Nightside Winds at the Lower Clouds of Venus with Akatsuki/IR2: Longitudinal, Local Time, and Decadal Variations from Comparison with Previous Measurements

IR2 acquired a total of 1671 images¹⁴ of the nightside of Venus with the filters 1.74, 2.26, and 2.32 μm (Satoh et al. 2017), among which ∼1370 were regarded as suitable for cloud tracking. Satoh et al. (2017, Figure 8(a) therein) reported a problem of light contamination in the IR2 images, consisting of the presence of halation rings and a cross pattern extending both horizontally and vertically around the saturated dayside of Venus and spreading with multiple reflections along the PtSi detector. Since this contamination is more reduced in the images taken with the 2.26 μm filter, we restricted our cloud tracking study to this data set composed of 466 images (although additional sets with 1.74 μm images were used in the case of automated cloud tracking). The calibration version of the IR2 images used in this work ("v20170601") does not include any of the corrections for the light contamination proposed by Satoh et al. (2017), and an alternative image processing technique was applied. This consisted of an adjustment of the brightness/contrast, followed by a sharpening of the images with an unsharp-mask technique, and finishing with the application of adaptive histogram equalization (see examples in Figure 1). Images acquired with ground-based instruments at TNG and IRTF also suffered from contamination from the illuminated side of the planet and were processed similarly. Some examples of these images are shown in Section 5.

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Uncertainties such as the thermal distortion affecting the Akatsuki spacecraft and the onboard cameras prevent high accuracy in the navigation of the Venus images at present, so additional corrections in the navigation are still required (Ogohara et al. 2017; Satoh et al. 2017). In recently published studies with Akatsuki (Fukuhara et al. 2017; Horinouchi et al. 2017a, 2018; Lee et al. 2017b), the navigation of the images was corrected with an algorithm able to perform an ellipse fitting from an automatic determination of the planetary limb pixels (Ogohara et al. 2012, 2017). This automated identification of the limb becomes rather uncertain in many of the IR2 nightside images because of the light contamination previously described and also due to the frequent darkening of the planetary limb because of the strong variability of the clouds' opacity at lower latitudes (McGouldrick et al. 2008; Satoh et al. 2017). Instead, we coded an interactive tool inspired by the software WinJupos (Hahn & Jacquesson 2012) which allows the interactive adjustment of the position, size, and orientation of the planet's grid,¹⁵ using as reference four locations on the limb chosen by the user (see Figure 2). The visualization of the limb was improved by interactively modifying the brightness and contrast in each image. In the case of the IR2 images, the position of the grid was adjusted with a precision of 1/10th of a pixel; its size was increased in some cases (less than 1.3% in all cases), while the orientation of the grid required no corrections. Regarding the ground-based images from NICS and SpeX, these were navigated using NASA's SPICE kernels (Acton 1996; Folkner et al. 2009); both the position and orientation of the grid were adjusted, and the predicted size was accurate enough to require no further corrections.

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After correcting the navigation and processing the images, these were geometrically projected onto an equirectangular (cylindrical) geometry with an angular resolution similar to the best resolution in the original images. In those cases where the nightside images were acquired when Akatsuki was closer to its pericenter, the better spatial resolution enabled the wind speeds to be measured at polar latitudes, and azimuthal equidistant (polar) projections were performed too using an angular resolution similar to that found at latitudes of about 70° in the original image. Figure 3 shows examples of the original observations, their navigation after corrections, and cylindrical and polar maps. To measure the wind speeds, two different techniques of cloud tracking were employed: (a) a manual method applied to the full data set of IR2 and ground-based images (see Table 1), consisting of a manual search of the cloud tracer followed by a fine adjustment using automatic template matching, which is visually accepted or rejected by a human operator (similar to that performed by Hueso et al. 2015); and (b) a fully automatic method used for images taken in 2016 July and August (see Table 1) and which uses the relaxation labeling technique (Ikegawa & Horinouchi 2016; Horinouchi et al. 2017b).

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Wind measurements are obtained by comparing the position on the map of cloud features that can be identified in two consecutive images with a given time difference. We obtained wind measurements using a manual method in which we perform automatic template matching with a phase-correlation technique that has also been applied for cloud tracking on Earth (Leese et al. 1970; Jun & Fengxian 1992; Humblot et al. 2005; Huang et al. 2012). We call this technique manual because a human user selects a region in the first image to obtain its best match in the second image and validates (or not) the final result. The phase correlation permits the translation between two images shifted relative to each other to be obtained (Kuglin 1975; Samritjiarapon & Chitsobhuk 2008), relying on a frequency-domain representation of the images calculated with the Fast Fourier Transform (FFT) that enables this shift from the location of a peak to be inferred in a cross-correlogram. Figure 4 shows an example of the use of this technique where the narrow peak in the phase cross-correlogram is shown for a well-identified tracer. A more complete explanation about the phase correlation is provided by Kuglin (1976). Compared to the standard correlation, its performance is faster, and it is less sensitive to random noise and illumination conditions (Ahmed & Jafri 2008). Even though the phase correlation can potentially register subpixel displacements (Reddy & Chatterji 1996; Foroosh et al. 2002), we did not test this capability in this work and only accounted for displacements expressed as integers of pixels. Besides, since the boundaries of any image imply discontinuities in the signal that can introduce a noisier result, zero padding is advisable before applying the FFT (Ahmed & Jafri 2008). For this reason, the initial and final templates (ranging in sizes of 32 × 32 to 48 × 48 pixels) were convolved with a Hanning window with a width of 0.7 before the FFT was applied. When no clear peak could be found with the phase correlation, or when the tracer identification was judged as not satisfactory by the human operator running the analysis, the wind measurement was rejected and the template matching was performed manually. Each of these manual measurements was verified by a human operator looking at visual reports such as the one shown in Figure 4.

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A fully automatic technique based on classical image correlation for identifying the cloud tracers' motions was also used by Horinouchi et al. (2017a, 2017b), and we will show here a detailed report of these measurements. This technique is similar to that used in many previous studies of Venus cloud dynamics (Rossow et al. 1990; Kouyama et al. 2012; Khatuntsev et al. 2017; Horinouchi et al. 2018), but in this case, the cross-correlation between images is computed along a sequence of images to estimate the horizontal velocity at a specific location (Horinouchi et al. 2017a, see the Methods section therein). This method was applied to images of the lower clouds acquired in 2.26 and 1.74 μm. All of the IR2 images were projected onto an equirectangular geometry with a fixed angular resolution of 0125 per pixel regardless of the resolution of the original images (Ogohara et al. 2017). Image processing was applied after the projection and consisted of a two-dimensional bandpass filter with Gaussian functions with sigma values of 025 for the low pass filter and 3° for the high pass filter with latitude and longitude. Finally, the template size for cloud tracking was set to a fixed value of 60 × 60 pixels for all images.

Panels (A)–(G) in Figure 3 exhibit a sample of the clouds' morphologies observed in the IR2/2.26 μm images. Pioneering ground-based observations at these wavelengths (Allen & Crawford 1984; Crisp et al. 1991) show global characteristics similar to those of the Akatsuki IR2 images. The deeper clouds on the nightside of Venus are normally characterized by a dark band with high-opacity clouds at low latitudes, while the midlatitudes are dominated by brighter bands with lower opacity (see Figures 3(A)–(B)). Prior to Akatsuki, the midlatitude bands appeared almost featureless (Crisp et al. 1991; Limaye et al. 2006; Hueso et al. 2012). However, the higher spatial resolution of the Akatsuki IR2 images reveals subtle though distinguishable wisps and patches (Figures 3(F)–(G)) on them, sometimes invaded by broad bands of clouds with higher opacity (Figure 3(B)) or unusual sharp dark spirals tilted relative to the latitude parallels (Figures 3(C)–(D)), which spread thousands of kilometers from latitudes higher than 30° toward equatorial ones (Horinouchi et al. 2017a; Satoh et al. 2017; Limaye et al. 2018).

In the IR2 images, the clouds' opacity exhibits higher variability at lower latitudes than at midlatitudes (Satoh et al. 2017; Limaye et al. 2018), confirming previous findings with ground-based observations (Crisp et al. 1991; Limaye et al. 2006; Tavenner et al. 2008; Mota Machado et al. 2016) and during the VEx mission (Hueso et al. 2012; McGouldrick et al. 2012; McGouldrick & Tsang 2017). Dark areas of high opacity normally dominate at lower latitudes, while their boundary with the midlatitude bright bands exhibits complex cloud features (Horinouchi et al. 2017a; Limaye et al. 2018), like small vortices or abundant bright swirls at around 30°, which sometimes adopt shapes suggestive of shear instabilities (Figures 3(B), (C), and (E)), similar to those found at the nightside upper clouds at ∼65 km in the 3.8 μm VEx images (Peralta et al. 2017a). The polar projection in Figure 3(G) clearly exhibits an example of the frequent episodes of hemispherical asymmetry of the morphology of the Venus clouds. A more complete survey of the clouds' morphologies apparent in the images from the Akatsuki/IR2 camera will be presented elsewhere.

Figure 5 shows zonally averaged profiles of the manually tracked winds at the lower clouds during the first year of Akatsuki observations. Bins of 5° latitude were considered to calculate the average wind speeds from 2016 March to October. The profiles of IR2 are compared with the VEx/VIRTIS-M results from 2006 April to 2008 August (Hueso et al. 2012). With regard to the zonal component of the wind (Figure 5(A)), the mean profile obtained with IR2 is faster than the VIRTIS-M measurements by 10 m s⁻¹. The zonal winds from IR2 observations show symmetric profiles between both hemispheres and, despite the higher dispersion at higher latitudes, due to the poorer spatial resolution of the IR2 images close to the poles, they show for the first time the decay of the winds toward the poles on both hemispheres simultaneously. This decay starts at about 60° and, compared to the VEx results, it seems more abrupt, although it retains similarities with the wind profiles obtained in 2004 with ground-based observations (Limaye et al. 2006, Figure 5 therein), albeit with higher errors. However, we cannot rule out that these differences at subpolar latitudes with respect to the VEx/VIRTIS-M subpolar winds are not caused by the worse spatial resolution at polar latitudes and the low number of measurements in the Akatsuki IR2 images (Figure 5(C)). Regarding the meridional component of the wind (Figure 5(B)), the results from 2016 and 2006–2008 are in good agreement, confirming the absence of clear global motions in the meridional circulation of the nightside lower clouds.

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Wind variability is also apparent during 2016 (see Figure 6). The profile obtained on 2016 March 25 (Figure 6(A)) corresponds to winds that are approximately constant between the equator and midlatitudes with small meridional shear. This seems to be the most frequent case found at the lower clouds during the Akatsuki observations and also during the VEx mission (Hueso et al. 2012, Figure 17 therein). Zonal wind profiles on other dates, such as July 11 (Figures 6(B)) and October 13 (6(C)), exhibit more intense equatorial zonal speeds, reported for the first time during the Galileo flyby (Crisp et al. 1991, Figure 4 therein) and later identified as recurrent episodes of jets at the equator (Horinouchi et al. 2017a). There is a reasonable correspondence between this local intensification of zonal speeds and the presence of features resembling shear instabilities (Figure 6(B)) or sharp opacity discontinuities at the equator (Figure 6(C)). The high dispersion in the horizontal speeds also suggests that these jets may be apparent only in a rather longitudinally narrow area. The profile during July 11 (Figure 6(B)) shows that these jets can sometimes show up at northern latitudes, as also reported in 1990 during the Galileo flyby (Carlson et al. 1991, Figure 6 therein) and in 1996 from observations at the Apache Point Observatory (Chanover et al. 1998, Figure 7 therein).

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It is still unclear whether the episodes of faster zonal speeds shown in Figure 6 are related to horizontal and/or vertical gradients of the zonal wind, or if the bright and dark clouds observed on the nightside at 1.74, 2.26, and 2.32 μm are moving at the same or at slightly different vertical levels. Early interpretations of Venus IR features considered that these clouds' contrasts might be caused either by scattered sunlight leaking in from the dayside along altitudes with low absorption in the CO₂ windows, or that the dark areas corresponded to opaque clouds lying in a broken layer at a certain altitude above the brighter regions and radiate as blackbodies at the lower temperature of their tops (Allen & Crawford 1984). Allen (1987) discarded both interpretations, confirming that the existence of important differences in the vertical elevation of bright and dark clouds was inconsistent with the small variations that the dark/bright areas exhibit in CO absorptions affecting the wide 2.2–2.3 μm band. Therefore, the dark and bright regions should correspond to horizontal inhomogeneities in the clouds' opacity to deeper background thermal emission (Allen 1987; Crisp et al. 1989), although certain discrepancies in the altitude between dark and bright clouds cannot be disregarded, since these inhomogeneities can be caused by variations in the size and distribution of the particles within the lower and middle clouds (McGouldrick & Toon 2008).

Assuming a range of 50 to 60 km for the altitudes sensed at the relevant wavelengths (McGouldrick et al. 2008), a comparison with in situ wind profiles from descending probes (Counselman et al. 1980; Gierasch et al. 1997; Moroz & Zasova 1997) indicates that variations of up to 30 m s⁻¹ could be explained in terms of the vertical shear (Peralta et al. 2017a, Figure 3 therein), which is also consistent with the magnitude of the reported jets (Horinouchi et al. 2017a, Figure 2(b) therein). Crisp et al. (1991) obtained a discrepancy between the velocities of large dark clouds and the smaller markings (sizes ranging from 400 to 1000 km), and also suggested that these might be produced at different altitudes. Figure 7 displays the distribution of zonal speeds obtained with the manual method compared with the average radiance (Figure 7(B)) and the size of the cloud tracers between 50°N and 50°S (Figure 7(A)) in the IR2 2.26 μm images. No dependence is apparent between the zonal velocities and these parameters. Since the low latitudes frequently exhibit cloud patterns that resemble shear instabilities for a wide range of scales (see Figures 3(C)–(E); Limaye et al. 2018, Figure 10 therein), the generation of jets due to meridional gradients in the horizontal winds seems probable. McGouldrick & Toon (2007, 2008) showed that large-scale dynamics can also explain the strong variations in the cloud opacity, and that even weak downwelling is able to produce optical-depth holes in the clouds.

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Since most of the sunlight absorption on Venus occurs within the clouds' layer, solar tides are expected to be excited and carry momentum upwards and downwards away from the region of excitation, thus accelerating the atmosphere westwards and contributing to the RZS (Gierasch et al. 1997; Sánchez-Lavega et al. 2017). The effect of solar tides has been unambiguously detected on both zonal and meridional components of the winds at the upper clouds at 65–70 km (Rossow et al. 1990; Limaye 2007; Sánchez-Lavega et al. 2008; Kouyama et al. 2012; Peralta et al. 2012; Khatuntsev et al. 2013; Hueso et al. 2015), but no evident influence has been found for the middle to lower clouds (50–60 km) for either day (Hueso et al. 2015; Khatuntsev et al. 2017) or night (Hueso et al. 2012, Figure 6 therein). Khatuntsev et al. (2017) argued that this negligible effect of the solar tides could be related to the absence of an unknown absorber downward of the middle clouds (which is responsible for most of the solar heat deposition on Venus).

Figure 8 allows the local time dependence for the winds at the nightside lower clouds of Venus during 2016 to be studied. In agreement with VEx during 2006–2008, no local time dependence is apparent in the meridional winds (Figure 8(B)). However, the zonal component of the wind speed displays a local increase of the zonal speeds between 19 and 22 LT, followed by a gradual decrease toward late nightside local times (a decrease of ∼10 m s⁻¹ between 19 LT and midnight). Hueso et al. (2012) also reported hints of faster retrograde winds close to dawn during 2006–2008, although this local time effect was weaker and disregarded due to the small number of measurements in this area. Recent results from Venus General Circulation Models (hereafter GCMs) predict that the diurnal tide should be also apparent on the zonal wind down to the middle clouds at ∼60 km with the slowest speeds centered at the evening terminator (Takagi et al. 2018, Figure 3(a) therein). Better agreement is found for the GCM results at the lower clouds (∼50 km), where the influence of the diurnal tide is predicted to weaken but a local maximum of about 10 m s⁻¹ is also found in the early night and before midnight (Takagi et al. 2018, Figure 4(a) therein).

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Evidences of how the surface topography may be influencing the atmospheric circulation through the excitation of atmospheric stationary waves (lee waves) have been accumulating. Vertical disturbances experienced by the VEGA-2 balloon over Aphrodite Terra (Blamont et al. 1986), strong asymmetries of the water vapor over certain geographical locations (Fedorova et al. 2016), and the finding of multiple stationary waves at the upper clouds of Venus, which are strongly correlated with the surface elevations (Fukuhara et al. 2017; Kouyama et al. 2017; Peralta et al. 2017a), strongly suggest surface effects on the upper atmosphere. Results of cloud tracking in the VEx/VMC dayside images seem to support the fact that the wind speeds at the cloud tops are decelerated as they pass over Aphrodite Terra and Atla Regio (Bertaux et al. 2016, Figures 4 and 6 therein). Other works have reported indications of an effect of surface elevations for the winds at the middle clouds (Khatuntsev et al. 2017, Figure 14 therein) and over the oxygen airglow patterns (Gorinov et al. 2018). Peralta et al. (2017a) discovered that during 2006–2008, the RZS at the nightside upper clouds exhibited a higher variability compared to the dayside ones, but the geographical coverage of the wind measurements was insufficient to confirm a correlated effect with the surface elevations. In the case of the lower clouds on the nightside, stationary waves are paradoxically missing in the clouds' opacity and no longitudinal dependence had been reported for the wind speeds (a reanalysis of VIRTIS-M winds for the lower clouds was inconclusive due to the lack of data to separate local time effects from time variability and the elusive surface dependence). Figure 9 displays the dependence on the longitude and latitude of the manual wind measurements with the Akatsuki IR2 images. While no clear effect is found for the meridional component of the wind (Figure 9(B)), the westward wind speeds exhibit a local maximum at low latitudes over the longitude range 300°–120° (Figure 9(A)).

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Unfortunately, separating the effects of the longitudinal and local time dependences is not feasible with our results, since the observed velocity disturbances are just marginally larger than the corresponding errors and the incomplete coverage of our wind measurements. Figure 10 shows the distribution of the mean zonal speeds (Figure 10(A)) and the number of measurements (Figure 10(B)) in terms of local time and longitude. To avoid the expected decrease of the zonal winds toward the poles, only the Akatsuki/IR2 wind measurements between 50 °N and 50 °S were considered. Figure 10(B) shows that the number of wind measurements during the year 2016 is irregularly distributed for both longitude and local time parameters, preventing a confirmation of either a local time dependence and/or influence of surface elevations.

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However, the influence of the surface elevations on the zonal winds at the nightside lower clouds might still be regarded as controversial. Published results for the zonal winds on the dayside originated from a huge set of wind measurements with noticeable dispersion (Bertaux et al. 2016, Figure 2 therein), and authors do not report filtering out other important sources of variability such as transient waves or the local time dependence. In the case of zonal wind speeds obtained with the Akatsuki/UVI images, Horinouchi et al. (2018) characterized and removed the local time dependence on the zonal winds at the dayside upper clouds. As a result, no effect of surface elevations was found for the dayside winds (Horinouchi et al. 2018, Figure 14(b) therein). In the case of our results for the nightside lower clouds, the local maximum extends mostly over rather plain areas and does not seem well correlated with the surface elevations at lower latitudes. Moreover, our results seem inconsistent with those on the dayside reported by Bertaux et al. (2016) and Khatuntsev et al. (2017) since, opposite to the zonal wind speeds on the dayside from VEx/VMC, which exhibit a local minimum with slower speeds extending along 0° to 200° (Bertaux et al. 2016, Figure 3 therein), our westward winds on the nightside display a local maximum between longitudes 300° and 120°.

We now explore possible wind dependencies on the factors shown above for our wind results obtained with the automatic cloud correlation technique and first reported by Horinouchi et al. (2017a). The dependence on both local time and longitude for the winds obtained with the fully automatic method is displayed in Figure 11. Since for these wind speeds only IR2 images obtained during 2016 July and August were used (see Table 1), the coverage is more limited than in the case of the manual method, and the dispersion is smaller than for the manual results (see Figures 8(D), (E), 9(D), and (E)).

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Except for the case of the meridional winds where no clear dependence on the local time and longitude is found, the zonal winds with automatic correlation are not fully consistent with those from manual wind measurements, and no influence over the zonal speeds is observed at any range of local time or longitude. Even though some discrepancies were expected between both cloud tracking methods and the different techniques to correct the navigation of the IR2 images, the homogeneity found for the zonal winds at low latitudes might be explained by the recurrence of the equatorial jet during 2016 August (Horinouchi et al. 2017a), which would mask a weaker dependence on local time and/or longitude.

We also combined the wind speeds at the nightside lower clouds from Akatsuki/IR2 in the year 2016 using the manual method with those obtained by Hueso et al. (2012) from VEx/VIRTIS-M from 2006 to 2008. Since the mean zonal winds during 2016 were faster than those during the VEx mission (see Figure 5(A)), the difference between the zonal averages of both data sets equatorward of the midlatitudes was suppressed by multiplying by a correcting factor of 1.06 the zonal speeds obtained from the VEx/VIRTIS-M images, while those from the Akatsuki/IR2 images were divided by 1.06. As a result, a correction of ∼4 m s⁻¹ was introduced in both sets of winds. Also, these two data sets have a comparable number of points, and both are based on cloud tracking inspected/validated by human operators. The result of their combination is presented in Figure 12. The dependence on the local time (Figures 12(A)–(B) and (E)–(F)) confirms the equatorial maximum of the zonal speeds between local hours 18–22 hr, while this dependence clearly decays toward higher latitudes. On the other hand, the meridional component of the wind displays no apparent dependence at low latitudes, while at the higher latitudes of the southern hemisphere (50 °S–90 °S) and between 21 and 22 hr, it exhibits an equatorward acceleration (Figure 12(B)). These results, again, suggest that the solar tide detected in the winds of the upper clouds (Limaye 2007; Peralta et al. 2012) might be able to propagate downwards to the middle clouds as predicted by some Venus GCMs (Takagi et al. 2018). Regarding the dependence on longitude and surface elevations, the local maximum between longitudes 300°–120° reported in Section 4.3 for the westward wind speeds is observed (Figure 12(C)), while the meridional component exhibits no clear influence of the surface elevation (Figure 12(D)).

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