Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation

• URL: http://arxiv.org/abs/2406.14775v1
• Date: Thu, 20 Jun 2024 22:57:38 GMT
• Title: Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation
• Authors: Aman Gupta, Aditi Sheshadri, Sujit Roy, Vishal Gaur, Manil Maskey, Rahul Ramachandran,
• Abstract summary: We present the first-ever global simulation of atmospheric mesoscale processes using machine learning (ML) models trained on the WINDSET dataset. Using an Attention U-Net-based architecture trained on globally resolved GW momentum, we illustrate the importance and effectiveness of global nonlocality.
• Score: 1.3108798582758452
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Global climate models typically operate at a grid resolution of hundreds of kilometers and fail to resolve atmospheric mesoscale processes, e.g., clouds, precipitation, and gravity waves (GWs). Model representation of these processes and their sources is essential to the global circulation and planetary energy budget, but subgrid scale contributions from these processes are often only approximately represented in models using parameterizations. These parameterizations are subject to approximations and idealizations, which limit their capability and accuracy. The most drastic of these approximations is the "single-column approximation" which completely neglects the horizontal evolution of these processes, resulting in key biases in current climate models. With a focus on atmospheric GWs, we present the first-ever global simulation of atmospheric GW fluxes using machine learning (ML) models trained on the WINDSET dataset to emulate global GW emulation in the atmosphere, as an alternative to traditional single-column parameterizations. Using an Attention U-Net-based architecture trained on globally resolved GW momentum fluxes, we illustrate the importance and effectiveness of global nonlocality, when simulating GWs using data-driven schemes.

Related papers

• RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models [0.0]
  Current climate models rely on complex mathematical parameterisations to represent sub-grid scale processes. This study explores integrating reinforcement learning with idealised climate models to address key parameterisation challenges.
  arXiv  Detail & Related papers  (2024-08-28T20:10:46Z)
• 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)
• Advances in Land Surface Model-based Forecasting: A comparative study of LSTM, Gradient Boosting, and Feedforward Neural Network Models as prognostic state emulators [4.852378895360775]
  We evaluate the efficiency of three surrogate models in speeding up experimental research by simulating land surface processes. Our findings indicate that while all models on average demonstrate high accuracy over the forecast period, the LSTM network excels in continental long-range predictions when carefully tuned.
  arXiv  Detail & Related papers  (2024-07-23T13:26:05Z)
• 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)
• 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)
• ClimaX: A foundation model for weather and climate [51.208269971019504]
  ClimaX is a deep learning model for weather and climate science. It can be pre-trained with a self-supervised learning objective on climate datasets. It can be fine-tuned to address a breadth of climate and weather tasks.
  arXiv  Detail & Related papers  (2023-01-24T23:19:01Z)
• Predictive World Models from Real-World Partial Observations [66.80340484148931]
  We present a framework for learning a probabilistic predictive world model for real-world road environments. While prior methods require complete states as ground truth for learning, we present a novel sequential training method to allow HVAEs to learn to predict complete states from partially observed states only.
  arXiv  Detail & Related papers  (2023-01-12T02:07:26Z)
• Machine-learned climate model corrections from a global storm-resolving model [0.0]
  We train neural networks to learn the state-dependent temperature, humidity, and radiative flux corrections needed to nudge a 200 km climate model to the evolution of a 3km fine-grid storm-resolving model (GSRM) When these corrective ML models are coupled to a year-long coarse-grid climate simulation, the time-mean spatial pattern errors are reduced by 6-25% for land surface temperature and 9-25% for land surface precipitation.
  arXiv  Detail & Related papers  (2022-11-21T19:39:05Z)
• Climate-Invariant Machine Learning [0.8831201550856289]
  Current climate models require representations of processes that occur at scales smaller than model grid size. Recent machine learning (ML) algorithms hold promise to improve such process representations, but tend to extrapolate poorly to climate regimes they were not trained on. We propose a new framework - termed "climate-invariant" ML - incorporating knowledge of climate processes into ML algorithms.
  arXiv  Detail & Related papers  (2021-12-14T07:02:57Z)
• 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)
• HECT: High-Dimensional Ensemble Consistency Testing for Climate Models [1.7587442088965226]
  Climate models play a crucial role in understanding the effect of environmental changes on climate to help mitigate climate risks and inform decisions. Large global climate models such as the Community Earth System Model (CESM), are very complex with millions of lines of code describing interactions of the atmosphere, land, oceans, and ice. Our work uses probabilistics like tree-based algorithms and deep neural networks to perform a statistically rigorous goodness-of-fit test of high-dimensional and man-made data.
  arXiv  Detail & Related papers  (2020-10-08T15:16:16Z)

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.