Cloud property retrievals from mm-wave radars

1. Cloud property retrievals from mm-wave radars Sally McFarlane Pacific Northwest National Laboratory

2. Outline Introduction to ARM mm-wave radars Liquid cloud retrievals Ice cloud retrievals Evaluation and application of cloud retrievals

3. Atmospheric Radiation Measurement (ARM) Program Research Sites Addu Atoll, Maldives

4. Millimeter-Wavelength Cloud Radar (MMCR) • 8.66 mm (Ka) wavelength • 35 GHz • zenith pointing • Dynamic range -50 to + 20 dBZ • 45 – 90 m vertical resolution, depending on mode • Primary ARM radar product - Active Remote Sensing of Cloud Layers (ARSCL) • Combines all radar modes (along with lidar cloud base) to remove clutter and produce gridded reflectivity, Doppler velocity, spectral width

5. MMCR (cont.) KAZR at Darwin • More information on ARM radars: https://engineering.arm.gov/~widener/ARM_Radars/Home.html • Historically, ARM vertically-pointing 35 GHz radar was called the MMCR • At AMIE/DYNAMO, we will have the KAZR (Ka ARM Zenith Pointing Radar) • KAZR = 2011 upgrade of MMCR • New receiver, other new hardware • Still 35 GHz, vertically pointing • Now dual polarization • Collects reflectivity, vertical velocity, spectral width and Doppler spectra

6. Scanning ARM Cloud Radar (SACR) • Beams not matched, dual wavelength retrievals difficult • Copolar and cross-polar radar reflectivity, Doppler velocity, spectral width, Doppler spectra (when not scanning), linear depolarization ratio • K-band: beamwidth ~ 0.3 deg, sensitivity -40 dBZ at 1 km • X-band beamwidth ~ 1.4 deg, -25 dBZ at 1km • AMIE/DYNAMO operation will be primarily boundary layer and cross wind RHI scans • For AMIE/DYNAMO: dual wavelength Ka (35 GHz) and X (9.7 GHz) band radar mounted on a common pedestal

8. MM-radar strengths/weaknesses • Strengths: • High sensitivity • using pulse compression and other techniques, dynamic range of -50 to 20 dBZ • Able to detect most clouds and precipitation • (except for thin clouds with small particles - high thin cirrus and very thin altocu layers)

10. MM-radar strengths/weaknesses • Weaknesses: • Large particles are out of Rayleigh scattering regime • Attenuation by water vapor, cloud liquid, and precipitation • Large uncertainties for ice clouds above rain • Limits scanning radar to ~ 20 km • Saturates in heavy precipitation • Narrow beam widths

11. 35 GHz 94 GHz

13. Retrievals from mm-cloud radar

14. Liquid cloud retrievals Given a droplet distribution, n(r), the xth moment of the distribution is For Rayleigh scattering and liquid clouds, Liquid water content, Liquid water path, Q = vertical integral of liquid water content over cloud depth

15. Liquid cloud retrievals (cont) • Measure radar reflectivity profile, Z and liquid water path, Q, from microwave radiometer • Assume a value for σ • Solve for N, ql, and r0 • Limitations: • Assumes fixed shape and width of droplet distribution; can’t handle multi-modal (such as cloud/drizzle) • Frisch et al. 1998 • Assume lognormal particle size distribution • Then:

16. Other liquid cloud retrieval methods • ARM standard product – Microbase • Uses a Z/LWC regression equation derived from adiabatic cloud model with fixed value of N (Liao & Sassen 1994) to retrieve LWC • Scales LWC to match total Q from microwave radiometer • Given LWC and assumed N, uses Frisch method to calculate effective radius • Mace and Sassen, 2000 • Applies an additional constraint of solar transmission at the surface measured by radiometer and retrieves σ • Only applicable during daytime • McFarlane et al., 2002 • Bayesian retrieval uses prior information on relationship between LWC and Z from aircraft in situ measurements rather than fixing form of droplet distribution • Can handle bimodal distribution (cloud + drizzle)

18. Ice cloud retrievals – overview • Types of retrievals • Reflectivity - ice water content (IWC) regressions • Reflectivity + Doppler velocity – IWC and particle size • Reflectivity + lidar extinction – IWC and particle size • Issues with ice particle retrievals • Ice particles can be large compared to mm wavelengths – non-Rayleigh scattering becomes important • Ice particles are not spherical – shape assumptions come into play for radar-velocity and radar-lidar retrievals • Ice particles are not solid – density assumptions needed to calculate IWC

19. Ice cloud retrievals – Z/IWC regressions • Regression relationships,IWC = aZeb developed for different field campaigns • Liao and Sassen 1994, Brown et al. 1995, Liu and Illingworth 2000 Z-IWC relationship for CEPEX aircraft data • Z-IWC retrievals: • Aircraft measurements of ice crystal size distributions used to calculate Z and IWC (using Mie scattering and density assumptions)

20. Example of Z-IWC retrieval

21. Ice cloud retrievals – Z/IWC regressions • Improvements: • Calculate regression equations as a function of temperature (Hogan et al. 2006) improves error estimates to +50% to -33% • log10(IWC) = 0.000242 Z T + 0.0699 Z − 0.0186 T − 1.63 • Issues: • Uncertainties in aircraft ice measurements (shattering, sampling, etc.) from which regression equations developed • Range of regression equations in the literature – due to wavelength and particle density assumptions • Uncertainty in an individual retrieval of IWC for a given value of Ze ranges from +100% to – 50%

22. Ice cloud retrievals – Radar Reflectivity and Doppler Velocity • Radar reflectivity (Z) + Doppler velocity (Vd) • For single particle: • σ = azD^bz (radar backscatter cross section) • Vf = avD^bv(fall velocity) • M = amD^bm(mass) • Assume size distribution with 2 parameters (N and λ) • Write equations for Z, Vz, IWC given size distribution • Assume values for a, b parameters • From aircraft measurements and assumed shape/density • Average Vd to remove air motions and get Vz •  Solve for N and λ, then derive IWC, D • Modifications/adaptations by: • Mace et al. 2002; Matrosov et al. 2002, Delanoe et al. 2007

23. Example Z-V Retrieval (Delanoe & Protat)

24. Example Z-V Retrieval (Delanoe & Protat)

25. Issues/Assumptions in Z-V retrieval • Assumptions: • Assumed particle density and/or shape plays large role in values of a,b parameters; some retrievals inconsistent in assumptions across equations • Single-mode size distribution • Need to average Vd to remove air motions and get Vz • Refinements: • Use IR radiance (if no underlying cloud) for additional constraint • Use wind profiler (if available) to get air motions

26. Ice cloud retrievals – radar + lidar • Radar reflectivity (Z, 6th moment) + lidar extinction (σ, 2nd moment) • Wang and Sassen (2002): • Parameterize σ = IWC[a0 + a1D] and Z = C(IWC/ρi)Db • Assume gamma distribution and shape to get a0, a1, C, b, and ρi • Issues: • Due to attenuation of lidar in thick clouds, only useful in thin cirrus or lower portion of cloud • Errors if lidar is partially attenuated • Shape assumptions important to extinction • Multiple scattering can affect lidar • Assumption of constant backscatter to extinction ratio for elastic scattering lidars • Other retrievals (slightly different assumptions): • Donovan and van Lammeren (2002) • Delanoe and Hogan (2008)

27. Example radar-lidar retrieval

28. Applying retrievals to *real* clouds: • Classification of cloud type: • Need to identify phase of cloud before applying retrieval • Temperature; Reflectivity and Doppler velocity thresholds; spectral width to identify mixed phase; lidar backscatter • Mixed phase: • Assume ice signal dominates the reflectivity and Doppler velocity because of large particle sizes • Partition into ice reflectivity and liquid reflectivity as a function of temperature and apply liquid retrieval to liquid portion of cloud and ice retrieval to ice portion • Use radar spectra to separate reflectivity ice/liquid components (Shupe et al. 2004)

29. Precipitation: • Identify drizzle and rain using: lidar cloud base, rain gauge, Z threshold, spectral width • Often, standard liquid cloud retrieval is simply applied to the precipitating portion of the cloud • For drizzle, can apply Z-R regression and droplet size distribution assumptions to retrieve rain rate and water content (Comstock et al. 2004) • Stratiform rain retrievals using attenuation of signal in rain (Matrosov et al. 2006)

30. 30 Radar Ze, VD, σD Lidar β Temperature MWR LWP PNNL Conditional Retrieval Algorithm Liao & Sassen 1994 Frisch et al. 1995 Retrieval Output (Profiles): Cloud Mask & Phase IWC/LWC Cloud Drop/Crystal Size Drizzle/Rain Rate & Water Content Wood (2005) Rogers & Yao

33. New frontiers in mm-wave cloud retrievals • Optimal estimation/variational retrievals • Combine measurements from all available instrumentation with prior information about cloud particle size distributions • Mace, Hogan, etc. • Use of Doppler spectra, especially in drizzle and mixed-phase clouds • Shupe et al. 2004; Luke et al. 2010; Kollias • Use of dual polarization measurements • Application to scanning radars

34. Evaluation and application of cloud retrievals

35. Evaluation of cloud retrievals • Apply to “synthetic” data from cloud model • Need to add realistic noise • Must develop forward models to create instrument signals • Compare to aircraft data • Sampling differences • Aircraft instrument artifacts • Inter-comparison of retrievals • Spread between retrievals not true measure of uncertainty as they may all be wrong • Calculate radiative fluxes from retrieved properties and compare to observations (radiative closure) • Habit assumptions • Indirect evaluation

36. Inter-comparison of Tropical Ice Cloud Retrievals Comstock, Protat, McFarlane, Delanoe, Deng

37. Inter-comparison of Tropical Ice Cloud Retrievals Comstock, Protat, McFarlane, Delanoe, Deng

38. Inter-comparison of Tropical Ice Cloud Retrievals Comstock, Protat, McFarlane, Delanoe, Deng

39. Application of mm cloud retrievals McFarlane & Grabowski, GRL, 2007 • Process studies of cloud properties

40. Application of mm cloud retrievals Observations Models Models • Climatological studies of cloud properties for model evaluation and process understanding

41. Application of mm cloud retrievals Mace et al. , JAS, 2006 • Climatological studies of cloud properties

42. Application of mm cloud retrievals Calculated radiative heating rate Retrieved water content • Calculation of radiative heating profiles

43. Wind shear in cloud layer from scanning mm radar Min Deng, U. Wyoming

44. AMIE/DYNAMOCloud Radar Objectives Evolution of cloud field (macrophysical and microphysical properties) during MJO Relation of cloud properties to properties of convection Measure water budget and diabatic heating by combining cloud radar and precipitation radar measurements Upper tropospheric wind shear from Doppler velocity of scanning cloud radar Comparison of cloud properties at Gan/Manus sites

46. Equivalent radar reflectivity factor or • Radar reflectivity is the sum of backscattering cross sections of individual particles over the radar volume • For Rayleigh scattering, where D << λ, the backscatter, σ is proportional to D6 where Z = radar reflectivity factor • Equivalent radar reflectivity factor, Ze, is defined as

47. IWC IWC retrieved / IWC measured Heymsfield et al. 2008

48. Temperature IWC retrieved / IWC measured Heymsfield et al. 2008