3d cloud visualizations

3D AIRS Data VisualizationsJohn Pham Section, 398B AffiliateElectrical Engineering, UC Riverside, Year 2Summer 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 concatenatedNASA JPL - AIRS

Generated using MatPlotLib© 2016, All rights reserved. California Institute of Technology

Government sponsorship acknowledged

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 CloudsProblem: 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)


PresentGoal: Color clouds by different schemes

Granule 33 09/04/2006No color

Granule 33 09/04/2006Colored by cloud phase

Granule 33 09/04/2006Colored by cloud type


InteractiveGoal: Explore new mediums of interacting and interpreting data

Virtual reality with Unity

Virtual reality with Google Cardboard/web viewer


OutreachGoal: Explore new mediums of interacting and interpreting data

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


AnimationsGoal: Explore new mediums of interacting and interpreting data

3D view animations

Fly-by animations with volumetric clouds


Comparing DataGoal: Explore new mediums of interacting and interpreting data

Sun Wong and Tau Wang



Preliminary comparison between AIRS and MODIS & CloudSat nadir



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 ImagesAugmented/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 ManningJPL Mentor

Sun WongCloudSat/MODIS

Brian KahnClouds reference

Tau WangCloudSat/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.


Questions?

3d cloud visualizations

Documents