1. Robin Hogan, Julien Delanoe and Nicola Pounder University of Reading Towards unified retrievals of clouds, precipitation and aerosols

2. Introduction Most exciting aspect to EarthCARE is synergy by design –A well formulated synergistic algorithm ought to always outperform a single-instrument algorithm –If different species present in the same profile then need to retrieve them simultaneously in order to interpret measurements that are simultaneously sensitive to both (e.g. path-integrated attenuation and radiances sensitive to whole column) In the RATEC project we will begin development of a unified retrieval algorithm for clouds, precipitation and aerosols –A variational formulation will weight information from all sources (radar, lidar, radiances and prior information) according to its error –This could also serve as 1- and 2-instrument algorithms (to insure against instrument degradation or failure) by simply removing certain observations This talk will present the ingredients that have been gathered so far...

3. Motivation and classification Cloudsat radar CALIPSO lidar Preliminary target classification Insects Aerosol Rain Supercooled liquid cloud Warm liquid cloud Ice and supercooled liquid Ice Clear No ice/rain but possibly liquid Ground Radar and lidar Radar only Lidar only Global-mean cloud fraction Radar misses a significant amount of ice Use radar and lidar together where they both detect a cloud Retrieve ice and liquid simultaneously as both affect MSI radiances

4. Retrieval framework Ingredients developed Not yet developed 1. New ray of data: define state vector Use classification to specify variables describing each species at each gate Ice: extinction coefficient, N 0, lidar extinction-to-backscatter ratio Liquid: extinction coefficient and number concentration Rain: rain rate and mean drop diameter Aerosol: extinction coefficient, particle size and lidar ratio 3a. Radar model Including surface return and multiple scattering 3b. Lidar model Including HSRL channels and multiple scattering 3c. Radiance model Solar and IR channels 4. Compare to observations Check for convergence 6. Gauss-Newton iteration Derive a new state vector 3. Forward model Not converged Converged Proceed to next ray of data 2. Convert state vector to radar-lidar resolution Often the state vector will contain a low resolution description of the profile 5. Convert Jacobian to state-vector resolution Jacobian initially will be at the radar-lidar resolution 7. Calculate retrieval error Include error covariance and averaging kernel

5. State variables: ice clouds Ice clouds already done by Delanoe and Hogan (2008), extended in CASPER to use HSRL lidar –Variational version of Donovan and Tinel radar-lidar algorithms –Blends seamlessly between regions of cloud detected by radar and lidar State vector contains these elements to describe ice clouds: –Visible extinction coefficient at each gate, –Normalized number concentration parameter, N 0 –Lidar extinction-to-backscatter ratio, S Prior information and other constraints: –Temperature dependence of N 0 (T) from aircraft in-situ data –Smoothness constraint on the state variables so that noisy observations (particularly lidar Mie and Rayleigh channels) dont result in noisy retrievals –Prior estimate of S (e.g. 20 sr) –Microphysical model assumptions, e.g. mass-size relationship, infrared scattering properties

6. State variables: liquid clouds Largely new, but will build on –Smith & Illingworth estimate of LWP from path-integrated attenuation –CloudSat radar + MODIS solar channels –Information from HSRL using multiple-scattering forward model Possible state variables for liquid clouds: –Liquid water content, LWC (or possibly ) at each gate –Droplet number concentration, constant in each contiguous layer (via size information from MSI channels, and combination of LWP from path-integrated attenuation and optical depth from MSI) Prior information and other constraints: –Smoothness constraint on profile of LWC –Prior estimate of number concentration (e.g. from sea versus land) –Assume lidar extinction-to-backscatter ratio is constant at 18.5 sr –LWC gradient at cloud base tends to the known adiabatic profile given the temperature and pressure

7. State variables: precipitation New; would need to build on results of other ESA/JAXA studies –Key ingredients would be radar multiple-scattering model, surface return from ocean, profile of attenuated reflectivity (e.g. CloudSat), and Doppler velocity in stratiform conditions Possible state variables for precipitation: –Rain rate profile, R –Normalized number concentration, N w (one value per profile) –Riming factor for snow and for ice above rain (one value per profile): invoked in convective conditions to account for higher density ice, and also in snow (treated as an extension to the ice-cloud retrieval) –Melting-layer thickness scaling factor... Prior information and other constraints: –Strong smoothness constraint on profile of rain rate –Estimate of N w dependent on warm rain (e.g. Sc drizzle) or cold rain Warning: this will be difficult!

8. State variables: aerosols New; would need to build on results of other ESA/JAXA studies –Key ingredients would be HSRL, MSI solar channels in the day and optical depth constraint from lidar ocean surface return –Relatively straightforward compared to precipitation! Possible state variables for aerosols: –Extinction coefficient at 355 nm –Exinction-to-backscatter ratio (one value per layer) –Mean particle size (one value per layer)? Prior information and other constraints: –Extinction-to-backscatter ratio estimate dependent on geographical region

9. Forward models: active instruments Radar –Microphysics: scattering library for cloud liquid, ice and precipitation particles, ideally based on DDA and T-matrix rather than Mie –Propagation: fast multiple-scattering model is available (Hogan and Battaglia 2008) but needs an analytic Jacobian model –Doppler: terminal fallspeeds straightforward; main challenge is to characterize error due to vertical wind and non-uniform beam filling –Surface return: requires first pass to interpolate between clear skies? Lidar –Microphysics: backscatter problem overcome by retrieving extinction- to-backscatter ratio, but some uncertainty between phase functions –Propagation: fast multiple-scattering forward model exists for ice clouds, where we are in the small-angle limit, but wide-angle model for liquid clouds currently lacks an analytic Jacobian model or the ability to represent the individual HSRL channels –Depolarization: currently no forward model for either single-scatter depolarization, or depolarization due to multiple scattering

10. Forward models: passive instruments Infrared radiances –Microphysics: scattering library for cloud liquid, ice and aerosols required –Propagation: two models suitable for use: RTTOV (used by ECMWF and Met Office data assimilation systems) and the Delanoe and Hogan (2008) scheme –Model inputs: note that the error in this model is significantly determined by the error in the temperature profile Solar radiances –Microphysics: scattering library required for liquid, ice and aerosols, with uncertainty in the asymmetry factor and single-scatter albedo –Propagation: fast Radiant code from Colorado State University could be implemented –Model inputs: Need to assume a surface albedo –Other uncertainties: three-dimensional scattering effects could be important but very difficult to incorporate in a 1D retrieval

11. Examples of wide-angle multiple scattering LITE lidar (<r, footprint~1 km) CloudSat radar (>r)Stratocumulus Intense thunderstorm Surface echo Apparent echo from below the surface

12. Fast multiple scattering forward model CloudSat-like example New method uses the time- dependent two-stream approximation Agrees with Monte Carlo but ~10 7 times faster (~3 ms) Added to CloudSat simulator Hogan and Battaglia (J. Atmos. Sci. 2008) CALIPSO-like example

13. Exploiting multiple scattering

14. Results First ~3 optical depths would be seen by HSRL 1D-Var retrievals using Hogan and Battaglia forward model (Nicola Pounder) Next ~10 optical depths from wide-angle returns Beyond, wide-angle returns provide constraint on total optical depth but not its vertical distribution

15. Test dataset: ER-2 radars and lidar 94-GHz radar 10-GHz radar Can perform 94-GHz radar precipitation retrievals (using surface return from the oceans), then evaluate them by forward modelling the less attenuated 10-GHz radar 94-GHz reflectivity in convection disappears very quickly: multiple scattering from CloudSat may be giving us a false impression of how far we are penetrating

16. Next steps Within RATEC –Code up flexible retrieval framework and error reporting –Add various forward models –Implement ice and liquid cloud capability –Test on A-train and aircraft datasets –Provide product description for 3D scene construction Post RATEC –Test in ECSIM –Via collaboration, implement precipitation and aerosol components –Test when in 1- and 2-instrument configurations in case of instrument degradation or failure