Oceanography and Atmospheric Sciences and Meteorology Commons^™

Physics-Informed Machine Learning To Predict Extreme Weather Events, Rthvik Raviprakash, Jonathan Buchanan, Mahdi Bu Ali

Discovery Undergraduate Interdisciplinary Research Internship

Extreme weather events refer to unexpected, severe, or unseasonal weather events, which are dynamically related to specific large-scale atmospheric patterns. These extreme weather events have a significant impact on human society and also natural ecosystems. For example, natural disasters due to extreme weather events caused more than $90 billion global direct losses in 2015. These extreme weather events are challenging to predict due to the chaotic nature of the atmosphere and are highly correlated with the occurrence of atmospheric blocking. A key aspect for preparedness and response to extreme climate events is accurate medium-range forecasting of atmospheric blocking events.

Unlike …

Automated Detection And Characterization Of Mesocyclones In X-Band Radar Observations, Raychel E. Nelson

The Journal of Purdue Undergraduate Research

No abstract provided.

Past The Tipping Point, But With Hope Of Return: How Creating A Geoengineering Compulsory Licensing Scheme Can Incentivize Innovation, Brooke Wilson

Washington and Lee Journal of Civil Rights and Social Justice

This Note explores the patenting of geoengineering technologies and issues arising from the early stages of this high-risk, high-reward technology. This Note focuses on one possible solution to solving the issues surrounding the patenting of geoengineering technology: Creating a specialized compulsory licensing scheme.

Pooling Data Improves Multimodel Idf Estimates Over Median-Based Idf Estimates: Analysis Over The Susquehanna And Florida, Abhishekh Kumar Srivastava, Richard Grotjahn, Paul Aaron Ullrich, Mojtaba Sadegh

Civil Engineering Faculty Publications and Presentations

Traditional multimodel methods for estimating future changes in precipitation intensity, duration, and frequency (IDF) curves rely on mean or median of models’ IDF estimates. Such multimodel estimates are impaired by large estimation uncertainty, shadowing their efficacy in planning efforts. Here, assuming that each climate model is one representation of the underlying data generating process, i.e., the Earth system, we propose a novel extension of current methods through pooling model data: (i) evaluate performance of climate models in simulating the spatial and temporal variability of the observed annual maximum precipitation (AMP), (ii) bias-correct and pool historical and future AMP data of …

Fifteen Years Of Hfc-134a Satellite Observations: Comparisons With Slimcat Calculations, Jeremy J. Harrison, Martyn P. Chipperfield, Christopher D. Boone, Sandip S. Dhomse, Peter F. Bernath

Chemistry & Biochemistry Faculty Publications

The phase out of anthropogenic ozone-depleting substances such as chlorofluorocarbons under the terms of the Montreal Protocol led to the development and worldwide use of hydrofluorocarbons (HFCs) in refrigeration, air conditioning, and as blowing agents and propellants. Consequently, over recent years, the atmospheric abundances of HFCs have dramatically increased. HFCs are powerful greenhouse gases and are now controlled under the terms of the 2016 Kigali Amendment to the Montreal Protocol. HFC-134a is currently the most abundant HFC in the atmosphere, breaking the 100 ppt barrier in 2018, and can be measured in the Earth's atmosphere by the satellite remote-sensing instrument …