ), development may be slowed down. This is especially is not yet available (EarthCARE launch currently expect in summer 2024). This data gap could be serious for sensors like METimage which most likely will not have any has no overlap with CALIPSO-CALIOP data (in contrast to VIIRS on Suomi-NPP and NOAA-20, where we have more than 10 years of useful CALIPSO data). However, since also the launch of the satellites in EPS-SG with METimage onboard is delayed (expected in 2025) this problem is less serious (provided that the EarthCARE launch is successful and on schedule).

New algorithm developments for retrieval of cloud parameters are likely to be based on machine learning approaches where also uncertainty measures will be added If the reference data availability is guaranteed, the next question is whether Bayesian cloud masking methods and/or physical retrieval methods including proper error propagation modelling (e.g., McGarragh et al., 2018) will be able to cope with the results provided by ANN-based methods. The latter have shown remarkable progress in generating various high-quality cloud products but they still lack appropriate methods for estimating the uncertainty of the products. However, promising attempts (e.g., as demonstrated by Pfreundschuh et al., 2018) to address this problem are under consideration. Intensive research in this field is expected to continueal., 2018).

Finally, a limiting factor for all climate monitoring activities from space is the very high requirement on radiometric calibration in order to be able to distinguish a climate trend from trends caused by instrumental inhomogeneities, errors or noise. Further work on achieving high quality radiance references is therefore needed in addition to securing the availability of active cloud sensing instruments in space.

3.5.2 2 Cloud Properties TCDR ESA AATSR v3.0 + ICDR ESA SLSTR v3.x

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3.6 Opportunities from exploiting the Sentinels and any other relevant satellite

3.6.1 Cloud Properties TCDR AVHRR CLARA

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v3.0 + ICDR

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v3.x

The Sentinel-3 mission carries the SLSTR and OLCI instruments which both have several AVHRR-heritage channels. In principle, data from these two instruments could be used to generate the same kind of cloud products as for AVHRR. For example, a new version the latest versions of the NWC SAF PPS cloud processing package (to be used for compiling the CLARA-A3 TCDR) is already capable of processing SLSTR data. This information could thus be added to the other products generated from pure AVHRR-data and from the previously mentioned AVHRR-heritage sensors.

However, since Sentinel-3 is orbiting in a morning orbit (daytime equator crossing time at 10 am) the observations unfortunately tend to duplicate observations from other satellites (e.g., Metop satellites and EPS-SG satellites). Thus, the added value could be questioned (unless some of the other morning satellites fail). This question could be broadened to also include the information from other polar orbiting satellites, like Aqua and Terra with MODIS data and the Chinese polar orbiting FY-3D+ satellites with data from the MERSI-2 imager (the latter now also handled by the latest NWC SAF PPS package). Again, it is clear that the satellite orbits used for these satellites are practically identical to the morning and afternoon satellites of the NOAA and Metop series of satellites and their successors. Thus, the added value from these observations will probably only be marginal.

More important seems to be to build the dataset consistently on observations from one morning satellite and one afternoon satellite (i.e., the prime satellites as demonstrated by by Stengel et al., 2017). A CDR based on only these prime satellites appears to be the most useful dataset from a climate monitoring perspective. Other satellites outside the primary AVHRR and AVHRR-heritage family could mainly serve as an important backup source of information if some of the prime satellites fail.

3.6.2 2 Cloud Properties TCDR ESA AATSR v3.0 + ICDR ESA SLSTR v3.1.1+v4.0

The ESA SLSTR v3.x ICDR directly exploits data from the Sentinel-3 platform. There have been examples shown of utilizing Sentinel-3 OLCI-like measurements (mainly using MERIS on ENVISAT) for cloud retrieval in conjunction with (A)ATSR or SLSTR (Carbajal Henken et al. 2014), but difficulties in cross-calibration and co-registration of the different instruments have meant these products have not shown improved performance over the (A)ATSR/SLSTR only algorithms. The availability of a well co-located and calibrated joint SLSTR-OLCI L1 product, could resurrect this approach to further improving cloud products derived from Sentinel-3 (and the preceding ENVISAT).

As discussed in in section 3.4.2, the ORAC retrieval scheme can be, and has been, applied to a wide range of satellite visible-IR imaging radiometers. A particular instrument, of direct relevance to the Sentinel satellite program, is the Flexible Combined Imager (FCI) to fly on MeteoSat Third Generation/Sentinel-4. This instrument is essentially a replacement for the SEVIRI sensors on MSG, with capabilities similar to those provided by Himawari-AHI and GOES-ABI imagers (which ORAC has already been applied to).

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