A modular framework for extreme weather generation

• URL: http://arxiv.org/abs/2102.04534v1
• Date: Fri, 5 Feb 2021 15:12:10 GMT
• Title: A modular framework for extreme weather generation
• Authors: Bianca Zadrozny, Campbell D. Watson, Daniela Szwarcman, Daniel Civitarese, Dario Oliveira, Eduardo Rodrigues, Jorge Guevara
• Abstract summary: Machine learning techniques can play a critical role in resilience planning. This paper proposes a modular framework that relies on interchangeable components to produce extreme weather event scenarios.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 learning techniques can play a critical role in resilience planning through the generation of realistic extreme weather event scenarios that can be used to evaluate possible mitigation actions. This paper proposes a modular framework that relies on interchangeable components to produce extreme weather event scenarios. We discuss possible alternatives for each of the components and show initial results comparing two approaches on the task of generating precipitation scenarios.

Related papers

• Schema-Guided Culture-Aware Complex Event Simulation with Multi-Agent Role-Play [69.57968387772428]
  Complex news events, such as natural disasters and socio-political conflicts, require swift responses from the government and society. We develop a controllable complex news event simulator guided by both the event schema representing domain knowledge. We introduce a geo-diverse commonsense and cultural norm-aware knowledge enhancement component.
  arXiv  Detail & Related papers  (2024-10-24T17:21:43Z)
• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests [47.285748922842444]
  We train a deep neural network to predict a phenological index from meteorological time series. We find that this approach outperforms traditional process-based models.
  arXiv  Detail & Related papers  (2024-01-08T15:29:23Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models [13.331224394143117]
  Uncertainty quantification is crucial to decision-making. dominant approach to representing uncertainty in weather forecasting is to generate an ensemble of forecasts. We propose to amortize the computational cost by emulating these forecasts with deep generative diffusion models learned from historical data.
  arXiv  Detail & Related papers  (2023-06-24T22:00:06Z)
• Improving extreme weather events detection with light-weight neural networks [0.0]
  We modify a light-weight Context Guided convolutional neural network architecture trained for semantic segmentation of tropical cyclones and atmospheric rivers in climate data. Our primary focus is on tropical cyclones, the most destructive weather events, for which current models show limited performance.
  arXiv  Detail & Related papers  (2023-03-31T23:38:54Z)
• A locally time-invariant metric for climate model ensemble predictions of extreme risk [8.347190888362194]
  We introduce a locally time-invariant method for evaluating climate model simulations with a focus on assessing the simulation of extremes. We explore the behaviour of the proposed method in predicting extreme heat days in Nairobi and provide comparative results for eight additional cities.
  arXiv  Detail & Related papers  (2022-11-26T16:41:50Z)
• A Multi-Scale Deep Learning Framework for Projecting Weather Extremes [3.3598755777055374]
  Weather extremes are a major societal and economic hazard, claiming thousands of lives and causing billions of dollars in damage every year. General circulation models (GCMs), which are currently the primary tool for climate projections, cannot characterize weather extremes accurately. We present a multi-resolution deep-learning framework that corrects a GCM's biases by matching low-order and tail statistics of its output with observations at coarse scales. We use the proposed framework to generate statistically realistic realizations of the climate over Western Europe from a simple GCM corrected using observational atmospheric reanalysis.
  arXiv  Detail & Related papers  (2022-10-21T17:47:05Z)
• ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods [89.61670857155173]
  We present our solution to simulate photo-realistic floods on authentic images. We propose ClimateGAN, a model that leverages both simulated and real data for unsupervised domain adaptation and conditional image generation.
  arXiv  Detail & Related papers  (2021-10-06T15:54:57Z)
• Deep Learning based Extreme Heatwave Forecast [8.975667614727648]
  Using 1000 years of state-of-the-art PlaSim Planete Simulator Climate Model data, it is shown that Convolutional Neural Network-based Deep Learning frameworks, with large-class undersampling and transfer learning achieve significant performance in forecasting the occurrence of extreme heatwaves.
  arXiv  Detail & Related papers  (2021-03-17T16:10:06Z)
• DeepClimGAN: A High-Resolution Climate Data Generator [60.59639064716545]
  Earth system models (ESMs) are often used to generate future projections of climate change scenarios. As a compromise, emulators are substantially less expensive but may not have all of the complexity of an ESM. Here we demonstrate the use of a conditional generative adversarial network (GAN) to act as an ESM emulator.
  arXiv  Detail & Related papers  (2020-11-23T20:13:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.