Low Resolution Sea Ice Drift Product User's Manual

Full text

Ocean & Sea Ice SAF
Low Resolution Sea Ice Drift
Product User’s Manual
GBL LR SID — OSI 405
Version 1.4 — March 2010
Thomas Lavergne and Steinar Eastwood

Documentation Change Record:
Document
version
Software
version Date Author Description
v0.9 - 03.12.2008 TL Initial version, before review
v1.0 - 14.01.2009 TL Amended by reviewers in the PCR
for OSI-405
v1.1 - 19.02.2009 TL Add a forgotten flag meanings de-
scription
v1.2 4.0 01.10.2009 TL Description of the multi sensor prod-
uct and of the product files
v1.3 4.0 15.11.2009 TL Change value of the time dataset in
product files (see p. 13). Document
the change in directory achitecture at
the FTP server (section 4.6).
v1.4 4.0 17.03.2010 TL Document the EUMETCast dissemi-
nation (section 4.6.2) and the direc-
tion of the dY axis (figure 2).
The software version number gives the corresponding version of the OSI SAF High Latitude
software chain for which the product manual is valid.

SAF/OSI/CDOP/met.no/TEC/MA/128
Table of contents
Table of contents
1 Introduction 1
1.1 The EUMETSAT Ocean and Sea Ice SAF . . . . . . . . . . . . . . . . . . . . 1
1.2 Scope of this document . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.3 Short introduction to the product . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.4 Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Algorithms 4
2.1 Building daily maps of satellite signal . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Ice motion tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.3 Merging daily products in a daily multi-sensor analysis . . . . . . . . . . . . . 7
3 Processing scheme 9
3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2 Primary processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.3 Daily calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4 Data description and distribution 12
4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4.2 Sea ice drift datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4.3 Rejection and Quality Index flags . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.4 Global attributes to the product file . . . . . . . . . . . . . . . . . . . . . . . . 15
4.5 Grid characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.6 Data distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
5 Examples of products 18
6 Acknowledgments 20
A Sea Ice drift products in NetCDF format 21
References 25
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 1
1. Introduction
1.1 The EUMETSAT Ocean and Sea Ice SAF
For complementing its Central Facilities capability in Darmstadt and taking more benefit from
specialized expertise in Member States, EUMETSAT created Satellite Application Facilities
(SAFs), based on co-operation between several institutes and hosted by a National Meteo-
rological Service. More on SAFs can be read from www.eumetsat.int.
The Ocean & Sea Ice Satellite Application Facility (OSI SAF) is producing on an oper-
ational basis a range of air-sea interface products, namely: wind, sea ice characteristics,
Sea Surface Temperatures (SST) and radiative fluxes, Surface Solar Irradiance (SSI) and
Downward Longwave Irradiance (DLI).
For the Continuous Development and Operation Phase (CDOP) — 2007 to 2012 — the
OSI SAF consortium is hosted by Mto-France. The sea ice processing is performed at the
High Latitude processing facility (HL centre), operated jointly by the Norwegian and Danish
Meteorological Institutes.
Note: All intellectual property rights of the OSI SAF products belong to EUMETSAT. The
use of these products is granted to every interested user, free of charge. If you wish to
use these products, EUMETSAT’s copyright credit must be shown by displaying the words
”copyright (year) EUMETSAT” on each of the products used.
1.2 Scope of this document
This document is one of the product manuals dedicated to the OSI SAF product users.
It describes the low resolution sea ice drift product. Two sets of ice motion products are
delivered by the SAF:
•Low resolution ice drift product (OSI-405);
•Medium resolution ice drift product (OSI-407).
This Product Manual only pertains to the low resolution product
(OSI-405).
See http://saf.met.no for real time examples of the products as well as updated infor-
mation. The latest version of this document can also be found there, along with up-to-date
validation and monitoring information.
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 2
General information about the OSI SAF is given at http://www.osi-saf.org.
Chapter 2presents a brief description of the algorithms and chapter 3gives an overview
of the data processing. Chapter 4provides detailed information on the file content and
format, and chapter 5proposes some visualization example of the product.
1.3 Short introduction to the product
For the time being, only Northern Hemisphere sea ice drift is
processed and distributed.
Low resolution ice drift datasets are computed on a daily basis from aggregated maps
of passive microwave (e.g. SSM/I, AMSR-E) or scatterometer (e.g. ASCAT) signals. The
typical resolution/spacing of those input images is 12.5 km. Wide swaths, high repetition
rates and independence with respect to the atmospheric perturbations permit daily coverage
of most of the sea ice covered regions. In summer, surface melting and a denser atmosphere
preclude from the retrieval of meaningful information. From October to Mai-June, however,
the excellent coverage makes it possible to extract 48 hours global ice drift vectors at a
spatial resolution of 62.5 km.
In the OSI SAF chain, one such ice drift map is derived for each sensor used as input to
the processing chain (single sensor product). An additional merged (multi-sensor) dataset
is distributed which combines the low resolution products in a daily analysis.
Sea ice drift vectors are not processed during summer (May, 1st to September, 30th) but
product files are distributed all year long (see section 3.3.6).
Product timeliness is at present approximately 7 hours (from last recorded swath). This
means that, on day 0 around 0600 UTC, low-resolution ice drift datasets are distributed
which cover the period from day -3 to day -1. For example, ice drift from 2008/02/16 to
2008/02/18 is delivered on 2008/02/19 around 0600 UTC.
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 3
1.4 Glossary
ASCAT Advanced SCATterometer
AVHRR Advanced Very High Resolution Radiometer
AMSR-E Advanced Microwave Scanning Radiometer for EOS
CDOP Continuous Development and Operations Phase
DMI Danish Meteorological Institute
DMSP Defense Meteorological Satellite Program
HL High Latitudes
met.no Norwegian Meteorological Institute
NetCDF Network Common Data Form
NH Northern Hemisphere
SAF Satellite Application Facility
SSM/I Special Sensor Microwave/Imager
UMARF Unified Meteorological Archive and Retrieval Facility
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 4
2. Algorithms
In this section, we briefly describe the algorithms used to extract ice drift information from
pairs of daily low resolution satellite images. Note, however, that a detailed Algorithm Theo-
retical Basis Document (ATBD, [8]) is available from the web pages, which intends at giving
an in depth understanding of the science and algorithm behind the low resolution ice drift
product.
First, we introduce the preprocessing steps implemented to prepare daily maps of satel-
lite signal from individual swath data. In the second section, we describe the tracking
methodology to compute drift vectors, as well as the filtering of obviously erroneous esti-
mates from the vector field. Finally, the merging strategy to obtain a multi sensor ice drift
product is presented.
2.1 Building daily maps of satellite signal
The ice tracking processor implemented in the OSI SAF first constitutes daily average maps
of satellite signals. The satellite signal is either brightness temperatures for passive mi-
crowave instruments (e.g. SSM/I and AMSR-E) or radar backscatter for scatterometers (e.g.
ASCAT). The specific wavelengths and polarization used are discussed at a later stage.
2.1.1 Daily average field of satellite signal
Because ice drift is tracked between two images, each daily run begins by building two aver-
age daily images, with central time 1200 UTC. All swath data relevant to one of the two dates
of interest are collected and remapped in a common grid. At each grid location in the daily
image is affected an average of the values coming from the selected swathes. Because sea
ice moves during the 24h aggregation temporal window, a strategy is implemented to reduce
the blurring due to motion : a linear temporal weighting function is used when computing the
average. The function is chosen so that swath pixels with sensing time 1200 UTC enter the
average with a weight of 1. Conversely, pixels with sensing time 0000 UTC and 2400 UTC
have a weight of 0. The average sensing time at each location in the daily image (tavg) is
also computed (with the same temporal weighting function) and stored for later use.
2.1.2 Laplacian filter
As proposed in [2] a Laplacian filter is applied to the daily maps resulting from the previous
section. This step aims at enhancing the signal’s intensity patterns that are to be tracked by
the ice drift processor. Conversely to [2], however, the Laplacian filter we apply is not from
an approximated formula and is not followed by a median filter. The ice tracking described
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 5
Figure 1: Example ice drift product in the Beaufort Sea retrieved from AMSR-E imagery
(37 GHz). Both are 48 hours ice drift from 29th to 31st January 2008. The product on the
left hand side was computed using MCC while the product on the right is retrieved with the
CMCC. The removal of the quantization noise on the right hand side ice drift field enhances
the angular resolution and spatial smoothness of the motion vectors.
in the next section is applied on pairs of Laplacian fields and not on pairs of daily average
images.
2.2 Ice motion tracking
2.2.1 Individual ice motion tracking using the CMCC method
As the case for the majority of ice drift products, the vectors are optimized independently
from the others, using a pattern matching algorithm which boils down to finding maximum
cross correlations between sub-images (aka patterns) extracted respectively from the start
and end images of the drift.
Continuous Maximum Cross Correlation
The algorithm implemented in the OSI SAF chain is, however, more advanced than the clas-
sical Maximum Cross Correlation (MCC) which is usually chosen, for example by [2] or [4].
Indeed, it implements the CMCC (Continuous Maximum Cross Correlation) method, where
pixel values in the sub-images are interpolated from those in the nominal pixels. At this
stage, a simple bi-linear interpolation is implemented. This strategy allows the formulation
of the image matching problem in a continuous formalism which permits avoiding the quan-
tization effect. The latter is an artifact of the MCC-based datasets and is responsible for
their limited angular resolution when applied with lowresolution signal on short time spans.
Figure 1shows the better angular resolution a CMCC-based product (right panel) can have,
with respect to one based on the MCC (left).
It is important to note that the product on the right of figure 1(CMCC) is not a smoothing
of the one on the left (MCC). In the CMCC, only the base satellite images are interpolated
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 6
’on the fly’, that is during the optimization procedure. Nor the vector field nor the correlation
function are interpolated or smoothed. Smoothing the MCC vector field would not bring such
a spatial continuity and would not bring any extra information in the regions with very small
motion. Interpolating the MCC-based correlation function returns a different vector field and
is part of the solution introduced by [5].
In order for the CMCC implementation to be fully described, some parameters have to
be known, such as 1) the size and shape of the sub-images, 2) the size of the search area
and 3) the spacing between the drift locations on the output product grid. Those are 3 tuning
parameters which have an influence on the retrieved vectors and that are decided upon by
taking into account the pixel resolution of the images. Refer to section 3.3 for numerical
values implemented in the OSI SAF chain.
Merging of polarization channels
As part of the enhanced ice-tracking methodology, the OSI SAF chain maximizes the sum of
the cross correlations of all the channels available, instead of delivering several products for
each satellite. In the case of the SSM/I 85 GHz product, for example, two pairs of images are
available. One for the vertically polarized channel and the other for the horizontal polariza-
tion. Instead of applying the ice tracking processor twice and merging the two independent
products at a later stage (like in [4]), the OSI SAF ice drift processor directly maximizes
the sum of two cross correlations, each using the differently polarized pairs of images. As
introduced in [7], this is an efficient way of taking into account the different uncertainty de-
riving from each channel and constitutes a first level of merging between ice drift datasets
(intra-platform merging). This is also the reason why only one ice drift product is available
per sensor, although two channels are often used.
Varying pattern dimensions
As in [4], the size of the sub-image is modified close to open water, land or missing data to
try and have vectors as close as possible to the border of the ice field. In those cases, half
the nominal radius (in km) is used. This behaviour is documented in the product file and
accessible to the user in the product flag (section 4.3).
2.2.2 Filtering of the vector field
Once all ice drift vectors in the product grid have been estimated independently from each
other, a tiny number look obviously wrong. Those need to be removed or corrected by a
filtering step, whose strategy is described in this section.
The reasons for having those erroneous vectors are mainly two :
•Local noise in one or both of the images can create an artificial maximum correlation
which is not related to the motion of sea ice;
•The continuous optimization algorithm implemented in the CMCC converges to a local
optimum and not necessarily to the true one.
In both cases, a filtering strategy is implemented to automatically detect and, if possible,
correct those vectors. It is based on the distance between the end point of a drift vector
to the end point of the average drift vector, computed over the 8 direct neighbours. This
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 7
distance we name ∆avg . It is built as a metric for the deviation of each estimate from the
local average flow. For filtering out obviously erroneous vectors, all ∆av g distances are to be
lower than a given threshold, ∆max . After computation of ∆avg for each ice drift vector, an
iterative processing starts. The vector exhibiting the worst ∆avg score, if the latter is greater
than ∆max, is re-optimized via the CMCC. For this new optimization, however, a domain
constraint is added so that the maximum is searched for in a limited circular area of centre
(xavg , yavg )and radius ∆max.(xavg , yavg )is the end point of the local average drift vector. If
a new, satisfying maximum cross correlation is found in this area, then the newly optimized
vector vnew replaces the old one vold. If not, both the erroneous vold is removed from the
vector field and vnew is discarded. In both cases, the average vectors as well as ∆avg of the
8 neighbours are updated and the list of all vectors (including the corrected one) is sorted
again. The filtering starts again until all ∆avg are lower than ∆max .
By starting from the worst ∆avg score and iteratively re-optimizing vectors and updating
their neighbours, this strategy is often able to correct several erroneous vectors even if they
are close together in the output grid. It has, furthermore, a good correction rate, which al-
lows having only few missing vectors in the product file. Vectors who do not have enough
neighbours to compute a meaningful average as well as those which are discarded or cor-
rected by this filtering step are flagged and the information is included in the product file, as
described in section 4.3.
A final filtering level is implemented which discards vectors having too low a maximum
cross correlation value. This is also reported in the status flag dataset, in the product
file.
2.3 Merging daily products in a daily multi-sensor analysis
The OSI SAF HL ice drift processing chain produces and distribute single sensor, daily
products from all available instruments. Each of those products can be used as such for
assimilation in geophysical models but might not be optimal when it comes to the tasks of
forcing an ice model or performing process studies. This for two reasons :
•The start and end times of single sensor products are not homogeneous over the grid.
This means that ice drift vectors in one product file do not correspond to exactly the
same period of time. This is only an issue in regions presenting long drift vectors, when
combined with rapidly changing drift directions, like is the case when an atmospheric
low pressure system travels above sea ice.
•The single sensor ice drift products have a tendency to exhibit areas of missing data.
Those might be due either to failure in the processing or from missing input swath data.
The latter is particularly true for the AMSR-E instrument for which we are often missing
swath data when the processing chain starts.
•There is a region with constantly missing value close to North Pole due to the lack of
satellite observations at very high latitudes.
In order to cope with those aspects and acknowledge that the various single sensor prod-
ucts have different quality statistics, a pragmatic merging procedure is setup which starts by
gathering the available single sensor ice drift products for the current date.
The product grid is then partitioned into the nogap and gap pixels. nogap pixels are
those with at least one single sensor product having a valid vector at this location.
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 8
At nogap locations, the merged ice drift vector is computed as a weighted average of the
single sensor vectors available. The weights are inverse to the standard deviation associated
to each sensor. Particularly, it means that a vector from AMSR-E will have more weight than
one from SSM/I or ASCAT. In our simple setup, there is no correlation between the x and y
components of the drift vector and no correlation between the vector and its neighbours.
Once all nogap locations are processed, they enter a spatial interpolation step for com-
puting the gap locations. The spatial interpolation is based on an exponentially decaying
weight of the distance to the grid cell. Only the gap locations are interpolated, the nogap
come only from the weighted average. All ice drift vectors which are computed as a spatial
interpolation are accordingly flagged in the status flag dataset (section 4.3).
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 9
3. Processing scheme
3.1 Overview
The delivered products are 48 hours sea ice drift, whose start and end time are centered
on 1200 UTC. Sensors that are currently processed into single sensor products are listed in
this section. A merged, multi sensor product is also distributed (section 2.3). We also give
a brief overview of the data flow and external data sources that are used for the processing
of each sensor. The sensors used for the OSI SAF High Latitude low resolution sea ice drift
processing are summarized in table 1.
Table 1lists only the instruments that are used in the daily processing at the date of
writing this document. Other sensors have been used for reprocessing activities but are no
more active. Check the OSI SAF sea ice web portal http://saf.met.no for an updated list of
those sensors.
3.2 Primary processing
3.2.1 Satellite data
All instrument swath data are used as NetCDF file formats and come from earlier processing
in the OSI HL chain. Some of those processing steps are described in the Product User’s
Manual for OSI SAF sea ice products ([1]).
3.2.2 Ancillary data
Sea ice mask
An ice mask product is necessary for the processing. It should provide, on a daily basis, the
sea ice extent as well as the ocean and land surface mask. The operational, multi sensor,
sea ice edge product of the OSI SAF is used for this purpose. This product is described in
the sea ice PUM. Two sea ice edge products are used in daily sea ice drift processing : one
for the start image and one for the end image.
Instrument Platform Channels Sampling [km] Footprint1[km]
SSM/I DMSP-F15 85 GHz, H+V pol. 12.5 14x16
AMSR-E EOS Aqua 37 GHz, H+V pol. 10 14x8
ASCAT Metop-A C band σ012.5 (25-34)x(25-34)
Table 1: Sensors and corresponding channels used in the OSI SAF ice drift processing.
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 10
3.3 Daily calculations
Daily calculations are performed each day (D) at 0400 UTC and are based on data collected
from the two earlier days : D-3 and D-1. The sea ice drift chain is run right after the concen-
tration, type and edge daily analysis since it relies on those daily sea ice state products (e.g.
atmospherically corrected SSM/I swath data, sea ice edge product, etc...).
3.3.1 sea ice mask
The 10 km gridded NH operational sea ice edge product2is remapped to a 12.5 km resolu-
tion polar stereographic grid. The same 12.5 km grid is used for remapping the swath data
and constitute the daily images.
3.3.2 Daily images
Swath files are remapped and averaged on a 12.5 km polar stereographic grid, the same
as for the ice mask. When several channels are present for a given instrument (e.g. two
polarizations) they are kept in the same file. The average sensing time for each pixel is also
recorded.
3.3.3 Laplacian filtering
Laplacian filtering is applied to sea ice pixels only. The ice mask is used. When a pixel is
close to the sea ice edge, to land, or to missing data, the Laplacian computation is adapted
to exclude those neighbor pixels which are not over sea ice, according to the mask. The
laplacian fields (one for each of the instrument’s channel) are appended to the file storing
the daily average images.
3.3.4 Ice motion extraction and filtering
As they share large portion of software code, the ice motion extraction algorithm implement-
ing the CMCC and the filtering are performed in the same software. This software takes as
input the following parameters :
•Radius of the sub-image : the radius (in kilometers) of the sub-images to be crosscor-
related at each step of the CMCC. The pattern’s shape approximate a disk which is
computed once, at North Pole. The disk is contained in a 11x11 pixels square.
•Maximum ice drift velocity : The maximum expected speed for the pattern’s displace-
ment. Once integrated over the time span separating the start and end images (48 h)
this parameter gives a maximum drift distance (in km) in which the CMCC will search
for the maximum of the correlation function. The value used is 0.45 m.s-1.
•Output product grid : The ice drift computations are only performed at location on this
grid. It is a polar stereographic 62.5 km grid covering the NH domain of the other
OSI SAF ice-state products. The parameters for the grid are given in section 4.5. In
practice it means that ice drift locations are every 5 image pixels and, thus, that the
2ice edge nh YYYYMMDDHHMN.hdf
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 11
sub-images used in the correlation matching do overlap. Those are the same values
as those used by [2].
•Maximum distance to average vector : For the filtering step (∆max ). This is set to
10 km.
•Minimum cross correlation threshold : As a last filtering step, all vectors with a cross
correlation of less than 0.3 are discarded and flagged.
3.3.5 Multi sensor merged product
The merging step does not imply any image correlation computation. It is implemented in a
different module and starts by searching for all the available single sensor products for the
daily product, then apply the strategy described in section 2.3.
3.3.6 Summer products
Due to surface melting and a denser Arctic atmospere, sea ice drift vectors cannot be re-
trieved reliably during summer from the instruments and channels we are currently using.
Therfore, and for disrupting as little as possible operational assimilation schemes using
the ice drift datasets, empty product files are made available through the normal distribution
methods (see section 4.6). Those files are formatted as normal ice drift product files, but
contain no valid vectors, while the status flag dataset is set accordingly (see table 2).
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 12
4. Data description and distribution
4.1 Overview
The OSI SAF ice drift products are available in NetCDF format. They are all built on the same
model and include a status flag dataset which is also described in this section. Results
from validation exercises and, especially, the bias and uncertainty estimates resulting from
them are available in a separate validation report ([6]), at the OSI SAFSea Ice web portal
http://saf.met.no.
The ice drift product files are designed to follow the CF conventions for gridded prod-
ucts. Those conventions (http://cf-pcmdi.llnl.gov/) give rules to present attributes, units and
projection as well as dimensions.
An example product file header in CDL notation is given in appendix A(page 21).
4.2 Sea ice drift datasets
4.2.1 Drift parameters : Definitions and units
A sea ice drift estimate is defined by 6 values : lat0,lon0,t0,lat1,lon1and t1, where
subscript 0 (respectively 1) refers to the start (resp. stop) time and position for the displace-
ment. The ice drift product thus expresses that a parcel of ice which was at position lat0,
lon0at time t0, is at position lat1,lon1at time t1. From those 6 quantities, all other ice
drift datasets (like drift distance, direction, eastward component, etc...) can be computed by
interested users.
Although they too can be retrieved from the above mentioned 6 quantities, the drift com-
ponents along the X and Y axis of the product grid (dX and dY) are included in the product
file. This is because :
1. their later derivation is more complex due to the use of the Earth mapping function;
2. they are the primary variables the CMCC estimates;
3. the uncertainty estimates of the ice drift product are given for those two parameters in
the validation report ([6]), as they do not scale with latitude.
All geographical coordinate fields are given as degrees (latitude or longitude). The X and Y
drift components have unit of km.
As any sea ice drift product processed from pair of satellite images ([3,4,5]), the prod-
uct at hand does not define an ice velocity, neither instantaneous nor averaged. The only
information contained in the dataset is that an ice parcel observed at position (lat0,lon0)
is at another position (lat1,lon1) at the end of the drift period (48 hours). Particularly, the
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 13
dataset does not say anything about the trajectory (hence the velocities) of the ice between
the two reference times t0and t1. Although an arrow-shaped simbol is commonly used for
representing the displacement, a straight line trajectory is not implied. This is the reason
why the name for the dataset is not sea ice velocity and the unit is not m.s-1.
In the NetCDF file, the provided datasets are : lat,lon,lat1,lon1,dX and dY.
4.2.2 Time information
An ice drift vector must come with two time values (see above for t0and t1). In the product
file, we give 4 of them. Depending on the usage the ice drift product is intended for, users
can chose between two types of time information :
•Because our processing is performed from daily maps, it can be considered a fair
approximation that t0=t1=1200 UTC everywhere in the grid and for all sensors.
This time information is given at two locations in the product file :
–Global attributes start date and time and stop date and time in a string
format (e.g. 2008-01-01 12:00:00).
–Dataset time bnds[2]:time bnds[0] =t0and time bnds[1] =t1. Those
values are given as seconds since 01/01/1978.
•More accurate time information is additionally available for each individual vectors.
This is because the scan pattern of the instrument and the orbit parameters of the
platform all influence the time at which a particular region of the surface is sensed.
This time we record while constituting the daily average images and report for each ice
drift vector in two datasets :
–dt0 which contains the delta time (in seconds) for each vector’s start time to the
central time in time bnds[0].
–dt1 which contains the delta time (in seconds) for each vector’s end time to the
central time in time bnds[1].
For compliance with CF (and COARDS) format conventions, a scalar value has to be
specified for the time dataset as well. Since the ice motion we report is a time-extensive
quantity (from t0to t1), this scalar value has no physical meaning and was decided upon
arbitrarily. As of version 1.3 of the product files (November 2009), the value of the time
dataset is t1. Note that it was t0in earlier versions of the product. This is to ensure that the
suite of daily OSI SAF sea ice products (concentration, edge, type, drift, etc...) all have the
same time value and can be displayed on the same time stamp.
4.3 Rejection and Quality Index flags
Except for the lat and lon datasets, all the above mentioned fields have valid values only
when the ice drift product could be retrieved and is of acceptable quality. A status flag
dataset is thus also included in the product file to indicate for each pixel :
•if no valid retrieval could be made at this location, why;
•if a valid retrieval is proposed, what is its a-priori quality.
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 14
Value Meaning Reason
0missing input Missing image data. Whether because one
or more swath are missing or because of the
observation hole close to North Pole.
1over land Location is over land
2no ice Location is over open water (or open ice)
3close to coast or edge Location is over ice, but too close to land or
ice edge to be processed.
4summer period Untrusty vector was removed because in
Summer period (start date between May 1st
to September 30th ).
10 processing failed The optimization of the correlation function
(CMCC) failed.
11 too low correlation Vector was removed because the maximum
cross correlation was below the minimum
threshold.
12 not enough neighbours Vector lies on its own and cannot be assessed
by enough neighbours.
13 filtered by neighbours Vector was removed because too inconsistent
with the average drift vector from neighbour-
ing pixels.
Table 2: Value and meaning for the Rejection Flags entering the status flag dataset.
Flags of the first flavor are called rejection flags while those from the second flavor are quality
index flags. Flags are encoded following CF conventions, that is with flag values and
flag meanings attributes. Both flavors are encoded in a unique status flag dataset,
since both form a non-overlapping partition of the pixels.
4.3.1 Rejection flags
Rejection flags range from 0 to 19. All pixels having a value of the status flag dataset in
this range do not have a valid value for the other ice drift datasets. Table 2lists the values
and meaning of the rejection flags for ice drift products.
4.3.2 Quality index flag
Quality index flags range from 20 to 30. All pixels having a value of the status flag
dataset in this range have a valid value for the other ice drift datasets. A status flag
between 20 and 29 is reported to draw the attention of the user to vectors with possible
degraded quality. A value of 30 indicates vectors which we trust have nominal quality. Table 3
lists the values and meaning of the quality index flags for ice drift products.
Although several vectors are assigned status flag values between 20 and 29 in all
daily products, those have not been shown to exhibit poorer quality than those with flag
value 30. For all practical purposes, and until otherwise proven, all vectors having a flag
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 15
Value Meaning Reason
20 smaller pattern The CMCC was applied with a smaller radius
for the sub-images, due to the proximity to
coast, edge or missing value.
21 corrected by neighboursThe vector was not retrieved in the first CMCC
step but was constrained using the neighbour-
ing vectors.
22 interpolated The vector was not retrieved by CMCC but
was interpolated from the neighbouring vec-
tors. Only appears in the multi-oi product.
30 nominal quality The vector was retrieved by CMCC, indepen-
dently of others.
Table 3: Value and meaning for the Quality Index Flags entering the status flag dataset.
value larger or equal to 20 can be used, in the limit of the quantitative uncertainty estimates
reported in the validation report (accessible through http://saf.met.no).
4.4 Global attributes to the product file
Following the CF convention, global attributes are added to describe the product file content.
They are mainly intended to be read by users (like the abstract) but some of them might also
be parsed and analyzed by visualization software or help find the product files in metadata
search tools. Global attributes for an example file are included in the CDL example file in
appendix A(page 21).
4.5 Grid characteristics
The ice drift product grid is adapted from the 10 km grid used for the other OSI SAF ice
product. Below are given the details of the grid definitions and approximate maps of the
grid extents, corner coordinates are referenced to pixel center. Projection definitions in the
form of PROJ-4 initialization strings are also given (see http://www.remotesensing.org/proj
for details).
Projection Polar sterographic projection true at 70◦N
Central Meridian 45◦W
Corner point 35.14838◦N;10.30485◦W — X: -3750 km; Y:5750 km
Earth’s shape a = 6378273 m / b = 6356889.44891 m
PROJ-4 string +proj=stere +a=6378273 +b=6356889.44891
+lat 0=90 +lat ts=70 +lon 0=45
Resolution 62.5 km
Size 119 columns, 177 lines
Table 4: Geographical definition for Northern Hemisphere grid, NH
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 16
dX
dY
Figure 2: Coverage of the Northern Hemisphere grid is shown by the black box. The
coordinate system used for the dX and dY components of the drift vectors
is also shown.
Note that xc and yc datasets in the NetCDF file contain the grid coordinates (in km) of
the center of each pixel. Figure 2plots the area covered by the grid described in table 4.
See also figure 5in chapter 5.
Figure 2documents that sea ice drift is retrieved in the central Arctic Ocean but also in
the Baffin Bay, along the East Greenland coast, in Hudson Bay, in the Bering Sea and Sea
of Okhotsk. Note the direction of the dY axis.
4.6 Data distribution
4.6.1 Sea Ice FTP server
Sea ice drift product files can be collected at the OSI SAF Sea Ice FTP server. At the
OSI SAF Sea Ice FTP server ftp://saf.met.no/prod/ice/ the products are available on NetCDF
format (under directory drift lr). Here, products from the last 31 days can be collected.
In addition there is a separate directory with archive of all the sea ice drift products under
ftp://saf.met.no/archive/ice/drift lr. The file name convention for these products is given in
the table below.
Naming convention for ice drift files at OSI SAF FTP server
ice drift <area> <gridInfo> <source> <startdate12>-<enddate12>.nc
<area> nh for Northern Hemisphere product.
<gridInfo> projection/grid information, polstere-625.
<source> Instrument used for the product. One of amsr-aqua,ssmi-fxx,
ascat-metopA or multi-oi.
<date12> Start or Stop date and time of the product, on format
YYYYMMDDhhmn.
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 17
Note that the primary separating character is (underscore) and that the secondary one
is -(dash). For compatibility with the other sea ice products from OSI SAF, a secondary
level separator appears between the two dates. This is because the two dates form together
a unique timeInfo.
The architecture at the OSI SAF Sea Ice FTP server is:
ftp://saf.met.no
‘-- prod
‘-- ice
‘-- drift_lr
|-- merged
| ‘-- ice_drift_*.nc
‘-- single_sensor
|-- amsr-aqua
| ‘-- ice_drift_*.nc
|-- ascat-metopA
| ‘-- ice_drift_*.nc
‘-- ssmi-f15
‘-- ice_drift_*.nc
4.6.2 EUMETCast dissemination and UMARF archiving
As of now, only the merged (multi-sensor) products are disseminated through EUMETCast.
Naming convention for ice drift files on EUMETCast
S-OSI -NOR -MULT-NH LRSIDRIFT-<enddate12>Z.nc.gz
Since the file name convention for OSI SAF files on EUMETCast does not allow for using
two date-stamps, it was chosen to use the end date for the motion as a date-stamp. Accord-
ingly, ice motion vectors from the 16th to 18th February 2010 are found in file S-OSI -NOR -MULT-NH LRSIDRIFT-2010
(which if another name for file ice drift nh polstere-625 multi-oi 201002161200-201002181200
that can be retrieved from the OSI SAF Sea Ice FTP server, see section 4.6.1).
In the future, the product files will be centrally archived at UMARF.
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 18
5. Examples of products
EUMETSAT OSI SAF Version 1.4 —March 2010

1 2
3 4
5
Five example products from the OSI SAF
sea ice drift processing chain:
1. Ice drift from SSM/I ’F13’ instru-
ment, from 13th to 15th March 2008;
2. Same dates but with AMSR-E sen-
sor;
3. Ice drift using AMSR-E instrument,
from 1st to 3rd January 2008;
4. Same dates, same instrument, but
using the MCC (this product is not
delivered but run as a comparison
processing);
5. Maximum ice extent in 2008, from
13th to 15th March.
Note : only example 5 is on the full prod-
uct grid. All others are ’cropped’ images.

SAF/OSI/CDOP/met.no/TEC/MA/128 20
6. Acknowledgments
Several other projects have financed the research and development efforts necessary to
setup this ice drift product. The DAMOCLES (http://www.damocles-eu.org), MERSEA IP
(www.mersea.eu.org) and iAOOS-Norway (www.iaoos.no) are acknowledged.
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 21
A. Sea Ice drift products in NetCDF
format
n e t c d f i c e d r i f t n h p o l s t e r e −625 m u l t i −oi 200911211200−200911231200 {
dimensi o n s :
tim e = 1 ;
nv = 2 ;
xc = 119 ;
yc = 177 ;
v a r i a b l e s :
i n t P o l a r S t e r e o g r a p h i c G r i d ;
P o l a r S t e r e o g r a p h i c G r i d : g r id m ap pi n g n am e = ”
p o l a r s t e r e o g r a p h i c ” ;
P o l a r S t e r e o g r a p h i c G r i d :
s t r a i g h t v e r t i c a l l o n g i t u d e f r o m p o l e = −45. f ;
P o l a r S t e r e o g r a p h i c G r i d : l a t i t u d e o f p r o j e c t i o n o r i g i n =
90 . f ;
P o l a r S t e r e o g r a p h i c G r i d : s t a n d a r d p a r a l l e l = 7 0 . f ;
P o l a r S t e r e o g r a p h i c G r i d : f a l s e e a s t i n g = 0 . f ;
P o l a r S t e r e o g r a p h i c G r i d : f a l s e n o r t h i n g = 0 . f ;
P o l a r S t e r e o g r a p h i c G r i d : s e m i m a j o r a x i s = 6 37 82 73 . f ;
P o l a r S t e r e o g r a p h i c G r i d : s e m i m i n o r a x i s = 6 35 68 90 . f ;
P o l a r S t e r e o g r a p h i c G r i d : p r o j 4 s t r i n g = ” + p r o j = s t e r e +a
=63 7827 3 +b= 63 56 88 9. 44 891 + l a t 0 =90 + l a t t s =7 0 + l o n 0
=−45” ;
double t i me ( tim e ) ;
tim e : long n am e = ” r e f e r e n c e ti m e o f p r o du c t ” ;
tim e : s tandard nam e = ” t i me ” ;
t im e : u n i t s = ” s ec on ds s in c e 1978−01−01 0 0 :0 0: 0 0” ;
t im e : a x i s = ” T ” ;
tim e : bounds = ” time b nds ” ;
t im e : c omment = ” As o f v e r s i o n 1 . 3 o f t he p r o d uc t , t h e \’
tim e \’ s c a l a r d a t a s e t c o n t a i n s t he en d da t e o f
m ot io n ( was \n ” ,
” b eg i n d at e i n p r e v i ou s v e r s i o n s ) . ” ;
double t i me b nd s ( ti me , n v ) ;
tim e b n ds : u n i t s = ” s eco nd s s i n ce 1978−01−01 00 : 00 : 00 ” ;
double xc ( x c ) ;
x c : a x i s = ” X ” ;
x c : u n i t s = ”km ” ;
xc : long n am e = ” x c o o rd i n at e o f p r o j e c t i o n ( e a s ti n g s ) ” ;
xc : stand a r d n am e = ” p r o j e c t i o n x c o o r d i n a t e ” ;
x c : g r i d s p a c i n g = ” 6 2. 5 0 km” ;
double yc ( y c ) ;
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 22
y c : a x i s = ” Y ” ;
y c : u n i t s = ”km ” ;
yc : long n am e = ” y c o o r di n a t e o f p r o j e c t i o n ( n o r t h i n g s ) ” ;
y c : s ta nd ar d n am e = ” p r o j e c t i o n y c o o r d i n a t e ” ;
y c : g r i d s p a c i n g = ” 6 2 .5 0 km ” ;
f l o a t l a t ( yc , x c ) ;
l a t : long name = ” l a t i t u d e c o or d in a te ” ;
l a t : s ta ndar d name = ” l a t i t u d e ” ;
l a t : u n i t s = ” d e gr e e s n o rt h ” ;
f l o a t l o n ( yc , xc ) ;
lo n : long n am e = ” l o n g i t u d e c o o r d i n at e ” ;
l o n : s ta nd a rd n am e = ” l o n g i t u d e ” ;
l o n : u n i t s = ” d e g re e s e as t ” ;
i n t d t0 ( t ime , yc , x c ) ;
dt 0 : lo ng na me = ” d e l t a t im e f o r s t a r t o f d i sp l ac e me n t ” ;
d t0 : s ta nd ar d n am e = ” s t a r t t i m e d i s p l a c e m e n t ” ;
d t0 : u n i t s = ” s eco nd s ” ;
dt 0 : F i l l V a l u e = −2147483648 ;
d t 0 : v a l i d m i n = −43200 ;
dt 0 : v al i d m ax = 43200 ;
dt 0 : g r id m a p p in g = ” P o l a r S t e r e o g r a p h i c G r i d ” ;
dt 0 : c oo rd in at es = ” l a t lo n ” ;
f l o a t l on 1 ( t im e , yc , x c ) ;
lon 1 : long n ame = ” l o n g i t u d e a t end o f d i sp l a ce m en t ” ;
l on 1 : s ta nd a rd n am e = ” e n d l o n g i t u d e d i s p l a c e m e n t ” ;
l on 1 : u n i t s = ” d e gr e e s e as t ” ;
l on 1 : F i l l V a l u e = −1.e+10 f ;
l on 1 : g r i d m a p p in g = ” P o l a r S t e r e o g r a p h i c G r i d ” ;
l on 1 : c o o r d i n a t e s = ” l a t l o n ” ;
f l o a t l a t 1 ( t im e , yc , x c ) ;
l a t 1 : l ong nam e = ” l a t i t u d e a t end o f dis pl ac em en t ” ;
l a t 1 : s ta nd ar d n am e = ” e n d l a t i t u d e d i s p l a c e m e n t ” ;
l a t 1 : u n i t s = ” d e gr e e s n o rt h ” ;
l a t 1 : F i l l V a l u e = −1.e+10 f ;
l a t 1 : g r i d m a p p i ng = ” P o l a r S t e r e o g r a p h i c G r i d ” ;
l a t 1 : c o o r d i n a t es = ” l a t l o n ” ;
i n t d t1 ( t ime , yc , x c ) ;
dt 1 : lo ng na me = ” d e l t a t im e f o r en d o f d i sp l ac e me n t ” ;
dt 1 : s tan d a r d name = ” en d ti m e d is p la c em en t ” ;
d t1 : u n i t s = ” s eco nd s ” ;
dt 1 : F i l l V a l u e = −2147483648 ;
d t 1 : v a l i d m i n = −43200 ;
dt 1 : v al i d m ax = 43200 ;
d t 1 : g r i d m a p p in g = ” P o l a r S t e r e o g r a p h i c G r i d ” ;
dt 1 : c oo rd in at es = ” l a t lo n ” ;
f l o a t dX ( t i me , yc , x c ) ;
dX : l ong na me = ” comp onent o f t he d is pl ac em en t a lo ng t he x
a x i s o f t h e g r i d ” ;
dX : s ta ndard n a me = ” s e a i c e x d is p l a c e m e n t ” ;
dX : u n i t s = ” km” ;
dX : F i l l V a l u e = −1.e+10 f ;
dX : g r i d m a pp i n g = ” P o l a r S t e r e o g r a p h i c G r i d ” ;
dX : c o o r d i n a t es = ” l a t l o n ” ;
f l o a t dY ( t i me , yc , x c ) ;
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 23
dY : l ong na me = ” co mpone nt o f t h e di sp la ce me nt a lo ng t h e y
a x i s o f t h e g r i d ” ;
dY : s ta ndard n a me = ” s e a i c e y d is p l a c e m e n t ” ;
dY : u n i t s = ” km” ;
dY : F i l l V a l u e = −1.e+10 f ;
dY : g r i d m a pp i n g = ” P o l a r S t e r e o g r a p h i c G r i d ” ;
dY : c o o r d i n a t es = ” l a t l o n ” ;
s h o r t s t a t u s f la g ( t i m e , yc , xc ) ;
s t a t u s f l a g : l ong n ame = ” r e j e c t i o n and q u a l i t y l e v e l f l a g
” ;
s t a t u s f l a g : s ta nd ar d n ame = ” i c e d r i f t x d i s p l a c e m e n t
s t a t u s f l a g ” ;
s t a t u s f l a g : F i l l V a l u e = −1s ;
s t a t u s f l a g : g r i d m a pp i n g = ” P o l a r S t e r e o g r a p h i c G r i d ” ;
s t a t u s f l a g : c o o r d i n a t e s = ” l a t l o n ” ;
s t a t u s f l a g : v a l i d r a n g e = 0 s , 30 s ;
s t a t u s f l a g : f l a g v a l u e s = 0s , 1 s , 2 s , 3s , 4s , 1 0s , 11 s ,
12s , 13s , 20s , 21 s , 22 s , 30s ;
s t a t u s f l a g : f la g me a n i ng s = ” m i s s i n g i n p u t d a t a o v e r l a n d
n o i c e c l o s e t o c o a s t o r e d g e s um m er pe r io d
p r o c e s s i n g f a i l e d t o o l o w c o r r e l a t i o n
n ot e n ou gh n ei gh b ou rs f i l t e r e d b y n e i g h b o u r s
s m a l l e r p a t t e r n c o r r ec t e d b y n ei g h bo u r s i n t e r p o l a t e d
nominal q u a l i t y ” ;
/ / g l o b a l a t t r i b u t e s :
: t i t l e = ” OSI SAF Low R e s o l u t i o n Sea I c e D is p la c em e nt ” ;
: p r o d u c t i d = ” OSI −405” ;
: pr o d u c t n ame = ” o s i s a f l r i c e d r i f t ” ;
: p r o d u c t s t a t u s = ” p r e o p e r a t i o n a l ” ;
: a b s t r a c t = ” G r id d ed i c e d i s pl a c em e nt f i e l d s o b ta i n e d
from s a t e l l i t e image \n ” ,
” p r oc e s si n g . I t i s a lo w r e s o l u t i o n p r o du c t ( 6 2. 5
km r e s o l u t i o n ) . \n ” ,
” The tim e span o f th e ic e di s p la c em e n t is
ap p ro xi ma te l y 48\n ” ,
” h ou r s . T h i s d a t a s e t i s i n t e nd e d b o t h f o r
p ro c es s s t u d i e s a nd\n ” ,
” d at a a s s i m i l a t i o n . D a i l y p r od u ct s a re f r e e l y
a v a i l a b l e fr om \n ” ,
” t he OSI SAF d i s t r i b u t i o n ch ain . ” ;
: t o p i c c a t e g o r y = ” O ceans Cl i ma t o lo g y Me t eo r o lo g y At m o sp h er e
” ;
: ke yw or ds = ” Sea I c e Mo ti on , Sea Ic e , Oc ean og rap hy ,
Met eo ro log y , Cli ma te , Remote S ensing ” ;
: gc md key words = ” Cryosphere >Sea I c e >Sea I c e Mo t io n \n
” ,
” Ocean >Sea I c e >Sea I c e Mo t io n \n ” ,
” Geo grap hic R egio n >N or t he r n Hemips here\n ” ,
” V e r t i c a l L o ca t io n >Sea S ur f ac e \n ” ,
”EUMETSAT / OSISAF >S a t e l l i t e A p p l i c a t i o n F a c i l i t y
on Ocean an d Sea Ic e , Eu rop ean O r g a n i s a t io n
f o r t h e E x p l o i t a t i o n o f M e t e o r o l o g i c a l
S a t e l l i t e s ” ;
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 24
: n o r t h e r n m o s t l a t i t u d e = 9 0. f ;
: s o u t h e r n m o s t l a t i t u d e = 32 .2 0 28 7 f ;
: e a s t e r n m o s t lo n g i t u d e = 1 8 0. f ;
: we s te rn mo st l o ng i tu de = −180. f ;
: a c t i v i t y t y p e = ” Sp ace b or n e i n s t r u me n t ” ;
: a re a = ” N o rt he r n H em isp he re ” ;
: s t a r t d a t e = ”200 9−11−21 1 2: 00 : 00 ” ;
: s t o p d a te = ”2009 −11−23 1 2:0 0: 00” ;
: pr o je ct n a me = ”EUMETSAT OSI SAF” ;
: i n s t i t u t i o n = ” EUMETSAT OSI SAF” ;
: PI n ame = ” Thomas La v er gn e ” ;
: c o n t a c t = ” o s i s a f −manager@met . no ” ;
: d i s t r i b u t i o n s t a t e m e n t = ” F re e ” ;
: r e f e r e n c e s = ” OSI SAF Low R e s o l u t i o n Sea I c e D r i f t
P ro d u ct Man ual , L av er gn e , T . , E as two od S . , v 1 . 2 ,
Octob e r 2009\n ” ,
” V a l i d a t i o n and M o n it o r i n g o f t h e OSI SAF Low
R e s o l u t io n Sea I c e D r i f t P ro d uc t , L av er gn e , T
. , v 1 . 0 , F eb r ua r y 2 009 \n ” ,
” h t tp : / / s af . met . no\n ” ,
” h t t p : / / www . o s i −s a f . o rg ” ;
: h i s t o r y = ”2009 −11−24 c r e a t i o n ” ;
: p r o d u c t v e r s i o n = ” 1 . 3 ” ;
: s o f t w a r e v e r s i o n = ” 4 . 0 ” ;
: n e t c d f v e r s i o n = ” 3 . 6 . 3 ” ;
: Conven t i o n s = ” CF−1 . 3” ;
}
EUMETSAT OSI SAF Version 1.4 —March 2010

SAF/OSI/CDOP/met.no/TEC/MA/128 25
References
[1] S. Andersen, L.-A. Breivik, S. Eastwood, Ø. Godøy, M. Lind, M. Porcires, and
H. Schyberg, “OSI SAF Sea Ice Product Manual – v3.5,” EUMETSAT OSI SAF – Ocean
and Sea Ice Sattelite Application Facility, Tech. Rep. SAF/OSI/met.no/TEC/MA/125,
January 2007. [Online]. Available: http://saf.met.no/docs/ss2 pmseaice v3p5.pdf
[2] R. Ezraty, F. Girard-Ardhuin, and J.-F. Pioll´
e, “Sea ice drift in the central Arctic esti-
mated from SeaWinds/QuikSCAT backscatter maps – User’s manual,” CERSAT, IFRE-
MER, France, v2.2, February 2007.
[3] ——, “Sea ice drift in the central Arctic combining QuikSCAT and SSM/I sea ice drift data
– User’s manual,” CERSAT, IFREMER, France, v3.0, April 2008.
[4] J. Haarpaintner, “Arctic-wide operational sea ice drift from enhanced-resolution
Quikscat/SeaWinds scatterometry and its validation,” IEEE Transactions on Geoscience
and Remote Sensing, vol. 44, no. 1, pp. 102–107, January 2006.
[5] R. Kwok, A. Schweiger, D. A. Rothrock, S. Pang, and C. Kottmeier, “Sea ice motion
from satellite passive microwave imagery assessed with ERS SAR and buoy motions,”
Journal of Geophysical Research, vol. 103, pp. 8191–8214, April 1998.
[6] T. Lavergne, “Validation and monitoring of the OSI SAF low resolution sea ice drift
product – v2,” EUMETSAT OSI SAF – Ocean and Sea Ice Sattelite Application Facility,
Tech. Rep. SAF/OSI/CDOP/met.no/T&V/RP/131, March 2010. [Online]. Available:
ftp://saf.met.no/docs/REPORT OSISAF LRSeaIceDrift Validation.pdf
[7] T. Lavergne, S. Eastwood, H. Schyberg, and L.-A. Breivik, “Ice drift monitoring
from low resolving sensors: an alternative method and its validation against in-situ
data,” MERSEA – Marine EnviRonment and Security for the European Area, Report
MERSEA WP02 METNO STR 005 1A, October 2008.
[8] ——, “Algorithm Theoretical Basis Document for the OSI SAF low resolution sea ice drift
product – v1.2,” EUMETSAT OSI SAF – Ocean and Sea Ice Sattelite Application Facility,
Tech. Rep. SAF/OSI/CDOP/met.no/SCI/MA/130, April 2009.
EUMETSAT OSI SAF Version 1.4 —March 2010