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Synergy of Raman Lidar and Microwave Radiometry for High Vertically Resolved

Synergy of Raman Lidar and Microwave Radiometry for High Vertically Resolved Temperature and Water Vapor Profiles. WWOSC 2014 Montreal, 17 th August 2014 María Barrera Verdejo 1 , Susanne Crewell 1 , Ulrich Löhnert 1 , Bjorn Stevens 2 , Emiliano Orlandi 1 , Paolo Di Girolamo 3

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Synergy of Raman Lidar and Microwave Radiometry for High Vertically Resolved

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  1. Synergy of Raman Lidar and Microwave Radiometry for High Vertically Resolved Temperature and Water Vapor Profiles WWOSC 2014 Montreal, 17th August 2014 María Barrera Verdejo1, Susanne Crewell1, Ulrich Löhnert1, Bjorn Stevens2, Emiliano Orlandi1, Paolo Di Girolamo3 1Universität zu Köln, Cologne, Germany, 2Max-Planck-Institut fürMeteorologie, Hamburg, Germany, 3Università della Basilicata, Potenza, Italy

  2. Introduction and motivation

  3. Abs. Humidity + uncertainties • MWR TBs

  4. Abs. Humidity + uncertainties Abs. Humidity + uncertainties

  5. Abs. Humidity + uncertainties Abs. Humidity + uncertainties

  6. Abs. Humidity + uncertainties Abs. Humidity + uncertainties

  7. Abs. Humidity + uncertainties Rel. Humidity + uncertainties Abs. Humidity + uncertainties

  8. Instruments

  9. Optimal Estimation Scheme

  10. Integrated Profiling Technique (IPT) Inversion OPTIMALESTIMATION Avariational approach towards multi-instrument retrieval Löhnert et al., 2004 and 2008

  11. Optimal Estimation Scheme • A priori information, xa, Sa • Radiosondes climatology • Measurements, y, Se • Lidar temp and humidity profiles • TB from MWR • Atmospheric retrieved parameters, x=[T,q] • Temperature and humidity profiles Rodgers 2000

  12. Optimal Estimation Scheme • A priori information, xa, Sa • Radiosondes climatology • Measurements, y, Se • Lidar temp and humidity profiles • TB from MWR • Atmospheric retrieved parameters, x=[T,q] • Temperature and humidity profiles Rodgers 2000

  13. Optimal Estimation Scheme • A priori information, xa, Sa • Radiosondes climatology • Measurements, y, Se • Lidar temp and humidity profiles • TB from MWR • Atmospheric retrieved parameters, x=[T,q] • Temperature and humidity profiles Rodgers 2000

  14. Optimal Estimation Scheme • A priori information, xa, Sa • Radiosondes climatology • Measurements, y, Se • Lidar temp and humidity profiles • TB from MWR • Atmospheric retrieved parameters, x=[T,q] • Temperature and humidity profiles Rodgers 2000

  15. Optimal Estimation Scheme FForward models Rodgers 2000

  16. Optimal Estimation Scheme KiJacobian represents the variation in the forward model if a layer is perturbed FForward models Rodgers 2000

  17. Results

  18. Example of a profile

  19. Example of a profile

  20. Example of a profile

  21. Example of a profile

  22. Example of a profile

  23. Example of a profile

  24. Example of a profile

  25. Abs. Humidity + uncertainties Abs. Humidity + uncertainties

  26. Abs. Humidity + uncertainties

  27. Abs. Humidity + uncertainties

  28. Abs. Humidity + uncertainties

  29. Integrated water vapor

  30. Scatter plots 500 m 1.5 km 5 km LIDAR MWR BOTH

  31. Scatter plots 500 m 1.5 km 5 km LIDAR MWR BOTH

  32. Theoretical error

  33. Theoretical error

  34. Theoretical error SYNERGY IMPROVEMENT

  35. Future work and conclusions

  36. Temperature+ uncertainties Rel. Humidity + uncertainties Abs. Humidity + uncertainties

  37. Temperature+ uncertainties • Improvement of the algorithm: • Include temperature • Extend to cloudy cases • Apply algorithm to Barbados data Rel. Humidity + uncertainties

  38. Conclusions

  39. Thanks for your attention

  40. Back up slides

  41. Average difference in HOPE

  42. Degrees of freedom

  43. Humidity and Atmospheric Profiler (HATPRO) • Passive instrument measuring thermal emission expressed as brightness temperatures (TB) • 7 channels from 22.235 - 31.4 GHz7 channels from 51.3 – 58.8 GHz • Statistical retrieval of T, q profiles, Liquid Water Path (LWP) and Integrated Water Vapor (IWV) • Rain sensor, GPS, clock • Noise in TB depends on channel band width and integration time. • Absolute calibration: LN2 and/or tipping curves. Precision ~ 0.5 K SUNHAT at Barbados Cloud Observatory. Max Planck Institute. Mentor: María Barrera Verdejo.

  44. Basil Lidar • Nd:YAG laser. Fundamental, Second and Third Harmonic Generation • Elastic channels: 1064, 532 and 355 nm • Nitrogen (387 nm) and water vapor (408 nm) • Profiles of backscatter coefficient, particle depolarization and particle extinction. • Daytime measurements more noisy than nighttime operation. • Water vapor mass mixing ratio from Raman channels • Temperature, water vapor • Calculation of relative humidity. • Calibration using RS Hamburg lidar at Barbados Cloud Observatory. Max Planck Institute.

  45. A priori information: xa, Sa Xa • Pprof_aver • Tprof_aver • Qprof_aver (and their standard deviations) • Improvements: take closest RS

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