Manning_3D_Cloud_ASTM_Fall_2016

National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Atmospheric Infrared Sounder
© 2016, All rights reserved. California Institute of Technology
Government sponsorship acknowledged
3D Visualization of AIRS Clouds
John Pham1,2
Evan M. Manning1
1 Jet Propulsion Laboratory, California Institute of Technology
2 University of California, Riverside
Thanks to Brian Kahn, Eric Fetzer, Oleg Pariser, Victor Ardulov, Charles
Thompson
19/16/2016

Outline
• AIRS Clouds
• 3D Clouds
• 3D Clouds plus
– Colors
– Comparisons
– Animation
• Beyond 3D
– Interaction
– Virtual Reality
• More
– Tools
– Applications
– Future Directions
9/16/2016 2

AIRS Cloud Products
• Among its many products, AIRS includes
several cloud products
• The primary cloud retrieval reports effective
cloud fraction (EFC) and cloud top pressure
(CTP) for up to 2 cloud layers in each 15
km spot
• There is also characterization of cloud
thermodynamic phase (ice/liquid) from
Shaima Nasiri
• A second “cirrus” retrieval from Brian Kahn
for ice clouds reports:
– Cloud particle effective diameter
– Optical depth
– Cloud top temperature
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2-D to 3-D
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Visualizing AIRS Primary Cloud Products
For each 15KM spot, the primary cloud retrieval provides only CTP and
ECF for up to 2 cloud layers
This is not a full characterization of the clouds’ appearance:
• Cloud top height (CTH) can be calculated from CTP
• But what is the cloud thickness?
• What is the cloud optical density? (visible or infrared)
• If the cloud does not fill the FOV ellipse, then what is the spatial
distribution within the area?
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Current Spatial Approach
The area of each cloud is adjusted to match the
reported ECF
• Keeping the horizontal shape constant, the
radius is multiplied by sqrt(ECF)
• This emphasizes accurately reflecting the
data over photorealistic presentation
• It also allows lower cloud layers to be seen
through higher ones
Depth is based on Miller et al. Cloudsat-derived
climatology of cloud thickness by cloud type
• We use data from his Table 1 all-season mode
for 15-45 degrees north
• For Dc and Ns, we modify this to put the cloud
bottom 0.5 km above the surface
• For cloud type determination, we use IR CTP
and IR ECF
– Thresholds are preliminary
• The lower cloud is reduced or eliminated when
the clouds overlap vertically
• Vertical coordinates are magnified 3-15x for
display
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Volumetric vs Solid clouds
• Clouds appear volumetric in the real world – we see light scattered
off particles throughout the volume, not on the surface
• Originally this effort focused on volumetric visualizations, but these
proved slower to produce and harder for viewers to interpret
• They may still be useful in the future for outreach or to provide more
realistic transparency in science visualizations
9/16/2016 7

Coloring clouds
Color is an important way to add an extra layer of information.
Some color schemes show info about the clouds:
• Cloud Type
• Cloud thermodynamic phase (Nasiri)
• Cloud top temperature (AIRS Team or Kahn)
• Ice cloud optical depth (Kahn)
• Ice cloud particle size (Kahn)
Other color schemes tell about the retrieval or environment:
• Retrieved surface temperature (Tsurf)
• Retrieved near-surface air temperature (NSAT)
• Difference: NSAT - Tsurf
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Test Case 2002-09-06 Granule 44
White
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Cloud Type
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Cloud Thermodynamic Phase
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Cloud Top Temperature (AIRS Team)
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Ice Cloud Top Temperature (Kahn)
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Ice Cloud Optical Depth (Kahn)
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Ice Cloud Effective Diameter (Kahn)
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Retrieved Tsurf
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Retrieved NSAT
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NSAT - Tsurf
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V5 vs v6 AIRS clouds
Case study: V5 vs V6 clouds from the AIRS science team algorithm
• V5 clouds had only one cloud top temperature/pressure per FOR; V6
clouds can be more independent per FOV
• V6 has new “fall back” logic to get good clouds when the main physical
retrieval fails
Visualizing the differences:
• Here we present simple “blink” tests, back and forth between the two
• Other options include:
– Presenting both superimposed semi-transparent
– Interactive/VR with user controlling transparency and and viewpoint
• In the future similar comparisons might be used to compare among:
– This algorithm
– Kahn
– NUCAPS, CLIMCAPS, Irion, AER
– CrIMSS, IASI, MODIS
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V5
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V6
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V5 Cloud Type
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V6 Cloud Type
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V5 Cloud Top Temperature
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V6 Cloud Top Temperature
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Satellite track
• This animation flips through the first 20 granules of 2002-09-06,
about 1.5 orbits.
• It is a teaser of what a more advanced animation along the satellite
track might show
• It hints how much data we have access to and how much conditions
vary.
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20-granule animation
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Comparing data sets
• One of the most important applications of 3D visualization is
comparing data sets.
• For a sample we look at Cloudsat.
• The image below (Sun Wong and Tau Wang) shows a CloudSat
“curtain” along with matched MODIS data
9/16/2016 28

CloudSat curtain embedded in volumetric
AIRS cloud
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CloudSat curtain embedded in cut-away
volumetric AIRS cloud
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Animations
• Animations provide a good way to show a scene from multiple
angles when full interactivity cannot be provided.
• This movie shows several simple 15-second animations around
sample granules with different color schemes.
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Animation reel
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Richness/Interactivity Progression
• 2-D figures
can show
spatial
patterns
quite well
• 3-D figures
add depth
• 3-D
animation
makes the
depth more
obvious and
exposes
different
elements.
9/16/2016 33

(2 of 3)
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A 3-D interactive environment lets users steer around and see what’s most
important to them. Layers of information can be selectively highlighted or
removed. This short movie was captured from a live session using WebGL.
Credit NASA JPL
MIPL lab –
Oleg Pariser,
Victor Ardulov,
Charles Thompson

(3 of 3)
• Virtual Reality Provides perspective
rendering.
• When an immersed viewer moves her
head, the world responds as though it
were actually before her.
• This feature provides improved perception
of the relationship between objects based
on that ability of moving one’s head to see
the data from a new perspective.
• VR also is ideally suited for looking at the
datasets from many different directions
and provides the ability to render
important details in front and center while
at the same time allowing for a larger
contextual data display.
9/16/2016 35

Tools
• Python is a modern language that is used here to read AIRS data and
create cloud objects for Blender.
• Blender is an open-source modelling program which has an embedded
interface to python. With that, it is possible to script the
modeling/creation of lights, objects, and cameras and create still and
dynamic renders
• Unity is a game engine which also comes with an IDE. Scripts can be
used to control properties such as a camera’s viewing angle from
sensor data of VR headsets.
• WebGL (Web Graphics Library) is a JavaScript API for rendering
interactive 3D computer graphics and 2D graphics within any
compatible web browser without the use of plug-ins. [wikipedia]
• VR hardware supported: Oculus Rift, HTC Vive, GearVR by Samsung
9/16/2016 36

3D Clouds -- Possible Applications
• NSSTM badges
• Science
– Data exploration
– Inter-instrument comparisons
• Algorithm development
support
– Cloud algorithms
– Cloud clearing & through it,
everything
• GES DISC DAAC browse
images
• AWIPS terminals
• Direct broadcast
• Public outreach
– Perhaps make the clouds
puffier and/or volumetric
– Perhaps repair retrieval
artifacts
– Formats include:
• Red/cyan glasses
• Lenticular
• Animations
• VR
9/16/2016 37

Future directions
• Bring in more sources of information
– Clouds from other sources
• Other AIRS algorithms
• CrIMSS
• MODIS
• CloudSat
• …
– Other geophysical fields
• AIRS q, T, O3, tropopause, boundary layer top, etc.
• Wind, Psurf,
– Requires georeferencing
• Make images/animations of more than 1 granule
– 2-3 granules
– Orbital track
– Daily and monthly maps
• Share tools to make 3D images and animations
• Interactive / virtual reality
9/16/2016 38

References
Miller, S. D., and Coauthors, 2014: Estimating three-dimensional cloud
structure via statistically blended satellite observations. J. Appl.
Meteor. Climatol., 53, 437–455, doi:10.1175/JAMC-D-13-070.1.
S. L. Nasiri, B. H. Kahn, and H. Jin, "Progress in Infrared Cloud Phase
Determination Using AIRS," in Advances in Imaging, OSA Technical
Digest (CD) (Optical Society of America, 2009), paper HWA3.
9/16/2016 39

9/16/2016 40

Manning_3D_Cloud_ASTM_Fall_2016

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