Exploring the Potential of Hybrid Machine-Learning/Physics-Based Modeling for Atmospheric/Oceanic Prediction Beyond the Medium Range

• URL: http://arxiv.org/abs/2405.19518v1
• Date: Wed, 29 May 2024 20:56:44 GMT
• Title: Exploring the Potential of Hybrid Machine-Learning/Physics-Based Modeling for Atmospheric/Oceanic Prediction Beyond the Medium Range
• Authors: Dhruvit Patel, Troy Arcomano, Brian Hunt, Istvan Szunyogh, Edward Ott,
• Abstract summary: This paper explores the potential of a hybrid modeling approach that combines machine learning (ML) with conventional physics-based modeling for weather prediction beyond the medium range. The model is based on the low-resolution, simplified parameterization atmospheric general circulation model (AGCM) SPEEDY. The model has skill in predicting the El Nino cycle and its global teleconnections with precipitation for 3-7 months depending on the season.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores the potential of a hybrid modeling approach that combines machine learning (ML) with conventional physics-based modeling for weather prediction beyond the medium range. It extends the work of Arcomano et al. (2022), which tested the approach for short- and medium-range weather prediction, and the work of Arcomano et al. (2023), which investigated its potential for climate modeling. The hybrid model used for the forecast experiments of the paper is based on the low-resolution, simplified parameterization atmospheric general circulation model (AGCM) SPEEDY. In addition to the hybridized prognostic variables of SPEEDY, the current version of the model has three purely ML-based prognostic variables. One of these is 6~h cumulative precipitation, another is the sea surface temperature, while the third is the heat content of the top 300 m deep layer of the ocean. The model has skill in predicting the El Ni\~no cycle and its global teleconnections with precipitation for 3-7 months depending on the season. The model captures equatorial variability of the precipitation associated with Kelvin and Rossby waves and MJO. Predictions of the precipitation in the equatorial region have skill for 15 days in the East Pacific and 11.5 days in the West Pacific. Though the model has low spatial resolution, for these tasks it has prediction skill comparable to what has been published for high-resolution, purely physics-based, conventional operational forecast models.

Related papers

• Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling [19.340636269420692]
  Storm-scale convection-allowing models (CAMs) are an important tool for predicting the evolution of thunderstorms and mesoscale convective systems. Deep learning models have thus far not proven skilful at km-scale atmospheric simulation. We present a generative diffusion model called StormCast, which emulates the high-resolution rapid refresh (HRRR) model-NOAA's state-of-the-art 3km operational CAM.
  arXiv  Detail & Related papers  (2024-08-20T15:56:01Z)
• 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)
• Embedding machine-learnt sub-grid variability improves climate model biases [0.44998333629984877]
  Under-representation of cloud formation is a long-standing bias associated with climate simulations. We overcome these biases by embedding a Multi-Output Gaussian Process (MOGP) trained on high resolution Unified Model simulations. Ten-year predictions are generated for both control and ML-hybrid models.
  arXiv  Detail & Related papers  (2024-06-13T19:35:58Z)
• 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)
• Forecasting Tropical Cyclones with Cascaded Diffusion Models [4.272401529389713]
  This work leverages generative diffusion models to forecast cyclone trajectories and precipitation patterns. Forecasts can be produced in as little as 30 mins on a single Nvidia A30/RTX 2080 Ti.
  arXiv  Detail & Related papers  (2023-10-02T23:09:59Z)
• 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)
• Dynamical Tests of a Deep-Learning Weather Prediction Model [0.0]
  Deep-learning weather prediction models have been shown to produce forecasts that rival those from physics-based models run at operational centers. It is unclear whether these models have encoded atmospheric dynamics, or simply pattern matching that produces the smallest forecast error. Here we subject one such model, Pangu-weather, to a set of four classical dynamical experiments that do not resemble the model training data. We conclude that the model encodes realistic physics in all experiments, and suggest it can be used as a tool for rapidly testing ideas before using expensive physics-based models.
  arXiv  Detail & Related papers  (2023-09-19T18:26:41Z)
• Expanding Mars Climate Modeling: Interpretable Machine Learning for Modeling MSL Relative Humidity [0.0]
  We propose a novel approach to Martian climate modeling by leveraging machine learning techniques. Our study presents a deep neural network designed to accurately model relative humidity in Gale Crater. We find that our neural network can effectively model relative humidity at Gale crater using a few meteorological variables.
  arXiv  Detail & Related papers  (2023-09-04T08:15:15Z)
• Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast [91.9372563527801]
  We present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast. For the first time, an AI-based method outperforms state-of-the-art numerical weather prediction (NWP) methods in terms of accuracy. Pangu-Weather supports a wide range of downstream forecast scenarios, including extreme weather forecast and large-member ensemble forecast in real-time.
  arXiv  Detail & Related papers  (2022-11-03T17:19:43Z)
• A Hybrid Model for Forecasting Short-Term Electricity Demand [59.372588316558826]
  Currently the UK Electric market is guided by load (demand) forecasts published every thirty minutes by the regulator. We present HYENA: a hybrid predictive model that combines feature engineering (selection of the candidate predictor features), mobile-window predictors and LSTM encoder-decoders.
  arXiv  Detail & Related papers  (2022-05-20T22:13:25Z)
• A generative adversarial network approach to (ensemble) weather prediction [91.3755431537592]
  We use a conditional deep convolutional generative adversarial network to predict the geopotential height of the 500 hPa pressure level, the two-meter temperature and the total precipitation for the next 24 hours over Europe. The proposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018 with the goal to predict the associated meteorological fields in 2019.
  arXiv  Detail & Related papers  (2020-06-13T20:53:17Z)

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.