-
Natural Hazards Tephra Fallout Lab
04 Mar 2011 | Educational Materials | Contributor(s): Leah Michelle Courtland
This lab walks students through using the tephra2 education graphical user interface (gui) to investigate tephra fallout at Colima volcano. By the end of this lab, students should be able to:•understand that every volcanic event is different and so produces a unique deposit•convert from units of...
-
1st IAVCEI/GVM Workshop: "From Volcanic Hazard to Risk Assessment", Geneva, 27-29 June 2018
18 Dec 2018 | Workshops | Contributor(s): Costanza Bonadonna, Sebastien Biass, Eliza S Calder, Corine Frischknecht, Chris Eric Gregg, Susanna Jenkins, Sue C Loughlin, Scira Menoni, Shinji Takarada, Tom Wilson
The complexity of volcanic risk analysis typically resides in the interaction of multiple hazard, vulnerability and exposure aspects dynamically acting over various spatial and temporal scales. Risk analyses provide an evidence-based approach to development and implementation of proactive...
-
A Matlab implementation of the Carey and Sparks (1986) model
04 Nov 2015 | Offline Tools | Contributor(s): Sebastien Biass, Gholamohssein Bagheri, Costanza Bonadonna
Download the code on GitHub: https://github.com/e5k/CareySparks86_MatlabFollow updates on: https://e5k.github.io/ This file is a Matlab implementation of the Carey and Sparks (1986) model to estimate i) the plume height above sampling altitude and ii) the wind speed at the...
-
A new method to identify the vent location of tephra fall deposits based on thickness or maximum clast size measurements (SVL)
17 Apr 2018 | Offline Tools | Contributor(s): Qingyuan Yang, Marcus I Bursik, E Bruce Pitman
This tool presents a new method to identify the vent location of tephra fall deposits based on thickness or maximum clast size measurements. It is temporarily named "svl", which is short for Source Vent Locator. The method estimates the vent location by coupling semi-empirical models...
-
A Survey on Causal Discovery Methods for IID and Time Series Data
16 Jul 2024 | Publications | Contributor(s): Uzma Hasan, Emam Hossain, Md Osman Gani
Abstract: The ability to understand causality from data is one of the major milestones of human-level intelligence. Causal Discovery (CD) algorithms can identify the cause-effect relationships among the variables of a system from related observational data with certain assumptions. Over the...
-
An Open Source Tool for Visualizing ISM Intercomparisons
14 Dec 2021 | *Tools | Contributor(s): Alex Becerra, sophie nowicki, Erika Simon
This tool produces visualizations from Seroussi et al. (2020).
-
Approximation by Localized Penalized Splines (ALPS)
22 Sep 2021 | *Tools | Contributor(s): Prashant Shekhar, Abani Patra
Approximation by Localized Penalized Splines (ALPS)
-
ASHEE
14 Aug 2015 | Offline Tools | Contributor(s): Matteo Cerminara, Tomaso Esposti Ongaro
A fluid-dynamic model is developed to numerically simulate the non-equilibrium dynamics of polydisperse gas-particle mixtures forming volcanic plumes. Starting from the three-dimensional N-phase Eulerian transport equations for a mixture of gases and solid particles, we adopt an asymptotic...
-
ATM-Based Crevasse Detection & Extraction workflow
29 Jul 2020 | *Tools | Contributor(s): Renette Jones-Ivey, Jeanette Sperhac, Kristin Poinar
ABCDE Tool
-
CESM ISMIP6 Forcing Data
20 Oct 2021 | *Data Sets/Collections | Contributor(s): Kate Thayer-Calder, Gunter Leguy, William Lipscomb
The Community Earth System Model (CESM) version 2.1 is a world-class coupled climate system model that includes components for the atmosphere, ocean, terrestrial system, river run-off, and fully active glaciers (Danabasoglu et al. 2020). This version of CESM was used in many experiments as part...
-
Civil protection exercises for volcanic risk management. The VUELCO experience at midway.
20 Nov 2014 | Courses | Contributor(s): Stefano Ciolli, Chiara Cristiani
This lecture was given during the short-course titled "Coping with volcanic unrest" held in Quito (Ecuador) on November 2014.The presentation illustrates some essential key-elements to take into account in the preparation of a simulation exercise, providing useful suggestion taken...
-
CmCt GRACE MASCON Tool
16 Nov 2020 | *Tools | Contributor(s): Erika Simon, sophie nowicki
The Cryosphere model Comparison tool (CmCt) GRACE Mascon Module compares user uploaded ice sheet models to the GRACE Mascon product derived by NASA GSFC.
-
CmCt Histogram Tool
21 May 2019 | *Tools | Contributor(s): Erika Simon, sophie nowicki
This Jupyter notebook based tool can be used to plot the comparison results from the Cryosphere model Comparison tool (CmCt).
-
Code Demos for "Regression on Ice" Lecture Notes
10 Oct 2023 | *Tools | Contributor(s): Noah J Bergam
Notebooks with visualizations of some basic regression / machine learning concepts for glaciology
-
Development and Initial Testing of XR-Based Fence Diagrams for Polar Science
23 Apr 2024 | Publications | Contributor(s): Naomi Tack, Nicholas Holschuh, Sharad Sharma, Rebecca Williams, Don Engel
Naomi Tack, Nicholas Holschuh, Sharad Sharma, Rebecca Williams, and Don Engel. 2023. Development and Initial Testing of XR-Based Fence Diagrams for Polar Science. In IGARSS 2023 – 2023 IEEE International Geoscience and Remote Sensing Symposium, July 16, 2023, Pasadena, CA, USA. IEEE,...
-
Estimating Volcanic Risk in the Lesser Antilles
26 Sep 2013 | Publications | Contributor(s): Michal Camejo, Richard E.A. Robertson
The potential catastrophic effects of future volcanic eruptions in the Lesser Antilles can be decreased by the utilisation of effective risk quantification measures and their subsequent incorporation into disaster risk reduction strategies. A volcanic risk study conducted by the Norwegian...
-
Evaluating Machine Learning and Statistical Models for Greenland Bed Topography
15 May 2024 | Publications | Contributor(s): Homayra Alam, Katherine Yi, Angelina Dewar, Tartela Tabassum, Jason Lu, Ray Chen, Jianwu Wang, Sikan Li, Mathieu Morlighem, Omar Faruque
Abstract:The purpose of this research is to study how different machine learning and statistical models can be used to predict bed topography in Greenland using ice-penetrating radar and satellite imagery data. Accurate bed topography representations are crucial for understanding ice sheet...
-
Evaluating Machine Learning and Statistical Models for Greenland Subglacial Bed Topography
15 May 2024 | Publications | Contributor(s): Homayra Alam, Jianwu Wang, Tartela Tabassum, Katherine Yi, Angelina Dewar, Jason Lu, Ray Chen, Omar Faruque, Mathieu Morlighem, Sikan LI
Abstract:The purpose of this research is to study how different machine learning and statistical models can be used to predict bedrock topography under the Greenland ice sheet using ice-penetrating radar and satellite imagery data. Accurate bed topography representations are crucial for...
-
GIS layer of Greenland Ice Sheet bed available for sub-ice drilling
15 Sep 2022 | *Data Sets/Collections | Contributor(s): jason briner
This dataset includes several geographical-information-system (GIS, ArcGIS) shapefiles relating to this study:Briner, J.P., Walcott, C.K., Schaefer, J.M., Young, N.E., MacGregor, J.A., Poinar, K., Keisling, B.A., Anandakrishnan, S., Albert, M.R., Kuhl, T., Boeckmann, G. (submitted). Where to...
-
GIS layers of North America ice sheet history
03 Oct 2022 | *Data Sets/Collections | Contributor(s): jason briner
Included are ArcGIS shapefiles of North American ice sheet extent in 36 time slices spanning from 18,000 to 1,000 years ago.