Quantum Artificial Intelligence for the Science of Climate Change
- URL: http://arxiv.org/abs/2108.10855v1
- Date: Wed, 28 Jul 2021 19:00:33 GMT
- Title: Quantum Artificial Intelligence for the Science of Climate Change
- Authors: Manmeet Singh, Chirag Dhara, Adarsh Kumar, Sukhpal Singh Gill and
Steve Uhlig
- Abstract summary: We argue that new developments in Artificial Intelligence algorithms designed for quantum computers may provide the key breakthroughs necessary to furthering the science of climate change.
The resultant improvements in weather and climate forecasts are expected to cascade to numerous societal benefits.
- Score: 4.678152517092125
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Climate change has become one of the biggest global problems increasingly
compromising the Earth's habitability. Recent developments such as the
extraordinary heat waves in California & Canada, and the devastating floods in
Germany point to the role of climate change in the ever-increasing frequency of
extreme weather. Numerical modelling of the weather and climate have seen
tremendous improvements in the last five decades, yet stringent limitations
remain to be overcome. Spatially and temporally localized forecasting is the
need of the hour for effective adaptation measures towards minimizing the loss
of life and property. Artificial Intelligence-based methods are demonstrating
promising results in improving predictions, but are still limited by the
availability of requisite hardware and software required to process the vast
deluge of data at a scale of the planet Earth. Quantum computing is an emerging
paradigm that has found potential applicability in several fields. In this
opinion piece, we argue that new developments in Artificial Intelligence
algorithms designed for quantum computers - also known as Quantum Artificial
Intelligence (QAI) - may provide the key breakthroughs necessary to furthering
the science of climate change. The resultant improvements in weather and
climate forecasts are expected to cascade to numerous societal benefits.
Related papers
- 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) - MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions.
Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations.
We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
arXiv Detail & Related papers (2024-08-20T13:45:49Z) - Quantum Computing for Climate Resilience and Sustainability Challenges [0.23558144417896584]
Review explores the application of quantum machine learning and optimization techniques for climate change prediction and enhancing sustainable development.
By synthesizing the latest research and developments, this paper highlights how QC and quantum machine learning can optimize multi-infrastructure systems towards climate neutrality.
arXiv Detail & Related papers (2024-07-23T08:54:12Z) - Building a temperature forecasting model for the city with the regression neural network (RNN) [0.0]
Research on weather forecast models is a recent development, having only begun around 2000.
along with advancements in computer science, mathematical models are being built and applied with machine learning techniques to create more accurate and reliable predictive models.
This article will summarize the research and solutions for applying recurrent neural networks to forecast urban temperatures.
arXiv Detail & Related papers (2024-05-27T18:32:36Z) - ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs [14.095897879222676]
We present ClimODE, a continuous-time process that implements key principle of statistical mechanics.
ClimODE models precise weather evolution with value-conserving dynamics, learning global weather transport as a neural flow.
Our approach outperforms existing data-driven methods in global, regional forecasting with an order of magnitude smaller parameterization.
arXiv Detail & Related papers (2024-04-15T06:38:21Z) - FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather
Forecasting [56.73502043159699]
This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution.
It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model.
The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
arXiv Detail & Related papers (2024-01-28T13:23:25Z) - Towards an end-to-end artificial intelligence driven global weather forecasting system [57.5191940978886]
We present an AI-based data assimilation model, i.e., Adas, for global weather variables.
We demonstrate that Adas can assimilate global observations to produce high-quality analysis, enabling the system operate stably for long term.
We are the first to apply the methods to real-world scenarios, which is more challenging and has considerable practical application potential.
arXiv Detail & Related papers (2023-12-18T09:05:28Z) - Quantum Machine Learning in Climate Change and Sustainability: a Review [0.5217870815854702]
We review existing literature that applies quantum machine learning to solve climate change and sustainability-related problems.
We discuss the challenges and current limitations of quantum machine learning approaches.
arXiv Detail & Related papers (2023-10-13T14:56:38Z) - Multi-scale Digital Twin: Developing a fast and physics-informed
surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
Climate change exacerbates the long-term soil management problem of groundwater contamination.
We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO)
In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
arXiv Detail & Related papers (2022-11-20T06:46:35Z) - Forecasting Future World Events with Neural Networks [68.43460909545063]
Autocast is a dataset containing thousands of forecasting questions and an accompanying news corpus.
The news corpus is organized by date, allowing us to precisely simulate the conditions under which humans made past forecasts.
We test language models on our forecasting task and find that performance is far below a human expert baseline.
arXiv Detail & Related papers (2022-06-30T17:59:14Z) - Quantum technologies for climate change: Preliminary assessment [0.0]
Climate change presents an existential threat to human societies and the Earth's ecosystems.
Quantum technologies in computing, sensing, and communication could become useful tools to diagnose and help mitigate the effects of climate change.
This report aims to identify potential high-impact use-cases of quantum technologies for climate change with a focus on four main areas.
arXiv Detail & Related papers (2021-06-23T18:02:19Z)
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.