Contributors: Richard Kidd (EODC GmbH), Christian Briese (EODC GmbH), Christopher Merchant (University of Reading), Laura Carrea (University of Reading), Ross Maidment (University of Reading), Beatriz Calmettes (Collecte Localisation Satellites)
Issued by: EODC GmbH/Richard A Kidd
Date: 16/06/2021
Ref: C3S_312b_Lot4_D1.S.1-2020_TRGAD_LK_i1.0.docx
Official reference number service contract: 2018/C3S_312b_Lot4_EODC/SC2
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Level 2 pre-processed (L2P): this is a designation of satellite data processing level. "Level 2" means geophysical variables derived from Level 1 source data on the same grid (typically the satellite swath projection). "Pre-processed" means ancillary data and metadata added following GHRSST Data Specification, adopted in the case of LSWT.
Level 3 /uncollated/collated/super-collated (L3U/L3C/L3S): this is a designation of satellite data processing level. "Level 3" indicates that the satellite data is a geophysical quantity (retrieval) that has been averaged where data are available to a regular grid in time and space. "Uncollated" means L2 data granules remapped to a regular latitude/longitude grid without combining observations from multiple source files. L3U files will typically be "sparse" corresponding to a single satellite orbit. "Collated" means observations from multiple images/orbits from a single instrument combined into a space-time grid. A typical L3C file may contain all the observations from a single instrument in a 24-hour period. "Super-collated" indicates that (for those periods where more than one satellite data stream delivering the geophysical quantity has been available) the data from more than one satellite have been gridded together into a single grid-cell estimate, where relevant.
Target requirement: ideal requirement which would result in a significant improvement for the target application.
Threshold requirement: minimum requirement to be met to ensure data are useful.
This document aims to provide users with the relevant information on requirements and gaps for each of the given products within the Lakes Service which is he part of the Land Hydrology and Cryosphere service. The gaps in this context refer to data availability to enable the ECV products to be produced, or in terms of scientific research required to enable the current ECV products to be evolved to respond to the specified user requirements.
The Lakes Service provides two products: a Lake Surface Water Temperature (LSWT) product, and a Lake Water Level (LWL) product.
Initially an overview of each product is provided, including the required input data and auxiliary products, a definition of the retrieval algorithms and processing algorithms versions; including, where relevant, a comment on the current methodology applied for uncertainty estimation. The target requirements for each product is then specified which generally reflect the GCOS ECV requirements. The result of a gap analysis is provided that identifies the envisaged data availability for the next 10-15 years, the requirement for the further development of the processing algorithms, and the opportunities to take full advantage of current, external, research activities. Finally, where possible, areas of required missing fundamental research are highlighted, and a comment on the impact of future instrument missions is provided.
The Lakes Service provides two ECV products, specifically lake surface water temperature (LSWT) and lake water level (LWL). The LSWT climate data record (CDR) is a daily gridded product derived from observations of one or more satellites and is an estimate of the daily mean surface temperature of the lake, from 1996 to 2016, and has been attempted for the 1000 GloboLakes1 lakes. The LSWT CDR v1.0 product is composed of the brokered GloboLakes CDR extended within the C3S service until October 2018. For LSWT CDR v2.0 the extension started in November 2018 until August 2019. For LSWT CDR v3.0 the extension will start in September 2019 until October 2020. The satellites contributing to the time series are: ATSR-2, AATSR and AVHRR MetOp-A and AVHRR MetOp-B (from January 2017 only) until August 2019. From September 2019 only SLSTR on Sentinel3A and Sentinel3B L1b data will be used.
The LWL CDR, which is both brokered and generated in the Lakes Service, is an estimate of the mean surface height of the lake, wherever at least three valid observations have been made within the intersect between the satellite ground track and a given lake. The LWL product targets 166 lakes worldwide, from 1993 to 2020, with daily to decadal monitoring (in CDR v3.1 with a status date of April 2021). The satellites contributing to the time series are: TOPEX-Poseidon, Jason-1/2/3 and Sentinel-3A. The data format for both LSWT and LWL products are netCDF4 classic, adopting relevant CF conventions.
The reliance of the LSWT product on data from the AVHRR sensor is guaranteed via the MetOp and MetOp SG programmes, thereby guaranteeing data up to 2042. The inclusion of data from VIIRS would have significant impact, but research is needed for its exploitation and none is presently planned or proposed.
The requirements for the Lakes Service products are largely reliant upon the statements from GCOS, published literature and experience from other CDR projects. For LWST, the threshold for user requirements are generally already reached, but more in situ data is required in order to be able to provide reliable assessments of product stability. For LWL, either the target or the threshold target has already been reached.
Further development of the retrieval methodologies is required. For the LSWT product, improvements in pixel classification and in the optimal estimation (OE) retrieval algorithm are required, and adaptation to a 0.025o gridding should be possible and useful if there is genuine user demand. For the LWL product, an automatic version for the geographic extraction zone for altimetry measurements is required along with improvements to the geophysical corrections of the extracted data.
The uncertainty estimation within LSWT has been fully developed within CCI SST activities and is considered to be mature. For LWL the uncertainty variable only estimates the precision of the measurements and not the accuracy, and this will be address in the CCI lakes project once this goes ahead.
In addition to LSWT and LWL, elements of lake surface reflectance, lake area and lake ice cover and thickness are included in the GCOS Lake ECV definition. A review of the opportunity to broker datasets addressing these gap areas is scheduled for early 2020.
Reliance on External Research
Since the C3S programme only supports the implementation, development and operation of the CDR processor, any scientific advances of the C3S products entirely rely on funding provided by external programmes, e.g. CCI+, H-SAF, Horizon2020. Thus, the implementation of new scientific improvements can only be implemented if external funding allows for it. This depends both on the availability of suitable programmes to support the R&D activities and the success of the C3S contractors in winning potential suitable calls.
1 See http://www.globolakes.ac.uk/overview.html (URL resource viewed 21/02/20) |
Section 2 briefly presents the Lake ECV products provided in the service - lake surface water temperature (LSWT) and lake water level (LWL) as background to the remainder of the report.
Section 3 presents known statements of requirements directly relevant to the products in the context of the C3S, in terms of definitional, coverage, resolution, uncertainty, format and timeliness requirements. The C3S team's view and interpretation of these statements of requirement and their relevance to the C3S service is stated.
Section 4 presents an analysis of gaps and opportunities:
The LSWT climate data record (CDR) brokered to the C3S is a daily gridded product derived from observations of one or more satellites (L3S, level-3 super-collated). The reported LSWT is an estimate of the daily mean surface temperature of the lake, wherever at least one valid observation has been made within the spatial grid cell on a given day. The grid is a regular latitude-longitude one at 0.05 degree intervals.
In addition to the cell-mean LSWT data, the product contains:
The data format is netCDF4 classic, adopting relevant CF conventions.
The CDR v1.0 covers:
The CDR v1.0 contains scientifically consistent time series since the same physics-based algorithm has been employed for all the sensors so that the brokered dataset can be used seamlessly with the extended one.
The generated LSWT v4.0 CDR v2.0 extends the CDR v1.0 time series to August 2019. The generated CDR v2.0 is identical in format and scientific methodology to the CDR v1.0 dataset. The CDR v2.0 starts from the day following the last in the CDR v1.0, is scientifically the same as the CDR, and is thus intended to be used seamlessly with it. The CDR v2.0 includes satellite data from AVHRR on MetOp-A and MetOp-B.
The generated LSWT ICDR v2.0 reprocesses the CDR v2.0 time series from October 2018 to August 2019 including Sentinle3A SLSTR data for testing. The generated ICDR v3.0 is identical in format and scientific methodology to the CDR v1.0 and v2.0 dataset.
The generated LSWT CDR v3.0 will extend the time series from September 2019 until October 2020 and it is identical in format and scientific methodology to the CDR v1.0/v2.0 dataset and is thus intended to be used seamlessly with it.
The LWL climate data record (CDR) brokered to the C3S is a timeseries product derived from observations of one or more satellites. The reported LWL is an estimate of the mean surface height of the lake, wherever at least three valid observations have been made within the intersect between the satellite ground track and a given lake.
In addition to the lake-mean LWL data, the timeseries contains:
The data format is netCDF4 classic, adopting relevant CF conventions.
The v3.1 CDR covers the period 1992 to 2020 under identical reprocessing, so there is no brokered/extended distinction in this case. The satellites contributing to the time series are: TOPEX/Poseidon, Jason-1/2/3,Sentinel-3A and Sentinel-3B
There not having been a precursor ESA Climate Change Initiative project addressing the Lake ECVs, the is no substantive survey of user requirements for satellite-derived lake products. Presently, this section relies on statements for the Lake ECV from GCOS, published literature, experience from other CDR projects, and requirements emerging from the definition of the service. The requirements will be updated in future versions using requirements that emerge from users of the service and their feedback, and from any user requirements survey that is undertaken in a future CCI+ project.
Property | Threshold | Target | Comments | Source |
LSWT | Provide | - | Satellites are sensitive to the skin temperature of the water, the sub-skin temperature being typically 0.2 K warmer. | GCOS (RD.1) |
Time base | UTC | - | Based on experience in SST service. | Experience |
Property | Threshold | Target | Comments | Sources |
Spatial coverage | Global | Global | Based on experience in SST service. | Experience |
Temporal coverage | 10 years | >30 years | Based on experience in SST service. | Experience |
Property | Threshold | Target | Comments | Sources |
Spatial resolution | 0.1° | 300 m | Threshold is resolution in the project ARC Lake, which has been used for lake-climate science. Target is from GCOS. | Experience, GCOS (RD.1) |
Temporal resolution | Weekly | Daily | Threshold comes from GCOS. Target is based on ARC Lake, where daily resolution has aided usage for identifying the day of year of stratification, etc. | GCOS (RD.1), Experience |
Property | Threshold | Target | Comments | Sources |
LSWT uncertainty | Provide | - | Provision of uncertainty is recognised as good practice for CDR | |
Quality flag | Provide | - | Use international norms for quality levels for SST, as the closest analogy | GHRSST (RD.3) |
Validate uncertainty | Document | - | Validation of uncertainty is recognised as good practice for CDR |
Property | Threshold | Target | Comments | Sources |
Standard uncertainty of LSWT | 1.0 K | 0.25 K | Threshold value is from GCOS, but seems a weak requirement for quantifying, for example, on-set of stratification; target value would be more appropriate | GCOS (RD.1), Experience |
Trend uncertainty (stability) | 0.01 K yr-1 | 0.01 K yr-1 | Presumed to apply at lake-mean level, although not stated | GCOS (RD.1) |
Property | Threshold | Target | Comments | Sources |
NetCDF, CF conventions | Provide | - | Service requirement | C3S |
Grid definition | Regular lat/lon | - | Based on experience in SST service | Experience |
Property | Threshold | Target | Comments | Sources |
Ongoing timely updates | Annually | Annually | Driver of this timescale is to make an annual state-of-the-climate assessment. Would not apply for lake quality monitoring, which requires a shorter delay with a greater tolerance of uncertainty and instability. | C3S |
Property | Threshold | Target | Comments | Source |
LWL | Provide | - | Satellite RADAR and Doppler altimeters are used for computing lake levels. | GCOS (RD.1) |
Time base | UTC | - | Based on experience in the Hydroweb service. | Experience |
Property | Threshold | Target | Comments | Sources |
Spatial coverage | Global | Global | Based on experience in the Hydroweb service and the list of lakes defined for the first version of the Lakes CCI project. | Experience, User's community |
Temporal coverage | 10 years | >25years | Based on experience in the Hydroweb service. | Experience |
Property | Threshold | Target | Comments | Sources |
Spatial resolution | area: 1000km² | area: 1km² | Threshold comes from experience in the Hydroweb service. Target comes from Copernicus Global Land User Requirements. In the current dataset, several lakes have surfaces lower than 300 km2. | Experience |
Temporal resolution | 1-10 days | Daily | Threshold comes from experience in the Hydroweb service. Target comes from GCOS and Copernicus Global Land User Requirements. This resolution depends on the altimetric missions overpassing the lake. | GCOS (RD.1), Experience |
Property | Threshold | Target | Comments | Sources |
Standard uncertainty of LWL | 15 cm | 3 cm for large lakes, 10 cm for the remainder | Threshold comes from experience in the Hydroweb service. Target comes from GCOS. | GCOS (RD.1), Experience, CCI target requirements |
Trend uncertainty (stability) | - | 1cm/decade | Target comes from GCOS. | GCOS (RD.1) |
Property | Threshold | Target | Comments | Sources |
Format | NetCDF, CF Convention | NetCDF, CF Convention | Service requirement | C3S |
Property | Threshold | Target | Comments | Sources |
Ongoing timely updates | Annually | Annually | Driver of this timescale is to make an annual state-of-the-climate assessment. | C3S |
The LSWT observing system from space consists of ~1 km resolution infra-red imaging radiometers. In particular, the following sensors can be exploited for LSWT retrieval:
Summary: with R&D, there are opportunities that would extend the LSWT CDR to earlier times (1991 globally, mid 1980s for Europe) with something like the current resolution and quality. In the contemporary extensions of the record, uncertainty decreases and coverage increases as MetOp-B, SLSTR A, SLSTR B (and possibly in the future MetOp-C) are brought into the service. To capture more small lakes, a better resolution instrument is required, and VIIRS is a possibility here, although presently no mechanism for the necessary R&D and practical measures can be identified to make the progress needed to take advantage of this opportunity. Against the targets, the gap analysis is as summarised, therefore, in Table 1.
Table 1: LSWT Gap Analysis Summary
Property | Threshold | Target | Currently Achieved | Gap analysis |
Spatial coverage | Global | Global | >900 target lakes delivering useful timeseries | To increase the success rate for smaller lakes, needs to use a higher resolution sensor such as VIIRS |
Spatial resolution | 0.1° | 300 m | 0.05° (gridded) | 0.025° gridding may be possible and useful with the present sensors |
Temporal resolution | Weekly | Daily | Variable because of clouds and change in spatial resolution across satellite swaths. Daily for large lakes under clear skies. | Effective temporal resolution increases as further MetOp and SLSTR input data streams are exploited within the service. |
Standard uncertainty of LSWT | 1.0 K | 0.25 K | SD of single-pixel differences to in situ are typically ~0.6 K | Addition of MetOp-B and SLSTR input data streams reduces uncertainty from averaging of LSWTs over multiple observations |
Trend uncertainty (stability) | 0.01 K yr-1 | 0.01 K yr-1 | Difficult to assess as there are no reference networks of known stability | Need to continue to collect as much in situ data as possible, including retrospectively |
Table 2: LWL Gap Analysis Summary
Property | Threshold | Target | Currently Achieved | Gap analysis |
Spatial coverage | Global | Global | Global coverage (166 Lakes in V3.1) | The number of Lakes monitored must be increased (ongoing activity) |
Temporal coverage | 10 years | >25years | Since Sept 1992 | Target reached |
Spatial resolution | area: 1000km² | area: 1km² | Lakes area > 100km² | Threshold reached; new algorithms must be implemented to improve the resolution. New missions/altimeters must be launched to reach target (e.g. SWOT) |
Temporal resolution | 1-10 days | Daily | 1-10 days | Threshold reached, new historic altimetry missions could be considered to improve the temporal resolution (ERS-1/2, EnviSat, SARAL). New missions/altimeters must be launched to reach target |
Standard uncertainty of LWL | 15 cm | 3 cm for large lakes, 10 cm for the remainder | 10cm for large lakes, 20cm for medium lakes, small lakes not processed | Threshold reached for most lakes in the product. New algorithms must be developed to reach target. New missions/altimeters will help to reach the target (e.g. SWOT) |
Trend uncertainty (stability) | - | 1cm/decade | Not estimated. For comparison, on oceanic surfaces, the trend uncertainty has been estimated up to 5cm/decade locally | - |
Format | NetCDF, CF Convention | NetCDF, CF Convention | NetCDF, CF Convention | Target Reached |
Ongoing timely updates | Annually | Annually | Annually | Target Reached |
LSWT estimation has three steps:
The priorities for improvement in each area are described in the following:
Classification: (1) The day-time classification of a pixel is based on a combination of threshold tests on the visible (VIS), near-IR (NIR), and short-wave-IR (SWIR) channels. This classification is achieved using a fixed of global thresholds on the VIS, NIR and SWIR channels. Since lakes present different optical properties, there are failures to detect water certain cases such as turbid lakes or shallow salty lakes. Lake-specific thresholds may improve this, although it is a significant R&D task to achieve this for ~1000 lakes. (2) The day-time water detection is not applicable at night-time and for the ATSR1 sensor since the VIS channels are not available. To include night-time LSWT observations requires thermal-only water/cloud/fog/ice discrimination and could almost double the density of observations and reduce uncertainties in gridded daily products. Bayesian methods used for SST have been used for lake observations from ATSRs, and this should be considered for future versions.
LSWT retrieval: The optimal estimation (OE) retrieval algorithm will continue to be the retrieval of choice for LSWT, because it is context specific. The main improvement to come will be to switch to ERA-5 as the source of prior information that is used in the radiative transfer model needed for OE. The LSWT records from different sensors are adjusted using overlap periods to be unbiased in the lake mean compared to AVHRR MetOp-A. The better calibrated SLSTRs may be considered as a reference for the future (e.g., for LSWT v5.0).
L3 gridding: Adaptation to a 0.025° gridding should be possible and useful, if there is genuine user demand. This may be addressed as a future evolution of the service after the priority tasks of bringing additional sensors into the data stream are successfully completed.
The context in which R&D to underpin some service evolutions can be pursued is, for LSWT, the ESA Lake CCI project. This project started in 2019. The R&D elements for LSWT are limited by resources to a few weeks' effort on each of the following:
All R&D progress in the ESA Lake CCI will ultimately enter the C3S service via the CCI-generated brokered dataset, and validated transition of the updated research code to generate future annual C3S time series extensions.
The current state-of-the-art R&D that lead to the V3.1 CDR relies partly on a manual approach to estimate the geographic extraction zone of altimetry measurements. An automatic version of this R&D is currently being implemented in the frame of the present project to ramp-up the products and be able to provide water level for a wider network of lakes. New lakes should thus be proposed in the future The method relies on a database of lake delineations and a land/water mask (from Global Surface Water Explorer, Pekel et al. 2016), intersected with the theoretical ground-track of the satellites.
Then, the extracted data must be corrected for various propagations (ionosphere, wet troposphere, dry troposphere…etc) and geophysical corrections (geoid, pole tide, solid earth tide…etc) based on models and with limitations. The geoid model, in particular, does not include small wavelengths of the geoid and this must be estimated based on altimetry data and a posteriori corrected. The algorithm is currently being improved to cover both simple (cf Figure 1 left panel) and complex (cf Figure 1, right panel) cases.
Figure 1: Automatic extraction of altimetry measures over specific lakes. The eft panel shows a simple case of automatic intersection between satellite ground tracks and the polygon defining a lake. The right panel shows a more complex case including some land zones in the target lake that need to be excluded in the processing.
These two implementations are performed to improve the number of lakes monitored in the LWL product (see Section 4.1.2). Additionally, other R&D algorithms should be developed within the CCI-Lakes project and then be implemented for operational use to improve the quality of the product.
L3C uncertainty: A comprehensive approach to estimate the LSWT uncertainty in L3 has been developed within the CCI SST work and it comprises the following components:
The different uncertainties are aggregated; in the products the total uncertainty is provided. The uncertainty can be validated, and the various components can be further refined (parameters better estimated and better validated) over time and understanding of the spatial and temporal scales of the error correlations over lakes can be improved. Alternative methods of representing the uncertainties (i.e. ensembles) can potentially also be considered.
L3S uncertainty: The per-lake inter-satellite bias correction generates an uncertainty which is included in the estimation of the L3S LSWT uncertainty.
The uncertainty estimate for LSWT is mature, and the ongoing evolution should focus on determining appropriate parameters to use for additional sensor data streams and updating such parameters for all sensors if reason to do so emerges.
The uncertainty variable distributed in the LWL product along the Water Level variable is currently estimated based on the Median Absolute Deviation of the consecutive along-track water level measurements before it is averaged. It estimates the precision of the measurements but not the accuracy part. The improvement of this uncertainty variable depends on the success of the CCI lakes project, but no strategy is currently foreseen to improve this variable.
The ongoing offline validation exercise will provide global statistics on the LWL product and a characterisation of the global uncertainty based on:
The GCOS definition (RD.1) of the Lake ECV includes, in addition to the LSWT and LWL, the elements of lake surface reflectance, lake area and lake ice cover and thickness. A review of the opportunity to broker datasets addressing these gap areas is ongoing.
Pekel, J.F, Cottam A., Gorelick N. et al. High resolution mapping of global surface water and its long-term changes. Nature 540, 418-422 (2016).
This document has been produced in the context of the Copernicus Climate Change Service (C3S). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view. |
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