On polarimetric characteristics in SAR images of mesoscale cellular ...

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116, C08028, doi:10.1029/2010JC006738, 2011

On polarimetric characteristics in SAR images of mesoscale cellular convection in the marine atmospheric boundary layer Haiyan Li,1,2,3 William Perrie,1 Lanli Guo,1 and Biao Zhang1 Received 25 October 2010; revised 4 May 2011; accepted 31 May 2011; published 25 August 2011.

[1] Convection is an important phenomenon in the marine atmospheric boundary layer (MABL). Previous spaceborne radar studies of such have been limited to single polarization data, and therefore their focus was on the variation in intensity of the radar return, which was constrained by the existence of a single polarization image pattern, representing different atmospheric and oceanic phenomena. In this paper, we study the polarimetric characteristics of mesoscale cellular convection (MCC) in the MABL using high‐resolution data from fully polarimetric (HH, VV, HV, and VH) RADARSAT‐2 (RS‐2) synthetic aperture radar (SAR) images, in conjunction with closely collocated mesoscale atmospheric model simulations, to identify the MCC signatures. To compare the polarimetric characteristics of MCC with those of the ocean surface, our analysis also includes 641 open ocean surface quad‐polarization RS‐2 SAR images collocated with 52 National Data Buoy Center buoys. The open ocean surface SAR images exhibit different polarimetric characteristics from those of MCC. Thus, we differentiate MCC from other open ocean phenomena, based on identifiable polarimetric SAR characteristics. Citation: Li, H., W. Perrie, L. Guo, and B. Zhang (2011), On polarimetric characteristics in SAR images of mesoscale cellular convection in the marine atmospheric boundary layer, J. Geophys. Res., 116, C08028, doi:10.1029/2010JC006738.

1. Introduction [2] Convection over the ocean constitutes a family of possible coherent structures that can occur in the marine atmospheric boundary layer (MABL). Because these structures exhibit coherent up and down drafts, they produce coherent perturbations in the wind, temperature, and humidity fields and contribute significantly to vertical MABL fluxes of heat, momentum, and humidity. Thus, it is important to forecast and parameterize these effects and to investigate their structure and dynamics [Young et al., 2002; Fairall et al., 2009]. Synthetic aperture radar (SAR) is a powerful sensor to study the vast array of MABL phenomena via their ocean surface signatures. MABL convection can produce signatures in the SAR images because these phenomena can modulate the local wind stress vector field and change the surface waves on scales comparable to the wavelength of the SAR [Alpers and Brümmer, 1994; Sikora and Ufermann, 2004; Sikora et al., 2011]. [3] Sikora et al. [1995] linked the mottled patterns in an ERS‐1 SAR image with microscale MABL convection, using high‐resolution concurrent boundary layer measurements. The depth of a convective MABL was estimated by 1 Fisheries and Oceans Canada, Bedford Institute of Oceanography, Dartmouth, Nova Scotia, Canada. 2 Also at Key Laboratory of Computational Geodynamics, Chinese Academy of Sciences, Beijing, China. 3 Also at Graduate University of the Chinese Academy of Sciences, Beijing, China.

Published in 2011 by the American Geophysical Union.

using spectral peaks along the mean wind direction to discern a typical wavelength for the mottled features [Sikora et al., 1997]. Young et al. [2000] attempted to calculate turbulence and stability statistics from SAR via similarity theories in MABL. Sikora and Thompson [2002] suggested that oceanographic phenomena and atmospherically induced inhomogeneities in SAR images can affect such results. Babin et al. [2003] investigated the signature of the spatial evolution of atmospheric convection in SAR imagery during an intense cold air outbreak over the North Atlantic Ocean. They proposed that the “blister‐like” appearance that prevails on the SAR image represents mesoscale cellular convection (MCC) [Atkinson and Zhang, 1996] in the MABL. The low backscatter centers and high backscatter edges of these “blisters” are consistent with the effects of strong downdrafts and updrafts, respectively. Young et al. [2005] used SAR data to study the fine‐scale processes associated with a synoptic‐scale front at sea, including convection. In related studies, Moderate Resolution Imaging Spectroradiometer (MODIS) images, SAR‐derived winds and marine variables estimated from MM5 (Pennsylvania State University/National Center for Atmospheric Research (NCAR) Mesoscale Model 5) were used to study open cellular convection in the Gulf of Alaska [Young et al., 2007]. Sikora et al. [2011] presented an 8 year (1999–2006) climatology of the frequency of open cell convection over the northeast Pacific Ocean and the thermodynamic and kinematic environment associated with its development based on SAR‐derived wind speed and reanalysis data. [4] These observations and studies relate the mottled and blistered appearance that often occurs in SAR images to

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LI ET AL.: POLARIMETRIC MARKS OF MARINE CONVECTION

convective processes, which helps us to understand and parameterize the structure and dynamics of these phenomena. However, previous studies of such are limited in that they are based on single polarization data and focus on the variations in the intensities of radar returns. Thus, until now, development of an understanding of the SAR imaging of convective processes was hampered by the existence of the single image patterns resulting from many possible different atmospheric and oceanic phenomena, such as the sea surface signature of rain [Atlas, 1994], upper/surface ocean convection [Romeiser et al., 2004], and surfactants [Migliaccio et al., 2009]. It is necessary to distinguish the various phenomena from one another before we can synthesize the results of observations, theory, and modeling studies, to achieve a comprehensive understanding of their structure and dynamics. In this regard, Ufermann and Romeiser [1999] proposed a numerical radar imaging model and presented a complete analysis of the normalized radar backscattering cross section (NRCS). They focused on the multifrequency/multipolarization signature of atmospheric convective cells and on theoretical differences between atmospherically induced radar signatures and those of oceanic phenomena. [5] Multipolarization remote sensing measurements can potentially yield more useful information than single polarimetric NRCS. Polarimetric parameters such as copolarization phase differences (hhvv), entropy (H), anisotropy (A), and polarization scattering angle (a), from the eigenvalue and eigenvector decomposition [Cloude and Pottier, 1996; Pottier and Cloude, 1997], are the basis for the development of new classification methods [Lee et al., 1999; Ferro‐Famil et al., 2001; Touzi et al., 2004; Lee and Pottier, 2009]. These polarimetric parameters use statistical properties of the polarimetric data, including eigencharacters of the coherency matrix; all elements of the coherency matrix are analyzed including NRCS, correlation, and phase differences among different polarimetric channels. Morris et al. [2003] collected X‐band quad‐polarization data of breaking ocean waves in the littoral zone at a low grazing angle and found that H can approach 1 and a can approach 80°. Schuler et al. [2003] detected colder, trapped water along the northern California coast at low wind speeds using anisotropy (A). The ocean surface slope can be obtained from the polarization orientation angle shift and a, without the estimation the complex hydrodynamic modulation function [Schuler et al., 2004a]. Schuler et al. [2004b] mapped an ocean eddy covered by biogenic slick fields using these polarimetric parameters. Migliaccio et al. [2009] detected oil spills with the statistical distribution of hhvv. Li et al. [2008] analyzed the variation of the polarimetric characteristics (hhvv, H, A, a) of ocean surface processes with incidence angles and radar wave number, using C‐, L‐ and P‐band, fully polarimetric SIR‐C/X‐SAR data. [6] In this paper, we use RADARSAT‐2 (hereinafter RS‐2) fully polarimetric (HH, VV, HV, and VH) SAR (hereinafter PolSAR) data to study the differences between the polarimetric characteristics of (1) MCC in the MABL and (2) the absence of other processes such as rain, atmospheric phenomena, or surfactants in the open ocean. We consider different incidence angles and sea states. The intensity of the radar returns of HH and VV, hhvv, H, a, and A are analyzed to investigate and interpret SAR images and to differentiate among imaged phenomena. The Weather Research

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and Forecasting (WRF) version 3.2 atmospheric model [Skamarock et al., 2008] is also used to investigate the synoptic meteorological processes and to confirm that the signals in the SAR imagery are MCC. Moreover, to compare the polarimetric characteristics of MCC with those of the ocean surface, we analyzed 641 open ocean surface RS‐2 PolSAR images collocated with 52 buoys. Thus, based on identifiable polarimetric characteristics, we differentiate the polarimetric SAR parameters of MCC from those of other open ocean phenomena. [7] This paper is organized as follows. The data are presented in section 2; for a single SAR image suspected of exhibiting MCC, we first describe the meteorological conditions and assess the features related to MCC. Section 3 presents the WRF model’s simulation of the associated atmospheric phenomena, including the small‐scale details, confirming that the phenomenon in the SAR image is MCC. The SAR polarimetric parameters used to aid in the interpretation of the processes in the SAR image are given in section 4. In order to investigate the polarimetric characteristics of the ocean surface in the absence of MCC, section 5 analyzes a large collection of RS‐2 PolSAR images at different incidence angles, wind speeds, wave heights, and wave steepness. Differences in polarimetric characteristics between MCC and the open ocean surface SAR imagery are discussed in section 6. Conclusions are given in section 7.

2. Data Description [8] The focus of this study is the RS‐2 PolSAR data. Associated in situ ocean surface observations were obtained from the National Data Buoy Center (NDBC) buoy measurements (http://www.ndbc.noaa.gov/). North American Regional Reanalysis (NARR) atmospheric data provided the vertical temperature profiles and synoptic data (http://www. emc.ncep.noaa.gov/mmb/rreanl/), colocated with SAR images. These data were assembled to assess near‐surface meteorology variables such as sea surface temperature (SST), air temperature, vertical temperature profiles, and wind speed. NARR data were also used as the initial and boundary data for WRF model simulations. To provide supplementary information on rain, wind and clouds, we used Special Sensor Microwave Imager (SSM/I) (http:// www.ssmi.com/ssmi/ssmi_description.html), QuikSCAT L3 data with 0.25° resolution (http://podaac.jpl.nasa.gov/ DATA_CATALOG/quikscatinfo.html) and MODIS data (http://rapidfire.sci.gsfc.nasa.gov/realtime). 2.1. PolSAR Data [9] RS‐2 fully polarimetric modes provide C‐band single‐ look complex PolSAR data, which means that each complex image is four combinations (HH, VV, HV, and VH) of H and V on both transmit and receive. Therefore, RS‐2 PolSAR data can provide more information than traditional single polarimetric data. All RS‐2 PolSAR data used in the study are fine quad‐polarization image mode with 25 km swath width. The sampled pixel spacing is 4.73 m in the range direction and 4.79 m in the azimuth direction. The fine quad‐polarization data have an extremely low noise floor, and cross‐talk between different channels was corrected [Morena et al., 2004; Touzi et al., 2010].

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Figure 1

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about 49.67°N and 169.95°W. The center incidence angle was 33.2° and track angle was 11.3°. The four polarization images are shown in Figures 1a–1d. The image is characteristic of many ocean SAR images in that it is dominated by blistered image patterns. Three areas, denoted by A, B, and C in Figure 1a, are about 10 km in the latitudinal direction and about 5–7 km in the longitudinal direction. These length scales are mesoscale [Orlanski, 1975]. Numerous examples of this type of blistered backscatter pattern have been seen in SAR images of sea surface, linking them to the MCC [Atkinson and Zhang, 1996; Babin et al., 2003]. Young et al. [2007] and Sikora et al. [2011] have described the morphology of open cell convection using RS‐1 and MODIS data; the morphology of our RS‐2 SAR image signals is similar to their descriptions. The MODIS image (Figure 1e) that is closest to the SAR image time occurred at 22:10:00 UTC on 1 February; it suggests that MCC did occur and that the SAR image area appears to be a transition zone between open and closed MCC. Thus, as an initial assessment, the morphology of the SAR signature and the MODIS image suggest the presence of MCC phenomena in the MABL. In the following study, we investigate the details of the meteorological conditions, using WRF model simulations and ancillary data, to support our assessment.

Figure 2. North American Regional Reanalysis (NARR) data showing (a) sea level pressure (hectopascals), (b) geopotential height field (geopotential meters) at 500 hPa, and (c) geopotential height field (geopotential meters) at 200 hPa, at 06:00:00 UTC on 2 February 2009. The SAR image area is indicated by the star. [10] The RS‐2 fully polarimetric SAR image that constitutes the basis for this study was acquired in the North Pacific near the Gulf of Alaska, at 05:05:35 UTC on 2 February 2009. The center latitude and longitude coordinates were

2.2. Meteorological Conditions With the Available Data [11] Figures 2a–2c illustrate the NARR sea level pressure and geopotential height fields at 500 and 200 hPa, respectively, at 06:00:00 UTC on 2 February 2009. The NARR data (from 18:00:00 UTC on 29 January to 21:00:00 UTC on 2 February) suggest that the SAR imaging area is located at a high‐pressure ridge with a strong low‐pressure system to the west and a decaying cyclone to the east. Additional details of the cyclone’s development and life cycle are given in the discussion of Figure 7. Figure 3 shows the associated 10 m wind field (U10) from QuikSCAT at 06:22:45 UTC, on 2 February, corresponding to 77 min after the SAR observation. Thus, the SAR image is in a low wind regime with mean wind speed of about 5.1 m/s. However, the closest NARR wind field occurs at 06:00:00 UTC, 25 min after the SAR image, and suggests that U10 is about 3.1 m/s. The winds are clearly lower than those of Young et al. [2007], which were in the range 10–12 m/s. NARR data also suggest that SST and the air temperature at 2 m height are about 4.5°C and 0.5°C, respectively. Thus, the air‐sea temperature difference is about −4°C, suggesting that the atmosphere is unstable in the boundary layer over the image area. Figure 4 shows the vertical profiles of temperature at 03:00:00 UTC and 06:00:00, indicating that the MABL height is about 850 hPa (∼1250 m assuming a typical air density rair of

Figure 1. RADARSAT‐2 synthetic aperture radar (SAR) images for (a) HH polarization and (b) VV polarization at 05:05:35 UTC on 2 February 2009, where the color bar denotes return intensity, in decibels. Interactions with the convective airflow opposite to the ambient wind direction result in a blistered signature, which is darker than the background; the blistered signature is brighter than the background when convective airflow is in the direction of ambient wind. The arrow in Figure 1a represents wind direction obtained from QuikSCAT. Thus, the brighter signal is in the wind direction, and the darker signal is in the opposite direction. The transect line in Figure 1a is used in Figure 10; blistered areas A, B, and C also appear in Figures 9b, 10, and 11. (c) HV image. (d) VH image. (e) Moderate Resolution Imaging Spectroradiometer (MODIS) image at 22:10:00 UTC on 1 February. The circled plus sign represents SAR image. RADARSAT‐2 data and products ©MacDonald, Dettwiler and Associates Ltd. (2008–2009). All rights reserved. 4 of 19

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Figure 3. QuikSCAT wind field U10 at 06:22:45 UTC, 77 min after the SAR image at 05:05:35 UTC on 2 February 2009. The parallelogram indicates the SAR image area. The color bar denotes wind speed, in units of meters per second. 1.2 kg/m3). In general, the aspect ratios of typical convection areas in our study case range from 4 to 8. Here the convection depth, the diameters, and aspect ratios of convection are similar to those described by Atkinson and Zhang [1996]. 2.3. Precipitation [12] Rainfall is potentially an important factor in the interpretation of SAR images [Alpers and Melsheimer, 2004]. The closest F15 SSM/I data to the SAR image occurred 29 min after the SAR image, as presented in Figure 5a, showing that the mean rain rate (in millimeters per hour) is almost zero in the SAR image; F15 denotes the instruments carried on board the DMSP polar‐orbiting satellites. These Figure 5. Precipitation as indicated by (a) the rain rate (millimeters per hour) from Special Sensor Microwave Imager (SSM/I) F15 on 2 February 2009, at 29 min after the SAR imaging time, and (b) accumulated precipitation from Weather Research and Forecasting (WRF) model estimates from 18:00:00 UTC on 1 February to 05:05:00 UTC on 2 February 2009. The parallelograms represent the SAR imaging area. The rain rate in Figure 5a is zero, and there is just one color and no color bar. The color bar in Figure 5b denotes the accumulated precipitation, in millimeters.

Figure 4. Vertical temperatures profiles on 2 February 2009 at (49.67°N 170°W) from NARR data, where the y axis is pressure in hectopascals and the x axis represents temperature (K). Black and green symbols represent temperature profiles at 03:00:00 UTC and 06:00:00 UTC, respectively.

results are confirmed in Figure 5b giving WRF simulation results, suggesting that the accumulated precipitation is less than 0.25 mm over the SAR image area (implementation details and further discussion in section 3). We also analyzed the 3 hourly total precipitation from NARR data from 00:00:00 UTC on 1 February until 06:00:00 UTC on 2 February, a period of 30 h. Table 1 suggests that the resulting maximum total precipitation is no more than 0.07 mm/3 h, indicating the precipitation rate was very low in the SAR image area. Thus, we exclude precipitation effects as factors in the SAR image.

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Table 1. Total Precipitation from 3‐Hourly North American Regional Reanalysis Data, for the Synthetic Aperture Radar Imaging Area, for the Period From 00:00:00 UTC on 1 February 2009 to 06:00:00 UTC on 2 February 2009a Time (h)

1 Feb 2 Feb

0

3

6

9

12

15

18

21

0.01 0.005

0.001 0.004

0.005 0.03

0.07 —

0.04 —

0.03 —

0.01 —

0.005 —

a

Units are in millimeters.

[13] Despite the spatial and temporal offsets of the SSM/I and QuikSCAT data and the coarse resolution of the NARR data relative to the SAR image, they do provide an overview of the meteorological conditions. In addition to temperature and wind measurements, the fact that there probably was no obvious precipitation in the test area in the preceding 30 h is important for interpretation of the SAR image. In principle, heavy rainfall can occur in combination with atmospheric convective cells, which can influence the radar signal [Melsheimer et al., 1998]. For the scenario studied here, we are confident that the rain effect can be excluded. [14] In section 3 we use the WRF3.2 [Skamarock et al., 2008] mesoscale atmospheric model to provide additional support for the suggestion that the phenomena are atmospheric MCC processes in MABL with very low precipitation rates.

3. Atmospheric Model Simulations 3.1. WRF Model [15] We used the WRF model to simulate the atmospheric and MABL processes related to the SAR image in space and time. WRF was implemented with one‐way nested domains telescoping from 10, 3.33, and 1.11 km horizontal grid resolutions, denoted as D1, D2, and D3 in Figure 6. All domains had 31 vertical levels topped at 100 hPa, with the lowest level 35 m above the ground. The outermost domain, D1, covered an area from 42°N to 56°N and from 175°E to 155°W, allowing enough space to describe the weather systems over the SAR imaging area, with the inner domains

Figure 6. The computation domains for WRF. D1 is the outermost area, D2 is the intermediate area, and D3 is the innermost area. The resolution of D1, D2, and D3 are 10, 3.33, and 1.11 km, respectively. The SAR image area is indicated by the star.

Figure 7. Cyclone development comparing NARR data (solid black line in Figure 7a and line with solid black circles in Figure 7b) and WRF (solid blue line in Figure 7a and line with open blue circles in Figure 7b) results. (a) Storm tracks with sea level pressure contours at 06:00:00 UTC on 2 February 2009; the star represents the SAR image area. (b) The x axis represents time, and y axis represents the minimum sea level pressure of the cyclone. as far away from the model’s lateral boundaries as possible. The 3 hourly NARR data were used as the initial and lateral boundary conditions for domain D1. The simulations start on 29 January at 06:00:00 UTC for D1 and on 1 February at 18:00:00 UTC for D2 and D3. All simulations end at 06:00:00 UTC on 2 February. [16] We focus on results for the finest grid, D3, at 1.11 km resolution. The time step for this grid is ∼6.7 s; model results are output at 5 min time intervals. Models always have limitations and uncertainty for mesoscale simulations, including WRF. But WRF can be an ideal tool to study SAR‐observed atmospheric phenomena in the MABL [Li et al., 2011]. Motivation for using the WRF model is that simulations can describe the small‐scale processes using the fine‐resolution D3 implementation. Thus, MCC processes can be identified at the bottom of atmosphere layer, which are lost in NARR data. 3.2. WRF Model Analysis [17] Both WRF model results and NARR data show that a cyclone was initially located at 06:00:00 UTC on 30 January 2009 (not shown), with center at 42°N, 175°W, approximately 900 km southwest of the SAR image area, and central sea level pressure of 998 hPa. Figure 7a shows the trajectory of the cyclone in space; Figure 7b shows its

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Figure 8. Transects of vertical profiles of equivalent potential temperature (K) obtained from WRF, where the y axis represents pressure (in hectopascals): (a) along 169.7°W and (b) along 49.7°N. corresponding variation in central sea level pressure with time. Although it moved northeastward, its center did not pass through the SAR imaging area but remained south of this area. The nearest distance between the cyclone center and SAR imaging area is about 200 km. The cyclone track and central intensity evolution simulated by the WRF model are in good agreement with NARR data, suggesting that the WRF simulation results are reliable. The correlation coefficient of cyclone tracks and central intensity evolutions between WRF and NARR data is about 0.9. The cyclone decayed and moved out of the simulation area after 2 February. However, a high‐pressure ridge gradually formed at the rear of the cyclone and continued to strengthen over the SAR imaging area at the time of observation. At this point, the airflow is mainly that of downdraft or synoptic‐scale subsidence, and the atmosphere is stable on the synoptic scale. This is clearly shown in Figure 8, indicating that the equivalent potential temperature increases with height. [18] Figure 9 shows the horizontal and vertical distributions of vertical velocity from the WRF simulation. In particular, Figure 9a presents the 1000 hPa distribution of

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vertical velocity, clearly showing upward and downward air motions with maximal speeds reaching 0.15 and −0.25 m/s, respectively. The upward and downward airflow constitutes MCC at the bottom of the atmosphere. Constrained by stability at the upper portion of the MABL (Figure 4), the convection does not extend above about 850 hPa, which is consistent with the convection depth estimates of 1 ∼ 3 km by Atkinson and Zhang [1996]. The updrafts and downdrafts in the parallelogram shown in Figures 9a and 9b correspond well with the light and dark areas in the SAR image (Figure 1a). Areas A, B, and C in Figure 9b have the same geographical positions as those in the SAR image indicated in Figure 1a. It is notable that although the WRF model simulation is performed independently from the SAR observation; its results reveal the MCC characteristics. Figure 9c is the vertical profile along 170.01°W, which demonstrates the consecutive upward and downward airflows that are particularly prevalent in the MABL from sea level to about 850 hPa; the SAR image extends from 49.52°N to 49.85°N, as indicated. Similar characteristics are also evident in the vertical profile along 49.65°N in Figure 9d. [19] One possible driver of MCC in the MABL is buoyancy, caused by the temperature differences between the ocean surface and the MABL air. When MCC occurs, the upward and downward air motions at the bottom of atmosphere interact with the ambient wind field and ocean waves and change the roughness of the ocean surface [Sikora and Ufermann, 2004; Sikora et al., 2011]. In SAR images, these processes appear as blistered signatures [Babin et al., 2003]. [20] We use WRF outputs from the 1.11 km resolution D3 domain to provide the integrated precipitation estimates for the time period from 18:00:00 UTC on 1 February until the moment of the SAR observation, at 05:05:35 UTC on 2 February. Figure 5b suggests that the precipitation that accumulated over ∼11 h prior to the time of the SAR image is less than 0.25 mm over the SAR imaging area, implying that precipitation has almost no effect on the SAR image. However, WRF results show that the SAR imaging area was influenced by cyclonic processes. After the passage of the vigorous cyclone, a high‐pressure ridge gradually developed and dominated this area; on the synoptic scale, the atmosphere was stable. Therefore, we exclude rain effects as factors in the SAR image, based on WRF’s precipitation estimation. Moreover, there clearly are upward and downward air motions at the bottom of atmosphere, which constitute convection. This suggests that the blistered signals in the SAR image are MCC signatures. In the following discussion, we will analyze the polarimetric characteristics of these phenomena.

4. SAR Image 4.1. Imaging Atmospheric Convection in SAR Imagery [21] The imaging mechanism for convection signatures on radar backscatter is briefly reviewed. The couplets of updrafts and downdrafts result in the alternation of ocean surface roughness features on the scale of centimeters [Sikora et al., 1995; Ufermann and Romeiser, 1999; Sikora and Ufermann, 2004; Young et al., 2007]. Assuming dry convection, the occurrence of downward‐moving, high‐momentum airflow at the sea surface results in increased vertical shear in the near‐surface horizontal wind. Increased wind shear provides enhanced momentum transfer to the sea surface via shear‐

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Figure 9. WRF results for vertical velocity of the atmosphere, showing (a) the horizontal distribution of vertical velocity field at 1000 hPa and (b) the enlarged figure of the parallelogram in Figure 9a representing the SAR imaging area, where A, B, and C are indicated as in Figure 1a. (c) Vertical profile of the vertical velocity along 170.01°W. SAR image extends from 49.52°N to 49.85°N on the transect at 170.01°W as indicated by red vertical lines on the x axis. (d) Vertical profile of vertical velocity along 49.65°N, with SAR image extending from 170.17°W to 169.71°W as indicated by red vertical lines on the x axis. The color bar denotes vertical wind speed (in meters per second). In each case, the color bar denotes vertical wind speed (in meters per second). driven surface layer turbulence. In an alternate view of things, Young et al. [2007] and Sikora et al. [2011] suggest that the signatures of open MCC are possibly tied to downdrafts fueled by evaporation, sublimation, or melting, which could constitute momentum mix‐down in conjunction with precipitation. In any case, enhanced momentum transfer impacts the mean wind in the boundary layer and associated ocean

waves. The reverse occurs within updrafts. Sea surface roughness and concomitant radar backscatter are increased, where the convection winds and mean surface wind are in the same direction, and minimized when they occur in opposite directions. Thus, convection appears in SAR images as mottled [Sikora et al., 1995] or blistered [Babin et al., 2003] patterns.

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Figure 9. (continued)

[22] MCC signatures in SAR intensity images analyzed in this study (Figure 1a) have mesoscale length of the order of 10 km. Radar backscatter increases with wind speed (all else being equal) at moderate incidence angles and wind speeds. For a given imaging geometry, the strongest signal return is obtained looking in the upwind direction [Romeiser et al., 1997; Young et al., 2008]. The mean wind direction obtained from the WRF model (not shown) is 162° (meteorological convention), which is consistent with 160° from the closest QuikSCAT data in time, as indicated in Figures 1a and 3. When the convective airflow is in the opposite direction to the ambient wind direction, the resulting signatures are darker than the background signals (indicated by A, B and C

in Figure 1a); signatures are brighter than the background signals when they are in the same direction as the ambient wind. The brighter and darker blistered areas are evident. 4.2. Polarimetric Characteristics of MCC [23] Polarimetric parameters are used to analyze the polarimetric characteristics of MCC with high‐resolution fully polarimetric RS‐2 SAR data. As noted in section 1, these parameters make use of statistical properties of the data, including eigencharacters of the covariance matrix; all elements of the covariance matrix are analyzed. The parameters include the intensity of the backscattered return for HH and VV, hhvv, H, a, and A. Details are given by Cloude and

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Figure 10. Variations of backscatter cross section (measured in decibels) on 2 February 2009 for HH, VV, HV, and noise level along the cross section indicated by the red transect line in Figure 1a, where A, B, and C are indicated in Figure 1a. Pottier [1996], Pottier and Cloude [1997], Lee et al. [1999], Ferro‐Famil et al. [2001], Li et al. [2008], and Lee and Pottier [2009]. 4.2.1. Intensity of the Return Wave of Different Polarizations [24] The variations of NRCS for the SAR image are presented in Figure 10, showing HH, VV, HV, and the noise level for a transect through areas A, B, and C in Figure 1a. The backscatter intensity of VV polarization is systematically several decibels higher than that of the HH polarization. The results are consistent with the conclusions of Romeiser et al. [1997] and Ufermann and Romeiser [1999] that the backscatter intensity for VV polarization for atmospheric phenomena is larger than that of oceanic phenomena. The largest modulation depth of area C (Figure 10) is almost 10 dB, which is much larger than results in the 1∼3 dB range, resulting from theoretical investigations of radar signatures for open ocean deep convection [Fischer et al., 1999]. By comparison, Romeiser et al. [2004] found that MABL cellular convection results in a modulation of RS‐1 ScanSAR backscatter intensity of about 9∼10 dB, which is a consistent approximation to the results shown in Figure 10 for our study case. [25] Figure 10 also presents the noise level, which is the same for HH, VV, HV, and VH polarizations, at about −36.5 ± 3 dB [Slade, 2009]. Almost all the pixels of HH and VV polarization along the transect line in Figure 1a are above the noise level. For HH polarization, several dozen pixels in areas typical of area C have lower NRCS than the noise level. However, for HV polarization, only a few pixels have NRCS values above the noise level. Ufermann and Romeiser [1999] simulated C‐band SIR‐C/X‐SAR HV

polarization signatures of convective cells associated with very low absolute NRCS values, ranging from −33.01 to −39.98 dB, which is similar with those studied in this paper (Figure 1c). 4.2.2. Copolarization Phase Differences hhvv [26] Copolarization phase differences hhvv are in the range from 0° to 180°. An ideal single‐bounce (or odd‐ bounce) scatterer will have a hhvv of 0°, whereas this difference will be about 180° for an ideal double‐bounce (or even‐bounce) scatterer. Generally, the radar backscattering at the ocean surface is dominated by resonant Bragg scattering with low hhvv at moderate incidence angles and wind speeds. However, in our case, Figure 11a shows that in areas A, B, and C the hhvv is larger than 20°, especially in area C, and the mean hhvv reaches 70°, which suggests that the dominant Bragg scattering has been reduced and modulated by convection processes, and the scattering mechanism at the ocean surface is not simply resonant Bragg scattering. 4.2.3. Polarimetric Entropy H [27] Polarimetric entropy H is in the range from 0 to 1, which is a measure of the randomization and inherent reversibility of the scattering mechanisms. At moderate incidence angles and wind speeds, the radar backscatter at the ocean surface is dominated by tilted resonant Bragg scattering, and the ocean can be regarded as a low‐entropy surface. Exceptions to this view (high H) appear at high‐ incidence angles and the surf zone [Morris et al., 2003; Li et al., 2008]. In the SAR image, there are some high H areas, as shown in Figure 11b. In area C, the mean value is 0.68, and the maximum value is 0.76. The value for H of these areas experiences a significant increase, which indicates that the Bragg scatter has been reduced by the convective pro-

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Figure 11. Analysis of the case on 2 February 2009 for (a) copolarization phase difference hhvv (measured in degree) and (b) entropy H. (c) Polarization scattering angle a with the units of degree and (d) anisotropy A. Positions A, B, and C are indicated in Figure 1a. Rectangles denote the areas used to calculate the mean polarimetric parameters. In Figures 11b, 11c, and 11d, areas A, B, and C are the same as those denoted in Figure 11a. cesses. The interactions among downward airflows, mean wind fields, and ocean waves generate complex, stochastic turbulence, which changes the roughness and scattering mechanisms of the ocean surface. 4.2.4. Polarization Scattering Angle a [28] The polarization scattering angle a indicates the dominant type of scattering mechanism. The a value has a range that varies from 0° to 90°. For the values below 40°, single bounce surface scatter dominates, whereas for values in the range from 40° to 50°, dipole scatter dominates. In the range from 50° to 90°, even‐bounce scatter dominates. These designations hold when the entropy is low. Figure 11c gives the a values in the convection areas. High a indicates that the scattering mechanism in the convection area might not be due to the single bounce mechanism as typically observed for the ocean surface. Thus, the mean a reaches 51° in area C,

suggesting that the scattering mechanisms might be even‐ bounce, whereas in areas A and B they are possibly single bounce. 4.2.5. Anisotropy A [29] Anisotropy A is important in striving to understand the properties related to SAR ocean backscatter. The values for A range from 0 to 1 and are sensitive to surface roughness; higher A values indicate a smoother surface when surface roughness less than 0.5 [Schuler et al., 2002, 2004b]. Thus, MCC areas are expected to have relatively high values for A. However, in our case, Figure 11d suggests that variations in A are not large over the entire SAR image, compared to variations in other parameters (backscatter intensity, hhvv, H, and a). This result may occur because A is very noisy when H is less than 0.7 (PolSARpro tutorial; http:// earth.eo.esa.int/polsarpro/tutorial.html). We note that in this

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Table 2. Mean Polarimetric Parameters of Mesoscale Cellular Convection in Areas A, B, and C, Denoted by Rectangles in Figure 11 ’hhvv H a A

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Area A

Area B

Area C

20° 0.57 38° 0.34

43° 0.60 40° 0.32

70° 0.68 51° 0.37

study, the values for anisotropy A are estimated based on an eigenvector‐eigenvalue decomposition A (p2, p3) following Pottier and Cloude [1997]. [30] When convection occurs, the upward and downward airflows interact with the ambient wind field resulting in modifications of the intensity and the distribution of centimeter‐scale waves. These processes transform the dominant resonant Bragg scattering mechanisms into more complex, diverse, and multiscattering processes. All of these processes increase the modulation depths of HH and VV polarizations, hhvv, a, and H. However, the contrast in A is not as obvious as that of the other three parameters; variances in A need further study. Table 2 reports the mean values of polarimetric parameters in areas A, B, and C. [31] In order to compare the polarimetric characteristics of MCC and those of open ocean surface SAR imagery at different sea states and incidence angles, we present the analyses of an additional 641 SAR images in section 5. It will be shown that open ocean surface SAR images that do not exhibit MABL convection give polarimetric parameters that have lower values than those suggested in Table 2 for MABL convection areas A, B, and C.

5. Polarimetric Characteristics of Open Ocean SAR Images [32] Until now, this paper has focused on MCC in the MABL. What are the other processes that can be present in SAR imagery of the open ocean? Clearly, the ocean surface is characterized by ocean waves, which undergo continuous variations, and development. Therefore, the NRCS of ocean waves are related to radar frequencies, wind speeds, and incidence and azimuth look angles [Kudryavtsev et al., 2003]. Polarimetric characteristics of ocean surface SAR images are modified by differing image geometry and sea state conditions. These include incident angle, azimuth angle, wind speed, wind direction, significant wave height, wave steepness, and wave direction [e.g., Morris et al., 2003; Mouche et al., 2005, 2006; Li et al., 2008; Lee and Pottier, 2009]. In this section, we explore the variation of polarimetric characteristics of the ocean surface with sea state parameters and incidence angles. [33] To compare the polarimetric characteristics of the open ocean with those of MCC in SAR imagery, we used RS‐2 fine quad‐polarization single‐look complex data collocated in situ sea state measurements from 52 NDBC buoys during the time period from December 2008 to February 2010. The buoys are located in the Gulf of Alaska and off the east and west coasts of the United States and the Gulf of Mexico. The original total number of SAR images is

641, with incidence angles in the range 20.5° ∼ 47.6°, wind speeds of 2 ∼ 25.9 m/s, significant wave heights of 0.2 ∼ 8.7 m, and wave steepness of 0 ∼ 0.08; these were obtained after we eliminated events with negative air‐sea temperature differences in order to avoid instances of atmospheric convection, which was the focus of the previous sections of this paper. Missing measurements reduced this number so that the resulting number of SAR images for analysis of the variation of polarimetric characteristics related to significant wave height is 636; and for wave steepness, to 358. For given significant wave heights and wave steepness, incidence angles are then in the range 20.5° ∼ 40.8°, and wind speeds are in the range 2 ∼ 19 m/s. [34] We adopt the following steps to get the functional characteristics of the polarimetric parameters. First, we find the position of the pixel in the SAR image that is nearest to the buoy. Using this pixel as a center point, we determined a 40 × 40 pixel box. In the box, we first average over 2 × 2 pixels, then we calculate the polarimetric parameters (’hhvv, H, a, and A) with a 9 × 9 moving window, following Lee and Pottier [2009, chapter 7]. Next, we average these variables over the box. In order to analyze the variation of the polarimetric parameters of the ocean surface SAR image with incidence angles, wind speeds, significant wave heights, and wave steepness, the radar incidence angles of the all RS‐2 PolSAR images are divided into six bins for incidence angles (20° ≤ < 25°, 25° ≤ < 30°, 30° ≤ < 35°, 35° ≤ < 40°, 40° ≤ < 45°, 45° ≤ < 50°). Within any given incidence angle bin, we neglect the variation in polarimetric parameters with incidence angle. Finally, in each bin, the variations of each polarimetric parameter with wind speeds, significant wave heights, and wave steepness are evaluated. For this analysis, wind speed is also binned in 2 m/s increments, significant wave height is binned in 1 m increments, and wave steepness is binned in 0.01 increments. [35] The observed wind speeds from the NDBC buoys are normalized to an equivalent anemometer height of 10 m assuming a logarithmic wind profile: U10 ¼ ½lnð10=zo Þ= lnðHei =zo Þ UH

ð1Þ

where zo is the surface roughness length, which equals 1.52 × 10−4 m assuming a drag coefficient of 1.3 × 10−3, and where Hei and UH are the buoy measurement height and the wind speed at that height, respectively. [36] The wave steepness S and significant wave height Hs have the following relation: S¼

Hs Hs ¼ p cT

ð2Þ

where lp is the peak wavelength, T is the average wave period, and c is the phase velocity of ocean wave, which can be calculated from the finite depth dispersion relation: !2p ¼ gkp tanh kp d

ð3Þ

where wp is the peak wave frequency, kp is the peak wave number, and d is water depth. Using these relations, we analyzed the variation of polarimetric parameters with incidence angles, wind speeds, and wave steepness.

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Figure 12. Variations of copolarization phase differences with incidence angles for (a) wind speeds, (b) significant wave heights, and (c) wave steepness. The symbols for the incidence angles bins in 5° increments are indicated in the legend. The 45° ≤ < 50° bin is only for variance in wind speed and significant wave height, not wave steepness. 5.1. Copolarization Phase Differences hhvv [37] Figures 12a, 12b, and 12c present the change of copolarization phase differences hhvv for the open ocean at different incidence angles, wind speeds, significant wave

heights, and wave steepness. As mentioned in section 5, incidence angles were binned in 5° increments from 20° to 50°. The distributions do not show any obvious regular pattern and the actual variations in hhvv are small, resulting from variations in these four parameters. The range in hhvv is

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Figure 13. Variations and standard deviation (vertical lines) of entropy with incidence angles, for (a) wind speeds, (b) significant wave heights, and (c) wave steepness. The symbols for the incidence angles bins in 5° increments are indicated in the legend. The 45° ≤ < 50° bin is only for variance in wind speed and significant wave height, not wave steepness. limited to between 0° and 6°, even though the standard deviation is taken into account in each bin. 5.2. Polarimetric Entropy H [38] Figures 13a, 13b, and 13c show the variation in polarimetric entropy H with different incidence angles, wind speeds, significant wave heights, and wave steepness,

respectively. H clearly tends to increase with increasing incidence angles, for each of the three sea state parameters. There are a few exceptional cases, however. For example, when wind speed is about 25.9 m/s and significant wave height is about 8.7 m, the H value in the incidence angle bin 20° ∼ 25° seems to be larger than that in the bin 25° ∼ 30°. When incidence angles are in the range from 40° to 50°, the

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Figure 14. Variations and standard deviation (vertical lines) of a with incidence angles for (a) wind speeds, (b) significant wave heights, and (c) wave steepness. Legend is same as Figure 13. variations of H with incidence angles, wind speeds, significant wave heights, and wave steepness are not clear. For example, in Figure 13c, for significant wave heights, when incidence angles are in the range from 40° to 45°, H appears to be larger than that for incidence angles in the range from 45° to 50°. In Figure 13c, when wave steepness is smaller than 0.025, for incidence angles in the range 40° ∼ 45°, H is smaller than that with incidence angles in the range 35° ∼ 40°.

[39] In each case in Figure 13, the trend for an incremental change in H’s amplitude with increasing incidence angle is larger than the incremental changes in H (for given incidence angle) for other variables, for example, with wind speed in Figure 13a, or with significant wave height in Figure 13b, or with wave steepness in Figure 13c. These results are notable because they imply that imaging geometry conditions have more effect on H than the effects due to variations in sea state parameters.

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[40] When wind speeds are larger than 2 m/s, the largest H value is less than 0.55, whereas the mean H of area C is 0.68 in Figure 11b; the maximum value for H is ∼0.68 in Figure 13a is probably noise, occurring at incidence angles in the range from 35° to 40° and wind speeds less than 2 m/s. 5.3. Polarization Scattering Angle a [41] Figures 14a, 14b, and 14c present the variations of polarization scattering angle a with different incidence angles, wind speeds, significant wave heights, and wave steepness. Generally, a increases with increasing incidence angles, for each of the three sea state parameters. There are a few exceptions to this trend, for example, (1) when incidence angles are in the range from 45° to 50° and (2) when wind speed is less than 2 m/s, which is probably noise. Overall, Figures 14a and 14b show that a tends to slowly decrease with increasing values of wind speed or significant wave height, respectively. One exception is that for incidence angles in the range from 20° to 25°, a slowly grows with increasing wind speeds (>5 m/s) and significant wave heights (>2 m). The other exception is for incidence angles in the range from 25° to 30°, and for significant wave heights in the range from 6 to 7 m. In general, Figure 14c shows that a tends to slowly decrease with increasing wave steepness. [42] Moreover, the variation in amplitude of a with wind speeds in each incidence angle bin is smaller than its variations with incidence angles for a given wind speed; its variations with significant wave heights or wave steepness are similar. These results indicate that conditions related to the imaging geometry have more influence on a than the sea state parameters. Except for the maximum in a, occurring in Figure 14a, when wind speed is about 2 m/s, the variation in a is from 3° to 25°, and taking its standard deviation into account, the variation of a is from 2° to 27°. 5.4. Anisotropy A [43] Figures 15a, 15b, and 15c describe the variation in anisotropy A with incidence angles, wind speeds, significant wave heights, and wave steepness. A generally tends to decrease with increasing incidence angles. The variation in A with the other three sea state parameters varies slowly at different incidence angles. When incidence angles are in the range from 20° to 30°, A decreases with increasing wind speeds, significant wave heights, and wave steepness. An exception occurs for incidence angles in the range from 25° to 30° and significant wave heights range from 7 to 8 m. For incidence angles in the range from 30° to 35°, wind speeds higher than 5 m/s, and significant wave height larger than 2 m, A has almost no variation. When incidence angles range from 35° to 45°, A tends to increase with increasing wind speeds, significant wave heights, and wave steepness.

6. Discussion [44] Comparing the polarimetric characteristics of open ocean SAR imagery in Figures 12–15 with those of the convection case in Figure 11 and Table 2, we suggest that the typical signatures of convective processes are higher copolarization phase differences (larger than 20°), entropy (larger than 0.5), and polarization scattering angle (larger than 38°). These values are clearly larger than those of the

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open ocean SAR imagery considered in section 5, even taking into account the standard deviation of each bin, when incidence angles range from 20.5° to 47.6°. Therefore, with those polarimetric parameters, it is potentially easy to distinguish atmospheric MCC from the other processes of open ocean SAR imagery. [45] However, incidence angle, wind speed, significant wave height, and wave steepness do influence the polarimetric characteristics of the ocean surface. It should be noted that in addition to these parameters, wind direction, wave direction, and azimuth look angle also may play a potential role in determining the polarimetric characteristics of the ocean surface. These are possible factors for the large variations in standard deviation of the polarimetric parameters that are presented in Figures 12–15. In future investigations, we will use more RS‐2 PolSAR images and study these processes. [46] MCC typical of areas A, B, and C in Figure 1a tend to show higher hhvv, H, and a compared to typical polarimetric characteristics of the ocean surface. However, we have no in situ observations, and therefore we cannot estimate the other possible contributions; for example, these may be from volume scattering from breaking waves or air bubbles in the water, compared to the downdraft air motion damping of the Bragg waves on the ocean surface, which are beyond the scope of this paper.

7. Conclusions [47] The frequent occurrence of mottled or blistered SAR imagery is linked with the presence of the convection in the MABL. A potentially complicating factor is precipitation. In the particular case study presented here, we used ancillary meteorological data to show that the lower (below 850 hPa) atmospheric levels are unstable, implying the presence of convection, and that the precipitation rate is quite low. Here high‐resolution (1.11 km) WRF model simulation results are also used to confirm that the phenomena in the SAR imagery of our case study are MCC with essentially no precipitation. Specifically, the WRF simulation indicates that a vigorous cyclone had passed prior to the SAR image and that upward and downward airflows were occurring at the bottom of the atmosphere. Thus, the downdrafts and updrafts interacted with the ambient wind fields and ocean waves and were imaged by SAR. [48] In order to differentiate the convection from other processes in the SAR image, we analyzed the polarimetric characteristics of MCC in the MABL in the PolSAR image of our case study. Many (641) additional quad‐polarization SAR images collocated with NDBC buoys were also analyzed. Thus, we present an assessment of the variation of polarimetric parameters (hhvv, H, a, and A) with incident angles and sea state variables. As expected, the intensity of VV polarization is systematically larger than that of HH polarization for MCC. Moreover, mean values of hhvv, H, and a for convective areas of our case study are also larger than those of open ocean areas; anisotropy A is exceptional in that it does not show a consistent variation for the signature of convection. Investigation of additional air‐sea factors and processes affecting the polarimetric characteristics of the ocean surface is the focus of future studies. These factors include wind direction, wave direction, and

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Figure 15. Variations and standard deviation (vertical lines) of anisotropy A with incidence angles, wind speeds, significant wave heights, and wave steepness. Legend is same as Figure 13.

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azimuth look angle and their contributions to variability in ocean surface polarimetric parameters in SAR imagery. [49] Although the imagery geometry and sea state parameters do affect the polarimetric characteristics of convection in open ocean SAR imagery, the variations in polarimetric parameters (hhvv, H, a, and A) due to convection are larger than variations resulting from different incidence angles, wind speeds, significant wave heights, and wave steepness. We suggest that the differences in polarimetric parameters such as hhvv, H, and a between cases involving MCC and those involving other processes such as ocean waves can help in the interpretation of SAR images and in the detection of atmospheric convection. Polarimetric parameters show good potential to identify atmospheric convection phenomena from other open ocean processes. Additional considerations include the imaging mechanism, theoretical models for atmospheric convection at the ocean surface, different polarimetric characteristics, and numerical NCRS studies for signatures of atmospheric phenomena in the MABL in SAR imagery [Ufermann and Romeiser, 1999; Romeiser et al., 2004; Li et al., 2011]. Full polarimetric model studies of the phenomena will further improve the unambiguous interpretation of SAR images and the retrieval of quantitative information. [50] Acknowledgments. The authors greatly thank the Canadian Space Agency for providing RADARSAT‐2 SAR images. This work is supported by the Canadian Space Agency GRIP projects Spaceborne Ocean Intelligence Network (SOIN) and Building Satellite Data into Operational Oceanography. We also thank two anonymous reviewers for comments that greatly improved the manuscript.

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