Blind tests of radar/lidar retrievals: Assessment of errors in terms of radiative flux profiles Malcolm Brooks Robin Hogan and Anthony Illingworth David.

Slides:



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

Robin Hogan, Chris Westbrook University of Reading Lin Tian NASA Goddard Space Flight Center Phil Brown Met Office Why it is important that ice particles.
Lidar observations of mixed-phase clouds Robin Hogan, Anthony Illingworth, Ewan OConnor & Mukunda Dev Behera University of Reading UK Overview Enhanced.
Quantifying sub-grid cloud structure and representing it GCMs
Robin Hogan, Chris Westbrook University of Reading, UK Alessandro Battaglia University of Leicester, UK Fast forward modelling of radar and lidar depolarization.
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
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.
Radar/lidar observations of boundary layer clouds
Robin Hogan, Julien Delanoë, Nicky Chalmers, Thorwald Stein, Anthony Illingworth University of Reading Evaluating and improving the representation of clouds.
Robin Hogan & Julien Delanoe
Robin Hogan, Malcolm Brooks, Anthony Illingworth
Joint ECMWF-University meeting on interpreting data from spaceborne radar and lidar: AGENDA 09:30 Introduction University of Reading activities 09:35 Robin.
Robin Hogan Anthony Illingworth Ewan OConnor Nicolas Gaussiat Malcolm Brooks University of Reading Cloudnet products available from Chilbolton.
Towards “unified” retrievals of cloud, precipitation and aerosol from combined radar, lidar and radiometer observations Robin Hogan, Julien Delanoë, Nicola.
Robin Hogan, Richard Allan, Nicky Chalmers, Thorwald Stein, Julien Delanoë University of Reading How accurate are the radiative properties of ice clouds.
Robin Hogan, Chris Westbrook University of Reading Lin Tian NASA Goddard Space Flight Center Phil Brown Met Office The importance of ice particle shape.
Use of ground-based radar and lidar to evaluate model clouds
Robin Hogan & Anthony Illingworth Department of Meteorology University of Reading UK Parameterizing ice cloud inhomogeneity and the overlap of inhomogeneities.
Robin Hogan (with input from Anthony Illingworth, Keith Shine, Tony Slingo and Richard Allan) Clouds and climate.
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 Julien Delanoe University of Reading Remote sensing of ice clouds from space.
Department of Meteorology, University of Reading, UK
Robin Hogan Ewan OConnor Anthony Illingworth Nicolas Gaussiat Malcolm Brooks Cloudnet Evaluating the clouds in European forecast models.
Modelling radar and lidar multiple scattering Modelling radar and lidar multiple scattering Robin Hogan The CloudSat radar and the Calipso lidar were launched.
Robin Hogan and Jon Shonk Implementation of multiple regions in Edwards-Slingo.
A thermodynamic model for estimating sea and lake ice thickness with optical satellite data Student presentation for GGS656 Sanmei Li April 17, 2012.
Exploiting multiple scattering in CALIPSO measurements to retrieve liquid cloud properties Nicola Pounder, Robin Hogan, Lee Hawkness-Smith, Andrew Barrett.
Review of model/obs/products. Models Fluxes –ECMWF fluxes will added v soon (Openshaw) UKMO Global model data –In testing –Damian to provide more data.
Shortwave Radiation Options in the WRF Model
Calibration Scenarios for PICASSO-CENA J. A. REAGAN, X. WANG, H. FANG University of Arizona, ECE Dept., Bldg. 104, Tucson, AZ MARY T. OSBORN SAIC,
Analysis of ARM cirrus data and the incorporation of Doppler Fall-velocity measurements in lidar/radar retrievals Outline of lidar/radar procedure. Problem.
Equation for the microwave backscatter cross section of aggregate snowflakes using the Self-Similar Rayleigh- Gans Approximation Robin Hogan ECMWF and.
EarthCARE: The next step forward in global measurements of clouds, aerosols, precipitation & radiation Robin Hogan ECMWF & University of Reading With input.
ESA Explorer mission EarthCARE: Earth Clouds, Aerosols and Radiation Explorer Joint ESA/JAXA mission Launch 2016 Budget 700 MEuro.
Robin Hogan Anthony Illingworth Marion Mittermaier Ice water content from radar reflectivity factor and temperature.
1. The problem of mixed-phase clouds All models except DWD underestimate mid-level cloud –Some have separate “radiatively inactive” snow (ECMWF, DWD) –Met.
Work Package 2 – Overview of instrumentation, data gathering and calibration issues Lidar Calibration Ewan O’Connor, Anthony Illingworth and Robin Hogan.
Remote sensing of Stratocumulus using radar/lidar synergy Ewan O’Connor, Anthony Illingworth & Robin Hogan University of Reading.
Lee Smith Anthony Illingworth
To compute the solar radiation flux density at the surface we need to know effects of atmosphere in filtering and depleting the beam from the top of the.
Initial 3D isotropic fractal field An initial fractal cloud-like field can be generated by essentially performing an inverse 3D Fourier Transform on the.
EarthCARE and snow Robin Hogan University of Reading.
Characterization of Arctic Mixed-Phase Cloudy Boundary Layers with the Adiabatic Assumption Paquita Zuidema*, Janet Intrieri, Sergey Matrosov, Matthew.
Anthony Illingworth, Robin Hogan, Ewan O’Connor, U of Reading, UK Nicolas Gaussiat Damian Wilson, Malcolm Brooks Met Office, UK Dominique Bouniol, Alain.
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Southern Ocean cloud biases in ACCESS.
Evaluating forecasts of the evolution of the cloudy boundary layer using radar and lidar observations Andrew Barrett, Robin Hogan and Ewan O’Connor Submitted.
Radiative Atmospheric Divergence using ARM Mobile Facility, GERB data and AMMA stations –led by Tony Slingo, ESSC, Reading University, UK Links the ARM.
Lidar+Radar ice cloud remote sensing within CLOUDNET. D.Donovan, G-J Zadelhof (KNMI) and the CLOUDNET team With outside contributions from… Z. Wang (NASA/GSFC)
Drizzle measurements with the HSRL and the KAZR: sensitivity to assumptions Ed Eloranta University of Wisconsin-Madison
Improvement of Cold Season Land Precipitation Retrievals Through The Use of Field Campaign Data and High Frequency Microwave Radiative Transfer Model IPWG.
BBHRP Assessment Part 2: Cirrus Radiative Flux Study Using Radar/Lidar/AERI Derived Cloud Properties David Tobin, Lori Borg, David Turner, Robert Holz,
The role of boundary layer clouds in the global energy and water cycle: An integrated assessment using satellite observations Ralf Bennartz University.
Ice-Phase Precipitation Remote Sensing Using Combined Passive and Active Microwave Observations Benjamin T. Johnson UMBC/JCET & NASA/GSFC (Code 613.1)
CLN QA/QC efforts CCNY – (Barry Gross) UMBC- (Ray Hoff) Hampton U. (Pat McCormick) UPRM- (Hamed Parsiani)
Cloud and precipitation best estimate… …and things I don’t know that I want to know Robin Hogan University of Reading.
Retrieval of Cloud Phase and Ice Crystal Habit From Satellite Data Sally McFarlane, Roger Marchand*, and Thomas Ackerman Pacific Northwest National Laboratory.
DODO RESULTS: Campaign Averages & BAe-146 Nephelometer Findings Claire McConnell Ellie Highwood Acknowledgements: Paola Formenti, Met Office, FAAM.
© Oxford University Press, All rights reserved. 1 Chapter 3 CHAPTER 3 THE GLOBAL ENERGY SYSTEM.
An Evaluation of Cloud Microphysics and Radiation Calculations at the NSA Matthew D. Shupe a, David D. Turner b, Eli Mlawer c, Timothy Shippert d a CIRES.
Cloudnet meeting Oct Martial Haeffelin SIRTA Cloud and Radiation Observatory M. Haeffelin, A. Armstrong, L. Barthès, O. Bock, C. Boitel, D.
Robin Hogan Anthony Illingworth Marion Mittermaier Ice water content from radar reflectivity factor and temperature.
Comparing various Lidar/Radar inversion strategies using Raman Lidar data D.Donovan, G-J Zadelhof (KNMI) Z. Wang (NASA/GSFC) D. Whiteman (NASA/GSFC)
12 April 2013 VARSY progress meeting Robin Hogan and Nicola Pounder (University of Reading)
UNIVERSITY OF BASILICATA CNR-IMAA (Consiglio Nazionale delle Ricerche Istituto di Metodologie per l’Analisi Ambientale) Tito Scalo (PZ) Analysis and interpretation.
What Are the Implications of Optical Closure Using Measurements from the Two Column Aerosol Project? J.D. Fast 1, L.K. Berg 1, E. Kassianov 1, D. Chand.
Slide 1 Robin Hogan, APRIL-CLARA-DORSY meeting 2016 ©ECMWF Towards a fast shortwave radiance forward model for exploiting MSI measurements Robin Hogan.
RadOn : Retrieval of microphysical and radiative properties of ice clouds from Doppler cloud radar observations J. Delanoë and A. Protat IPSL / CETP.
Presentation transcript:

Blind tests of radar/lidar retrievals: Assessment of errors in terms of radiative flux profiles Malcolm Brooks Robin Hogan and Anthony Illingworth David Donovan and Claire Tinel

Introduction First blind test showed that –Both Donovan and Tinel algorithms could retrieve extinction coefficient very accurately –Effective radius and IWC depend on assumption of habit (i.e. density or the mass-size relationship) Second blind test included multiple scattering, molecular scattering and instrument noise: –Reasonable extinction profiles were generally obtained if multiple scattering was included in the retrieval, otherwise extinction was underestimated –Retrieval only possible where lidar still has good signal What are the radiative implications? How do these retrievals compare to radar-only?

Blind test 1 (From aggregation study) No instrument noise No multiple scattering No molecular scattering High lidar sensitivity Two versions of each profile provided, with variable or constant extinction/backscatter ratio k, which was not known by the algorithms

Blind test 1: Results 1 Constant k: –Both Donovan and Tinel (after modification) algorithms produce highly accurate extinction Variable k: –Error in extinction varies with k, but not unstable

Blind test 1: Results 2 Effective radius: –Good, but difficult if r e > 80 microns because of radar Mie scattering –Sensitive to particle habit Ice water content: –Extinction ~ IWC/r e –Hence if extinction is correct then the % error in effective radius is equal to the % error in IWC

Best case: radar/lidar retrieval Excellent extinction, good r e if same mass-size relationship is used (otherwise 40% too low) Mitchell relationship Francis et al. relationship Radar only retrieval Extinction coefficientEffective radius

Best case: radiation calculations Used Edwards-Slingo radiation code Excellent longwave, good shortwave but slight effect of habit and k; better than radar alone Longwave up Shortwave up Clear sky profile Cloudy profile Error W m -2 depending on habit and k

Worst case: radar/lidar retrieval Radar/lidar extinction excellent, r e underestimated Extinction poor from radar only Extinction coefficientEffective radius Effective radius underestimate Poor radar- only retrieval, particularly at cloud top

Worst case: radiation calculations Excellent longwave, still good shortwave! Effective radius not very important? Longwave up Shortwave up Error W m -2

Blind test 1: Heating rates Radar/lidar: very accurate Radar alone: OK but some biases Best case Worst case Error due to higher Z here

Blind test 2 (From EUCREX) Instrument noise Multiple scattering Molecular scattering True lidar sensitivity Constant extinction to backscatter ratio Note: radar-only relationships derived using this dataset so not independent!

Donovan retrieval: with multiple scattering

Tinel retrieval: no multiple scattering

Good case: radar/lidar retrieval Extinction and effective radius reasonable when use same habit and include multiple scattering Extinction coefficientEffective radius Full profile retrieved Difference between Mitchell and Francis et al.

Good case: radiation calculations OLR and albedo good for both radar/lidar and radar-only (but radar-only not independent) Longwave up Shortwave up Mass-size relationship has modest effect: Error<10 Wm -2 Underestimate radiative effect if multiple scattering neglected

Poor case: radar/lidar retrievals No retrieval in lower part of cloud Extinction coefficientEffective radius Wild retrieval where lidar runs out of signal Good retrieval at cloud top

Poor case: radiation calculations At top-of-atmosphere, lower part of cloud important for shortwave but not for longwave Longwave up Shortwave up OLR excellent despite lower part not retrieved Albedo underestimated (90 W m -2 ): lower part of cloud is important

Blind test 2: Heating rates Heating profile reasonable if full profile retrieved Best case Worst case Erroneous 80 K/day heating No cloud observed so no heating by cloud here

Sensitivity of radiation to retrievals Longwave: easy! –Sensitive to extinction coefficient –Insensitive to effective radius, habit or extinction/backscatter –OLR insensitive to lower half of cloud undetected by lidar Shortwave: difficult to get to better than 20 W m -2 –Most sensitive to extinction coefficient –Need full cloud profile to get correct albedo –Some sensitivity to habit and therefore effective radius –Slight sensitivity to extinction/backscatter ratio –Note: not included habit dependence of asymmetry parameter or single-scattering albedo

Conclusions Extinction much the most important parameter: –Good news: this can be retrieved accurately independent of assumption of crystal type –But need to include multiple scattering in retrieval Need to retrieve something when no more lidar: –Switch to radar-only retrieval? –Assign error to both radar/lidar and radar-only retrievals and produce a consensus value, weighted accordingly? –Must avoid erroneous spikes where lidar loses signal! –Use imager (VIS & IR) synergy to give top-of-atmosphere radiances and provide a constraint for the retrieval: how would this be incorporated into the algorithms? –Do SW radiances provide multiple-scattering information?

Scaling the radar-only retrieval Where radar/lidar retrieval fails, can we scale the radar-only retrieval to get a seamless join? –Dubious: the profiles are not real but simulated! Good fitPartial fit