Towards “unified” retrievals of cloud, precipitation and aerosol from combined radar, lidar and radiometer observations Robin Hogan, Julien Delanoë, Nicola Pounder, Nicky Chalmers, Thorwald Stein, Anthony Illingworth University of Reading Thanks to Alessandro Battaglia and Richard Forbes

Clouds in climate models Via their interaction with solar and terrestrial radiation, clouds are one of the greatest sources of uncertainty in climate forecasts But cloud water content in models varies by a factor of 10 Need instrument with high vertical resolution… 14 global models (AMIP) 90N 80 60 40 20 -20 -40 -60 -80 90S 0.05 0.10 0.15 0.20 0.25 Latitude Vertically integrated cloud water (kg m-2) But all models tuned to give about the same top-of-atmosphere radiation The properties of ice clouds are particularly uncertain Stephens et al. (2002)

Vertical structure of liquid water content Cloudnet: several years of retrievals from 3 European ground-based sites Observations in grey (with range indicating uncertainty) How do these models perform globally? 0-3 km Supercooled liquid water content from seven forecast models spans a factor of 20 ECMWF has far too great an occurrence of low LWC values Illingworth, Hogan et al. (2007)

Spaceborne radar, lidar and radiometers EarthCare The A-Train NASA 700-km orbit CloudSat 94-GHz radar (launch 2006) Calipso 532/1064-nm depol. lidar MODIS multi-wavelength radiometer CERES broad-band radiometer AMSR-E microwave radiometer EarthCARE: launch 2012 ESA+JAXA 400-km orbit: more sensitive 94-GHz Doppler radar 355-nm HSRL/depol. lidar Multispectral imager Broad-band radiometer Heart-warming name 2018 2019 2017 2014 2013 2015 2016

Overview What do spaceborne radar and lidar see? Classification of targets from radar and lidar Global distribution of supercooled clouds from the LITE lidar Towards a “unified” retrieval of cloud, precipitation and aerosol Variational retrieval framework Results from CloudSat-Calipso ice-cloud retrieval Consistency with top-of-atmosphere radiative fluxes Evaluation and improvement of models Challenges and opportunities from multiple scattering Fast forward model Multiple field-of-view lidar retrieval EarthCARE First results from prototype unified retrieval Outlook for model evaluation and improvement

What do CloudSat and Calipso see? Cloudsat radar Radar: ~D6, detects whole profile, surface echo provides integral constraint Lidar: ~D2, more sensitive to thin cirrus and liquid but attenuated Radar-lidar ratio provides size D CALIPSO lidar Target classification Insects Aerosol Rain Supercooled liquid cloud Warm liquid cloud Ice and supercooled liquid Ice Clear No ice/rain but possibly liquid Ground Delanoe and Hogan (2008, 2010)

CloudSat and Calipso sensitivity In July 2006, cloud occurrence in the subzero troposphere was 13.3% The fraction observed by radar was 65.9% The fraction observed by lidar was 65.0% The fraction observed by both was 31.0%

Distribution versus temperature & latitude No supercooled water colder than –40°C (as expected) Supercooled water more frequent in southern hemisphere storm track Hogan et al. (2004)

Ingredients of a variational retrieval Aim: to retrieve an optimal estimate of the properties of clouds, aerosols and precipitation from combining these measurements To make use of integral constraints must retrieve components together For each ray of data, define observation vector y: Radar reflectivity values Lidar backscatter values Infrared radiances Shortwave radiances (not yet implemented) Surface radar echo providing two-way attenuation (ditto) Define state vector x of properties to be retrieved: Ice cloud extinction, number concentration and lidar-ratio profile Liquid water content profile and number concentration Rain rate profile and number concentration Aerosol extinction coefficient profile and lidar ratio Forward model H(x) to predict the observations from the state vector Microphysical component: particle scattering properties Radiative transfer component

The cost function + Smoothness constraints The essence of the method is to find the state vector x that minimizes a cost function: Some elements of x are constrained by a prior estimate This term can be used to penalize curvature in the retrieved profile Each observation yi is weighted by the inverse of its error variance The forward model H(x) predicts the observations from the state vector x + Smoothness constraints

Ingredients developed Work in progress Unified retrieval 1. New ray of data: define state vector x Use classification to specify variables describing each species at each gate Ice: extinction coefficient , N0’, lidar extinction-to-backscatter ratio Liquid: extinction coefficient and number concentration Rain: rain rate, drop diameter and melting ice Aerosol: extinction coefficient, particle size and lidar ratio Ingredients developed Work in progress 2. Forward model 4. Iteration method Derive a new state vector Adjoint of full forward model Quasi-Newton or Gauss-Newton scheme 2a. Radar model Including surface return and multiple scattering 2b. Lidar model Including HSRL channels and multiple scattering 2c. Radiance model Solar and IR channels 3. Compare to observations Check for convergence Not converged Converged 5. Calculate retrieval error Error covariances and averaging kernel Proceed to next ray of data

Unified retrieval: Forward model From state vector x to forward modelled observations H(x)... Ice & snow Liquid cloud Rain Aerosol x Adjoint of radar model (vector) Adjoint of lidar model (vector) Adjoint of radiometer model Gradient of cost function (vector) xJ=HTR-1[y–H(x)] Vector-matrix multiplications: around the same cost as the original forward operations Adjoint of radiative transfer models yJ=R-1[y–H(x)] Ice/radar Liquid/radar Rain/radar Ice/lidar Liquid/lidar Rain/lidar Aerosol/lidar Ice/radiometer Liquid/radiometer Rain/radiometer Aerosol/radiometer Lookup tables to obtain profiles of extinction, scattering & backscatter coefficients, asymmetry factor Radar scattering profile Lidar scattering profile Radiometer scattering profile Sum the contributions from each constituent Radar forward modelled obs Lidar forward modelled obs Radiometer fwd modelled obs H(x) Radiative transfer models

Minimization methods - in 1D Quasi-Newton method (e.g. L-BFGS) Rolling a ball down a hill Intelligent choice of direction in multi-dimensions helps convergence Requires the gradient J/x A vector (efficient to store) Efficient to calculate using adjoint method Used in data assimilation Gauss-Newton method Requires the curvature 2J/x2 A matrix More expensive to calculate Faster convergence Assume J is quadratic and jump to the minimum Limited to smaller retrieval problems J x x1 J/x 2J/x2 J x1 J/x x2 x3 x4 x5 x6 x7 x8 x2 x3 x4 x5 x

Ice retrieval First challenge: How do we model radar scattering from complex ice particles? In Rayleigh regime (l»D), backscatter proportional to mass squared Particle shape irrelevant Use a mass-D relationship But Rayleigh assumption often not valid at 94 GHz (l=3 mm) Most papers assume homogeneous ice-air spheres with a diameter equal to the maximum dimension D of the particle, and apply Mie theory Problems with this approach: Non-Rayleigh behaviour depends on the vertical dimension of the particle Most are irregular aggregates with an average axial ratio of 0.6 They fall with their maximum dimension horizontal Alternative approach: Approximate as horizontally aligned oblate spheroids

Spheres versus spheroids Transmitted wave Sphere: returns from opposite sides of particle out of phase: cancellation Spheroid: returns from opposite sides not out of phase: higherb Spheroid Sphere Hogan et al. (2011)

Test with dual-wavelength aircraft data Sphere produces ~5 dB error (factor of 3) Spheroid approximation matches Rayleigh reflectivity (mass is about right) and non-Rayleigh reflectivity (shape is about right) Hogan et al. (2011)

Test with 3-GHz differential reflectivity Horizontal polarization Zh Differential reflectivity Zdr is larger for more extreme axial ratios Agreement with Z and Zdr confirms the Brown & Francis (1995) mass-D relationship and axial ratio = 0.6 Zdr = 10log10(Zh/Zv) Hogan et al. (2011)

Ice cloud: non-variational retrieval Aircraft-simulated profiles with noise (from Hogan et al. 2006) Donovan et al. (2000) Observations State variables Derived variables Retrieval is accurate but not perfectly stable where lidar loses signal Donovan et al. (2000) algorithm can only be applied where both lidar and radar have signal Delanoe and Hogan (2008)

Variational radar/lidar retrieval Observations State variables Derived variables Lidar noise matched by retrieval Noise feeds through to other variables Noise in lidar backscatter feeds through to retrieved extinction Delanoe and Hogan (2008)

…add smoothness constraint Observations State variables Derived variables Retrieval reverts to a-priori N0 Extinction and IWC too low in radar-only region Smoothness constraint: add a term to cost function to penalize curvature in the solution (J’ = l Si d2ai/dz2) Delanoe and Hogan (2008)

…add a-priori error correlation Observations State variables Derived variables Vertical correlation of error in N0 Extinction and IWC now more accurate Use B (the a priori error covariance matrix) to smooth the N0 information in the vertical Delanoe and Hogan (2008)

Example ice cloud retrievals Lidar observations Delanoe and Hogan (2010) Lidar forward model Radar forward model Visible extinction Ice water content Effective radius Radar observations MODIS radiance 10.8um Forward modelled radiance

Evaluation using CERES TOA fluxes Radar-lidar retrieved profiles containing only ice used with Edwards-Slingo radiation code to predict CERES fluxes Small biases but large random shortwave error: 3D effects? Shortwave Bias 4 W m-2, RMSE 71 W m-2 Longwave Bias 0.3 W m-2, RMSE 14 W m-2 Chalmers (2011)

CERES versus a radar-only retrieval How does this compare with radar-only empirical IWC(Z, T) retrieval of Hogan et al. (2006) using effective radius parameterization from Kristjansson et al. (1999)? Shortwave Bias 48 W m-2, RMSE 110 W m-2 Longwave Bias –10 W m-2, RMSE 47 W m-2 Bias 10 W m-2 RMS 47 W m-2 Chalmers (2011)

How important is lidar? Remove lidar-only pixels from radar-lidar retrieval Change to fluxes is only ~5 W m-2 but lidar still acts to improve retrieval in radar-lidar region of the cloud Shortwave Bias –5 W m-2, RMSE 17 W m-2 Longwave Bias 4 W m-2, RMSE 9 W m-2 Chalmers (2011)

A-Train versus models Ice water content 14 July 2006 Half an orbit 150° longitude at equator Delanoe et al. (2011)

Evaluation of gridbox-mean ice water content In-cloud mean ice water content Both models lack high thin cirrus ECMWF lacks high IWC values; using this work, ECMWF have developed a new prognostic snow scheme that performs better Met Office has too narrow a distribution of in-cloud IWC

Radiative transfer forward models Infrared radiances Delanoe and Hogan (2008) model Currently testing RTTOV (widely used, can do microwave, has adjoint) Solar radiances Currently testing LIDORT Radar and lidar Simplest model is single scattering with attenuation: b’=b exp(-2d) Problem from space is multiple scattering: contains extra information on cloud properties (particularly optical depth) but no-one has previously been able to rigorously make use of data subject to pulse stretching Use combination of fast “Photon Variance-Covariance” method and “Time-Dependent Two-Stream” methods Adjoints for these models recently coded Forward model for lidar depolarization is in progress

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

Scattering regimes Regime 2: Small-angle multiple scattering Regime 0: No attenuation Optical depth d << 1 Regime 1: Single scattering Apparent backscatter b’ is easy to calculate from d at range r : b’(r) = b(r) exp[-2d(r)] Mean free path l Regime 2: Small-angle multiple scattering Occurs when Ql ~ x Only for wavelength much less than particle size, e.g. lidar & ice clouds No pulse stretching Footprint x Regime 3: Wide-angle multiple scattering (pulse stretching) Occurs when l ~ x

Time-dependent 2-stream approx. Describe diffuse flux in terms of outgoing stream I+ and incoming stream I–, and numerically integrate the following coupled PDEs: These can be discretized quite simply in time and space (no implicit methods or matrix inversion required) Time derivative Remove this and we have the time-independent two-stream approximation Source Scattering from the quasi-direct beam into each of the streams Gain by scattering Radiation scattered from the other stream Loss by absorption or scattering Some of lost radiation will enter the other stream Spatial derivative Transport of radiation from upstream Hogan and Battaglia (J. Atmos. Sci., 2008)

Fast multiple scattering forward model Hogan and Battaglia (J. Atmos. Sci. 2008) New method uses the time-dependent two-stream approximation Agrees with Monte Carlo but ~107 times faster (~3 ms) CloudSat-like example CALIPSO-like example

Multiple field-of-view lidar retrieval To test multiple scattering model in a retrieval, and its adjoint, consider a multiple field-of-view lidar observing a liquid cloud Wide fields of view provide information deeper into the cloud The NASA airborne “THOR” lidar is an example with 8 fields of view Simple retrieval implemented with state vector consisting of profile of extinction coefficient Different solution methods implemented, e.g. Gauss-Newton, Levenberg-Marquardt and Quasi-Newton (L-BFGS) lidar Cloud top 100 m 10 m 600 m

Results for a sine profile Simulated test with 200-m sinusoidal structure in extinction With one FOV, only retrieve first 2 optical depths With three FOVs, retrieve structure of extinction profile down to 6 optical depths Beyond that the information is smeared out Pounder, Hogan et al. (2011)

Optical depth from multiple FOV lidar Despite vertical smearing of information, the total optical depth can be retrieved to ~30 optical depths Limit is closer to 3 for one narrow field-of-view lidar Useful optical depth information from one 100-m-footprint lidar (e.g. Calipso)! Pounder, Hogan et al. (2011)

THOR lidar

EarthCARE The ESA/JAXA “EarthCARE” satellite is designed with synergy in mind We are currently developing synergy algorithms for its instrument specification

EarthCARE lidar High Spectral Resolution capability enables direct retrieval of extinction profile

EarthCARE radar: Doppler capability 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 10-GHz radar 94-GHz radar Example from NASA airborne cloud radar demonstrates Can estimate ice fall-speed globally: important for radiation budget Can identify strong updrafts in convective cores

Unified algorithm: progress Bringing all the aspects of this talk together… Done: Functioning algorithm framework exists C++: object orientation allows code to be completely flexible: observations can be added and removed without needing to keep track of indices to matrices, so same code can be applied to different observing systems Preliminary retrieval of ice, liquid, rain and aerosol Adjoint of radar and lidar forward models with multiple scattering and HSRL/Raman support Interface to L-BFGS quasi-Newton algorithm in GNU Scientific Library In progress / future work: Estimate and report error in solution and averaging kernel Interface to radiance models Test on a range of ground-based, airborne and spaceborne instruments

Observations vs forward models Lidar backscatter Radar reflectivity factor Radar and lidar backscatter are successfully forward modelled (at final iteration) in most situations Can also forward model Doppler velocity (what EarthCARE would see)

Three retrieved components Liquid water content Ice extinction coefficient Rain rate

Outlook Use of radiances in retrieval should make retrieved profiles consistent with broadband fluxes (can test this with A-Train and EarthCARE) EarthCARE will take this a step further Use imager to construct 3D cloud field 10-20 km wide beneath satellite Use 3D radiative transfer to test consistency with broadband radiances looking at the cloud field in 3 directions (overcome earlier 3D problem) How can we use these retrievals to improve weather forecasts? Assimilate cloud products, or radar and lidar observations directly? Assimilation experiments being carried out by ECMWF Still an open problem as to how to ensure clouds are assimilated such that the dynamics and thermodynamics of the model are modified so as to be consistent with the presence of the cloud How can we use these retrievals to improve climate models? We will have retrieved global cloud fields consistent with radiation So can diagnose in detail not only what aspects of clouds are wrong in models, but the radiative error associated with each error in the representation of clouds

Scattering models First part of a forward model is the scattering and fall-speed model Same methods typically used for all radiometer and lidar channels Radar and Doppler model uses another set of methods Particle type Radar (3.2 mm) Radar Doppler Thermal IR, Solar, UV Aerosol Aerosol not detected by radar Mie theory, Highwood refractive index Liquid droplets Mie theory Beard (1976) Rain drops T-matrix: Brandes et al. (2002) shapes Ice cloud particles T-matrix (Hogan et al. 2010) Westbrook & Heymsfield Baran (2004) Graupel and hail TBD Melting ice Wu & Wang (1991)

Unified algorithm: state variables Proposed list of retrieved variables held in the state vector x State variable Representation with height / constraint A-priori Ice clouds and snow Visible extinction coefficient One variable per pixel with smoothness constraint None Number conc. parameter Cubic spline basis functions with vertical correlation Temperature dependent Lidar extinction-to-backscatter ratio Cubic spline basis functions 20 sr Riming factor Likely a single value per profile 1 Liquid clouds Liquid water content One variable per pixel but with gradient constraint Droplet number concentration One value per liquid layer Rain Rain rate Cubic spline basis functions with flatness constraint Normalized number conc. Nw One value per profile Dependent on whether from melting ice or coallescence Melting-layer thickness scaling factor Aerosols Extinction coefficient One value per aerosol layer identified Climatological type depending on region Ice clouds follows Delanoe & Hogan (2008); Snow & riming in convective clouds needs to be added Liquid clouds currently being tackled Basic rain to be added shortly; Full representation later Basic aerosols to be added shortly; Full representation via collaboration?

Radiative transfer forward models Computational cost can scale with number of points describing vertical profile N; we can cope with an N2 dependence but not N3 Radar/lidar model Applications Speed Jacobian Adjoint Single scattering: b’=b exp(-2t) Radar & lidar, no multiple scattering N N2 Platt’s approximation b’=b exp(-2ht) Lidar, ice only, crude multiple scattering Photon Variance-Covariance (PVC) method (Hogan 2006, 2008) Lidar, ice only, small-angle multiple scattering N or N2 Time-Dependent Two-Stream (TDTS) method (Hogan and Battaglia 2008) Lidar & radar, wide-angle multiple scattering N3 Depolarization capability for TDTS Lidar & radar depol with multiple scattering Lidar uses PVC+TDTS (N2), radar uses single-scattering+TDTS (N2) Jacobian of TDTS is too expensive: N3 We have recently coded adjoint of multiple scattering models Future work: depolarization forward model with multiple scattering Radiometer model Applications Speed Jacobian Adjoint RTTOV (used at ECMWF & Met Office) Infrared and microwave radiances N Two-stream source function technique (e.g. Delanoe & Hogan 2008) Infrared radiances N2 LIDORT Solar radiances Infrared will probably use RTTOV, solar radiances will use LIDORT Both currently being tested by Julien Delanoe

Minimizing the cost function Gradient of cost function (a vector) Gauss-Newton method Rapid convergence (instant for linear problems) Get solution error covariance “for free” at the end Levenberg-Marquardt is a small modification to ensure convergence Need the Jacobian matrix H of every forward model: can be expensive for larger problems as forward model may need to be rerun with each element of the state vector perturbed and 2nd derivative (the Hessian matrix): Gradient Descent methods Fast adjoint method to calculate xJ means don’t need to calculate Jacobian Disadvantage: more iterations needed since we don’t know curvature of J(x) Quasi-Newton method to get the search direction (e.g. L-BFGS used by ECMWF): builds up an approximate inverse Hessian A for improved convergence Scales well for large x Poorer estimate of the error at the end

Comparison of convergence rates Solution is identical Gauss-Newton method converges in < 10 iterations L-BFGS Gradient Descent method converges in < 100 iterations Conjugate Gradient method converges a little slower than L-BFGS Each L-BFGS iteration >> 10x faster than each Gauss-Newton one! Gauss-Newton method requires the Jacobian matrix, which must be calculated by rerunning multiple scattering model multiple times

Unified algorithm: first results for ice+liquid But lidar noise degrades retrieval Convergence! Truth Retrieval First guess Iterations Observations Retrieval Observations Forward modelled retrieval Forward modelled first guess

Add smoothness constraint Smoother retrieval but slower convergence Truth Retrieval First guess Iterations Observations Retrieval Observations Forward modelled retrieval Forward modelled first guess