MASKING OF SEA ICE FOR METOP SST

RETRIEVAL

STEINAR EASTWOOD, met.no SØREN ANDERSEN, DMI

JANUARY 2007 (VERSION 1.0)

1 INTRODUCTION

This report describes the work that has been done in the OSI SAF Global SST project for defining a method for masking of sea ice as part of the high resolution SST retrieval from METOP AVHRR data. By masking of sea ice is understood identification of all areas that are contaminated by sea ice on the resolution of the AVHRR data (so that SST retrieval from AVHRR pixels covering these areas would be affected by the sea ice).

This sea ice masking is meant to be used in addition to a cloud masking to assure that all ice covered areas are masked or labelled in the SST retrieval. An additional procedure will be performed after the cloud masking to calculate a sea ice index which is related to the possibility of a pixel being contaminated by sea ice.

This sea ice masking will consist of two steps. First, the OSI SAF sea ice edge or concentration product is used to mask areas that are certainly covered by sea ice. Secondly, all pixels within a certain distance from this ice are tested for the possibility of being ice contaminated by applying a Bayesian algorithm that gives the probability for sea ice based on the AVHRR data.

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2 USE OF THE OSI SAF SEA ICE PRODUCTS

The OSI SAF delivers three daily gridded sea ice products; sea ice concentration, edge and type. They are delivered in 10km grid resolution, but the true resolution is coarser because the field-of-view of the sensors used are coarser (SSM/I). Ice concentration gives the fraction of sea ice within each grid point. The ice edge classifies each grid point in three classes: open water, open ice and closed ice. Open ice roughly corresponds to ice concentration of 35-70% and closed ice to 70-100%. The ice type classifies each open ice and closed ice point in two classes: first year ice and multi year ice.

2.1 CHOOSING BETWEEN THE ICE EDGE AND ICE CONCENTRATION PRODUCTS

Both the ice concentration and ice edge product are well suited for ice masking, except that the resolution is too coarse for some application. The ice edge product can be used by assuming that all areas with open or closed are ice covered. The ice concentration product can be used by assuming all areas with concentration above a certain concentration threshold is ice contaminated, e.g. 30%. The advantage of the concentration product is that the threshold for defining ice covered can be adjusted to be more or less strict. The advantage of the edge product is that more sensors are used in this product than in the concentration product. At present the 85GHz SSM/I instrument (resolution = 15x13km2) is used only in the edge product. This is thought to give better resolution in the ice edge product than in the concentration product, which is based on the 19 and 37 Ghz instruments (69x43 km2 and 37x29 km2

resolution). From 2007 the 85Ghz data and data from AMSR-E will also be introduced in the ice concentration product. The ice concentration product is know to exaggerate the ice extent a bit, but the ice concentration product also better detects thin and fresh ice (e.g. during ice formation).

While the coarse resolution results in the failure to detect small scale ice features, it also causes overestimation near the ice edge. The signal from the relatively bright sea ice tends to spill into the open water and the “ real” ice edge is in most cases not at the 0% ice concentration isoline but probably closer to 30%. Experience with the validation of the OSISAF sea ice products show that this is the case in general and the ice edge product compares relatively well with the ice edge taken from navigational ice charts. Important exceptions can be found where the ice edge is less sharp and/or in case of thin ice that has microwave emissive properties similar to low concentration sea ice. Examples of areas that are often affected by thin ice are the Labrador Coast, where particular large differences between ice charts and passive microwave products are found [Agnew and Howell, 2003]. The Bering Sea and Sea of Okhotsk areas [Cavalieri, 1994] and the Odden area [Andersen et al., 2006; Wadhams and Comiso, 1999] in the Greenland Sea are other examples.

For applications such as high resolution SST retrieval from AHVRR data, neither the ice edge or ice concentration product will be optimal for ice masking. Some open water between ice covered areas will be masked as ice covered, and small areas of drifting ice will not be detected. Figure 1 and Figure 2 shows an example from early September between Greenland and Svalbard, with both open water between ice

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sheets and a tongue of ice which is transported south with ocean currents. Figure 2 shows a zoom from Figure 1 where the AVHRR image have been overlaid with transparent ice edge and ice concentration products. Comparing with Figure 1 it is possible to see the steps of the boarder between the ice edge classes, as well as the low concentration intervals. The ice edge product misses some of the small ice sheets and the thin ice tongue. The ice concentration product indicates some of these features as low concentration ice, but also some open water as low concentration. Using a low concentration threshold for ice masking will work if the objective is to be rigid regarding sea ice, but will also mask interesting open water close to the sea ice.

Additional insight is provided by the monthly comparisons of the ice edge product vs. the ice edge defined by the 15% concentration isoline are provided in appendix 1. Concentrating on the winter season first, we see that:

1. in regions where the ice edge is relatively stable and sharp (e.g. most of the Greenland East) the overestimation of the ice edge as determined from 15% concentration isoline can be determined to be 1 to 2 pixels.

2. In general the differences follow the ice edge very closely and naturally ap-pear larger where the ice edge is moving. In the following section we will inspect these areas closer to determine the possible advantage of more fre-quent product updates.

3. Excess weather effects in either product appear as a purple, coherent and irregularly shaped spot of several pixels extent. These are generally not found in winter in the ice concentration products, however they are present in the ice edge product (e.g. January 2006 in the Labrador Sea). This is mainly due to the use of the polarisation ratio in the ice edge multi-sensor scheme, which introduces a sensitivity to wind speed. The ice concentration product is insensitive to wind speed [Andersen et al., 2006]. A change in the multi-sensor ice edge scheme to rely on the passive microwave spectral gradient is in progress and should solve this problem.

In summer (May/June through August/September) the ice edge is generally retreating and again the concentration product reflects this change in the ice edge position. Atmospheric effects are relatively frequent in the ice edge product in July. In July also the ice concentration product displays the only occurrence of atmospheric effects observed throughout the year in the Barents Sea.

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Figure 1: Sea ice conditions between Svalbard and Greenland on 2006-09-04 12:42 UTC. Sea ice concentration upper left, ice edge upper right, AVHRR ch 2,3A,4 RBG false colour image. In AVHRR image the red coloured areas are sea ice and snow, black/dark blue are open sea, green/yellow are low clouds and snow/ice free land, white areas are high/cold clouds.

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Figure 2: AVHRR ch2,3A,4 false colour image overlaid with partly transparent ice concentration (upper) and ice edge (lower). Same scene as in Figure 1.

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2.2 DAILY OR TWICE DAILY SEA ICE PRODUCTS

The sea ice conditions in an area can change significant from day to day because ice is transported with currents and new ice is formed. A daily ice product might not be sufficient for ice masking in areas with rapid changes. In Figure 3 is shown an example of an ice sheet that moves 15km during a day.

Such an event is not extraordinary, yet even if it extends several SST product pixels, its areal extent is not large by ice product standards and it is close to the +/- 1 sea ice product pixel threshold that defines the basic limit to the precision of the sea ice edge.

Satellite Ascending node Descending node

DMSP F13 18:33 06:33

DMSP F14 19:08 07:08

DMSP F15 21:05 09:05

Aqua (AMSR-E) 13:30 01:30

Table 1: Local equator crossing times of DMSP satellites used in the OSISAF sea ice products. Information for the AMSR-E sensor, currently in pre-operational use, is given for reference.

Figure 3: Example of sea ice movement during 24 hours (north east coast of Greenland, AVHRR ch2,3A,4 colour composite giving the ice red colours). Ice sheet A has moved about 15km during the time of the two images.

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Rapid and widespread changes in ice extent result during freezeup and melt, it is thought that these may be periods where more frequent updates of the sea ice products may be helpful. To investigate this, met.no has produced a twice daily product update since 1 November 2006 with one missing product on December 12 at 06. Equator crossing times of the DMSP satellites used in the OSISAF sea ice products are given in Table 1. Unfortunately for this application, the orbits of the DMSP satellites are very similar and it was found that an 18 hour window was necessary to obtain full coverage of the product grid. The products were centred at 06 and 18 UTC, where the operational products are centred at 12 UTC of the preceding day with a 24 hour time window. We will concentrate on the differences in the Baffin Bay, the Labrador Sea and the East Siberian Sea where rapid ice growth occurred in the period as well as in East Greenland where variations in ice edge position are smaller. Figure 4 shows the ice edge products at the beginning and end of the study period with definition of profiles in the areas of interest along which the temporal evolution of the ice edge is analysed.

Figure 4: Ice edge products from 20061101 and 20061220 with profile tracks marked at the Northern Labrador Sea, the Baffin Bay, North East Greenland and in the East Siberian Sea through the Bering Strait.

Figures 5 through 7 show the resulting ice edge evolutions during the freeze-up in Arctic peripheral seas. Especially the Bering and Labrador profiles display very large and abrupt increases in ice edge position, whereas in the Baffin Bay the ice cover is gradually expanding southward. An artefact of the missing 06 UTC product on December 12 is seen in Figure 6 and 7 where the operational/daily product seems to be leading the increase relative to the twice-daily product. In comparison Figure 8 shows the more stable position of the East Greenland ice edge. In East Greenland the difference between the twice-daily and the daily product is typically limited to 1

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pixel, except for on November 16 where missing data in the Greenland Sea result in a larger difference. This indicates that the noise level in the twice-daily products is not significantly higher. Along the remaining profiles, differences are higher by a few pixels. In particular the Baffin Bay throughout November displays differences that are markedly larger than in December. Along the Labrador and Bering profiles the differences are concentrated in large spikes of 10-15 pixels coincident with episodes of rapid ice growth.

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Figure 5: Ice edge evolution along the Baffin Bay profile (see Figure 4). Top panel shows the evolution of the ice edge in the twice-daily ice edge product (black solid), the operational (daily) ice edge product (black dash) and the ice edge defined by the daily ice concentration product isoline at 10 % (black dash-dot), 15 % (red dash-dot) and 30 % (blue dash-dot). Lower panel shows twice-daily minus daily ice edge (black solid) and differences between ice edge and ice concentration at 10 % (black dash), 15% (red dash) and 30% (blue dash) (concentration minus daily ice edge).

Figure 6: As Figure 5, but for the Bering Strait profile

Figure 7: As Figure 5, but for the Northern Labrador Sea profile.

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Figure 8: As Figure 5, but for the North-East Greenland profile.

The Labrador profile is also thought to show the significance of thin ice occurrences. As noted by Agnew and Howell (2003), thin ice is a common feature along the Canadian East Coast and its microwave radiative characteristics are similar to a mixture of ice and water, hence it will resemble low concentration sea ice [Cavalieri, 1994]. We focus on the situation at the end of November/early December, where the 15% ice concentration isoline is seen to be systematically much higher than the ice edge products. As exemplified in Figure 9 the ice concentration product is closer to the real ice extent as given by Canadian Ice service ice charts from the period. Conditions favourable for such prolonged occurrences of thin ice are most often met in the Arctic peripheral seas at lower latitude; as mentioned, in the Pacific, the Sea of Okhotsk is another area displaying significant thin ice extents. Comparison to the Bering profile shows how, at higher latitudes, the ice growth occurs much faster and the transition to first year ice occurs much faster.

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Figure 9: Ice product from 28. November 2006 in the Davis Strait region. From left to right: ice edge product, ice concentration overlaid with transparent ice edge product, and Canadian Ice Service ice chart.

3 SEA ICE PROBABILITY FROM AVHRR DATA

For high resolution SST retrieval close to the ice edge, it is desirable to have a sea ice mask on the same spatial and temporal resolution as the retrieval. To achieve this ice information can be retrieved directly from the AVHRR data.

A method for sea/ice/cloud classification has been developed at met.no for the purpose of discriminating between cloud free sea, cloud free ice and clouds. The main focus has been on areas in the vicinity of the ice edge. The method has been developed and tuned with this in mind. So the method can be expected to give false results in very different areas, such as the tropics. The method can therefore be applied within a certain distance from an coarser ice edge product (for example 150km) or poleward of a certain latitude (for example 40N/40S).

The method was developed by manually collecting training data of sea, ice and clouds. These training data were used to define probability distribution functions for the classes and a Bayesian method was defined to estimate the probabilities for each class.

In SST retrieval the important matter is to detect both ice and cloud contaminated pixels that the cloud mask has erroneously labelled as cloud free or ice free sea, and not necessarily to distinguish between cloud and ice. This is an advantage, since some clouds and sea ice have similar signatures in the spectral bands of the AVHRR channels.

3.1 COLLECTING TRAINING DATA FOR THE ALGORITHM

The algorithm was trained by visual inspection of AVHRR data from NOAA-17 in the areas around Greenland and Svalbard from spring, summer and autumn months in 2003 and 2004. Spectral signatures from three different classes (cloudy, cloud free ice and cloud free water) were collected by manually selecting areas in the images that clearly corresponds to these classes. Figure 10 shows an example from collecting samples of different classes.

In the training data base all AVHRR channel values, sun and satellite geometry, position and time information were stored for each pixel together with the class identification. By studying the spectral signatures of the classes from different channel features (single channels and combination of channels), the channel features best suited for separating the classes can be found. Figure 11 shows histograms of two AVHRR channel features under daytime conditions. The histograms have been normalized so that each histogram cover the same area. The fraction between channel 2 and 1 is suited for separating between sea and ice or clouds, and the fraction between channel 3A and 1 is suited for separating between ice and clouds.

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3.2 DEFINING AN ALGORITHM FOR DAYTIME CONDITIONS

Based on the histograms in Figure 11 the probability density functions (PDF) for each class and channel feature can be estimated by fitting distribution functions to the shape of the histograms. Assuming gaussian normal distributions will be a sufficient approximation for these histograms (except maybe cloud in ch3a/ch1, which looks a bit more like a tailed distribution). The function fitting was done by calculating the mean value and standard deviation of the histograms, and then adjusting the these values by overlaying the distribution on the histograms. Figure 12 shows the estimated PDFs for the two channel features ch2/ch1 and ch3a/ch1 for each of the classes. The distribution function values are given in Table 2.

Table 2: Values defining the probability distribution functions for daytime conditions.

ch2/ch1 ch3a/ch1

mean std.dev mean std.dev

Ice 0.7477 0.06163 0.09522 0.03729

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Figure 10: Example of collection of AVHRR training data by visual inspection. From NOAA-17 2006-09-24 11:42 UTC, west of Svalbard (ch2,3A,4 colour composite).

ch2/ch1 ch3a/ch1

mean std.dev mean std.dev

Water 0.4043 0.05729 0.07902 0.06691

Cloud 0.8135 0.06434 0.6288 0.2007

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Figure 11: Normalized histograms of ice, cloud and sea for two AVHRR channel features at daytime, the fraction between ch2 and ch1 (left) and between ch3A and ch1 (right) (from NOAA-17 data).

Figure 12: Gaussian probability distribution functions for AVHRR ch2/ch1 (left) and ch3a/ch1 (right) given each of the three classes ice, water and cloud.

The PDF is the probability for a measurement given a certain class (ice, cloud, water). For a classification method the opposite is needed, namely the probability for a certain class given the measurement. In the combined case where several channel features are used as measurements, the probability of a certain class given several channels features is needed. This can be calculated using the Bayesian approach, which is defined in equation (1).

p I k∣A1 ,... , An=p A1∣I k ⋅p A2∣I k ⋅...⋅p An∣I k

∑j

p A1∣I jP I j⋅...⋅p An∣I jP I jP I k (1)

Equation (1) gives the probability p for a class Ik given the series of measurements A1,...,An as a function combining the probabilities for each of the single observations Ai given the class Ik and an a priori probability for each class P(Ik). The a priori probabilities for each class can be assumed to be equal, and each a priori term therefore cancels. For the case of the probability for ice given an observation of ch2/ch1 (labelled c21), equation (1) simplifies to:

p I∣c21= p c21∣I pc21∣I pc21∣W p c21∣C

(2)

In Figure 13 the probabilities for each of the classes given an observation of either ch2/ch1 or ch3a/ch1 have been plotted.

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Figure 13: Probabilities for each of the three classes ice, water and clouds given either the feature ch2/ch1 (left) or ch3a/ch1 (right).

For the case of the probability for ice given observations of both ch2/ch1 and ch3a/ch1 (labelled c21 and c31), equation (1) can be written:

p I∣c21 , c31= p c21∣I p c31∣I p c21∣I pc31∣I pc21∣W pc31∣W p c21∣C p c31∣C (3)

where I is ice, W is water and C is cloud. In Figure 14 the probabilities of the most probable class given observations of both ch2/ch1 and ch3a/ch1 have been plotted.

3.3 DEFINING AN ALGORITHM FOR NIGHTTIME AND TWILIGHT CONDITIONS

At nighttime channel 1, 2 and 3a are not available and in twilight conditions the signal from these channels can be too weak and the daytime algorithm no longer applies (twilight is here defined as solar zenith angle between 85 and 95 degrees, day below 85 degrees and night above 95 degrees). Training data must therefore be collected specific for both nighttime and twilight conditions and new channel features studied to define methods for ice masking in these conditions. This work is being done at present and the results will be presented in a later version of this report.

3.4 APPLYING THE METHOD IN ICE MASKING SST RETRIEVAL

As noted earlier, the important matter in SST retrieval is to detect both ice and cloud contaminated pixels that the cloud mask has erroneously labelled as cloud/ice free sea. When all three channels 1, 2 and 3a are available, the method developed here

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Figure 14: Probability of the most probable class ice (red), water (blue) or cloud (green) given both the feature ch2/ch1 and ch3a/ch1.

is capable of separating between ice, water and clouds. When only channel 1 and 2 are available the method is capable of separating water from ice/clouds. For some AVHRR instruments (e.g. on-board NOAA-18) only channel 1 and channel 2 is available. For others, the instrument switches between channel 3a and channel 3b.

If it is not important that the ice mask is purely an ice mask, but also can mask some undetected clouds, a good option is then to use the sum of probabilities for ice and clouds in the ice masking. This gives the probability of not being open water. As seen in Figure 13 ch2/ch1 can then be used alone when ch3a is not available, or in fact ch3a is not necessary if separation of clouds and ice is not needed.

An example of applying the ch2/ch1 for calculating the combined probability of ice + clouds is shown in Figure 15. Black areas in the probability image is areas where the PPS NWP SAF cloud type product gives ice or clouds, and the ice algorithm has not been applied there. The new algorithm is able to detect more clouds and ice. Many of the small scale structures and the thin ice in the scattered ice cover gives high probabilities for ice as well as some of the undetected clouds (see Figure 1 to better see the difference between clouds and ice).

When masking for sea ice, the probability for ice (or probability for ice and clouds) can be used directly by applying a threshold depending on how strict the ice masking should be (e.g. 75%). Another approach is to introduce an ice indicator which takes this threshold into account. Such an indicator has been suggested by CMS/Meteo-France, defined by:

ice_indicator=100 x ice_prob−ice_prob_limice_prob_crit−ice_prob_lim

ice_indicator=0 if ice_probice_prob_limice_indicator=100 if ice_probice_prob_crit

(4)

where ice_prob_lim is the lower limit where the ice probability algorithm is sure not to miss ice (ice_indicator = 0), and ice_prob_crit is the upper critical limit where the ice probability algorithm is sure not to be wrong about the presence of ice (ice_indicator = 100). ice_prob_lim and ice_prob_crit should be tuned with respect to validation results of the ice probability algorithm. A set of initial values can be ice_prob_lim = 10% and ice_prob_crit = 90%. These values will be tuned when gaining more experience with the method.

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Figure 15: AVHRR ch1,2,4 colour composite image and corresponding ice + cloud probability (same situation as Figure 1). Black colour in ice probability image corresponds to where initial cloud mask gives cloud or ice (from PPS, NWC SAF)

4 CONCLUSION

A method for masking of sea ice in SST retrieval from AVHRR data has been suggested. This method combines the use of a coarse resolution sea ice mask using the OSI SAF sea ice edge or sea ice concentration product, with an algorithm estimating the probability of each AVHRR pixel in the vicinity of the ice edge being contaminated by sea ice. The method will help to alleviate some of the shortcomings in daily global ice analyses; in particular the lack of resolution and summertime underestimation of the sea ice edge.

The algorithm for estimating ice probability from AVHRR data has been developed for daytime use on NOAA17. The coefficients for NOAA17 should work for AVHRR instruments on other satellites as well, but fine tuning is necessary to optimize the algorithm for each satellite. When METOP AVHRR data are operational available the daytime algorithm will be fine tuned for use on these data. The night time and twilight algorithm is under development and will be presented later in 2007. Until then the ice masking method should be based on the ice concentration or ice edge product alone.

The possible advantage to more frequent updating of the daily sea ice products was investigated. The differences between the ice edge in a new twice-daily and the daily product typically amounted to 2-4 pixels, with sporadic differences up to about 10-15 pixels in situations where the ice edge is evolving fast. The more frequent updating was not found to introduce significant problems with weather contamination, however in a few cases there was areas of missing data in the twice daily product. To decision whether to use the twice-daily products in the OSISAF SST products should weigh these rather local and sporadic benefits against the additional system complexity introduced.

Larger and more long-lived differences were found along the Canadian East Coast, South of Baffin Island - an area abundant with thin ice. Here for prolonged periods, the 15 % ice concentration contour was systematically estimating an ice edge 5-15 pixels beyond the ice edge product. In areas with a more stable ice edge and/or less thin ice abundance, the differences were generally less than 5 pixels. The 15 % ice concentration contour was not found to be more susceptible to atmospheric contamination. In fact comparison of the entire 2006 OSISAF sea ice data set rather showed atmospheric contamination in the ice edge product, which is thought to be related to the use of the polarisation ratio in the multi-sensor scheme and is in the process of being rectified.

For these reasons, the use of the inclusion of an ice concentration data set in the SST product would seem justified, allowing users to set their own thresholds. However the SST product quality flags might rely on both the ice edge and ice concentration products.

In the near future resolution of the operational sea ice products will be improved through inclusion of 85 GHz information and the release of an AMSR-E processing system. We expect the performance concerning thin ice detection may be improved through the use of ASCAT information in the multi sensor scheme. It would be

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natural to pursue the combination of the AVHRR based sea ice flagging with information from ice concentration or ice edge products in particular during the winter season, where the infrared signal alone does not allow unambiguous classifications, but does have the potential to mitigate some of the problems in the sea ice products caused by coarse resolution.

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5 REFERENCES

Agnew, T., S. Howel (2003), The use of operational ice charts for evaluating passive microwave ice concentration data, Atmosphere-Ocean, 41(4), 317-331.

Andersen, S., R.T. Tonboe, S. Kern, H. Schyberg (2006), Improved retrieval of sea ice total concentration from spaceborne passive microwave observations using Numerical Weather Prediction model fields: An intercomparison of nine algorithms, Rem. Sens. Environ., 104, 4, 374—3 92.

Cavalieri, D.J. (1994), A microwave technique for mapping thin sea ice, J. Geophys. Res., 99(C6), 12561-12572.

Wadhams, P., J. C. Comiso (1999), Two modes of appearance of the Odden ice tongue in the Greenland Sea, Geophys. Res. Lett., 26(16), 2497-2500, 10.1029/1999GL900502.

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6 APPENDIX I

In the following we present monthly images of exclusive ice occurrences in the ice edge product (left) and in the ice edge defined by the 15% concentration isoline (right). Colours indicate the number of occurrences during a month where one product reported ice and the other reported open water.

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January 2006

February 2006

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March 2006

April 2006

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May 2006

June 2006

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July 2006

August 2006

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September 2006

October 2006

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November 2006

December 2006

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