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VARSY progress meeting

VARSY progress meeting. Robin Hogan and Nicola Pounder (University of Reading). 12 April 2013. Brief summary of progress. No plots today: Full error descriptors now implemented for liquid clouds and rain (ice already done)

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VARSY progress meeting

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  1. VARSY progress meeting Robin Hogan and Nicola Pounder (University of Reading) 12 April 2013

  2. Brief summary of progress No plots today: • Full error descriptors now implemented for liquid clouds and rain (ice already done) • Solar radiance forward model: code included to describe scattering phase function with Legendre polynomials but still needs to be coupled to the LIDORT radiative transfer model Plots today: • Liquid cloud retrievals using multiple scattering from single field-of-view lidar Calipso • Overcoming multiple minima in the cost function for liquid cloud • Possible algorithm speed-up being investigated: Levenberg-Marquardt minimization rather than quasi-Newton, plus GPU computation of Jacobian matrix • Ability to simulate EarthCARE data (including Doppler and HSRL) from A-Train retrievals, then retrieve from the simulated EarthCARE data

  3. Unified retrieval 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, N0’,lidar extinction-to-backscatter ratio, riming factor Liquid: extinction coefficient and number concentration Rain: rain rate, drop diameter and melting ice Aerosol: extinction coefficient, particle size and lidar ratio 2. Convert state vector to radar-lidar resolution Often the state vector will contain a low resolution description of the profile 3. Forward model 6. Iteration method Derive a new state vector 3a. Radar model With surface returnand multiple scattering 3b. Lidar model Including HSRL channels and multiple scattering 3c. Radiance model Solar & IR channels Not converged 4. Compare to observations Check for convergence Converged 7. Calculate retrieval error Error covariances & averaging kernel Proceed to next ray of data

  4. Liquid cloud retrieval • We have found that the multiple scattering signal from Calipso can be inverted to get extinction profile for optical depth up to at least 30 • Benefits from a constraint on LWC to be no steeper than adiabatic • We can validate with CloudSat PIA, or assimilate PIA too • Example from 1 minute (~400 km) of oceanic stratocumulus: • Forward modelled backscatter • Observed backscatter

  5. Assimilate only Calipso backscatter • LWC • Effective radius • Optical depth • CloudSat PIA

  6. Assimilate also CloudSat PIA • LWC • Effective radius • Optical depth • CloudSat PIA

  7. Simulated retrieval of optical depth for idealized adiabatic clouds, using spaceborne lidar with varying field of view (FOV) For FOV less than around 50 m, there is simply too little multiple scattering signal to retrieve extinction and optical depth Will need to rely more on radar PIA over ocean and solar radiances in the day Night-time land a problem Will this work with EarthCARE? FOV >= 55 m (e.g. Calipso) FOV <= 50 m (e.g. EarthCARE)

  8. Why can the first guess matter? • Consider a cloud with an optical depth of 50 • If the first guess had an optical depth of 1 then the simulated molecular scattering below the cloud would look a bit like the measured multiple scattering • Algorithm has difficulty getting over hump in cost function because increasing optical depth first reduces simulated backscatter below cloud top (leading to poorer agreement with obs) before multiple scattering builds up (leading to better agreement) First guess Truth

  9. Possible solution • Consider all possible true optical depths (but only triangular profiles so that profiles can be described uniquely by optical depth) • Algorithm will converge provided first guess is outside the shaded areas • Should be able to pre-analyse the profile (e.g. by integrating the backscatter with height) to tell if we are in the low or high optical depth regime, then set the first guess appropriately Previous plot considered true optical depth of 50

  10. Potential optimization • We need to speed-up the retrieval algorithm • Can we exploit parallel architectures, e.g. multicore machines or GPUs? • Trade-off between minimization schemes: • Quasi-Newton (L-BFGS) • Uses only the gradient of the cost function, which is fast to calculate • Many iterations required • Levenberg-Marquardt (LM; more stable version of Gauss-Newton) • Uses also the curvature of the cost function which is slow to calculate • But few iterations required, and a little more robust (in my experience) • Currently works for ice and rain, not yet for liquid • Adept’s algorithm for computing the Jacobian matrix (needed by LM) is potentially parallelizable • “m” parallel threads, where “m” is number of observations (~100) • At best, the cost of an LM iteration would be the same as a quasi-Newton iteration, so LM would be much faster overall • I am currently employing a programmer with GPU experience to code up a parallel Jacobian algorithm using CUDA (for NVIDIA hardware)

  11. Levenberg-Marquardt algorithm run on ice and rain region CloudSat and Calipso observations and forward model Example case

  12. Convergence comparison • Quasi-Newton needs around five times more iterations on average (depending on convergence criterion) Levenberg-Marquardt Quasi-Newton

  13. Convergence comparison cont. Levenberg-Marquardt Quasi-Newton CloudSat Calipso

  14. Levenberg-Marquardt is already competitive but if Jacobian can be sped up it would be much faster than qausi-Newton Further change: perform wide-angle multiple scattering at half the vertical resolution would gain factor 4 speed-up Computational cost Proportional to number of iterations Computational cost (arbitrary) Potentially parallelizable

  15. CloudSat • EarthCARE CPR Z • Higher sensitivity • CPR Z error • CPR Doppler • Use Japanese random error • CPR Doppler error Unified retrieval of cloud +precip …then simulate EarthCARE instruments

  16. Calipso backscatter • ATLID Mie channel • Note liquid! • ATLID Mie error • Not rigorous! • ATLID Rayleigh channel • ATLID Rayleigh error Unified retrieval of cloud +precip …then simulate EarthCARE instruments Liquid cloud

  17. A-Train retrieval Pseudo-EarthCARE retrieval Assimilate Doppler and HSRL (Some difference due to lidar ratio not being carried between retrieval and simulation) Compare ice retrievals Extinction Number concentration Extinction Number concentration

  18. A-Train EarthCARE Poor LWC: not enough lidar multiple scattering! Compare liquid clouds and rain Liquid water content Rain rate Liquid water content Rain rate

  19. Remaining algorithm development • Minimization • Parallelize Jacobian calculation on GPU and compare speed of Levenberg-Marquardt to quasi-Newton • Forward models • Finish implementation of LIDORT solar radiance model • Ice clouds • Add “riming” factor • Add Baran phase functions where appropriate • Liquid clouds • Test impact of solar radiances on retrievals • Test size retrieval from two solar wavelengths • Rain • Test impact of various observations (PIA, radar multiple scattering) • Aerosols • Implement an aerosol retrieval scheme (contract extension)

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