Retrieval of liquid water cloud properties from ground-based remote sensing observations

1. Retrieval of liquid water cloud properties from ground-based remote sensing observations C. L. BrandauandH. W. J. Russchenberg Delft University of Technology

2. The liquid water cloud properties of interest: Liquid water content (LWC) Droplet concentration (N) Particle size (r) Cloud optical depth (τ)

3. The used measured quantities and their source of instrument

4. Fundamental assumptions (1) Mono-modal droplet size distribution: generalized gamma distribution (Flatau et al. 1989): (2) The moments of the DSD are proportional to each other: is a function of the shape parameter or relative dispersion (3) Assumptions on in-cloud vertical structure of the DSD parameters so that any moment of the DSD can be computed Note: The points (1) and (2) restrict the application to liquid water clouds without drizzle formation

5. The in-cloud vertical structure

6. The scaled-adiabatic stratified cloud model probable impacts of mixing are characterized by a constant reduction of the adiabatic LWC with height (1) (2) Approximation (3) Assuming (4) are functions of the DSD shape parameter

7. The homogenous mixing cloud model impact of mixing accounts for the observed vertical variation in Z, conform with the assumptions made in the Frisch retrieval algorithm (Frisch et al., 1998, 2002 and 2004) Frisch LWC

8. Example: Output for various Z profiles The shapes of the vertical profiles of the SAS cloud model retrievals are independent from the radar reflectivity

9. Sensitivity to input observations LWP and Z

10. Uncertainties in the retrieval estimates

11. Choice of gamma DSD shape parameter Brenguier et al. (2011) reanalyzed in-situ data of warm boundary clouds from five different field campaigns: Reassessment of the relationship between the mean surface radius and the mean volume radius of the measured DSDs k=0.737+-0.061 differences in k for pristine and polluted clouds (e.g. Martin et al.,1994) are not confirmed, but explained by instrumental biases For the gamma DSD

12. ASTEX (with drizzle parts): Homogenous mixing • Very low values of the input LWP • Drizzle sized particles produce biased results, • because they are dominating Z while their • contribution to LWC, re and N is negligible small LWC is essentially a function of LWP and H, but re and N depend on Z

13. Assumption of N using Miles et al., 2000 results to retrieve effective radius Maritime clouds: N=75/cm3, DSD shape parameter = 8.7 Assumption of N (75/cm3) reduces the maximum particle size from 25 to 9 microns

14. ASTEX (drizzle case): Scaled-adiabatic stratified Degree of adiabaticty is very small, (Fr>0.9 near adiabatic, 0.5<FR<0.9 diluted, Fr<0.5 strongly diluted cloud properties), because of the very low input LWP

15. ASTEX (drizzle case): Scaled-adiabatic stratified Very low amounts of N values cause the large particle sizes Similar results to the HM model output

16. FIRE: Homogenous mixing Although the reflectivities increase up to -5 dBZ (drizzle?), the profiles show a gradual increase (quasi-adiabatic processes) except in vicinity at cloud top, so that the cloud layer can be considered as drizzle free

17. Assumption of N using Miles et al., 2000 results to retrieve effective radius Maritime clouds: N=75/cm3, DSD shape parameter = 8.7

18. Fire: Scaled-adiabatic stratified

19. Fire: Scaled-adiabatic stratified