Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Zadrozny, Bianca"'
Autor:
Jakubik, Johannes, Roy, Sujit, Phillips, C. E., Fraccaro, Paolo, Godwin, Denys, Zadrozny, Bianca, Szwarcman, Daniela, Gomes, Carlos, Nyirjesy, Gabby, Edwards, Blair, Kimura, Daiki, Simumba, Naomi, Chu, Linsong, Mukkavilli, S. Karthik, Lambhate, Devyani, Das, Kamal, Bangalore, Ranjini, Oliveira, Dario, Muszynski, Michal, Ankur, Kumar, Ramasubramanian, Muthukumaran, Gurung, Iksha, Khallaghi, Sam, Hanxi, Li, Cecil, Michael, Ahmadi, Maryam, Kordi, Fatemeh, Alemohammad, Hamed, Maskey, Manil, Ganti, Raghu, Weldemariam, Kommy, Ramachandran, Rahul
Significant progress in the development of highly adaptable and reusable Artificial Intelligence (AI) models is expected to have a significant impact on Earth science and remote sensing. Foundation models are pre-trained on large unlabeled datasets t
Externí odkaz:
http://arxiv.org/abs/2310.18660
Wildfire events have caused severe losses in many places around the world and are expected to increase with climate change. Throughout the years many technologies have been developed to identify fire events early on and to simulate fire behavior once
Externí odkaz:
http://arxiv.org/abs/2203.15558
Autor:
Wong, Ken C. L., Wang, Hongzhi, Vos, Etienne E., Zadrozny, Bianca, Watson, Campbell D., Syeda-Mahmood, Tanveer
Global warming leads to the increase in frequency and intensity of climate extremes that cause tremendous loss of lives and property. Accurate long-range climate prediction allows more time for preparation and disaster risk management for such extrem
Externí odkaz:
http://arxiv.org/abs/2112.05254
One of the consequences of climate change is anobserved increase in the frequency of extreme cli-mate events. That poses a challenge for weatherforecast and generation algorithms, which learnfrom historical data but should embed an often un-certain b
Externí odkaz:
http://arxiv.org/abs/2108.00048
Future climate change scenarios are usually hypothesized using simulations from weather generators. However, there only a few works comparing and evaluating promising deep learning models for weather generation against classical approaches. This stud
Externí odkaz:
http://arxiv.org/abs/2107.08074
An impact of climate change is the increase in frequency and intensity of extreme precipitation events. However, confidently predicting the likelihood of extreme precipitation at seasonal scales remains an outstanding challenge. Here, we present an a
Externí odkaz:
http://arxiv.org/abs/2107.06846
Operational forecasting centers are investing in decadal (1-10 year) forecast systems to support long-term decision making for a more climate-resilient society. One method that has previously been employed is the Dynamic Mode Decomposition (DMD) algo
Externí odkaz:
http://arxiv.org/abs/2106.11111
Autor:
Zadrozny, Bianca
Publikováno v:
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Thesis (Ph. D.)--University of California, San Diego, 2003.
Vita. Includes bibliographical references (leaves 136-143).
Vita. Includes bibliographical references (leaves 136-143).
Externí odkaz:
http://wwwlib.umi.com/cr/ucsd/fullcit?p3099556
Statistical learning theory provides the foundation to applied machine learning, and its various successful applications in computer vision, natural language processing and other scientific domains. The theory, however, does not take into account the
Externí odkaz:
http://arxiv.org/abs/2102.08791
Autor:
Zadrozny, Bianca, Watson, Campbell D., Szwarcman, Daniela, Civitarese, Daniel, Oliveira, Dario, Rodrigues, Eduardo, Guevara, Jorge
Extreme weather events have an enormous impact on society and are expected to become more frequent and severe with climate change. In this context, resilience planning becomes crucial for risk mitigation and coping with these extreme events. Machine
Externí odkaz:
http://arxiv.org/abs/2102.04534