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Assessment of ground-based cloud liquid water profiling retrieval techniques

Assessment of ground-based cloud liquid water profiling retrieval techniques. Ulrich Löhnert (University of Cologne) Dave Donovan (KNMI) Kerstin Ebell (University of Cologne) Giovanni Martucci (Galway University) Simone Placidi (TU Delft) Christine Brandau (TU Delft)

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Assessment of ground-based cloud liquid water profiling retrieval techniques

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  1. Assessment of ground-based cloud liquid water profiling retrieval techniques Ulrich Löhnert (University of Cologne) Dave Donovan (KNMI) Kerstin Ebell (University of Cologne) Giovanni Martucci (Galway University) Simone Placidi (TU Delft) Christine Brandau (TU Delft) Ewan O’Connor (FMI/University of Reading) Herman Russchenberg (TU Delft) weatherreport.com

  2. Why focus on pure liquid water clouds? • they occurr very frequently and significantly influence solar radiative forcing •  large climate impact! • however, these clouds are difficult to measure and model •  especially in global climate models! Turner et al. 2007 Turner et al. 2007

  3. Objective of this study • Evaluate current liquid cloud profiling techniques • identify errors and discuss assumptions • correct for errors • recommendations for an optimal retrieval method • Simulation case • “truth” known • direct evaluation • need to simulate measurements • Real case • application to real measurements • evaluation with radiation closure • ECSIM (EarthCareSimulator) • radar, lidar, microwave radiometer • SW, LW fluxes

  4. Overview of measurements and parameters • ... measurements which are used ... • MWR: microwave radiometer • TB: microwave radiometer brightness temperature (K) • LWP: liquid water path (gm-2) • radar Z: cloud radar reflectivity (dBZ) • lidar: backscatter profile (sr-1m-1) • ... parameters to be retrieved ... • LWC: liquid water content (gm-3) • Reff: cloud droplet effective radius (μm) • N0: cloud droplet concentration (cm-3)

  5. Simulated cases • Two maritime cases simulated with LES • ASTEX: spectrally resolved microphysics, low LWP (< 100 gm-2), partially drizzling • FIRE: only bulk microphysics (LWC), reasonable drop size distribution assumed (uni-modal) ASTEX FIRE ASTEX

  6. Retrievals • TUD (Brandau et al. / Frisch et al.)  retrieve LWC(z), Reff(z) and N0 • input: MWR LWP and radar Z • uni-modal drop size distribution • relation between moments of the drop size distribution (Brenguier et al. 2011) • SYRSOC (Martucci et al.)  retrieve LWC(z), Reff(z) and N0(z) • input: MWR LWP, radar Z and lidar backscatter • extrapolate extinction retrieved in not fully attenuated part of lidar signal to cloud top  N0(z) • Gamma size distribution • IPT (Löhnert et al. / Ebell et al.)  retrieve LWC(z), Reff(z) • input: MWR TB, radar Z and a priori LWC • minimize cost function to meet TB, LWC a priori profiles and radar Z-LWC relation

  7. Prior categorization of clouds • Cloudnet scheme following Hogan & O‘Connor (Univ. of Reading) • cloud particle detection vs. aerosol and insects • discriminate between thermodynamic phase of water • precipitating (drizzle) / non-precipitating only cloud ASTEX case only drizzle clouds & drizzle

  8. FIRE case: LWC IPT TUD rand. error: <10% sys. error: 25-30% rand. error: 10-20% sys. error: < 15% SYRSOC rand. error: 30% sys. error: < 20%

  9. FIRE: Reff rand. error: < 10% sys. error: < 10% IPT TUD rand. error: < 10% sys. error: < 40% rand. error: < 20% sys. error: < 15% N0 assumed too large SYRSOC

  10. ASTEX: TUD method LWC Reff rand. error: 20-50% sys. error: ~50% rand. error: 70-100% sys. error: < 40% • small LWP  high relative LWP uncertainties lead to large LWC uncertainties • drop size distribution no longer uni-modal  small number of drizzle droplets lead to Reff overestimation

  11. ASTEX: IPT rand. error: <60% sys. error: 20-30% LWC Reff rand. error: <40% sys. error: 20-100% • complex inter-play between LWC, radar Z, TB, and a priori profile • a priori covariance information „smoothes“ LWC profile towards a non-representative a priori profile

  12. Real measurements 2007 ARM mobile facility COPS (Black Forest) deployment  8.9.07: uniform stratus cloud deck (non-precipitating) with LWP up to 400 gm-2

  13. Application to real data: radiative closure study SW flux measurements 0 400 1000 Wm-2 SW flux simulations IPT SW flux simulations SYRSOC SW flux simulations TUD 0 400 1000 Wm-2

  14. Summary • correct cloud categorization is essential for all retrieval approaches • for uni-modal case (FIRE) all methods show acceptable LWC accuracies (20-30 %) • methods (TUD & SYRSOC) that retrieve N0 show good Reff performance (10-20 %) • accurate LWP retrieval essential for TUD and SYRSOC • larger problems occur in drizzle case (ASTEX)  systematic overestimation (underestimation) of Reff (N0) • IPT a prioi assumptions significantly determine shape of LWC profile

  15. Outlook • carry out long-term radiative closure experiments • further simulation study: continental strato-cumulus case • adjust physical constraints within the algorithm depending on situation (e.g. non-drizzle/drizzle) • implement more realistic a priori assumptions • using higher moments of cloud radar Doppler spectra to better discriminate cloud droplets and drizzle

  16. Back ups ...

  17. Real data: radiative closure 20070908

  18. Real data: radiative closure 20070908 • closed cloud deck (20070908) shows acceptable SW closure for IPT and TUD (~80 Wm-2 STDEV, BIAS close to zero) • SYRSOC shows overestimation due to lower LWC and Reff values, cloud boundary issue? • keep in mind inhomogeneity • broken cloud case from June shows much larger differences ...

  19. Results TUD (V1) ASTEX

  20. Results TUD (V1) ASTEX

  21. Results TUD(V1) FIRE

  22. Results IPT ASTEX

  23. Results IPT FIRE

  24. Results SYRSOC ASTEX

  25. Results SYRSOC FIRE

  26. FIRE CASE: estimation of cloud optical depth by SW broadband data.

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