Active synergistic observations for improving our knowledge on - - PowerPoint PPT Presentation
Active synergistic observations for improving our knowledge on clouds Julien Delano*, Quitterie Cazenave* + , Silke Gross + , Jacques Pelon*, Florian Ewald + , Abdenour Irbah*, Oliver Reitebuch + , Robin Hogan e And instrumental teams
Julien Delanoë*, Quitterie Cazenave*+, Silke Gross+, Jacques Pelon*, Florian Ewald+, Abdenour Irbah*, Oliver Reitebuch+, Robin Hogane…
And instrumental teams *LATMOS / + DLR / e ECMWF-U. Reading
Clouds Ice crystals Super-cooled droplets aggregates Precipitation Snow flake Rain drops Nature, size and distribution, density and shape… In-situ: good description but… very local Remote sensing instruments => sample a volume remotely Interaction depends on the wavelength and the size/density of the hydrometeors
Cloud radar 95 GHz (3.2 mm) / Lidar (355nm – 1064 nm)
Clouds Ice crystals Super-cooled droplets aggregates Precipitation Snow flake Rain drops Nature, size and distribution, density and shape… In-situ: good description but… very local Remote sensing instruments => sample a volume remotely Interaction depends on the wavelength and the size/density of the hydrometeors
Z = λ4 2 w K π5 1018 N(D)
∫
σbsc(λ,D,ρ)dD
Z = 1018 N(D)
∫
D6dD
σbsc(D, λ, ρ) scattering coefficients
(Mie,1908) or T-matrix… Rayleigh approximation Assuming no attenuation
α = 2.103 N(D)A(D)
∫
dD
A(D) represents the projected cross sectional area
β(r) = α(r) S(r) exp −2 α(r')dr'
r
∫
⎡ ⎣ ⎢ ⎤ ⎦ ⎥
Assuming no multiple scattering
Cloud radar 95 GHz (3.2 mm) / Lidar (355nm – 1064 nm)
Cloud radar 95 GHz (3.2 mm) Lidar (355nm – 1064 nm)
Radar more sensitive to size Lidar more sensitive to concentration
Cloud radar (95GHz) Lidar (355 -532-1064nm) CloudSat CALIPSO 2006- today: A revolution for vertical cloud/aerosol studies! CALIOP CloudSat EXAMPLE CALIOP lidar EXAMPLE CloudSat radar Radar more sensitive to ice (large particles) Only attenuated in liquid cloud/rain + Can penetrate thick ice clouds Lidar more sensitive than radar but attenuated in ice cloud, extinguished in liquid
Cloud radar (95GHz) Lidar (355 -532-1064nm) CloudSat CALIPSO 2006- today: A revolution for vertical cloud/aerosol studies! Only lidar CALIOP CloudSat EXAMPLE CALIOP lidar EXAMPLE CloudSat radar Radar more sensitive to ice (large particles) Only attenuated in liquid cloud/rain + Can penetrate thick ice clouds Lidar more sensitive than radar but attenuated in ice cloud, extinguished in liquid
Cloud radar (95GHz) Lidar (355 -532-1064nm) CloudSat CALIPSO 2006- today: A revolution for vertical cloud/aerosol studies! Radar and lidar Only lidar CALIOP CloudSat EXAMPLE CALIOP lidar EXAMPLE CloudSat radar Radar more sensitive to ice (large particles) Only attenuated in liquid cloud/rain + Can penetrate thick ice clouds Lidar more sensitive than radar but attenuated in ice cloud, extinguished in liquid
Cloud radar (95GHz) Lidar (355 -532-1064nm) CloudSat CALIPSO 2006- today: A revolution for vertical cloud/aerosol studies! Radar and lidar Only lidar Only radar CALIOP CloudSat EXAMPLE CALIOP lidar EXAMPLE CloudSat radar Radar more sensitive to ice (large particles) Only attenuated in liquid cloud/rain + Can penetrate thick ice clouds Lidar more sensitive than radar but attenuated in ice cloud, extinguished in liquid
In 2008, Cloud in the subzero troposphere:
Radar (CloudSat) 68.4% Lidar (CALIPSO) 62.6% of tropospheric ice
cloud
31.0% observed by both the radar and the
lidar
Stein et al. (2011): Supercooled water layers Model temperature (ECMWF) => Ice / Liquid water Different response of radar and lidar in presence of supercooled liquid water:
Within a 300m cloud layer Cold cloud CALIOP CloudSat radar-lidar categorisation: Ceccaldi et al. 2013
Radar and lidar Only lidar Only radar CALIOP CloudSat
We know the observations (instrument measurements) and we would like to know cloud properties : α, IWC, re… everywhere we have a cloud : lidar, radar, radar+lidar areas Possible to add external constraints (radiances...
! ~ # $ % %&'% ( ~ # $ % %)'% 2 independent measurements (radar+lidar) If one measurement is missing => a priori information
New ray of data: define state vector Use classification to specify variables describing ice cloud at each gate: extinction coefficient and N0* Radar model Lidar model Including multiple scattering (Hogan 2006) Radiance model IR channels Compare to observations: with an a-priori and measurement errors as a constraint Check for convergence Gauss-Newton iteration Derive a new state vector
Forward model Not converged Converged
Proceed to next ray of data Delanoë and Hogan JGR,2008-2010
Variational scheme:
Varcloud
Radar-lidar example CALIPSO lidar CloudSat radar
ice water Pacific Ocean /2006-9-22
Radar-lidar example CALIPSO lidar CloudSat radar Forward modelled lidar Forward modelled radar
ice water Pacific Ocean /2006-9-22
Radar-lidar example CALIPSO lidar CloudSat radar Visible extinction Ice water content Effective radius
— MODIS radiance 10.8um — Forward modelled radiance
Forward modelled lidar Forward modelled radar
ice water Pacific Ocean /2006-9-22
Climate and global forecast models:
Validation (scores or direct comparison) Parameterisations
Cloud processes:
High resolution cloud properties
Spaceborne measurements
+ Global coverage + Long term series + Observation from top
Global radiative forcing:
Cloud properties at large scale
High resolution models
Validation (scores or direct comparison)
Climate and global forecast models:
Validation (scores or direct comparison) Parameterisations
Cloud processes:
High resolution cloud properties
Airborne measurements
+ Very detailed + Satellite cal/val + Large payload (including in-situ) + Observation from top
Spaceborne measurements
+ Global coverage + Long term series + Observation from top
Global radiative forcing:
Cloud properties at large scale
Cal/Val
High resolution models
Validation (scores or direct comparison)
Climate and global forecast models:
Validation (scores or direct comparison) Parameterisations
Cloud processes:
High resolution cloud properties
Airborne measurements
+ Very detailed + Satellite cal/val + Large payload (including in-situ) + Observation from top
Spaceborne measurements
+ Global coverage + Long term series + Observation from top
Ground-based measurements
+ Long-term series + Large payload
Global radiative forcing:
Cloud properties at large scale
Cal/Val
High resolution models
Validation (scores or direct comparison)
Cal/Val
The Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) (Illingworth et al. 2015) (ESA/JAXA) One satellite but : high spectral resolution lidar at 355nm and a Doppler cloud radar => first time in space 2006
Train of satellites (CloudSat/CALIPSO/MODIS, NASA/CNES) –(Stephens et al. 2002, Winker et al. 2010)
.
2020
F20 SAFIRE HALO
Aircraft:
Payload:
532 and 1064 nm
to 6 antennas
temperature 8-10-12 micron)
u, v)
Aircraft:
jet
Payload:
(532 nm) and water vapor DIAL
p, hum, u, v) Z, Vd
β,vr
http://rali.projet.latmos.ipsl.fr
F20 SAFIRE HALO The use of combined active and passive remote sensing payload
EarthCARE (Grob et al.)
Aircraft:
Payload:
532 and 1064 nm
to 6 antennas
temperature 8-10-12 micron)
u, v)
Aircraft:
jet
Payload:
(532 nm) and water vapor DIAL
p, hum, u, v)
Horizontal cloud wind direction Horizontal cloud wind speed Vertical cloud wind IWC from radar-lidar retrieval Extinction from radar-lidar retrieval Re from radar-lidar retrieval EPATAN-NAWDEX Oct 2016
Ice crystal aggregation
RALI French airborne platform (POLARCAT 2008)
Delanoë et al. 2013
HSR-Lidar (WALES) 532 nm Cloud Radar (MIRA) 35 GHz Hyper-Spectral Imager 400 – 2500 nm (specMACS) WALES MIRA
Ewald et. al. (2018), in prep.
Can we validate the lidar+radar retrieval using additional remote sensing measurements?
VarCloud
WALES MIRA specMACS
HSR-Lidar (WALES) 532 nm Cloud Radar (MIRA) 35 GHz WALES MIRA
Ewald et. al. (2018), in prep.
Can we validate the lidar+radar retrieval using additional remote sensing measurements? VARCLOUD retrieval Hyper-Spectral Imager 400 – 2500 nm (specMACS)
HSR-Lidar (WALES) 532 nm Cloud Radar (MIRA) 35 GHz
Ewald et. al. (2018), in prep.
Can we validate the lidar+radar retrieval using additional remote sensing measurements? Hyper-Spectral Imager 400 – 2500 nm (specMACS)
Retrieved cloud microphysics Simulated cloud reflectance libRadtran
VarCloud
Ewald et. al. (2018), in prep.
WALES MIRA specMACS Solar radiance Ice water content Effective radius Forward simulated solar radiances with lidar+radar retrieved microphysics compare mostly well with measured solar radiances!
WALES MIRA