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Anticipated A-Train highlights Sensor data used CloudSat & CALIPSO MLS, CloudSat,AIRS AIRS, MODIS Cloudsat,AMS RE & CALIPSO AMSR-E, CERES, CloudSat, MODIS & CALIPSO What is provided Why useful Interesting tidbits Vertical pro<strong>file</strong>s of cloud occurrence, new definitions of high thin cloud, cloud base, cloud layering, etc Ice water content and path comparison and relation to UTH Cloud & precipitation information from different sensors can be tested including cloud liquid water path of raining/nonraining clouds More integrated view of aerosol indirect effects on observed cloud albedos This vertical structure is required for many weather and climate related processes Important climate feedbacks revolve around high, thin ice clouds - agreement between these two data sets confirms validity of products Can calibrate longer time records of other sensor data, like cloud top heights, precipitation, – useful for other applications like cloud track winds Large uncertainties in AIE is one of the principle tools that constrain our ability to predict cliimate warming. Multiple layering is prevalent in tropics (60%), total cloud cover ~76% Connection to water vapor implies processes. Gross errors in the relation between UT ice and vapor in climate models - Major biases in cloud/radiance climatologies exposed, AMSR-E precip occurrence is ~ 2X less than CloudSat, exposes large uncertainty in mid alt precipitation AIE are inferred to be small composed due to combination of processes that buffer one another. Production of precipitation grossly influences AIE
Sensor data used A-Train What is provided Serendipity Why useful Interesting tidbits MODIS IR, CloudSat, CALIPSO AMSR-E, CALIPSO CloudSat & CALIPSO Convective buoyancy, entrainment Surface wind from lidar surface reflection Aerosol optical depth via PIA –radar surface reflectivity is used to define lidar surface reflection Provides unique, global information that is beginning to revolutionize model convection parameterization CALIPSO surface wind sees in between clouds and is less contaminated by cloud effects AOD much less sensitive to aerosol model assumptions that plague all other methods Verified hot tower hypothesis – 0.02% of tropics contain undilute convective cores 1m/s rms, near zero bias compared to AMSR-E
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: NASA A-Train member missions NASA E
Page 43 and 44: A-Train Science Concepts 4 slides f
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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