Radar/lidar observations of boundary layer clouds

Slides:



Advertisements
Similar presentations
Robin Hogan, Julien Delanoe and Nicola Pounder University of Reading Towards unified retrievals of clouds, precipitation and aerosols.
Advertisements

Lidar observations of mixed-phase clouds Robin Hogan, Anthony Illingworth, Ewan OConnor & Mukunda Dev Behera University of Reading UK Overview Enhanced.
Ewan OConnor, Anthony Illingworth, Robin Hogan and the Cloudnet team Cloudnet.
Ewan OConnor, Robin Hogan, Anthony Illingworth Drizzle comparisons.
Ewan OConnor, Robin Hogan, Anthony Illingworth, Nicolas Gaussiat Liquid water path from microwave radiometers.
Proposed new uses for the Ceilometer Network
Ewan OConnor, Robin Hogan, Anthony Illingworth, Nicolas Gaussiat Radar/lidar observations of boundary layer clouds.
1 Drizzle rates inferred from CloudSat & CALIPSO compared to their representation in the operational Met Office and ECMWF forecast models. Lee Hawkness-Smith.
Convection Initiative discussion points What info do parametrizations & 1.5-km forecasts need? –Initiation mechanism, time-resolved cell size & updraft.
Anthony Illingworth, + Robin Hogan, Ewan OConnor, U of Reading, UK and the CloudNET team (F, D, NL, S, Su). Reading: 19 Feb 08 – Meeting with Met office.
Robin Hogan, Julien Delanoë, Nicky Chalmers, Thorwald Stein, Anthony Illingworth University of Reading Evaluating and improving the representation of clouds.
Robin Hogan Julien Delanoë Nicola Pounder Chris Westbrook
Robin Hogan Anthony Illingworth Ewan OConnor Nicolas Gaussiat Malcolm Brooks University of Reading Cloudnet products available from Chilbolton.
DYMECS: Dynamical and Microphysical Evolution of Convective Storms (NERC Standard Grant) University of Reading: Robin Hogan, Bob Plant, Thorwald Stein,
Robin Hogan Department of Meteorology University of Reading Cloud and Climate Studies using the Chilbolton Observatory.
Robin Hogan, Richard Allan, Nicky Chalmers, Thorwald Stein, Julien Delanoë University of Reading How accurate are the radiative properties of ice clouds.
Use of ground-based radar and lidar to evaluate model clouds
Robin Hogan Ewan OConnor Changes to the Instrument Synergy/ Target Categorization product.
Robin Hogan & Anthony Illingworth Department of Meteorology University of Reading UK Parameterizing ice cloud inhomogeneity and the overlap of inhomogeneities.
Robin Hogan Ewan OConnor Anthony Illingworth Department of Meteorology, University of Reading UK PDFs of humidity and cloud water content from Raman lidar.
Integrated lidar backscatter: Quantifying the occurrence of supercooled water and specular reflection Robin Hogan and Anthony Illingworth Enhanced algorithm.
Robin Hogan Ewan OConnor Damian Wilson Malcolm Brooks Evaluation statistics of cloud fraction and water content.
Robin Hogan Julien Delanoe University of Reading Remote sensing of ice clouds from space.
Robin Hogan Ewan OConnor Anthony Illingworth Nicolas Gaussiat Malcolm Brooks Cloudnet Evaluating the clouds in European forecast models.
DYMECS: Dynamical and Microphysical Evolution of Convective Storms (NERC Standard Grant) University of Reading: Robin Hogan, Bob Plant, Thorwald Stein,
Integrated Profiling at the AMF
Cloud Radar in Space: CloudSat While TRMM has been a successful precipitation radar, its dBZ minimum detectable signal does not allow views of light.
Application of Cloudnet data in the validation of SCIAMACHY cloud height products Ping Wang Piet Stammes KNMI, De Bilt, The Netherlands CESAR Science day,
Exploiting multiple scattering in CALIPSO measurements to retrieve liquid cloud properties Nicola Pounder, Robin Hogan, Lee Hawkness-Smith, Andrew Barrett.
Nicolas Gaussiat and Robin Hogan Progress meeting 4 – Toulouse – Oct 2003 Dual wavelength retrieval of LWC and IWC at Chilbolton.
TRMM Tropical Rainfall Measurement (Mission). Why TRMM? n Tropical Rainfall Measuring Mission (TRMM) is a joint US-Japan study initiated in 1997 to study.
Wesley Berg, Tristan L’Ecuyer, and Sue van den Heever Department of Atmospheric Science Colorado State University Evaluating the impact of aerosols on.
Matthew Shupe Ola Persson Paul Johnston Cassie Wheeler Michael Tjernstrom Surface-Based Remote-Sensing of Clouds during ASCOS Univ of Colorado, NOAA and.
1. The problem of mixed-phase clouds All models except DWD underestimate mid-level cloud –Some have separate “radiatively inactive” snow (ECMWF, DWD) –Met.
Aerosol and Cloud Microphysics Working Group Dietrich Althausen, Andrea Riede, Herman Russchenberg, Aldo Amodeo, Susanne Crewell, Paolo Di Girolamo, Stephen.
Atmospheric structure from lidar and radar Jens Bösenberg 1.Motivation 2.Layer structure 3.Water vapour profiling 4.Turbulence structure 5.Cloud profiling.
Remote sensing of Stratocumulus using radar/lidar synergy Ewan O’Connor, Anthony Illingworth & Robin Hogan University of Reading.
Lee Smith Anthony Illingworth
Ewan O’Connor Anthony Illingworth Comparison of observed cloud properties at the AMF COPS site with NWP models.
Observed and modelled long-term water cloud statistics for the Murg Valley Kerstin Ebell, Susanne Crewell, Ulrich Löhnert Institute for Geophysics and.
Remote-sensing of the environment (RSE) ATMOS Analysis of the Composition of Clouds with Extended Polarization Techniques L. Pfitzenmaier, H. Russchenbergs.
Characterization of Arctic Mixed-Phase Cloudy Boundary Layers with the Adiabatic Assumption Paquita Zuidema*, Janet Intrieri, Sergey Matrosov, Matthew.
SIRTA Site Instrumental de Recherche par Télédétection Atmosphérique Martial Haeffelin SIRTA Coordinator CLOUDNET Meeting, Paris May 2002.
Matthew Shupe, Ola Persson, Amy Solomon CIRES – Univ. of Colorado & NOAA/ESRL David Turner NOAA/NSSL Dynamical and Microphysical Characteristics and Interactions.
DYMECS: Dynamical and Microphysical Evolution of Convective Storms (NERC Standard Grant) University of Reading: Robin Hogan, Bob Plant, Thorwald Stein,
The three-dimensional structure of convective storms Robin Hogan John Nicol Robert Plant Peter Clark Kirsty Hanley Carol Halliwell Humphrey Lean Thorwald.
Anthony Illingworth, Robin Hogan, Ewan O’Connor, U of Reading, UK Nicolas Gaussiat Damian Wilson, Malcolm Brooks Met Office, UK Dominique Bouniol, Alain.
RICO Modeling Studies Group interests RICO data in support of studies.
Water cloud retrievals O. A. Krasnov and H. W. J. Russchenberg International Research Centre for Telecommunications-transmission and Radar, Faculty of.
Robin Hogan Ewan O’Connor The Instrument Synergy/ Target Categorization product.
Matthew Shupe Ola Persson Paul Johnston Duane Hazen Clouds during ASCOS U. of Colorado and NOAA.
Radiometer Physics GmbH
1 Atmospheric profiling to better understand fog and low level cloud life cycle ARM/EU workshop on algorithms, May 2013 J. Delanoe (LATMOS), JC.
KNMI 35 GHz Cloud Radar & Cloud Classification* Henk Klein Baltink * Robin Hogan (Univ. of Reading, UK)
Towards a Characterization of Arctic Mixed-Phase Clouds Matthew D. Shupe a, Pavlos Kollias b, Ed Luke b a Cooperative Institute for Research in Environmental.
Modelling and observations of droplet growth in clouds A Coals 1, A M Blyth 1, J-L Brenguier 2, A M Gadian 1 and W W Grabowski 3 Understanding the detailed.
JAPAN’s GV Strategy and Plans for GPM
Nicolas Gaussiat, Anthony Illingworth and Robin Hogan Beeskow, 12 Oct 2005 Liquid Water Path from radiometers and lidar.
UNIVERSITY OF BASILICATA CNR-IMAA (Consiglio Nazionale delle Ricerche Istituto di Metodologie per l’Analisi Ambientale) Tito Scalo (PZ) Analysis and interpretation.
Emma Hopkin University of Reading
The three-dimensional structure of convective storms
Understanding warm rain formation using CloudSat and the A-Train
Group interests RICO data required
RadOn : Retrieval of microphysical and radiative properties of ice clouds from Doppler cloud radar observations J. Delanoë and A. Protat IPSL / CETP.
Radiometer Physics GmbH
Radar-lidar synergy for the retrieval of water cloud parameters
Cloud liquid water and ice content by multi-wavelength radar
M. De Graaf1,2, K. Sarna2, J. Brown3, E. Tenner2, M. Schenkels4, and D
Group interests RICO data in support of studies
Radiometer retrievals of LWP
Presentation transcript:

Radar/lidar observations of boundary layer clouds Ewan O’Connor, Robin Hogan, Anthony Illingworth, Nicolas Gaussiat

Overview Radar and lidar can measure boundary layer clouds at high resolution: Cloud boundaries - radar and lidar LWP – microwave radiometer LWC – cloud boundaries and LWP Cloudnet – compare forecast models and observations 3 remote-sensing sites (currently), 6 models (currently) Cloud fraction, liquid water content statistics Microphysical profiles: Water vapour mixing ratio - Raman lidar LWC - dual-wavelength radar Drizzle properties - Doppler radar and lidar Drop concentration and size – radar and lidar

Vertically pointing radar and lidar Radar: Z~D6 Sensitive to larger particles (drizzle, rain) Lidar: b~D2 Sensitive to small particles (droplets, aerosol)

Statistics - liquid water clouds 2 year database Use lidar to detect liquid cloud base Low liquid water clouds present 23% of the time (above 400 m) Summer: 25% Winter: 20% Use radar to determine presence of “drizzle” 46% of clouds detected by lidar contain occasional large droplets Summer: 42% Winter: 52 %

Dual wavelength microwave radiometer Brightness temperatures -> Liquid water path Improved technique – Nicolas Gaussiat Use lidar to determine whether clear sky or not Adjust coefficients to account for instrument drift Removes offset for low LWP LWP - initial LWP - lidar corrected

LWC - Scaled adiabatic method Use lidar/radar to determine cloud boundaries Use model to estimate adiabatic gradient of lwc Scale adiabatic lwc profile to match lwp from radiometers http://www.met.rdg.ac.uk/radar/cloudnet/quicklooks/

Compare measured lwp to adiabatic lwp obtain ‘dilution coefficient’ Dilution coefficient versus depth of cloud

Stratocumulus liquid water content Problem of using radar to infer liquid water content: Very different moments of a bimodal size distribution: LWC dominated by ~10 m cloud droplets Radar reflectivity often dominated by drizzle drops ~200 mm An alternative is to use dual-frequency radar Radar attenuation proportional to LWC, increases with frequency Therefore rate of change with height of the difference in 35-GHz and 94-GHz yields LWC with no size assumptions necessary Each 1 dB difference corresponds to an LWP of ~120 g m-2 Can be difficult to implement in practice Need very precise Z measurements Typically several minutes of averaging is required Need linear response throughout dynamic range of both radars

Drizzle below cloud Doppler radar and lidar - 4 observables (O’Connor et al. 2005) Radar/lidar ratio provides information on particle size

Drizzle below cloud Retrieve three components of drizzle DSD (N, D, μ). Can then calculate LWC, LWF and vertical air velocity, w.

Drizzle below cloud Typical cell size is about 2-3 km Updrafts correlate well with liquid water flux

Profiles of lwc – no drizzle Examine radar/lidar profiles - retrieve LWC, N, D

Profiles of lwc – no drizzle 260 cm-3 90 cm-3 80 cm-3 Consistency shown between LWP estimates.

Profiles of lwc – no drizzle Cloud droplet sizes <12μm no drizzle present Cloud droplet sizes 18 μm drizzle present Agrees with Tripoli & Cotton (1980) critical size threshold

Conclusion Relevant Sc properties can be measured using remote sensing; Ideally utilise radar, lidar and microwave radiometer measurements together. Cloudnet project provides yearly/monthly statistics for cloud fraction and liquid water content including comparisons between observations and models. Soon - number concentration and size, drizzle properties. Humidity structure, turbulence. Satellite measurements A-Train (Cloudsat + Calipso + Aqua) EarthCARE IceSat

Importance of Stratocumulus Most common cloud type globally Global coverage 26% Ocean 34% Land 18% Average net radiative effect is about –65 W m-2 Cooling effect on climate Mean annual low cloud amount – ISCCP

Cloud Parameters Use radar and lidar to provide vertical profiles of: Cloud droplet size distribution (N, mean D, broad/narrow) Drizzle droplet size distribution (N, mean D, broad/narrow) Relate drizzle to cloud N Is stratocumulus adiabatic? Entrainment rates

Data

Drizzle-free stratocumulus Z = ND6 & LWC  ND3  Z  LWC2/N Assume adiabatic ascent and constant N LWC increases linearly with height (z) If we know T and p  dLWC /dz Assume dLWC /dz is a constant, a  LWC(z) = az Z(z)  (az)2 / N Adiabatic profile: Z should vary as z2

Aircraft data - ACE 2 Brenguier et al. (2000) 1005 UTC 1545 UTC Reflectivity profiles

Refined technique Nad Allow dilution from adiabatic profile of LWC LWC(z) = k LWCad(z) N = k Nad D(z) = Dad(z) Z(z)  k (az)2 / Nad Nad

Plots of N High N, small D  low Z Nad = 264 cm-3

Plots of N Nad = 91 cm-3

Plots of N Nad = 82 cm-3

Presence of drizzle can lead to an overestimate of N  an overestimate of LWC (and LWP)

Conclusion Consistency shown between LWP estimates from this technique, and from microwave radiometers. Additional techniques to investigate Sc are also available: Doppler radar/lidar – Drizzle properties (O’Connor et al. 2004) Dual wavelength radar – LWC profile (Gaussiat et al.) Doppler spectra Raman humidity measurements – WV structure, mixed layer depths Aircraft verification? CloudNet – 3 years, 3 sites, provide climatology of Sc properties

Dual wavelength microwave radiometer Brightness temperatures -> Liquid water path Improved technique – Nicolas Gaussiat Use lidar to determine whether clear sky or not Adjust coefficients to account for instrument drift Removes offset for low LWP LWP - initial LWP - lidar corrected

LWC - Scaled adiabatic method Use lidar/radar to determine cloud boundaries Use model to estimate adiabatic gradient of lwc Scale adiabatic lwc profile to match lwp from radiometers http://www.met.rdg.ac.uk/radar/cloudnet/quicklooks/

Compare measured lwp to adiabatic lwp obtain ‘dilution coefficient’ Dilution coefficient versus depth of cloud

Stratocumulus liquid water content Problem of using radar to infer liquid water content: Very different moments of a bimodal size distribution: LWC dominated by ~10 m cloud droplets Radar reflectivity often dominated by drizzle drops ~200 mm An alternative is to use dual-frequency radar Radar attenuation proportional to LWC, increases with frequency Therefore rate of change with height of the difference in 35-GHz and 94-GHz yields LWC with no size assumptions necessary Each 1 dB difference corresponds to an LWP of ~120 g m-2 Can be difficult to implement in practice Need very precise Z measurements Typically several minutes of averaging is required Need linear response throughout dynamic range of both radars

Drizzle below cloud Doppler radar and lidar - 4 observables (O’Connor et al. 2005) Radar/lidar ratio provides information on particle size

Drizzle below cloud Retrieve three components of drizzle DSD (N, D, μ). Can then calculate LWC, LWF and vertical air velocity, w.

Drizzle below cloud Typical cell size is about 2-3 km Updrafts correlate well with liquid water flux

Profiles of lwc – no drizzle Examine radar/lidar profiles - retrieve LWC, N, D

Profiles of lwc – no drizzle 260 cm-3 90 cm-3 80 cm-3 Consistency shown between LWP estimates.

Profiles of lwc – no drizzle Cloud droplet sizes <12μm no drizzle present Cloud droplet sizes 18 μm drizzle present Agrees with Tripoli & Cotton (1980) critical size threshold