Download presentation

Presentation is loading. Please wait.

Published byLauren Barton Modified over 2 years ago

1
A variational cloud retrieval scheme combining radar, lidar and radiometer observations Robin Hogan & Julien Delanoe University of Reading, UK. The CloudSat radar and the Calipso lidar were launched on 28 th April 2006 They join Aqua, hosting the MODIS, CERES, AIRS and AMSU radiometers An opportunity to tackle questions concerning role of clouds in climate Need to combine all these observations to get an optimum estimate of global cloud properties

2

3
Eastern RussiaJapan Sea of JapanEast China Sea Calipso lidar CloudSat radar Molecular scattering Aerosol from China? Cirrus Mixed-phase altocumulus Drizzling stratocumulus Non-drizzling stratocumulus Rain 7 June km

4
Motivation Why combine radar, lidar and radiometers? –Radar Z D 6, lidar D 2 so the combination provides particle size –Radiances ensure that the retrieved profiles can be used for radiative transfer studies Some limitations of existing radar/lidar ice retrieval schemes (Donovan et al. 2000, Tinel et al. 2005, Mitrescu et al. 2005) –Only work in regions of cloud detected by both radar and lidar –Noise in measurements results in noise in the retrieved variables –Elorantas lidar multiple-scattering model is too slow to take to greater than 3rd or 4th order scattering –Other clouds in the profile are not included, e.g. liquid water clouds –Difficult to make use of other measurements, e.g. passive radiances –Difficult to also make use of lidar molecular scattering beyond the cloud as an optical depth constraint –Some methods need the unknown lidar ratio to be specified A unified variational scheme can solve all of these problems

5
Formulation of variational scheme Observation vector State vector –Elements may be missing Attenuated lidar backscatter profile Radar reflectivity factor profile (on different grid) Ice visible extinction coefficient profile Ice normalized number conc. profile Extinction/backscatter ratio for ice Visible optical depth Aerosol visible extinction coefficient profile Liquid water path and number conc. for each liquid layer Infrared radiance Radiance difference

6
Solution method Find x that minimizes a cost function J of the form J = deviation of x from a-priori + deviation of observations from forward model + curvature of extinction profile New ray of data Locate cloud with radar & lidar Define elements of x First guess of x Forward model Predict measurements y from state vector x using forward model H(x) Also predict the Jacobian H Has solution converged? 2 convergence test Gauss-Newton iteration step Predict new state vector: x i+1 = x i +A -1 {H T R -1 [y-H(x i )] -B -1 (x i -x a )-Tx i } where the Hessian is A=H T R -1 H+B -1 +T Calculate error in retrieval No Yes Proceed to next ray

7
Radar forward model and a priori Create lookup tables –Gamma size distributions –Choose mass-area-size relationships –Mie theory for 94-GHz reflectivity Define normalized number concentration parameter –The N 0 that an exponential distribution would have with same IWC and D 0 as actual distribution –Forward model predicts Z from extinction and N 0 –Effective radius from lookup table N 0 has strong T dependence –Use Field et al. power-law as a-priori –When no lidar signal, retrieval relaxes to one based on Z and T (Liu and Illingworth 2000, Hogan et al. 2006) Field et al. (2005)

8

9
Lidar forward model: multiple scattering 90-m footprint of Calipso means that multiple scattering is a problem Elorantas (1998) model –O (N m /m !) efficient for N points in profile and m-order scattering –Too expensive to take to more than 3rd or 4th order in retrieval (not enough) New method: treats third and higher orders together –O (N 2 ) efficient –As accurate as Eloranta when taken to ~6th order –3-4 orders of magnitude faster for N =50 (~ 0.1 ms) Hogan (2006, Applied Optics, in press). Code: Ice cloud Molecules Liquid cloud Aerosol Narrow field-of-view: forward scattered photons escape Wide field-of- view: forward scattered photons may be returned

10
Radiance forward model MODIS solar channels provide an estimate of optical depth –Only very weakly dependent on vertical location of cloud so we simply use the MODIS optical depth product as a constraint –Only available in daylight MODIS, Calipso and SEVIRI each have 3 thermal infrared channels in atmospheric window region –Radiance depends on vertical distribution of microphysical properties –Single channel: information on extinction near cloud top –Pair of channels: ice particle size information near cloud top Radiance model uses the 2-stream source function method –Efficient yet sufficiently accurate method that includes scattering –Provides important constraint for ice clouds detected only by lidar –Ice single-scatter properties from Anthony Barans aggregate model –Correlated-k-distribution for gaseous absorption (from David Donovan)

11
Ice cloud: non-variational retrieval Donovan et al. (2000) algorithm can only be applied where both lidar and radar have signal Observations State variables Derived variables Retrieval is accurate but not perfectly stable where lidar loses signal Donovan et al. (2000) Aircraft- simulated profiles with noise (from Hogan et al. (2006) Optical depth 13.9; lidar sees to 3.6

12
Variational radar/lidar retrieval Noise in lidar backscatter feeds through to retrieved extinction Observations State variables Derived variables Lidar noise matched by retrieval Noise feeds through to other variables

13
…add smoothness constraint Smoothness constraint: add a term to cost function to penalize curvature in the solution (J = i d 2 i /dz 2 ) Observations State variables Derived variables Retrieval reverts to a-priori N 0 Extinction and IWC too low in radar-only region

14
…add a-priori error correlation Use B (the a priori error covariance matrix) to smooth the N 0 information in the vertical Observations State variables Derived variables Vertical correlation of error in N 0 Extinction and IWC now more accurate

15
…add visible optical depth constraint Integrated extinction now constrained by the MODIS-derived visible optical depth Observations State variables Derived variables Slight refinement to extinction and IWC

16
…add infrared radiances Better fit to IWC and r e at cloud top Observations State variables Derived variables Poorer fit to Z at cloud top: information here now from radiances

17
Radar-only retrieval Observations State variables Derived variables

18
Radar plus optical depth Observations State variables Derived variables

19
Radar, optical depth and IR radiances Observations State variables Derived variables

20
Observed 94-GHz radar reflectivity Observed 905-nm lidar backscatter Forward model radar reflectivity Forward model lidar backscatter Ground-based example Lidar fails to penetrate deep ice cloud

21
Retrieved extinction coefficient Retrieved effective radius r e Retrieved normalized number conc. parameter N 0 Error in retrieved extinction Lower error in regions with both radar and lidar Radar only: retrieval tends towards a-priori

22
Conclusions and ongoing work A variational method has been described for combining radar, lidar, radiometers and any other relevant measurements, to retrieve profiles of cloud microphysical properties In progress: –Testing radiance part of retrieval using geostationary-satellite radiances from Meteosat/SEVIRI above ground-based radar & lidar –Add capability to retrieve properties of liquid-water layers, drizzle and aerosol Then apply to A-train data! CloudSat observations over the UK on 18th June 2006 Scotland England Lake district Isle of Wight France

23
13.10 UTC June 18 th Scotland England Lake district Isle of Wight France MODIS RGB composite

24
Scotland England Lake district Isle of Wight France MODIS Infrared window UTC June 18 th (Sunday)

25
Scotland England Lake district Isle of Wight France Met Office rain radar network UTC June 18 th (Sunday)

26
Sd sdf Banda Sea An island of Indonesia

27
Antarctic ice sheet Southern Ocean

Similar presentations

© 2016 SlidePlayer.com Inc.

All rights reserved.

Ads by Google