UWBRAD UltraWideband SoftwareDefined Microwave Radiometer for Ice Sheet
UWBRAD: Ultra-Wideband Software-Defined Microwave Radiometer for Ice Sheet Subsurface Temperature Sensing Joel T. Johnson, K. C. Jezek, L. Tsang, C. C. Chen, M. Durand, G. Macelloni Biweekly Telecon 22 nd April 2014 Columbus, OH
Project Status • First increment of funds in place at OSU as of 4/10; start date 4/1 – ~ 57% of year 1 budget • OSU separate account for BPRC project • OSU contracts working on getting UW subcontract in place • OSU team working on getting purchase order to Leuski • Andrews/Leuski/Johnson reviewing radiometer front end design – Some components no longer available – Also investigating calibration procedures • Also reviewing digital backend component selection – Pentek, Inc. or Agilent also provide high speed sampling boards – Need to make selection soon – Alazar. Tech board apparently can be purchased with wider analog bandwidth option • Still no cost quote from Ken Borek Airlines
Kickoff Meeting Wrapup • April 10 th; NASA IIP program manager Parminder Ghuman attended – Tom Wagner (NASA Cryosphere manager) also invited but did not dial in • No major concerns expressed; bimonthly reports will be required – Try to stay up-to-date on spending! • This project may be more suited for eventual long term airborne deployment (high altitude) instead of space • Will need to complete a TRL level spreadsheet at some point showing activities that will advance TRL level if achieved successfully
Model Comparisons • A code intercomparison makes sense as a first activity in the modeling/retrieval arena – Goal: have a first set of comparisons by mid-May • Models to compare: – – – DMRT-ML (Aksoy/Macelloni/Brogioni) Coherent wave approach (Tsang) Bicontinuous medium approach (Tsang)? MEMLS (Durand) 0 th order RT (Jezek) • Simulating the DOME-C environment to start with makes sense since tower measurements are available
Model Requirements • Output Tb as a function of: – frequency from 0. 1 -3 GHz, 0. 1 GHz steps – 0 to 60 degrees incidence angle – H- and V- pols • Marco has produced reasonable match to DOME-C data already using DMRT-ML using “layered method” (i. e. Monte Carlo average of many random layers) • Proposal: Use Marco’s “best” fit parameters; compare: – – i) results with no “layering” Ii) results for one realization with “layering” (need to specify file) Iii) results after averaging over 100 realizations with “layering” Iv) consider “rock” or “ice” or “water” at base of icesheet
Model analysis: inputs The model used in this work was the DMRT-ML which was developed under the Quasi-Crystalline Approximation with Coherent Potential - QCACP (implemented at LGGE). It simulates the snowpack emission under the Rayleigh approximation. The input to the model were: -Snowpack temperature profile 0 -10 m : seasonal temperature swing (Bingham and Drinkwater, 2000). 0 to 10 m Tm = mean annual value ; Ta = seasonal T amplitude. below 10 m : (Jezek et al. 2013) Ø The match of the two formula is performed at 10 m below the surface
Model analysis: inputs Tsurface (July) Tsurface (January) T mean annual value G = 53. 3*10^-3 M = 50*10^-3 mel = 0. 7*10^-3 H = 3200 Kc = 2. 7 Kd = 45 Ts = 217 geothermal heat flux accumulation rate basal melting rate altitude thermal conductivity thermal diffusivity temperature at the surface
Model analysis: inputs -Snowpack density profile The density profile was obtained by slightly modify the one in Bingham and Drinkwater for coarse grains in order to fit the measurements collected at Dome C. -Snowpack grain radius profile The grain radius profile was the one published in Zwally adjusted to fit the Tb measurements at L- and C- band -Layers profile In order to better analyze the problem and to speed up the computations we considered: 0 to 100 m 1000 layers 10 cm each 100 to 300 m 400 layers 25 cm each 300 to 3200 m 480 layers 6 m each Below 3200 pure ice – half medium In case of the C-band the number of layers has been limited to 1402 (approx. 301 m) because of the lower penetration depth.
Model analysis: layering - Real data shows fluctuation of density and grain radius along the profile - Density and grain radius profiles where perturbed with a dumped gaussian noise Ground Measurements at Dome-C
Model analysis: obtained results For σ =60 Kg/m 3 and α=30 m we obtain L-band Tb (K) C-band Angle (deg) The introduction of fluctuations in the snowpack density profile makes possible: - to fit quite well the experimental measurements - to reproduce the difference between V and H polarizations - to reproduce the trend of the V pol
Modeling of Polar Ice Sheet Emission 0. 5 GHz to 2 GHz UWBRAD teleconference Leung Tsang, Shurun Tan, Tianlin Wang 04/08/2014
Outline �Review of the DMRT-ML model �Density Fluctuation Modeling using coherent wave approach �Snow/Ice modeling with bicontinuous Medium �Review of the Bicontinuous medium/DDA/DMRT approach and key results �Model ice sheet with bicontinuous medium
both reflections and volume scattering � The snowpack viewed by DMRT-ML G. Picard, et. al, Simulation of the microwave emission of multi-layered snowpacks using the dense media Radiative transfer theory: the DMRT-ML model, Geosci. Model Dev. , 6, 1061 -1078, 2013.
Wave approach of Density Fluctuation Effects reflections only, no volume scattering �Measurement shows fine scale (5 cm-10 cm) density fluctuation �UWBRAD frequency range 0. 5 GHz~2. 0 GHz, with wavelength 15 cm~60 cm. Layer thickness is smaller than wavelength. �Coherent wave interaction between successive layers �Compare results of wave approach and DMRT approach Figures from Macelloni and Brogioni, UWBRAD Teleconference , 03/01/2013
Brightness temperature Modeling for layered ice sheet with internal temperature distribution � Fluctuation Dissipation Theory � Dyadic Green’s function for stratified medium A, B, C, D are the upward and downward going wave coefficients in each layer for TE and TM polarization, respectively
Antarctica Ice sheet Density Profile setup Density profile Standard deviation Damping factor Effective permittivity, spheres , Maxwell-Garnett mixing formula Ice permittivity, Mӓtzler and Wegmuller, 2006 Absorption coefficient estimated from effective permittivity
Antarctica Ice Sheet Temperature Profile Temperature profile following Aksoy and Johnson, UWBRAD teleconference, 02/25/2014
Brightness Temperature of ice sheet with density fluctuation One realization Monte Carlo analysis with 300 realization
Snow/Ice sheet modeling with bicontinuous medium � Review of the Bicontinuous medium/DDA/DMRT approach � Model ice sheet with bicontinuous medium
EM Scattering Models: Bicontinuous �Computer-generated snow samples �Solve Maxwell equations numerically o. To calculate effective permittivity, absorption/scattering/extinction coefficient and phase matrix �Combine with DMRT equations to 20 predict brightness temperature and backscattering 10/7/2020
Bicontinuous Medium Generation �Computer generated snow versus real snow picture Depth Hoar (30%): 3 cm * 3 cm A. Wiesmann, C. Mätzler, and T. Weise, "Radiometric and structural measurements of snow samples, " Radio Sci. , vol. 33, pp. 273 -289, 1998. 21 10/7/2020
Bicontinuous Parameters �Bicontinuous parameters: density, <ζ>, b o One to one relation between α and f. V o Size parameter <ζ> § Mean wave number, relating to inverse of mean grain size o Size parameter b determines the size distribution § Related to clustering § Size distribution becomes uniform as b increases �ζn follows Γ-distribution 22 10/7/2020
Dependence on the Size Parameter <ζ> �The mean grain size changes 23 10/7/2020
Dependence on the Size Parameter b �Clustering changes 24 10/7/2020
Specific Surface Area (SSA) � Definition: surface area per unit mass [cm 2/g] � Analytical expression Example: <ζ>=6000 [m-1], b=1. 5, f. V=30% Bicontinuous SSA=71. 8 [cm 2/g] � Numerical SSA o Use digitized pictures of snow; Discretize according to microwave resolution 0. 5 1. 0 1. 5 o Count ice surfaces contacting with air b <ζ> = 5000 [m 1] f. V = 0. 2 25 Numerical SSA [cm 2/g] 57. 0 56. 6 53. 9 Analytical SSA [cm 2/g] 63. 5 60. 2 58. 2 10/7/2020
Auto-Correlation Function �Analytical expression �Comparison with exponential correlation function in linear scale 26 10/7/2020
Solve Maxwell’s Equations Numerically • For each computer-generated sample, Maxwell • Equations are solved numerically based on DDA Volume integral equation • DDA in each cube • Matrix equations • Matrix-vector product sped up by FFT 27 10/7/2020
Model ice sheet with bicontinuous medium – Real Snow/Ice cross section images Representative vertical cross section micro-CT images of firn column from Summit, Greenland. Ice is white and the pores are black. Image size 8 mm x 8 mm. Lomonaco et. al, Journal of Glaciology, 57 (204), 755 -762. 28 10/7/2020
Model ice sheet with bicontinuous medium – Computer generated Snow/Ice cross section images 29 Bicontinuous medium cross sections resampled into cubes. Ice is white and air pores are black. Image 10/7/2020 size 8 mm x 8 mm. Ice fractional volume varies from 0. 2 to 0. 9.
Quantifying the geometry SSA = 5. 56 m 2/kg Correlation length = 0. 241 mm (exponential) 30 SSA = 0. 765 m 2/kg Correlation length = 0. 197 mm 10/7/2020
Characterizing EM property �Solve Maxwell’s Equation for each computer generated sample to calculate �Effective permittivity �Absorption/Scattering/Extinction coefficient �Phase matrix 31 Discrete dipole approximation (DDA) 10/7/2020
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