MODIS Regional and Global Cloud Variability Brent C. Maddux 1,2 Steve Platnick 3, Steven A. Ackerman 1,2, Paul Menzel 1, Kathy Strabala 1, Richard Frey.

Slides:

Advertisements

Similar presentations

MODIS Atmosphere Solar Reflectance Issues 1. Aqua VNIR focal plane empirical re-registration status Ralf Bennartz, Bob Holz, Steve Platnick 2 1 U. Wisconsin,

Advertisements

A New A-Train Collocated Product : MODIS and OMI cloud data on the OMI footprint Brad Fisher 1, Joanna Joiner 2, Alexander Vasilkov 1, Pepijn Veefkind.

Daytime Cloud Shadow Detection With MODIS Denis Grljusic Philipps University Marburg, Germany Kathy Strabala, Liam Gumley CIMSS Paul Menzel NOAA / NESDIS.

VIIRS LST Uncertainty Estimation And Quality Assessment of Suomi NPP VIIRS Land Surface Temperature Product 1 CICS, University of Maryland, College Park;

Cloud Masking and Cloud Products MODIS Operational Algorithm MOD35 Paul Menzel, Steve Ackerman, Richard Frey, Kathy Strabala, Chris Moeller, Liam Gumley,

MOD06 Cloud Top Properties Richard Frey Paul Menzel Bryan Baum University of Wisconsin - Madison.

Exploring the similarities and differences between MODIS, PATMOS and ISCCP Amato Evan, Andrew Heidinger & Michael Pavolonis Collaborators: Brent Maddux,

Menglin Jin Department of Atmospheric & Oceanic Science University of Maryland, College park Observed Land Impacts on Clouds, Water Vapor, and Rainfall.

CoRP Symposium, 10-11August 2010, Fort Collins, CO 1 A daytime multispectral technique for detecting supercooled liquid water- topped mixed-phase clouds.

Evaluating Remote Sensing Data Or How to Avoid Making Great Discoveries by Misinterpreting Data Richard Kleidman ARSET-AQ Applied Remote Sensing Education.

Cooperative Institute for Meteorological Satellite Studies University of Wisconsin - Madison Steve Ackerman Director, Cooperative Institute for Meteorological.

Seasonal and Inter-annual Variations of Polar Cloud Cover Seiji Kato 1, Norman G. Loeb 2, Patrick Minnis 3, Jennifer A. Francis 4, Thomas P. Charlock 3,

Cooperative Institute for Meteorological Satellite Studies University of Wisconsin - Madison Steve Ackerman P. Menzel, R. Frey, K. Strabala, R Holz, B.

Spatial & Temporal Distribution of Clouds as Observed by MODIS onboard the Terra and Aqua Satellites  MODIS atmosphere products –Examples from Aqua Cloud.

Reflected Solar Radiative Kernels And Applications Zhonghai Jin Constantine Loukachine Bruce Wielicki Xu Liu SSAI, Inc. / NASA Langley research Center.

Cooperative Institute for Meteorological Satellite Studies University of Wisconsin - Madison E. Eva Borbas, Zhenglong Li and W. Paul Menzel Cooperative.

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.

TEMPLATE DESIGN © Total Amount Altitude Optical Depth Longwave High Clouds Shortwave High Clouds Shortwave Low Clouds.

Surface Atmospheric Radiation Budget (SARB) working group update Seiji Kato 1, Fred Rose 2, David Rutan 2, Alexander Radkevich 2, Tom Caldwell 2, David.

Spatially Complete Global Surface Albedos Derived from MODIS Data

SeaDAS Training ~ NASA Ocean Biology Processing Group 1 Level-2 ocean color data processing basics NASA Ocean Biology Processing Group Goddard Space Flight.

Orbit Characteristics and View Angle Effects on the Global Cloud Field

MODIS Sea-Surface Temperatures for GHRSST-PP Robert H. Evans & Peter J. Minnett Otis Brown, Erica Key, Goshka Szczodrak, Kay Kilpatrick, Warner Baringer,

OpenDAP Server-side Functions for Multi-Instrument Aggregation ESIP Session: Advancing the Power and Utility of Server-side Aggregation Jon C. Currey (NASA),

Introduction Invisible clouds in this study mean super-thin clouds which cannot be detected by MODIS but are classified as clouds by CALIPSO. These sub-visual.

AGU 2002 Fall Meeting NASA Langley Research Center / Atmospheric Sciences Validation of GOES-8 Derived Cloud Properties Over the Southeastern Pacific J.

Seasonal Climate Cycles Solar Radiation at the Earth’s Surface.

An Estimate of Contrail Coverage over the Contiguous United States David Duda, Konstantin Khlopenkov, Thad Chee SSAI, Hampton, VA Patrick Minnis NASA LaRC,

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,

Cloud Top Properties Bryan A. Baum NASA Langley Research Center Paul Menzel NOAA Richard Frey, Hong Zhang CIMSS University of Wisconsin-Madison MODIS Science.

Andrew Heidinger and Michael Pavolonis

Testing LW fingerprinting with simulated spectra using MERRA Seiji Kato 1, Fred G. Rose 2, Xu Liu 1, Martin Mlynczak 1, and Bruce A. Wielicki 1 1 NASA.

Trends & Variability of Liquid Water Clouds from Eighteen Years of Microwave Satellite Data: Initial Results 6 July 2006 Chris O’Dell & Ralf Bennartz University.

Cloud Mask: Results, Frequency, Bit Mapping, and Validation UW Cloud Mask Working Group.

DEPARTMENT OF GEOMATIC ENGINEERING MERIS land surface albedo: Production and Validation Jan-Peter Muller *Professor of Image Understanding and Remote Sensing.

MODIS Level 3: Future Issues and Considerations Brent Maddux MODIS Level 3: Future Issues and Considerations Brent Maddux Cloud Fraction Mean.

ARCTIC CLIMATE CHARACTERISTICS AND RECENT TRENDS FROM SPACE Xuanji Wang 1, Jeffrey R. Key 2, Taneil Uttal 3, and Shelby Frisch 4 1 Cooperative Institute.

Consistency of reflected moonlight based nighttime precipitation product with its daytime equivalent. Andi Walther 1, Steven Miller 3, Denis Botambekov.

Using MODIS and AIRS for cloud property characterization Jun W. Paul Menzel #, Steve Chian-Yi and Institute.

MODIS Atmosphere Team Summary S. Platnick and the atmosphere team Atmosphere Team Agenda Day 1: Collection 6 – L2 Algorithm Plans/Status – L3 Discussion:

Bryan A. Baum, Richard Frey, Robert Holz Space Science and Engineering Center University of Wisconsin-Madison Paul Menzel NOAA Many other colleagues MODIS.

Initial Analysis of the Pixel-Level Uncertainties in Global MODIS Cloud Optical Thickness and Effective Particle Size Retrievals Steven Platnick 1, Robert.

Point Comparison in the Arctic (Barrow N, 156.6W ) Part I - Assessing Satellite (and surface) Capabilities for Determining Cloud Fraction, Cloud.

MODIS Atmosphere Data Products and Science Results Steven Platnick 1,2 Michael D. King, 2 W. Paul Menzel, 3 Yoram J. Kaufman, 2 Steve Ackerman, 4 Didier.

Validation of Satellite-derived Clear-sky Atmospheric Temperature Inversions in the Arctic Yinghui Liu 1, Jeffrey R. Key 2, Axel Schweiger 3, Jennifer.

Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.

S. Platnick 1, M. D. King 1, J. Riedi 2, T. Arnold 1,3, B. Wind 1,3, G. Wind 1,3, P. Hubanks 1,3 and S. Ackerman 4, R. Frey 4, B. Baum 4, P. Menzel 4,5,

Michael D. King, EOS Senior Project ScientistMarch 21, Early Results from the MODIS Atmosphere Algorithms Michael D. King, 1 Steven Platnick, 1,2.

Evidence in ISCCP for regional patterns of cloud response to climate change Joel Norris Scripps Institution of Oceanography ISCCP at 30 Workshop City College.

Data acquisition From satellites with the MODIS instrument.

Analysis of Simultaneous Nadir Observations of MODIS from AQUA and TERRA Andrew Heidinger and many others NOAA/NESDIS Office of Research and Applications.

Barbuda Antigua MISR 250 m The Climatology of Small Tropical Oceanic Cumuli New Findings to Old Problems (Analysis of EOS-Terra data) Larry Di Girolamo,

Collect 5 Calibration Issues Chris Moeller and others Univ. Wisconsin March 22, 2005 Presented at MCST Calibration breakout meeting, March 22, 2005.

Interannual Variability and Decadal Change of Solar Reflectance Spectra Zhonghai Jin Costy Loukachine Bruce Wielicki (NASA Langley research Center / SSAI,

Steve Platnick 1, Gala Wind 2,1, Zhibo Zhang 3, Hyoun-Myoung Cho 3, G. T. Arnold 2,1, Michael D. King 4, Steve Ackerman 5, Brent Maddux NASA Goddard.

MODIS Atmosphere Products: The Importance of Record Quality and Length in Quantifying Trends and Correlations S. Platnick 1, N. Amarasinghe 1,2, P. Hubanks.

Shaima Nasiri University of Wisconsin-Madison Bryan Baum NASA - Langley Research Center Detection of Overlapping Clouds with MODIS: TX-2002 MODIS Atmospheres.

MOLLY E. BROWN, PHD NASA GODDARD GIMMS Group Challenges of AVHRR Vegetation Data for Real Time Applications.

Spatial & Temporal Distribution of Cloud Properties observed by MODIS: Preliminary Level-3 Results from the Collection 5 Reprocessing Michael D. King,

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

A comparison of AMSR-E and AATSR SST time-series A preliminary investigation into the effects of using cloud-cleared SST data as opposed to all-sky SST.

Cloud Detection: Optical Depth Thresholds and FOV Considerations Steven A. Ackerman, Richard A. Frey, Edwin Eloranta, and Robert Holz Cloud Detection Issues.

MODIS Atmosphere Group Summary Summary of modifications and enhancements in collection 5 Summary of modifications and enhancements in collection 5 Impacts.

TOA Radiative Flux Estimation From CERES Angular Distribution Models

Winds in the Polar Regions from MODIS: Atmospheric Considerations

Remote Sensing of Cloud, Aerosol, and Land Properties from MODIS

VIIRS Cloud Mask Validation Exercises

Average Monthly Temperature and Rainfall

HIRS Observations of a Decline in High Clouds since 1995 February 2002

The Aqua-MODIS calibration transfer using DCC

Presentation transcript:

MODIS Regional and Global Cloud Variability Brent C. Maddux 1,2 Steve Platnick 3, Steven A. Ackerman 1,2, Paul Menzel 1, Kathy Strabala 1, Richard Frey 1 1 Cooperative Institute for Meteorological Satellite Studies 2 Department of Atmospheric and Oceanic Sciences-U of Wisconsin 3 NASA Goddard Space Flight Center, Greenbelt, Maryland 7 th CoRP Science Symposium, Fort Collins, CO, August 11, 2010

Overview CF and CTP Big Picture - Trend? - Aqua/Terra difference MODIS Case Studies - View Angle - Surface Type - Other Cloud Fractions from MODIS? - Averaging games

Global Means Collection 5 Data 36 spectral bands 10:30 am/pm Terra 1:30 am/pm Aqua CTP: 4 CO 2 bands plus 10μm Biggest differences are over land-deep daytime convection CTP Aqua Daytime CTP Aqua Nighttime

Global Means Daytime cloud fraction Coast lines evident Differences where expected: maritime stratocumulus decks, SH land, etc. CF Terra 9 Year Mean CF Aqua 7 Year Mean

CF daytime decadal change.35% Trend is not significant Would need a 14 year record with this trend If pre-2003 data is removed no trend at all Aqua and Terra agree very well Terra until recently was slightly higher than Aqua

Daily Zonal Hovmoller: Terra Daytime Cloud Top Pressure Anomaly hPa

Cloud Fraction Day Difference for 7 yrs (Aqua minus Terra) OCT JUL APR JAN NOV AUG MAY FEB DEC SEP JUN MAR

View Angle Biases Single Day in the MODIS Orbit Terra Daytime Cloud Fraction Geostationary-like view 16 day orbit procession U-Shape at edges of convection and cloudy regions See through holes in clouds or through thin high clouds near nadir Most other data sets show similar characteristics

Daytime Cloud Fraction Mean Nadir to 10° 60° to edge of scan° Nadir to 10° minus 60 and greater Differences not uniform Largest differences not where thin high clouds exist

Cloud Fraction vs Viewing Angle 7 years of Aqua and Terra 16% increase from near nadir to edge of scan View angle effect not constant for all cloud types Cloud Fraction vs Sensor Zenith Angle Sensor Zenith Angle

Nadir vs Edge of Scan Near NadirNear Edge of Scan Cloud Fraction (%) Cloud Top Pressure (hPa) Cloud Effective Radius Ice (μm) Cloud Effective Radius Liquid (μm) Cloud Optical Depth Ice Cloud Optical Depth Liquid Changes are not uniform Largest changes in CF aren’t the same as largest changes in optical properties Maddux, B. C., S. A. Ackerman, and S. Platnick, 2010: View Geometry Dependencies in MODIS Cloud Products, J. Tech A, Accepted.

Day of year Mean Cloud Fraction Cloud Fraction Difference Cloud Fraction Difference Due to Sensor Zenith Angle Cloud Fraction Mean for High (Red) and Low (Blue) Sensor Zenith Angle Day of year

Active vs. Passive Surface Type MODIS vs Calipso for 2-years of data over Arctic Liu, Y., S. Ackerman, B. Maddux, J. Key, and R. Frey, 2010: Errors in cloud detection over the Arctic using a satellite imager and implications for observing feedback mechanisms, J. Climate, 23(7),

Active vs. Passive MODIS vs Calipso for 2-years of data over Arctic Large bias at night over ice assuming Calipso is truth 21% error in CF translates to a trend error of 2.6%/decade Night Day MODISCalipsoMODIS-Calipso MODISCalipsoMODIS-Calipso

Who sees a cloud? Comparison to other cloud datasets (apples- to-apples) Place ‘error bars’ on global mean statistics Quantify cloud variability globally Cloud Fraction Retrieval Fraction Anomaly Difference Absolute Difference

Mean Cloud Fraction Difference (MOD35-MOD06) (%) MOD06 cloud fraction is a quality assured subset of MOD35 to retrieve better cloud optical properties

Mean Cloud Fraction Difference in Percent (MOD35-MOD06)/MOD (%) Differences are due to the QA stuff (clear sky restoral and cloud edges, thin clouds, and surfaces influences).

CTP (red,all cloud) and CTP (blue, retrieval) histogram % of Clouds at Pressure Level COD, Re, and WP are blue CTP and CF are red

Grid Cell Size and Swath Overlap Latitudes Included EQ More mid level cloud at high latitudes But temporal averaging puts high and low clouds together from multiple swaths

Grid Cell Size and Swath Overlap Cloud Top Pressure Histogram High Cloud <400 hPa Mid Cloud >400 and <700 hPa Low Cloud >700 hPa 21% of mid level clouds in a 1x1 degree grid cell are mid level clouds

Pixel vs Area Weighting Not a uniform offset Doesn’t change long term mean (.2%) Polar regions oscillate opposite mid-latitudes MODIS Terra Cloud Fraction Area (Red) and Pixel (Blue)

Conclusions MODIS Annual Cycle: 2.1% Decadal Change:.35% Aqua Terra Difference: <2% Local Surface Type Biases: 40% Local View Angle Bias: 60% (globally ≈16% or 4%)