Joint ECMWF-University meeting on Joint ECMWF-University meeting on interpreting data from spaceborne radar interpreting data from spaceborne radar

and lidar: AGENDAand lidar: AGENDA09:30 Introduction

University of Reading activities• 09:35 Robin Hogan - Overview of CloudSat/CALIPSO/EarthCARE work at University• 09:50 Julien Delanoe - Ice cloud retrievals from CloudSat, CALIPSO & MODIS• 10:05 Lee Smith - Retrieval of liquid water content from CloudSat and CALIPSO

10:20-10:35 Coffee

ECMWF Activities• 10:35 Marta Janiskova - Overview of CloudSat/CALIPSO activities at ECMWF• 10:50 Olaf Stiller - Estimating representativity errors• 11:05 Richard Forbes - ECMWF model cloud verification• 11:20 Maike Ahlgrimm - Lidar derived cloud fraction for model comparison

11:35-12:30 Discussion• Retrievals, forward models and error characteristics• Verification of models• Possibilities for collaboration

12:30 Lunch in the canteen

Recent Recent CloudSat/CALIPSO/EarthCARE-CloudSat/CALIPSO/EarthCARE-

related work at University of related work at University of ReadingReading• Forward models and model evaluation

– Lidar forward modelling to evaluate the ECMWF model from IceSAT

– Multiple scattering model for spaceborne radar and lidar (Hogan)

• Retrievals and model evaluation– LITE lidar estimates of supercooled water occurrence– Radar retrievals of liquid clouds (Lee Smith, Anthony Illingworth)– Variational radar-lidar-radiometer retrieval of ice clouds (Delanoe)

• ESA “CASPER” project (Clouds and Aerosol Synergy Products from EarthCARE Retrievals)– Defined the required cloud, aerosol and precipitation products– Developed variational ice cloud retrieval for EarthCARE that uses

the cloud radar, the “High Spectral Resolution Lidar” (HSRL; the same technology as ADM) and the infrared channels of the multispectral imager

Ongoing/future workOngoing/future work• Forward models and model evaluation

– Use the CloudSat simulator to evaluate the 90-km resolution HiGEM version of the Met Office climate model (Margaret Woodage)

– Use the CloudSat simulator to evaluate 1-km large-domain simulations of tropical clouds in “CASCADE” (Thorwald Stein)

• Retrievals and model evaluation– Ongoing comparisons with MO and ECMWF models (Smith & Delanoe)– Use of retrievals to evaluate the CASCADE model (Thorwald Stein)

• CloudSat, CALIPSO and EarthCARE algorithm development– Develop a “unified” retrieval algorithm for clouds, precipitation and

aerosols simultaneously using radar, lidar, infrared radiances and possibly microwave radiances (Nicola Pounder, Hogan, Delanoe)

• Science questions– What is the radiative impact of errors in model clouds? Use retrievals,

CERES observations and radiative transfer calcs. (Nicky Chalmers)– What is the distribution of supercooled water in the atmosphere and

why is it so difficult to model? (Andrew Barrett)

ECMWF clouds vs IceSAT using a lidar forward model

• Cloud observations from IceSAT 0.5-micron lidar (first data Feb 2004)

• Global coverage but lidar attenuated by thick clouds: direct model comparison difficult

Optically thick liquid cloud obscures view of any clouds beneath

• Solution: forward-model the measurements (including attenuation) using the ECMWF variables

Lidar apparent backscatter coefficient (m-1 sr-1)

Latitude

Wilkinson, Hogan, Illingworth and Benedetti (Monthly Weather Review 2008)

Simulate lidar backscatter:– Create subcolumns with max-rand

overlap– Forward-model lidar backscatter from

ECMWF water content & particle size– Remove signals below lidar sensitivity

ECMWF raw cloud fraction

ECMWF cloud fraction after processing

IceSAT cloud fraction

Global cloud fraction comparison

ECMWF raw cloud fraction ECMWF processed cloud fraction

IceSAT cloud fraction

• Results for October 2003– Tropical convection peaks too

high– Too much polar cloud– Elsewhere agreement is good

• Results can be ambiguous– An apparent low cloud

underestimate could be a real error, or could be due to high cloud above being too thick

Examples of multiple scattering• LITE lidar (<r, footprint~1 km)

CloudSat radar (>r)

StratocumulusStratocumulus

Intense thunderstormIntense thunderstorm

Surface echoSurface echoApparent echo from below the surface

Fast multiple scattering forward Fast multiple scattering forward modelmodel

CloudSat-like example

• New method uses the time-dependent two-stream approximation

• Agrees with Monte Carlo but ~107 times faster (~3 ms)

• Added to CloudSat simulator

Hogan and Battaglia (J. Atmos. Sci. 2008)

CALIPSO-like example

Combining radar and lidar…

Cloudsat radar

CALIPSO lidar

Preliminary target classificationInsectsAerosolRainSupercooled liquid cloudWarm liquid cloudIce and supercooled liquidIceClearNo ice/rain but possibly liquidGround

Radar and lidarRadar onlyLidar only

Global-mean cloud fraction

Radar misses a

significant amount of

ice

“Unified” retrieval framework

New ray of data: define state vector

Use classification to specify variables describing each species at each gate• Ice: extinction coefficient and N0*

• Liquid: liquid water content and number concentration• Rain: rain rate and mean drop diameter• Aerosol: extinction coefficient and particle size

Radar model

Including surface return and multiple scattering

Lidar model

Including HSRL channels and multiple scattering

Radiance model

Solar and IR channels

Compare to observations

Check for convergence

Gauss-Newton iteration

Derive a new state vector

Forward model

Not converged

Converged

Proceed to next ray of data

(Black) Ingredients already developed

(Delanoe and Hogan JGR 2008)

(Red) Ingredients remaining to be

developed

• Supercooled water layers have large radiative impact

• Poorly modelled

Hogan et al. (GRL 2004)

Mixed-phase clouds

LITE lidar showed more supercooled

water in SH than NH

Two independent methods from MODIS show the same thing

What does CALIPSO show?

What is the explanation?How can we

model mixed-phase clouds?

Discussion points• Is the intention to assimilate cloud radar and lidar directly?

– If so, are fast radar and lidar forward models of interest?

• If retrievals are to be assimilated, what variables are needed?• Do you need error covariances, averaging kernels and

information content? Straightforward to calculate, but:– Complicated to store (state vector is a different size for each

profile)– Increases the data volume by an order of magnitude

• What are best diagnostics for assessing model performance?– Means, PDFs, skill scores…

• ECMWF model variables are required by retrievals– What is the error of model temperature, pressure and humidity?

CloudSat simulator (Bodas et CloudSat simulator (Bodas et al)al)

• Simulated radar reflectivity from sub-grid model

• Simulated radar reflectivity averaged to model grid– How would this

look with high-res model?

• Observed CloudSat radar reflectivity

Example of mid-Pacific Example of mid-Pacific convectionconvection

CloudSat radar

CALIPSO lidar

MODIS 11 micron channel

Time since start of orbit (s)

Heig

ht

(km

)H

eig

ht

(km

)

Cirrus detected only by lidar

Mid-level liquid clouds

Deep convection penetrated only by radar

Retrieved extinction (m-1)

Supercooled water in models

• A year of data from the Met Office and ECMWF– Easy to calculate occurrence of supercooled water with > 0.7

Prognostic ice and liquid+vapour variables

Prognostic cloud water: ice/liquid diagnosed from temperature