1. FIM Cloud Visualization for SOS and TerraViz Steve Albers

2. Team Members Jebb Stewart Eric Hackathorn Julien Lynge Bob Lipschutz Judy Henderson

3. Visualization Overview – Simulated VIS Assume both sun and viewpoint are overhead at all points on the sphere Cloud Albedo derived from model data is combined with multi-spectral land albedo inferred from NASA’s Blue Marble image Simulated VIS provides best realism to produce an “animated Blue Marble” (more so than IR) Physically and Empirically based for best efficiency and reasonable accuracy

4. Visualization Technique Vertically integrated hydrometeor fields are computed o Cloud liquid, cloud ice, rain, snow, graupel (as available) o A.k.a. liquid/ice water path (LWP/IWP) o Units can be either kg/m**2 or m (based on water density) o FIM presently groups everything into cloud liquid Convert LWP/IWP into optical depth o Use typical values of droplet/crystal size (r e ) and density for each type of hydrometeor ( ρ) o Account for lower density of snow or graupel o τ ≈ (1.5 LWP)/(r e ρ) (Stephens 1978, with ρ term added)

5. Scattering of Sunlight by Clouds & Precip Calculate fraction of incident sunlight scattered upward ○ Based on optical depth and backscattering efficiency Backscattering efficiency ○ Ratio of backscatter coefficient to extinction coefficient ○.063 for liquid,.14 for cloud ice or snow, 0.3 for graupel ○ Low values explain why clouds can look opaque yet still darker gray as seen from above

6. Cl oud / Precip Scattering - II Apply equation to yield cloud albedo (a) using optical depth ( τ ) and backscatter efficiency (b) ○ a = τ / ( τ + 1 / b) ○ Reproduces figure in Mishchenko et. al. (1996) within a few % (for non-absorbing clouds) ○ Works with cloud liquid and cloud ice (random fractal crystals) ○ Reduces to expected relationship: a = τ × b for small values of τ

7. Cloud / Precip Scattering - III Cloud albedo definition and assumption ○ Top Of Atmosphere (TOA) albedo (fraction of sunlight scattered upward by clouds) assuming dark surface TOA albedo (a t ) used for visualization ○ Combine cloud albedo (a c ) derived from FIM with ground albedo (a g ) from NASA’s Blue Marble image ○ Consider cloud semi-transparency and multiple reflections between ground and cloud ○ a t = a c + (1 – a c ) 2 × (a g ⁄(1-a c a g )) ○ Equivalent to equation (12) in Stephens (1978) ○ Using just cloud albedo (a c ) in a linear fashion with a g (e.g. in TerraViz) would introduce a further approximation

8. Use of Blue Marble Image Pros ○ The high resolution Blue Marble image allows for finer detail to be shown (10km resolution), compared with the FIM at ~13km ○ Allows for visualization in color ○ Blue Marble RGB values accurately convert to albedo (still to do) ○ Allows for more accurate blending of cloud albedo and ground albedo, compared with a simple overlay ○ Relatively simple and efficient, already demonstrated Questions / Cons ○ Wouldn’t use TOA (total) albedo or outgoing short-wave (if available) from model ○ Wouldn’t allow “progressive disclosure” compared with an overlay

9. Potential use of cloud overlay Pros ○ Can allow very high resolution (much less than 10km) for land ○ Ease of use with layering in TerraViz Questions / Cons ○ Can it “blur” the land underneath translucent clouds when scales go finer than ~10km? ○ Can it consider more accurate calculation of TOA (top of atmosphere albedo), based on cloud & ground albedo?

10. Implementation Case study with 3-D cloud water from FIM ○ Shown recently at AMS conference ○ 700MB of input for each of 168 time steps ○ Takes ~2.5 hours to process in IDL on SOS server ○ Will rerun to include recent refinements Speedup of processing for real-time runs ○ Precalculate vertically integrated hydrometers (i.e. LWP) ○ About 40 times less data for ITS to write to /public

11. Implementation - II Should we switch to a more rigorous RTM? ○ Would be more somewhat more accurate, particularly when NIM comes online with improved microphysics ○ Would TOA or cloud albedo already be available from RTM output within the FIM/NIM? ○ Can it be configured for a “sun/viewer always overhead” setup ○ Consider just cloud albedo output to merge with higher resolution multi-spectral land surface (e.g. Blue Marble) data, or alternatively with “progressive disclosure” ○ Would it allow for visualization in color (if multi-spectral radiances are available)? ○ Separate radiation package (e.g. CRTM)? ○ What are computational resource needs?

12. References Stephens, G., 1978: Radiation Profiles in Extended Water Clouds. II: Parameterization Schemes. J. Atmos. Sci., 35, 2123-2132 Mishchenko, M., Rossow, W.B., Macke, A., 1996: Sensitivity of cirrus albedo, bidirectional reflectance, and optical thickness retrieval accuracy to ice particle shape. JGR, 101, 16973-16985

13. Backup Slides

14. Other Wavelengths? 11μ IR also shown at AMS ○ Less physically consistent with Blue Marble (visible) ○ Simplified approach works well for brightness temperatues ○ Model OLR converted to brightness temperature with Stefan- Boltzmann relationship, then a linear correction applied ○ Agrees within 5-10K with satellite observations 3.9μ, 6.7μ, 13μ, etc. ○ More impacted by various absorption lines, etc. ○ RTM more needed and appropriate