Citation Information

References

This software is developed with the goal of supporting science applications for the NASA Operation IceBridge and ICESat-2 missions. This work is developed in conjunction with other ICESat-2 projects. The programs included in this software have contributed most recently to the following work:

B. E. Smith, B. Medley, X. Fettweis, T. Sutterley, P. Alexander, D. Porter, and M. Tedesco, “Evaluating Greenland surface-mass-balance and firn-densification data using ICESat-2 altimetry”, The Cryosphere, 17(2), 789-808, (2023). doi: 10.5194/tc-17-789-2023

T. C. Sutterley, I. Velicogna, X. Fettweis, E. Rignot, B. Noël, and M. van den Broeke, “Evaluation of Reconstructions of Snow/Ice Melt in Greenland by Regional Atmospheric Climate Models Using Laser Altimetry Data”, Geophysical Research Letters, 45(16), 8324-8333, (2018). doi: 10.1029/2018GL078645

Contributors

• Patrick Alexander (LDEO Columbia University)

• Brooke Medley (NASA Goddard Space Flight Center)

• David Porter (LDEO Columbia University)

• Ben Smith (University of Washington)

• Matt Siegfried (Colorado School of Mines)

• Tyler Sutterley (University of Washington)

• Marco Tedesco (LDEO Columbia University)

• Hannah Verboncoeur (Colorado School of Mines)

Development

This is an open source project. We welcome any help in maintaining and developing the software and documentation. Anyone at any career stage and with any level of coding experience can contribute. Please see the Contribution Guidelines for more information.

Problem Reports

If you have found a problem in these programs, or you would like to suggest an improvement or modification, please submit a GitHub issue and we will get back to you.

Dependencies

This software is also dependent on other commonly used Python packages:

• numpy: Scientific Computing Tools For Python

• scipy: Scientific Tools for Python

• pyproj: Python interface to PROJ library

• dateutil: powerful extensions to datetime

• netCDF4: Python interface to the netCDF C library

• scikit-learn: Machine Learning in Python

Disclaimer

This product includes software developed at:

• Lamont-Doherty Earth Observatory, Columbia University

• University of Washington, Applied Physics Laboratory, Polar Science Center

It is provided here for your convenience but with no guarantees whatsoever.