1. Summary of JPSS Ice Products Presented at Arctic Summit 2018
Jeff Key
NOAA Satellite and Information Services
@ Cooperative Institute for Meteorological Satellite Studies,
Madison, Wisconsin USA
SSEC/CIMSS JPSS Internal Meeting, 21 June 2018

2. JPSS Arctic Initiative Demonstration
Overview
A collaborative effort where JPSS cryosphere data products are demonstrated and evaluated in an operational setting within the NWS Alaska Sea Ice Program in Anchorage, AK from April 9, 2018 to April 30, 2018.
NOAA National Environmental Satellite, Data, and Information Service (NESDIS)
NOAA National Weather Service (NWS) Alaska Sea Ice Program (ASIP)
UW Cooperative Institute for Meteorological Satellite Studies (CIMSS), and
University of Alaska (UAF) Geographic Information Network of Alaska (GINA)
Purpose and Scope
Purpose: To evaluate the use of S-NPP data products for NWS Sea Ice Program (ASIP) applications and as a contribution to the Year of Polar Prediction
Scope: Focus on the ASIP usage of polar satellite products
Goal of the Arctic Demonstration
Ensure the JPSS cryosphere data products are providing value added information to the ASIP in support of its mission
Identify areas for data product improvement, whether related to data quality or data visualization
(Slide courtesy of Bonnie Reed)

3. Demonstration Participants
NOAA/NESDIS/STAR
Provide Product/Algorithm Support: Jeff Key - Algorithm Lead for VIIRS Ice Age/Thickness, VIIRS Ice Concentration, VIIRS Ice Surface Temperature, and VIIRS/AMSR-2 Ice Motion
Provide Product Training
UAF GINA
Receive S-NPP data downlink via Direct Readout and generate S-NPP products using CSPP and CLAVR-x
Provide required files to CIMSS so they can produce the JPSS S-NPP Ice products
Receive S-NPP Ice products from CIMSS and reformat S-NPP Ice products into GIS-friendly (if not already GIS-friendly)
Provide S-NPP Ice products (GIS-friendly format) to ASIP via webpage
Work with ASIP and ingest/display data in ArcGIS
UW CIMSS
Work with GINA to receive data products generated from CSPP and CLAVR-x
Generate S-NPP Ice Products using the Enterprise Algorithm software
Provide the S-NPP Ice Products to GINA in GIS-friendly format for distribution
NWS Alaska Sea Ice Program
Work with GINA to integrate the JPSS Ice products into ice-related tasks/duties and
Evaluate the utility/usefulness of the products to include data quality, latency, visualization, and format
Log comments/feedback
(Slide courtesy of Bonnie Reed)

4. Demonstration Participants
(Slide courtesy of Bonnie Reed)

5. The Cryosphere and JPSS✓
River and lake ice
Ice sheets, ice caps, ice shelves ✘ ✓
Sea ice
Permafrost and seasonally-frozen ground ✓
Snow ✘
The point of the labels is that the Cryosphere Teams actually only do floating ice and snow. There are other cryosphere elements that are not covered. ✘
Glaciers

6. 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)
Used in demonstration

7. 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.

8. 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.

9. 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.

10. 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).

11. 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.

12. Quick Guides These are now being modified for AWIPS.
Four product ”quick guides” and this presentation are available at:
These are now being modified for AWIPS.

13. Additional JPSS Snow and Ice Products (not in demo)
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)

14. Recent Arctic Climate Trends
VIIRS Snow Fraction
Some artifacts exist due to the cloud mask
Gridded 1km resolution global maps of VIIRS reflectance-based snow fraction have been generated daily since the beginning of 2014.

15. AMSR2 Snow Depth and Snow Water Equivalent (SWE)

16. AMSR2 Sea Ice Concentration
Examples of AMSR2 sea ice concentration over the Arctic (left) and Antarctic (right) on 1 May 2018.

17. Multiyear Ice Concentration
Initial comparison with independent ice age fields (Lagrangian tracking of ice parcels) indicates good agreement in terms of spatial distribution of multi-year ice cover.

18. VIIRS+AMSR2 Blended Ice Concentration
Passive infrared/visible ice concentration:
Con: clear-sky only
Pro: high spatial resolution
Passive microwave ice concentration:
Con: low spatial resolution
Pro: all-weather
Compared to AMSR, the blended snow depth showed improved ability to detect real time events, adjust based on surface obs, adjust for high elevations, fill observations between overpass observational gaps, and reduce errors in high desert areas.
Blended sea ice concentration at 1 km resolution on June 24, 2015 using AMSR-2 and the Suomi NPP VIIRS ice concentration products
Blended ice concentration:
high spatial resolution under all-weather conditions

19. VIIRS+AMSR2 Ice Motion
Combining AMSR2+VIIRS ice motion vectors creates output with high spatial resolution, full Arctic coverage
AMSR2+VIIRS Blended products available for Arctic
Working to add SAR ice motions to the algorithm to improve capacities
VIIRS M15, VIIRS DNB, AMSR2, and blended