Variational cloud retrievals from radar, lidar and radiometers - PowerPoint PPT Presentation

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Robin Hogan

JulienDelanoë

Nicola Pounder

University of Reading

Variational cloud retrievals from radar, lidar and radiometers

Introduction

Best estimate of the atmospheric state from instrument synergy

Use a variational framework / optimal estimation theory

Some important measurements are integral constraints

E.g. microwave, infrared and visible radiances
Affected by all cloud types in profile, plus aerosol and precipitation
Hence need to retrieve different particle types simultaneously

Funded by ESA and NERC to develop unified retrieval algorithm

For application to EarthCARE
Will be tested on ground-based, airborne and A-train data

Algorithm components

Target classification input
State variables
Minimization techniques: Gauss-Newton vs. Gradient-Descent
Status of forward models and their adjoints

Progress with individual target types

Ice clouds
Liquid clouds

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

Liquid: extinction coefficient and number concentration

Rain: rain rate and mean drop diameter

Aerosol: extinction coefficient, particle size and lidar ratio

6. Iteration method

Derive a new state vector

Either Gauss-Newton or quasi-Newton scheme

Retrieval framework

2. Convert state vector to radar-lidar resolution

Often the state vector will contain a low resolution description of the profile

3. Forward model

3a. Radar model

Including surface return and multiple scattering

3c. Radiance model

Solar and IR channels

3b. Lidar model

Including HSRL channels and multiple scattering

Ingredients developed before

In progress

Not yet developed

Not converged

4. Compare to observations

Check for convergence

5. Convert Jacobian/adjoint to state-vector resolution

Initially will be at the radar-lidar resolution

Converged

7. Calculate retrieval error

Error covariances and averaging kernel

Proceed to next ray of data

Target classification

In Cloudnet we used radar and lidar to provide a detailed discrimination of target types (Illingworth et al. 2007):
A similar approach has been used by Julien Delanoe on CloudSat and Calipso using the one-instrument products as a starting point:
More detailed classifications could distinguish “warm” and “cold” rain (implying different size distributions) and different aerosol types

Example from the AMF in Niamey

94-GHz radar reflectivity

Forward model at final iteration

532-nm lidar backscatter

94-GHz radar reflectivity

Observations

532-nm lidar backscatter

Results: radar+lidar only

Large error where only one instrument detects the cloud

Retrievals in regions where radar or lidar detects the cloud

Retrieved visible extinction coefficient

Retrieved effective radius

Retrieval error in ln(extinction)

Results: radar, lidar, SEVERI radiances

Cloud-top error greatly reduced

Retrieval error in ln(extinction)

TOA radiances increase retrieved optical depth and decrease particle size near cloud top

Delanoe & Hogan (JGR 2008)

Retrieved visible extinction coefficient

Retrieved effective radius

Proposed list of retrieved variables held in the state vector xUnified algorithm: state variables

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?

The cost function

Some elements of x are constrained by an a priori estimate

The forward model H(x) predicts the observations from the state vector x

Each observation yi is weighted by the inverse of its error variance

This term penalizes curvature in the extinction profile

The essence of the method is to find the state vector x that minimizes a cost function:

+ Smoothness constraints

Gauss-Newton method

See Rodgers’ book (p85): write the cost function in matrix form:

Define its gradient (a vector):

…and its second derivative (a matrix):

Approximate J as quadratic and apply this:

Advantage: rapid convergence (instant convergence for linear problems)

Another advantage: get the error covariance of the solution “for free”

Disadvantage: need the Jacobian matrix of every forward model: can be expensive for larger problems

Gradient descent methods

Just use gradient information:

Advantage: we don’t need to calculate the Jacobian so forward model is cheaper!

Disadvantage: more iterations needed since we don’t know curvature of J(x)

Use a quasi-Newton method to get the search direction, such as BFGS used by ECMWF: builds up an approximate form of the second derivative to get improved convergence

Scales well for large x

Disadvantage: poorer estimate of the error at the end

Why don’t we need the Jacobian H?

The “adjoint” of a forward model takes as input the vector {.} and outputs the vector Jobs without needing to calculate the matrix H on the way

Adjoint can be coded to be only ~3 times slower than original forward model

Tricky coding for newcomers, although some automatic code generators exist

Typical convergence behaviour

Forward model components

From state vector x to forward modelled observations H(x)...

Jacobian matrix

H=y/x

x

Ice & snow

Liquid cloud

Rain

Aerosol

Lookup tables to obtain profiles of extinction, scattering & backscatter coefficients, asymmetry factor

Ice/radar

Ice/lidar

Ice/radiometer

Liquid/radar

Liquid/lidar

Liquid/radiometer

Computationally expensive matrix-matrix multiplications: the most expensive part of the entire algorithm

Rain/radar

Rain/lidar

Rain/radiometer

Aerosol/radiometer

Aerosol/lidar

Sum the contributions from each constituent

Lidar scattering profile

Radar scattering profile

Radiometer scattering profile

Radiative transfer models

Gradient of radar measurements with respect to radar inputs

Gradient of lidar measurements with respect to lidar inputs

Gradient of radiometer measurements with respect to radiometer inputs

H(x)

Lidar forward modelled obs

Radar forward modelled obs

Radiometer forward modelled obs

Jacobian part of radiative transfer models

Equivalent adjoint method

From state vector x to forward modelled observations H(x)...

Gradient of cost function (vector)

J/x=HTy*

x

Ice & snow

Liquid cloud

Rain

Aerosol

Lookup tables to obtain profiles of extinction, scattering & backscatter coefficients, asymmetry factor

Ice/radar

Ice/lidar

Ice/radiometer

Liquid/radar

Liquid/lidar

Liquid/radiometer

Vector-matrix multiplications: around the same cost as the original forward operations

Rain/radar

Rain/lidar

Rain/radiometer

Aerosol/radiometer

Aerosol/lidar

Sum the contributions from each constituent

Lidar scattering profile

Radar scattering profile

Radiometer scattering profile

Radiative transfer models

Adjoint of radar model (vector)

Adjoint of lidar model (vector)

Adjoint of radiometer model

H(x)

Lidar forward modelled obs

Radar forward modelled obs

Radiometer forward modelled obs

Adjoint of radiative transfer 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

Graupel and melting ice still uncertain; but normal ice is decided...

Scattering models

Computational cost can scale with number of points describing vertical profile N; we can cope with an N2dependencebut not N3Radiative transfer forward models

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

Infrared will probably use RTTOV, solar radiances will use LIDORT
Both currently being tested by Julien Delanoe

Gradient constraint

We have a good constraint on the gradient of the state variables with height for:

LWC in stratocu (adiabatic profile, particularly near cloud base)
Rain rate (fast falling so little variation with height expected)

Not suitable for the usual “a priori” constraint
Solution: add an extra term to the cost function to penalize deviations from gradient c:

A. Slingo, S. Nichols and J. Schmetz, Q. J. R. Met. Soc. 1982

Example in liquid clouds

Using simulated observations:

Triangular cloud observed by a 1- or 2-field-of-view lidar
Retrieval uses Levenberg-Marquardt minimization with Hogan and Battaglia (2008) model for lidar multiple scattering

Two FOVs: very good performance

One 10-m footprint: saturates at optical depth=5

One 100-m footprint: multiple scattering helps!

One 10-m footprint with gradient constraint: can extrapolate downwards successfully

Optical depth=30

Footprint=100m

Footprint=600m

Progress

Done:

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
Code to generate particle scattering libraries in NetCDF files
Adjoint of radar and lidar forward models with multiple scattering and HSRL/Raman support
Radar/lidar model interfaced and cost function can be calculated