A Quick Look at JPSS Ice Products Jeff Key NOAA Satellite and Information Services @ Cooperative Institute for Meteorological Satellite Studies, Madison, Wisconsin USA US AON Telecon, 11 June 2018

JPSS Snow and Ice Products NPP/JPSS VIIRS Snow cover (binary) Snow fraction Ice thickness and age Ice concentration Ice surface temperature Ice motion (experimental) Sea ice leads (under devel.) Polar winds AMSR-2 on GCOM-W1 Snow cover Snow depth Snow water equivalent (SWE) Ice characterization Ice age class (first-, multi-year) Ice concentration Ice motion (experimental) Ice products

VIIRS Ice Surface Temperature IST provides the radiating, or “skin”, temperature of the sea and fresh water ice surface under clear-sky conditions. It includes the aggregate temperature of objects comprising the ice surface, including snow and melt water on the ice. The VIIRS IST EDR provides surface temperatures retrieved at VIIRS moderate resolution for ice-covered oceans both day and night.

VIIRS Ice Surface Temperature Advantages: High spatial resolution (750 m) and high accuracy, as determined through validation studies with surface temperature measurements from aircraft campaigns (NASA IceBridge). It has been shown to have a near-zero bias and a root-mean-square error (RMSE) of less than 1 K. Limitations: It is a clear-sky only product, and errors in the cloud mask produce errors in IST.

VIIRS Ice Concentration (animation) Ice concentration provides the fraction of an area covered by ice. It is calculated for every VIIRS moderate resolution band pixel (750 m) over unfrozen ocean and inland water bodies.

VIIRS Ice Concentration Weekly Composite, 27 Oct 2016 VIIRS Ice Concentration Advantages: High spatial resolution (750 m) and detail relative to passive microwave. Comparisons to Landsat-8 indicate that differing concentrations have an absolute magnitude of less than 10%.   Limitations: Clear-sky only. Errors in the cloud mask produce errors in the ice concentration. The performance of cloud mask is generally better during the “day” (sunlit conditions) than at night. This difference can lead to cloud mask discontinuity in regions that include the terminator.

AMSR2 Ice Concentration AMSR2 vs VIIRS Right: Enterprise VIIRS Sea Ice Concentration (SIC) along the Alaska Coast on April 27, 2017. Left: Passive microwave-derived sea ice concentration from NSIDC for the same area and day.

Recent Arctic Climate Trends Satellite-Derived Ice Thickness Products APP-x CryoSat-2 SMOS IceBridge ICESat PIOMAS Wang, X., J. R. Key, and Y. Liu (2010), A thermodynamic model for estimating sea and lake ice thickness with optical satellite data, J. Geophys. Res., 115, C12035, doi:10.1029/2009JC005857. Kwok, R., and G. F. Cunningham (2008): ICESat over Arctic sea ice: Estimation of snow depth and ice thickness, J. Geophys. Res., 113, C08010, doi:10.1029/2008JC004753. Laxon S. W., K. A. Giles, A. L. Ridout, D. J. Wingham, R. Willatt, R. Cullen, R. Kwok, A. Schweiger, J. Zhang, C. Haas, S. Hendricks, R. Krishfield, N. Kurtz, S. Farrell and M. Davidson (2013), CryoSat-2 estimates of Arctic sea ice thickness and volume, Geophys. Res. Lett., 40, 732–737, doi:10.1002/grl.50193. N. T. Kurtz, S. L. Farrell, M. Studinger, N. Galin, J. P. Harbeck, R. Lindsay, V. D. Onana, B. Panzer, and J. G. Sonntag (2013), Sea ice thickness, freeboard, and snow depth products from Operation IceBridge airborne data, The Cryosphere, 7, 1035-1056, 2013. X. Tian-Kunze, L. Kaleschke, N. Maaß, M. Mäkynen, N. Serra, M. Drusch, and T. Krumpen (2014), SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification, The Cryosphere, 8, 997-1018, 2014. Zhang, J.L. and D.A. Rothrock (2003), Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates, Mon. Weather Rev., 131, 845-861, 2003. SMOS instrument is Microwave Imaging Radiometer using Aperture Synthesis (MIRAS). L-band, 1.4 GHz, passive. Emission is a function of salinity and temperature; attenuation for very high salinity and high temperature is only a few cm; 1.5 m for low salinity and cold. IceBridge snow radar from U. Kansas. 2-7 GHz (broadband); mainly C band with a little of S band on the low frequency end. CryoSat-2 instrument is SIRAL (SAR/Interferometric Radar Altimeter), Ku band, 13.575 GHz; 250 m footprint These are the monthly mean results for March 2012, except for ICESat sea ice thickness, which is a 34-day average from 2 February to 31 March 2008.

Recent Arctic Climate Trends Alaska VIIRS Sea Ice Thickness 14 Feb 2013, 14:10 UTC VIIRS ice thickness provides estimates of sea, lake, and river ice thickness under clear-sky conditions. The ice surface may be covered by snow or, in the summer, melt ponds. The depth of snow is not included in ice thickness product. Calculations are done at VIIRS moderate resolution (750 m) for ice-covered water bodies both day and night. It is not a direct measurement, but rather a model-based approach driven by other satellite products.

Recent Arctic Climate Trends VIIRS Sea Ice Thickness Alaska Left: VIIRS ice thickness on the Ob River, western Siberia, on 16 January 2016. The actual river ice thickness on 7 February was 55-60 cm, as determined by surface-based radar and drilled holes in the area indicated by the red circle. Above: Collecting validation data on Green Bay.

Recent Arctic Climate Trends VIIRS/AVHRR Sea Ice Thickness Advantages: Solid physical foundation with all components of surface energy budget. Capable of retrieving daytime and nighttime sea, lake, and river ice thickness under both clear and cloudy sky conditions. Computationally efficient, easy to implement and maintain, flexible, and fast. Built-in parameterizations can be used if various satellite products are not available. Limitations: The accuracy of input parameters can significantly impact the accuracy of the ice thickness estimates. Thickness is sensitive to rapid changes in surface temperature. Averages over time are usually better than instantaneous estimates. Daytime retrievals are less reliable than nighttime retrievals. The uncertainty is large for ice more than a few meters thick.

Ice Motion The ice motion products provide the speed and direction of ice features over the past 24 hours. Ice motion is currently generated from AMSR2, VIIRS infrared window (M15), blended AMSR2+VIIRS(IR), and the VIIRS day-night band (DNB).

Ice Motion Advantage: It clearly illustrates the medium- to large-scale motion of ice over a 24-hour period. Limitations: AMSR2 is ambiguous for melting ice, so AMSR2 ice motion is of the highest quality during the winter, spring, and autumn. Cloud cover restricts the area coverage of VIIRS ice motion. Spatial averaging must be employed to achieve a useable result. Data latency and processing time constrain real-time generation.