sea ice drift product validation plan
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ESA UNCLASSIFIED - For Official Use
Sea Ice Climate Change Initiative: Phase 2
Sea Ice Drift Product Validation Plan
Doc Ref: SICCI-PVP-05-16
Version: 1.0
Date: 30 May 2016
Product Validation Plan
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Change Record
Issue Date Reason for Change Author
1.0 30 May 2016 First Issue Fanny Ardhuin, Thomas Lavergne, Leif Toudal Pedersen, Roberto Saldo, Thomas Hollands, Stefan Muckenhuber
Authorship
Role Name Signature
Written by: Fanny Ardhuin, Thomas Lavergne, Leif Toudal Pedersen, Roberto Saldo, Thomas Hollands, Stefan Muckenhuber
Checked by: Gary Timms (CGI)
Approved by: Stein Sandven (NERSC)
Authorised by: Pascal Lecomte (ESA)
Distribution
Organisation Names Contact Details
ESA Pascal Lecomte [email protected]
NERSC Stein Sandven, Natalia Ivanova, Kirill Khvorostovsky
[email protected]; [email protected]
CGI Gary Timms, Sabrina Mbajon, Clive Farquhar
[email protected]; [email protected]; [email protected]
MET Norway Thomas Lavergne, Atle
Sørensen [email protected]; [email protected]
DMI Leif Toudal Pedersen, Rasmus Tonboe
[email protected]; [email protected]
DTU Roberto Saldo, Henriette Skourup
[email protected]; mailto:[email protected]
FMI Marko Mäkynen, Eero Rinne [email protected]; [email protected];
University of Hamburg
Stefan Kern, Lars Kaleschke, Xiangshan Tian-Kunze
[email protected]; [email protected]; [email protected]
University of Bremen
Georg Heygster [email protected]
MPI-M Dirk Notz, Felix Bunzel [email protected]; [email protected]
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Organisation Names Contact Details
Ifremer Fanny Ardhuin [email protected]
AWI Marcel Nicolaus, Stefan
Hendricks, Thomas Hollands
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Table of Contents
1 Introduction ................................................................................... 7 1.1 Document Structure ........................................................................7 1.2 Document Status ............................................................................7 1.3 Applicable Documents .....................................................................7 1.4 Reference Documents ......................................................................7 1.5 Acronyms and Abbreviations ............................................................7
2 Sea Ice Drift (SID) ECV ................................................................... 9
3 RRDP activities ............................................................................. 10 3.1 Inputs ......................................................................................... 10 3.2 Reference data ............................................................................. 12 3.3 Format of the RRDP ...................................................................... 14 3.4 Collocation method ....................................................................... 14 3.5 Algorithms evaluation: parameters/statistics, criteria ......................... 14 3.6 Sensitivity ................................................................................... 15 3.7 Uncertainties ................................................................................ 15
4 Roles and Responsibilities ............................................................. 16
5 Master Time Schedule ................................................................... 17
6 Summary ...................................................................................... 18
Appendix A References ................................................................................ 19
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List of Figures
Figure 3-1: Geographical distribution of buoys (and some land data) ....................... 13
Figure 3-2: Number of observations distributed per year of the current SICCI2 in situ
data record. Left is NH and right is SH. .......................................................... 14
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List of Tables
Table 1-1: Applicable Documents ......................................................................... 7
Table 1-2: Acronyms .......................................................................................... 8
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1 Introduction
1.1 Document Structure
This document describes the validation plan for the Sea Ice Drift (SID)
Essential Climate Variable algorithm to be selected in the ESA's Sea Ice
Climate Change Initiative project.
This document is composed of the presentation of the SID activities in Phase
2 of the project, a Round Robin Data Package (RRDP) detailed presentation,
definition of responsibilities of the team, and master time schedule of these
activities.
1.2 Document Status
This is the first version of the PVP for SID which was not included in Phase 1
of the project. This includes additional internal (consortium) review
comments.
1.3 Applicable Documents
The following table lists the Applicable Documents that have a direct impact
on the contents of this document.
Acronym Title Reference Issue
AD-1 Sea Ice ECV Project
Management Plan
ESA-CCI_SICCI_PMP_D6.1_v1.1 1.1
Table 1-1: Applicable Documents
1.4 Reference Documents
All references are listed at the end of the document
1.5 Acronyms and Abbreviations
Acronym Meaning
AMSR Advanced Microwave Scanning Radiometer
ASCAT Advanced Scatterometer
ASCII American Standard Code for Information Interchange
ATBD Algorithm Theoretical Basis Document
CRREL Cold Region Research and Engineering Laboratory
DMSP Defence Meteorological Satellite Program
ECV Essential Climate Variable
Envisat Environmental Satellite
ESA European Space Agency
FY First Year
GPS Global Positioning System
H Horizontal polarization
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Acronym Meaning
IABP International Arctic Buoys Program
IPAB International Program for Antarctic Buoys
ITP Ice Tethered Profiler
MCC Max Cross Correlation
MIZ Marginal ice zone
MY MultiYear
n.a. Not applicable
NetCDF Network Common Data Format
NH Northern hemisphere
NSIDC National Snow and Ice Data Center
OSI-SAF Ocean and Sea Ice Satellite Application Facility
PMW Passive Microwave
PVP Product Validation Plan
RADAR Radio Detection and Ranging
RRDP Round Robin Data Package
SAR Synthetic Aperture Radar
SID Sea Ice Drift
SH Southern hemisphere
SMMR Satellite Multichannel Microwave Radiometer
SMOS Soil Moisture and Ocean Salinity
SSM/I Special Sensor Microwave / Imager
SSM/IS Special Sensor Microwave / Imager+Sounder
V Vertical polarization
Table 1-2: Acronyms
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2 Sea Ice Drift (SID) ECV
The SID maps exist from different sensors, at different time and space
scales. Each existing dataset has been validated with references, some have
also been inter-compared at the basin scale (Sumata et al, 2014) or on
specific area (Rozman et al, 2011). The goal here is not to do again a
product comparison we will focus here on the algorithms, independently of
the data and scale used. At the end of this study we will be able to select an
algorithm (which we call “the best one”) that could be applied to any
dataset for a climate record (the project does not include the production of
this dataset).
The goal of the Product Validation Plan (PVP) is to define a strategy of
validation to select the “best” algorithm(s), this includes:
- choose some metrics
- choose satellite data and reference data
- design the RRDP
Main existing SID datasets are defined as displacement estimate, we choose
here to apply the RRDP with the same parameter: a time integrated
displacement rather than speed values which are used by modellers.
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3 RRDP activities
The RRDP aims to compare algorithms on several satellite datasets in order
to select the “best” algorithm. This requires a number of input satellite
datasets, algorithms, and validation datasets compiled in a so-called Round
Robin data package (RRDP).
This consists in collecting available algorithms as well as establishing a
validation dataset by which the algorithms can be tested for performance
against a number of performance criteria. This requires also a collection of
satellite data. The main challenges of the RRDP are the compilation of
algorithms, satellite and reference datasets.
This section describes the selection and comparison of algorithms with the
satellite and reference data.
The protocols contain:
1. Specification of input data (satellites, algorithms)
2. Specification of validation data (reference data)
3. Definition of format of RRDP
4. Collocation methods
5. Specification of validation parameters/statistics, criteria.
6. Sensitivity of the results
3.1 Inputs
3.1.1 Satellite data
The database will consist of daily averaged maps of
1) Gridded brightness temperature (TB) data measured by passive
microwave radiometers (PMW) onboard satellites:
SMMR (if needed)
SSM/I(S) onboard DMSP at low resolution (the successive SSMI
sensors onboard F8 to F17 are calibrated)
AMSR-E at medium resolution
AMSR2 at medium resolution
2) Gridded low resolution backscatter data :
SeaWinds/QuikSCAT
ASCAT/MetOp (-A, -B if needed)
3) High resolution radar sensor (SAR)
EnviSATSentinel-1
Other high resolution data exist but have not the potential for a climate data
record.
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These reference datasets will be detailed in the DBT2 document.
The possibilities of optical/infrared data are not considered because they are
highly contaminated by clouds and limited by the polar night (little potential
for climate data record).
The SID team will keep an eye on future/new sensors to continue the SID
ECV dataset if available (for example future European Scatterometer...).
Most of products are based on daily averaged maps of TB (radiometry) or
Sigma0 (scatterometry), we have to pay attention at the loss of accuracy of
the averaging of swaths data, this will be analysed on the uncertainties work
among other parameters to take into account.
3.1.2 Algorithms and methods
Several algorithms have been applied to infer SID. We will focus here on the
a) technics to infer vector estimate
b) but also pre-processing of the raw data
c) post-processing consistency check of the vectors
d) application to single or multiple data (channels/sensors)
a) We will test 2 main families of algorithms based on :
1) Pattern Matching / Correlation based methods
1.1. Max Cross Correlation (MCC)
This method is applied on successive and lagged averaged daily maps, it
consists in tracking common features on pairs of sequential images (Ninnis
et al 1986, Emery et al 1997). A correlation is estimated between two
arrays of data, one at a given day and another one lagged in time, and the
location of the maximum correlation is the location of the maximum
similarity between the two original subimages. The displacement is thus
inferred.
MCC is widely used since a long time for SID estimate, it has been used with
low resolution data (SSM/I – Agnew et al 1997, QuikSCAT - Girard-Ardhuin
and Ezraty 2012) but also medium resolution data (AMSRs, Girard-Ardhuin
and Ezraty 2012) and high resolution SAR data (Kwok et al 1990).
Lavergne et al (2010) applied the MCC with a continuous optimization step
resulting in a attenuation of the quantization noise due to the MCC technics.
1.2. Phase correlation
Another correlation based way to detect displacement is the analysis of
(time and space) varying signals through wavelet/Fourier transform. After
applications to wind patterns, Liu et al 1999 have been tested this method
to infer sea ice motion. Since the 2010's, others have applied this kind of
decomposition using high resolution data (SAR) (Thomas, 2005 and
Karvonen, 2012). While the application of phase correlation only can be
computationally more efficient than the correlation in the spatial domain, it
is less prone to high frequency noise.
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2) Feature-based detection
While pattern matching algorithms just correlate regular search windows,
feature tracking algorithms include a semantic step, identifying a list of
features to compare between two scenes. This method is described and
tested with Sentinel-1 SAR data by Muckenhuber et al (2015). It consists
first in a sampling of the data to lower resolution in order to decrease the
influence of speckle noise and increase the computational efficiency. Then a
tracking features method is applied with a new quality measure using the
amount and deviation of vectors in a grid cell.
Compared to patter matching, the advantage of feature tracking is that each
drift vector is independent of the surrounding motion; it is in particular of
interest for areas with high gradient displacements. The disadvantage
compared to purely correlation based methods is the irregular distribution
of vectors and a bias towards motion of clearly identifiable features
Sampling of the images, area of sub-window, correlation areas and
distances between inferred vectors are problems to be investigated in this
study.
b) Pre-processing of the images will also be tested, for example :
- Laplacian fields (Girard-Ardhuin & Ezraty 2012)
- Laplace (Komarov et al 2014)
- missing values filled with mean neighboured pixels (Kimura et al,
2013)
c) The existing SID datasets have been made using method for vector
detection but also using consistency check technics that could be tested
here, such as
- consistency with wind direction
- threshold of correlation coefficient
- consistency with averaged neighbours vectors
- consistency with lower resolution vectors pattern (for high
resolution)
- back matching verification (Hollands, 2012, 2015, Komarov, 2014)
d) We will also test different ways to apply the algorithms over:
- single sensor or single channel sensor
- multi-channels or multi-sensors (Haarpainter 2006, Girard-Ardhuin &
Ezraty 2012, Lavergne, et al. 2010)
3.2 Reference data
An in situ reference data set is being compiled from mainly buoys and
drifters mainly available from different programs such as:
ITP (Woods Hole Oceanographic Institute,
http://www.whoi.edu/page.do?pid=20756)
IABP (International Arctic Buoy Programme,
http://iabp.apl.washington.edu/index.html)
CRREL IMB Ice Mass Balance Buoy Program
(http://www.erdc.usace.army.mil/Media/FactSheets/FactSheetArt
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icleView/tabid/9254/Article/553850/ice-mass-balance-imb-buoy-
program.aspx)
SAMS (The Scottish Association for Marine Science,
http://www.sams.ac.uk/)
IPAB (for southern hemisphere- SH)
GTS drifters (Global Telecommunication System from ECMWF
MARS archive)
Atlas of Antarctic Sea Ice Drift (http://imkbemu.physik.uni-
karlsruhe.de/~eisatlas/HTML/eisatlas_download.html)
SOOS (Southern Ocean Observing System,
http://soos.aq/data/overview)
AWI Buoy data (Alfred Wegener Institute)
Norwegian drift station (Yngve Christoffersen)
Marginal Ice zone Program (University of Washington,
(http://www.apl.washington.edu/project/project.php?id=miz)
ISTI (International surface temperature initiative,
http://www.surfacetemperatures.org/)UPMC Buoys
(http://www.iaoos-equipex.upmc.fr/fr/index.html)
University of Tasmania, Southern Ocean buoy data (if
possible....)
This list is neither complete nor final. It is a live list for the duration of the
project. The list does also contain doublets, but they will removed. The
reference data will be read and stored in uniform and consistent data
format, i.e. in netCDF with CF compliancy where possible. The reference
data will undergo a quality check in its final version.
The data set is under construction, but at any time a version is available for
testing. Careful attention will be drawn to the availability of the data (NH
and SH, MY/FY, period) and of the quality of them. When new data are
available and/or when improved data formats and/or better quality control
procedures are developed, the available data set will be reprocessed.
Statistics of the in situ data distribution of
the current in situ data set version is
presented in the figures x and y.
Figure 3-1: Geographical distribution of buoys (and some land data)
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Figure 3-2: Number of observations distributed per year of the current
SICCI2 in situ data record. Left is NH and right is SH.
(The current data set not complete, as mentioned, in particular drifters from
the southern hemisphere is anticipated to increase drastically in numbers)
These reference datasets will be detailed in the DBT2 document.
3.3 Format of the RRDP
We aim for a robust ASCII based (csv) flat file. Each line records information
for a unique (validation data, collocated) pair.
The source/institution, ref. SID value (+a confidence level) are part of the
information to be recorded. Follow the latitude, longitude, time, sensor
(<instr>-<platform>, e.g. ssmi-f15), channels if needed, polarization if
needed. The data format (e.g. order of the columns, number of decimals,
format for the date-time string) was not decided upon. This simple format
should enable easy exchange between the partners and reading from any
programming language or data-analysis tools.
3.4 Collocation method
The validation of the results with references needs to take the closest start
point for the vectors of the two datasets.
3.5 Algorithms evaluation: parameters/statistics, criteria
There are several ways to compare data to reference dataset, relevant
metrics for the SID are Firstly:
standard deviation estimate of the difference between the data and
the references
the bias of the difference between the data and the references (in
particular for high displacement values)
Secondly:
the computation time
the number of estimated vectors
the resolution, spatial and temporal coverage
The potential for back tracking will be also analysed and quantified.
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We will start these estimates on
the U and V components (East and North)
the X and Y components (grid)
magnitude and direction
The method will be applied only where reference data are available, which
has limitations (in particular to test the sensitivity to different ice conditions,
periods, areas...). One can note here that the RRDP is designed by the
reference data rather than the satellite data.
The method will be applied to compare algorithms, independently of the
satellites data (frequency, active/passive, resolution, etc...), the RRDP
should be designed to allow processing on gridded daily maps but also
swath data.
The goal of the activity is to select the “best” algorithm(s), it is not obvious
that only one algorithm has the best performance for each criteria and we
may select a pair of algorithms with description of advantages,
performances and limitations of each.
3.6 Sensitivity
During the validation of the final SID algorithm, if possible we will have a
look at the relative performance of the algorithms regarding
sea ice conditions (melt, pack ice, season...)
summer data analysis
hemisphere (Arctic and Antarctic areas)
drift values (areas with mean low values, and with mean high
values)
ice type (MY, FY...)
area (MIZ, pack ice, high and low values areas...)
grid
These analysis will be made if possible (i.e. if data available).
3.7 Uncertainties
A specific work will be conducted to deduce uncertainties for the inferred
vectors, depending on the time and space scales, algorithms, and other
parameters. The performace of the algorithm depends on the uniqueness of
the available patterns. It can therefore vary between for different datasets
and even within a scene. The Project partners will derive a proxy for the
performance and related uncertainty of the algorithm based on measures,
which describe:
texture itself (e.g. Hollands, 2015)
phase correlation surface (e.g. Karvonen, 2012)
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4 Roles and Responsibilities
Project partners are tasked with working on the RRDP, deriving high quality
SID information from a number of independent satellites and several
algorithms. The RRDP will be published on the project web site.
The following tasks will be mainly followed by
Prepare the database for RRDP : DMI (lead), DTU
Build the RRDP and implement the algorithms : DTU (lead),
MetNorway
Algorithm selection : MetNorway (lead), all
Uncertainties estimate : AWI (lead), all
Document the SID algorithms for future production : MetNorway
(lead), all
Future new sensors : NERSC (lead)
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5 Master Time Schedule
Compilation of database and algorithms of RRDP (Feb-May 2016)
Implement of algorithms (April-June 2016)
Test runs of algorithms (June-Sept 2016)
Evaluation of results and selection of algorithms (Sept 2016-March
2017)
Study of the uncertainties estimate of the ECV (Sept 2016-March 2017)
Document the whole processing and the results of the WP (August 2017)
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6 Summary
This report describes
how we will compose the RRDP dataset for SID algorithm comparison
and selection
how we will analyse the candidate SID algorithms to select the final SID
ECV algorithm.
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Appendix A References
Agnew, T., H. Le, T. Hirose, 1997 : Estimation of large scale sea ice motion
from SSM/I 85.5 GHz imagery, Ann. Glaciology, 24, 305-311.
Emery, W.J., C.W. Fowler, J.A. Maslanik, 1997: Satellite-derived maps of
Arctic and Antarctic sea ice motion : 1988-1994, Geophys. Res. Lett., 24
(8), 897-900. doi : 10.1029/97GL00755
Girard-Ardhuin, F., R. Ezraty, 2012 : Enhanced Arctic sea ice drift estimation
merging radiometer and scatterometer data. IEEE Trans. Geosci. Remote
Sensing, vol. 50 (7), pp 2639-2648. Doi : 10.1109/TGRS.2012.2184124.
Haarpaintner, J., 2006 : Arctic wide operational sea ice drift from enhanced
resolution QuikSCAT/SeaWinds scatterometry and its validation. IEEE Trans.
Geosci. Rem. Sens., 44 (1).
Hollands, T. (2012) : Motion tracking of sea ice with SAR satellite data , PhD
thesis, Universität Bremen. hdl:10013/epic.40814, http://nbn-
resolving.de/urn:nbn:de:gbv:46-00102948-13
Hollands, T., S. Linow, W. Dierking, 2015 : Reliability measures for sea ice
motion retrieval from synthetic aperture radar images, IEEE JSTARS, 8 (1),
pp. 67-75. doi: 10.1109/JSTARS.2014.2340572
Karvonen, J., 2012: Operational SAR-based sea ice drift monitoring over the
Baltic sea, Ocean Sci., 8, doi:10.5194/os-8-473-2012.
Kimura, N., A. Nishimura, Y. Tanaka, H. Yamaguchi, 2013: Influence of
winter sea ice motion on summer ice cover in the Arctic, Polar Res., 32,
doi:10.3402/polar.v32i0.20193.
Komarov A.S., D.G. Barber, 2014 : Sea ice motion tracking from sequential
dual-polarization RADARSAT-2 images. IEEE Trans. Geosci. Rem. Sens., 52
(1).
Kwok, R., J.C. Curlander, R. Mc Connell, S.S Pang, 1990 : An ice motion
tracking system at the Alaska SAR facility. IEEE J. Ocean. Eng, 15.
Lavergne T., S. Eastwood, Z. Teffah, H. Schyberg, L. Breivik, 2010: Sea ice
motion from low-resolution satellite sensors: an alternative method and its
validation in the Arctic. J. Geophys. Res., C10032, doi:
10.1029/2009JC005958
Linow S., T. Hollands, W. Dierking, 2015: An assessment of the reliability of
sea ice motion and deformation from synthetic aperture radar data, Annals
of Glaciology, 56 (69), pp. 229-234 . doi: 10.3189/2015AoG69A826
Liu, A. K., Y. Zhao and S. Y. Wu, 1999 : Arctic sea ice drift from wavelet
analysis of NSCAT and special sensor microwave imager data. J. Geophys.
Res., 104 (C5), 11529-11538.
Muckenhuber, S., A. Korosov, S. Sandven, 2015 : Sea ice drift from
Sentinel-1A SAR imagery using open source feature tracking. The
Cryosphere, under discussion
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Ninnis, R.M., W.J.Emery, M.J. Collins, 1986: Automated extraction of pack
ice motion from advanced very high resolution radiometer imagery. J.
Geophys. Res., 91, doi: 10.1029/JC091iC09p10725. issn: 0148-0227.
Rozman, P., J. A. Holemann, T. Krumpen, R. Gerdes, C. Koberle, T.
Lavergne, S. Adams, and F. Girard-Ardhuin, 2011: Validating satellite
derived and modelled sea-ice drift in the Laptev Sea with in situ
measurements from the winter of 2007/08, Polar Res., 30, 7218,
doi:10.3402/polar.v30i0.7218.
Sumata, H., T. Lavergne, F. Girard-Ardhuin, N. Kimura, M. A. Tschudi, F.
Kauker, M. Karcher, R. Gerdes, 2014: An intercomparison of Arctic ice drift
products to deduce uncertainty estimates, J. Geophys. Res. Oceans, 119,
4887–4921, doi:10.1002/2013JC009724.
Thomas, M., S. Misra, C. Kambhamettu and J.T. Kirby, 2005: A robust
motion estimation algorithm for PIV, Measurment Science and Technology,
16, pp. 865-877, doi:10.1088/0957-0233/16/3/031