robin hogan, julien delanoe department of meteorology, university of reading, uk richard forbes...
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Robin HoganRobin Hogan, Julien Delanoe, Julien DelanoeDepartment of Meteorology, University of Reading, UKDepartment of Meteorology, University of Reading, UK
Richard ForbesRichard ForbesEuropean Centre for Medium Range Weather ForecastsEuropean Centre for Medium Range Weather Forecasts
Alejandro Bodas-SalcedoAlejandro Bodas-SalcedoMet Office, UKMet Office, UK
Radar/lidar/radiometer Radar/lidar/radiometer retrievals of ice clouds retrievals of ice clouds
from the A-trainfrom the A-train
MotivationMotivation• Clouds are important for climate due to interaction with radiation
– A good cloud retrieval must be consistent with broadband fluxes at surface and top-of-atmosphere (TOA)
• Advantages of combining radar, lidar and radiometers– Radar ZD6, lidar ’D2 so the combination provides particle size– Radiances ensure that the retrieved profiles can be used for radiative
transfer studies
• How do we do we combine them optimally?– Use a “variational” framework: takes full account of observational errors– Straightforward to add extra constraints and extra instruments– Allows seamless retrieval between regions of different instrument
sensitivity
• In this talk a new variational radar-lidar-radiometer algorithm is applied to a month of A-Train data– Comparison with MODIS retrievals– Evaluation of Met Office and ECMWF model ice clouds– Investigation of the morphology of tropical cirrus
Formulation of variational Formulation of variational schemescheme
m
m
m
n
I
I
Z
Z
0.127.8
7.8
1
1
ln
ln
y
aer1
liq1
1
ice
ice1
ice1
ln
ln
LWP
ln
ln
ln
ln
N
S
N
N
m
n
x
For each ray of data we define:• Observation vector • State vector
– Elements may be missing– Logarithms prevent unphysical negative values
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
(TBD) Aerosol visible extinction coefficient profile
(TBD) Liquid water path and number conc. for each liquid layer
Infrared radiance
Radiance difference
Solution methodSolution method• An iterative method is required
to minimize the cost function
New ray of dataLocate cloud with radar & lidarDefine elements of xFirst guess of x
Forward modelPredict measurements y from state vector x using forward model H(x)Predict the Jacobian H=yi/xj
Has solution converged?2 convergence test
Gauss-Newton iteration stepPredict new state vector:
xk+1= xk+A-1{HTR-1[y-H(xk)]
-B-1(xk-b)-Txk}where the Hessian is
A=HTR-1H+B-1+T
Calculate error in retrieval
No
Yes
Proceed to next ray
Lidar forward model: multiple Lidar forward model: multiple scatteringscattering
• 90-m footprint of Calipso means that multiple scattering is a problem
• Eloranta’s (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 (Applied Optics, 2006). Code: www.met.rdg.ac.uk/clouds
Ice cloud
Molecules
Liquid cloud
Aerosol
Narrow field-of-view:
forward scattered
photons escape
Wide field-of-view:
forward scattered
photons may be returned
Wide-angle multiple Wide-angle multiple scatteringscattering
CloudSat multiple scattering
• To extend to precip, need to model radar multiple scattering– Talk on Wednesday, session B!
New model agrees well with Monte Carlo
Radiance forward modelRadiance forward model• MODIS and CALIPSO each have 3 thermal infrared channels in
the 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 Baran’s aggregate
model– Correlated-k-distribution for gaseous absorption (from David
Donovan and Seiji Kato)
• MODIS solar channels provide an estimate of optical depth– Only available in daylight– Likely to be degraded by 3D radiative transfer effects– Only usable when no liquid clouds in profile … currently not used
Ice cloud: non-variational Ice cloud: non-variational retrievalretrieval
• 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
Aircraft-simulated profiles with noise (from Hogan et al. 2006)
Variational radar/lidar Variational radar/lidar retrievalretrieval
• 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
……add smoothness constraintadd smoothness constraint
• Smoothness constraint: add a term to cost function to penalize curvature in the solution ( J’ = id2i/dz2)
Observations
State variables
Derived variables
Retrieval reverts to a-priori N0
Extinction and IWC too low in radar-only region
……add a-priori error add a-priori error correlationcorrelation
• Use B (the a priori error covariance matrix) to smooth the N0 information in the vertical
Observations
State variables
Derived variables
Vertical correlation of error in N0
Extinction and IWC now more accurate
CloudSat-CALIPSO-MODIS CloudSat-CALIPSO-MODIS exampleexample
1000 km
Lidar observations
Radar observations
CloudSat-CALIPSO-MODIS CloudSat-CALIPSO-MODIS exampleexample
Lidar observations
Lidar forward model
Radar observations
Radar forward model
• Extinction coefficient
• Ice water content
• Effective radius
Forward modelMODIS 10.8-m observations
Radar-lidar retrievalRadar-lidar retrieval
Radiances matched by increasing extinction near cloud top
……add infrared radiancesadd infrared radiances
Forward modelMODIS 10.8-m observations
One orbit in July 2006
A-Train
Model
Comparison with Met Office Comparison with Met Office modelmodel
log10(IWC[kg m-3])
Antarctica
CentralPacific
ArcticOcean
CentralAtlantic
SouthAtlantic
Russia
Effective radius versus Effective radius versus temperaturetemperature
All clouds
An effective radius parameterization?
log10(IWC [kg m-3])
log10(IWC)
Lidar only
log10(IWC)
Radar only
log10(IWC)
Radar+lidar only
Frequency of IWC vs. Frequency of IWC vs. temperaturetemperature
• Mean and variance of IWC both increase with temperature
• Clearly need both radar and lidar to detect full range of ice clouds
Comparison of mean effective Comparison of mean effective radiusradius
• July 2006 mean value of re=3IWP/2i from CloudSat-CALIPSO only
• Just the top 500 m of cloud
• MODIS/Aqua standard product
Comparison of ice water pathComparison of ice water pathMean of all skies
Mean of clouds
CloudSat-CALIPSO MODIS
• Need longer period than just one month (July 2006) to obtain adequate statistics from poorer sampling of radar and lidar
Comparison of optical depthComparison of optical depthMean of all skies
Mean of clouds
CloudSat-CALIPSO MODIS
• Mean optical depth from CloudSat-CALIPSO is lower than MODIS simply because CALIPSO detected many more optically thin clouds not seen by MODIS
• Hence need to compare PDFs as well
A-Train
Tem
pera
ture
(°C
)Comparison with model IWCComparison with model IWC
Met Office ECMWF
• Global forecast model data extracted underneath A-Train• A-Train ice water content averaged to model grid
– Met Office model lacks observed variability– ECMWF model has artificial threshold for snow at around 10-4 kg m-3
Tem
pera
ture
(°C
)
Observations- Note limitation
of each instrument
Retrievals
Tropical Tropical Indian Indian Ocean Ocean cirruscirrus
MODIS infrared window radiance
Turbulent fall-streaks in lower half of cloud?
Stratiform region in upper half of
cloud?
Hogan and Kew (QJ 2005) found that mid-latitude
cirrus structure affected by cloud top turbulence
with a typical outer scale of 50-100 km
Outer scale 90 km
-5/3 law
600 km 120 km
Stratiform upper region dominated by larger scales
A-Train data show quite different structure above ~12.5 km in tropical cirrus: gravity waves?
Mid-latitude cirrus Mid-latitude cirrus Tropical cirrusTropical cirrus320 km
1300 km
Summary and future workSummary and future workNew dataset provides a unique perspective on global ice clouds• Planned retrieval enhancements
– Retrieve liquid clouds and precipitation at the same time to provide a truly seamless retrieval from the thinnest to the thickest clouds
– Incorporate microwave and visible radiances– Adapt for EarthCARE satellite (ESA/JAXA: launch 2013)
• Model evaluation– How can Met Office and ECMWF model cloud schemes be
improved?– High-resolution simulations of tropical convection in “CASCADE”– Use CERES to determine the radiative error associated with
misrepresented clouds in model
• Cloud structure and microphysics– What is the explanation for the different regions in tropical cirrus?– What determines the outer scale of variability?– Can we represent tropical cirrus in the Hogan & Kew fractal model?– Can we resolve the “small crystal” controversy?
ConvergenceConvergence• The solution generally
converges after two or three iterations– When formulated in terms
of ln(), ln(’) rather than ’ the forward model is much more linear so the minimum of the cost function is reached rapidly