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  1. 1. CoReH2O – A Dual Frequency Radar Satellite for Cold Regions HydrologyH. Rott1, D. Cline2, C. Duguay3, R. Essery4, P. Etchevers5, I. Hajnsek6,M. Kern7, G. Macelloni8, E. Malnes9, J. Pulliainen10, S. Yueh111 University of Innsbruck & ENVEO IT, Austria2 NOAA, NWS, Hydrology Laboratory, USA3 University of Waterloo, Canada4 University of Edinburgh, UK5 Meteo-France, Saint Martin d’Héres, France6 DLR-HR, Germany & ETH Zürich, Switzerland7 ESA-ESTEC, Noordwijk, NL8 IFAC-CNR, Firenze, Italy9 NORUT IT, Tromsǿ, Norway10 Finish Meteorological Institute, Helsinki, Finland11 JPL-Caltech, Pasadena, USA H. Rott –CoReH2O IGARSS 2011
  2. 2. Outline of the Presentation• Summary of mission objectives• Observation requirements• Retrieval concept for snow mass• Inversion of RT model• Examples for performance analysis - with simulated data - with experimental data• Conclusions H. Rott –CoReH2O IGARSS 2011
  3. 3. Objectives: Improved Snow and Ice ObservationsFor climate research • Snow and ice – two essential climate elements not well represented in climate models • In particular, snow mass is poorly knownHydrology and surface/atmosphere exchange processes • High-resolution data are needed to account for spatial variability of snowGlacier mass balance – climate interactions • An essential climate variable measured only for few glaciers • Global data are needed to quantify response to climate forcingSnowmelt and glacier runoff - a crucial water resource • Snow cover and glacier retreat caused by climate change may affect the water supply to hundreds of millions of people. • New models using spatially detailed snow observations are needed to improve water management and support adaptation to changes. H. Rott –CoReH2O IGARSS 2011
  4. 4. Observation Requirements Spatial scale Sampling AccuracyPrimary parameters Regional/Global (days) (rms) 3 cm for SWE  30 cm,Snow water equivalent 200 m / 500 m 3-15 10% for SWE > 30 cmSnow extent 100 m / 500 m 3-15 5% of areaGlacier snow 200 m / 500 m 15 10% of winter maximumaccumulationSecondary parameters Snow Glaciers Lake and river ice Sea ice Melting snow Diagenetic Ice area; freeze Snow on ice (SWE, area, snow facies types, up and melt melt onset and area); depth glacial lakes onset type and thickness of thin ice H. Rott –CoReH2O IGARSS 2011
  5. 5. CoReH2O – Instrument Design ParametersParameter Ku-band SAR X-band SARFrequency 17.2 GHz 9.6 GHzPolarization VV, VHSwath width, Inc angle ≥ 100 km; 30° to 45° rangeSpatial resolution ≤ 50 m x 50 m (≥ 4 ENL)NESZ ≤ -25dB VH ≤ -27dB VHRadiom. Stability / Bias ≤ 0.5 dB / ≤ 1.0 dBAntenna concept Single reflector with multiple beam feed arrayPeak RF power 1.2 kW; 1.8 kW (2 concepts) 1.8 kW; 3.5 kWNr. of ScanSAR beams 6 6 H. Rott –CoReH2O IGARSS 2011
  6. 6. Flowline for SWE Retrieval Algorithm H. Rott –CoReH2O IGARSS 2011
  7. 7. SWE Retrieval Algorithm - Iteration A semi-empirical radiative transfer model is used for forward computations to enable efficient iteration for 2 free parameters: SWE, re H. Rott –CoReH2O IGARSS 2011
  8. 8. Semi-empirical RT-Formulation for Snow over SoilSemi-empirical RT Model (sRT) – Single Layer P r P tBasic Equation: Air qi   s qi   s qt    qt s qt t qt  qs t pq as pq v pq 2 pq g pq sas q Snow svOne-Way Loss Factor: d s, t s sg Lqt   exp ke d s secqt   exp k e SWE secqt  Ground keke  ka  k s ´ Scattering s Extinction coefficient for unit massFormulation for forward computation:     2k e SWE    2k e SWE s t qi   s as qi    qt 0.75 pq cosqt 1  exp  2    s pq qt exp   g  cos q    cos qt pq pq pq      t T(q).. Power transmission coefficient;  … Scattering albedo H. Rott –CoReH2O IGARSS 2011
  9. 9. sRT – Parameterization of Snow Volume BackscatterInitial value of Scattering coefficient:The sRT scattering coefficient, ks , at f1 (17.2 GHz VV) is related to “effective grain size” rewhich is used as input parameter for specifying the scattering efficiency in this channel.In order to provide a link to common formulations, the initial value of ks is computed withthe Rayleigh approach for frequency f1 =17.2 GHz as f(re).In the iteration ks is a free parameter to match forward computations and measurements.Frequency dependence of scattering is parameterized based on experimental data and numerical simulations for closely packed snow grains: Wavelength exponent A = 3.2 is used as default value for seasonal snow, based on experimental data and numerical simulations (e.g. Tse et al., 2007). Further work needed to establish relations to snow morphology/snow type. 2   J x    2 i x1 ,...., xq ; c1i , c2i ....., cri   Zi   2 x j  xj  n q 1 2 1 Cost function i 1 2s i j 1 2 j For iteration Forward model a-priori SWE, re H. Rott –CoReH2O IGARSS 2011
  10. 10. Input Parameters for sRT Forward ModelSymbol Name Source / Role in retrieval and forward modelSnow pack (single layer)SWE Snow water equivalent Free variablere Effective grain radius Free variable , related to ks at f1 = 17 GHz Configuration Parameter: from auxiliary data / forTs Mean snow pack temperature computing ka (”) Configuration Parameter: auxiliary data or default value/s Mean snow pack density for computing T(pq) and q(t) Std. deviation of surface height at Configuration Parameter: Pre-scribed / for computingrmsas air/snow interface sas (small contribution to total backscatter) Backscatter coefficient at ground From pre-snowfall backscatter measurements in 4sg (f, pq) surface channelsRT model parameters (empirical) Coefficient for frequency Relation based on experimental data for linking ks(f2) toAs dependence of ks ks(f1). Presently used default value As=3.2 Cross- to co-polarized ratio for ks Relation based on experimental data for deriving ks (pq)Ap (depolarization factor) from ks(pp); presently linked to grain size H. Rott –CoReH2O IGARSS 2011
  11. 11. Performance Analysis for SWE Retrieval - SimulationsExample for test case using SIMULATED RADAR BACKSCATTER - X_vv -2Synthetic Scene Generator xvv_snow_mean -3 xvv_ref_mean -4 SIGMA_0 [dB] Input for simulation -5 -6 -7 FP-ID SWE [m] re [mm] -8 -9 FP01 0.1 0.3 -10 FP02 0.1 0.5 FP01 FP02 FP03 FP04 FP05 FP06 FP07 FP08 FP09 Basic Test ID FP03 0.1 0.7 SIMULATED RADAR BACKSCATTER - Ku_vv kuvv_snow_mean FP04 0.3 0.3 -2 kuvv_ref_mean FP05 0.3 0.5 -3 SIGMA_0 [dB] -4 FP06 0.3 0.7 -5 -6 FP07 0.5 0.3 -7 -8 FP08 0.5 0.5 -9 -10 FP09 0.5 0.7 FP01 FP02 FP03 FP04 FP05 FP06 FP07 FP08 FP09 H. Rott –CoReH2O IGARSS 2011 Basic Test ID
  12. 12. Performance Analysis – Effect of Snow Density H. Rott –CoReH2O IGARSS 2011
  13. 13. Performance Analysis – Effect of Snow Density Retrieval statistics for different snow cover states using Synthetic Scene Generator H. Rott –CoReH2O IGARSS 2011
  14. 14. Performance Analysis with NOSREX DataField campaignSodankylä 2010-11SnowScat s°17 GHz, 10 GHzSWE time seriesGWI H. Rott –CoReH2O IGARSS 2011
  15. 15. Retrieval Tests – Effect of Background s°Retrieval input data Snow Density Snow RV – Grain radius Cost-function Reference Temperature (mean, stdev) (0 without RV-SWE) Backscatter 200 kg/m³ -5 0.5, 0.4 mm 0 December 200 kg/m³ -5 0.5, 0.4 mm 0 October H. Rott –CoReH2O IGARSS 2011
  16. 16. Conclusion• The CoRe-H2O mission addresses a particular gap in present cryosphere monitoring: spatially detailed observations of snow mass (SWE).• A dual frequency, dual polarized Ku- and X-band SAR sensor is proposed as tool for SWE measurements.• The baseline retrieval method for SWE is based on iterative inversion of a semi-empirical RT model, applying a statistical concept.• Experimental data are essential for calibrating and testing the forward model and inversion algorithm.• Important contributions to the experimental data base are supplied by the NOSREX Campaign (17& 10 GHz in situ), CAN-SCI (17 & 10 GHz in situ), CLPX PolScat (14 GHz), TerraSAR-X (9.6 GHz).• Activities for scientific mission preparation are dealing with assimilation of CoRe-H2O products in snow process models, including the extraction of auxiliary data for input to the SWE retrieval, and further field campaigns (with the new 17 & 10 GHz airborne SnowSAR of ESA and in situ sensors). H. Rott –CoReH2O IGARSS 2011