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

Slides:



Advertisements
Similar presentations
Ewan OConnor, Robin Hogan, Anthony Illingworth Drizzle comparisons.
Advertisements

Ewan OConnor, Robin Hogan, Anthony Illingworth, Nicolas Gaussiat Liquid water path from microwave radiometers.
Proposed new uses for the Ceilometer Network
R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading ECMWF Cloud and Radiation Parametrization: Recent Activities Richard Forbes, Maike.
Radar/lidar observations of boundary layer clouds
Robin Hogan, Richard Allan, Nicky Chalmers, Thorwald Stein, Julien Delanoë University of Reading How accurate are the radiative properties of ice clouds.
Integrated Profiling at the AMF
Global, Regional, and Urban Climate Effects of Air Pollutants Mark Z. Jacobson Dept. of Civil & Environmental Engineering Stanford University.
Allison Parker Remote Sensing of the Oceans and Atmosphere.
Probing continental boundary layer clouds using first-time, extended-term aircraft observations: Low-level boundary layer clouds include stratus, stratocumulus,
By : Kerwyn Texeira. Outline Definitions Introduction Model Description Model Evaluation The effect of dust nuclei on cloud coverage Conclusion Questions.
Applications of satellite measurements on dust-cloud-precipitation interactions over Asia arid/semi-arid region Jianping Huang Key Laboratory for Semi-Arid.
Radiative Properties of Clouds SOEE3410 Ken Carslaw Lecture 3 of a series of 5 on clouds and climate Properties and distribution of clouds Cloud microphysics.
1. The problem of mixed-phase clouds All models except DWD underestimate mid-level cloud –Some have separate “radiatively inactive” snow (ECMWF, DWD) –Met.
The Radiative Budget of an Atmospheric Column in Tropical Western Pacific Zheng Liu Department of Atmospheric Science University of Washington.
Clouds and Climate: Cloud Response to Climate Change ENVI3410 : Lecture 11 Ken Carslaw Lecture 5 of a series of 5 on clouds and climate Properties and.
Radiative Properties of Clouds SOEE3410 Ken Carslaw Lecture 3 of a series of 5 on clouds and climate Properties and distribution of clouds Cloud microphysics.
Lee Smith Anthony Illingworth
1 Cloud Droplet Size Retrievals from AERONET Cloud Mode Observations Christine Chiu Stefani Huang, Alexander Marshak, Tamas Várnai, Brent Holben, Warren.
Radiative Properties of Clouds ENVI3410 : Lecture 9 Ken Carslaw Lecture 3 of a series of 5 on clouds and climate Properties and distribution of clouds.
The Atmospheric Radiation Measurement (ARM) Program: An Overview Robert G. Ellingson Department of Meteorology Florida State University Tallahassee, FL.
Figure 2.10 IPCC Working Group I (2007) Clouds and Radiation Through a Soda Straw.
J. L. Brenguier, Météo-France CNRSCNRM/GAME Warsaw April 2015 How observations shall be used to constrain numerical models of the Earth System Workshop.
Observed and modelled long-term water cloud statistics for the Murg Valley Kerstin Ebell, Susanne Crewell, Ulrich Löhnert Institute for Geophysics and.
Profiling Clouds with Satellite Imager Data and Potential Applications William L. Smith Jr. 1, Douglas A. Spangenberg 2, Cecilia Fleeger 2, Patrick Minnis.
Ten Years of Cloud Optical/Microphysical Measurements from MODIS M. D. King 1, S. Platnick 2, and the entire MOD06 Team 1 Laboratory for Atmospheric and.
Cloud Biases in CMIP5 using MISR and ISCCP simulators B. Hillman*, R. Marchand*, A. Bodas-Salcedo, J. Cole, J.-C. Golaz, and J. E. Kay *University of Washington,
Motivation Many GOES products are not directly used in NWP but may help in diagnosing problems in forecasted fields. One example is the GOES cloud classification.
Effects of size resolved aerosol microphysics on photochemistry and heterogeneous chemistry Gan Luo and Fangqun Yu ASRC, SUNY-Albany
Lidar algorithms to retrieve cloud distribution, phase and optical depth Y. Morille, M. Haeffelin, B. Cadet, V. Noel Institut Pierre Simon Laplace SYMPOSIUM.
Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University.
Characterization of Arctic Mixed-Phase Cloudy Boundary Layers with the Adiabatic Assumption Paquita Zuidema*, Janet Intrieri, Sergey Matrosov, Matthew.
Case Study Example 29 August 2008 From the Cloud Radar Perspective 1)Low-level mixed- phase stratocumulus (ice falling from liquid cloud layer) 2)Brief.
Matthew Shupe, Ola Persson, Amy Solomon CIRES – Univ. of Colorado & NOAA/ESRL David Turner NOAA/NSSL Dynamical and Microphysical Characteristics and Interactions.
Estimation of Cloud and Precipitation From Warm Clouds in Support of the ABI: A Pre-launch Study with A-Train Zhanqing Li, R. Chen, R. Kuligowski, R. Ferraro,
ARM Data Overview Chuck Long Jim Mather Tom Ackerman.
Comparison on Cloud and radiation properties at Barrow between ARM/NSA measurements and GCM outputs Qun Miao and Zhien Wang University of Wyoming 1. Introduction.
RICO Modeling Studies Group interests RICO data in support of studies.
Matthew Shupe Ola Persson Paul Johnston Duane Hazen Clouds during ASCOS U. of Colorado and NOAA.
1 Atmospheric profiling to better understand fog and low level cloud life cycle ARM/EU workshop on algorithms, May 2013 J. Delanoe (LATMOS), JC.
Boundary Layer Clouds.
Diagnosis and improvement of cloud parametrization schemes in NCEP/GFS using multiple satellite products 1 Hyelim Yoo, 1 Zhanqing Li 2 Yu-Tai Hou, 2 Steve.
Mixed-phase cloud physics and Southern Ocean cloud feedback in climate models. T 5050 Liquid Condensate Fraction (LCF) Correlation between T5050 and ∆LWP.
Cloud optical properties: modeling and sensitivity study Ping Yang Texas A&M University May 28,2003 Madison, Wisconsin.
Thomas Ackerman Roger Marchand University of Washington.
Shaocheng Xie, Renata McCoy, and Stephen Klein Lawrence Livermore National Laboratory Statistical Characteristics of Clouds Observed at the ARM SGP, NSA,
BBHRP Assessment Part 2: Cirrus Radiative Flux Study Using Radar/Lidar/AERI Derived Cloud Properties David Tobin, Lori Borg, David Turner, Robert Holz,
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.
A Sharper View of Fuzzy Objects: Warm Clouds and their Role in the Climate System as seen by Satellite Ralf Bennartz University of Wisconsin – Madison.
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.
ASAP In-Flight Icing Research at NCAR J. Haggerty, F. McDonough, J. Black, S. Landolt, C. Wolff, and S. Mueller In collaboration with: P. Minnis and W.
Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment, Part II: Multi-layered cloud GCSS.
Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.
Cloud Microphysics and Atmospheric Structure : Application to MEGHA TROPIQUES Sachchida Nand Tripathi Department of Civil Engineering IIT.
Comparison between aircraft and A-Train observations of midlevel, mixed-phase clouds from CLEX-10/C3VP Curtis Seaman, Yoo-Jeong Noh, Thomas Vonder Haar.
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.
UCLA Vector Radiative Transfer Models for Application to Satellite Data Assimilation K. N. Liou, S. C. Ou, Y. Takano and Q. Yue Department of Atmospheric.
Testing a new ice parameterization scheme based on CloudNET & ARM observations. CloudNet Final meeting: October 2005 Gerd-Jan van Zadelhoff Co-authors:
PAPERSPECIFICS OF STUDYFINDINGS Kohler, 1936 (“The nucleus in and the growth of hygroscopic droplets”) Evaporate 2kg of hoar-frost and determined Cl content;
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Southern Ocean Clouds Characterization.
Cloudnet meeting Oct Martial Haeffelin SIRTA Cloud and Radiation Observatory M. Haeffelin, A. Armstrong, L. Barthès, O. Bock, C. Boitel, D.
NMSC Daytime Cloud Optical and Microphysical Properties (DCOMP) 이은희.
Discussion of Radiation
Simulation of the Arctic Mixed-Phase Clouds
What are the causes of GCM biases in cloud, aerosol, and radiative properties over the Southern Ocean? How can the representation of different processes.
Group interests RICO data required
Characterizing the response of simulated atmospheric boundary layers to stochastic cloud radiative forcing Robert Tardif, Josh Hacker (NCAR Research Applications.
Studying the cloud radiative effect using a new, 35yr spanning dataset of cloud properties and radiative fluxes inferred from global satellite observations.
Group interests RICO data in support of studies
Presentation transcript:

Toward Continuous Cloud Microphysics and Cloud Radiative Forcing Using Continuous ARM Data: TWP Darwin Analysis Goal: Characterize the physical properties of the atmospheric column continuously using a suite of cloud property retrieval algorithms and parameterizations. Corollary: Create an operational description (complete with uncertainty) of the thermodynamic state, properties of condensate (mass and particle size), radiative properties, and solar, IR, clear and cloudy radiative fluxes over the ARM sites. Purpose: 1) For comparison with large-scale models 2) To develop understanding of the role of clouds in the climate system using long-term data.

The Easy Part: Profiles with only liquid water and/or cirrus can be addressed using existing retrieval algorithms. The Hard Part: Because the MWR observes the total liquid water path, the challenge is to treat profiles that contain supercooled clouds/mixed phase volumes along with perhaps cloud volumes that are warm (T>273K). Approach to supercooled/mixed phase liquid: Estimate a normalized distribution of LWC using parameterizations and MMCR observations. From this normalized profile and the MWR LWP define a supercooled LWP and a warm LWP. For the warm LWP, goto easy part. For Supercooled liquid: Distribute the supercooled LWP vertically using the normalized parameterization of LWC and the MMCR cloud occurrence. Important: The layer then is guaranteed to have the observed LWP distributed vertically in the column. Approach: (Presently Optimized of SGP! See Mace et al., Part 1 and 2 in JGR 2006)

Case Study: Darwin, 28 January 2006.

TWP ICE data will allow us to optimize the cloud property algorithms and provide essential validation for this work….