Active synergistic observations for improving our knowledge on - PowerPoint PPT Presentation

Active synergistic observations for improving our knowledge on clouds Julien Delanoë*, Quitterie Cazenave* + , Silke Gross + , Jacques Pelon*, Florian Ewald + , Abdenour Irbah*, Oliver Reitebuch + , Robin Hogan e … And instrumental teams *LATMOS / + DLR / e ECMWF-U. Reading

Clouds are funny/not well known objects • Better understand cloud/precipitation processes • Microphysical • Dynamical properties • Radiative • Role of clouds in climate and water and radiative budgets • Better constrain Climate and Forecast models: • Through processes analyses • Parameterisations Our answer… Airborne and spaceborne radar-lidar

Outline • Why radar-lidar synergy for clouds ? • How do we convert radar-lidar measurements into cloud properties? • Example of radar-lidar platforms? • Spaceborne • Airborne • Conclusion

Why radar-lidar synergy for clouds?

Optical and microwave… In-situ: good description but… very local Clouds Ice crystals Remote sensing instruments => sample a volume remotely Super-cooled droplets aggregates Interaction depends on the Precipitation wavelength and the size/density of Snow flake the hydrometeors Rain drops Cloud radar 95 GHz (3.2 mm) / Lidar (355nm – 1064 nm) Nature, size and distribution, density and shape…

Optical and microwave… In-situ: good description but… very local Clouds Ice crystals Remote sensing instruments => sample a volume remotely Super-cooled droplets aggregates Interaction depends on the Precipitation wavelength and the size/density of Snow flake the hydrometeors Rain drops Cloud radar 95 GHz (3.2 mm) / Lidar (355nm – 1064 nm) Nature, size and distribution, density and shape… Lidar (355nm – 1064 nm) Cloud radar 95 GHz (3.2 mm) Radar more sensitive to size Lidar more sensitive to concentration λ 4 α = 2.10 3 N ( D ) A ( D ) 10 18 N(D) ∫ ∫ Z = σ bsc ( λ ,D, ρ )dD dD A(D) represents the projected 2 cross sectional area Z = 10 18 N(D) π 5 ∫ D 6 dD K w Assuming no attenuation ⎡ ⎤ β ( r ) = α ( r ) r Assuming no multiple scattering ∫ S ( r ) exp − 2 α ( r ') dr ' Rayleigh approximation ⎢ ⎥ σ bsc (D, λ, ρ) scattering coefficients ⎣ ⎦ (Mie,1908) or T-matrix… 0

Optical / microwave duet EXAMPLE CALIOP lidar Cloud radar (95GHz) Lidar (355 -532-1064nm) CALIOP EXAMPLE CloudSat radar CloudSat CloudSat CALIPSO 2006- today: A revolution for vertical cloud/aerosol studies! 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

Optical / microwave duet EXAMPLE CALIOP lidar Cloud radar (95GHz) Lidar (355 -532-1064nm) CALIOP Only lidar EXAMPLE CloudSat radar CloudSat CloudSat CALIPSO 2006- today: A revolution for vertical cloud/aerosol studies! 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

Optical / microwave duet EXAMPLE CALIOP lidar Cloud radar (95GHz) Lidar (355 -532-1064nm) CALIOP Only lidar Radar and lidar EXAMPLE CloudSat radar CloudSat CloudSat CALIPSO 2006- today: A revolution for vertical cloud/aerosol studies! 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

Optical / microwave duet EXAMPLE CALIOP lidar Cloud radar (95GHz) Lidar (355 -532-1064nm) CALIOP Only lidar Radar and lidar Only radar EXAMPLE CloudSat radar CloudSat CloudSat CALIPSO 2006- today: A revolution for vertical cloud/aerosol studies! 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

Why two are better than one? radar-lidar categorisation: Ceccaldi et al. 2013 Stein et al. (2011): CALIOP Cold cloud Supercooled water layers CloudSat In 2008, Cloud in the subzero troposphere: ž Radar (CloudSat) 68.4% Model temperature (ECMWF) => Ice / Liquid water ž Lidar (CALIPSO) 62.6% of tropospheric ice Different response of radar and lidar in presence of supercooled cloud liquid water: ž 31.0% observed by both the radar and the -Very strong lidar signal lidar -Very weak radar signal Within a 300m cloud layer

How can we convert measurements into ice cloud properties? We know the observations (instrument measurements) and we would like to know CALIOP cloud properties : α, IWC, re… everywhere we have a cloud : lidar, radar, radar+lidar areas Possible to add external constraints (radiances... other wavelengths) => Variational approach ! ~ # $ % % & '% ( ~ # $ % % ) '% Radar and lidar Only lidar Only radar CloudSat 2 independent measurements (radar+lidar) If one measurement is missing => a priori information

Radar-lidar ice cloud retrieval method Variational scheme: New ray of data: define state vector Varcloud Delanoë and Hogan Use classification to specify variables describing ice cloud JGR,2008-2010 at each gate: extinction coefficient and N 0 * Radar model Lidar model Radiance model Including multiple IR channels Forward model scattering (Hogan 2006) Gauss-Newton iteration Compare to observations: Not converged Derive a new state vector with an a-priori and measurement errors as a constraint Check for convergence Converged Proceed to next ray of data

DARDAR-product example Radar-lidar example CALIPSO lidar ice water CloudSat radar Pacific Ocean /2006-9-22

DARDAR-product example Radar-lidar example CALIPSO lidar ice water Forward modelled lidar CloudSat radar Forward modelled radar Pacific Ocean /2006-9-22

DARDAR Radar-lidar example CALIPSO lidar ice water Visible extinction Forward modelled lidar Ice water content CloudSat radar Effective radius Forward modelled radar — MODIS radiance 10.8um Pacific Ocean — Forward modelled radiance /2006-9-22

Radar-lidar platforms Climate and global forecast models: Validation (scores or direct comparison) Parameterisations High resolution models Validation (scores or direct comparison) Cloud processes: High resolution cloud properties Global radiative forcing: Cloud properties at large scale Spaceborne measurements + Global coverage + Long term series + Observation from top - Limited payload - Diurnal cycle

Radar-lidar platforms Climate and global forecast models: Validation (scores or direct comparison) Airborne measurements Parameterisations + Very detailed High resolution models + Satellite cal/val + Large payload (including in-situ) Validation (scores or direct comparison) + Observation from top - Time x space coverage Cloud processes: High resolution cloud properties Cal/Val Global radiative forcing: Cloud properties at large scale Spaceborne measurements + Global coverage + Long term series + Observation from top - Limited payload - Diurnal cycle

Radar-lidar platforms Climate and global forecast models: Validation (scores or direct comparison) Airborne measurements Parameterisations + Very detailed High resolution models + Satellite cal/val + Large payload (including in-situ) Validation (scores or direct comparison) + Observation from top - Time x space coverage Cloud processes: High resolution cloud properties Cal/Val Global radiative forcing: Cloud properties at large scale Spaceborne measurements + Global coverage Ground-based measurements + Long term series + Observation from top + Long-term series Cal/Val - Limited payload + Large payload - Diurnal cycle - Only one location - Bottom up sight

Spaceborne platforms Slicing Clouds from space : piece of cake 2006 2020 A-Train EarthCare . The Earth C louds, A erosol and R adiation E xplorer (EarthCARE) (Illingworth et al. 2015) (ESA/JAXA) One satellite but : Train of satellites (CloudSat/CALIPSO/MODIS, high spectral resolution lidar at 355nm and a Doppler cloud NASA/CNES) –( Stephens et al. 2002, Winker et al. radar => first time in space 2010)

European airborne platforms (synergy) F20 SAFIRE HALO Aircraft : Aircraft : • Lidar pointing direction • Modified Gulfstream G550 business • Dassault Falcon 20 • Radar pointing directions jet • Endurance: 3.5 flight hours • Endurance: > 10 flight hours • Maximum cruising altitude: 13 km • Maximum cruising altitude: > 15 km Payload : Payload : • LNG High spectral resolution lidar (355 nm), • WALES High spectral resolution lidar 532 and 1064 nm (532 nm) and water vapor DIAL Z, V d • RASTA Doppler cloud Radar (95 GHz) – up β,vr • MIRA Doppler cloud Radar (35 GHz) to 6 antennas • Hyper-spectral radiometer • IR radiometer CLIMAT (brightness temperature 8-10-12 micron) • Microwave package • L/S fluxes • Dropsonde launching (profiles of T, p, hum, u, v) • Dropsonde launching (profiles of T, p, hum, u, v) http://rali.projet.latmos.ipsl.fr