ArticlePDF Available
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
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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.
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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.
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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
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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
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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
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’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
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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.
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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).
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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.
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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
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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).
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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
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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.
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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
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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 70N
Central Meridian 45W
Corner point 35.14838N;10.30485W 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
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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.
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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.
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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.
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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 200911211200200911231200 {
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 19780101 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 19780101 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 ) ;
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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 ) ;
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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 ;
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: 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 91121 1 2: 00 : 00 ;
: s t o p d a te = ”2009 1123 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 1124 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 = CF1 . 3” ;
}
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References
[1] S. Andersen, L.-A. Breivik, S. Eastwood, Ø. Godøy, M. Lind, M. Porcires, and
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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´
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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
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and Remote Sensing, vol. 44, no. 1, pp. 102–107, January 2006.
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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
... However, due to the advantages of (a) regular large-scale coverage with a consistent grid, (b) relative ease of calculation, and (c) relative ease of use and interpretation for the dataset end user, DM products have been widely used for recent studies of sea ice kinematics (Szanyi et al., 2016;Hutter et al., 2018). Several PM-derived DM products have been used for investigating sea ice motion in both the Arctic and Antarctic, including the Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea ice drift product OSI-405-c (Lavergne, 2016), henceforth referred to as the OSI SAF product; the NASA Polar Pathfinder Daily 25 km Equal-Area Scalable Earth Grid (EASE-Grid) Sea Ice Motion Vectors product (Tschudi et al., 2020) from the National Snow and Ice Data Center (NSIDC), henceforth referred to as the NSIDC product; and the daily sea ice motion product produced by Kimura et al. (2013), henceforth referred to as the KIMURA product. ...
... Recently sea ice motion has been estimated from PMderived T B partial-overlap swath pairs from a wide range of temporal baselines (from one orbit of ∼ 90 min to 3 d). This approach is referred as "swath-to-swath" (S2S) (Lavergne et al., 2021). For S2S, the time base for derived motion is a function of the temporal separation of individual swaths in each pair and hence differs between pairs . ...
... Here, we investigate this hypothesis by performing comparisons against drifting buoy-derived motion in different latitude ranges with the expectation that the DM-based KIMURA product performs better at lower latitudes, where swath overlap averaging is lower. We note that the OSI SAF sea ice motion product is derived by averaging over 2 d to increase the signal-to-noise ratio (SNR) of this operational product (Lavergne, 2016). This extended averaging interval arises, in part, due to OSI SAF integrating multiple sensors. ...
Article
Full-text available
Antarctic sea ice kinematics plays a crucial role in shaping the Southern Ocean climate and ecosystems. Satellite passive-microwave-derived sea ice motion data have been used widely for studying sea ice motion and deformation, and they provide daily global coverage at a relatively low spatial resolution (in the order of 60 km × 60 km). In the Arctic, several validated datasets of satellite observations are available and used to study sea ice kinematics, but far fewer validation studies exist for the Antarctic. Here, we compare the widely used passive-microwave-derived Antarctic sea ice motion product by Kimura et al. (2013) with buoy-derived velocities and interpret the effects of satellite observational configuration on the representation of Antarctic sea ice kinematics. We identify two issues in the Kimura et al. (2013) product: (i) errors in two large triangular areas within the eastern Weddell Sea and western Amundsen Sea relating to an error in the input satellite data composite and (ii) a more subtle error relating to invalid assumptions for the average sensing time of each pixel. Upon rectification of these, performance of the daily composite sea ice motion product is found to be a function of latitude, relating to the number of satellite swaths incorporated (more swaths further south as tracks converge) and the heterogeneity of the underlying satellite signal (brightness temperature here). Daily sea ice motion vectors calculated using ascending- and descending-only satellite tracks (with a true ∼ 24 h timescale) are compared with the widely used combined product (ascending and descending tracks combined together, with an inherent ∼ 39 h timescale). This comparison reveals that kinematic parameters derived from the shorter-timescale velocity datasets are higher in magnitude than the combined dataset, indicating a high degree of sensitivity to observation timescale. We conclude that the new generation of “swath-to-swath” (S2S) sea ice velocity datasets, encompassing a range of observational timescales, is necessary to advance future research into sea ice kinematics.
... The values for CIMR are from Donlon et al. (2020), and those for AMSR2 are from the Observing Systems Capability Analysis and Review (OSCAR) tool of the World Meteorological Organization. See also Lavergne (2018) A variety of buoy types are included in our validation data, but we are only concerned with three pieces of information per trajectory record: timestamp, latitude and longitude. Most buoys report positions on an hourly basis. ...
... The sea-ice motion tracking methodology implemented here, including the quality control steps, is very similar to that described by Lavergne et al. (2010) and implemented in the operational chains of the EUMETSAT OSI SAF (Lavergne et al., 2016). We recall below three unique features of this processing chain. ...
... As in Lavergne et al. (2010) and in the EUMETSAT OSI SAF sea-ice drift product (Lavergne et al., 2016), we process sea-ice drift vectors with a grid spacing of 62.5 km on two polar stereographic grids (Arctic and Antarctic). 3.2 "Swath-to-swath" and "daily map" sea-ice drift products ...
Article
Full-text available
Across spatial and temporal scales, sea-ice motion has implications for ship navigation, the sea-ice thickness distribution, sea-ice export to lower latitudes and re-circulation in the polar seas, among others. Satellite remote sensing is an effective way to monitor sea-ice drift globally and daily, especially using the wide swaths of passive microwave missions. Since the late 1990s, many algorithms and products have been developed for this task. Here, we investigate how processing sea-ice drift vectors from the intersection of individual swaths of the Advanced Microwave Scanning Radiometer 2 (AMSR2) mission compares to today's status quo (processing from daily averaged maps of brightness temperature). We document that the “swath-to-swath” (S2S) approach results in many more (2 orders of magnitude) sea-ice drift vectors than the “daily map” (DM) approach. These S2S vectors also validate better when compared to trajectories of on-ice drifters. For example, the RMSE of the 24 h winter Arctic sea-ice drift is 0.9 km for S2S vectors and 1.3 km for DM vectors from the 36.5 GHz imagery of AMSR2. Through a series of experiments with actual AMSR2 data and simulated Copernicus Imaging Microwave Radiometer (CIMR) data, we study the impact that geolocation uncertainty and imaging resolution have on the accuracy of the sea-ice drift vectors. We conclude by recommending that a swath-to-swath approach is adopted for the future operational Level-2 sea-ice drift product of the CIMR mission. We outline some potential next steps towards further improving the algorithms and making the user community ready to fully take advantage of such a product.
... This ice motion product was selected to be used for the following reasons. (a) It allows identification of the long-term change of sea ice kinematics because it covers a more extensive time period than other products, for example, those from the Centre ERS d'Archivage et de Traitement (CERSAT; Girard-Ardhuin and Ezraty, 2012) and the Ocean and Sea Ice Satellite Application Facility (OSI-SAF; Lavergne and Eastwood, 2010 F I G U R E 1 Deployment sites of the CHINARE ice camps (triangles) and the trajectories of the buoys deployed at the camps. Also shown are the backward (to 31 August of the previous year) and forward (to 31 August of the following year or to the end of the buoy operation) trajectories estimated using the NSIDC product. ...
... We note that our buoy data have not been assimilated in any ice motion product. The OSI-SAF ice motion vectors with a time span of 48 hr are estimated using a continuous maximum cross-correlation method on pairs of satellite images (Lavergne and Eastwood, 2010). Because the OSI-SAF data were available only for the freezing season from October to April, the comparison focuses on this period using the starting positions measured by the buoys on 1 October. ...
Article
To track sea ice motion, four ice‐tethered buoys were deployed at 84.6°N and 144.3°W, 87.3°N and 172.3°W, 81.1°N and 157.4°W, and 82.8°N and 166.5°W in summers of 2008, 2010, 2014, and 2016, respectively. In addition, the remote sensed ice motion product provided by National Snow and Ice Data Center was used to reconstruct backward and forward ice drifting trajectories from the buoy deployment sites during 1979–2016. Sea ice in the central Arctic Ocean in late summer is trending to have travelled from lower latitudes, and to be advected to the region more involved in the Transpolar Drift Stream (TDS) during 1979–2016. The strengthened TDS has played a crucial role in Arctic sea ice loss from a dynamic perspective. The trajectory of ice is found to be significantly related to atmosphere circulation indices. The Central Arctic Index (CAI), defined as the difference in sea level pressure between 84°N, 90°W and 84°N, 90°E, can explain 34–40% of the meridional displacement along the backward trajectories, and it can explain 27–40% of the zonal displacement along the forward trajectories. The winter Beaufort High (BH) anomaly can explain 18–27% of the zonal displacement. Under high positive CAI values or high negative winter BH anomalies, floes from the central Arctic tended to be advected out of the Arctic Ocean through Fram Strait or other marginal gateways. Conversely, under high negative CAI values or high positive winter BH anomalies, ice tended to become trapped within a region close to the North Pole or it drifted into the Beaufort Gyre region. The long‐term trend and spatial change in Arctic surface air temperature were more remarkable during the freezing season than the melt season because most energy from the lower troposphere is used to melt sea ice and warm the upper ocean during summer. This article is protected by copyright. All rights reserved.
... Here, we investigate this hypothesis by performing comparisons against drifting buoy-derived motion in different latitude ranges with the expectation that DM-based KIMURA dataset performs better at lower latitudes, where swath overlap averaging is lower. We note that the OSI SAF sea ice motion product is derived by averaging over two days to increase the signal-to-noise ratio of this operational product (Lavergne, 2016). This extended averaging interval arises, in part, due to OSI SAF integrating multiple sensors. ...
Preprint
Full-text available
Antarctic sea ice kinematic plays a crucial role in shaping the polar climate and ecosystems. Satellite passive microwave-derived sea ice motion data have been used widely for studying sea ice motion and deformation processes, and provide daily, global coverage at a relatively low spatial-resolution (in the order of 60 × 60 km). In the Arctic, several validated data sets of satellite observations are available and used to study sea ice kinematics, but far fewer validation studies exist for the Antarctic. Here, we compare the widely-used passive microwave-derived Antarctic sea ice motion product by Kimura et al. (2013) with buoy-derived velocities, and interpret the effects of satellite observational configuration on the representation of Antarctic sea ice kinematics. We identify two issues in the Kimura et al. (2013) product: (i) errors in two large triangular areas within the eastern Weddell Sea and western Amundsen Sea relating to an error in the input satellite data composite, and (ii) a more subtle error relating to invalid assumptions for the average sensing time of each pixel. Upon rectification of these, performance of the daily composite sea ice motion product is found to be a function of latitude, relating to the number of satellite swaths incorporated (more swaths further south as tracks converge), and the heterogeneity of the underlying satellite signal (brightness temperature here). Daily sea ice motion vectors calculated using ascending- and descending-only satellite tracks (with a true ~24 h time-scale) are compared with the widely-used combined product (ascending and descending tracks combined together, with an inherent ~39 h time-scale). This comparison reveals that kinematic parameters derived from the shorter time-scale velocity datasets are higher in magnitude than the combined dataset, indicating a high degree of sensitivity to observation time-scale. We conclude that the new generation of “swath-to-swath” (S2S) sea ice velocity datasets, encompassing a range of observational time scales, is necessary to advance future research into sea ice kinematics.
... This set is used to calculate model state statistics (section 4.2). The third and last set is similar to the second except that drifters' initial locations are in this case matching with the OSISAF ice drift dataset 2 (which provides estimated drift vectors with a distance of 62.5 km between them, [24]) and the drifters being redeployed every 2 days in order to be compared in a consistent manner to the OSISAF observations. This later set is used to calibrate the model parameters (section 3.5). ...
Preprint
We study the response of the Lagrangian sea ice model neXtSIM to the uncertainty in the sea surface wind and sea ice cohesion. The ice mechanics in neXtSIM is based on a brittle-like rheological framework. The study considers short-term ensemble forecasts of the Arctic sea ice from January to April 2008. Ensembles are generated by perturbing the wind inputs and ice cohesion field both separately and jointly. The resulting uncertainty in the probabilistic forecasts is evaluated statistically based on the analysis of Lagrangian sea ice trajectories as sampled by virtual drifters seeded in the model to cover the Arctic Ocean and using metrics borrowed from the search-and-rescue literature. The comparison among the different ensembles indicates that wind perturbations dominate the forecast uncertainty i.e. the absolute spread of the ensemble, while the inhomogeneities in the ice cohesion field significantly increase the degree of anisotropy in the spread i.e. trajectories drift differently in different directions. We suggest that in order to get a full flavor of uncertainties in a sea ice model with brittle-like rheologies, to predict sea ice drift and trajectories, one should consider using ensemble-based simulations where both wind forcing and sea ice cohesion are perturbed.
Article
Wide-swath C-band synthetic aperture radar (SAR) has been used for sea ice classification and estimates of sea ice drift and deformation since it first became widely available in the 1990s. Here, we examine the potential to distinguish surface features created by sea ice deformation using ice type classification of SAR data. Also, we investigate the cross-platform transferability between training sets derived from Sentinel-1 Extra Wide (S1 EW) and RADARSAT-2 (RS2) ScanSAR Wide A (SCWA) and fine quad-polarimetric (FQ) data, as the same radiometrically calibrated backscatter coefficients are expected from the two C-band sensors. We use a novel sea ice classification method developed based on Arctic-wide S1 EW training, which considers per-ice-type incident angle (IA) dependency of backscatter intensity. This study focuses on the region near Fram Strait north of Svalbard to utilize expert knowledge of ice conditions during the Norwegian young sea ICE (N-ICE2015) expedition. Manually drawn polygons of different ice types for S1 EW, RS2 SCWA and RS2 FQ data are used to retrain the classifier. Different training sets yield similar classification results and IA slopes, with the exception of leads with calm open water, nilas or newly formed ice (the “leads” class). This is caused by different noise floor configurations of S1 and RS2 data, which interact differently with leads, necessitating dataset-specific retraining for this class. SAR scenes are then classified based on the classifier retrained for each dataset, with the classification scheme altered to separate level from deformed ice to enable direct comparison with independently derived sea ice deformation maps. The comparisons show that the classification of C-band SAR can be used to distinguish areas of ice divergence occupied by leads, young ice and level first-year ice (LFYI). However, it has limited capacity in delineating areas of ice deformation due to ambiguities between ice types with higher backscatter intensities. This study provides reference to future studies seeking cross-platform application of training sets so they are fully utilized, and we expect further development of the classifier and the inclusion of other SAR datasets to enable image-classification-based ice deformation detection using only satellite SAR.
Article
Full-text available
The neXtSIM-F (neXtSIM forecast) forecasting system consists of a stand-alone sea ice model, neXtSIM (neXt-generation Sea Ice Model), forced by the TOPAZ ocean forecast and the ECMWF atmospheric forecast, combined with daily data assimilation of sea ice concentration. It uses the novel brittle Bingham–Maxwell (BBM) sea ice rheology, making it the first forecast based on a continuum model not to use the viscous–plastic (VP) rheology. It was tested in the Arctic for the time period November 2018–June 2020 and was found to perform well, although there are some shortcomings. Despite drift not being assimilated in our system, the sea ice drift is good throughout the year, being relatively unbiased, even for longer lead times like 5 d. The RMSE in speed and the total RMSE are also good for the first 3 or so days, although they both increase steadily with lead time. The thickness distribution is relatively good, although there are some regions that experience excessive thickening with negative implications for the summertime sea ice extent, particularly in the Greenland Sea. The neXtSIM-F forecasting system assimilates OSI SAF sea ice concentration products (both SSMIS and AMSR2) by modifying the initial conditions daily and adding a compensating heat flux to prevent removed ice growing back too quickly. The assimilation greatly improves the sea ice extent for the forecast duration.
Preprint
Full-text available
Across spatial and temporal scales, sea-ice motion has implications on ship navigation, the sea-ice thickness distribution, sea ice export to lower latitudes and re-circulation in the polar seas, among others. Satellite remote sensing is an effective way to monitor sea-ice drift globally and daily, especially using the wide swaths of passive microwave missions. Since the late 1990s, many algorithms and products have been developed for this task. Here, we investigate how processing sea-ice drift vectors from the intersection of individual swaths of the Advanced Microwave Scanning Radiometer 2 (AMSR2) mission compares to today's status-quo (processing from daily averaged maps of brightness temperature). We document that the swath-to-swath (S2S) approach results in many more (two orders of magnitude) sea-ice drift vectors than the daily-maps (DM) approach. These S2S vectors also validate better when compared to trajectories of on-ice drifters. For example, the RMSE of the 24 hour Arctic sea-ice drift is 0.9 km for S2S vectors, and 1.3 km for DM vectors from the 36.5 GHz imagery of AMSR2. Through a series of experiments with actual AMSR2 data and simulated Copernicus Imaging Microwave Radiometer (CIMR) data, we study the impact that geo-location uncertainty and imaging resolution have on the accuracy of the sea-ice drift vectors. We conclude by recommending that a swath-to-swath approach is adopted for the future operational Level-2 sea-ice drift product of the CIMR mission. We outline some potential next steps towards further improving the algorithms, and making the user community ready to fully take advantage of such a product.
Article
Full-text available
The neXtSIM-F forecast system consists of a stand-alone sea ice model, neXtSIM, forced by the TOPAZ ocean forecast and the ECMWF atmospheric forecast, combined with daily data assimilation. It was tested for the northern winter of 2018–2019 with different data being assimilated and was found to perform well. Despite drift not being assimilated in our system, we obtain quite good agreement between observations, comparing well to more sophisticated coupled ice-ocean forecast systems. The RMSE in drift speed is around 3 km/day for the first three days, climbing to about 4 km/day for the next day or two; computing the RMSE in the total drift adds about 1 km/day to the error in speed. The drift bias remains close to zero over the whole period from Nov 2018–Apr 2019. The neXtSIM-F forecast system assimilates OSISAF sea ice concentration products (both SSMI and AMSR2) and SMOS sea ice thickness by modifying the initial conditions daily and adding a compensating heat flux to prevent removed ice growing back too quickly. This greatly improved the agreement of these quantities with observations for the first 3–4 days of the forecast.
Article
Full-text available
We present a sensitivity analysis and discuss the probabilistic forecast capabilities of the novel sea ice model neXtSIM used in hindcast mode. The study pertains to the response of the model to the uncertainty on winds using probabilistic forecasts of ice trajectories. neXtSIM is a continuous Lagrangian numerical model that uses an elasto-brittle rheology to simulate the ice response to external forces. The sensitivity analysis is based on a Monte Carlo sampling of 12 members. The response of the model to the uncertainties is evaluated in terms of simulated ice drift distances from their initial positions, and from the mean position of the ensemble, over the mid-term forecast horizon of 10 days. The simulated ice drift is decomposed into advective and diffusive parts that are characterised separately both spatially and temporally and compared to what is obtained with a free-drift model, that is, when the ice rheology does not play any role in the modelled physics of the ice. The seasonal variability of the model sensitivity is presented and shows the role of the ice compactness and rheology in the ice drift response at both local and regional scales in the Arctic. Indeed, the ice drift simulated by neXtSIM in summer is close to the one obtained with the free-drift model, while the more compact and solid ice pack shows a significantly different mechanical and drift behaviour in winter. For the winter period analysed in this study, we also show that, in contrast to the free-drift model, neXtSIM reproduces the sea ice Lagrangian diffusion regimes as found from observed trajectories. The forecast capability of neXtSIM is also evaluated using a large set of real buoy's trajectories and compared to the capability of the free-drift model. We found that neXtSIM performs significantly better in simulating sea ice drift, both in terms of forecast error and as a tool to assist search and rescue operations, although the sources of uncertainties assumed for the present experiment are not sufficient for complete coverage of the observed IABP positions.
Article
Full-text available
The near-real-time enhanced-resolution QuikScat/SeaWinds (QS) scatterometry composite data are used for daily automatic 48-h sea ice tracking by maximum cross-correlation over the entire Arctic. A correlation window of 61 × 61 pixels is used for best performance. Both QS polarizations, vertical (VV) and horizontal (HH), are used independently, which permits to filter the final results for erroneous vectors by comparing the two drift vectors, u&oarr;<sub>VV</sub> and u&oarr;<sub>HH</sub>, respectively. Additional filtering is performed by setting a minimum correlation coefficient and by considering the spatial consistency of the motion field. The algorithm has been validated with winter 2002/2003 buoy data from the International Arctic Buoy Program showing error standard deviations in the 48-h displacement of 3.1 and 3.2 km in the latitude and longitude direction, respectively. This corresponds to an error standard deviation in ice drift speed of just 2.6 cm/s. Errors are largest in dynamic regions with lower ice concentrations as for example the southern Fram Strait. The enhanced-resolution data improve previous drift results by about 25%, but are still blurred by the necessary 36-h period to produce the composites.
Article
Observing the motion of sea ice from space is analogous to observing wind stress over the wet oceans; both provide surface forcing for modeling ocean dynamics. Ice motion also directly provides the advective component of the equations governing the mass balance of the sea ice cover. Thus its routine observation from space would be of great value to understanding ice and ocean behavior. To demonstrate the feasibility of creating a global multidecadal ice motion record from satellite passive microwave imagery and to quantitatively assess the errors in the estimated ice motions, we have tracked ice every 3 days in the Arctic Ocean and daily in the Fram Strait and Baffin Bay during the 8 winter months from October 1992 to May 1993 and daily in the Weddell Sea during the 8 winter months from March to October 1992. The method, which has been well used previously, involves finding the spatial offset that maximizes the cross correlation of the brightness temperature fields over 100-km patches in two images separated in time by from 1 to 3 days. The resulting ice motions are compared with contemporaneous buoy- and SAR-derived ice motions. The uncertainties in the displacement vectors, between 5 and 12 km, are better than the spatial resolution of the data. Both 85-GHz data with 12-km spatial resolution and 37-GHz data with 25-km resolution are tracked. These trials with the 37-GHz data are new and show quite surprisingly that the error is only about 1 km larger with these data than with the 12-km 85-GHz data. Errors are typically larger than average in areas of lower ice concentration; in the most dynamic regions, particularly near the ice edge in the Barents and Greenland Seas; and in zones of high shear. These passive microwave ice motions show a large increase in spatial detail over motion fields optimally interpolated from buoy and wind observations, especially where buoy data are virtually absent such as near coasts and in some passages between the Arctic Ocean and its peripheral seas. The feasibility of obtaining ice motion from the 37-GHz data in addition to the 85-GHz data should allow an important record of ice motion to be established for the duration of the scanning multichannel microwave radiometer (SMMR), special sensor microwave/imager (SSM/I), and future microwave sensors, that is, from 1978 into the next millenium.
Validation and monitoring of the OSI SAF low resolution sea ice drift product – v2 EUMETSAT OSI SAF – Ocean and Sea Ice Sattelite Application Facility Available: ftp://saf
  • T Lavergne
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
Ice drift monitoring from low resolving sensors: an alternative method and its validation against in-situ data
  • T Lavergne
  • S Eastwood
  • H Schyberg
  • L.-A Breivik
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.
OSI SAF Sea Ice Product Manual – v3 EUMETSAT OSI SAF – Ocean and Sea Ice Sattelite Application Facility
  • S Andersen
  • L.-A Breivik
  • S Eastwood
  • Ø Godøy
  • M Lind
  • M Porcires
  • H Schyberg
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
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
——, " 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-Ocean and Sea Ice Sattelite Application Facility
  • S Andersen
  • L.-A Breivik
  • S Eastwood
  • Ø Godøy
  • M Lind
  • M Porcires
  • H Schyberg
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