Related papers: Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast

Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast

URL: http://arxiv.org/abs/2211.02556v1
Date: Thu, 3 Nov 2022 17:19:43 GMT
Title: Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast
Authors: Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu, Qi Tian
Abstract summary: 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.
Score: 91.9372563527801
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In this paper, we present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast. For this purpose, we establish a data-driven environment by downloading $43$ years of hourly global weather data from the 5th generation of ECMWF reanalysis (ERA5) data and train a few deep neural networks with about $256$ million parameters in total. The spatial resolution of forecast is $0.25^\circ\times0.25^\circ$, comparable to the ECMWF Integrated Forecast Systems (IFS). More importantly, for the first time, an AI-based method outperforms state-of-the-art numerical weather prediction (NWP) methods in terms of accuracy (latitude-weighted RMSE and ACC) of all factors (e.g., geopotential, specific humidity, wind speed, temperature, etc.) and in all time ranges (from one hour to one week). There are two key strategies to improve the prediction accuracy: (i) designing a 3D Earth Specific Transformer (3DEST) architecture that formulates the height (pressure level) information into cubic data, and (ii) applying a hierarchical temporal aggregation algorithm to alleviate cumulative forecast errors. In deterministic forecast, Pangu-Weather shows great advantages for short to medium-range forecast (i.e., forecast time ranges from one hour to one week). Pangu-Weather supports a wide range of downstream forecast scenarios, including extreme weather forecast (e.g., tropical cyclone tracking) and large-member ensemble forecast in real-time. Pangu-Weather not only ends the debate on whether AI-based methods can surpass conventional NWP methods, but also reveals novel directions for improving deep learning weather forecast systems.

Related papers

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere [53.22497376154084]
We propose FengWu-Weather to Subseasonal (FengWu-W2S), which builds on the FengWu global weather forecast model and incorporates an ocean-atmosphere-land coupling structure along with a diverse perturbation strategy. Our hindcast results demonstrate that FengWu-W2S reliably predicts atmospheric conditions out to 3-6 weeks ahead, enhancing predictive capabilities for global surface air temperature, precipitation, geopotential height and intraseasonal signals such as the Madden-Julian Oscillation (MJO) and North Atlantic Oscillation (NAO) Our ablation experiments on forecast error growth from daily to seasonal timescales reveal potential
arXiv Detail & Related papers (2024-11-15T13:44:37Z)
Super Resolution On Global Weather Forecasts [0.1747623282473278]
Group seeks to improve upon existing deep learning based forecasting methods by increasing spatial resolutions of global weather predictions. Specifically, we are interested in performing super resolution (SR) on GraphCast temperature predictions by increasing the global precision from 1 degree of accuracy to 0.5 degrees.
arXiv Detail & Related papers (2024-09-17T19:07:13Z)
Aardvark weather: end-to-end data-driven weather forecasting [30.219727555662267]
Aardvark Weather is an end-to-end data-driven weather prediction system. It ingests raw observations and outputs global gridded forecasts and local station forecasts. It can be optimised end-to-end to maximise performance over quantities of interest.
arXiv Detail & Related papers (2024-03-30T16:41:24Z)
Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh [3.2785715577154595]
We present a parsimonious deep learning weather prediction model to forecast seven atmospheric variables with 3-h time resolution for up to one-year lead times on a 110-km global mesh. In comparison to state-of-the-art (SOTA) machine learning (ML) weather forecast models, such as Pangu-Weather and GraphCast, our DLWP-HPX model uses coarser resolution and far fewer prognostic variables.
arXiv Detail & Related papers (2023-09-11T16:25:48Z)
Deep Learning for Day Forecasts from Sparse Observations [60.041805328514876]
Deep neural networks offer an alternative paradigm for modeling weather conditions. MetNet-3 learns from both dense and sparse data sensors and makes predictions up to 24 hours ahead for precipitation, wind, temperature and dew point. MetNet-3 has a high temporal and spatial resolution, respectively, up to 2 minutes and 1 km as well as a low operational latency.
arXiv Detail & Related papers (2023-06-06T07:07:54Z)
FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead [93.67314652898547]
We present FengWu, an advanced data-driven global medium-range weather forecast system based on Artificial Intelligence (AI) FengWu is able to accurately reproduce the atmospheric dynamics and predict the future land and atmosphere states at 37 vertical levels on a 0.25deg latitude-longitude resolution. The results suggest that FengWu can significantly improve the forecast skill and extend the skillful global medium-range weather forecast out to 10.75 days lead.
arXiv Detail & Related papers (2023-04-06T09:16:39Z)
GraphCast: Learning skillful medium-range global weather forecasting [107.40054095223779]
We introduce a machine learning-based method called "GraphCast", which can be trained directly from reanalysis data. It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute. We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets.
arXiv Detail & Related papers (2022-12-24T18:15:39Z)
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators [45.520430157112884]
FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor. It has important implications for planning wind energy resources, predicting extreme weather events such as tropical cyclones, extra-tropical cyclones, and atmospheric rivers. FourCastNet generates a week-long forecast in less than 2 seconds, orders of magnitude faster than IFS.
arXiv Detail & Related papers (2022-02-22T22:19:35Z)
Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models [6.882042556551611]
We present an ensemble prediction system using a Deep Learning Weather Prediction (DLWP) model. This model uses convolutional neural networks (CNNs) on a cubed sphere grid to produce global forecasts. Ensemble spread is primarily produced by randomizing the CNN training process to create a set of 32 DLWP models.
arXiv Detail & Related papers (2021-02-09T20:14:43Z)
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.