Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 2 (NSIDC-0051)
This is the most recent version of these data.
Conversion to netCDF format.
This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km.
SEA ICE CONCENTRATION
DMSP 5D-2/F11, DMSP 5D-2/F13, DMSP 5D-2/F8, DMSP 5D-3/F17, Nimbus-7
SMMR, SSM/I, SSMIS
26 October 1978 to 31 May 2022
1 day, 1 month
Strengths and Limitations
- Long-term continuous record, with complete daily coverage of the Antarctic and the Arctic (excluding the “pole-hole”) since August 1987, preceded by every-other-day coverage since late October 1978. This makes it useful for tracking climate trends and variability and as a large-scale climate indicator (Parkinson et al., 1999; Zwally et al., 2002; Parkinson, 2019).
- Thorough inter-calibration between sensors for consistency throughout the record (Cavalieri et al., 1999; Cavalieri et al., 2012)
- Manual corrections and spatial and temporal interpolation to remove errors and fill in data gaps (Cavalieri et al., 1999)
- Less sensitive to temperature variations because it uses ratios instead of differences (Comiso et al., 1997)
- Concentrations are generally reliable within the ice pack (away from the ice edge) during cold (non-melt) conditions (Comiso et al., 1997)
- Microwave observations provide surface snow and ice coverage during cloudy and night-time (including polar night) conditions (Cavalieri et al., 1999)
- Useful input/validation of climate model simulations (National Center for Atmospheric Research Staff, 2017)
- Low spatial resolution (25 km gridded) limits detail on concentration and precision of ice edge (Cavalieri et al., 1999)
- Underestimates sea ice concentration during melt season (Kern et al., 2020) and/or when the ice is thin (Ivanova et al., 2015)
- Higher uncertainties in Antarctica due to flooded snow and other ice characteristics (Comiso et al., 1997)
- Algorithm coefficients are fixed for a given sensor, so biases can occur if characteristic surface conditions change (Cavalieri et al., 1999)
- False coastal ice can occur due to mixed land and ocean within a sensor footprint (Cavalieri et al., 1999)
Data Access & Tools
Sensor and Instrument Information
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.
Many NSIDC DAAC data sets can be accessed using the NSIDC DAAC's Data Access Tool. This tool provides the ability to search and filter data with spatial and temporal constraints using a map-based interface. Users have the option to
The NSIDC Data Map Services Application Programming Interface (API) provides HTTP URLs for requesting geo-registered map images from NSIDC's geospatial database. A WMS request defines the geographic layer(s) and area of interest to be processed.
You will first need to have GDAL installed on your system before proceeding on to the following steps.
The following are instructions describing how to import sea ice binary files into ArcGIS. These instructions were tested with ArcMap 10.5 and 10.6.
How to display and analyze the Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data
The Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data are distributed in gridded binary format. NSIDC provides IDL routines to ingest and read the data.
The NSIDC Python Reformatting and Subsetting (PyRS) tool is a command line tool which prompts the user to specify data reformatting and subsetting preferences. Output formats available are native, GeoTIFF, and NetCDF.