Diffusion Model-based Probabilistic Downscaling for 180-year East Asian Climate Reconstruction

• URL: http://arxiv.org/abs/2402.06646v2
• Date: Fri, 5 Apr 2024 15:27:07 GMT
• Title: Diffusion Model-based Probabilistic Downscaling for 180-year East Asian Climate Reconstruction
• Authors: Fenghua Ling, Zeyu Lu, Jing-Jia Luo, Lei Bai, Swadhin K. Behera, Dachao Jin, Baoxiang Pan, Huidong Jiang, Toshio Yamagata,
• Abstract summary: We introduce a diffusion probabilistic downscaling model (DPDM) into the meteorological field. This model can efficiently transform data from 1deg to 0.1deg resolution. We apply the model to generate a 180-year dataset of monthly surface variables in East Asia.
• Score: 8.132450337453525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As our planet is entering into the "global boiling" era, understanding regional climate change becomes imperative. Effective downscaling methods that provide localized insights are crucial for this target. Traditional approaches, including computationally-demanding regional dynamical models or statistical downscaling frameworks, are often susceptible to the influence of downscaling uncertainty. Here, we address these limitations by introducing a diffusion probabilistic downscaling model (DPDM) into the meteorological field. This model can efficiently transform data from 1{\deg} to 0.1{\deg} resolution. Compared with deterministic downscaling schemes, it not only has more accurate local details, but also can generate a large number of ensemble members based on probability distribution sampling to evaluate the uncertainty of downscaling. Additionally, we apply the model to generate a 180-year dataset of monthly surface variables in East Asia, offering a more detailed perspective for understanding local scale climate change over the past centuries.

Related papers

• Towards Kriging-informed Conditional Diffusion for Regional Sea-Level Data Downscaling [3.8178633709015446]
  Given coarser-resolution projections from global climate models or satellite data, the downscaling problem aims to estimate finer-resolution regional climate data. This problem is societally crucial for effective adaptation, mitigation, and resilience against significant risks from climate change. We propose a novel Kriging-informed Conditional Diffusion Probabilistic Model (Ki-CDPM) to capture spatial variability while preserving fine-scale features.
  arXiv  Detail & Related papers  (2024-10-21T04:24:10Z)
• 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)
• Generative Diffusion-based Downscaling for Climate [0.0]
  Machine learning algorithms are proving themselves to be efficient and accurate approaches to downscaling. We show how a generative, diffusion-based approach to downscaling gives accurate downscaled results. This research highlights the potential of diffusion-based downscaling techniques in providing reliable and detailed climate predictions.
  arXiv  Detail & Related papers  (2024-04-27T01:49:14Z)
• Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method [66.80344502790231]
  We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Our proposed method outperforms other specially designed baseline models on multiple surface variables.
  arXiv  Detail & Related papers  (2024-01-22T14:02:56Z)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• Downscaling Extreme Rainfall Using Physical-Statistical Generative Adversarial Learning [0.0]
  We develop a data-driven downscaling (super-resolution) method that incorporates physics and statistics in a generative framework to learn the fine-scale spatial details of rainfall. Our method transforms coarse-resolution ($0.25circ times 0.25circ$) climate model outputs into high-resolution ($0.01circ times 0.01circ$) rainfall fields while efficaciously quantifying uncertainty.
  arXiv  Detail & Related papers  (2022-12-02T21:04:32Z)
• Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning [112.69497636932955]
  Federated learning aims to train models across different clients without the sharing of data for privacy considerations. We study how data heterogeneity affects the representations of the globally aggregated models. We propose sc FedDecorr, a novel method that can effectively mitigate dimensional collapse in federated learning.
  arXiv  Detail & Related papers  (2022-10-01T09:04:17Z)
• Increasing the accuracy and resolution of precipitation forecasts using deep generative models [3.8073142980733]
  We train a conditional Generative Adversarial Network -- coined CorrectorGAN -- to produce ensembles of high-resolution, bias-corrected forecasts. CorrectorGAN, once trained, produces predictions in seconds on a single machine. Results raise exciting questions about the necessity of regional models, and whether data-driven downscaling and correction methods can be transferred to data-poor regions.
  arXiv  Detail & Related papers  (2022-03-23T09:45:12Z)
• Deep Learning Based Cloud Cover Parameterization for ICON [55.49957005291674]
  We train NN based cloud cover parameterizations with coarse-grained data based on realistic regional and global ICON simulations. Globally trained NNs can reproduce sub-grid scale cloud cover of the regional simulation. We identify an overemphasis on specific humidity and cloud ice as the reason why our column-based NN cannot perfectly generalize from the global to the regional coarse-grained data.
  arXiv  Detail & Related papers  (2021-12-21T16:10:45Z)
• Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change Projections [1.7503398807380832]
  We present auxiliary informed-temporal neural architecture for statistical downscaling. Current study performs daily downscaling of precipitation variable from an ESM output at 1.15 degrees (115 km) to 0.25 degrees (25 km) over the world's most climatically diversified country, India.
  arXiv  Detail & Related papers  (2020-09-23T17:52:09Z)

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.