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History of modifications

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Issue

Date

Description of modification

Editor

i0.1

22/11/2020

Created from D1.S.1-2019.

RK

i1.0

13/06/2021

Finalised. Created Lakes TRGAD from approved D1.S.1-2020_TRGAD_LHC.

CR, LC, RM, BC, RK


Related documents
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titleClick here to expand the list of related documents (D1-D12)


Reference ID

Document

Anchor
LK_RD_1
LK_RD_1
RD.1

Global Climate Observing System (2016) THE GLOBAL OBSERVING SYSTEM FOR CLIMATE: IMPLEMENTATION NEEDS, GCOS-200, https://library.wmo.int/doc_num.php?explnum_id=3417

Anchor
LK_RD_2
LK_RD_2
RD.2

Merchant, C. J., Paul, F., Popp, T., Ablain, M., Bontemps, S., Defourny, P., Hollmann, R., Lavergne, T., Laeng, A., de Leeuw, G., Mittaz, J., Poulsen, C., Povey, A. C., Reuter, M., Sathyendranath, S., Sandven, S., Sofeiva, V. F. and Wagner, W. (2017) Uncertainty information in climate data records from Earth observation. Earth System Science Data, 9 (2). pp. 511-527. ISSN 1866-3516 doi: https://doi.org/10.5194/essd-9-511-2017

Anchor
LK_RD_3
LK_RD_3
RD.3

Group for High Resolution Seas Surface Temperature Data Specification (GDS) v2, Casey and Donlon (eds.), 2012, https://www.ghrsst.org/wp-content/uploads/

2016

2021/

10

04/GDS20r5.pdf

Image Removed

Anchor
SM_RD_4
SM_RD_4
RD.4

W. Dorigo, T. Scanlon, P. Buttinger, , A. Pasik, C. Paulik,R. Kidd, 2020. C3S D312b Lot 4, D3.SM.5-v2.0, Product User Guide and Specification (PUGS): Soil Moisture (v201912).

Anchor
SM_RD_5
SM_RD_5
RD.5

R. van der Schalie, R. De Jeu, C. Paulik, W. Dorigo, T. Scanlon, A. Pasik, C. Reimer, R. Kidd, 2020. C3S D312b Lot 4 D1.SM.2-v2.0,.Algorithm Theoretical Basis Document (ATBD): Soil Moisture (v201912).

Anchor
SM_RD_6
SM_RD_6
RD.6

W. Dorigo, T. Scanlon, W. Preimesberger, P. Buttinger, A. Pasik, R. Kidd, C. Chatzikyriakou, 2020. C3S D312b Lot 4 D2.SM.1_v2.0 Product Quality Assurance Document (PQAD): Soil Moisture.

Anchor
SM_RD_7
SM_RD_7
RD.7

T. Scanlon, W. Dorigo , , W. Preimesberger, R. Kidd, C. Chatzikyriakou, 2020. C3S D312b Lot 4, D2.SM.2-v2.0, Product Quality Assessment Report (PQAR): Soil Moisture (v201912).

RD.8

D1.GL.2-v3.0 ATBD Area Change

RD.9

D1.GL.2-v3.0 ATBD Elevation and Mass Change

RD.10

ATBD CCI:
climate.esa.int/media/documents/glaciers_cci_ph2_d21_atbd_v26_161114.pdf

RD.11

ATBD CCI Soil Moisture: ATBD CCI: ESA Climate Change Initiative Plus, Soil Moisture, Algorithm Theoretical Baseline Document (ATBD), Supporting Product Version 06.1, D2.1 Version 2, 19-04-2021 See https://admin.climate.esa.int/media/documents/ESA_CCI_SM_RD_D2.1_v2_ATBD_v06.1_issue_1.1.pdf

RD.12

PUG CCI Soil Moisture: ESA Climate Change Initiative Plus, Soil Moisture Product User Guide (PUG), Supporting Product Version v06.1, Deliverable ID: D4.2 Version 2, 16-04-2021. See

https://admin.climate.esa.int/media/documents/ESA_CCI_SM_D4.2_v2_Product_Users_Guide_v06.1_i1.0.pdf


Acronyms

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titleClick here to expand the list of acronyms


Acronym

Definition

AATSR

Advanced Along-Track Scanning Radiometer

AMI-WS

Active Microwave Instrument - Windscat (ERS-1 & 2)

AFRL

Air Force Research Laboratory

ALS DEM

Airborne Laser Scanner Digital Elevation Model

AMI

Active Microwave Instrument

AMSR2

Advanced Microwave Scanning Radiometer 2

AMSR-E

Advanced Microwave Scanning Radiometer-Earth Observing System

AntIS

Antarctic Ice Sheet

ASCAT

Advanced Scatterometer (MetOp)

ASTER

Advanced Spaceborne Thermal Emission and Reflection Radiometer

ASTER GDEM

ASTER Global Digital Elevation Model

ATBD

Algorithm Theoretical Baseline Document

ATSR-2

Along Track Scanning Radiometer 2

AVHRR

Advanced Very-High Resolution Radiometer

C3S

Copernicus Climate Change Service

CCI

Climate Change Initiative

CDF

Cumulative Distribution Function

CDM

Common Data Model

CDR

Climate Data Record

CDS

Climate Data Store

CF

Climate Forecast

CMA

China Meteorological Administration

CNES

Centre national d'études spatiales

DEM

Digital Elevation Model

DMSP

Defense Meteorological Satellite Program

DOD

Department of Defense

ECMWF

European Centre for Medium-Range Weather Forecasts

ECV

Essential Climate Variable

EODC

Earth Observation Data Centre for Water Resources Monitoring

ERS

European Remote Sensing Satellite (ESA)

ESA

European Space Agency

ESGF

Earth System Grid Federation

ESRI

Environmental Systems Research Insitute

ETM+

Enhanced Thematic Mapper plus

EUMETSAT

European Organisation for the Exploitation of Meteorological Satellites

FAO

Food and Agriculture Organization

FoG

Fluctuations of Glaciers

FPIR

Full-polarized Interferometric synthetic aperture microwave radiometer

FTP

File Transfer Protocol

GCOM

Global Change Observation Mission

GCOS

Global Climate Observing System

GDS

Glacier Distribution Service

GHRSST

Group for High Resolution Sea Surface Temperature

GIA

Glacial Isostatic Adjustment

GLCF

Global Land Cover Facility

GLIMS

Global Land Ice Measurements from Space

GLL

Grounding Line Location

GLS

Global Land Survey

GMB

Gravimetric Mass Balance

GMI

GPM Microwave Imager (GMI)

GPM

Global Precipitation Mission

GRACE

Gravity Recovery and Climate Experiment

GRACE-FO

Gravity Recovery and Climate Experiment Follow On

GrIS

Greenland Ice Sheet

GTN-G

Global Terrestrial Network for Glaciers

HDF

Hierarchical Data Format

H-SAF

Hydrological Satellite Application Facility (EUMETSAT)

HRV

High Resolution Visible

ICDR

Interim Climate Data Record

ICESat

Ice, Cloud and Elevation Satellite

IFREMER

Institut Français de recherche pour l'exploitation de la mer

IGOS

Integrated Global Observing Strategy

IMBIE

Ice sheet Mass Balance Intercomparison Exercise

InSAR

Interferometric SAR

IPCC

Intergovernmental Panel on Climate Change

ISRO

Indian Space Research Organisation

IV

Ice Velocity

JAXA

Dokuritsu-gyosei-hojin Uchu Koku Kenkyu Kaihatsu Kiko, (Japan Aerospace Exploration Agency)

KPI

Key Performance Indicators

L2

Retrieved environmental variables at the same resolution and location as the level 1 (EO) source.

L3

Level 3

LIDAR

Light Detection and Ranging

LOS

Line of Sight

LPDAAC

Land Processes Distributed Active Archive Center

LPRM

Land Parameter Retrieval Model

LSWT

Lake Surface Water Temperature

LWL

Lake Water Level

MERRA

Modern-Era Retrospective analysis for Research and Applications

MetOp

Meteorological Operational Satellite (EUMETSAT)

MetOp SG

Meteorological Operational Satellite - Second Generation

MSI

Multi Spectral Imager

MWRI

Micro-Wave Radiation Imager

NASA

National Aeronautics and Space Administration

NED

National Elevation Data

NetCDF

Network Common Data Format

NIR

Near Infrared

NISAR

NASA-ISRO SAR Mission

NOAA

National Oceanic and Atmospheric Administration

NRL

Naval Research Laboratory

NSIDC

National Snow and Ice Data Center

NWP

Numerical Weather Prediction

OE

Optimal Estimation

OLI

Operational Land Imager

PMI

Polarized Microwave radiometric Imager

PQAD

Product Quality Assurance Document

PQAR

Product Quality Assessment Report

PUG

Product User Guide

QA4ECV

Quality Assurance for Essential Climate Variables

RFI

Radio Frequency Interference

RGI

Randolph Glacier Inventory

RMSE

Root Mean Square Error

SAOCOM

SAtélite Argentino de Observación COn Microondas

SAF

Satellite Application Facilities

SAR

Synthetic Aperture Radar

SCA

Scatterometer

SEC

Surface Elevation Change

SLC

Single Look Complex

SLSTR

Sea and Land Surface Temperature Radiometer

SMAP

Soil Moisture Active and Passive mission

SMMR

Scanning Multichannel Microwave Radiometer

SMOS

Soil Moisture and Ocean Salinity (ESA)

SPIRIT

Stereoscopic survey of Polar Ice: Reference Images & Topographies

SPOT

Satellites Pour l'Observation de la Terre

SRTM

Shuttle Radar Topography Mission

SRTM DEM

SRTM Digital Elevation Model

SSM

Surface Soil Moisture

SSM/I

Special Sensor Microwave Imager

SST

Sea Surface Temperature

SWIR

Shortwave Infrared

TCA

Triple Collocation Analysis

TM

Thematic Mapper

TMI

TRMM Microwave Imager

TOPEX-Poseidon

Topography Experiment - Positioning, Ocean, Solid Earth, Ice Dynamics, Orbital Navigator

TOPS

Terrain Observation with Progressive Scan (S-1)

TRMM

Tropical Rainfall Measuring Mission

TU

Technische Universität

TU Wien

Vienna University of Technology

URD

User Requirements Document

USGS

United States Geological Survey

UTC

Universal Time Coordinate

VIIRS

Visible Infrared Imaging Radiometer Suite

VNIR

Visible and Near Infrared

VOD

Vegetation Optical Depth

WARP

Water Retrieval Package

WCOM

Water Cycle Observation Mission

WGI

World Glacier Inventory

WGMS

World Glacier Monitoring Service

WGS

World Geodetic System

WindSat

WindSat Radiometer


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.

...

Threshold requirement: minimum requirement to be met to ensure data are useful.

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 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.

...

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.

Executive Summary

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.

...

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.

Info
iconfalse

1 See http://www.globolakes.ac.uk/overview.html (URL resource viewed 21/02/20)

Introduction

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.

...

  • current observational constraints and additional/future sources of satellite data
  • known areas for improvement of LSWT and LWL estimation methods
  • known areas for improvement of LSWT and LWL uncertainty estimation methods
  • lake ECV components not presently delivered by the Hydrology service within the C3S 312b LHC service.

The Lake ECV products

Brokered and Generated LSWT CDR v1.0

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.

...

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.

Generated LSWT CDR v2.0


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.

Generated LSWT CDR v3.0 and LSWT ICDR v3.0

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.

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.

...

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

Lakes Service: User requirements

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.

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 service.

Experience

Coverage

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

Spatial and temporal resolution

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

Uncertainty requirements

Communication of uncertainty

Property

Threshold

Target

Comments

Sources

LSWT uncertainty

Provide

-

Provision of uncertainty is recognised as good practice for CDR

RD.2

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

RD.2

Data uncertainties

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)

Format requirements

Property

Threshold

Target

Comments

Sources

NetCDF, CF conventions

Provide

-

Service requirement

C3S

Grid definition

Regular lat/lon

-

Based on experience in SST service

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. Would not apply for lake quality monitoring, which requires a shorter delay with a greater tolerance of uncertainty and instability.

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 Hydroweb service.

Experience

Coverage

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

Spatial and temporal resolution

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

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. Target comes from GCOS.

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

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

Lakes Service: Analysis of gaps and opportunities

Satellite observational constraints and opportunities

Lake surface water temperature

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.

Anchor
table1
table1
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

Lake water level

Anchor
table2
table2
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

Improvement of retrieval algorithms

Lake surface water temperature

LSWT estimation has three steps:

...

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.

Lake water level

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.

...

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.

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:

...

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.

Lake water level

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.

...

  • Comparison to other altimetry products (e.g. G-REALM)
  • Comparison to in situ data (e.g. HYDROLARE)

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.

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).

...

Info
iconfalse

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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