Testing a new ice parameterization scheme based on CloudNET & ARM observations. CloudNet Final meeting: October 2005 Gerd-Jan van Zadelhoff Co-authors:

Slides:

Advertisements

Similar presentations

Ewan OConnor, Anthony Illingworth, Robin Hogan and the Cloudnet team Cloudnet.

Advertisements

Ewan OConnor, Robin Hogan, Anthony Illingworth Drizzle comparisons.

Dynamical and Microphysical Evolution of Convective Storms (DYMECS) University: Robin Hogan, Bob Plant, Thorwald Stein, Kirsty Hanley, John Nicol Met Office:

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 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.

Use of ground-based radar and lidar to evaluate model clouds

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.

Joint GABLS-GLASS/LoCo workshop, September 2004, De Bilt, Netherlands Interactions of the land-surface with the atmospheric boundary layer: Single.

Evaluation of HARMONIE using a single column model in the KNMI

Application of Cloudnet data in the validation of SCIAMACHY cloud height products Ping Wang Piet Stammes KNMI, De Bilt, The Netherlands CESAR Science day,

A thermodynamic model for estimating sea and lake ice thickness with optical satellite data Student presentation for GGS656 Sanmei Li April 17, 2012.

Allison Parker Remote Sensing of the Oceans and Atmosphere.

3D Radiative Transfer in Cloudy Atmospheres: Diffusion Approximation and Monte Carlo Simulation for Thermal Emission K. N. Liou, Y. Chen, and Y. Gu Department.

Lidar-Based Microphysical Retrievals During M-PACE Gijs de Boer Edwin Eloranta The University of Wisconsin - Madison ARM CPMWG Meeting, October 31, 2006.

Analysis of ARM cirrus data and the incorporation of Doppler Fall-velocity measurements in lidar/radar retrievals Outline of lidar/radar procedure. Problem.

Microphysical and radiative properties of ice clouds Evaluation of the representation of clouds in models J. Delanoë and A. Protat IPSL / CETP Assessment.

© Crown copyright Met Office Radiation developments Latest work on the radiation code in the Unified Model James Manners, Reading collaboration meeting.

Robin Hogan Anthony Illingworth Marion Mittermaier Ice water content from radar reflectivity factor and temperature.

The Radiative Budget of an Atmospheric Column in Tropical Western Pacific Zheng Liu Department of Atmospheric Science University of Washington.

Ice-Cloud effective particle size parameterization based on combined lidar, radar reflectivity, and mean Radar Doppler velocity Data Rational Brief outline.

Atmospheric structure from lidar and radar Jens Bösenberg 1.Motivation 2.Layer structure 3.Water vapour profiling 4.Turbulence structure 5.Cloud profiling.

The Radiative Budget of an Atmospheric Column in Tropical Western Pacific Zheng Liu 1 Thomas Ackerman 1,2, Sally McFarlane 2, Jim Mather 2, University.

Profiling Clouds with Satellite Imager Data and Potential Applications William L. Smith Jr. 1, Douglas A. Spangenberg 2, Cecilia Fleeger 2, Patrick Minnis.

Uintah Basin WRF Testing Erik Neemann 20 Sep 2013.

2013 DOE/EU Retrieval Workshop, Köln “Common Algorithm Evaluation Approaches ” Session Shaocheng Xie, Kerstin Ebell, Dave Turner, and Ulrich Lohnert EU/DOE.

WMO Cloud Modelling Workshop, Hamburg, 12. Juli 2004 The BALTEX BRIDGE Campaign (BBC) cases Susanne Crewell, Nicole van Lipzig, Wenchieh Yen Meteorological.

Gerd-Jan van Zadelhoff & Dave Donovan Comparing ice-cloud microphysical properties using Cloudnet & ARM data.

Characterization of Arctic Mixed-Phase Cloudy Boundary Layers with the Adiabatic Assumption Paquita Zuidema*, Janet Intrieri, Sergey Matrosov, Matthew.

CAUSES (Clouds Above the United States and Errors at the Surface) "A new project with an observationally-based focus, which evaluates the role of clouds,

Cabauw Experimental Site for Atmospheric Research - CESAR - Henk Klein Baltink Atmospheric Research Section.

DYMECS: Dynamical and Microphysical Evolution of Convective Storms (NERC Standard Grant) University of Reading: Robin Hogan, Bob Plant, Thorwald Stein,

Anthony Illingworth, Robin Hogan, Ewan O’Connor, U of Reading, UK Nicolas Gaussiat Damian Wilson, Malcolm Brooks Met Office, UK Dominique Bouniol, Alain.

IACETH Institute for Atmospheric and Climate Sciences Indirect aerosol effects in EC earth Trude Storelvmo and Ulrike Lohmann, ETH-Zurich.

Evaluating forecasts of the evolution of the cloudy boundary layer using radar and lidar observations Andrew Barrett, Robin Hogan and Ewan O’Connor Submitted.

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)

KNMI 35 GHz Cloud Radar & Cloud Classification* Henk Klein Baltink * Robin Hogan (Univ. of Reading, UK)

Boundary Layer Clouds.

Investigations of Artifacts in the ISCCP Datasets William B. Rossow July 2006.

Cloud optical properties: modeling and sensitivity study Ping Yang Texas A&M University May 28,2003 Madison, Wisconsin.

Cloudnet Observing Stations Instrumentation C L Wrench Cloudnet Final Symposium – 12 October 2005.

Thomas Ackerman Roger Marchand University of Washington.

09-Nov-2004Brussels, 09-Nov CM-SAF Surface Radiation Budget: First Results and Plans R. Hollmann, A. Gratzki, R. Mueller O. Sievers Deutscher Wetterdienst.

BBHRP Assessment Part 2: Cirrus Radiative Flux Study Using Radar/Lidar/AERI Derived Cloud Properties David Tobin, Lori Borg, David Turner, Robert Holz,

Representation of Subgrid Cloud-Radiation Interaction and its Impact on Global Climate Simulations Xinzhong Liang (Illinois State Water Survey, UIUC )

Retrieval of Cloud Phase and Ice Crystal Habit From Satellite Data Sally McFarlane, Roger Marchand*, and Thomas Ackerman Pacific Northwest National Laboratory.

Page 1© Crown copyright 2005 Damian Wilson, 12 th October 2005 Assessment of model performance and potential improvements using CloudNet data.

EUCLIPSE Toulouse meeting April 2012 Roel Neggers Process-level evaluation at selected grid-points: Constraining a system of interacting parameterizations.

Validation of Cloud Top Temperature with CLOUDNET cloud radar data during the BBC2 Period Erwin Wolters (D. Donovan Presenting)

1 The RadOn method and associated error analysis Delanoë J., Protat A., Bouniol D., Testud J. C entre d’étude des E nvironnements T errestre et P lanétaires.

Uintah Basin WRF Testing Erik Neemann 20 Sep 2013.

Evaluation of Cloud-Radiation Interaction in the Canadian GEM Model Using ARM Observations Danahé Paquin-Ricard 1 Colin Jones 1, Paul Vaillancourt 2 1.

Evaluation of cloudy convective boundary layer forecast by ARPEGE and IFS Comparisons with observations from Cabauw, Chilbolton, and Palaiseau  Comparisons.

Stephan de Roode The art of modeling stratocumulus clouds.

Rob Roebeling CloudNET meeting 18 – 19 October 2004, Delft METEOSAT-8 OBSERVATIONS AND DERIVED CLOUD MICROPHYSICAL PROPERTIES.

1 Recent advances in CALIPSO cloud retrievals: Progress over the last 7 years Looking ahead to the next 30 ISCCP at 30: City College of New York, 23 April.

UNIVERSITY OF BASILICATA CNR-IMAA (Consiglio Nazionale delle Ricerche Istituto di Metodologie per l’Analisi Ambientale) Tito Scalo (PZ) Analysis and interpretation.

NASA Langley Research Center / Atmospheric Sciences CERES Instantaneous Clear-sky and Monthly Averaged Radiance and Flux Product Overview David Young NASA.

Rob Roebeling, Hartwig Deneke and Arnout Feijt GEWEX Cloud Assessment Meeting Madison, United States of America 6 -7 July 2006 "METEOSAT-8 (SEVIRI) CLOUD.

Toward Continuous Cloud Microphysics and Cloud Radiative Forcing Using Continuous ARM Data: TWP Darwin Analysis Goal: Characterize the physical properties.

"CRIME Investigations" Henk Klein Baltink (RDWD, KNMI)

W. Smith, D. Spangenberg, S. Sun-Mack, P.Minnis

Investigating Cloud Inhomogeneity using CRM simulations.

Quantitative verification of cloud fraction forecasts

CIRRUS IN LEM & LARGE-SCALE MODELS

RadOn : Retrieval of microphysical and radiative properties of ice clouds from Doppler cloud radar observations J. Delanoë and A. Protat IPSL / CETP.

Cloud fraction and Ice Water Content in Various Weather Regimes

RCA and CLIWANET results

Presentation transcript:

Testing a new ice parameterization scheme based on CloudNET & ARM observations. CloudNet Final meeting: October 2005 Gerd-Jan van Zadelhoff Co-authors: Dave Donovan, Erik van Meijgaard

KNMI October 27, 2004 April 18, 1996, CLARA

ARM Cabauw ARM Particle size vs. Temperature Doppler Velocity vs. Particle size Size-vs-temperature relationship different for ARM-SGP and Cabauw ! (Chilbolton and Cabauw similar ) Size-vs-Fall speed similar between ARM-SGP and Cabauw Comparison of results obtained over coastal Europe and Central U.S. Comparing microphysical ice cloud properties

Cabauw ARM Particle size to cloud depth from cloud-top R’ eff –vs-distance from cloud-top [R’ eff (  z)] - R’ eff (T)  ARM-SGP & Cabauw results are different - R’ eff (  Z)  ARM-SGP & Cabauw results are similar (|  Z| < 3 km) ARM H> 4.5 km 3.0 < H < 4.5 km 1.5 < H < 3.0 km H < 1.5 km

Comparing mean profiles to normalized cloud thickness ARM Cabauw - R eff (  Z, complex polycrystals)  ARM-SGP & Cabauw results are the same within the errors.

R eff =A+B(  Z/H)+C(  Z/H) 2 ARM Parameterization of R eff versus cloud thickness - R eff (  Z, complex polycrystals): Use parabolic fits to the observed values A=A 0 +A 1. H B=B 0 +B 1. H C=C 0 +C 1. H

Current Reff(T) relationship in Racmo vs. Obs Mean R eff -T relationship observed, using Complex polycrystals as habit. Current Racmo parameterization

Reff parameterization & distribution. Reff parameterization & distribution. Resulting distribution (left) of the new R eff parameterizations based on cloud- thickness.

Differences due to changes in parameterization Differences in the mean net short wave flux at the surface and cloud cover. Comparing the new parameterization with the current operational one. Hindcast run (1995) Climate run (1995)

Differences due to changes in parameterization Differences in the mean net short wave flux at the surface and cloud cover. Comparing the new parameterization with the current operational one. Hindcast run (1995) Climate run (1995)

Conclusions New parameterization gives back both the mean Reff-T relationship and its distribution. There is no temperature dependent input needed. Current vertical resolution RACMO too large for thinnest clouds. Monthly mean (day + night) planetary albedo changes by up to 4 % in the hindcast case due to smaller ice particles. Combination of both IWC and Reff has to change in RACMO to give observed SW fluxes. Comparison of R eff (  H) has to be confirmed using all three CloudNET, the ARM SGP, TWP and NSA sites.