3D Cloud Visualizations

3D AIRS Data Visualizations
John Pham Section, 398B Affiliate
Electrical Engineering, UC Riverside, Year 2
Summer FIELDS Intern, 2016
Evan Manning, Section 398B
© 2016, All rights reserved. California Institute of Technology
Government sponsorship acknowledged

Introducing AIRS
AIRS is a hyperspectral infrared sounder on the EOS-Aqua platform
● Launched in 2002 - has retrieved over 13 years worth of data
● Sun-synchronous, polar orbit, 1:30 PM equator crossing
● “Whisk-Broom” scan pattern
AIRS retrieves 90 Fields of View (FOVs) every 2.67 seconds
● FOVs are ~15 km at nadir, larger at the scan edges
Each FOV has 2378 channels (colors), sensitive to unique combinations
of:
● Surface temperature and emissivity
● Atmospheric temperature
● Water vapor at different heights
● Trace gases
● clouds
NASA GES DISC
NASA GES DISC

Granule Map
Data is packaged in 240 6-minute granules per day
Each granule is 90 FOVs cross-scan * 135 scans
● 12,150 spots per granule
Granules can be concatenated
NASA JPL - AIRS
Generated using MatPlotLib

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)
A second “cirrus” retrieval from Brian Kahn for ice clouds report:
● Cloud particle effective diameter
● Optical depth
● Cloud top temperature
NASA JPL - AIRS

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
Assuming it’s at a standard atmosphere
● What is the cloud thickness?
● What is the cloud optical density? (visible or infrared)
● If the cloud does not fill the FOV, then what is the spatial distribution within the
area?

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
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 Generated in Blender

2D vs 3D Visuals
Generated in Blender
NASA JPL - Bill Irion

Rendering Problems (Z-Fighting)
Solution: Pushed my change of Blender package “Cloud Generator” to resolve particle position within a
volume.
Clouds wireframe view in Blender Clouds rendered view in Blender

Granule 50 as a fluffy cloud
Volumetric Clouds
Problem: Not a true representation of the data, generalizes shape of the entire granule
Clouds Wireframe View Clouds Rendered View
“Granule 50” as a volumetric cloud
With AQUA and Earth (model and texture from NASA)

Present
Goal: Color clouds by different schemes
Granule 33 09/04/2006
No color
Granule 33 09/04/2006
Colored by cloud phase
Granule 33 09/04/2006
Colored by cloud type

Interactive
Goal: Explore new mediums of interacting and interpreting data
Virtual reality with Unity
Virtual reality with Google Cardboard/web viewer

Outreach
Blue-Red stereograph image of volumetric cylindersBlue-Red stereograph image of volumetric clouds

Animations
3D view animations
Fly-by animations with volumetric clouds

Comparing Data
Sun Wong and Tau Wang

Comparing Data
Preliminary comparison between AIRS and MODIS & CloudSat nadir

Comparing Data
Comparing v5 and v6 of cloud retrieval algorithms

Future Direction
Create tools to let scientists generate these visualizations on their own
Display AIRS clouds together with more AIRS data:
● Surface parameters
● Kahn cloud optical properties
Display AIRS clouds with cloud data from other sources:
● MODIS
● CrIMSS
● ECMWF
Global Images
Augmented/Virtual Reality for interactive data exploration
More photorealistic clouds for public outreach NASA Goddard
Globe view in Blender with 1 granule

Acknowledgements
UC Riverside FIELDS Program
Evan Manning
JPL Mentor
Sun Wong
CloudSat/MODIS
Brian Kahn
Clouds reference
Tau Wang
CloudSat/MODIS

References
Aumann, H. H., Chahine, M. T., Gautier, C., Goldberg, M., Kalnay, E., McMillin, L., Revercomb, H., Rosenkranz, P. W.,
Smith, W. L., Staelin, D. H., Strow, L. and Susskind, J., "AIRS/AMSU/HSB on the Aqua Mission: Design, Science
Objectives, Data Products and Processing Systems," IEEE Trans. Geosci. Remote Sensing, 41, 253-264 (2003).
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.

3D Cloud Visualizations

More Related Content

What's hot (20)

Viewers also liked (16)

Similar to 3D Cloud Visualizations (20)

3D Cloud Visualizations