Polar Regions and Cryosphere Monitoring from Space: Future Outlook. P. Silvestrin EOP-SF ESA ESTEC

Transcription

1 Polar Regions and Cryosphere Monitoring from Space: Future Outlook P. Silvestrin EOP-SF ESA ESTEC

2 Programmatic Context Copernicus Evolution now top of agenda, seen over two time horizons (services extension / expansion): next-generation (horizon 2030+): enhanced continuity of observations of current CSC and new capabilities to meet user requirements (UR) shorter horizon (~2025) evolution, for additional capabilities in support of currently identified needs ESA to ensure that evolutions of the requirements framework shall also incorporate the results of capability-driven and technology-driven R&D to improve the match between user needs and the services offered (ESA-EU Copernicus Agreement) ESA inputs on observation capabilities to address new and emerging applications for consideration in the UR definition Analyses not exhaustive Evolution of other (European) capabilities shall be considered, e.g. EUMETSAT missions (for instance, new radiometer data), to achieve synergies (cf EUM presentation at EC User Requirements Workshop on Polar and Snow Cover Appl.)

3 Copernicus Space Component (CSC) Evolution: potentially relevant concepts Below is a very tentative ordering of concepts in terms of expected need time vs. technical readiness (in-orbit deployment from ~2025) Only limited subset can be realised è user requirements process, thematic Task Forces,.. Only concepts relevant to polar and snow observations outlined CO 2 fossil emissions monitor Polar ice & ocean topography Land hyperspectr imaging Mass transport Securityoriented imaging Thermal imaging Polar imaging from HEO Low frequ. passive radiometry Polar ice imaging etc ~2025 > 2025 >2030

4 Polar Ice and Sea Topography by Interferometric SAR Altimetry Main objectives: Monitor critical and direct climate change signals: ice cap melting and sea level Support monitoring Arctic ice and sea conditions Support applications related to coastal and inland waters Support Arctic policies for environmental protection, sustainable development and international cooperation, operational services Application areas (examples): Climate (ice and snow monitoring, ocean circulation, sea state, sea level,..) Weather, climate and seasonal forecasting Coastal and marine environment Global marine and inland water resources incl. glaciers Emergency management (hydrology, ocean monitoring) Required observations: Enhanced continuity of ice thickness In blue: relative contribution of CS-2 to sea level maps (source: GODAE OceanView) Snow depth and snow cover High-latitude ocean circulation Lakes, rivers, glaciers, and coastal water levels

5 Polar Ice and Sea Topography by Interferometric SAR Altimetry Baseline: interferometric SAR altimeter, POD in NRT, CryoSat orbit Various ways to ensure enhanced continuity of CryoSat: affordable enhancements considered in order to operationalise mission and unlock all applications of previous slide Enhancement Mission heritage Applications/Benefits CS2 (2010) Technology Enablers/Improvements 1 Optimisation of altimeter modes, continuous operation SAR(In) Poseidon-4 (Sent-6) Improved performance Digital architecture (Pos-4) à flexibility over waveform parameters, bands used 2 Swath processing mode SIRAL (CryoSat-2) All applications requiring frequent revisits Capacity improvement in data handling Optimisation of instrument parameters 3 Addition of Kaband channel AltiKa Poseidon-4 (Sent-6) [GPM, OIB and Cryovex campaigns] Snow thickness Improved ice thickness Coupling ice-atmosphere Glacier topography Inland waters (lakes, rivers) Ionospheric correction Ka band electronics (AltiKa) Digital architecture (Poseidon-4) 4 Dual- or tri-band radiometer MWR (Sent-3) AMR-C (Sent-6) Ocean and coastal altimetry Sea level rise (global) Atmospheric correction in NRT products 5 High resolution multi-frequency radiometer (as extension to 4) MHS (MetOp) MWS (MetOp-SG) Coastal & inland altimetry Glacier topography Sea ice concentration Snow cover and water content

6 Imminent activities: Ku+Ka-band Synergy Campaign Objectives a. Document additional value of coincident Kaand Ku-band acquisitions in cryosphere b. Support detailed investigations into signal properties (e.g. penetration into snow cover, signal strength) c. Technical inputs to Ku- and Ka-band mission feasibility studies with industry Campaign Details a. First measurement campaign: October 2016 (Greenland). Follow-on in b. Prime Contractor: DTU (DK) c. Ku-band RA ASIRAS (RST-CH) d. Ka-band RA (Metasensing NL) e. Ground teams (Univ. Leeds UK) In addition: Arctic+ STSE Study starting Other science / user oriented activities also in planning Technical definition (antenna, HPA,..)

7 (Ku+Ka)-Band radar preliminary configuration (Ku+Ka)-Band nadir altimeter timeline (courtesy TAS) a. Ku-Band chronogram not modified wrt nominal configuration b. Alternate operation between Ku- and Ka-Band pulses LRM mode SAR mode SARin mode

8 Low frequency radiometry for polar and snow applications microwave passive radiometry: all-weather technique capable to observe many parameters e.g. sea ice concentration, sea ice thickness, freeze and thaw state of soil, snow parameters, storm tracking,.. backbone: long data record of sea ice parameters (~37 y) complemented by active sensors e.g. scatterometers (for operational products like sea ice drift from OSI SAF) typ. low resolution; supports other measurements (SST, near-surface wind) Europe will continue to contribute with suite of sensors of EUM polar missions e.g. on MetOp-SG: MicroWave Imager (MWI), Ice Cloud Imager (ICI) [complemented by SCA] major gap at low-frequency (<~10 GHz), lack of heritage (large sensor if good resolution wanted; multi-frequency)

9 Low frequency radiometry for polar and snow applications microwave passive radiometry: all-weather technique capable to observe many parameters e.g. sea ice concentration, sea ice thickness, freeze and thaw state of soil, snow parameters, storm tracking,.. backbone: long data record of sea ice parameters (~37 y) complemented by active sensors e.g. scatterometers (for operational products like sea ice drift from OSI SAF) typ. low resolution; supports other measurements (SST, near-surface wind) Europe will continue to contribute with suite of sensors of EUM polar missions e.g. on MetOp-SG: MicroWave Imager (MWI), Ice Cloud Imager (ICI) [complemented by SCA] major gap at low-frequency (<~10 GHz), lack of heritage (large sensor if good resolution wanted; multi-frequency) however, for L-band (1.4 GHz): SMOS (Soil Moisture and Ocean Salinity) Earth Explorer (launched 2009 healthy mission that will be further extended, like CryoSat-2) SMOS demonstrated synthetic-aperture radiometry (specific configuration, extendable)

10 SMOS ESA UNCLASSIFIED For Official Use

11 Data products from SMOS (to be updated)

12 Cryosphere Applications of SMOS Proven objective: daily sea ice thickness estimates based on SMOS observations up to maximum values of ~0.5 m Applications developed: Sea Ice (thin ice) q thickness q snow depth Land Ice q internal ice temperature q bedrock topography q surface characteristics Ice Shelves q ice temperature q marine ice q iceberg tracking Terrestrial Cryosphere q freeze / thaw state q snow density/ground permittivity q temperature gradient SMOS-derived sea ice thickness for February and March (average) from 2011 to Credit: University of Hamburg

13 Sea Ice Thickness From SMOS (1) Why thin ice matters [credit: Kaleschke, UHH]

14 Sea Ice Thickness from SMOS (2) ü 6 years of SMOS sea ice thickness data (Oct- Apr) ü h"p://icdc.zmaw.de (latency ~1 day) ü SMOSIce 2014 campaign provided unique thin ice validaeon data set ü Thickness retrieval up to 1.5 m ü Impact studies suggest that SMOS ice data are valuable for assimilaeon in prediceve models ü SMOS+CryoSat2 synergy product to be released soon ü Ice thickness informaeon useful for NWP, climate monitoring, ship roueng, ice chareng,...

15 Soil Freeze / Thaw Areas affected by soil freezing cover ~50% of Earth s landmass Soil freezing affects Surface energy balance Surface and subsurface water flow Carbon exchange Photosynthesis Microbial acevity InformaEon on soil freezing and thawing is important to forest industry (harvesters) and hydro power plants (river runoff) Possibility to acquire daily informaeon on soil freeze/thaw state in global scale is verified using SMOS observaeons Currently only two demonstraeon L- band radiometry missions: SMOS (ESA) and SMAP (NASA) Soil freeze onset date for year 2013, determined using SMOS- based soil freeze/thaw product AddiEonal informaeon and be\er spaeal resolueon possible with combined use of L- band passive and C- band aceve instruments (e.g. SenEnel- 1) Regional resolueon enhancement: combined use of L- band SMOS and C- band SAR (ENVISAT ASAR)

16 Snow Density Amount, distribueon, and variability of terrestrial SWE: key missing parameters in numerical weather prediceon, climate projeceons and hydrological forecasts ESA GlobSnow SWE and EUMETSAT H- SAF products Provide +35 years of hemispheric, daily Eme series; 25 km EASE grid Known deficiencies: assumpeon of constant snow density (240 kg/m3) and soil permigvity (6- j) L- band passive microwave data can provide informaeon on snow density and soil permigvity (Schwank et al., 2015) SynergisEc use of L- band with sensors sensieve to SWE Schwank et al., 2015 a) L- band Dry- snow permigvity as a funceon of its density b) L- band PropagaEon angles in snow as funceon of observaeon angle Snow Density product prototype using NASA SMAP (credit GAMMA Remote Sensing, FMI, JPL)

17 Applications to Antarctica availability of L-band data opens new scenarios for ice sheets / shelves because of the very low permittivity of dry snow at L-band (Re: 1.5-2, Im: 10-4) and then to an high penetration depth ( 500 m). Moreover L-band can detect surface processes. Monitoring ice shelves stability Example of applications Ice sheet Temperature Profile SMOS SMOS at nadir for northern Larsen C ice shelf (nadir) showing the strong and continuous decline over 5 years. Glaciological model Snow Melting Number of melting days derived by SMOS: deeper layers are detected. signature over the Antarctic plateau (not visible with higher frequencies) reveals sensitivity to ice sheet temperature profile

18 Polar Atmosphere: Polaris Hosted Arctic Imager (HAI) one of the concepts (with strong heritage) in ESA s Polaris studies current observations based on GEO stop at o latitude WMO identified filling this gap as 2025 priority, but past attempts, e.g. Canada PCW - endorsed by meteo agencies and others incl. US CMTS (Ten-year Prioritization of Infrastructure Needs in US Arctic, 2016) - seem stalled concept: HAI in Highly Elliptical Orbit (HEO, apogee ~50000 km) telecom satellites

19 Polar Atmosphere Polaris HAI basic data for weather and climate (and crucial for extreme events at midlatitudes), but applications also for e.g. land and emergency management sensor imager like SEVIRI on MSG: ~12 channels, 3 km resolution, imaging every 15 min, flying in HEO (TAP) orbit è specifications to be adapted to constraints Volcano ash Atmospheric Motion Vector (AMV) Aerosol Optical Depth Land/Sea surface temperature Contrails Couplings ice-arctic weather-extreme events

20 Synergistic mission concept HAI mission GEO gap common to EO and telecom, hence multi-domain HEO missions synergistic mission relevant to EU Arctic Policy needs: mobile communications, EO multi-domain scenario: Hosted Arctic Imager (HAI) alongside a telecom P/L in HEO best scenario is with polar system in PPP with telecom operator (delivering EO data e.g. SAR-based imagery) no technical issue, studies show that resulting constraints (mass/size) are compatible with MSG-level specifications Gaps in Inmarsat mobile comms coverage PCW

21 Polar Sea Ice: Polaris Train L-band Polaris studies of observation gaps ranked multi-frequency (near-simultaneous) L+C- (L+X-) band SAR observations as crucial for polar applications like navigation safety better ice penetration added bands improve characterisation detection of melting ponds in summer and better classification with summer or thin ice (higher frequency for winter ice) recurrent goal of users: e.g. NASA/ESA proposed missions (EOS-C, CLIMACS for sea ice) after experience of Shuttle SIR-C (L, C, X) in 1990s some issues to be addressed e.g. snow cover induced errors promising new possibilities from (passive) bistatic SAR RadarSat 1, 15 July 07 PALSAR, 16 July 07 C-Band SAR L-Band SAR

22 A polar train (Polaris) concept 1. Polaris concept is strongly operationally oriented and considers context: Sentinel-1, Radarsat Constellation Mission, other (commercial) SAR 2. Evolutive convoy concept leading to a polar convoy or P-train 3. Ongoing work on main element: L-band satellite companion to Sentinel-1 enabling L+C dual-frequency SAR Sentinel-1 A+B P-train Main element L-Band SAR P-Train Secondary Passive C-band SAR P-Train Secondary Ku-band SAR Sea Ice daily Full Sea Ice daily 3D Ice mapping Snow Cover Extent Concentration Type Thickness (coarse) +Melting ponds +Iceberg drift +Ridges, structure +Thickness (fine) +Topography +Fine structure +Edges +Snow Water +Snowmelt Operator-led Winter-focused Fully automated sea ice charting Summer-capable State-of-the-art mapping Full domain Cryosphere capacity

23 Polar Sea Ice: Polaris Train L-band goal: automated year-round high-resolution NRT (daily) ice charting, from L-SAR tandem with existing C- or X-band SARs mid/long-term endeavour, but build-up possible with SAR in development (L-band: NISAR, SAOCOM, Tandem-L,..), incl. imminent commercial EO (e.g. UrtheCast s L+X OptiSAR const.) Sentinel-1 L-band SAR Passive C-band SAR

24 Mass Transport from Space Gravimetry CHAMP GRACE 2002-? GOCE GRACE-FO : continuity of GRACE + laser tracking demonstrator (5 year from 2017)

25 What can we do with (future) gravimetry data? direct measure of land ice mass change (crucial to è remove largest uncertainty in sea level predictions) ice sheets: mass change and sea level

26 Spatial and temporal scales associated with gravity changes relevant to cryosphere Pail, R. et al, Science and User Needs for Observing Global Mass Transport to Understand Global Change and to Benefit Society, Surv. Geophysics (2015) 36: , DOI /s

27 Mapping Snow Water Equivalent (SWE) with passive microwave data significant progress recently made from innovative field campaigns, improved modeling (physical; emission), and new retrieval approaches Global SWE data sets are available at coarse scale (25 km resol.; time-series for ) nature of brightness temperature versus SWE relationship, combined with limitations of passive microwave measurements, means retrieval challenges remain: regional variability in land cover, snow properties temporal variability (snowpack evolution) though valuable for some climate and hydrological applications, current generation of satellite-derived SWE products unsuitable to address user needs in many applications and locations GLOBSNOW SWE product over Northern Hemisphere: Greenland, glaciers & mountains masked out Takala, M., Luojus, K., Pulliainen, J., Derksen, C., Lemmetyinen, J., Kärnä, J.- P, Koskinen, J., Bojkov, B., EsPmaPng northern hemisphere snow water equivalent for climate research through assimilapon of spaceborne radiometer data and ground- based measurements, Remote Sensing of Environment, Vol. 115, Issue 12, 15 December 2011, doi: /j.rse

28 Main SWE and SE product issues main reasons for differences and errors in SWE products from passive microwave measurements: limited spatial resolution saturation in deep snow (typically at >12 cm SWE), dense forest cover glaciers, mountains and all areas with some topography (need higher resolution data) main reasons for differences and errors in snow extent products: cloud cover dense forest cover low solar elevation angle (due to topography and in winter months in high latitudes regions in general) temporal and spatial variations of snow albedo, and anisotropy of snow reflectance mission concepts that could address some of the issues include high-frequ. radars (in convoy with eg MetOp-SG active/passive synergy), passive bistatic SAR with Sentinel-1 è need more studies, campaigns,. Results from ESA SnowPEx study