Studies of Austfonna ice cap (Svalbard) using radar altimetry with other satellite techniques

Similar documents
SNOW DEPTH AND SURFACE CONDITIONS OF AUSTFONNA ICE CAP (SVALBARD) USING FIELD OBSERVATIONS AND SATELLITE ALTIMETRY

GAMINGRE 8/1/ of 7

GLOBAL WAVEFORM SHAPE ANALYSIS FOR THE DETECTION AND MONITORING OF EPHEMERAL SURFACE WATER

Eddy and Chlorophyll-a Structure in the Kuroshio Extension Detected from Altimeter and SeaWiFS

The Arctic Energy Budget

Merged sea-ice thickness product from complementary L-band and altimetry information

High resolution geoid from altimetry & bathymetry: requirements for a future mission

Using Remote-sensed Sea Ice Thickness, Extent and Speed Observations to Optimise a Sea Ice Model

ERS-ENVISAT CROSS-INTERFEROMETRY SIGNATURES OVER DESERTS. Urs Wegmüller, Maurizio Santoro and Christian Mätzler

Life Cycle of Convective Systems over Western Colombia

The Climate of Marshall County

Ice sheet mass balance from satellite altimetry. Kate Briggs (Mal McMillan)

WHEN IS IT EVER GOING TO RAIN? Table of Average Annual Rainfall and Rainfall For Selected Arizona Cities

TECH NOTE. New Mean Sea Surface for the CryoSat-2 L2 SAR Chain. Andy Ridout, CPOM, University College London

The Climate of Seminole County

The Climate of Bryan County

Scarborough Tide Gauge

Yellow Sea Thermohaline and Acoustic Variability

CryoSat-2: A new perspective on Antarctica

The Climate of Pontotoc County

Lake level variations from satellite radar altimetry with retracking of multi-leading edge

The Climate of Payne County

Snowcover along elevation gradients in the Upper Merced and Tuolumne River basin of the Sierra Nevada of California from MODIS and blended ground data

Local Ctimatotogical Data Summary White Hall, Illinois

S3-A Land and Sea Ice Cyclic Performance Report. Cycle No Start date: 30/09/2017. End date: 27/10/2017

Supplementary appendix

The Climate of Texas County

Chiang Rai Province CC Threat overview AAS1109 Mekong ARCC

Mass balance of sea ice in both hemispheres Airborne validation and the AWI CryoSat-2 sea ice data product

Atmospheric circulation analysis for seasonal forecasting

Variability and trends in daily minimum and maximum temperatures and in diurnal temperature range in Lithuania, Latvia and Estonia

The impact of the assimilation of altimeters and ASAR L2 wave data in the wave model MFWAM

Aquarius Data Release V2.0 Validation Analysis Gary Lagerloef, Aquarius Principal Investigator H. Kao, ESR And Aquarius Cal/Val Team

The Climate of Kiowa County

ESA Sea Level Climate Change Initiative. Sea Level CCI project. Phase II 1 st annual review

Recent improvements on the wave forecasting system of Meteo-France: modeling and assimilation aspects

Interannual variation of MODIS NDVI in Lake Taihu and its relation to climate in submerged macrophyte region

Swath Mode Altimetry. Noel Gourmelen

Geophysical Correction Application in Level 2 CryoSat Data Products

The Climate of Murray County

S3 Product Notice Altimetry

Recent Improvements in the U.S. Navy s Ice Modeling Efforts Using CryoSat-2 Ice Thickness for Model Initialization

Geoid and MDT of the Arctic Ocean

The Climate of Grady County

Jackson County 2013 Weather Data

Sentinel-3 A STM (Altimetry) Disclaimer

Location. Datum. Survey. information. Etrometa. Step Gauge. Description. relative to Herne Bay is -2.72m. The site new level.

Average temperature ( F) World Climate Zones. very cold all year with permanent ice and snow. very cold winters, cold summers, and little rain or snow

S3-A Land and Sea Ice Cyclic Performance Report. Cycle No Start date: 21/04/2017. End date: 18/05/2017

Fri. Apr. 06, Map Projections Environmental Applications. Reading: Finish Chapter 9 ( Environmental Remote Sensing )

Predictability of Sudden Stratospheric Warmings in sub-seasonal forecast models

Location. Datum. Survey. information. Etrometa. Step Gauge. Description. relative to Herne Bay is -2.72m. The site new level.

On the impact of the assimilation of ASAR wave spectra in the wave model MFWAM

The Climate of Haskell County

Agricultural Science Climatology Semester 2, Anne Green / Richard Thompson

An Atlas of Oceanic Internal Solitary Waves (February 2004) by Global Ocean Associates Prepared for Office of Naval Research Code 322 PO

2015 Fall Conditions Report

GTR # VLTs GTR/VLT/Day %Δ:

Climatography of the United States No

Eddy Shedding from the Kuroshio Bend at Luzon Strait

Climatography of the United States No

Climatography of the United States No

Jackson County 2018 Weather Data 67 Years of Weather Data Recorded at the UF/IFAS Marianna North Florida Research and Education Center

Snow property extraction based on polarimetry and differential SAR interferometry

Quantification of energy losses caused by blade icing and the development of an Icing Loss Climatology

The Climate of Oregon Climate Zone 4 Northern Cascades

P7.7 A CLIMATOLOGICAL STUDY OF CLOUD TO GROUND LIGHTNING STRIKES IN THE VICINITY OF KENNEDY SPACE CENTER, FLORIDA

Appendix B. A proposition for updating the environmental standards using real Earth Albedo and Earth IR Flux for Spacecraft Thermal Analysis

TEMPORAL VARIABILITY OF ICE FLOW ON HOFSJÖKULL, ICELAND, OBSERVED BY ERS SAR INTERFEROMETRY

Winter Season Resource Adequacy Analysis Status Report

A Factor of 2-4 Improvement in Marine Gravity and Predicted Bathymetry from CryoSat, Jason-1, and Envisat Radar Altimetry: Arctic and Coastal Regions

Climatic and Ecological Conditions in the Klamath Basin of Southern Oregon and Northern California: Projections for the Future

CloudSat Data Processing Center (DPC)

Climatography of the United States No

Climatography of the United States No

Climatography of the United States No

Climatography of the United States No

Changes in spatial distribution of chub mackerel under climate change: the case study using Japanese purse seine fisheries data in the East China Sea

1 The satellite altimeter measurement

Remote sensing of sea ice

The impact of combined assimilation of altimeters data and wave spectra from S-1A and 1B in the operational model MFWAM

Physical Features of Monsoon Asia. 192 Unit 7 Teachers Curriculum Institute 60 N 130 E 140 E 150 E 60 E 50 N 160 E 40 N 30 N 150 E.

Today s Lecture: Land, biosphere, cryosphere (All that stuff we don t have equations for... )

Climatography of the United States No

Climatography of the United States No

2018 Annual Review of Availability Assessment Hours

ALASKA REGION CLIMATE FORECAST BRIEFING. January 23, 2015 Rick Thoman ESSD Climate Services

Optimal Spectral Decomposition (OSD) for Ocean Data Analysis

Climatography of the United States No

Climatography of the United States No

Sierra Weather and Climate Update

British Colombia Knight Inlet Strait of Georgia Strait of Juan de Fuca

Habitat Suitability for Forage Fishes in Chesapeake Bay

Analysis of Antarctic Sea Ice Extent based on NIC and AMSR-E data Burcu Cicek and Penelope Wagner

The MODIS Cloud Data Record

Funding provided by NOAA Sectoral Applications Research Project CLIMATE. Basic Climatology Colorado Climate Center

Spectral Albedos. a: dry snow. b: wet new snow. c: melting old snow. a: cold MY ice. b: melting MY ice. d: frozen pond. c: melting FY white ice

YACT (Yet Another Climate Tool)? The SPI Explorer

Analysis of mining deformations based on PSInSAR technique case study of the Walbrzych coal mines (Poland)

Project No India Basin Shadow Study San Francisco, California, USA

Transcription:

15 Years of progress in Radar Altimetry Symposium Ocean surface topography science team (OSTST) International Doris Service (IDS) Workshop, Argo Workshop 13-18 March 2006, Venice, Italy Alexei V. Kouraev, Benoit Legrésy, Frédérique Remy Studies of Austfonna ice cap (Svalbard) using radar altimetry with other satellite techniques Laboratoire d'etudes en Geophysique et Oceanographie Spatiales (LEGOS), Toulouse France State Oceanography Institute, St. Petersburg branch, St. Petersburg, Russia

Austfonna - largest ice cap in the Eurasian Arctic AUSTFONNA Austfonna - 8120 km 2, Vestfonna - 2500 km 2 Etonbreen 0 m 767 m Digital Elevation Model (DEM), courtesy of the Norwegian Polar Institute (NPI), Tromsoe, Norway

ETONBREEN: RECENT CHANGES FROM SPOT 1 km Etonbreen + Basin 03: continuous rapid retreat 1-1987 April 15-21 2-1988 July 28 3-1991 July 18-29 4-1993 July 19 5-1998 March 28 1 2 3 4 5 Etonbreen DATA: SPOT 1-4 imagery for 1987, 1988, 1991, 1993, 1998 from ISIS project 0506-778. Period Change, km2 Rate, km2/yr 15/04/1987-28/07/1988-0.75-0.58 28/07/1988-29/07/1991-3.04-1.01 29/07/1991-19/07/1993-1.42-0.72 19/07/1993-28/03/1998-2.25-0.48 Basin 03

DATA: ENVISAT RA2 data for cycles 10 to 40 (Oct 2002 - Aug 2005). ICE2 retracker. Data quality control: Non-blank values of backscatter and range in Ku band, heights more than -1 m above the geoid. Number of observations 25-31 20-24 15-19 10-14 ENVISAT AUSTFONNA COVERAGE Eastern coast - few data (altimeter locked), central part - OK 4500 4000 3500 3000 2500 2000 1500 1000 500 0 Number of observations: monthly changes for cycles 10 to 36 1 2 3 4 5 6 7 8 9 10 11 12 Number of observation decreases in summer (melting)

Orbit changes across track are much larger than distance between 18 Hz measures (1500 m vs 400 m) CHOICE OF REFERENCE POINTS FOR TEMPORAL ANALYSIS Use of latitude or longitude for study of temporal changes is misleading 1500 1500 1500 1500 1500 1500 1500 1500 1500 m m m m m m m m m Solution: Choosing one cycle as reference and referring data for each cycle to the nearest reference point 400 m Location of 18 Hz ENVISAT points for cycles 10 to 36. Track 20.

Height: good relation to DEM, temporal variability low on top, high on slopes SPATIAL DISTRIBUTION: MEDIAN VALUES Backscatter: low values for ice, low temporal variability Haight (Ku) Backscatter (Ku)

Latitude 80.4 80.2 79.9 79.7 ENVISAT GR*1000 Monthly values GR= (TB365 - TB238) / (TB238 + TB365) JAN FEB MAR SNOW DEPTH GR ratio: linear relation to snow depth 79.4 79.2 80.4 80.2 APR MAY JUN ENVISAT: maximal values NE, GPR - NE and SE Latitude 79.9 79.7 79.4 79.2 80.4 JUL AUG SEP 50 40 30 20 10 NPI Ground Penetrating Radar (GPR) snow depth (2004) and interpolated map. Courtesy of A. Taurisano, NPI 80.2 0 Latitude 79.9 79.7 79.4-10 -30-50 79.2 80.4 OCT NOV DEC 80.2 Latitude 79.9 79.7 79.4 79.2 18 20 22 24 26 Longitude 18 20 22 24 26 Longitude 18 20 22 24 26 Longitude

D 2005.2 C B Backscatter (S) D C A B TEMPORAL VARIABILITY ALONG THE TRACK Backscatter: high values in summer near the ice edge (melting), low values in winter on top dtb - strong melting signal in summer A 2005.2 dtb (TB23.8-TB36.5) D C B A 2005.0 2005.0 2004.8 2004.6 2004.8 2004.6 SUMMER 30000 2004.4 12 2004.4 2000 1750 1500 2004.2 2004.0 8 4 2 2004.2 2004.0 1250 1000 800 600 400 200-1000 2003.8 2003.6 2003.4 2003.2 2003.0-480 -470-460 -450-440 -430-420 -410-400 -390-380 -370-360 0-2 -4-6 -8-12 -16 2003.8 2003.6 SUMMER 2003.4 2003.2 2003.0-480 -470-460 -450-440 -430-420 -410-400 -390-380 -370-360

D C B HEIGHT PROFILE - MEAN AND ANOMALIES A TOP A B C D A B C D Mean height (mm relative to reference ellipsoid) in Ku band for track 853. Opposite changes (swinging mode) before and after top of AID. No real seasonal signal, but something intriguing is evident... Anomalies in mm (from median values for cycles 11 to 31) of height in Ku band for track 853.

Artefact related to changes in orbit over topography! INFLUENCE OF ORBIT CHANGE ON ALTIMETRIC MEASURES) Anomalies of Height in Ku band, color scale is the same as previous slide D C B A

Sloping and hilly terrain: complicated radar echo SLOPE AND TOPOGRAPHY INFLUENCE Nadir line (shortest distance in theory) R A Shortest distance in reality Case 2: first echo comes not from the nadir point Simulation of radar return echo at various time steps for different topography

Overestimation on slopes SLOPE AND CURVATURE INFLUENCE Theoretical line (Measured = DEM) Good agreement, topography influence is low Altimeter locked Comparison of measured height in Ku band vs NPI DEM

Slope, cycle 18 SLOPE AND CURVATURE CORRECTIONS Altimeter X and Y position DEM DEM subset in +-20 km direction from X and Y Curvature, cycle 18 Approach: for a given DEM subset approximate (least square solution) Hdem=a*x2+ b*y2+ c*x*y + d*x + e*y + f Coefficients (a,b,c,d,e,f) Slope and curvature values for a given DEM subset

DEM SOURCE 2: SAR INTERFEROMETRY (1995-1996) Amplitude Coherence DEM Three ERS-1 and ERS-2 tandem pairs from SPRI November 1995, December 1995, January 1996

DEM SOURCE 3: SPOT STEREO Austfonna (Etonbreen region): stereo pairs for 1987 (SPOT1) and 1991 (SPOT2) from the ISIS project 0506-778

ITERATIVE APPROACH: SIMULATED AND MEASURED RA DATA DEM SIMULATOR ENVISAT Correcting errors due to DEM inclination plane SIMULATED WAVEFORM COMPARISON MEASURED WAVEFORM DEM CORRECTIONS QC, DATA SELECTION AND INTERPRETATION