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ICE CLOUD PROPERTIES FROM THE A-TRAI N

CloudSat Meeting- Seattle 2008. ICE CLOUD PROPERTIES FROM THE A-TRAI N. Julien Delano ë & Robin Hogan University of Reading, UK Thanks to Richard Forbes (ECMWF) Alejandro Bodas-Salcedo (Met-Office). CloudSat Meeting: August 19-21 2008. Remote sensing synergy for Clouds.

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ICE CLOUD PROPERTIES FROM THE A-TRAI N

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  1. CloudSat Meeting- Seattle 2008 ICE CLOUD PROPERTIES FROM THE A-TRAIN Julien Delanoë & Robin Hogan University of Reading, UK Thanks to Richard Forbes (ECMWF) Alejandro Bodas-Salcedo (Met-Office) CloudSat Meeting: August 19-21 2008

  2. Remote sensing synergy for Clouds CloudSat Meeting- Seattle 2008 A-Train: 28th April 2006 CloudSat: Cloud profiler radar 94GHz CALIPSO: Cloud profiler lidar 532, 1064nm + Infra Red Imager AQUA: radiometers MODIS, AIRS, CERES, AMSR-E • An opportunity to tackle questions concerning role of clouds in climate • Need to combine all these observations to get an optimum estimate of global cloud properties

  3. CloudSat Meeting- Seattle 2008 An algorithm to combine radar, lidar, radiometers • Why combine radar, lidar and radiometers? • Radar ZD6, lidar b’D2 so the combination provides particle size • Radiances ensure that the retrieved profiles can be used for radiative transfer studies • Single channel: information on extinction near cloud top • Pair of channels: ice particle size information near cloud top • Some limitations of existing radar/lidar ice retrieval schemes (Donovan et al. 2000, Tinel et al. 2005, Mitrescu et al. 2005) • Only work in regions of cloud detected by both radar and lidar • Difficult to make use of other measurements, e.g. passive radiances • A “unified” variational scheme can solve all of these problems

  4. Formulation of variational scheme Ice visible extinction coefficient profile Attenuated lidar backscatter profile Radar reflectivity factor profile Ice normalized number conc. profile Extinction/backscatter ratio for ice Visible optical depth Infrared radiance Radiance difference CloudSat Meeting- Seattle 2008 We know the observations (instrument measurements) and we would like to know cloud properties : visible extinction, Ice water content, effective radius… • Observation vector • State vector (which we want to retrieve) • Elements may be missing Iterative process: compare predicted observations and measurements, with an a-priori and measurement errors as a constraint Delanoë and Hogan 2008, JGR (doi:10.1029/2007JD009000)

  5. CloudSat Meeting- Seattle 2008 Ice cloud properties from A-TRAINCloudSat-CALIPSOCase study

  6. CALIPSO lidar Pacific Ocean 2006-9-22 Visible extinction Forward modelled lidar Ice water content CloudSat radar Effective radius Forward modelled radar • MODIS radiance 10.8um • Forward modelled radiance CloudSat Meeting- Seattle 2008

  7. CloudSat Meeting- Seattle 2008 Ice cloud properties from A-TRAINCloudSat-CALIPSOStatistics :one month data July 2006Ice water content and optical thickness

  8. Radar+lidar only log10(IWC) log10(IWC) Radar only Lidar only log10(IWC) log10(IWC) CloudSat Meeting- Seattle 2008 Frequency of occurrence of IWC vs temperature IWC increases with temperature: • but spread over 2 to 3 orders of magnitude at low temperatures • reach 5 orders of magnitude close to 0° C Advantage of the algorithm: Deep ice clouds: radar Thin ice clouds: lidar When radar and lidar work well together very good confidence in the retrievals • Obvious complementarity radar-lidar

  9. CloudSat Meeting- Seattle 2008 Differences between Hemispheres: July 2006 No obvious differences in the general trend IWC shifted to low temperatures in southern hemisphere Temperature [˚C] Temperature [˚C] Boreal Summer Austral Winter:

  10. Comparison of ice water path CloudSat Meeting- Seattle 2008 Mean of all skies Mean of clouds CloudSat-CALIPSO MODIS Need longer period than just one month (July 2006) to obtain adequate statistics from poorer sampling of radar and lidar

  11. Comparison of optical depth CloudSat Meeting- Seattle 2008 Mean of all skies Mean of clouds CloudSat-CALIPSO MODIS Mean optical depth from CloudSat-CALIPSO is lower than MODIS simply because CALIPSO detected many more optically thin clouds not seen by MODIS => need to compare PDFs as well

  12. CloudSat Meeting- Seattle 2008 Ice cloud properties from A-TRAINCloudSat-CALIPSOModel comparisons (July 2006):UK Met-OfficeECMWF

  13. A-Train vs UK met-Office A-Train vs ECMWF A-Train vs ECMWF IWC cut-off @ 10-4 kg m-3 between 0°C & -20°C: explained by the parameterization of the ice-to-snow autoconversion rate. When ice water contents reach 10-4 kg m-3 => precipitating snow. CloudSat Meeting- Seattle 2008 Forecasts Vertical profiles were extracted from the model along the CloudSat-Calipso tracks at the closest time to the observations A-train data averaged to models grid Good trend but can’t capture variability lack of IWC @ T< -50°C => Models capture the trend of the IWC-T distribution (not the rest)

  14. CloudSat Meeting- Seattle 2008 Ice cloud properties from A-TRAINCloudSat-CALIPSOModel comparison (July 2006):UK Met-OfficeDoes the model capture the latitude characteristics?

  15. CloudSat Meeting- Seattle 2008 A-Train vs UK met-Office Northern Hemisphere Southern Hemisphere A-Train A-Train UK met-Office UK met-Office Temperature °C log10(IWC) log10(IWC) log10(IWC) log10(IWC) Ok for the trend but not the variability

  16. Conclusion /Future work CloudSat Meeting- Seattle 2008 Conclusion • New variational scheme, combining radar, lidar, radiometer and/or any other relevant measurement in order to retrieve ice cloud properties profiles • Develop ice cloud climatology and use to evaluate forecast/climate models Future work • More than one month… • Develop algorithms for ESA’s “EarthCARE” satellite, HSRL lidar and Doppler radar • Retrieve properties of liquid-water layers, drizzle and aerosol • Incorporate microwave radiances for deep precipitating clouds • A-train data and validate using in-situ underflights Acknowledgements: These data were obtained from the NASA Langley Research Center Atmospheric Science Data Center and the NASA CloudSat project.

  17. Supercooled water layers These pixels are not retrieved Can be bad detection Attenuated lidar backscatter from CALIPSO Radar Reflectivity from CloudSat We have developed a simple cloud phase identification algorithm Cloud seen by lidar Temperature model (ECMWF) => Ice / Liquid water Simple method : Exploit the different response of radar and lidar in presence of supercooled liquid water: -Very strong lidar signal -Very weak radar signal Within a 300m cloud layer Cloud seen by radar

  18. Northern hemisphere July 2006 Southern hemisphere

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