Last modified on Nov 08, 2023 12:20

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

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

Date: 31/05/2020

Ref:C3S_312b_Lot4_D3.LK.5-v2.0_202001_Product_User_Guide_and_Specification_LSWT_v1.0

Official reference number service contract: 2018/C3S_312b_Lot4_EODC/SC2

History of modifications

List of datasets covered by this document

Related documents

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 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.

Scope of the document

This document is the user guide for the LSWT v4.0 product in the Hydrology service of C3S. This document is applicable to both components of this dataset: the brokered timeseries from the GloboLakes project and the C3S extension in time produced within the Copernicus Climate Change Service. The brokered and extended CDR 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.
Note that internally to C3S, the product referred to is contractually "CDR V2.0". In this document, the versioning and product name relevant to users is employed ("LSWT v4.0").

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 v4.0. This product includes a static, brokered timeseries from 1995 through end-2016 (produced within the NERC project, GloboLakes), and an ongoing extension in time of the record from Jan-2017 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 1000 GloboLakes lakes. The list of this is available in a browsable table: http://www.laketemp.net/home_GL/GLTargets.php¹
This document describes the key points about the data set generation and product contents and format needed by users intending to apply the CDR.

Product Change Log

The following Table, Table 1, provides an overview of the differences between different versions of the product up-to, and including, the current version.

Table 1: Changes in the product between versions.

1. Data description

LSWT is the surface expression of the thermal structure of lakes and is changing in response to climatic trends. LSWT is needed for climate change studies, water budget analysis (linked to evaporation), lake physical and ecological modelling.

1.1. The retrieval methodology

For full details of the basis of the data, refer to the Algorithm Theoretical Basis Document [D3].

The algorithms to derive LSWT products from imagery of visible and infrared radiometers consist of many components which aim to retrieve the LSWT from the observed reflectance and brightness temperature for only-water pixels. The core 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 numerical weather prediction, where relevant), and any pre-processing adjustment to the data themselves.

Classification:  It identifies valid pixels for LSWT retrieval. Although sometimes referred to as cloud detection, this 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 is estimated only for pixels that are fully water and free of cloud. The algorithm for the discrimination of water and non-water pixels in presence of clouds is based on threshold tests on the Visible (VIS), Near-InfraRed (NIR), and Short-Wave-InfraRed (SWIR) channels of the ATSR and AVHRR instruments. The water detection algorithm is applied only to candidate pixels identified as potential inland water in the water-bodies identifier mask [Carrea et al., 2015] built from the ESA CCI Land Cover Project.

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]. 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 (clear-sky) 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).

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

Daily collation: The complete 14-15 orbits each day per sensor stored in the LSWT L3U outputs 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 for changes in satellite sensor, an adjustment using overlaps of sensors is made, using as the (unadjusted) reference the LSWTs from the AVHRR on MetOpA.

1.1.2. Input data

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

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

• ATSR L1b:

For the GloboLakes LSWT v4.0, L1b data from the following sensors have been processed to produce LSWT:

• • ATSR2 on the ESR-2 platform from 1995 to 2003
  • AATSR on the Envisat platform from 2002 to 2012

• AVHRR L1b:

For the GloboLakes LSWT v4.0, L1b data from the following sensors have been processed to produce LSWT

• • AVHRR on the MetOpA platform from 2007 to 2016

For the LSWT v4.0 C3S extension, L1b data from the following sensors have been processed to produce LSWT:

• • AVHRR on the MetOpA and MetOpB platforms from 2017 to 2019

In addition to the L1b data, the auxiliary data inputs are NWP data and a lake mask as described in the Overview.

1.2. Limitations of the product

The 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": 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 relatively small). The retrieved LSWT reflects the skin temperature of the lake (the radiating layer of surface water), and a cool offset of order 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 area overall is large. Some lakes that are targeted in the products, but whose geometry is unfavourable, may have associated with them few or no high quality LSWTs. Some targeted lakes (such as lake 799 in the Global Lakes and Wetlands Database, i.e., the Hawizeh Marshes in Iran) does not seem to contain pixels of pure water, at least since 1995.

Prior to the availability of global full-resolution AVHRR (MetOpA) observations, the temporal density of observations is generally lower because of the narrower swath of the ATSR series instruments.

2. Product description

2.1. Product content

The product contains all the descriptive metadata in the global attributes of the netCDF file (Table 2):

Table 2: Metadata 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 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 GloboLakes or C3S); <Level> is the processing level; <Dataset> indicates the scientific version number, which is v4.0. Finally, fv01.0 indicates the file version. An example of filename is:

20100101120000-GloboLakes-L3S-LSWT-v4.0-fv01.0.nc

An example of the file structure (ncdump output) is reported in the Appendix A.

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

Table 3: 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 1000 lakes prioritised in GloboLakes.

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 4 summarizes the characteristics of the C3S LSWT product and their contrast with target requirements.

Table 4: User requirements

4. Data usage information

4.1. File naming convention

The data files are in netCDF-4 classic format and are compatible with the NERC GloboLakes 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 5: 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 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
GloboLakes: NERC GloboLakes

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. Current string in use is:
v4.0

4.2. Data format

The data files are in netCDF-4 format and are CF-compliant [D2], following the GloboLakes 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 6 following. These are recognised by most tools and utilities for working with netCDF files.

Table 6: Standard variable attributes.

4.2.2. Coordinate grid

The coordinate variables are listed in Table 7 and discussed in the following sections.

Table 7: 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 GDS:

L3C, 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 8: 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 9: Quality indicator

4.2.5. Auxiliary variables and uncertainties

There are auxiliary variables and the total LSWT uncertainty listed in Table 10 following.

Table 10: Auxiliary variables and uncertainty

4.3. Product contents

Examples of the data contained in one L3S product are shown for the Aral Sea (lakeid=4) and for lake Qamystybas (lakeid=1360) in Kazakhstan. Figure 2 shows the LSWT on the 3-Aug-2016, Figure 3 shows the quality levels, Figure 4 shows the LSWT uncertainty and Figure 5 shows the static lake identifier mask.

Figure 2: LSWT for the Aral Sea and the Qamystybas lake in Kazakhstan on the 3-Aug-2016.

Figure 3: Quality levels for the Aral Sea and the Qamystybas lake in Kazakhstan on the 3-Aug-2016 

Figure 4: LSWT uncertainty for the Aral Sea and the Qamystybas lake in Kazakhstan on the 3-Aug-2016 

Figure 5: Lake id for the Aral Sea and the Qamystybas lake in Kazakhstan.

Although the uncertainty is quite low, not all the pixels have high quality levels. Around coastal areas where the distance to land is lower than 1.5km, generally lower quality levels can be found. In the case of the Aral Sea the distance to land has been computed on a maximum (2005-2010) extent mask shown in Figure 5. However, in this case the water extent has been highly dynamic and therefore the distance to land values are not accurate.

Appendix A – Specifications for L3S CDR

Example file structure (ncdump output):
netcdf \19950601120000-GloboLakes-L3S-LSWT-v4.0-fv01.0 {
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) ;
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 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:standard_name = "lake_surface_water_temperature" ;
short lswt_uncertainty(time, lat, lon) ;
lswt_uncertainty:_FillValue = -32768s ;
lswt_uncertainty:units = "Kelvin" ;
lswt_uncertainty:long_name = "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:standard_name = "lake_surface_water_temperature_uncertainty" ;
lswt_uncertainty:comment = "Total uncertainty was computed with LSWT uncertainties from the Optimal Estimation and bias correction uncertainty." ;
byte quality_level(time, lat, lon) ;
quality_level:_FillValue = 0b ;
quality_level:flag_meanings = "no_data bad_data worst_quality low_quality acceptable_quality best_quality" ;
quality_level:flag_masks = 0b, 1b, 2b, 3b, 4b, 5b ;
quality_level:long_name = "quality levels" ;
quality_level:valid_min = 0b ;
quality_level:valid_max = 5b ;
quality_level:comment = "These are overall quality indicators." ;
quality_level:standard_name = "lake_surface_water_temperature_quality_level" ;
byte obs_instr(time, lat, lon) ;
obs_instr:_FillValue = 0b ;
obs_instr:long_name = "observation instruments" ;
obs_instr:flag_meanings = "ATSR2 ATSR2-AATSR AATSR AATSR-AVHRR AVHRR" ;
obs_instr:flag_masks = 1b, 2b, 4b, 8b, 16b ;
obs_instr:comment = "If the bit is set to 1 the observation from the correspondent instrument/instruments have been used to generate the LSWT." ;
obs_instr:standard_name = "instrument_for_observation" ;
byte flag_bias_correction(time, lat, lon) ;
flag_bias_correction:_FillValue = 0b ;
flag_bias_correction:long_name = "bias correction flag" ;
flag_bias_correction:flag_meanings = "ATSR2 AATSR ATSR2-AATSR" ;
flag_bias_correction:flag_masks = 1b, 2b, 3b ;
flag_bias_correction:comment = "The reference instrument was the AVHRRMTA, consequently no bias correction has been applied to observations from the AVHRR instrument." ;
flag_bias_correction:standard_name = "instrument_bias_correction_flag" ;
int lakeid(lat, lon) ;
lakeid:_FillValue = -2147483648 ;
lakeid:units = "1" ;
lakeid:long_name = "Lake ID" ;
lakeid:valid_min = 2 ;
lakeid:valid_max = 999999 ;
lakeid: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." ;
lakeid:standard_name = "lake_identifier" ;
// global attributes:
:title = "NERC GloboLakes Lake Surface Water Temperature L3S product" ;
:summary = "L3S product from the NERC GloboLakes project, produced using the GloboLakes v4.0 algorithm." ;
:citation = "MacCallum, S.N. and Merchant, C.J. (2012) Surface water temperature observations of large lakes by optimal estimation. Canadian Journal of Remote Sensing, 38 (1). pp. 25-45.; Carrea, L., Embury, O. and Merchant, C. J. (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." ;
:license = "Creative Commons by Attribution ShareAlike CC-BY-SA v4.0 (https://creativecommons.org/licenses/by-sa/4.0/)" ;
:reference = "http://www.globolakes.ac.uk/ http://www.laketemp.net/home_GL/" ;
:institution = "NERC" ;
:history = "Created with using GBCS library v2.6.1-146-gfe50b81" ;
:id = "NERCGloboLakes-L3S-CDR" ;
:product_version = "4.0" ;
:uuid = "6624f392-1b3e-11e9-8fd7-f452141d3dd0" ;
:tracking_id = "6624f392-1b3e-11e9-8fd7-f452141d3dd0" ;
:netcdf_version_id = "4.4.1.1" ;
:source = "ATSR2-ESA-L1-v3, AATRS-ESA-L1-v3, AVHRRMTA-EUMETSAT-L1-v1, ERA_INTERIM-ECMWF-WSP-v1.0, GloboLakes-Mask-v1.0" ;
:platform = "ERS-2" ;
:sensor = "ATSR2" ;
:Metadata_Conventions = "Unidata Dataset Discovery v1.0" ;
:Conventions = "CF-1.6" ;
: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 the UK Natural Environment Research Council (NERC)" ;
:creator_name = "NERC GloboLakes" ;
:creator_email = "l.carrea@reading.ac.uk" ;
:creator_url = "http://www.globolakes.ac.uk/ http://www.laketemp.net/home_GL/" ;
:creator_processing_institution = "These data were produced on the Jasmin infrastructure at STFC as part of the NERC GloboLakes project" ;
:project = "GloboLakes" ;
:publisher_name = "NERC GloboLakes" ;
:publisher_url = "http://www.globolakes.ac.uk/" ;
:publisher_email = "l.carrea@reading.ac.uk" ;
:date_created = "2019-01-18T16:30:42Z" ;
:time_coverage_start = "19950601T000000Z" ;
:time_coverage_end = "19950601T235959Z" ;
: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" ;
}

References

Carrea, L., Embury, O. and Merchant, C. J. (2015) Datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifiers and lake-centre co-ordinates. Geoscience Data Journal, 2(2). pp. 83-97. ISSN 2049-6060 doi:10.1002/gdj3.32

MacCallum, S.N. and Merchant, C. J. (2012) Surface water temperature observations for large lakes by optimal estimation. Canadian Journal of Remote Sensing, 38(1). pp. 25-45. ISSN 1712-7971 doi:10.5589/m12-010

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