NOAA AMSR2 SNOW AND ICE PRODUCTS Jeff Key NOAANESDIS Madison Wisconsin USA AMSR2 Snow and Ice Products Snow Cover SC Presenceabsence of snow Snow Depth SD The depth of snow on land ID: 761251
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NOAA AMSR2 SNOW AND ICE PRODUCTS Jeff KeyNOAA/NESDISMadison, Wisconsin USA
AMSR-2 Snow and Ice Products Snow Cover (SC) – Presence/absence of snowSnow Depth (SD) – The depth of snow on landSnow Water Equivalent (SWE) – The amount of water in the snowpack Sea Ice Characterization (SIC) – Ice concentration (area fraction in a pixel) Ice type or A ge 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).
NOAA AMSR2 SNOW PRODUCTSYong-Keun Lee1, Cezar Kongoli2 , Jeff Key 3 1 Cooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin-Madison 2 Cooperative Institute for Climate Studies (CICS), University of Maryland 3 NOAA/NESDIS
Snow Algorithms 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 algorithmSnow depth and SWE: NASA AMSR-E algorithm (Kelly, 2009; Tedesco and Narvekar, 2010)
Product Examples: Snow Cover Snow cover on January 15, 2015The Snow Cover product provides the presence/absence of snow cover for every pixel.
Product Examples: Snow Depth Snow depth (cm) on January 15, 2015The Snow Depth product provides the depth of the snow cover (cm).
Product Examples: SWE Snow water equivalent (kg/m2) on January 15, 2015The Snow Water Equivalent (SWE) product provides the water equivalent (mm) of the snow cover.
Snow Cover Validation If wet snow is not included, detection accuracy is higher.Wet snow excludedWith wet snow Snow classes are from Sturm et al. (1995)
Statistics are computed daily, automatically November December January February
Snow Depth Validation 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 By elevation By forest fraction
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: bias std rmse mean1 mean2 number of pixels -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 ThresholdRequirementValidation 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 Meier 1, Scott Stewart 2, and Ludovic Brucker 3 1 National Snow and Ice Data Center (NSIDC; formerly NASA GSFC) Cooperative Institute for Research in the Environmental Sciences University of Colorado, Boulder 2 NSIDC contractor 3 NASA 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.Additional information on validation is in the notes section of this slide
Sea Ice Concentration Validation Comparison of AMSR2 and VIIRS sea ice concentration over the Arctic on 31 January 2015.(animation)
Comparison of AMSR2 and VIIRS sea ice concentration over the Antarctic on 31 January 2015. (animation)Sea Ice Concentration Validation
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 Performance drops in May (melt onset)Comparison of NOAA vs. Canadian Ice Service (CIS) charts in high Arctic NOTE: Summer months are not included in plot.
Performance drops in May Comparison of NOAA vs. ASCAT scatterometer Lower performance expected from ASCAT as well Ice Type Validation: ASCAT NOTE: Summer months are not included in plot.
Ice Type Validation: OSI-SAF Confusion Matrix results, 2012-2015 OSI-SAF MYI OSI-SAF no-MYI NOAA MYI 28.1% 2.1% NOAA no-MYI 4.8% 65.1% Average over all 3.5 years (Oct. 2012 – Dec. 2015) Mid-October through mid-April each year NOAA agrees with OSISAF (i.e., “correct” retrieval) Accuracy: 93.2 ± 2.3% Precision: 84.5 ± 8.5%
Error Budget Summary Attribute AnalyzedThresholdRequirementValidation 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 ProductsAMSR2/in situ blended snow depth and SWEBlended Ice Concentration Blended Ice motion
Blended AMSR2 + In Situ Snow Depth AMSR2 Snow Depth Improved SD areas 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). Blended AMSR2 + in situ
VIIRS Operational Cryosphere Products Snow Cover (binary) Ice Surface Temperature Ice Thickness/Age Ice Concentration Snow Fraction
Blended Sea Ice Concentration Blended sea ice concentration from passive Microwave and infrared/visiblePassive microwave ice concentration:Pro: all-weatherCon: low spatial resolutionVisible/IR ice concentration:Pro: high spatial resolutionCon: clear-sky only 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 MotionSea 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 MotionLeft: 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)
35 NAVOCEANO Operational Sea Ice data for ACNFS/GOFS Assimilation JAXA AMSR2 Ice Concentration FNMOC SSMIS Ice Concentration NIC 4km IMS Current FY18 NAVO AMSR2 Ice Concentration FNMOC SSMIS Ice Concentration NIC 4km IMS NAVO NPP VIIRS Ice Concentration 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 Approved for Public Release; Distribution Unlimited
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% Distance Along the Cut (km) 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 Mean ice edge errors (km) between the observed and forecasts 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 7 July 2016 Sea ice concentration observations 36 Approved for Public Release; Distribution Unlimited