NOAA AMSR2 SNOW AND ICE PRODUCTS Jeff Key NOAA/NESDIS Madison, Wisconsin USA

AMSR-2 Snow and Ice Products Snow Cover (SC) – Presence/absence of snow Snow Depth (SD) – The depth of snow on land Snow Water Equivalent (SWE) – The amount of water in the snowpack Sea Ice Characterization (SIC) – Ice concentration (area fraction in a pixel) Ice type or Age class (first-year or multiyear ice) Snow and ice algorithms are built around heritage products with a few low-risk improvements. All products are now operational (September 2016 for snow; March 2017 for ice). SWE = depth (m) x density (kg/m3) (units: kg/m2) SWE = depth (m) x density (kg/m3) / density of water (kg/m3) (units: m)

NOAA AMSR2 SNOW PRODUCTS Yong-Keun Lee1, Cezar Kongoli2, Jeff Key3 1Cooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin-Madison 2Cooperative Institute for Climate Studies (CICS), University of Maryland 3NOAA/NESDIS

Snow Algorithms Snow cover: Grody (1991) SSM/I algorithm Lee, K.-L., C. Kongoli, and J. Key, 2015, An in-depth evaluation of heritage algorithms for snow cover and snow depth using AMSR-E and AMSR2 measurements, J. Atmos. Oceanic Tech., 32, 2319-2336, doi: 10.1175/JTECH-D-15-0100.1. Snow cover: Grody (1991) SSM/I algorithm Snow depth and SWE: NASA AMSR-E algorithm (Kelly, 2009; Tedesco and Narvekar, 2010)

Product Examples: Snow Cover The Snow Cover product provides the presence/absence of snow cover for every pixel. Snow cover on January 15, 2015

Product Examples: Snow Depth The Snow Depth product provides the depth of the snow cover (cm). Snow depth (cm) on January 15, 2015

Snow water equivalent (kg/m2) on January 15, 2015 Product Examples: SWE The Snow Water Equivalent (SWE) product provides the water equivalent (mm) of the snow cover. Snow water equivalent (kg/m2) on January 15, 2015 SWE = depth (m) x density (kg/m3) (units: kg/m2) SWE = depth (m) x density (kg/m3) / density of water (kg/m3) (units: m)

Snow Cover Validation With wet snow Wet snow excluded The overall accuracy is calculated as the number of pixels where both AMSR2 and IMS detect snow or no-snow divided by the whole number. The whole number is the sum of the number of pixels that are reported as snow or no snow by both AMSR2 and IMS. Snow detection rate is calculated as the number of pixels where both AMSR2 and IMS detect snow divided by the number of pixels where IMS detects snow. Omission error is calculated as the number of pixels where AMSR2 misses snow divided by the whole number. Commission error is calculated as the number of pixels where AMSR2 incorrectly detects snow divided by the whole number. In this study, the term, ‘‘false alarm’’ (commission error) is used to indicate a situation where IMS reports no snow and AMSR2 reports snow. If wet snow is not included, detection accuracy is higher. Snow classes are from Sturm et al. (1995)

Statistics are computed daily, automatically November December overall accuracy (overall): the number of pixels where both AMSR2 and IMS detect snow or no-snow divided by the whole number => The whole number is the sum of numbers of pixels that are reported as snow or no snow by both AMSR2 and IMS snow detection rate (snow detect): the number of pixels where both AMSR2 and IMS detect snow divided by the number of pixels where IMS detects snow omission error (omission): the number of pixels where AMSR2 misses snow (compared to IMS) divided by the whole number commission error (commission): the number of pixels where AMSR2 incorrectly detects snow divided by the whole number January February

Snow Depth Validation By elevation By forest fraction Tundra Taiga Maritime Ephemeral Prairie Alpine Bias (cm) 4.51 3.77 -5.34 6.05 2.75 -4.45 RMSE (cm) 18.77 20.96 19.37 14.95 18.93 21.97 Mean (cm) of in-situ obs 25.10 19.18 20.20 8.40 18.49 25.14

Snow Water Equivalent (SWE) Validation SWE comparison between AMSR2 retrievals and GHCN: When 10 < AMSR2 SWE < 100 and 10 < GHCN SWE < 100 and the altitude < 3000m: bias std rmse mean1 mean2 number of pixels -7.97 30.77 31.79 46.54 54.52 45033 When AMSR2 SWE > 100 and GHCN SWE > 100 and the altitude < 3000m: -29.91 50.91 59.05 115.56 145.47 657 Units are mm mean1: average of AMSR2 SWE mean2: average of GHCN SWE bias: mean of AMSR2 SWE - GHCN SWE GHCN: Global Historical Climatology Network

Error Budget Summary Attribute Analyzed Threshold Requirement Validation Result Error Summary Snow cover 80% prob of correct snow/no-snow classification 72-97% correct classification If wet snow is excluded, > 90% correct Snow depth 20 cm snow depth uncertainty 15-22 cm depth uncertainty If alpine excluded, depth uncertainty < 20 cm SWE 50-70% uncertainty (shallow to thick snowpacks) 20-60% Larger validation dataset would improve reliability of results. More thin snowpack cases needed. NOAA’s requirements are in the extra slides.

AMSR2 SEA ICE CHARACTERIZATION Walt Meier1, Scott Stewart2, and Ludovic Brucker3 1National Snow and Ice Data Center (NSIDC; formerly NASA GSFC) Cooperative Institute for Research in the Environmental Sciences University of Colorado, Boulder 2NSIDC contractor 3NASA Goddard Space Flight Center

Sea Ice Algorithms Meier, W.N., J.S. Stewart, Y. Liu, J. Key, and J. Miller, 2017, Operational implementation of sea ice concentration estimates from the AMSR2 sensor, IEEE J. Selected Topics Appl. Earth Obs. Remote Sens. (J-STARS), 10(9), 3904-3911, doi: 10.1109/JSTARS.2017.2693120. Ice concentration is essentially the NASA Team 2 algorithm with the Bootstrap algorithm as secondary

AMSR2 Sea Ice Concentration Examples Examples of AMSR2 sea ice concentration over the Arctic (left) and Antarctic (right) on 9 November 2017.

Validation Comparison of AMSR2 (left) and VIIRS (below) sea ice concentration over the Arctic on 31 January 2015. Numerous validation studies have been done on BT and NT2 algorithms via comparisons with aircraft and other satellite (vis/IR, SAR) imagery e.g., Cavalieri et al., 2006; Meier, 2005; Comiso et al., 1997; Comiso and Nishio, 2008; Andersen et al., 2007; Ivanova et al., 2014 Concentration errors for the central ice pack during cold, winter periods are <5% Errors for melting ice, thin ice, and near the ice edge may be higher Precision of the ice edge limited by spatial resolution of the channel with the largest footprint (IFOV), ~25 km for AMSR2 AIT test cases were validated through a comparison of the AMSR2 ice concentration results to S-NPP VIIRS sea ice concentration. Differences are expected due to: Cloud cover – VIIRS retrievals are clear-sky only Spatial resolution – VIIRS is < 1 km Additional information on validation is in the notes section of this slide 16 16

Sea Ice Concentration Validation Comparison of AMSR2 and VIIRS sea ice concentration over the Arctic on 31 January 2015. (animation)

Sea Ice Concentration Validation Comparison of AMSR2 and VIIRS sea ice concentration over the Antarctic on 31 January 2015. (animation)

Sea Ice Concentration Validation: Arctic Comparison of AMSR2 minus VIIRS ice concentrations for different AMSR2 ice concentration ranges/bins in the Arctic. Note that the y-axis range is different for "All", "90-100%", and the other plots. Data are from January to October 2016.

Sea Ice Concentration Validation: Antarctic Same as previous slide except for the Antarctic.

Multiyear Ice Validation 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.

Ice Type Validation: Ice Charts Comparison of NOAA vs. Canadian Ice Service (CIS) charts in high Arctic Performance drops in May (melt onset) Precision = (TruePositives) / (TruePositives + FalsePositives) NOTE: Summer months are not included in plot.

Ice Type Validation: ASCAT Comparison of NOAA vs. ASCAT scatterometer Lower performance expected from ASCAT as well Precision = (TruePositives) / (TruePositives + FalsePositives) Performance drops in May NOTE: Summer months are not included in plot.

Ice Type Validation: OSI-SAF Confusion Matrix results, 2012-2015 Average over all 3.5 years (Oct. 2012 – Dec. 2015) Mid-October through mid-April each year OSI-SAF MYI OSI-SAF no-MYI NOAA MYI 28.1% 2.1% NOAA no-MYI 4.8% 65.1% Accuracy: 93.2 ± 2.3% Precision: 84.5 ± 8.5% NOAA agrees with OSISAF (i.e., “correct” retrieval)

Error Budget Summary Attribute Analyzed Threshold Requirement Validation Result Error Summary Concentration 10% uncertainty (see note) 1-4% accuracy 9-15% precision Most errors well below 10% threshold, higher errors near ice edge Ice type (MYI) 70% correct typing 80-90% (preliminary) during Arctic winter Multiyear ice (MYI) detection only

Experimental Products AMSR2/in situ blended snow depth and SWE Blended Ice Concentration Blended Ice motion

Blended AMSR2 + In Situ Snow Depth A snow depth bias-correction method has been developed based on optimal interpolation of in situ measurements. The method is being refined and tested with the NOAA AMSR2 snow depth product using in situ data from the Global Historical Climatology Network (GNCN). AMSR2 Snow Depth Improved SD areas Blended AMSR2 + in situ

VIIRS Operational Cryosphere Products Ice Concentration Ice Surface Temperature Ice Thickness/Age Snow Cover (binary) Snow Fraction

Blended Sea Ice Concentration Blended sea ice concentration from passive Microwave and infrared/visible Visible/IR ice concentration: Pro: high spatial resolution Con: clear-sky only Passive microwave ice concentration: Pro: all-weather Con: low spatial resolution Blended ice concentration: high spatial resolution under all-weather conditions Blended sea ice concentration at 1 km resolution on June 24, 2015 using AMSR-2 and the S-NPP VIIRS ice concentration products

AMSR2 and VIIRS Ice Motion Sea ice motion is retrieved individually from AMSR2, the S-NPP VIIRS 11 micron band, and from the VIIRS Day-Night Band (DNB)

VIIRS+AMSR2 Ice Motion Left: Combining AMSR2+VIIRS ice motion vectors creates output with high spatial resolution, full Arctic coverage Right: Ice motion from AMSR2 alone.

Applications

National Ice Center Interactive Multisensor Snow and Ice Mapping System (IMS)

National Ice Center Interactive Multisensor Snow and Ice Mapping System (IMS)

NAVOCEANO Operational Sea Ice data for ACNFS/GOFS Assimilation NAVO AMSR2 Ice Concentration FNMOC SSMIS Ice Concentration NIC Ice concentration NAVO J-1 VIIRS Ice Concentration FY19 Ice Thickness NIC 4km IMS NAVO NPP VIIRS Ice Concentration FY18 NAVO AMSR2 Ice Concentration FNMOC SSMIS Ice Concentration NIC 4km IMS NAVO NPP VIIRS Ice Concentration JAXA AMSR2 Ice Concentration FNMOC SSMIS Ice Concentration NIC 4km IMS Current INTERACTIVE MULTISENSOR SNOW AND ICE MAPPING SYSTEM test/evaluate the assimilation of ice thickness into GOFS by the end of FY19 Approved for Public Release; Distribution Unlimited

Sea ice concentration observations Assimilation of the NPP VIIRS Sea Ice Concentration (SIC) Data for Arctic forecasts VIIRS/AMSR2 AMSR2 only AMSR2 SIC VIIRS/AMSR2 SIC 7 July 2016 Sea ice concentration observations Mean ice edge errors (km) between the observed and forecasts Region Assimilation without VIIRS data Assimilation including VIIRS data Pan-Arctic 45.8 33.4 Greenland 43.8 34.6 Barents 37.7 25.3 Laptev 64.8 51.6 Sea of Okhotsk 40.1 35.8 Bering/Beaufort 43.0 35.6 Canadian Arch 57.6 33.3 Distance Along the Cut (km) Mean ice edge errors were using NAVO VIIRS ice Average errors for the time period of Jan – Dec 2016. Adding VIIRS SIC products into the operational sea ice forecast reduces ice edge error by an average of 25% Approved for Public Release; Distribution Unlimited