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VARIATIONAL SYNERGISTIC ICE-CLOUD RETRIEVALS

1 st JADE Meeting- ESTEC 2009. VARIATIONAL SYNERGISTIC ICE-CLOUD RETRIEVALS. Julien Delano ë, Robin Hogan & Thorwald Stein University of Reading, UK. First JADE Meeting, 22-23 April 2009 ESTEC. Radar-Lidar-IRradiometers. 1 st JADE Meeting- ESTEC 2009. Now. EARTHCARE. A-TRAIN.

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VARIATIONAL SYNERGISTIC ICE-CLOUD RETRIEVALS

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  1. 1st JADE Meeting- ESTEC 2009 VARIATIONAL SYNERGISTIC ICE-CLOUD RETRIEVALS Julien Delanoë, Robin Hogan & Thorwald Stein University of Reading, UK First JADE Meeting, 22-23 April 2009 ESTEC

  2. Radar-Lidar-IRradiometers 1st JADE Meeting- ESTEC 2009 Now EARTHCARE A-TRAIN CPR (Cloud Profiling Radar) ATLID (ATmospheric LIDar) MSI (Multi-Spectral Imager) BBR (Broad Band Radiometer) CloudSat: CPR 94GHz CALIPSO: Lidar (532, 1064nm) + IIR AQUA: radiometers MODIS, AIRS, CERES, AMSR-E Ice cloud retrievals using radar-lidar-radiometer synergy: 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. Radar-Lidar-Radiometer Radar misses a significant amount of ice 1st JADE Meeting- ESTEC 2009 Radar+lidar=> Vertical description of clouds July 2006 Global-mean cloud fraction CALIPSO lidar CloudSat radar Radar and lidar Radar only Lidar only Radar ZD6, lidar b’D2: combination provides particle size • Lidar: sensitive to particle concentration, but extinguished when cloud to thick • Radar: sensitive to particle size, can’t detect small crystals 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

  4. How to combine them? 1st JADE Meeting- ESTEC 2009 Variational scheme: We know the observations (instrument measurements) and we would like to know cloud properties : visible extinction, Ice water content, effective radius… Ingredients already developed (Delanoë and Hogan JGR 2008-2009) New ray of data: define state vector Use classification to specify variables describing ice cloud at each gate: extinction coefficient and N0* Radar model Lidar model Including HSRL channels and multiple scattering Radiance model IR channels Forward model Not converged Compare to observations: with an a-priori and measurement errors as a constraint Check for convergence Gauss-Newton iteration Derive a new state vector Converged Proceed to next ray of data

  5. 1st JADE Meeting- ESTEC 2009 A-Train EarthCare No HSRL available: S is assumed constant with height Or can be assumed linearly varying with height if radiance used When HSRL available: S can be relaxed!

  6. 1st JADE Meeting- ESTEC 2009 What do we do with A-Train?

  7. Categorisation 1st JADE Meeting- ESTEC 2009 We merge radar, lidar, MODIS data Using CloudSat and CALIPSO mask + ECMWF temperature => New categorisation Temperature model (ECMWF) => Ice / Liquid water Supercooled liquid layers : 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 Cloudsat radar CALIPSO lidar Insects Aerosol Rain Supercooled liquid cloud Warm liquid cloud Ice and supercooled liquid Ice Clear No ice/rain but possibly liquid Ground Preliminary target classification

  8. Radar-lidar example 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 1st JADE Meeting- ESTEC 2009 ice water

  9. Radar-lidar-radiances 1st JADE Meeting- ESTEC 2009 (+radiances Sct) (+radiances Svar) Radar+lidar lidar Latitude [°] Radar a a a Latitude [°] N0* N0* N0* re re re Latitude [°] Latitude [°] Latitude [°]

  10. Radar+lidar only log10(IWC) log10(IWC) Radar only Lidar only log10(IWC) log10(IWC) 1st JADE Meeting- ESTEC 2009 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

  11. Comparison with other products 1st JADE Meeting- ESTEC 2009 Comparison CloudSat IWC – IWC-Z-T- Variational method IWC vs Temperature CloudSat CloudSat IWC-Z-T Stein et al. 2009

  12. Comparison with other products 1st JADE Meeting- ESTEC 2009 Optical depth: MODIS vs radar-lidar variational method As a function of latitude (2 weeks in July 2006) 20 • MODIS • Radar-lidar 15 Optical depth 10 5 0 -50 0 50 Latitude [°]

  13. Model comparison 1st JADE Meeting- ESTEC 2009 Work in Collaboration with Alejandro Bodas-Salcedo (Met-Office) 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 A-Train vs UK met-Office x10-4 x10-4 => Models capture the trend of the IWC-T distribution (not the rest)

  14. 1st JADE Meeting- ESTEC 2009 Model comparison Work in Collaboration with Richard Forbes (ECMWF) 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 A-Train vs ECMWF x10-4 x10-4 x10-4 => Models capture the trend of the IWC-T distribution (not the rest)

  15. Future for A-Train 1st JADE Meeting- ESTEC 2009 We will treat the entire period of CloudSat-CALIPSO, however doing this requires resources… Icare (http://www-icare.univ-lille1.fr/) They provide various services to support the research community in fields related to atmospheric research, such as aerosols, clouds, radiation, water cycle, and their interactions. Production and distribution of remote sensing data derived from Earth observation missions from CNES, NASA, and EUMETSAT. They provide: • Merged files (radar-lidar-radiometers colocated, CloudSat track) • Categorisation (under development) • Retrievals (using our radar-lidar algorithm)

  16. 1st JADE Meeting- ESTEC 2009 What do we do to prepare EarthCare? CASPER project

  17. Variational for EarthCare 1st JADE Meeting- ESTEC 2009 Simulate EarthCare measurements: (cf Blind Test Hogan et al. 2006) Simulated profiles (Lidar&Radar) and MSI radiances are taken as truth CPR ATLID S Visible a Effective radius

  18. ECSIM 1st JADE Meeting- ESTEC 2009 Attenuated lidar backscatter (Mie channel) Attenuated lidar backscatter (Ray channel) Measurements Forward modelled Reflectivity a profile Visible extinction (truth) Measurements Forward modelled Retrieved visible extinction

  19. Future work 1st JADE Meeting- ESTEC 2009 Future work • More than one month… Icare • Develop algorithms for EarthCARE, HSRL lidar and Doppler radar • Retrieve properties of liquid-water layers, drizzle and aerosol (Robin Hogan’s talk) • A-train data and validate using in-situ underflights

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