Magdalena D. Anguelova Michael H. Bettenhausen Michael H. Bettenhausen William F. Johnston William F. Johnston Peter W. Gaiser Peter W. Gaiser Whitecap.

Slides:



Advertisements
Similar presentations
Recent Evidence for Reduced Climate Sensitivity Roy W. Spencer, Ph.D Principal Research Scientist The University of Alabama In Huntsville March 4, 2008.
Advertisements

Whitecaps, sea-salt aerosols, and climate Magdalena D. Anguelova Physical Oceanography Dissertation Symposium College of Marine Studies, University of.
Sea salt aerosols: Their generation and role in the climate system Ph. D. Dissertation Proposal Magdalena D. Anguelova November 12, 1999 College of Marine.
Global trends in air-sea CO 2 fluxes based on in situ and satellite products Rik Wanninkhof, NOAA/AOML ACE Ocean Productivity and Carbon Cycle (OPCC) Workshop.
TRMM Tropical Rainfall Measurement (Mission). Why TRMM? n Tropical Rainfall Measuring Mission (TRMM) is a joint US-Japan study initiated in 1997 to study.
Variability of Liquid Water Path in Marine Boundary Layer Clouds
Complex dielectric constant of sea foam for microwave remote sensing Magdalena D. Anguelova Peter W. Gaiser Naval Research Laboratory, Washington, DC 15th.
CO 2 in the middle troposphere Chang-Yu Ting 1, Mao-Chang Liang 1, Xun Jiang 2, and Yuk L. Yung 3 ¤ Abstract Measurements of CO 2 in the middle troposphere.
ATS 351 Lecture 8 Satellites
SMOS – The Science Perspective Matthias Drusch Hamburg, Germany 30/10/2009.
Remote Sensing of the Ocean and Atmosphere: John Wilkin Sea Surface Temperature IMCS Building Room 214C ext 251.
Using Scatterometers and Radiometers to Estimate Ocean Wind Speeds and Latent Heat Flux Presented by: Brad Matichak April 30, 2008 Based on an article.
Climate Variability and Prediction in the Little Colorado River Basin Matt Switanek 1 1 Department of Hydrology and Water Resources University of Arizona.
MWR Algorithms (Wentz): Provide and validate wind, rain and sea ice [TBD] retrieval algorithms for MWR data Between now and launch (April 2011) 1. In-orbit.
Satellite Remote Sensing of Surface Air Quality
Observations of Ocean response to Hurricane Igor: A Salty Tropical Cyclone Wake observed from Space N.Reul 1, Y, Quilfen 1, B. Chapron 1, E. Vincent 2,
Proxy Data and VHF/Optical Comparisons Monte Bateman GLM Proxy Data Designer.
Magdalena D. Anguelova, Ferris Webster, Peter Gaiser 12 May, 2004 Effects of Environmental Variables in Sea Spray Generation Function via Whitecap Coverage.
Magdalena D. Anguelova, Justin P. Bobak, William E. Asher, David J. Dowgiallo, Ben I. Moat, Robin W. Pascal, Margaret J. Yelland 16th Conference on Air-Sea.
Satellite Imagery and Remote Sensing NC Climate Fellows June 2012 DeeDee Whitaker SW Guilford High Earth/Environmental Science & Chemistry.
Ensemble-variational sea ice data assimilation Anna Shlyaeva, Mark Buehner, Alain Caya, Data Assimilation and Satellite Meteorology Research Jean-Francois.
STATISTICAL ANALYSIS OF ORGANIZED CLOUD CLUSTERS ON WESTERN NORTH PACIFIC AND THEIR WARM CORE STRUCTURE KOTARO BESSHO* 1 Tetsuo Nakazawa 1 Shuji Nishimura.
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.
Evaluation of Microwave Scatterometers and Radiometers as Satellite Anemometers Frank J. Wentz, Thomas Meissner, and Deborah Smith Presented at: NOAA/NASA.
Development and evaluation of Passive Microwave SWE retrieval equations for mountainous area Naoki Mizukami.
Séverine Fournier, Nicolas Reul, Bertrand Chapron Laboratoire Océanographie Spatiale, IFREMER Joe Salisbury, Doug Vandemark University of New Hampshire,
Generation of Sea-Salt Aerosols Magdalena Anguelova Bridging the Gap October , 1999.
GOES and GOES-R ABI Aerosol Optical Depth (AOD) Validation Shobha Kondragunta and Istvan Laszlo (NOAA/NESDIS/STAR), Chuanyu Xu (IMSG), Pubu Ciren (Riverside.
Magdalena D. Anguelova, Justin P. Bobak, William E. Asher, David J. Dowgiallo, Ben I. Moat, Robin W. Pascal, Margaret J. Yelland 16th Conference on Air-Sea.
Oceanic Whitecaps: Good or Bad? Magdalena Anguelova Bridging the Gap October , 2000.
Optimization of L-band sea surface emissivity models deduced from SMOS data X. Yin (1), J. Boutin (1), N. Martin (1), P. Spurgeon (2) (1) LOCEAN, Paris,
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
Why We Care or Why We Go to Sea.
Magdalena D. Anguelova Michael H. Bettenhausen William F. Johnston* Peter W. Gaiser Novel Applications of Remote Sensing for Improved Quantification of.
Modern Era Retrospective-analysis for Research and Applications: Introduction to NASA’s Modern Era Retrospective-analysis for Research and Applications:
Whitecaps, sea-salt aerosols, and climate Magdalena D. Anguelova Oceans and Ice Branch Seminar College of Marine Studies University of Delaware18 October,
2nd GODAE Observing System Evaluation Workshop - June Ocean state estimates from the observations Contributions and complementarities of Argo,
The New Geophysical Model Function for QuikSCAT: Implementation and Validation Outline: GMF methodology GMF methodology New QSCAT wind speed and direction.
As components of the GOES-R ABI Air Quality products, a multi-channel algorithm similar to MODIS/VIIRS for NOAA’s next generation geostationary satellite.
Graduate Course: Advanced Remote Sensing Data Analysis and Application RETRIEVAL OF SURFACE AIR HUMIDITY FROM SSM/I Shu-Hsien Chou Dept. of Atmospheric.
Remote Sensing Division Naval Research Lab, Washington, DC Separating Whitecap Fraction of Active Wave Breaking From Satellite Estimates of Total.
Locally Optimized Precipitation Detection over Land Grant Petty Atmospheric and Oceanic Sciences University of Wisconsin - Madison.
Observations of Ocean response to Hurricane Igor: A Salty Tropical Cyclone Wake observed from Space Nicolas Reul 1, Joseph Tenerelli 2 1 IFREMER, Laboratoire.
Global high-resolution marine isoprene emission derived from VIIRS-SNPP and MODIS-Aqua ocean color observations 1/25/2016Air Resources Laboratory1 Daniel.
Daily observation of dust aerosols infrared optical depth and altitude from IASI and AIRS and comparison with other satellite instruments Christoforos.
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
Combining Altimeter-derived Currents With Aquarius Salinity To Study The Marine Freshwater Budget Gary Lagerloef Aquarius Principal Investigator Yi Chao.
Infrared and Microwave Remote Sensing of Sea Surface Temperature Gary A. Wick NOAA Environmental Technology Laboratory January 14, 2004.
NOAA Environmental Technology Laboratory Gary A. Wick Observed Differences Between Infrared and Microwave Products Detailed comparisons between infrared.
SeaWiFS Views Equatorial Pacific Waves Gene Feldman NASA Goddard Space Flight Center, Lab. For Hydrospheric Processes, This.
Magdalena D. Anguelova Michael H. Bettenhausen Michael H. Bettenhausen William F. Johnston William F. Johnston Peter W. Gaiser Peter W. Gaiser Oceanic.
EuroGOOS Arctic Task Team Workshop September 2006 Satellite data portals for Arctic monitoring Stein Sandven Nansen Environmental and Remote Sensing.
Evaluation of Satellite-Derived Air-Sea Flux Products Using Dropsonde Data Gary A. Wick 1 and Darren L. Jackson 2 1 NOAA ESRL, Physical Sciences Division.
Radiometric Measurements of Whitecaps and Surface Fluxes Magdalena D. Anguelova Remote Sensing Division Naval Research Laboratory Washington, DC, USA In.
SCM x330 Ocean Discovery through Technology Area F GE.
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
In order to accurately estimate polar air/sea fluxes, sea ice drift and then ocean circulation, global ocean models should make use of ice edge, sea ice.
Passive Microwave Remote Sensing
T. Meissner, F. Wentz, J. Scott, K. Hilburn Remote Sensing Systems 2014 Aquarius / SAC-D Science Team Meeting November , 2014.
ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course
Blending multiple-source wind data including SMOS, SMAP and AMSR-2 new products Joe TENERELLI, Fabrice COLLARD OceanDataLab, Brest, France.
Whitecaps, sea-salt aerosols, and climate
5th Workshop on "SMART Cable Systems: Latest Developments and Designing the Wet Demonstrator Project" (Dubai, UAE, April 2016) Contribution of.
Jackie May* Mark Bourassa * Current affilitation: QinetiQ-NA
Intraseasonal latent heat flux based on satellite observations
Microwave Emissivity of a Vertically Inhomogeneous Sea-Foam Layer: Application to the WindSat Retrieval Algorithm Magdalena D. Anguelova, Karen St.
Nicolas Reul1, Joseph Tenerelli2
Satellite Foundational Course for JPSS (SatFC-J)
Igor Appel Alexander Kokhanovsky
Why do satellite-based estimates of whitecap fraction depend on
Presentation transcript:

Magdalena D. Anguelova Michael H. Bettenhausen Michael H. Bettenhausen William F. Johnston William F. Johnston Peter W. Gaiser Peter W. Gaiser Whitecap fraction database for studies of whitecaps variability and its influence on sea-spray source function Workshop: Primary Marine Aerosol Fluxes National University of Ireland, Galway May 10-11, 2010

Workshop 11 May2Whitecaps database Anguelova et al., NRL Rate of production of sea spray per unit area per increment of droplet radius, r (s -1 m -2  m -1 ). Sea spray generation function Explicit forms for 4 size regions covering 1.6 to 500  m range. Andreas (2001) (Monahan and O’Muircheartaigh, 1980)

Workshop 11 May3Whitecaps database Anguelova et al., NRL Objective Model the high variability of foam fraction Model the high variability of foam fraction U – wind speed (U 10 or u * )  T – atmospheric stability (= T air – T sea ) X – wind fetch d – wind duration U cur – water currents T s – sea surface temperature S – salinity C k – concentration, type (k) of surface active materials

Workshop 11 May4Whitecaps database Anguelova et al., NRL Motivation Sea spray Sea spray Heat exchange Heat exchange Tropical storm intensification Tropical storm intensification Sea-salt aerosols Sea-salt aerosols Direct effect – cooling Direct effect – cooling Indirect effect Indirect effect Dominate the activation of CCN Dominate the activation of CCN Compete with SO42- aerosols Compete with SO42- aerosols Halogen chemistry Halogen chemistry Reactive Cl and Br Reactive Cl and Br Tropospheric O3 Tropospheric O3 Sink of S Sink of S Whitecaps Whitecaps Gas exchange Gas exchange Ocean albedo Ocean albedo Geophysical retrievals Geophysical retrievals Surface wind Surface wind Ocean color Ocean color Salinity Salinity Photo courtesy of C. Fairall

Workshop 11 May5Whitecaps database Anguelova et al., NRL Framework Improve existing or develop new models Improve existing or develop new models Investigate correlations Investigate correlations Extensive database: W + various factors Extensive database: W + various factors Measurements: W + various factors Measurements: W + various factors Existing W measurements Existing W measurements Photographs/video images Photographs/video images Insufficient for extensive database Insufficient for extensive database Alternative approach: From satellites to get Alternative approach: From satellites to get Global coverage Global coverage Wide range of meteo & environ conditions Wide range of meteo & environ conditions Whitecap variability:   

Workshop 11 May6Whitecaps database Anguelova et al., NRL Remote sensing of sea foam Strong whitecaps signature in Vis, IR,  W Strong whitecaps signature in Vis, IR,  W Microwave region Microwave region Improvements Improvements More physical models More physical models Independent variables Independent variables

Workshop 11 May7Whitecaps database Anguelova et al., NRL Data Independent sources Independent sources T B from WindSat T B from WindSat V, L from SSM/I or TMI V, L from SSM/I or TMI U 10 and  from QuikSCAT or GDAS U 10 and  from QuikSCAT or GDAS T s from GDAS T s from GDAS S = 34 psu S = 34 psu Trade-off: Sampling issues Trade-off: Sampling issues GDAS (6-hr analyses) GDAS (6-hr analyses) Only 4 full swaths Only 4 full swaths Large time differences Large time differences QuikSCAT QuikSCAT Chunks of swaths Chunks of swaths Asc/desc passes opposite Asc/desc passes opposite Sample count

Workshop 11 May8Whitecaps database Anguelova et al., NRL Whitecaps data base All available orbits for All available orbits for Low resolution (50×70 km2) Low resolution (50×70 km2) Time period Time period Entire 2006 Entire 2006 Months of 2003, 2007 and 2008 Months of 2003, 2007 and 2008 Gridding data Gridding data With 0.5  x 0.5  grid box With 0.5  x 0.5  grid box Any other N  x N  possible Any other N  x N  possible Time periods: Time periods: Daily; Daily; Monthly Monthly Weekly (7 days) Weekly (7 days) 3-days 3-days

Workshop 11 May9Whitecaps database Anguelova et al., NRL Other factors besides W 6 additional variables 6 additional variables Wind speed ( U 10 ) Wind speed ( U 10 ) Wind direction (  ) Wind direction (  ) Sea surface temperature ( T s ) Sea surface temperature ( T s ) Air 2 m ( T a ) Air 2 m ( T a ) Wave field Wave field Significant wave height ( H s ) Significant wave height ( H s ) Mean wave period ( T p ) Mean wave period ( T p ) Various sources Various sources Other satellites (QuikSCAT) Other satellites (QuikSCAT) Models Models GDAS GDAS NWW3 NWW3 Mar 2006

Workshop 11 May10Whitecaps database Anguelova et al., NRL Derived environmental factors Mar 2006 Stability,  T () Stability,  T (  C ) Mar 2006 Fetch, X (km)  T > 0 | Stable | Reduced mixing  T < 0 | Unstable | Increased mixing Atmospheric stability proxy Atmospheric stability proxy Fetch Fetch

Workshop 11 May11Whitecaps database Anguelova et al., NRL Geographic characteristics of W March,  x 0.5  Wind speed formula Satellite, GHz, H pol. Satellite, 10.7 GHz, H pol.

Workshop 11 May12Whitecaps database Anguelova et al., NRL Seasonal variations of W 37H Dec-Jan-Feb

Workshop 11 May13Whitecaps database Anguelova et al., NRL Mar-Apr-May Seasonal variations of W 37H

Workshop 11 May14Whitecaps database Anguelova et al., NRL Jun-Jul-Aug Seasonal variations of W 37H

Workshop 11 May15Whitecaps database Anguelova et al., NRL Sep-Oct-Nov Seasonal variations of W 37H

Workshop 11 May16Whitecaps database Anguelova et al., NRL Spatial and temporal variations Every 5th day in March 2006

Workshop 11 May17Whitecaps database Anguelova et al., NRL Spatial and temporal variations Every 5th day in July 2006

Workshop 11 May18Whitecaps database Anguelova et al., NRL Spatial and temporal variations Every 5th day in November 2006

Workshop 11 May19Whitecaps database Anguelova et al., NRL Correlation maps Used 0.5  0.5  gridded data of W and other variables Used 0.5  0.5  gridded data of W and other variables Time period to investigate Time period to investigate Seasonal variations over 2006 Seasonal variations over 2006 Time series of W and x assembled for each grid box Time series of W and x assembled for each grid box Monthly data  up to 12-points per grid box Monthly data  up to 12-points per grid box Weekly data  up to 43-points per grid box Weekly data  up to 43-points per grid box 3-days data  up to 109-points per grid box 3-days data  up to 109-points per grid box Find correlation coefficients ( r ) for Find correlation coefficients ( r ) for each W-x pair each W-x pair in each grid box in each grid box r is a measure of the strength (or presence) of linear dependence between two variables r is a measure of the strength (or presence) of linear dependence between two variables

Workshop 11 May20Whitecaps database Anguelova et al., NRL Correlation maps W vs  T W vs XW vs U 10

Workshop 11 May21Whitecaps database Anguelova et al., NRL Factors contribution to W variance Use the correlation maps Use the correlation maps For each grid box obtain r 2 (coefficient of determination) for each W-x relation For each grid box obtain r 2 (coefficient of determination) for each W-x relation Rank r2 from min to max in each grid box Rank r2 from min to max in each grid box The factor with the max( r 2 ) contributes the most at this grid box The factor with the max( r 2 ) contributes the most at this grid box Color-code each factor contribution Color-code each factor contribution

Workshop 11 May22Whitecaps database Anguelova et al., NRL Contributions to W variance Monthly data, correlations on up to 12 data points

Workshop 11 May23Whitecaps database Anguelova et al., NRL Summary Foam fraction W estimates from WindSat TB data Foam fraction W estimates from WindSat TB data Data base of W assembled with other variables Data base of W assembled with other variables Whitecaps variability Whitecaps variability Correlations Correlations Regional contributions of various factors Regional contributions of various factors Work in progress Work in progress

Workshop 11 May24Whitecaps database Anguelova et al., NRL Additional slides

Workshop 11 May25Whitecaps database Anguelova et al., NRL Validation Insufficient ground truth values Insufficient ground truth values Data collection Data collection Slow and expensive Slow and expensive Sporadic and non-systematic Sporadic and non-systematic Limited range of conditions Limited range of conditions Fewer in situ-satellite matches in time and space Fewer in situ-satellite matches in time and space Different principles of measurement Different principles of measurement Visible photography vs microwave radiometry Visible photography vs microwave radiometry

Workshop 11 May26Whitecaps database Anguelova et al., NRL Various validation approaches

Workshop 11 May27Whitecaps database Anguelova et al., NRL Direct validation: results 180-min time window

Workshop 11 May28Whitecaps database Anguelova et al., NRL Samples available for 1 month Sample count High latitudes with high winds are under represented. High latitudes with high winds are under represented.

Workshop 11 May29Whitecaps database Anguelova et al., NRL Seasonal variations Dec-Jan-Feb Mar-Apr-MayJun-Jul-AugSep-Oct-Nov 10H

Workshop 11 May30Whitecaps database Anguelova et al., NRL Seasonal variations Dec-Jan-FebMar-Apr-May Jun-Jul-Aug Sep-Oct-Nov 10H