ASCAT sensors onboard MetOps : application for sea ice

Slides:

Advertisements

Similar presentations

David Prado Oct Antarctic Sea Ice: John N. Rayner and David A. Howarth 1979.

Advertisements

Discussion about two papers concerning the changing Arctic sea ice GEO6011Seminar in Geospatial Science and Applications Wentao Xia 11/19/2012.

1 Information on the sea ice conditions in the Eurasian Arctic Vasily Smolyanitsky head of laboratory of sea ice climate manuals Arctic and Antarctic Research.

ESA Climate Change Initiative - Sea Ice project CMUG Fourth Integration Meeting Met Office Exeter, 2 – 4 June 2014 Stein Sandven, Nansen Environmental.

Sea Ice Exchange across the Canadian Arctic Archipelago from Microwave Satellites Sea Ice Exchange across the Canadian Arctic Archipelago from Microwave.

Using Scatterometers and Radiometers to Estimate Ocean Wind Speeds and Latent Heat Flux Presented by: Brad Matichak April 30, 2008 Based on an article.

Monitoring the Arctic and Antarctic By: Amanda Kamenitz.

Ron Kwok Jet Propulsion Laboratory California Institute of Technology Critically Needed: Continued 3-day RADARSAT coverage of the Western Arctic Ocean.

SWARP KO Bergen, 4 Feb WP4 : Satellite remote sensing of wave in ice

National Ice Center Science and Applied Technology Program Dr. Michael Van Woert, Chief Scientist.

Edge of the Arctic multiyear 83.2N, 62.0E on August 14, 2007 Some thoughts on sea ice retreat in 2007 Takashi Kikuchi (& Jun Inoue) Institute of.

Comparisons of Sea Winds Scatterometer with a hydrologic process model for the assessment of snow melt dynamics V. Sridhar a, Kyle C McDonald b and Dennis.

Comparison of SSM/I Sea Ice Concentration with Kompsat-1 EOC Images of the Arctic and Antarctic Hyangsun Han and Hoonyol Lee Department of Geophysics,

Precipitation and altimeter missions Jean Tournadre Laboratoire d’Océanographie Spatiale IFREMER Plouzane France.

Earth Observation from Satellites GEOF 334 MICROWAVE REMOTE SENSING A brief introduction.

Ocean and sea-ice data assimilation and forecasting in the TOPAZ system L. Bertino, K.A. Lisæter, I. Kegouche, S. Sandven NERSC, Bergen, Norway Arctic.

Results of the assimilation of sea ice concentration and velocity into a sea-ice-ocean model. John Stark, Mike Bell, Matt Martin, Adrian.

Connecting Sensors: SSM/I and QuikSCAT -- the Polar A Train.

Development and evaluation of Passive Microwave SWE retrieval equations for mountainous area Naoki Mizukami.

SWARP: WP2, wave modelling

1http://saf.met.noSAF on Ocean and Sea Ice SAF=Satellite Application Facility Distributed MSG/Metop ground segmentDistributed MSG/Metop ground segment.

WIND STRESS OVER INDIAN OCEAN Abhijit Sarkar, K Satheesan, Anant Parekh Ocean Sciences Division Space Applications Centre, INDIA ISRO-CNES Joint Programme.

Arctic System Model workshop III Montreal, Canada, July 17 th, :50 – 11:10 Ocean/Atmosphere observations A. Proshutinsky, WHOI a)Model forcing validation.

1) Canadian Airborne and Microwave Radiometer and Snow Survey campaigns in Support of International Polar Year. 2) New sea ice algorithm Does not use a.

Enhanced Resolution SSM/I Data Julienne Stroeve, David Long.

IOVWST Meeting May 2011 Maryland Calibration and Validation of Multi-Satellite scatterometer winds Topics  Estimation of homogeneous long time.

Evaluation of Passive Microwave Sea Ice Concentration in Arctic Summer and Antarctic Spring by Using KOMPSAT-1 EOC Hoonyol Lee and Hyangsun Han

SMOS Science Meeting September 2011 Arles, FR Simulating Aquarius by Resampling SMOS Gary Lagerloef, Yann Kerr & Eric Anterrieu and Initial Results.

Validation of ORCA05 regional configuration of the Arctic North Atlantic Christophe HERBAUT and Marie-Noëlle HOUSSAIS Charles DELTEL LOCEAN, Université.

EuroGOOS Arctic Task Team Workshop September 2006 Satellite data portals for Arctic monitoring Stein Sandven Nansen Environmental and Remote Sensing.

Global Ice Coverage Claire L. Parkinson NASA Goddard Space Flight Center Presentation to the Earth Ambassador program, meeting at NASA Goddard Space Flight.

SeaWiFS Highlights July 2002 SeaWiFS Celebrates 5th Anniversary with the Fourth Global Reprocessing The SeaWiFS Project has just completed the reprocessing.

Variability of Arctic Cloudiness from Satellite and Surface Data Sets University of Washington Applied Physics Laboratory Polar Science Center Axel J.

Figure 1. The three overlapping study regions. The small region is centered on Disko Bay. The areas of the small, medium, and large regions (not including.

Detection of Wind Speed and Sea Ice Motion in the Marginal Ice Zone from RADARSAT-2 Images Alexander S. Komarov 1, Vladimir Zabeline 2, and David G. Barber.

Monitoring of Sea Ice in the Arctic (ICEMON) Stein Sandven Research director Polar and Environmental Remote Sensing Group 20 October.

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.

ESTIMATING SEA ICE TRANSPORT INTO THE NORTH ATLANTIC USING THE AMSR-E SATELLITE SENSOR Tom A. Agnew, Climate Research Branch, Meteorological Service of.

Fig Wilkins Ice Shelf breakup events of (a) MODIS band 1 image 10 days after the end of the first event; (b) Envisat ASARimage during the second.

L2P and L3 SST data produced by EUMETSAT/OSI SAF and EC/MyOcean

NOAA use of Scatterometry Products Presented to CGMS-43 Working Group 2 session, agenda item 10 Author: Paul Chang.

Spaceborne Polarimetric Microwave Radiometer Brandon Ravenscroft

HSAF Soil Moisture Training

Changes in the Melt Season and the Declining Arctic Sea Ice

An Assessment of the Navy's Sea Ice Outlook Predictions for 2013

Satellite sea ice observations for validating midterm climate models

W. N. Meier, J. C. Stroeve, and J. Smith (Correspondence: Introduction

Yinghui Liu1, Jeff Key2, and Xuanji Wang1

Blending multiple-source wind data including SMOS, SMAP and AMSR-2 new products Joe TENERELLI, Fabrice COLLARD OceanDataLab, Brest, France.

American Geophysical Union San Francisco, December 5th - 9th 2011

Plans for Met Office contribution to SMOS+STORM Evolution

Operational Oceanography Science and Services for Europe and Mediterranean Srdjan Dobricic, CMCC, Bologna, Italy on behalf of National Group of Operational.

Release plan of the data sets (year)

V. Sridhara, Kyle C McDonaldb and Dennis P Lettenmaiera

Japanese perspective on GIIPSY Jinro Ukita (Chiba University)

W. N. Meier, J. C. Stroeve, and J. Smith (Correspondence: Introduction

Institute of Low Temperature Science, Hokkaido University

The Global Hydrological Cycle

Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing

Rain Gauge Data Merged with CMORPH* Yields: RMORPH

Calibration, Validation and Status of OSI SAF ScatSat-1 products

Hyangsun Han and Hoonyol Lee

ESTEC Contract N° 4000/10/NL/AF

Daily ”snapshot” of the Cryosphere (terrestrial SWE + Sea Ice)

FAY Dates School Year Traditional Fall 4x4 Spring 4x

Greenland U.K. RUSSIA CANADA 북극권 ~ 5,300 km JAPAN.

Space and time scales in satellite oceanography

Sea Ice 16 Sept 2009.

FAY Dates School Year Traditional Fall 4x4 Spring 4x

Recent activities of the OSVW-VC

Presentation transcript:

ASCAT sensors onboard MetOps : application for sea ice Fanny GIRARD-ARDHUIN IFREMER, Laboratory of Oceanography from Space, Plouzané, France Email : fanny.ardhuin@ifremer.fr ©ESA/EUMETSAT Backscatter values from scatterometers are commonly used to estimate wind field over oceans, their ability to monitor sea ice coverage, age and drift has been also demonstrated. Thanks to the work on previous scatterometers, the operational ASCAT scatterometer sensor onboard MetOp is used for sea ice applications with very good results. Incidence-adjusted backscatter coefficient maps are routinely processed over the polar areas for sea ice geophysical interpretation in order to increase the long time series of ice parameters already available through the previous scatterometers since the 1990's. The backscatter maps enable the discrimination between multi year ice and first year ice, these maps can also be used as a basic product for Arctic sea ice drift estimation. The synergy between ASCATs sensors onboard MetOp-A and -B enables to have an almost daily full coverage of the sea ice areas at both hemispheres. These routinely produced data are available at CERSAT/Ifremer for research and models/operational systems. Spatial coverage improved using both ASCATs on MetOp-A and -B Sea ice extent ASCAT-B Contour of the radiometer (SSM/I) sea ice edge (concentration=15%) Data gap patches ASCAT-A ASCAT A+B Homogeneous values over sea ice High values over open ocean areas ASCAT-A ASCAT-A ASCAT- A&B Sea ice search area Standard deviation of the backscatter for one day for each pixel, ASCAT/MetOp-A data. March 10, 2010. ASCAT A+B ASCAT-A 60 100 80 40 20 Antarctic daily sea ice 40° incidence-adjusted backscatter maps from ASCATs. Sept 15, 2014 The sea ice edge can be detected from scatterometers, in particular C-band frequency scatterometer (ASCAT) detects the first steps of freeze of the sea ice at fall. Arctic daily number of data for a pixel in the sea ice search area. ASCAT-B Using both ASCATs onboard MetOp-A & -B is very useful to improve the spatial coverage, needed in particular in the Antarctic area Sea ice roughness ERS, NSCAT, QuikSCAT, ASCAT scatterometers time series Following the ice type evolution during winter Russia Pixel resolution : 12,5 km (25 km for ERS) Since 1992 NOV NOV DEC FEB APR Daily (weekly for ERS) Multi year ice has survived at least a summer melt, it is thicker than first year ice. FY MY FY : first year ice MY : multi year ice Unique time series 1992-present available at CERSAT, ongoing with the ASCAT sensors Canada Greenland Sea ice roughness evolution during 2009-2010 winter using ASCAT/MetOp-A data Daily mean Arctic sea ice roughness at adjusted constant 40° incidence angle. April 7, 2015. From ASCAT/MetOp-A data. Sea ice displacement estimate at low resolution From daily backscatter maps, sea ice displacement can be inferred (method and validation in Girard-Ardhuin & Ezraty, IEEE TGRS 2012) Monthly drift maps 3 day-lag 6 day-lag Drift grid spacing: 62,5 km Daily except melt season SSMI Merged ASCAT/SSMI ASCAT IFREMER algorithm Examples of monthly merged ASCAT&SSMI sea ice displacement map for October and March, made from the 6 and 3 day-lags map estimate. Drift data density for Individual sensor (black, blue, green), Merged ASCAT&SSMI (red) and interpolated map (brown) for 2013-2014 winter. Individual sensor sea ice motion maps and Merged ASCAT&SSMI map, 3 day-lag (Oct. 5–8, 2007). Drift vectors less than one pixel are marked with a cross. Better data density for the Merged and filled product, in particular for early fall and early spring The merging of multi-sensors enables to have more vectors, sharply increases the number of data of the monthly drift maps also during freeze period, worst period of estimation  enables to estimate sea ice drift from September until May More than 80% data density Combination of the radiometer and scatterometer data increases the density by 20 to 30% Several applications ASCATs in tandem increase the vectors density up to 8 to 12% Estimation of sea ice fluxes (Laptev seas, Fram strait…) Assimilation of sea ice drift → greatly improves results on drift but also on concentration and thickness (Rollenhagen et al., JGR 2009) Estimation of the backtrajectory to find pollution origin Unique time series of sea ice displacement maps 1992-present, with SSM/I, SSM/I merged with QuikSCAT, ongoing with the ASCATs sensors merging with SSM/Is sensors Ifremer/CERSAT products available at ftp://ftp.ifremer.fr/ifremer/cersat/products/gridded/psi-drift/ ftp://ftp.ifremer.fr/ifremer/cersat/products/gridded/psi-backscatter/ and Products used in EU, GMES, space agencies projects : SWARP, SPICES, Climate Change Initiative, Copernicus...