Arctic sea ice drift from wavelet analysis of N SCAT and special sensor microwave imager data

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 104, NO. C5, PAGES 11,529-11,538, MAY 15, 1999 Arctic sea ice drift from wavelet analysis of N SCAT and special sensor microwave imager data Antony K. Liu and Yunhe Zhao Oceans and Ice Branch, NASA Goddard Space Flight Center, Greenbelt, Maryland Sunny Y. Wu Universities Space Research Association, Greenbelt, Maryland Abstract. Wavelet analysis of NASA scatterometer (NSCAT) backscatter and DMSP special sensor microwave imager (SSM/I) radiance data can be used to obtain daily sea ice drift information for the Arctic region. This technique provides improved spatial coverage over the existing array of Arctic Ocean buoys and better temporal resolution over techniques utilizing data from satellite synthetic aperture radars. Comparisons with ice motion derived from ocean buoys give good quantitative agreement. Both comparison results from NSCAT and SSM/I are compatible, and the results from NSCAT can definitely complement that from SSM/I when there are cloud or surface effects. Then, three sea ice drift daily results from NSCAT, SSM/I, and buoy data can be merged as a composite map by some data fusion techniques. The ice flow streamlines are highly correlated with surface air pressure contours. Examples of derived ice drift maps in December 1996 illustrate large-scale circulation reversals over a period of 4 days. These calibrated/validated results indicate that NSCAT, SSM/I merged daily ice motions are suitably accurate to identify and closely locate sea ice processes and to improve our current knowledge of sea ice drift and related processes through the data assimilation of ocean-ice numerical model. 1. Introduction In 1996 the NASA scatterometer (NSCAT) rode into orbit on the Japanese satellite Advanced Earth Observing System (ADEOS) and gathered 8.5 months of valuable wind data. NSCAT, the active microwave sensor, measured return signals from 600-km-wide swaths on both sides of the satellite. The resolution within the swaths was approximately 25 km. The microwaves were Bragg scattered by short water waves in the open ocean and by ice surface roughness in the Arctic region. In this paper, the daily sea ice motion derived from NSCAT is demonstrated and its applications are indicated. Prior to these applications, however, the uncertainty of NSCAT data must be determined from calibration and validation with in situ and other remote sensing observations. Early estimates of ice drift were obtained from ship records, manned ice stations, and reconnaissance aircraft. With the advent of satellite systems a much better understanding of polar ice drift was realized. The Argos system on National Oceanic and Atmospheric Administration (NOAA) satellites, for example, can relay data from buoys (or other automated platforms) and determine locations to an accuracy of a few hundred meters about 10 times a day [Thorndike, 1986]. Sequential satellite images obtained from the advanced very high resolution radiometer (AVHRR) have also been used to determine ice motion through ice feature tracking [Emery et al., 1991]. Emery et al. combined the automated maximum crosscorrelation method with a spatial filtering technique to retrieve coherent ice motion vectors in cloud-contaminated imagery. Copyright 1999 by the American Geophysical Union. Paper number 1998JC /99/1998JC ,529 Nonetheless, AVHRR images are obscured by cloud cover and low light levels (for visible channel), limiting their usefulness. Radar systems such as the ERS 1 synthetic aperture radar (SAR) and the recently launched RADARSAT are independent of weather conditions and should provide particularly good estimates of ice drift [Kwok et al., 1995]. However, SAR is valuable for ice motion owing to the fine spatial resolution of the data, but this spatial resolution comes at a cost in terms of temporal resolution and amount of processing required. Therefore less than daily coverage by SAR may be a serious problem for some applications. Drinkwater [1996] discussed NSCAT data for ice analyses and ice motion for Antarctic sea ice anomalies study. A preliminary study by Liu et al. [1998] shows sea ice motion derived from NSCAT agrees with buoy observations. Since 1987, the Defense Meteorological Satellite Program (DMSP) special sensor microwave imager (SSM/I) has been providing global maps of radiometric brightness from which sea ice extent, concentration, and type are derived routinely for operational and research purposes. While the SSM/I measures radiances at 19, 22, 37, and 85 GHz, only the lower three frequencies are used routinely for extracting sea ice information. Recently, more attention has been given to the 85-GHz channels because of their higher spatial resolution (12.5 km). It has recently been demonstrated byagnew et al. [1997], Liu and Cavalieri [1998], Liu and Zhao [1998], and Kwok et al. [1998] that sequential imagery from SSM/I can provide ice motion observations. Maslanik et al. [1997] also show examples of ice motion derived from SSM/I data, including mention of blending with buoy and AVHRR-derived velocities. A concern with using SSM/I 85-GHz radiance data is the known effect of atmospheric emission at this frequency as well as surface el-

2 11,530 LIU ET AL.: SEA ICE DRIFT FROM NSCAT AND SSM/I DATA fects, which may give a false indication of ice drift. To check this concern, a similar procedure has been applied to the NSCAT 13.9-GHz backscattering data covering a period (November and December 1996) for which SSM/I data and ocean buoy data are available for validation. Generally, NSCAT and SSM/I measure different physical ice properties: surface briefly described in the next section. Wavelet analysis for ice feature tracking and sea ice motion from NSCAT and SSM/I data is then presented, along with some discussion of algorithms and techniques. Both wavelet analysis results from NSCAT and SSM/I are compared with the ice motion observed from buoys for calibration/validation. Three sea ice drift daily roughness and brightness temperature anomalies, respectively. results from NSCAT, SSM/I, and buoy data are then merged as Thus they provide two independent measurements of sea ice drift and can be checked by comparisons with buoy data. a composite map for the evolution study of sea ice drift in December The results and applications of satellite- A new method of time-varying signal analysis, called the derived sea ice motion are discussed in the final section. In this wavelet transform, has been developed and used for image processing applications at NASA Goddard Space Flight Cenpaper, the focus is on the merging of NSCAT, SSM/I, and buoy data for the evolution study of sea ice motion. ter (GSFC) during the past 3 years. Basically, wavelet transforms are analogous to Fourier transforms but are localized both in frequency and time [e.g., Combes et al., 1989]. Recent 2. Two-Dimensional Gaussian Wavelet investigations in physical oceanography justify the efficiency and utility of these transforms to analyze nonlinear dynamical ocean systems. Wavelet analysis of wind fluctuations over In general, the continuous wavelet transform, W (a, b), of a function, s(r), where r = (x, y) is expressed in terms of the complex valued wavelet function, w(r), as follows: ocean wave groups has been reported by Liu et al. [1995] and Peng et al. [1995] using a one-dimensional Morlet wavelet transform. A two-dimensional wavelet transform is a highly ' a Ws(a b) = - s(r)w, dr (1) efficient band-pass filter. It has been applied to SAR imagery to separate various scale processes including relative phase/ in which the wavelet function is dilated by a factor a and location information [Liu and Peng, 1993]. The two-dimen- shifted by b. The function w(r) is the basic wavelet [Combes et sional Gaussian wavelet (often referred to as Mexican hat) has al., 1989]. The asterisk indicates complex conjugate. For data been used as an edge detector in SAR imagery for small-scale analysis the wavelets frequently used are a Gaussian modufeatures [Canny, 1986]. In a marginal ice zone study by Liu et lated sine and cosine wave packet (the Motlet wavelet), the al. [1994], the ice edge in each SAR image was delineated by second derivative of a Gaussian (the Mexican hat). In this using the two-dimensional wavelet transform, which has also study, we use a real value function as the analyzing wavelet, been applied to satellite images (SAR, AVHRR, and ocean which is defined as the second derivative of a Gaussian as color) to separate various scale processes including relative follows: phase/location information for coastal watch applications [Liu et al., 1997a]. The wavelet transforms of satellite images can be w, = 2 exp used for near-real-time "quick look" analyses of satellite data 2a 2 (2) for feature detection, for data reduction using a binary image, where a is the scale of the wavelet transform. Since convoluand for image enhancement by edge linking. The use of wavelet transforms in the tracking of sequential ice features, such as ice edges and floes, in the ERS 1 SAR imagery has been demonstrated by Liu et al. [1997b], especially in situations (e.g., summer ice and marginal ice zone) where feature correlation tion is commutative with respect to differentiation, the resulting wavelet transform is the Laplacian of a Gaussian smoothed function. Thus its zeroes correspond to the inflection points of the original function [Canny, 1986]. The contours of zero crossing indicate the edges in the pattern of the input function. techniques may fail to yield reasonable results. In the marginal ice zone the ice processes are very dynamic with shear and deformation from wind, current, and wave forcing [Liu et al., 1994; Liu and Peng, 1998]. For the summer ice the presence of 3. Wavelet Analysis of Satellite Images A flow chart of satellite imaging analysis using 2-D wavelet liquid water on the ice surface greatly reduces the backscatter transform technique of different scales to track various ice contribution from the sea ice. As shown by Agnew et al. [1997] and Kwok et al. [1998] the traditional correlation method is robust and operational. However, it may fail when the signaltextures from NSCAT (backscatter) and SSM/I (brightness temperature) is shown in Figure 1 for reference. Satellite data will first be interpolated to fit the numerical grid, with land to-noise ratio is very low (e.g., for summer ice or contamina- masked. The satellite image will be wavelet transformed at tion by the atmospheric effects) and where ice processes are highly dynamic (e.g., in the marginal ice zone or coastal various scales to separate various ice textures or features. The Laplacian of the Gaussian wavelet is used as a band-pass filter polynya). The wavelet transform is a very efficient band-pass with a threshold for feature detection. The choice of the scales filter for feature extraction. Wavelet analysis is more interactive and is a research tool at this stage since one needs some knowledge of physical scales of ice features involved for the for wavelet transform depends on the physical scales of the ice signatures (brightness temperature for SSM/I and backscatter/ roughness for NSCAT) to be extracted. Then, the following wavelet transform. Since the wavelet transform is based on fast two tracking regions are considered: coast/bay for fast ice mo- Fourier transform (FFT), it is very efficient computationally tion (with a 2-day sliding window) and central Arctic for slow and needs about 10 min for a motion map using a workstation. Therefore wavelet analysis is definitely complemental to the correlation method. Wavelet analysis of SSM/I 85-GHz radiance data can be used to obtain daily sea ice drift information for the Arctic and Antarctic regions [Liu and Cavalieri, 1998]. The two-dimensional (2-D) Gaussian wavelet transform is ice motion (with a 4-day sliding window). Template matching is performed with the results from the wavelet transform of the images between day 1 and day 5 for central Arctic and between day 2 and day 4 for coast/bay. Because the displacement of the ice feature may move just a few pixels in a few days, the domain of the template matching can be restricted to an area with a

3 LIU ET AL.: SEA ICE DRIFT FROM NSCAT AND SSM/I DATA 11,531 [SSMI($5 GHz 37 GHz) / SMMR / NSCAT (14 GHz) IMAGES [COAST/BAY I iitolatiosl 2D WAVELET [CENTRAL ARCTIC I TRANSFORM CA na t O A S$ -) SMALL SCALE IMDIA TEMPLATE/NUMERICAL FEATURE SCALZ I GRID I BLOCK AVERAGE AND F LTERIN SCALZl ]SEA ICE DRIFT (DATA FUSION)I ]BUOY DATA[ [ARCTIC ICE MOTION (NEIGHBOR FH.,TER) [ STREAMLINES SURFACE PRESSURE CONTOURS I lice-ocean INTERACTION MODEL] [SA Figure 1. Flow chart of satellite imaging analysis using a two-dimensional (2-D) wavelet transform technique with var- ious scales to track ice textures in NASA scatterometer (NSCAT) and special sensor microwave imager (SSM/I) data for sea ice motion. few pixels (e.g., 10 pixels) larger than the template window. The template match is done by shifting the template over each pixel in the domain. The summation of the absolute value of the differences between the shifted template values and the target values is computed at each location. The sequence of the summation values is then used as a metric of the degree of match of the ice feature. Its minimum indicates a possible match of two displaced ice features. Once the templates have been matched, the velocity vector can be easily estimated from dividing the relative displacement over the time interval. Finally, the sea ice drift map can be merged by block average with outlier filtered. Wavelet analysis of NSCAT backscattering and SSM/I radiance data can be used to obtain daily sea ice drift information for both the northern and southern polar regions. The application of the wavelet transform technique described in the previous section results in an ice motion map. Plate la shows an 85-GHz SSM/I image (12.5-km resolution) of the Arctic Ocean for December 20, 1996, where the ice circulation motion in the central Arctic has been clearly derived as indicated by the white arrows. In this SSM/I image of size 512 x 512 pixels, several scales (a = 1.0, 1.21, units of pixel spacing) for the wavelet transform are chosen. The resultant ice motion vectors have been block averaged to 100 km x 100 km grid with outlier filtered. The detailed procedure has been reported by Liu and Cavalieri [1998] and outlined above. Notice that the ice motion in Fram Strait/Baffin Bay and Green- l land coast can be clearly identified. The empty areas with no vectors in the map indicate regions where the template failed to identify displacement (i.e., no matching within a threshold value). In this study, the threshold is set to be the maximum neighboring buoy displacement plus two pixels. For NSCAT data, first the daily map of backscatter (O-o) has been constructed with all data in the pixel corrected to the same incidence angle of 40 ø [Ezraty and Cavanie, 1997]. Then, the ice surface roughness features are tracked from sequential days (4-day sliding window for the central Arctic and 2-day sliding window for coast/bay) using wavelet analysis almost exactly the same as described above and used on SSM/I data. Plate lb shows the sea ice drift derived from NSCAT data on December 20, The white arrows indicate velocities derived from feature tracking using wavelet analysis, while red arrows indicate velocities from Arctic Ocean buoys (4-day sliding window). Notice that the ice motion in Fram Strait/Baffin Bay and Greenland coast can be clearly identified and the major circulation in the central Arctic is clearly shown in both SSM/I and NSCAT results. The flow patterns from both results are extremely similar. Since NSCAT is an active sensor with lower frequency and is not affected by cloud cover, the agreement between SSM/I and NSCAT certainly increases our con- fidence in the derived sea ice drift results. In general, the results from SSM/I are smoother than those from NSCAT because of its higher-resolution capability. The regions with no ice tracking from NSCAT and SSM/I are generally not collocated since they are physically different areas: surface roughness and brightness temperature anomalies, respectively. Therefore the results from NSCAT can definitely complement those from SSM/I when there are cloud or surface effects as shown in Plate Data Comparisons Using almost 100 station years of Arctic drift data from 1893 to 1983, Colony and Thomdike [1984] applied optimum estimation techniques to obtain quantitative estimates of the properties of the mean motion. The mean motion is made up of day-to-day motions driven by surface wind and ocean currents. Drift speeds over a 1-day period have a mean value of about 2 cm s - and a standar deviation of 7 cm s - [Colony and Thomdike, 1984]. While this historical data set provides a good measure of the field of mean motion, the sparsity of data results in large uncertainties. A network of automatic data buoys to monitor synoptic-scale fields of pressure, temperature, and ice motion throughout the Arctic Basin, the Arctic Ocean Buoy Program (ADBP), was established in 1978 to support the Global Weather Experiment. The basic objective is to maintain a network of drifting buoys in the Arctic Ocean with which to provide data needed for real-time operations and meteorological and oceanographic research. These ADBP data are used in the following section to assess the accuracy of the remotely sensed ice displacements. Two months (November and December 1996) of data of NSCAT, SSM/I, and buoy, and the resultant daily sea ice motion are used for data comparisons. The red arrows in Plate 1 indicate the corresponding ice velocities computed from ocean buoy data from December 18 to 22, 1996 (centered on December 20 with a 4-day sliding window as used in wavelet feature tracking). These buoy velocities are consistent with both the direction and magnitude of the white arrows qualitatively. For quantitative comparisons the ice velocities of wave-

4 11,532 LIU ET AL.' SEA ICE DRIFT FROM NSCAT AND SSM/I DATA 3O RMS of diff. of speed between buoy and NSCAT: 2.83 k RMS of diff. of speed between buoy and SSM/I: / ' o o Time (day) 100 RMS of an gles between buoy and NSCAT: 32. RMS of angles between buoy and SSM/I: ,tvT,, Time (day) 3O RMS of diff. of speed btween buoy and NSCAT: 2.02 C RMS of diff. of speed btween buoy and SSM/h 3.06 'l 2/ ' R ls'o; a gl;s t ee; u;y mlfi S A : i RMS of angles btween buoy and SSM/I: 29.36, loo Time (day) Time (day) Figure 2. Comparisons of (a) ice speed and (b) drift direction in November 1996 and (c) ice speed and (d) drift direction in December 1996, from buoy 1101 in the Barents Sea, SSM/I, and NSCAT data with feature tracking. Solid line represents buoy displacement; stars and diamonds represent tracking results from SSM/I and NSCAT data. let-derived features from the satellite data closesto the buoys are identified (within a few pixels). For demonstration purposes a direct comparison of the drifts (speed and direction) of buoy (number 1101) located in the Beaufort Sea with the nearest ice feature derived from the wavelet analysis over November and December 1996 is shown in Figure 2. The solid line in Figure 2 represents buoy displacement, while stars and squares represent tracking results from SSM/I and NSCAT data, respectively. The speed and direction of ice feature mo- tion are tracked from the wavelet transform results nearest to the buoy. The agreement is very good in both speed and direction. The speed differences between the wavelet-derived NSCAT features and this buoy for November and December 1996 (Figures 2a and 2c) have mean values of 2.5 and 1.6 cm s -, respectively, with a standardeviation of 1.3 cm s-. The root-mean-square (rms) of speed differences are 2.8 and 2.0 cm/s, respectively. The direction differences (Figures 2b and 2d) for November and December 1996 have mean values of 22.9 ø and 13.3 ø with a standard deviation of 23.4 ø and 11.0 ø, respectively, and the general trend agrees very well. The rms of ice drift direction differences between NSCAT and buoy observations are 32.4 ø and 17.1 ø. Notice that the flow direction almost reversed near December 21 to 22 (from 40 ø to 320 ø ) as shown in Figure 2d. The speed differences between the wavelet-derived SSM/I features and this buoy for November and December 1996 have mean values of 2.6 and 2.3 cm s - with standard deviations of 2.0 and 2.1 cm/s, respectively. The rootmean-square values of speed differences are 3.3 and 3.1 cm s-, respectively. The direction differences for November and December 1996 have mean values of 27.8 ø and 17.2 ø with standard deviations of 26.1 ø and 24.2 ø, respectively, and the general trend also agrees very well. The rms of ice drift direction differences between SSM/I and buoy observations are 37.8 ø and 29.4 ø. In this case the NSCAT results agree slightly better with buoy data than the SSM/I results. Furthermore, the agreements between satellite-derived results and buoy data are better in December than in November. The cross-correlation coefficients of ice speed between NSCAT and buoy and between SSM/I and buoy are 0.92 and 0.80 for December, but they are only 0.86 and 0.81 for November, respectively. For the drift direction the cross-correlation coefficients between NSCAT and buoy and between SSM/I and buoy are 0.98 and 0.95 for December and 0.97 and 0.96 for November, respectively. The NSCAT sea ice velocities in 100 km x 100 km grids are compared to the observed ice motion from all buoys (about 21 buoys) in Figure 3 for November and December The proximity of the linear fit to the solid line (the ideal fit) shows that there is an extremely good match. The rms difference between NSCAT-derived speeds and buoy observations (ranging within 45 km) is 2.8 cm s -, with a total of 576 data points (Figure 3a). The mean value of speed differences between the NSCAT-derived feature and the buoy for November and December 1996 is 2.1 cm s -, and the standar deviation is 1.8 cm s -. The rms difference between NSCAT-derived ice drift di- rections and buoy observations is 28.6 ø (Figure 3b). The mean value of ice drift direction between the NSCAT-derived feature and the buoy for November and December 1996 is 19.3 ø, and the standard deviation is 21.2 ø. Figure 3c shows the drift

5 LIU ET AL.: SEA ICE DRIFT FROM NSCAT AND SSM/I DATA 11, cm/sec I Dec. 2.0, ('6 Dec. 2.0, 96 Plate 1. Arctic Sea ice drift maps of the Arctic basin in a grid of 100 x 100 km derived from (a) SSM/I 85-GHz radiance data and (b) NSCAT data on December 20, White arrows indicate velocities derived from feature tracking using wavelet analysis, while red arrows indicate velocities from Arctic Ocean buoys. 20 c n/sec t0361 Dec 20, 96 Plate 2. (a) Merged Arctic sea ice motion map and (b) ice flow streamlines (solid curves) from buoy, NSCAT, and SSM/I data on December 20, Contours (dashed curves) in ice flow streamlines map are surface air pressure field (units in millibars), which are highly correlated with sea ice motion.

6 ß &.. Dec 04, 96 Dec, 08., ß.9:6 20,,\ Dec 1.2, 96 '}co. 16 ' "Dee- 28, 9'6.',.

7 LIU ET AL.: SEA ICE DRIFT FROM NSCAT AND SSM/I DATA 11,535 A Plate 4. Ice flow streamlines (solid curves) from the merged ice drift maps on (top) December 24 and (bottom) December 28, 1996, to overlay with the contours of surface pressure (dashed curves). direction difference as a function of ice speed between buoy and NSCAT in November and December The discrepancy in direction may result, in part, from the uncertainty in the relatively slow motion areas (Figure 3c), but also from possible localized shearing motion. Sometimes, owing to a sharp turning of the buoy (see Figure 2d), a small time shift caused by collocation mismatch between wavelet-derived features and buoy may have a significant effect on the direction Plate 3. (opposite) A series of merged ice drift maps from December 4, 8, 12, 16, 24, and 28, 1996, with a 4-day interval (December 20 is shown in Plate 2). The major circulation patterns are changing and shifting significantly within every 4 days. and Cavalieri, 1998]. The rms of direction difference is 34.4 ø, which is slightly worse than that from NSCAT. These results may be affected by the missing data gaps in SSM/I on November 2, 3, 21, 29, and 30. However, both results from NSCAT and SSM/I are compatible and agree well with the buoy data. The ice drift direction in the slow motion areas may have larger uncertainty as shown in Figure 3c. Therefore another way to quantify all the differences between NSCAT, SSM/I, and buoy data is to calculate the magnitude of the difference of comparison (especially in very slow motion areas). As shown in Figure 3, the ice speed and direction derived from NSCAT have some discretized values due to the resolution of 25 km. ice velocities (MDV), especially in relatively slow motion ar- These types of discrete values are much less evident for the eas. In November and December 1996 data sets, MDV has also SSM/I case with a 12.5-km resolution shown in Figure 4. If we been calculated for data comparisons. The mean value of increase the range of NSCAT wavelet features from buoy to 80 MDV between NSCAT and buoy is 3.4 cm s -1, and the stankm for data comparison, then the rms of speed difference dard deviation of MDV is 2.3 cm s -l. Between SSM/I and buoy increases from 2.7 to 2.9 cm s-, with a total of 486 data points the mean value of MDV is 3.7 cm s-l and the standard devi- (from 294 data points), and rms of drift direction difference ation is 3.6 cm s -. The close values of rms and MDV means increases from 27.9 ø to 33.6 ø in December. The increase of that most of the large direction difference is probably due to the uncertainty from relatively slow motion areas. The comparison of NSCAT- and SSM/I-derived ice motion with the Arctic Ocean buoy data (Plate 1, red arrows) shows good overall agreement as indicated by these rms and MDV values. These values are also consistent with the estimate of collocation range for data comparison will add more data points, but the agreement is also deteriorating somewhat, as expected. However, the speed difference increases only slightly, while the direction difference increases more owing to the shearing motion in the sea ice circulation region. The ice speed differences between ice features from SSM/I satellite data uncertainty. For example, a feature displaced 25 and buoys in November and December 1996 have a mean value of 2.1 cm s - with a standard deviation of 2.0 cm s - km (NSCAT resolution) over 4 days will have a computed speed of 6.25 km d - (7.2 cm s-q). In this case, for template (Figure 4a). The rms of spee difference is 2.9 cm s-, with a matching, the uncertainty the estimate is 3.6 cm s-. Theretotal of 486 data points, which is less than that from NSCAT. The direction differences have a mean value of 22.7 ø with a fore a 4-day sliding window is required in central Arctic for the estimate of sea ice velocity. Using a daily map, the temporal standard deviation of 25.8 ø (Figure 4b). These values are conuncertainty of exact starting and ending time for a 4-day sliding sistent with the comparison results from December 1992 [Liu window is approximately 1 day, which is 25% of sea ice drift. Therefore a shorter-time sliding window has larger uncertainty on sea ice drift estimate (e.g., 2-day sliding window may have 50% error for sea ice drift). Another major uncertainty is the collocation of buoy (point) and satellite-derived grid (area) as demonstrated above by increasing the range between observations for data comparison.

8 11,536 LIU ET AL.: SEA ICE DRIFT FROM NSCAT AND SSM/I DATA 3O 20 ] l ß o - o o oo :. o d,. of,: 2 0 > o t o No. ofd da? poin : Buoy Based Ice Speed (c s) o o o o) o - o o o. ofaa in :s7 ' o o >% o o oo Buoy Based Ice Direction (degree) :i: Buoy Based Ice Speed (c s) Figure 3. NSCAT versus buoy (a) ice speed, (b) ice drift direction, and (c) drift direction difference as a function of ice speed in November and December Solid line indicates a perfect fit. 5. Sea Ice Motion Since the comparisons of both results with ice drift derived from buoys give good quantitative agreement, then three sea ice drift daily results from NSCAT, SSM/I, and buoy data can be merged as a composite map by some data fusion techniques. Plate 2a shows the merged Arctic sea ice motion map from buoy, NSCAT, and SSM/I data on December 20, 1996, by a weighted-block average method. Weights of 50% for buoy, 25% for NSCAT, and 25% for SSM/I results are used in this study. Notice that the sea ice motion is much more smooth and the large circulation pattern is clearly shown. Now, the sea ice motion map is almost completely filled up, except in the mar- ginal ice zone and near the ice edge where ice motion is too dynamic (with deformation and rotation) to track. However, there is an empty area (approximate 300 km in length and width) in the East Siberian Sea, which may be caused by regional heavy snow cover (only lasted for 3 days), so both roughness and brightness temperature signatures cannot be tracked. Using the grid analysis and display system (GRADS) [Doty and Kinter, 1994], sea ice motion streamlines can be constructed based on the ice motion vectors. GRADS is an interactive desktop tool currently in use worldwide for the analysis and display of Earth science data. The basic assumptions are using bilinear interpolation within grid boxes and the streamline is always tangential to the interpolated vectors. These ice motion streamlines (solid curves) can then be overlaid with the surface air pressure contours (dashed curves) as shown in Plate 2b. The sea ice motion streamlines are highly correlated with surface pressure contours as demonstrated by the good matching of major circulation in the central Arctic. In order to demonstrate the evolution of sea ice drift in the Arctic, a series of merged daily ice drift maps from December 4 to 28, 1996, with a 4-day interval are shown in Plate 3 (December 20 in Plate 2). Notice that the major circulation patterns are changing and shifting significantly within every 4 days. Furthermore, the sea ice motion vectors illustrate a total reversal within 4 days in the Beaufort Sea (by comparing December 20 to 24) and in the Laptev Sea (December 12 to 16). These circulation reversals can also be observed simulta- neously in daily surface air pressure contour/field over the same period [Rigor and Heiberg, 1997]. Local convergence regions in the central Arctic (December 4 and 24), in the Chukchi Sea (December 20), and in the Barents Sea (December 12) and divergence regions north of Greenland (December 20) and in the Beaufort Sea (December 24) can be easily identified in Plate 3. On the basis of these sea ice drift data and ice type and concentration derived from SSM/I, the regional mass flux, open water production, and heat flux can be estimated in the convergence/divergence regions and coastal polynya (e.g., Chukchi Sea). Since sea ice serves as an insulator between the ocean and atmosphere, the heat transfer between the two depends on the determination of ice convergence and divergence, where the divergence generates open water areas (such as leads and polynyas) within the pack. Because open water is exposed directly to the atmosphere, they are regions of large heat flux and ice growth. Therefore the estimate and prediction of these divergence regions are critical to global climate change study. As shown in Plate 2b for December 20, 1996, the dominant circulation features of sea ice motion derived from satellite data are highly correlated with the surface air pressure field. Plate 4 shows the ice flow streamlines (solid curves) constructed from the merged ice drift maps on December 24 and 28, 1996, to overlay with the contours of surface pressure (dashed curves) for comparison. Again, a weak cycloni circulation in the central Arctic and a very strong anticycloni circulation in the Beaufort Sea are highly correlated with the surface pressure contours on December 24. However, on December 28 the ice flow circulation pattern matches only qual- itatively with surface pressure contours. Nonetheless, a weak anticycloni circulation in the Beaufort Sea is consistent with the local pressure field. On the basis of these results, the sea ice motion is dominated by wind forcing and the ice motion field change significantly for every 4 to 7 days since the polar rows are passing through the Arctic basin in the same timescale.

9 LIU ET AL.: SEA ICE DRIFT FROM NSCAT AND SSM/I DATA 11,537 3O ø o oo oo k * ; ; vo RM S of diff. of sp : 2.96 o. of points: 486 0,,...,..., Buoy Based Ice Speed (c s) _ orms o les:m Buoy Based Ice Direction (degree) Figure 4. SSM/I versus buoy (a) ice speed and (b) ice drift direction in December Solid line indicates a perfect fit. Therefore a weekly mean motion map may not be useful, and daily motion fields are required for ice dynamics study and data assimilation in the ice-ocean interaction model [Hakkinen and Mellor, 1992]. The daily sea ice drift data from satellite and buoy can also be used in the numerical model as a direct application of surface forcing as indicated in Figure 1. Furthermore, on the basis of this ice drift data set and ice momentum equations, the ice model of generalized viscous rheology can be calibrated and improved. 6. Discussion Satellite measurements are providing the full view of sea ice motion in the polar regions daily. This unprecedented capability will lead to routine monitoring of the ice flow for climate change study and ship-routing applications. NSCAT, an instrument designed to observe wind speeds and directions over the ocean surface, has made exciting and totally unanticipated success in applications for which it was never intended, namely, sea ice motion. The exceptional ability of both NSCAT and SSM/I to determine daily sea ice motion in the polar regions with high resolution and no cloud effects should have profound impacts on global climate change applications. However, prior to these applications, the uncertainty of NSCAT and SSM/I data must be determined from calibration and validation with in situ and other remote sensing observations. Arctic Ocean buoys provide ground truth to evaluate the accuracy of satel- lite-derived sea ice motion. In this paper, the 2-D Gaussian wavelet transform is used at several scales to track ice texture and features in the NSCAT and SSM/I images. The technique is applied to derive the sea ice motion in the Arctic. The application of wavelet transforms for tracking ice features in the satellite imagery has been demonstrated through comparisons with ice displacements derived from Arctic drifting buoy data. This wavelet analysis procedure complements the correlation method and can make a major contribution to the understanding of ice drift over large areas at relatively high temporal resolutions. This new source of ice drift data offers a potential solution to the problem of inadequate temporal sampling. Pairs of daily NSCAT and SSM/I images can provide ice displacements, and that greatly increase temporal sampling. The accuracy of this technique is only limited by the persistence of the features and by the spatial resolution of NSCAT and SSM/I. A feature displaced 25 km over 4 days will have a maximum uncertainty of 3.6 cm s -, which agrees with the quantitative estimates of the differences between the wavelet-derived features from NSCAT and buoys in Figure 3 (rms of 2.8 cm s- and 28.6 ø for speed and direction differences, respectively). Future generations of scatterometers and radiometers will have smaller footprints, which will further reduce the uncertainty. Comparison data sets for further assessing the present technique include ice drift stations, AVHRR-derived ice motion, and output from the RADAR- SAT Geophysical Processing System. Comparisons of results from both NSCAT and SSM/I with ice drift derived from buoys give good quantitative agreement. This technique provides improved spatial coverage over the existing array of Arctic Ocean buoys and better temporal resolution over techniques utilizing data from satellite SAR. However, there are regions in the daily map where no surface roughness features from NSCAT or no brightness temperature features from SSM/I can be tracked; but, two daily maps can complement each other. Then, three sea ice drift daily results from NSCAT, SSM/I, and buoy data can be merged as a composite map by a data fusion technique. These calibrated/ validated results indicate that NSCAT, SSM/I merged ice motion data are suitably accurate to identify and closely locate sea ice processe such as ridging and leads. Then, sea ice flow streamlines can be constructed based on the ice motion vec- tors. The streamlines derived from merged ice flow maps are highly correlated with the surface pressure contours, which implies the sea ice motion is dominated by wind forcing. These ice motion streamlines can also be used with the ice surface pressure field for regional dynamic process study. Further study of wind forcing on ice motion is undergoing and will be reported in the near future. A series of daily ice drift maps from December 4 to 28, 1996, are produced for the evolution study of sea ice motion. It is found that the major circulation patterns are changing and shifting significantly within every 4 days. Furthermore, an example of derived ice drift maps in the Arctic from both NSCAT and SSM/I illustrates large-scale circulation reversal over a period of 4 days (December 20 to 24) in December 1996, as also indicated in Figure 2d. This circulation reversal can also be observed simultaneously in the surface air pressure contour/ field in the same period. A local convergence region in the Barents Sea and divergence regions north of Greenland and in

10 11,538 LIU ET AL.: SEA ICE DRIFT FROM NSCAT AND SSM/I DATA the Beaufort Sea are also observed. On the basis of these motion results, the significant ice motion field change for every 4 to 7 days is probably because the polar rolls are passing through the Arctic region in the same timescale. Therefore weekly mean motion may not be used and daily motion fields are required for data assimilation. On the basis of this preliminary assessment, wavelet analysis of SSM/I and NSCAT images definitely can help to improve our current knowledge of sea ice drift and related processes through the data assimilation of an ocean-ice numerical model. Further development of this technique for data fusion of the SSM/I and NSCAT data is undergoing to optimize its use for extracting ice drift information, especially for summer ice. Once the ocean ice model is calibrated with the sea ice drift derived from satellite data, then open water production, ice thickness, and heat flux can be estimated with greater confidence. Acknowledgments. The authors wish to thank Tim Liu of Jet Propulsion Laboratory/CIT for providing the NSCAT data and Don Cavalieri of NASA GSFC for preparing the DMSP SSM/I data sets and for their encouragement. They would like to thank David Long and Robert Ezraty for valuable discussions and suggestions on NSCAT data processing. The DMSP SSM/I data were acquired on CD-ROM from the National Snow and Ice Data Center in Boulder, Colorado. The Arctic Ocean Buoy data were obtained from the Polar Science Center at the University of Washington in Seattle. This work was supported by the National Aeronautics and Space Administration NSCAT project and the National Oceanic and Atmospheric Administration Coastal Ocean Program. References Agnew, T. A., H. Le, and T. 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Untersteiner, Plenum, New York, A. K. Liu and Y. Zhao, Ocean and Ice Branch, NASA Goddard Space Flight Center, Code 971, Greenbelt, MD (liu@neptune.gsfc.nasa.gov) S. Y. Wu, Universities Space Research Association, NASA Goddard Space Flight Center, Greenbelt, MD (Received February 27, 1998; revised December 3, 1998; accepted December 14, 1998.)