Search Results for author: Daniela Szwarcman

Found 12 papers, 4 papers with code

Prithvi WxC: Foundation Model for Weather and Climate

2 code implementations20 Sep 2024 Johannes Schmude, Sujit Roy, Will Trojak, Johannes Jakubik, Daniel Salles Civitarese, Shraddha Singh, Julian Kuehnert, Kumar Ankur, Aman Gupta, Christopher E Phillips, Romeo Kienzler, Daniela Szwarcman, Vishal Gaur, Rajat Shinde, Rohit Lal, Arlindo Da Silva, Jorge Luis Guevara Diaz, Anne Jones, Simon Pfreundschuh, Amy Lin, Aditi Sheshadri, Udaysankar Nair, Valentine Anantharaj, Hendrik Hamann, Campbell Watson, Manil Maskey, Tsengdar J Lee, Juan Bernabe Moreno, Rahul Ramachandran

Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downscaling, or nowcasting.

Evaluating the transferability potential of deep learning models for climate downscaling

no code implementations17 Jul 2024 Ayush Prasad, Paula Harder, Qidong Yang, Prasanna Sattegeri, Daniela Szwarcman, Campbell Watson, David Rolnick

Climate downscaling, the process of generating high-resolution climate data from low-resolution simulations, is essential for understanding and adapting to climate change at regional and local scales.

Deep Learning

Fine-tuning of Geospatial Foundation Models for Aboveground Biomass Estimation

no code implementations28 Jun 2024 Michal Muszynski, Levente Klein, Ademir Ferreira da Silva, Anjani Prasad Atluri, Carlos Gomes, Daniela Szwarcman, Gurkanwar Singh, Kewen Gu, Maciel Zortea, Naomi Simumba, Paolo Fraccaro, Shraddha Singh, Steve Meliksetian, Campbell Watson, Daiki Kimura, Harini Srinivasan

In this paper, we explore the effectiveness of fine-tuning of a geospatial foundation model to estimate above-ground biomass (AGB) using space-borne data collected across different eco-regions in Brazil.

Transfer Learning

A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network

no code implementations20 Dec 2023 Takuya Kurihana, Kyongmin Yeo, Daniela Szwarcman, Bruce Elmegreen, Karthik Mukkavilli, Johannes Schmude, Levente Klein

To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial resolution and monitored in time to ensure the reduction and ultimately elimination of the pollution source.

Generative Adversarial Network Super-Resolution

Hard-Constrained Deep Learning for Climate Downscaling

1 code implementation8 Aug 2022 Paula Harder, Alex Hernandez-Garcia, Venkatesh Ramesh, Qidong Yang, Prasanna Sattigeri, Daniela Szwarcman, Campbell Watson, David Rolnick

In order to conserve physical quantities, here we introduce methods that guarantee statistical constraints are satisfied by a deep learning downscaling model, while also improving their performance according to traditional metrics.

Deep Learning Super-Resolution

Extreme Precipitation Seasonal Forecast Using a Transformer Neural Network

no code implementations14 Jul 2021 Daniel Salles Civitarese, Daniela Szwarcman, Bianca Zadrozny, Campbell Watson

An impact of climate change is the increase in frequency and intensity of extreme precipitation events.

A modular framework for extreme weather generation

no code implementations5 Feb 2021 Bianca Zadrozny, Campbell D. Watson, Daniela Szwarcman, Daniel Civitarese, Dario Oliveira, Eduardo Rodrigues, Jorge Guevara

Extreme weather events have an enormous impact on society and are expected to become more frequent and severe with climate change.

BIG-bench Machine Learning

Semantic Segmentation of Seismic Images

no code implementations10 May 2019 Daniel Civitarese, Daniela Szwarcman, Emilio Vital Brazil, Bianca Zadrozny

We compare our approach with two well-known deep neural network topologies: Fully Convolutional Network and U-Net.

Segmentation Semantic Segmentation

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