Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent, Version 4
Data set id:
NISE
DOI: 10.5067/VF7QO90IHZ99
There is a more recent version of these data.
Version Summary
Version Summary
Updated metadata field values for PGEVERSION (collection level remains
Version 4)Removed 15% sea ice concentration threshold that assigned pixels
with a sea ice concentration of <15% as ocean (data value of 255)Reprocessed NISE Version
4 to include sea ice concentration values of 1-14%
This revision is an update to NISE Version 4. All
Version 4 data (17 August 2009 - present) have been reprocessed
with this system.
Overview
The Near-real-time Ice and Snow Extent (NISE) data set provides daily, global maps of sea ice concentrations and snow extent. These data are not suitable for time series, anomalies, or trends analyses. They are meant to provide a best estimate of current ice and snow conditions based on information and algorithms available at the time the data are acquired. Near-real-time products are not intended for operational use in assessing sea ice conditions for navigation.
This NISE Version 4 product contains DMSP-F17, SSMIS-derived sea ice concentrations and snow extents derived from the Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program (DMSP) F17 satellite. For DMSP-F16, SSMIS-derived data, see NISE Version 3. For DMSP-F18, SSMIS-derived data, see NISE Version 5. For the older, DMSP-F13, Special Sensor Microwave Imager (SSMI) derived data, see NISE Version 2.
Parameter(s):
SEA ICE CONCENTRATIONSNOW COVER
Platform(s):
DMSP 5D-3/F17
Sensor(s):
SSMIS
Data Format(s):
HDF-EOS2
Temporal Coverage:
17 August 2009 to present
Temporal Resolution:
- 1 day
Spatial Resolution:
- 25 km
- 25 km
Spatial Reference System(s):
NSIDC EASE-Grid North
EPSG:3408
NSIDC EASE-Grid South
EPSG:3409
Spatial Coverage:
N:
90
S:
-90
E:
180
W:
-180
Blue outlined yellow areas on the map below indicate the spatial coverage for this data set.
Strengths and Limitations
Strengths
- Near-real-time combined sea ice and snow product (Armstrong and Brodzik, 2001; Maslanik and Stroeve, 1999)
- Useful for large-scale monitoring of sea ice and snow conditions (Armstrong and Brodzik, 2001; Maslanik and Stroeve, 1999)
- Uses data from up to 5 days previous to fill in all spatial gaps (particularly important for lower latitude snow cover) (Armstrong and Brodzik, 2001)
- Microwave observations provide surface snow and ice coverage during cloudy and night-time (including polar night) conditions (Cavalieri et al., 1999)
- Good source as input to products/models requiring a spatially complete snow and sea ice cover field at moderate/low resolution (Armstrong and Brodzik, 2001)
Limitations
- Low spatial resolution (25 km gridded) limits detail on concentration and precision of sea ice edge; is unsuitable for operational/navigational support (Cavalieri et al., 1999) and/or for detailed mapping of snow extent (Armstrong and Brodzik, 2002)
- Underestimates sea ice concentration during melt season (Kern et al., 2020) and/or when the ice is thin (Ivanova et al., 2015)
- Underestimates thin snow (Armstrong and Brodzik, 2002)
- Snow in dense forest and mountainous regions may be missed or underestimated (Armstrong and Brodzik, 2002)
- Wet snow is not accurately retrieved (Armstrong and Brodzik, 2002)
- There is a gap in coverage near the coast due to low spatial resolution and mixed land-ocean footprints (Armstrong and Brodzik, 2002)
- Near-real-time product with no planned reprocessing for long-term consistency; should not be used to derive long-term trends in sea ice or snow (Cavalieri et al., 1999)
Data Access & Tools
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Documentation
Help Articles
General Questions & FAQs
NSIDC currently archives passive microwave sea ice concentration products based on two algorithms: the NASA Team algorithm and the Bootstrap algorithm. Both algorithms were developed by researchers at the NASA Goddard Space Flight Center in the 1980s.
OPeNDAP, the Open-source Project for a Network Data Access Protocol, is a NASA community standard DAP that provides a simple way for researchers to access and work with data over the internet.
How to Articles
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The HDF Group has example code for access and visualization of MODIS, GLAS HDF5, AMSR-E, and NISE data in MATLAB, IDL, Python, and NCL.
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