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General definitions
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.
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LWL: Lake Water Level refers to the water level above the geoid. The product for this service consists of one value of the water level for each lake. The level is in metre.
Scope of the document
This document aims to provide users with the relevant information on requirements and gaps for each of the given products within 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 developed in response to the specified user requirements.
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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 are then specified which generally reflect the Global Climate Observing System (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.
Executive Summary
The Lakes Service provides two Essential Climate Variable (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. It contains estimates of the daily mean surface temperature of the lake, from 1995 to 2020, and has been attempted for about 2000 lakes examined by the European Space Agency (ESA) Climate Change Initiative (CCI) Lakes1 initiative. The LSWT CDR v4.0 product is composed of the brokered ESA CCI Lakes CDR extended within the Copernicus Climate Change Service (C3S) service up to October 2021. The satellites contributing to the time series are: ATSR-2, AATSR and AVHRR MetOp-A, AVHRR MetOp-B, MODIS Terra, SLSTR Sentinel3A and SLSTR Sentinel3B.
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1 See https://climate.esa.int/en/projects/lakes/ (URL resource viewed 10/12/22) 2 See http://cfconventions.org/ (URL resource viewed 10/12/22) |
Product description: Lake ECV Service
Introduction
The Lake ECV Service consists of two products – lake surface water temperature (LSWT) and lake water level (LWL) presented as a Climate Data Record (CDR) which is updated once a year. The CDRs are created as an adaptation of state-of-the-art products specifically for the C3S service where scientific advancements have been investigated and applied. For these versions of the CDRs, the latest ESA CCI Lakes datasets are employed. The C3S extends every year the CDR and if available includes new advancements of the datasets.
The Lake ECV products
Brokered and Generated LSWT CDR v4.0
The LSWT climate data record (CDR) brokered to the C3S is a daily gridded product derived from observations of one or more satellite sensors (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 (for more details see the Product User Guide and Specifications document, RD.4):
- an uncertainty estimate for the LSWT as an estimate of the daily cell-mean value;
- a quality level indicator for the LSWT between 0 (invalid) and 5 (excellent), the recommended quality levels for most applications being 4 and 5;
- the satellite/s and instrument/s from which LSWTs were combined to make the gridded estimate;
- a flag indicating whether a cross-sensor offset adjustment has been applied to the temperatures.
- metadata, including funder and citation instructions;
- the main lake ID for each cell (from ESA CCI Lakes).
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3 For more information on the NetCDF Classic model, see https://www.unidata.ucar.edu/software/netcdf/ (URL resource viewed 10/12/2022) 4 For more information on these CF conventions, see https://cfconventions.org/Data/cf-conventions/cf-conventions-1.10/cf-conventions.pdf (URL resource viewed 10/12/2022) |
LWL V3.1: Brokered and Generated CDR
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.
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5 For more information on the NetCDF Classic model, see https://www.unidata.ucar.edu/software/netcdf/ (URL resource viewed 10/12/2022) 6 For more information on these CF conventions, see https://cfconventions.org/Data/cf-conventions/cf-conventions-1.10/cf-conventions.pdf (URL resource viewed 10/12/2022) |
User requirements
Based upon the precursor ESA Climate Change Initiative project addressing the Lake ECVs survey of user requirements for satellite-derived lake products, this section reports updates on the GCOS requirements which, however, are not yet publicly available yet. This section relies also on statements for the Lake ECV published literature, experience from other CDR projects, and requirements emerging from the definition of the service. The requirements are continuously updated using perspectives that emerge from users of the service and their feedback, and from any user requirements survey that is undertaken in the ESA CCI Lakes project.
The requirements involve different aspects of the product such as the definition, coverage, the spatial and temporal resolution, uncertainty, format and timeliness.
LSWT
Definitional requirements
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 and Lake services. | Experience |
Coverage
Property | Threshold | Target | Comments | Sources |
Spatial coverage | Global | Global | Based on experience in SST and Lake services. | Experience |
Temporal coverage | 10 years | >30 years | Based on experience in SST and Lake services. | Experience |
Spatial and temporal resolution
Property | Threshold | Target | Comments | Sources |
Spatial resolution | 0.1° | between 10m and 5km | Threshold is the resolution most commonly used for SST (sea surface temperature). Target is from GCOS (latest version not yet available). | Experience, GCOS (RD.1) |
Temporal resolution | Daily | between 3 hours and 10 days | Target comes from GCOS. Threshold is based on ARC Lake (http://www.laketemp.net/home_ARCLake/), where daily resolution has aided the usage of the dataset for identifying the day of year of stratification, etc. | GCOS (RD.1), Experience |
Uncertainty requirements
Communication of uncertainty
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. |
Data uncertainties
Property | Threshold | Target | Comments | Sources |
Standard uncertainty of LSWT | 1.0 K | Between 0.1 K and 0.6 K | Threshold value seems a weak requirement for quantifying on-set of stratification (for example); the suggested target value would be more appropriate. | GCOS latest version |
Trend uncertainty (stability) | Between 0.01 and 0.025 K yr-1 | Between 0.01 and 0.025 K yr-1 | Presumed to apply at lake-mean level, although not stated in the GCOS documentation. | GCOS latest version |
Format requirements
Property | Threshold | Target | Comments | Sources |
Format | NetCDF, CF conventions | NetCDF, CF conventions | This is a service requirement. | C3S |
Grid definition | Regular lat/lon | Based on experience in SST/Lake services. | Experience |
Timeliness requirements
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 |
LWL (V3.1)
Definitional requirements
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 Hydroweb7 service. | Experience |
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7 For more information on Hydroweb see https://hydroweb.theia-land.fr/ (URL resource viewed 10/12/2022). |
Coverage
Property | Threshold | Target | Comments | Sources |
Spatial coverage | Global | Global | Based on experience in the Hydroweb service and the list of lakes defined in the Lakes CCI8 Project. | |
Temporal coverage | 10 years | >25 years | Based on experience in the Hydroweb service. | Experience |
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8 For more information on the CCI Lakes project see https://climate.esa.int/en/projects/lakes/ (URL resource viewed 10/12/2022). |
Spatial and temporal resolution
Property | Threshold | Target | Comments | Sources |
Spatial | area: 400 km² | area: 1km² | The spatial resolution refers to the minimum lake area needed to estimate a water level value. | Experience |
Temporal | 1-10 days | Daily | Threshold comes from experience in the Hydroweb service. | GCOS (RD.1), Experience |
Data uncertainties
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. | GCOS (RD.1), Experience, CCI target requirements |
Trend uncertainty (stability) | 1cm/decade | Target comes from GCOS. | GCOS (RD.1) |
Format requirements
Property | Threshold | Target | Comments | Sources |
Format | NetCDF, CF Convention | NetCDF, CF Convention | This is a service requirement. | C3S |
Timeliness requirements
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 |
Analysis of gaps and opportunities
Satellite observational constraints and opportunities
Lake surface water temperature (LSWT)
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:
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In summary, with R&D, there are opportunities that would extend the LSWT CDR to earlier times (1991 globally, mid 1980s for Europe), with characteristics similar to the current resolution and quality. In the current extensions of the record, uncertainty decreases and coverage increases as AVHHR 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.
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Property | Threshold | Target | Currently Achieved | Gap analysis |
Spatial coverage | Global | Global | >2000 target lakes delivering useful timeseries. | Use of a higher resolution sensor such as VIIRS is needed, to increase the success rate for smaller lakes. |
Spatial resolution | 0.1° | between 10m and 5km | 0.05o (gridded) | Production of 0.025° gridding may be possible and useful with the present sensors. |
Temporal resolution | Weekly | between 3 hours and 10 days | Variable because of clouds and change in spatial resolution across satellite swaths. | Effective temporal resolution increases as further MetOp and SLSTR input data streams are exploited within the service. |
Standard uncertainty of LSWT | 1.0 K | Between 0.1 K and 0.6 K | Standard deviation of single-pixel differences to in situ are typically ~0.6 K. | Addition of MetOp-C input data streams reduces uncertainty from averaging of LSWTs over multiple observations. |
Trend uncertainty (stability) | Between 0.01 and 0.025 K yr-1 | Between 0.01 and 0.025 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. |
Lake water level
Table 2: LWL Gap Analysis Summary.
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Property | Threshold | Target | Currently Achieved | Gap analysis |
Spatial coverage | Global | Global | Global coverage (166 Lakes in V3.1) | The number of Lakes being monitored must be increased (ongoing activity). |
Temporal coverage | 10 years | >25 years | almost 30 years for some lakes ( Sept 1992 - Dec 2020) | Target has been reached. |
Spatial resolution | area: 1000km² | area: 1 km² | Lakes area > 100 km² | Threshold reached. New algorithms must be implemented to improve the resolution. New missions/altimeters must be launched to reach target (e.g. SWOT: Surface Water & Ocean Topography). |
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 |
Improvement of retrieval algorithms
Lake surface water temperature
LSWT estimation has three steps:
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All R&D progress in the ESA Lake CCI will ultimately enter the C3S service, via the CCI-generated, and then brokered to C3S, dataset, and validated transition of the updated research code to generate future annual C3S time series extensions.
Lake water level
The current state-of-the-art R&D leading to the V3.1 CDR relies partly on a manual approach to estimate the geographic extraction zone of altimetry measurements. An automated version of this R&D has been 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. This has enabled a threefold increase in the number of lakes monitored between the first (V1.0) and the latest (v3.1) version of the C3S dataset. New lakes will be available in future versions. 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 and the lakes polygons defined in the CCI Lakes project.
Then, the extracted data must be corrected for various propagations (e.g. corrections for ionosphere, wet troposphere, and dry troposphere amongst others) and geophysical corrections (e.g. geoid, pole tide, solid earth tide amongst others) 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 has been improved to cover both simple (cf Figure 1, left panel) and complex (cf Figure 1, right panel) cases.
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These two implementations are performed to improve the number of lakes monitored in the LWL product (see Section 3.1.2). 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.
Improvement of uncertainty estimation
Lake Surface Water Temperature
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:
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The uncertainty estimate for LSWT is mature, and the ongoing refinement 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.
Lake water level
The uncertainty variable distributed in the LWL product, associated to the Water Level variable, is currently estimated as the Median Absolute Deviation of selected water level measurements along-track (at level 2). The median value of the selected measurements at level 2 provides the median water level (level 3) . It estimates the precision of the measurements but not the accuracy part. The improvement of this uncertainty variable depends on achievements in 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:
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10 For more information on Glili-REALM, see https://ipad.fas.usda.gov/cropexplorer/global_reservoir/ (URL resource viewed 10/12/2022). 11 For more information on Hydrolare, see http://hydrolare.net/ (URL resource viewed 10/12/2022). |
Lake ECV components not presently in the service
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, and is not included in this report.
References
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).
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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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