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A-Train Serendipity Sensor data used MODIS vis, nir, CloudSat, OMIi , CloudSat, CloudSat & MODIS ColudSat & CALIPSO What is provided Why useful Interesting tidbits Correlation between radar reflectivity and MODIS particle size Inferred cloud top heights fro UV scattering matched to cloud pro<strong>file</strong>s A confirmation of MODIS particle size and its relation to precipitation Identification of thin winter time ice clods and it precipitation Provides unique identification of the transition from cloud to rain and time scale of rain formation Impacts ozone estimation above clouds Passive measures particle size of low clouds can be used to characterize drizzle/precio occurrence. Explosive development of precipitation altered by aerosol affecting the rate of dehydration of polar clouds Time scale is much longer in nature than is assumed in models Considerable UV multiple scattering makes OMI cloud tops appear many kms low Drizzle is so persistent in oceanic clouds that it measurably affects the mean particle size
A-Train Science Concepts 4 slides from: “ESA-NASA Constellation Management Workshop May 17, 2011 Saint-Hubert, Quebec, Canada Perspectives on Maximizing Science Return Chip Trepte, CALIPSO Project Scientist, NASA/LARC”
Page 1 and 2: A-Train formulation with the arriva
Page 3 and 4: A-Train science is having an profou
Page 6 and 7: Selected highlights with a little i
Page 8 and 9: A-Train demonstrated how formation
Page 10 and 11: Example of combining data CloudSat,
Page 12 and 13: The classic Riehl and Malkus “hot
Page 14 and 15: Buoyancy Entrainment rate ΔT = T p
Page 16 and 17: A-Train reveals important insights
Page 18 and 19: Aerosol effects on clouds - largest
Page 20 and 21: The more global picture from the A-
Page 22 and 23: A- Train Precipitation
Page 24 and 25: MODIS Insight on the rain process -
Page 26 and 27: Global Precipitation - combining di
Page 28 and 29: Summary (i) New science continues t
Page 31 and 32: Comparison of level 2 products Asse
Page 33 and 34: CloudSat-CALIPSO-AMSR-E data docume
Page 35 and 36: Multi-sensor fusion - new insights
Page 37 and 38: Access to Information • Public av
Page 39 and 40: NASA A-Train member missions NASA E
Page 41: Sensor data used A-Train What is pr
Page 45 and 46: Leadership • Mission leads (Princ
Page 47 and 48: Open Validation • Sustained calib
Page 49 and 50: Identifying core cloud Tested sever
Page 52 and 53: A-Train Constellation Evolution Aqu

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