Last modified on Sep 04, 2023 16:39

Contributors: L. Carrea (University of Reading), C.J. Merchant (University of Reading), B. Calmettes (CLS),  N. Taburet (CLS)

Issued by: L. Carrea, C.J. Merchant

Date: 06 Jun 2023 

Official reference number service contract: 2021/C3S2_312a_Lot4_EODC/SC1

Ref: C3S2_312a_Lot4.WP2-FDDP-LK-v1_202212_LSWT_PUGS-v4_i1.1

History of modifications

List of datasets covered by this document

Related documents

Click here to expand the list of related documents (D1-D7))

Reference ID	Document
D1	Carrea, L. et al. (2022) C3S Lakes Service: Target Requirement and Gap Analysis Document. Document ref. C3S2_312a_Lot4.WP3-TRGAD-LK-v1_202204_LK_TR_GA_i1.1
D2	Carrea, L. et al. (2023) C3S Lake Surface Water Temperature version 4.5: System Quality Assurance Document. Document ref. C3S2_312a_Lot4.WP3-SQAD-LK-v1_202301_LSWT_SQAD-v4_i1.1
D3	Carrea, L. et al. (2023) C3S Lake Surface Water Temperature version 4.5: Algorithm Theoretical Basis Document. Document ref. C3S2_312a_Lot4.WP2-FDDP-LK-v1_202212_LSWT_ATBD-v4_i1.1
D4	Carrea, L. et al. (2022) C3S Lake Surface Water Temperature version 4.5: Product Quality Assurance Document. Document ref. C3S2_312a_Lot4.WP1-PDDP-LK-v1_202206_LSWT_PQAD-v4_i1.1
D5	Carrea, L. et al. (2023) C3S Lake Surface Water Temperature version 4.5: Product Quality Assessment Report. Document ref. C3S2_312a_Lot4.WP2-FDDP-LK-v1_202212_LSWT_PQAR-v4_i1.1
D6	Group for High Resolution Seas Surface Temperature (GRHSST) Data Specification (GDS) v2, Casey and Donlon (eds.), 2012,  https://doi.org/10.5281/zenodo.4700466
D7	Climate and Forecast (CF) Conventions and Metadata; http://cfconventions.org

Acronyms

General definitions

L2P – Geophysical variables derived from Level 1 source data on the Level 1 grid (typically the satellite swath projection). Ancillary data and metadata added following Group for High Resolution Seas Surface Temperature (GHRSST) Data Specification.

L3U – Level 3 Un-collated data are 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.

L3C – Level 3 Collated data are observations 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.

L3S – Level 3 Super-collated data are observations from more than one satellite that have been gridded together into a single grid-cell estimate, for those periods where more than one satellite data stream delivering the geophysical quantity has been available.

Brokered dataset – Dataset provided by another institution/initiative and not produced within this service.

Scope of the document

This document is the user guide for the Lake Surface Water Temperature (LSWT) v4.5 product in the Hydrology service of Copernicus Climate Change Service (C3S). This document is applicable to both components of this dataset: the brokered timeseries from the European Space Agency (ESA) Climate Change Initiative (CCI) Lakes project and the C3S extension in time produced within the Copernicus Climate Change Service. The brokered and extended Climate Data Records (CDRs) are intended to be used seamlessly together by users.

The main aim of the document is to enable the users to read and use the data and to aid them in understanding its features and limitations. Details of the data format are provided including: data and flag variables, metadata, and naming conventions. The Appendix A - Specifications contains the specification of the product, based on the statements of the lake Essential Climate Variable (ECV) from Global Climate Observing System (GCOS). The Appendix B - List of lakes, contains the list of the 2024 lakes in the LSWT v4.5.

Note that internally to C3S, the product referred to is contractually "CDR V4.0". In this document, the versioning and product name relevant to users is employed ("LSWT v4.5").

Executive summary

The C3S Lake production system (C3S ECV LK) provides an operational service, generating lake surface water temperature and lake water level climate datasets for a wide variety of users within the climate change community. The present document covers the CDR for lake water temperature product, LSWT v4.5. This product includes a static, brokered timeseries from 1995 through end-2020 produced within the ESA CCI Lakes project [Carrea et al, 2023], and an ongoing extension in time of the record from Jan-2021 onwards, generated within the Hydrology service of C3S, providing an operational annual update to the LSWT record in line with user needs.
The LSWT product has been attempted for the 2024 lakes selected within the ESA CCI Lakes project. For 12 of the 2024 lakes, no LSWT could be retrieved at all. The list of the lakes is available at https://climate.esa.int/en/projects/lakes/data/ and in a browsable table: https://gws-access.jasmin.ac.uk/public/cds_c3s_lakes/CCI_LAKES/CCI_LAKE_LIST_v2/LAKE_LIST_MASK_CCI_v2_UoR_fv1.0.html   accessible at http://www.laketemp.net/home_CCI/LMPolygons.php  

This document describes the key points about the data set generation and product contents and format needed by users intending to apply the CDR. In particular, Chapter 1 contains a description of the  data in the CDR and a brief description of the water surface height estimation process. A detailed description of the algorithm is included in the Algorithm Technical Basis Document (ATBD). Chapter 2 contains a description of the C3S lake surface water temperature product including the description of the data in the NetCDF files and the product characteristics in terms of temporal and spatial coverage and resolution. Chapter 3 recalls the requirements as defined by the GCOS and the results obtained in the C3S Climate Data Record V4.0. The last chapter includes information on the use of the data, with examples of metadata and figures.

The Product User Guide and Specification (PUGS) is a self-contained document which gathers all necessary information to use the Lake Surface Water Temperature products in an efficient and reliable way.

Product Change Log

The following Table 1, provides an overview of the differences between different versions of the product up-to, and including, the current version, with algorithmic versions being provided in Table 2.

Table 1: Changes in the product between versions.

Table 2: Algorithmic versions and temporal coverage of the CDR v4.0

1. Data description

Lake Surface Water Temperature (LSWT) is the surface expression of the thermal structure of lakes and is changing in response to climatic trends. LSWT is a very important variable for climate change studies, water budget analysis (due to its link to evaporation), lake physical and ecological modelling, and numerical weather prediction (NWP).

1.1. The retrieval methodology

Below, the retrieval methodology of this product is shortly summarized. For full details of the basis of the data, refer to the Algorithm Theoretical Basis Document (ATBD) [D3].

The algorithm to derive LSWT products from imagery of visible and infrared radiometers consists of many components which aim to retrieve the LSWT from the observed reflectance and brightness temperature for only-water pixels. The core of the algorithm is the retrieval using Optimal Estimation (OE) of LSWT given the observations and prior simulations. The other components of the algorithm prepare the inputs for the retrieval part, namely simulate the brightness temperatures and classify a pixel as water or non-water. Finally, the observations are gridded in a regular 0.05^o resolution grid and subsequently a cross-sensor adjustment is estimated and applied in order to obtain a harmonized product.

1.1.1. Overview

Preparatory processing: This includes orbit file reading, validity checks, association of auxiliary information to the orbit file being processed (including prior fields from NWP, where relevant), and any pre-processing adjustment to the data themselves.

Classification:  Within this step, valid pixels for LSWT retrieval are identified. Although this step is sometimes referred to as cloud detection, it also involves identifying which image pixels cover only lake water (no coast or islands within the pixel), and exclusion of pixels affected by ice (for which LSWT cannot be obtained). Valid LSWT values are estimated only for pixels that are fully covered by water and free of cloud. The algorithm for the discrimination of water and non-water pixels in the presence of clouds is based on threshold tests on the Visible (VIS), Near-InfraRed (NIR), and Short-Wave-InfraRed (SWIR) channels of the Along Track Scanning Radiometer (ATSR) and Advanced Very-High Resolution Radiometer (AVHRR) instruments. The water detection algorithm is applied only to candidate pixels which have been identified as potential inland water by the water-bodies identifier mask [Carrea et al., 2015], built from the ESA CCI Land Cover Project for the 2024 ESA CCI LAKES lakes [Carrea et al., 2022].

Retrieval of LSWT (geophysical inversion): For pixels classified as water, LSWT is calculated dynamically given prior information with the Optimal Estimation technique [MacCallum and Merchant, 2012, Carrea et al., 2023]. The prior information comprises NWP fields as inputs to a radiative transfer model, whose simulations in comparison to the observations are used in the retrieval. The LSWT is estimated for each (cloud-free) water pixel using joint optimal estimation of surface temperature and Total Column Water Vapour (TCWV), given the simulations and observations. The form of OE used is to return the Maximum A-posteriori Probability (MAP) assuming Gaussian error characteristics. OE also gives an uncertainty estimate for each retrieval. Quality levels are also estimated which reflects the degree of confidence in the validity of the uncertainty estimate (not the magnitude of data uncertainty). We refer the readers to the ATBD [D3] for more detailed description.

Gridding/averaging: The algorithm grids the full resolution imagery (L2P) into a L3U product on a 0.05^o grid.

Daily collation: The output of the previous step (L3U product) is comprised of 14 to 15 orbits each day per sensor. These are collated to produce one file for each 24-hour period, corresponding to day-time observations. The average of the best quality L3U observations from all available sensors is used as LSWT for each cell in the L3S.

Inter-sensor adjustment: To stabilise the record with respect to changes in satellite sensor, an adjustment using overlaps of sensors is made, using the LSWTs from the AVHRR onboard the MetOp A satellite as the (unadjusted) reference. This is valid for all input data except data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the TERRA satellite, in which case fixed adjustments of 0.19 K and 0.11 K has been applied for LSWTs of quality level 4 and 5 respectively for all the lakes. MODIS LSWTs of quality level less than 4 have not been utilised for this dataset [Carrea et al., 2023].

1.1.2. Input data

Input data are shown in Figure 1 and briefly described below.

Figure 1: Input data for the ESA CCI LAKES brokered CDR (blue) and for the C3S extension CDR (red).

• ATSR L1b:

For the ESA CCI LAKES LSWT v4.5, L1b data from the following sensors have been processed to produce LSWT:

• • ATSR2 on the ESR-2 platform from 1995 to 2003
  • Advanced Along Track Scanning Radiometer (AATSR) on the Envisat platform from 2002 to 2012

• MODIS L1b:

For the ESA CCI LAKES LSWT v4.5, L1b data from the following sensors have been processed to produce LSWT:

• • MODIS on the Terra platform from 2000 to 2020

For the LSWT v4.5 C3S extension, L1b data from the following sensors have been processed to produce LSWT:

• • MODIS on the Terra platform from 2021 to 2022

• AVHRR L1b:

For the ESA CCI LAKES LSWT v4.5, L1b data from the following sensors have been processed to produce LSWT:

• • AVHRR on the MetOp A platform from 2007 to 2019
  • AVHRR on the MetOp B platform from 2012 to 2019

• SLSTR L1b:

For the ESA CCI LAKES LSWT v4.5, L1b data from the following sensors have been processed to produce LSWT:

• • Sea and Land Surface Temperature Radiometer (SLSTR) on the Sentinel-3A platform from 2016 to 2020
  • SLSTR on the Sentinel-3B platform for the year 2020

For the LSWT v4.5 C3S extension, L1b data from the following sensors have been processed to produce LSWT

• • SLSTR on the Sentinel-3A and Sentinel-3B platform from 2021 to Sep 2022

In addition to the L1b data, the auxiliary data inputs are NWP data and a lake mask as described in the Overview and in [Carrea et al., 2023].

1.2. Limitations of the product

In the following, the known limitations of the LSWT v4.5 product are listed.

The water/no-water classification algorithm relies on threshold tests, which ideally would be tuned to individual lakes (since lakes may have different reflectivities). Presently, the water detection algorithm uses one generic set of thresholds for all the lakes. For any classification scheme, some water pixels may have not been detected as water and some non-water pixels may have been included in the set of pixel where the retrieval has been applied. The classification scheme is fuzzy-like: the confidence of the water detection is captured in a water detection score which is used (together with other parameters) to set the value of the LSWT quality levels.

The LSWT quality levels range from 2 (suspect/marginal quality) to 5 (best quality). For most applications, we recommend use of quality levels 5 only, or 4 and 5. However, LSWT with quality levels = 2 and 3 are present in the product, and users can assess their usefulness for their own application.

The emissivity assumed in the LSWT retrieval is always set to that of fresh water, and for highly saline lakes, this may introduce some bias (whose magnitude is yet to be assessed, but is likely to be relatively small).

The retrieved LSWT reflects the skin temperature of the lake (the radiating layer of surface water), and a cool offset of ~ 0.2 K should be expected relative to sub-surface water temperature measurements.

The temporal density of observations of any particular quality varies greatly between lakes. Lakes that are narrow (only a couple of kilometres across) generally obtain few water-only pixels with these sensors (whose best resolution is 1 km), even if the lake is extensive and its overall area is large. Some lakes that are targeted in the products, but whose geometry is unfavourable, may have few or no high quality LSWTs associated with them. Some targeted lakes (such as lake 799, i.e., the Hawizeh Marshes in Iran) do not seem to contain pixels of pure water, at least since 1995. Furthermore, prior to the availability of global MODIS (Terra) observations (year 2000), the temporal density of observations is generally lower because of the narrower swath of the ATSR series instruments.

2. Product description

The product provides an estimation of lake surface water temperature at the time when a given satellite overpasses the lake and no clouds are present. It also contains the uncertainty and the quality level associated with this measurement and the lake mask with the lake identifiers as listed in Appendix B [Carrea et al, 2022]. The product also contains the metadata necessary for the correct use of the data.

2.1. Product content

The product contains all the descriptive metadata in the global attributes of the netCDF file (Table 3):

Table 3: Metadata (global attribute) included in the product files

The data are global per-day files in netCDF-4 format and are guided by the standard specification defined by the Group for High Resolution Sea Surface Temperature (GHRSST) [D6]. The file names have the following format:

<Date><Time>-<RDAC>-<Level>-LSWT-<Dataset>-fv01.0.nc

where <Date> is in the form YYYYMMDD; <Time> is HHMMSS; <RDAC> indicates which project generated the dataset (either ESACCI_LAKES or C3S); <Level> is the processing level; <Dataset> indicates the scientific version number, which is v4.5. Finally, fv01.0 indicates the file version. An example of filename is:

20100101120000-ESACCI_LAKES-L3S-LSWT-v4.5-fv01.0.nc

A summary of the key data fields within the files is given below.

Table 4:  Variables in the Lake Surface Water Temperature product

The level 3 data are provided as global per-day files, on a 0.05° regular latitude-longitude grid and hence the dimension of the data fields is 7200 in longitude and 3600 in latitude. The fields also have a time dimension, which always has a length of one.

2.2. Product characteristics

2.2.1. Projection and grid information

Longitude and latitude values are expressed with respect to the WGS84 ellipsoid.

2.2.2. Spatial information

The level 3 data are provided on a 0.05° regular latitude-longitude grid and hence the dimension of the data fields is 7200 in longitude and 3600 in latitude. LSWT retrieval has been attempted for the 2024 lakes selected in the ESA CCI LAKES project and only for 12 lakes LSWT could not be retrieved at all.

2.2.3. Temporal information

The fields have a time dimension, which always has a length of one.

Lake Surface Water Temperature product consists of daily files. Note these do not (and are not expected to) contain spatially complete temperatures for all the targeted lakes each day, because of limitations of satellite swaths and obscuring cloud cover.

3. Target requirements

The target requirements and the gap with the current product characteristics are described in the Target Requirement and Gap Analysis Document [D1]. Table 5 summarizes the characteristics of the C3S LSWT product and their contrast with target requirements based on GCOS. See the Product Quality Assessment Report (PQAR) [D5] for an assessment of product quality.

Table 5 : Target and Threshold user requirements compared to the characteristics of the LSWT v4.5 product.

4. Data usage information

4.1. File naming convention

The data files are in netCDF-4 classic format and are adapted for C3S from the ESA CCI LAKES LSWT product. The file names have the format:

<Date><Time>-<RDAC>-<Level>-LSWT-<Dataset>-fv01.0.nc

Note:

• fv01.0 refers to the file version.

Table 6: Filenaming convention components

4.1.1. Date

The identifying date for this file, using the ISO8601 basic format: YYYYMMDD.

4.1.2. Time

The identifying time for this file in Universal Time Coordinate (UTC), using the ISO8601 basic format: HHMMSS. The time used depends on the processing level of the dataset:

L3S: centre time of collation window (120000 for daily files)

4.1.3. RDAC

GHRSST Regional Data Assembly Centre (RDAC) where the dataset was generated. Two codes are used for C3S products:

C3S: Copernicus Climate Change Service

ESACCI_LAKES: European Space Agency Climate Change Initiative LAKES

4.1.4. Level

The GHRSST processing level for this product will be L3S.

4.1.5. Dataset

Indicates the scientific version number of the product. The current string in use is:

v4.5

4.2. Data format

The data files are in netCDF-4 format and are CF-compliant [D7], following the ESA CCI LAKES data format.

All L3S are on a global regular latitude/longitude grid.

4.2.1. netCDF Variable attributes

Variables in the netCDF files will include the standard metadata attributes listed in Table 7. These are recognised by most tools and utilities for working with netCDF files.

Table 7: Standard variable attributes.

4.2.2. Coordinate grid

The coordinate variables are listed in Table 8 and discussed in the following sections.

Table 8: Coordinate variables

Time coordinate

All LSWT files include time as a dimension and coordinate variable to represent the reference time of the LSWT data array. The reference time used follows GHRSST Data Specification (GDS) [D6]:

L3S: centre time of collation window (midday for daily files)

Regular latitude/longitude grid (L3S)

Level 3 files are stored on a global regular latitude/longitude grid and variables have the following dimensions:

time: UNLIMITED (1)

lat:Number of latitude points (3600)

lon:Number of longitude points (7200)

The resolution used for the products is 0.05° hence the full size of the arrays is 7200x3600. The time dimension is specified as unlimited, allowing standard netCDF tools to easily concatenate and manipulate multiple files, but each L3 file will be distributed with a single time slice (corresponding to a day).

4.2.3. LSWT Data Variable

The data files contain one LSWT variable, the primary satellite measurement which is the temperature of the skin at the time the satellite observes it.

Table 9: LSWT data variable

4.2.4. Quality indicator

Each pixel also has an associated quality_level which indicates the general quality of that pixel – higher values being better. Quantitative analyses should use the higher quality levels (4 or 5). Quality levels 2 and 3 may be useful for qualitative analyses, but the pixels have an increased chance of being cloud contaminated.

Table 10: Quality indicator

4.2.5. Auxiliary variables and uncertainties

There are auxiliary variables and the total LSWT uncertainty listed in Table 11.

Table 11: Auxiliary variables and uncertainty

4.3. Product contents

Examples of the data contained in one L3S product are shown for lake Vattern in Sweden (lakeid=96). Figure 2 shows the LSWT on the 15-Sep-2022, Figure 3 shows the quality levels, Figure 4 shows the LSWT uncertainty and Figure 5 shows the instruments used to create the product. In each of the plots the lake mask [Carrea et al, 2022] is plotted in white to indicate which are the pixels where the LSWT has been attempted. These pixels define the lake extent.

Figure 2: LSWT for lake Vattern in Sweden on the 15-Sep-2022.

Figure 3: Quality levels for lake Vattern in Sweden on the 15-Sep-2022.

Figure 4: LSWT uncertainty for lake Vattern in Sweden on the 15-Sep-2022.

Figure 5: Instruments used for each pixel to create the C3S product for lake Vattern in Sweden on the 15-Sep-2022.

Although the uncertainty is quite low in coastal areas, not all the pixels have high quality levels. Around coastal areas where the distance to land is lower than 1.5km, lower quality levels can be found in general. In Figure 4, an area of higher uncertainty can be seen with high uncertainty levels with values close to 1 while the LSWT quality level in the same area is 5 (best quality level). This demonstrates that the two variables have to be used together for a better understanding of the data. The uncertainty expresses the interval within which the result can be found and the quality level expresses the confidence in the estimate.

An example of the content of one of the file (header and variables) is reported in Table 12.

. 

Table 12: Header and variable metadata for the file for the 01-Jun-1995.

Example file structure (ncdump output):
netcdf \19950601-ESACCI_LAKES-L3S-LSWT-CDR-4.5-fv01.0.nc {
dimensions:
lat = 3600 ;
lon = 7200 ;
time = UNLIMITED ; // (1 currently)
variables: float lat(lat) ;
		lat:long_name = "latitude" ;
		lat:standard_name = "latitude" ;
		lat:units = "degrees_north" ;
		lat:valid_min = -90.f ;
		lat:valid_max = 90.f ;
		lat:axis = "Y" ;
		lat:reference_datum = "geographical coordinates, WGS84 projection" ;
	float lon(lon) ;
		lon:long_name = "longitude" ;
		lon:standard_name = "longitude" ;
		lon:units = "degrees_east" ;
		lon:valid_min = -180.f ;
		lon:valid_max = 180.f ;
		lon:axis = "X" ;
		lon:reference_datum = "geographical coordinates, WGS84 projection" ;
	int time(time) ;
		time:long_name = "reference time of the lswt file" ;
		time:standard_name = "time" ;
		time:units = "seconds since 1981-01-01 00:00:00" ;
		time:calendar = "gregorian" ;short lake_surface_water_temperature(time, lat, lon) ; short lake_surface_water_temperature(time, lat, lon) ;
		lake_surface_water_temperature:_FillValue = -32768s ;
		lake_surface_water_temperature:units = "kelvin" ;
		lake_surface_water_temperature:scale_factor = 0.01f ;
		lake_surface_water_temperature:add_offset = 273.15f ;
		lake_surface_water_temperature:long_name = "lake surface skin water temperature" ;
		lake_surface_water_temperature:valid_min = -200s ;
		lake_surface_water_temperature:valid_max = 5000s ;
		lake_surface_water_temperature:comment = "The observations from different instruments have been combined." ;
		lake_surface_water_temperature:ancillary_variables = "lswt_uncertainty lswt_quality_level lswt_obs_instr lswt_flag_bias_correction" ;
	short lswt_uncertainty(time, lat, lon) ;
		lswt_uncertainty:_FillValue = -32768s ;
		lswt_uncertainty:units = "kelvin" ;
		lswt_uncertainty:long_name = "lake surface water temperature total uncertainty" ;
		lswt_uncertainty:scale_factor = 0.001f ;
		lswt_uncertainty:add_offset = 0.f ;
		lswt_uncertainty:valid_min = 0s ;
		lswt_uncertainty:valid_max = 10000s ;
		lswt_uncertainty:comment = "Total uncertainty was computed with LSWT uncertainties from the Optimal Estimation and bias correction uncertainty." ;
	byte lswt_quality_level(time, lat, lon) ;
		lswt_quality_level:_FillValue = 0b ;
		lswt_quality_level:flag_meanings = "bad_data worst_quality low_quality acceptable_quality best_quality" ;
		lswt_quality_level:flag_masks = 1b, 2b, 3b, 4b, 5b ;
		lswt_quality_level:long_name = "lake surface water temperature quality levels" ;
		lswt_quality_level:valid_min = 1b ;
		lswt_quality_level:valid_max = 5b ;
		lswt_quality_level:comment = "These are overall quality indicators and they are important to properly use the data." ;
	byte lswt_obs_instr(time, lat, lon) ;
		lswt_obs_instr:_FillValue = 0b ;
		lswt_obs_instr:long_name = "lake surface water temperature observation instruments" ;
		lswt_obs_instr:flag_meanings = "ATSR2 AATSR MODIS_T AVHRRA AVHRRB SLSTRA SLSTRB" ;
		lswt_obs_instr:flag_masks = 1b, 2b, 4b, 8b, 16b, 32b, 64b ;
		lswt_obs_instr:comment = "If the bit is set to 1 an observation from the correspondent instrument has been used to generate the LSWT." ;
	byte lswt_flag_bias_correction(time, lat, lon) ;
		lswt_flag_bias_correction:_FillValue = 0b ;
		lswt_flag_bias_correction:long_name = "lake surface water temperature bias correction flag" ;
		lswt_flag_bias_correction:flag_meanings = "ATSR2 AATSR ATSR2-AATSR MODIS_T MODIS_T-ATSR2 MODIS_T-AATSR MODIS_T-ATSR2-AATSR" ;
		lswt_flag_bias_correction:flag_masks = 1b, 2b, 3b, 4b, 5b, 6b, 7b ;
		lswt_flag_bias_correction:comment = "The reference instrument for the bias correction was the AVHRRMTA, consequently no bias correction has been applied to observations from the AVHRR instruments. For ATSR2 and AATSR the bias correction was estimated per lake.                                  For MODIS an overall bias correction of +0.11 kelvin and +0.19 kelvin has been applied to the LSWT of QL=5 and QL=4 respectively for all the lakes." ;
	int lakeid_CCI(lat, lon) ;
		lakeid_CCI:_FillValue = -2147483648 ;
		lakeid_CCI:units = "1" ;
		lakeid_CCI:long_name = "CCI lake identifier" ;
		lakeid_CCI:valid_min = 2 ;
		lakeid_CCI:valid_max = 999999999 ;
		lakeid_CCI:comment = "CCI lake ID as defined in Carrea L., Merchant C.J., Simis S. (2022) Lake mask and distance to land dataset of 2024 lakes for the European Space Agency Climate Change Initiative Lakes v2 (Version 2.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6699376" ;
	int lakeid_GloboLakes(lat, lon) ;
		lakeid_GloboLakes:_FillValue = -2147483648 ;
		lakeid_GloboLakes:units = "1" ;
		lakeid_GloboLakes:long_name = "GloboLakes lake identifier" ;
		lakeid_GloboLakes:valid_min = 2 ;
		lakeid_GloboLakes:valid_max = 999999999 ;
		lakeid_GloboLakes:comment = "GLWD (Global Lakes and Wetlands Database) lake ID of GloboLakes lakes as defined in Carrea L. et al. (2015)  Datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates. Geoscience Data Journal, 2 (2) pp. 83-97." ; 

// global attributes:
		:title = "Copernicus Climate Change Service (C3S) Lake Surface Water Temperature L3S product" ;
		:summary = "L3S product from the ESA CCI project, produced using the LSWT 4.5 algorithm." ;
		:citation = "Carrea, L., Crétaux, J.-F., Liu, X., Wu, Y., Bergé-Nguyen, M., Calmettes, B., Duguay, C., Jiang, D., Merchant, C.J., Mueller, D., Selmes, N., Simis, S., Spyrakos, E., Stelzer, K., Warren, M., Yesou, H., Zhang, D. (2022): ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 2.0.2. NERC EDS Centre for Environmental Data Analysis, 06 July 2022. http://dx.doi.org/10.5285/a07deacaffb8453e93d57ee214676304; Carrea, L., Crétaux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. Sci Data 10, 30 (2023). https://doi.org/10.1038/s41597-022-01889-z" ;
		:licence = "Creative Commons Attribution 4.0 International (CC-BY 4.0, https://creativecommons.org/licenses/by/4.0/)" ;
		:reference = "https://cds.climate.copernicus.eu" ;
		:institution = "C3S, University of Reading" ;
		:history = "University of Reading LSWT v4.5, created with using GBCS library v2.6.1-146-gfe50b81_RES20" ;
		:id = "C3S-L3S-CDR" ;
		:product_version = "4.5" ;
		:uuid = "17dbb188-98aa-11ed-84fa-149ecf94399a" ;
		:tracking_id = "17dbb188-98aa-11ed-84fa-149ecf94399a" ;
		:netcdf_version_id = "4.6.1" ;
		:source = "ATSR2-ESA-L1-v3, AATSR-ESA-L1-v3, AVHRRMTA-EUMETSAT-L1-v1, AVHRRMTB-EUMETSAT-L1-v1, SLSTRA-ESA-L1-v1, SLSTRB-ESA-L1-v1, MODIS_T-NASA-L1-v61, ERA_INTERIM-ECMWF-WSP-v1.0, ERA5-ECMWF-v1.0, CCI-Mask-v2.0" ;
		:platform = "ERS-2" ;
		:sensor = "ATSR2" ;
		:Metadata_Conventions = "Unidata Dataset Discovery v1.0" ;
		:Conventions = "CF-1.8" ;
		:gemet_keywords = "inland water; temperature; climate; seasonal variations; hydrology; limnology; environmental data; environmental  monitoring; monitoring; remote sensing" ;
		:gcmd_keywords = "LAKES/RESERVOIRS" ;
		:iso19115_topic_categories = "Environment; Inland water; GeoscientificInformation" ;
		:standard_name_vocabulary = "NetCDF Climate and Forecast (CF) Metadata Convention" ;
		:acknowledgment = "Funded by Copernicus Climate Change Service. Use of these data should acknowledge the Copernicus Climate Change Service" ;
		:creator_name = "University of Reading" ;
		:creator_url = "http://www.laketemp.net/home/; https://www.reading.ac.uk/meteorology/" ;
		:creator_email = "l.carrea@reading.ac.uk" ;
		:creator_processing_institution = "These data were produced on the JASMIN infrastructure at STFC as part of the C3S Hydrology project" ;
		:project = "Copernicus Climate Change Service (C3S) Hydrology" ;
		:publisher_name = "Copernicus Climate data Store" ;
		:publisher_url = "https://cds.climate.copernicus.eu" ;
		:publisher_support = "https://cds.climate.copernicus.eu/cdsapp#!/usersupport" ;
		:date_created = "2023-01-20T10:06:23Z" ;
		:time_coverage_start = "19840531T000000Z" ;
		:time_coverage_end = "19840531T235959Z" ;
		:time_coverage_duration = "1 day" ;
		:geospatial_lat_units = "degrees_north" ;
		:geospatial_lat_resolution = 0.05f ;
		:geospatial_lon_units = "degrees_east" ;
		:geospatial_lon_resolution = 0.05f ;
		:geospatial_vertical_min = -1.e-05f ;
		:geospatial_lat_min = -90.f ;
		:geospatial_lat_max = 90.f ;
		:geospatial_lon_min = -180.f ;
		:geospatial_lon_max = 180.f ;
		:northernmost_latitude = 90. ;
		:southernmost_latitude = -90. ;
		:easternmost_longitude = 180. ;
		:westernmost_longitude = -180. ;
		:processing_level = "L3S" ;
		:cdm_data_type = "grid" ;
		:source_file = "19950601120000-ESACCI-L3C_GHRSST-ATSR2-CDR2.0-v02.0-fv01.0.nc" ;
}

Appendix A – Specifications for L3S CDR: GCOS User Requierements for LSWT

Appendix B – List of the 2024 ESA CCI LAKES lakes for LSWT v4.5

The table contains the list of the 2024 lakes selected within the ESA CCI LAKES project and for which the LSWT v4.5 are availble. In the table, the CCI ID is the lake identifier used in the variable lakeid_CCI which is the ESA CCI LAKES lake mask [Carrea et al, 2022], the MAX DISTANCE TO LAND is the distance to the nearest land of the point on the lake which is the furthest from land. We define this point as the lake centre [Carrea et al, 2015], and LAT CENTRE and LON CENTRE are the coordinate of the centre. The string 'Zzzz' in the NAME field refers to lakes without a name.