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The Equal-Area Scalable Earth (EASE) Grids are intended to be versatile formats for global-scale gridded data, including remotely sensed data. Data can be expressed as a digital array with one of many possible grid resolutions, which are defined in relation to one of four possible projections: Northern / Southern Hemispheres (Lambert's equal-area, azimuthal), temperate zones (cylindrical, equal-area), or global (cylindrical, equal-area).
NSIDC's Polar Stereographic Projection was originally designed to be optimal for sea ice applications, though it is now used for many other products.
Satellite remote sensing has made possible the collection of data over large areas of the Earth. These data are often stored in grids. Grids are an efficient means of storing data because the location of a value within the grid is implicit--it is not explicitly stored in the grid. The location is also constant, which makes it easy to compare data from different sensors or different time periods.
Programmatic Access to Data with Filtering and Customization Services
The elevation data are relative to the ellipsoid. The data also have a parameter indicating the geoid height, which is the height of the EGM2008 geoid above the TOPEX/Poseidon ellipsoid for the first and last shot in the record:
The regions differ slightly between the Sea Ice Index and the Multisensor Analyzed Sea Ice Extent - Northern Hemisphere (MASIE-NH). The two data sets are a source for sea ice extent, but they use different methods and data to estimate this parameter and their intended uses are also different. There are 14 regions used in the Sea Ice Index and 16 regions are used in MASIE-NH (Table 1). Even if the same regions are referred to in both data sets, they may not necessarily both cover exactly the same area.
Fortran readers for AMSR-E Level-2B rain data are available upon request. Please contact NSIDC User Services for more information.
Yes, one user of IMS has created and made available a suite of Python tools. Code examples and tutorials for spatial subsetting, pixel area calculations, timeseries construction and concatenation, and plotting can be found on this external website: https://www.itsonlyamodel.us/Tibet-Snow-Man.html. NSIDC does not support these tools and users of the scripts should reach out to the provider, who has an-email contact listed on the webpage. Last Updated April 2018
There are several ways to programmatically access NSIDC data products and metadata using Application Programming Interfaces (APIs). We also provide the ability to access data using an FTP client. To determine whether these methods are available for a specific data set, please visit the data set landing page you are interested in working with.
For the snow cover and sea ice products, users can use MODIS Collections 6 and 6.1 together. Users should be aware that there were minor changes in calibration for C6.1 that could potentially induce some differences, however the impacts of these changes on the snow and sea ice products should not be significant. There weren't any changes in the science algorithm, so the two collections are largely compatible.
SNODAS is a model output, so it is not recommended for use for quantitative water budget analysis. However, it is reasonable to sum values over a given area for a period of time, if, for example, you wanted to compare annual totals of snow water equivalent (SWE) for a given area. The solid precipitation variable (code 1025) can be used to calculate a running sum, with one caveat. The precipitation forcing data are separated into non-snow (L00) and snow (L01) components and both are provided as water equivalent.
Some data sets specify dates using the year and day of year rather than the year, month, and day of month. The day of year (DOY) is the sequential day number starting with day 1 on January 1st. There are two calendars--one for normal years with 365 days, and one for leap years with 366 days. Leap years are divisible by 4. Centuries, like 1900, are not leap years unless they are divisible by 400. So, 2000 was a leap year.
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. Provided below are two short summaries of the algorithms, an overview of the differences between them, and relevant references.
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.
The HDF Group has example code for access and visualization of SMAP data in MATLAB, Python, IDL, and NCL. Jupyter notebooks are also available on the NSIDC GitHub page. The notebooks demonstrate how to utilize Python for downloading, plotting, and visualizing SMAP L3/L4 data.
The answer is yes. Below the image in this article, you will find sample code in IDL, MATLAB, and Python to read in a GeoTIFF file, extract the metadata, and create an image. The code has been tested with data from:
Yes. This code is provided in files called Delivered Algorithm Packages (DAPs), which are available upon special request from NSIDC User Services. The DAP to generate Level-2A Brightness Temperatures runs only on Windows platforms; other DAPs run only on Unix platforms. Last Updated January 2018
NSIDC provides software and tools for geolocating and displaying EASE-Grid data sets at NSIDC. This article provides links to software and tools for geolocating and displaying EASE-Grid projection data sets available at NSIDC. All tools are available through the NSIDC Github. The software and tools are grouped into the following categories:
NSIDC provides software tools to extract and geolocate data in a polar stereographic projection derived from passive microwave instruments, as well as masking tools that limit the influence of weather effects on sea ice concentrations. All tools are available by FTP. The tools work with the polar stereographic gridded passive microwave data sets at NSIDC. Types of tools include:
This webinar introduces the ICESat-2 mission and shows you how to explore, access and customize ICESat-2 data with the OpenAltimetry application, using NSIDC DAAC tools, and shows you how to subset, reformat and analyze the data using Python. This webinar was originally presented on July 23, 2019, and is available on the NASA Earthdata Youtube channel.