manning_3d_cloud_astm_fall_2016

National Aeronautics and Space Administration

Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadena, California

Atmospheric Infrared Sounder

© 2016, All rights reserved. California Institute of TechnologyGovernment sponsorship acknowledged

3D Visualization of AIRS Clouds

John Pham1,2

Evan M. Manning1

1 Jet Propulsion Laboratory, California Institute of Technology2 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

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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

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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 44White

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Test Case 2002-09-06 Granule 44Cloud Type

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Test Case 2002-09-06 Granule 44Cloud Thermodynamic Phase

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Test Case 2002-09-06 Granule 44Cloud Top Temperature (AIRS Team)

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Test Case 2002-09-06 Granule 44Ice Cloud Top Temperature (Kahn)

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Test Case 2002-09-06 Granule 44Ice Cloud Optical Depth (Kahn)

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Test Case 2002-09-06 Granule 44Ice Cloud Effective Diameter (Kahn)

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Test Case 2002-09-06 Granule 44Retrieved Tsurf

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Test Case 2002-09-06 Granule 44Retrieved NSAT

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Test Case 2002-09-06 Granule 44NSAT - 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

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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.

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Richness/Interactivity Progression(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





Richness/Interactivity Progression(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.

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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

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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

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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

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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.

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