What did Venus Express tell us about the winds? PPT summary of Hueso et al. 2014

Transcription

1 What did Venus Express tell us about the winds? PPT summary of Hueso et al. 2014

2 Observations Data selected from first 2115 orbits (6 Earth years = 9 Venusian days) UV: km, VIS and NIR a few km below that

3 Complications Cloud motions: proxy for atmospheric conditions Apparent motion of clouds can be affected due to atmospheric waves (algorithms find wave motion instead of clouds) Lots of orbit-to-orbit variation and single-orbit scatter of measurements Must attain large data sets and averages to separate atmospheric variability from measurement noise Measurement error comparable to expected perturbations from eddies, waves, or tides

4 More Complications Low cloud contrast Worsened by stray sunlight Lower spatial resolution at the equator than at the poles But, lower illumination at poles which reduces detail in visible band

5 Image Sharpening Solution to low contrast in observations

6 Cloud Tracking Correlate sub-sections of 2 images separated in time Most separated by min, some as short as 15 min Errors scale with (spatial resolution/time separation) Shorter time intervals: pro: better correlation (cloud changes shape less) con: less displacement -> worse velocity measurement

7 Cloud Tracking

8 Individual Orbit Results

9 Sharp decrease common and coincides w/ changing cloud morphology zonal (E/W) meridional (N/S) Most common result! cyan circles (UV), orange diamonds (VIS) red crosses (NIR) Fits in blue (UV), green (VIS) and magenta (NIR)

10 another example with shorter time steps (same cloud morphology as image sharpening slide)

11 Global Averages

12 Zonal Meridional -UV (solid navy), VIS (dashed orange), NIR (solid magenta) -Previous VIRTIS data: UV (dashed cyan), NIR (dashed green)

13 Maps of Mean Velocity Thermal solar tide causes acceleration Possible Hadley cell circulation shown in meridional data

14 More maps of mean velocity

15 Long-term time variation of UV observations

16 4/12/06-8/9/07!! 1/6/08-11/23/08! 2/26/09-7/25/10

17 10/16/10-5/29/11!! 8/30/11-2/3/12 Periods 1,2: lower zonal velocities from -40 to 0 degrees Meridional circulation: variable. Intense Hadley circulation in period II & partial reversal at low latitudes in period V NIR observations do not show same magnitude of variability

18 Waves UV observations see waves Other work identifies them as gravity waves mean value of phase speed: -15 m/s (accelerates retrograde winds) Do not have net effect on cloud-tracking algorithm (just add noise)

19 Left: waves Right: none!!! they say: similar cloud motions..

20 Altimetry of NIR clouds Previous work: height of cloud tops observed in UV same as those in NIR But we see differing wind speeds Pioneer Venus probes & Vega balloons: measured zonal wind speed with altitude for tropical latitude VEX observations in NIR combined with these insitu measurements -> altitude of km Same analysis for Pioneer Venus North probe data (60 o N) implies altitude of km

21 More Ideas about NIR Altimetry Clouds in UV and NIR resemble each other morphologically at the poles -> same altitude there? Previous IR (deeper cloud layer) observations: similar global circulation patterns but different morphology than VEX NIR obs Means either: they re at same altitude or small vertical wind shear (the wind speed doesn t change much with height) All these pieces combined result in two models

22 Altimetry Model 1: constant altitude of 58.5 km! Model 2: mimics previously found profiles in UV and IR, and approximately fits the data

23 Vertical wind shear based on the 2 models (how much does the velocity change with height?)

24 Horizontal Divergence in UV Tells you about upwelling and downwelling Mid-latitudes: global upwelling that increases with time Downwelling in cold collar region (Hadley cell?)

25 Horizontal Divergence in NIR No divergence within the noise of the measurements Consistent with low-turbulence morphology Combined w/ mean velocity maps, indicates solar heating affects cloud tops almost exclusively

26 The Future Need to fit this new wind data to atmospheric models This work: long-scale variability Akatsuki mission: short-term variability Comparison with Earth-based wide-field observations could contextualize variability within global context