Global Snow Cover ECV Status and Progress in Observations

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Global Snow Cover ECV Status and Progress in
Observations
Richard L. Armstrong University of
Colorado Boulder, Colorado USA

• 16th Session of the GCOS/WCRP Terrestrial
  Observation Panel for Climate (TOPC-16)
• 10-11 March 2014, Ispra, Italy

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• Outline
• Why a snow cover ECV?
• Users and requirements
• Previous ECV documents
• Review of currently available products
• Some user statistics
• Enhanced data set activities
• Innovative data sources
• Snow cover trends
• Community activities/efforts

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Snow Cover ECV Why? Relevance and Benefits
Recognized as a fundamental indicator of
climate variability and change. Highly sensitive
to changes in temperature and precipitation
regimes. Affects albedo, soil moisture,
permafrost state, growth conditions for
vegetation. Required for snow melt runoff
forecasts and flood prediction. Approximately
1/6 worlds population relies on snow melt runoff
for water supply. (As much as 75-80 percent of
water in the western US results from
snowmelt.) Assessment and improvement of climate
model performance.
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Snow Cover ECV The ECV snow
cover may include four categories snow covered
area (SCA), snow water equivalent (SWE), snow
depth, and snow wetness (presence of liquid water
in the snow cover). There is a remote sensing
capability to determine, with varying degrees of
accuracy, all four of these parameters. The
primary monitoring product with the longest time
series is a continuous data record of
hemispheric/global snow covered area.
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Users and Requirements Snow covered area
(SCA) (binary, percent) - presence or
absence of snow -
surface reflectance -
trend analysis/climate variability -
cal/val NWP and GCM products Snow water
equivalent (SWE) - (mm) -
hydrology - frozen
soil/permafrost - trend
analysis/climate variability Time period and
spatial resolution ? - Typically first
question asked by potential user - both being
continually enhanced
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2009 GTOS ECV T5 Snow Cover

• Assessment of the status of the development of
  the standards for the Terrestrial Essential
  Climate Variables
• Definition and Units of Measure
• Existing Measurement Methods, Protocols and
  Standards
• In situ and satellite
• Contributing Networks and Agencies
• Available Data and Products
• Recommendations

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2011 GCOS -154 Systematic Observation
Requirements for Satellite Data Products for
Climate 2011 Update
The primary monitoring product is a continuous
data record of global snow areal extent (SCA).
Also desirable to have supplemental global
information of three additional properties snow
depth, snow water equivalent, and the presence of
water in the liquid phase. Passive microwave
sensors provide reasonable estimates of snow
depth, water equivalent and wetness in many
areas. Combined or integrated products linking
two or more snow products have been generated by
research groups e.g. ESA Globsnow.
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Satellite Remote Sensing of Snow Hemispheric to
Global Scale
SCA Visible GOES, AVHRR, MODIS, ASTER Higher
resolution (30 to 500 m) Clouds and darkness
obscure surface Limited to surface
characteristics SWE Passive Microwave SMMR,
SSM/I, AMSR-E Lower resolution ( 10-25 km)
All weather day/night Sub-surface
characteristics (mass estimate, soil state ) --
for dry snow only
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NOAA visible climate data record
Primary Monitoring Product Continuous Record of
SCA
1973
1998
2007
ESSA, NOAA, GOES Series
Weekly 190 km digitized
METEOSAT GMS added
Reanalysis of 1966-71
Moving back in time decreases spatial resolution,
spatial coverage, temporal resolution, and
retrieval/algorithm sophistication.
IMS 24 km
IMS 4 km
Slide courtesy of Dave Robinson/Rutgers Univ.
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Satellite-derived Data Sets at NSIDC - pre-NASA
EOS 2000 NSIDC Northern Hemisphere EASE-Grid
Weekly Snow Cover and Sea Ice Extent Product,
beginning October 1966 - derived from weekly
NOAA (AVHRR optical sensor) snow maps, 25 km.
Evolved to NOAA IMS (Interactive Multisensor Snow
and Ice Mapping System) in 1997, daily data at 24
km, and 4 km data starting in 2004.
http//nsidc.org/data/nsidc-0046.html NSIDC
Monthly Snow Water Equivalent Climatology
Product. (25 km EASE-Grid) beginning November
1978 . Snow water equivalent derived from SMMR
and SSM/I passive microwave sensors.
http//nsidc.org/data/nsidc-0271.html (no longer
being updated)
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NASA EOS Global Snow Cover Products at NSIDC
Visible/Infrared - Snow Covered Area (SCA)
MODIS (Moderate Resolution Imaging
Spectroradiometer) 500 m and 0.05 deg (CMG)
resolution, daily and 8-day, derived from two EOS
visible/infrared instruments Terra satellite
beginning December 1999, and Aqua, May 2002.
http//nsidc.org/data/modis/index.html Passive
Microwave Snow Water Equivalent AMSR-E
(Advanced Microwave Scanning Radiometer) 25 km
resolution, daily, 5-day, monthly, Aqua, May 2002
to Oct. 2011. http//nsidc.org/data/amsre/index.ht
ml Near-Real-Time SSM/I-SSMIS EASE-Grid Daily
Global Ice Concentration and Snow Extent (NISE)
25 km resolution, updated daily.
http//nsidc.org/data/nise1
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MODIS Snow Cover Products

• Number of users between 2010 and 2013
  10,174 (12 products)
• Number of countries acquiring data between 2010
  and 2012 70
• Product summary 12 snow cover products at daily,
  8-day, and monthly temporal resolution. Temporal
  coverage is February 2000 to present.

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Ongoing and future activities
Various efforts are underway to develop new
satellite data products to extend length of
records, increase spatial resolution, and
frequency of observation.
For example as recommended by 2011 GCOS Sat.
Supp. Exploit capability of the full
resolution (1 km) AVHRR data to extend data back
to 1985. Take advantage of extensive
oversampling of multi-sensor footprints to
enhance gridding resolution for passive microwave
data. Provide percent snow cover rather than
only binary.     Integrate data types.
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A snow cover climatology for the European Alps
derived from AVHRR data (1985-2011) Hüsler, F. et
al. 2014. The Cryosphere, 8, 73-90

• Data archive AVHRR 1-km, daily, back to 1985,
  pan-European coverage
• Snow detection algorithm applicable to any kind
  of AVHRR generation
• Extensively validated and tested for long-term
  consistency (inter-sensor)
• Overall accuracy around 90 POD
• Application-dependent parameters and products can
  be delivered (for climatology, phenology,
  hydrology, etc.)

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Passive Microwave Earth System Data Record
(ESDR)(a NASA MEaSUREs project)

• Objective
• Produce an improved, enhanced-resolution, gridded
  passive microwave ESDR for monitoring cryospheric
  and hydrologic time series
• Full record will include SMMR, all SSM/I-SSMIS
  and AMSR-E
• Use newly recalibrated L1B SSM/I-SSMIS FCDRs
• Enhance gridded resolution to as much as 1 km
  (channel-specific)
• EASE-Grid 2.0

Image reconstruction takes advantage of extensive
oversampling in sensor footprints to enhance
gridding resolution.  Resolution enhancement
using these techniques depends on
frequency.  Highest-frequency (85 GHz) data
resolution may be enhanced to 1-3 km."

• Investigators
• M. J. Brodzik, NSIDC
• D. G. Long, BYU
• R. L. Armstrong, NSIDC

http//nsidc.org/pmesdr
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MODSCAG MODIS snow covered-area and grain size,
percent w/in 500 m grid cell --- T. Painter,
JPLProvides Fractional Snow Cover
MODSCAG is a multiple endmember spectral mixture
analysis model combined with radiative transfer
directional reflectance spectrally unmixes
allowing number of endmembers and the endmembers
themselves to vary on pixel by pixel basis.
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Combined or integrated products linking two or
more data sources. Matias Takala,et al. 2011,
Estimating northern hemisphere snow water
equivalent for climate research through
assimilation of space-borne radiometer data and
ground-based measurements, Remote Sensing of
Environment, Volume 115, Issue 12, 15 December
2011, Pages 35173529 ESA Globsnow -
 www.globsnow.info/ .
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Combined or integrated products linking two or
more data sources, contd. CMC Global Snow Depth
Analysis Daily from March 12, 1998 Resolution
1/3 Uses all available real time snow depth
observations from synops, metars (meteorological
aviation reports) and sas (special aviation
reports) on the WMO information system. Updated
every 6 hours using the method of optimum
interpolation with an initial guess field
provided by a simple snow accumulation and melt
model.  The dataset includes monthly snow depth
and SWE, the latter based on snow density
estimates.  Data are provided on a 24-km polar
stereographic grid covering the NH.
(http//nsidc.org/data/nsidc-0447)
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Snow Cover Extent Anomalies
produced by D. Robinson
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Seasonal Snow Anomalies
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iSWGR

• NASA (international) Snow Working Group for
  Remote Sensing
• Dr. Matthew Sturm et al. 2013

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 Snow from Air SpaceBuilding a Vision

• Mission To engage U.S. and international science
  communities in building a vision for future snow
  remote sensing efforts, including but not limited
  to, future NASA missions, to promote
  international collaboration, and engage in
  capacity-building within the U.S. and
  international snow remote sensing communities.
  Encourage snow snow remote sensing research.

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MODIS Albedo Product
Global Albedo is a MODIS Standard Data Product
that began routine production in 2000. Products
are operationally produced operationally every 16
days at a 1km spatial resolution and archived as
equal area tiles in a sinusoidal projection
(HDF-EOS format) at the EROS Data Center (EDC)
DAAC. (currently available daily by special
request)
Products include Global Albedo (MOD43B3), BRDF
Model Parameters (MOD43B1), Nadir-BRDF Ajusted
Reflectance (NBAR) (MOD43B4)
http//www-modis.bu.edu/brdf/product.html
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• Snow Cover Product Validation
• - SCA -- Common approach is to validate a given
  spatial resolution against a higher spatial
  resolution satellite product, typically assuming
  the higher resolution to be truth often without
  objective evidence.
• SWE validation data are in situ point and
• transect manual measurements.
• Problems associated with validating
  area-integrated satellite data against sparse
  point data will always exist (sub-grid
  heterogeneity, especially in mountain regions).
  Recent applications of downscaled reanalysis
  data.