1 / 19

Barbuda

The Climatology of Small Tropical Oceanic Cumuli New Findings to Old Problems (Analysis of EOS-Terra data). Larry Di Girolamo, Guangyu Zhao, Bill Chapman and Iliana Genkova Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign. Barbuda. Antigua. MISR 250 m.

mariah
Download Presentation

Barbuda

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Climatology of Small Tropical Oceanic Cumuli New Findings to Old Problems (Analysis of EOS-Terra data) Larry Di Girolamo, Guangyu Zhao, Bill Chapman and Iliana Genkova Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign Barbuda Antigua MISR 250 m

  2. Satellite remotely sensed small cloud propertiescarries large errors Properties may include cloud fraction, height, optical depth, effective radius, LWP… Barbuda Antigua MISR 250 m

  3. Known Problems Measured cloud fraction = fraction of pixels detected as cloudy If we have “perfect” cloud detection (i.e., if pixel contains any amount of cloud, however defined, then label it cloudy), then measured cloud fraction will be an overestimate of the “true” cloud fraction: Perfect cloud detection is bad for estimating the true cloud fraction (but good as a cloud mask for retrieving clear sky properties) Based on 684 stochastic cloud fields for ri/rt = 32 (Di Girolamo and Davies 1997)

  4. Known Problems “Perfect” cloud detection does not exist. Two competing effects in estimating cloud fraction: (1) overestimation caused by partially-filled cloud pixels that were classified as cloud (2) underestimation by optically thinner, partially-filled cloud pixels that were classified as clear “… spatial resolution errors in cloud fraction using an ISCCP-type algorithm with MODIS data would be less than 0.02.”Wielicki and Parker (1992) “For broken clouds, the average ISCCP cloud amounts are about 5%”… smaller/larger than that estimated by surface observer/Landsat. Rossow et al. (1993)

  5. DJF 2004/05 ISCCP (D2) D2 Daytime MODIS (MOD35) Many spectral tests Clear + Probably Clear = Clear MISR (Nadir RCCM) 1 spectral test 1 spatial test No angular test ClearHC + ClearLC = Clear

  6. DJF 2004/05

  7. MISR AN BRF MISR RCCM MODIS MOD35 Orbit 26396, Block 107-111, South Pacific, December 3, 2004

  8. Ae = 4% Ae = 11% Ae = 1% Zhao and Di Girolamo (submitted to GRL)

  9. ASTER RGB 15m ASTER on EOS-Terra • 15-m Visible bands; 90-m Thermal IR bands • Tasked for RICO between September 2004 and March 2005 • Analysis between September and December: 448 scenes (~60 km x 60 km) over 38 separate days • Manually eliminated scenes containing any amount of cirrus: 124 scenes from 28 separate days • Cloud masks derived manually for each scene

  10. No Sunglint (32 scenes) Sunglint (92 scenes) Zhao and Di Girolamo (submitted to GRL)

  11. ASTER 15-m Cloud Masks f15 = 8% f1000 =30% fRCCM =21% fMOD =8% f15 = 9% f1000 =81% fRCCM=72% fMOD=12%

  12. Cloud Mask Comparisons between ASTER, RCCM, and MOD35 for the 124 ASTER scenes

  13. Trade Wind Cumuli Statistics from ASTER - RICO (fraction, size distribution, area vs. perimeter, clustering, height) Zhao and Di Girolamo (submitted to JGR)

  14. Trade Wind Cumuli Statistics from ASTER - RICO Zhao and Di Girolamo (submitted to JGR)

  15. ASTER 90m, MISR 1100m, and MODIS 5000m Cloud Top Altitude Over 41 ASTER scenes Genkova et al. (Submitted to RSE)

  16. Stereo Height (m) Cloud Top Pressure (mb) 0 m > 3450 m or ocean 830mb > 1000mb or ocean ASTER MISR MODIS

  17. Summary for Small Clouds • MISR-RCCM does a great job at identifying pixels that contain some clouds… this leads to large overestimates of the “true” cloud fraction over regions dominated by broken clouds. • Outside of sunglint, uncertainties in cloud fraction estimates using MODIS-MOD35 are as predicted from earlier studies when looking at the mean. However, there is a bias that increases with increasing true cloud fraction, reaching an overestimate in cloud fraction of ~ 0.1 when true cloud fractions are ~ 0.25 - 0.35. • Over sunglint, cloud fraction estimates using MODIS-MOD35 are of questionable value. We need to worry about such issues in regional trend analysis. • Estimates of cumulus cloud fraction from MISR and MODIS (… and others) strongly depend on the spatial distribution of the underlying cloud field. • For the trade cumuli observed over the RICO domain, MISR cloud top heights provide distributions that are consistant with ASTER and in situ observations, and provides excellent coverage of the cloud field. MODIS cloud top height distributions are skewed low, and provides only marginal coverage. • Robust statistics on the macrophysical properties of small clouds can be had by tasking ASTER at “no cost” (next: 10 weeks over Gulf of Mexico as part of GoMACCS; 6 months over Indian Ocean)

  18. ASTER 15m

More Related