SSM/I Ice Motion
Sensor Microwave/Imager SSM/I is
flown on the satellites of the DMSP
(Defence Meteorological Satellite Program) since 1987. The sun synchronous orbit,
the period of about 100 minutes and the sensors swath wide of 1394 km allows a
regular cover of the polar regions at least once a day. (Hollinger et al.,
and 37 GHz channels have a resolution of 25km, the 85 GHz channel has the double
resolution of 12,5 km, but is more sensible to atmospheric influence.
was proceeded by the Scanning Multichannel Microwave Radiometer SMMR on the
NIMBUS 7 satellite from 1978 to 1987, which could provide information on 5
frequencies from 6 to 37 GHz. (Cavalieri
et al., 1984)
Methods for calculation of sea ice motion out of 85 GHz data are
described in detail by Agnew and Le (1997). They give a theoretical error of these estimations of about
4,6 km/d when looking at the geolocation error, tracking error, swath smearing
effect and timing error. Velocity errors are divided by the time interval
between the images and so can be improved with higher time intervals between the
compared images. But this is related to worse temporal resolution and greater
SSM/I drift data is available as optimal interpolated and not
interpolated 37 GHz and 85GHz
values, with errors in 37 GHz being smaller than expected in comparison to the
double resolution 85 GHz Channel (Maslanik et al., 1998) .
Drift estimates are calculated
every second day and are available from 1979 to 1997, while one day calculations
exist only in the time after 1992 (since the 85 GHz channel was only available
more recently on SSM/I).
of the search window of the feature tracking algorithm has an effect of the results, too, cause a large window
allows reducing noise but also leads to more spatial averaging and so reduces
the number of independent velocity vectors (SEALION- Lemke et al., 2001).
The size of the search window leads to the spatial resolution of the acquired
drift vectors. The ground resolution of the pixels and
the temporal intervals between two images for drift determination lead to the
minimum velocity which is detectable.
The Sea Ice Drift Atlas uses SSM/I Optimal
Interpolated data (further cited as OI-data) as an advanced product,
combining results from the 37GHz and the 85GHz passive microwave radiometer
channel with additional drift information from buoys when geographically and
The data is calculated with a weighting function :
out of the two channels with different spatial resolution
on a final rectangular grid with 100km cell spacing.
The weighting coefficients alpha, beta and gamma are determined after Colony
and Thorndike (1984). Solutions for each point are obtained based on the
uncertainties, the expected variance of the motion and the distance to available
observations. (Kwok et al., 2000 + personal communications). SSM/I_OI drift data is only available for month from March to November because
satellite ice drift products are not generated for austral summer. The ice
surface decorrelates and prevents tracking during the melt season. Data is
available at one-day and two-day resolution, with the two-day resolution
covering the longer timespan from 1979 to 1997 and therefore is being used for
the Atlas products. The ice motion data were produced by the Polar Remote Sensing Group at the Jet Propulsion Laboratory,
California Institute of Technology, and are provided by courtesy R. Kwok.
Drifting Buoy Ice Motion
Data from buoys, drifting on sea ice floes and measuring meteorological
parameters provide better temporal and spatial higher resolution than
satellite based data. Sea ice drift
velocities from buoys were included in the Atlas for a more complete
presentation and for verification
of satellite drift fields . Additionally, some
information of the buoy data is already included by calculating the SSM/I OI
products. Data were collected and provided within the International Programme
for Antarctic Buoys (IPAB)
since 1995. Former data were collected at the Alfred-Wegener-Institute
figure below shows the absolute number of buoys per month, and how they are
divided in the different ocean regions around Antarctica.
Icedrift is calculated from the change of buoy position reports at different times.
The accuracy of the ice drift and its derivatives is
estimated from the rms error of Argos positioning at approx. 350 m by Doppler
shift of the transmission frequency at different satellite positions. It is
assumed that position errors are Gaussian distributed and temporally
uncorrelated, which is confirmed by the Argos positioning of an automatic
weather station on the Filchner-Ronne Ice Shelf. Position reports of different
temporal intervals are interpolated to 3h resolution, with gaps being filled by
linear interpolation between two points to get regularly spaced time series, see
Sellmann, 1996). The errors of the ice drift components decrease with
increasing averaging period. Typically they are 4,6 cm/s for three-hourly data
and around 1 cm/s for daily data. From 1994 onwards
several buoys were equipped with Global Positioning System (GPS) receivers,
which provide an improved location accuracy. Using the GPS transmission of the
Standard Positioning Service (SPS) and averaging times of 10 min, position
accuracies of better than 50 m are obtained. The errors of the drift velocity
for daily data goes down to 0.3 cm/s.
Sea Ice Concentration
Ice concentration data is taken out of the CD of the EU
funded Project Pelicon
(Project for Estimation of Long-term variability in Ice Concentration,
Heygster et al., 1996). For temporal constancy, data calculated with the
NASA2 Team algorithm (Comiso et
al., 1997) are chosen, which are available for the whole examination
period. The algorithm uses 17 and 37 GHz channels
to derive ice concentrations from brightness temperatures. In the Pelicon
algorithm, a correction scheme of Thomas (1998) for areas with ice
concentration below 50% is
included, which reduces the overestimation due to the atmospheric influences.
Model output data from NCEP Reanalysis
Project (NNRP) datasets, a 40 year record of global analysis
of atmospheric fields, are used to
combine ice drift and atmospheric parameters. The datasets in use were
at pressure level partition or as surface data on
a 2,5°x 2,5° latitude/longitude grid and have a temporal resolution of 6 hours.
The principle of this long time reanalysis dataset is to
use a fixed system for assimilation, analysis and forecasting for using the data
from 1957 to present. This allows
investigation of interannual variability with being sure that no model changes
will have influence. It involves land surface, ship, rawinsonde, pibal, aircraft,
satellite and other data from various organisations. For an analysis as accurate
as possible for the past 40 years, all available data at given times are used.
Additional information is available
from Kalnay et al., (1996).
Geographic Information System
Antarctic land topographic data, which are included in the
geoinformation system come from DEM
GLOBE . The Global Land One-kilometer Base Elevation (GLOBE) digital
elevation model is the most thoroughly designed, reviewed, and documented global
digital elevation dataset to date. GLOBE was developed by an international group
of specialists, cooperating with the Committee on Earth Observation Satellites (CEOS)
Working Group on Information Systems and Services (WGISS), International
Geosphere-Biosphere Programme's Data and Information System (IGBP-DIS), and IGBP
Working Group IV/6. GLOBE comprises a global 30 arc-second latitude-longitude
array, with land areas populated with integer elevation data. Full GLOBE
documentation is available on the CD-ROM and GLOBE Website. (Hastings
et al. 1998)
All data is uniformly georeferenced and combinated in the
Geographic Information System PCI. This
allows the spatial and temporal overlay of the different data and makes a
combined analysis, for example like the ice drift-wind forcing relation,
The following steps were undertaken, to get homogenous, temporal averaged datasets of vector- and
Data with cartesian
x-y-coordinates (polarstereographic) like the SSM/I and Pelicon data are converted to latitude-longitude-coordinates, according to the grid in which
they exist. Georeferencing is done using Hughes Ellipsoid with a latitude of
true scale at 70°S. This leads to the smallest distortions in length at the
areas around Antarcticas coast.
- Temporal averaging:
Out of daily data,
different mean values are calculated. Due to the restricted availability of
satellite products of only nine months per year, there are monthly means
from March to November, seasonal means for the three seasons March-May,
June-August and September-November and annual means including all the
nine months. For the same three averaging intervals, interannual means,
including the mean of a month or period out of the complete data-range from
1979 to 1997, are available. Due
to an average ice motion of
around 200 km in 30 days (Kottmeier and Sellmann, 1996) , a spatial
averaging of ice drift must be taken in account in the mean values.
- Buoy data preparation:
Buoy data at different temporal resolution is careful
checked and controlled, according to the buoy documentation, and then put
together to daily means. These data, as well as daily means of 6hourly NCEP
data is further on treated like the satellite data to calculate monthly means
and statistics, so that the monthly or seasonal temporal variances always
relate to daily input values. Combinated images for comparison of satellite
and buoy drift data is only built for monthly means, because averaging
intervals longer than one month is not suitable for most of the buoy data. In
this images, OI-vectors that are calculated with inclusion of buoy data are
highlighted by white dots. So it is to see, that not all available buoy data
was included in the OI data. A more detailed description on the effects of
this can be found in the Quality Check.
- Statistical parameters and
calculation of covariance ellipses:
all kind of mean products, statistical values like the variance of each
velocity component, the complete drift variance and covariance is calculated
and added to the ASCII-datasets in the Download area. For
better illustration of the coupling variability, covariance ellipses are
calculated and plotted. Their size and orientation describe the magnitude of
the variance as well as the relation of variance in the longitudinal
u-component and meridional v-component.
Covariance ellipses are developed out of the covariance matrix
like described in Nikolaj Nawri (1996).
- NCEP and additional data preparation:
NECP reanalysis data is first being interpolated to daily
values to have the same temporal
resolution like drift data for getting the statistic parameters of different
averaging periods. In the graphical outputs, a vector thinning for points in
high latitudes is done, because the 2,5 degrees resolution could not be
optical resolved in a polarstereographic projection.
- Interpolation of gridded fields:
SSM/I and NCEP data is imported to the geoinformation
system as vector data with its geographic coordinates for positioning and the
drift components and statistic values as vector attributes. For evaluation of
climatological patterns and changes in sea ice motion and atmospheric data
over the last 20 years, gridded fields of this values are best to use. Gridded
fields for sea ice motion, variance, air pressure and temperature are
created by interpolation of vector data to raster data. Interpolation is done
by using a nearest-neighbour-interpolation with 6 nearest neighbours (i.e.,
the 6 nearest of all sample points to the grid point in question) weighted by
their distances from the grid point. The interpolation equation then
Where for each k-nearest
neighbour, Zi is its grey level, Di is its distance from
the current grid point being interpolated. For each grid point, the k nearest
neighbours must be identified from the entire data set before the
distance-weighted equation can be employed. This spatial interpolation range
for the single grid points is similar to interpolation of
gridded fields out of buoy positions wit an acceptance circle of around
300 km like done in Kottmeier et al.(1997).
All gridded fields related to ice motion data are circumscribed by the ice
margin out of Pelicon data. Because often drift vectors are not available at
all points of the ice covered region, it should be noted that interpolated
values are only representative in the regions with drift arrows.
- Calculation of differential kinematic parameters:
Differential kinematic parameters like vorticity,
divergence and shear give a detailed overview of the motion of the sea ice
Based on its mathematical sense of the divergence of a
continuous vector field, the divergence of the 2-D vector field of sea ice
motion means the relative change
of an area covered all neighbouring ice floes within an enveloping curve,
whereas changes of the shape or orientation of this curves does not affect
divergence . Divergent ice motion causes opening of leads, causing enhanced heat losses from the
ocean in direct contact to the atmosphere. , Convergent
ice motion decreases the size of leads between the floes and may result in
larger internal ice stress.
changes of the orientation of the enveloping curve. Ice
drift vorticity has been found empirically to be related to the vorticity of
the geostrophic wind (Kottmeier and Sellmann, 1996).
This is mainly documented
by investigation of buoy drift for certain regions and can be seen here for
all areas and longer periods.
Shear means the changes in shape, having two components in
direction of the mean
motion and perpendicular to it. The shear rate of ice drift is found to be
relatively close to the shear rate of the geostrophic wind field, too
in case studies of drift.
parameters are calculated from the drift vector D with components (u,v)
in the following way:
div(D) = du/dx
= dv/dx - du/dy
Shearing rate of ice drift
Normal Deformation Rate
NDR(D) = du/dx - dv/dy
and Shear Deformation Rate
SDR (D) = dv/dx + du/dy.
To suppress effects of noise errors of u and v
on derivatives, dx and dy are chosen to be 600km, which comes
close to correlation lengths of ice motion detected in many investigations of
ice motion in the Southern Ocean. For different seasons during the Winter Weddell Sea Project, Kottmeier
et al. (1992) calculated the spatial correlation lengths of the buoy drift
speeds and components. The longitudinal correlation lengths of the drift
speeds varied between 490 and 680 km; the lateral ones between 270 and 540 km.
Vihma et al. (1996)
provide correlation lengths of 700 km for the longitudinal and 550 km for the
lateral drift speeds for the central and western Weddell Sea. As a result of
the large correlation lengths, spatial gradients of ice drift obtained from
SSM/I are able to reflect meaningful vorticity and divergence on these scales.
Smaller-scale DKPs may be much more important for lead and polynya formation,
but cannot be obtained reliably from SSM/I-based ice drift.
- Quality Check on Ice Motion DataAn
extensive quality check of satellite OI data compared to buoy data is done out
of the whole dataset, see the pdf-file Quality
studies on comparisons of satellite drift data with drifting buoy measurement (Maslanik
et al., 1998; Kwok et al., 1998; Geiger et al., 2000)
show that it is not sufficient to just compare a speed value at a certain buoys
position and time to the nearest gridpoint on SSMI grid. Thus a method is used
and tested for best temporal and spatial scales for comparison. In the coastal
regions of the western Weddell Sea, more perennial ice is expected and the buoy
motion is constrained with the ice being compressed against the coastal barrier.
In the central region, there is predominantly seasonal ice with relatively free
drift and divergent motion (Kottmeier and Sellman, 1996; Drinkwater, 1998;
Drinkwater and Liu, 1999). Results of seasonal and regional rms differences
show a quite smaller error of OI data compared to not interpolated drift vectors
and furthermore illustrate the big impact of different ice regimes on the drift
of seasonally/spatially varying estimates of rms errors are provided in the
Ice Drift Altas
and as additional quality flag included in the datasets for download.