Authors: JR Lee (Water Resources Research Center @ UH Mānoa), Prithviraj Pramanik (National Institute of Technology Durgapur, India / University of New South Wales), Veda Yakkali (Rutgers University), and Anastasiia Lehova (University of Hawaiʻi at Mānoa)
Author: Nathan Severyns (NEIU / Crocus)
Clouds have been widely studied in a variety of fields. The shape and distribution of clouds are not only important to modeling weather, but also to understand interactions between aerosol and cloud for weather research, and to develop environment forecasting models including radiation and cloud properties. Additionally, detecting and understanding cloud cover over the sky have been studied to estimate and forecast solar irradiance and performance of renewable solar photovoltaic energy generation. For this reason, examining solar irradiance in photovoltaic power grids has been investigated in many ways. Even though the purpose of each study is diverse, it is common that they have approached to analyze the magnitude of cloud coverage. In this context, answering how much cloud covers the sky is a striking problem along with other factors such as wind direction, speed, temperature, and other meteorological factors.
Lightning causes significant damage, starts wildfires, and poses a threat to human safety. Lightning strike releases a large pulse of electromagnetic energy that can be detected and recorded with software-defined radio (SDR). These recordings can be used to:
Overview
Authors: Samuel Watson (University of Hawaiʻi at Mānoa / HCDP)
Ice and snowfall are incredibly important parts of a river ecosystem. The Bad River is home to wild rice, which is very temperamental and prone to natural boom/bust years. Having a snow classifier can be used to create a larger dataset of snow that can be used for a variety of these additional tasks including assisting with predicting wild rice yields.
Hello! My name is Neelanshi Varia and I am a student at Northwestern University pursuing MS in Artificial Intelligence. During my undergraduate studies, I performed research in applications of Machine Learning in the Remote Sensing area, Conversational Artificial Intelligence and 360-degree Computer Vision. This summer at Argonne, I am working on a very interesting problem involving snowflakes! (Yes, isn’t that cool?) I am working on the classification of habits of snowflakes on images acquired real-time during snowfall and blizzards.
Solar energy is one of the cleanest and most renewable sources of energy in today’s day and age. Through the development of this project there can be a vast economic impact in the sense that power grid operators will be able to manage power supply much more efficiently and even begin to automate processes for solar energy generation. We created a model to estimate solar irradiance in the sky based on ground images taken from Waggle/Sage nodes. We are excepting that this application will support:
Clouds have long fascinated humans because of their complex and diverse nature. To gain a deeper understanding of these atmospheric phenomena, a multidisciplinary team of computer scientists, meteorologists, and machine learning experts from Northwestern-Argonne Institute of Science and Engineering (NAISE) collaborated on the National Science Foundation (NSF)-supported Sage project. The project's goal was to develop new edge computing technologies that would allow scientists to collect and analyze large amounts of data from advanced sensors in real time.
LIDAR technology, with its ability to create detailed maps of atmospheric environments, can potentially solve the issue of blockers by providing precise information about the location and height of obstructions. It proposes many benefits that can’t be offered by a regular camera, especially the fact that it provides accurate data in all different types of weather conditions including fog, rain, and low light. This information can be used to adjust solar irradiance estimates and sky predictions, making them more accurate in any setting. Current models experience difficulties in the presense of blockers and unfamiliar sky conditions. We believe LiDAR can potentially solve this issue and allow us to deploy these nodes in urban areas.