Zobrazeno 1 - 10
of 146
pro vyhledávání: '"GARG, PIYUSH"'
Autor:
Pathak, Jaideep, Cohen, Yair, Garg, Piyush, Harrington, Peter, Brenowitz, Noah, Durran, Dale, Mardani, Morteza, Vahdat, Arash, Xu, Shaoming, Kashinath, Karthik, Pritchard, Michael
Storm-scale convection-allowing models (CAMs) are an important tool for predicting the evolution of thunderstorms and mesoscale convective systems that result in damaging extreme weather. By explicitly resolving convective dynamics within the atmosph
Externí odkaz:
http://arxiv.org/abs/2408.10958
Autor:
Manshausen, Peter, Cohen, Yair, Pathak, Jaideep, Pritchard, Mike, Garg, Piyush, Mardani, Morteza, Kashinath, Karthik, Byrne, Simon, Brenowitz, Noah
Data assimilation of observational data into full atmospheric states is essential for weather forecast model initialization. Recently, methods for deep generative data assimilation have been proposed which allow for using new input data without retra
Externí odkaz:
http://arxiv.org/abs/2406.16947
Online social media platforms, such as Twitter, provide valuable information during disaster events. Existing tweet disaster summarization approaches provide a summary of these events to aid government agencies, humanitarian organizations, etc., to e
Externí odkaz:
http://arxiv.org/abs/2405.06551
The abundance of situational information on Twitter poses a challenge for users to manually discern vital and relevant information during disasters. A concise and human-interpretable overview of this information helps decision-makers in implementing
Externí odkaz:
http://arxiv.org/abs/2405.06541
Autor:
Stock, Jason, Pathak, Jaideep, Cohen, Yair, Pritchard, Mike, Garg, Piyush, Durran, Dale, Mardani, Morteza, Brenowitz, Noah
This work presents an autoregressive generative diffusion model (DiffObs) to predict the global evolution of daily precipitation, trained on a satellite observational product, and assessed with domain-specific diagnostics. The model is trained to pro
Externí odkaz:
http://arxiv.org/abs/2404.06517
Online social media platforms, such as Twitter, are one of the most valuable sources of information during disaster events. Therefore, humanitarian organizations, government agencies, and volunteers rely on a summary of this information, i.e., tweets
Externí odkaz:
http://arxiv.org/abs/2305.11592
Disaster summarization approaches provide an overview of the important information posted during disaster events on social media platforms, such as, Twitter. However, the type of information posted significantly varies across disasters depending on s
Externí odkaz:
http://arxiv.org/abs/2305.11536
We examine the longitudinal dispersion of spheroidal microswimmers in pressure-driven channel flow. When time scales corresponding to swimmer orientation relaxation, and diffusion in the gradient and flow directions, are well separated, a multiple sc
Externí odkaz:
http://arxiv.org/abs/2212.01817
The huge amount of information shared in Twitter during disaster events are utilized by government agencies and humanitarian organizations to ensure quick crisis response and provide situational updates. However, the huge number of tweets posted make
Externí odkaz:
http://arxiv.org/abs/2203.01188
The huge popularity of social media platforms like Twitter attracts a large fraction of users to share real-time information and short situational messages during disasters. A summary of these tweets is required by the government organizations, agenc
Externí odkaz:
http://arxiv.org/abs/2201.06545