1. AOL Confidential Sea Ice Concentration Retrievals from Variationally Retrieved Microwave Surface Emissivities Cezar Kongoli, Sid-Ahmed Boukabara, Banghua Yan, Fuzhong Weng and Ralph Ferraro NOAA/NESDIS/STAR, Camp Springs, MD, USA June 27, 2016 Microrad 2008, March 11-14, 2008 Florence, Italy

2. 2June 27, 2016 Outline Retrieval concepts  Microwave Signatures of sea ice and water  Description of heritage algorithms Microwave Surface Emissivities over Ice and Ocean  1D Variational (1DVAR) Retrievals of Surface Properties  Analysis of 1DVAR Retrieved Emissivities for the AMSU-MHS Sensor New Sea Ice Concentration Algorithm  Methodology  Exploration of New Retrieval Techniques  Validation Examples & Future Improvements Conclusions

3. 3June 27, 2016 Passive MW Principles for Sea Ice Type & Concentration Retrievals: Significant Contrast in Emissivity and V-H Polarization Water  Low surface emissivity (0.5-0.75 in the 20-200 GHz range)  Increase of emissivity with increasing frequency  Large V-H polarization in a wide frequency range Sea Ice  High surface emissivity (1.0 – 0.75 in the 20-200 GHz range)  Constant to decreasing emissivity with increasing frequency  Low to moderate V-H polarization Sea Ice Type Discrimination  Emissivity spectra and V-H polarization differences

4. 4June 27, 2016 Measured Surface Emissivity Spectra display characteristic signatures important for surface type identification  Large contrast in gradient at a wide frequency (23-157 GHz) between water and some ice types  Contrast in gradient “narrows” to lower frequency, e.g., first-year to consolidated ice, to deep snow  Contrast in emissivity generally highest at lower frequencies  Contrast in emissivity largest for younger ice types, reduces for deep snow/fast ice

5. 5June 27, 2016 Emissivity Spectra at V and H Polarizations for Water, FY ice and MY ice  Water V-H Polarization difference large in a wide frequency range  First-year Ice V-H Polarization difference small at freq >= 19 GHz  Multi-Year Ice V-H Polarization larger than FY ice, reduces significantly at higher frequencies

6. 6June 27, 2016 AMSU Heritage Sea Ice Algorithm (MSPPS) Algorithm Decision Tree Key features & assumptions:  Mixed scene composed of ice- ocean surfaces; Only total sea ice concentration retrieved  Scene emissivity at 23 GHz estimated from linear regression with TB23, TB31, and TB50  Emissivy of pure ice at 23 GHz estimated on the fly from the TB difference at 23 and 31 GHz  Min ice conc. set at 30% Example of seasonal retrievals of total sea ice concentration

7. 7June 27, 2016 Retrievals of Surface Emissivities and Temperature from the Microwave Integrated Retrieval System (MIRS) MIRS Key features:  Global retrievals of surface and atmospheric parameters - radiances the only dynamic input  Retrieved surface emissivities and skin temperature used as inputs for the retrievals of surface properties, e.g., Sea Ice  CRTM module hook-up for forward simulations of radiances  One-Dimensional Variational (1DVAR) retrievals  Sensor independent (AMSU-MHS, AMSR-E, SSMIS, WINDSAT, etc) Example of MIRS retrieved Tskin and emissivities over ocean 1DVARPost 1DVAR

8. 8June 27, 2016 The two-step Algorithm for estimating Sea Ice Concentrations of first and multi-year ice Inversion of Brightness Temperatures Data using IDVAR  Surface Emissivities and temperature Inversion of Surface Emissivities into Sea Ice Concentration  Off-line Catalog of mean emissivity spectra for mixed First Year, Multi-Year and Ocean surfaces for a range of fractions  Closest match between 1DVAR-retrieved surface emissivity spectrum and those from the catalog

9. 9June 27, 2016 Catalog Generation of Emissivity Spectra For a Range of ocean-ice Fractions Key assumptions:  Mixed scene composed of Multi-Year Ice, First-Year ice and Ocean Surfaces  Fixed mean emissivity spectra for pure ice constituents obtained from reported observations  Ocean emissivity spectrum modeled for zero deg surface temperature and calm seas  Scene emissivity estimated as a weighted linear mixture of pure consituents  Scan dependent Catalog generation methodology

10. 10June 27, 2016 Utility of a wide range emissivity spectrum for achieving higher accuracy in sea ice concentration retrievals  Stronger signal to total sea ice concentration in the low frequency range (23, 31 GHz)  Stronger signal to FY and MY ice consituents concentrations in the higher frequency range (50,89, 157 GHz)  Improved signal by combining both bands

11. 11June 27, 2016 Utility of a gradents in a wide range emissivity spectrum for achieving higher accuracy in sea ice concentration retrievals  Total concentration is retrieved with higher accuracy at higher frequency channels when emissivities at these channels are subtracted from that at 23 GHz  A very accurate, single channel retrieval of total sea ice at 157 GHz.

12. 12June 27, 2016 Utility of a low and high frequencies in a wide range emissivity spectrum for achieving improved discrimination  Improved disrimination of ocean, ice type constituents is achieved by only combining low and high frequency channel emissivities

13. 13June 27, 2016 Utility of a gradents in a wide range emissivity spectrum for achieving higher accuracy in sea ice concentration retrievals  Much improved discrimination of FY,MY and Ocean types is achieved by a two pairs variable: EM23-EM31 vs EM23-EM50, EM23-EM89 or EM23-EM157

14. 14June 27, 2016 Time Series of retrieved MIRS Emissivities and Sea Ice Concentrations over an Arctic region for January-December, 2006 Comparing the normalized emissivities (slopes) yield improved total sea ice concentration retrievals

15. 15June 27, 2016 Time Series of retrieved MIRS Emissivities and Sea Ice Concentrations over an Arctic region for January-December, 2006 – Slope for 31 and 50 GHz only – Slope for 31, 50, 89 and 157 GHz

16. 16June 27, 2016 Hybrid approach – Switch between emissivies and slopes to maximize contrast yields improved results – Higher sea ice concentrations in both hemispheres Hybrid MIRS Algorithm being tested)NASA Team-2 Current MIRS (emissivity spectrum) MSPPS