Advancing Data-driven Weather Forecasting: Time-Sliding Data
Augmentation of ERA5
- URL: http://arxiv.org/abs/2402.08185v1
- Date: Tue, 13 Feb 2024 03:01:22 GMT
- Title: Advancing Data-driven Weather Forecasting: Time-Sliding Data
Augmentation of ERA5
- Authors: Minjong Cheon, Daehyun Kang, Yo-Hwan Choi, and Seon-Yu Kang
- Abstract summary: We introduce a novel strategy that deviates from the common dependence on high-resolution data.
This paper improves on conventional approaches by adding more variables and a novel approach to data augmentation and processing.
Our findings reveal that despite the lower resolution, the proposed approach demonstrates considerable accuracy in predicting atmospheric conditions.
- Score: 3.3748750222488657
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Modern deep learning techniques, which mimic traditional numerical weather
prediction (NWP) models and are derived from global atmospheric reanalysis
data, have caused a significant revolution within a few years. In this new
paradigm, our research introduces a novel strategy that deviates from the
common dependence on high-resolution data, which is often constrained by
computational resources, and instead utilizes low-resolution data (2.5 degrees)
for global weather prediction and climate data analysis. Our main focus is
evaluating data-driven weather prediction (DDWP) frameworks, specifically
addressing sample size adequacy, structural improvements to the model, and the
ability of climate data to represent current climatic trends. By using the
Adaptive Fourier Neural Operator (AFNO) model via FourCastNet and a proposed
time-sliding method to inflate the dataset of the ECMWF Reanalysis v5 (ERA5),
this paper improves on conventional approaches by adding more variables and a
novel approach to data augmentation and processing. Our findings reveal that
despite the lower resolution, the proposed approach demonstrates considerable
accuracy in predicting atmospheric conditions, effectively rivaling
higher-resolution models. Furthermore, the study confirms the model's
proficiency in reflecting current climate trends and its potential in
predicting future climatic events, underscoring its utility in climate change
strategies. This research marks a pivotal step in the realm of meteorological
forecasting, showcasing the feasibility of lower-resolution data in producing
reliable predictions and opening avenues for more accessible and inclusive
climate modeling. The insights gleaned from this study not only contribute to
the advancement of climate science but also lay the groundwork for future
innovations in the field.
Related papers
- Leveraging data-driven weather models for improving numerical weather prediction skill through large-scale spectral nudging [1.747339718564314]
This study illustrates the relative strengths and weaknesses of physics-based and AI-based approaches to weather prediction.
A hybrid NWP-AI system is proposed, wherein GEM-predicted large-scale state variables are spectrally nudged toward GraphCast predictions.
Results indicate that this hybrid approach is capable of leveraging the strengths of GraphCast to enhance the prediction skill of the GEM model.
arXiv Detail & Related papers (2024-07-08T16:39:25Z) - EWMoE: An effective model for global weather forecasting with mixture-of-experts [6.695845790670147]
We propose EWMoE, an effective model for accurate global weather forecasting, which requires significantly less training data and computational resources.
Our model incorporates three key components to enhance prediction accuracy: meteorology-specific embedding, a core Mixture-of-Experts layer, and two specific loss functions.
arXiv Detail & Related papers (2024-05-09T16:42:13Z) - Forecasting the Future with Future Technologies: Advancements in Large Meteorological Models [3.332582598089642]
The field of meteorological forecasting has undergone a significant transformation with the integration of large models.
Models like FourCastNet, Pangu-Weather, GraphCast, ClimaX, and FengWu have made notable contributions by providing accurate, high-resolution forecasts.
arXiv Detail & Related papers (2024-04-10T00:52:54Z) - 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) - FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation [67.20588721130623]
We develop an AI-based cyclic weather forecasting system, FengWu-4DVar.
FengWu-4DVar can incorporate observational data into the data-driven weather forecasting model.
Experiments on the simulated observational dataset demonstrate that FengWu-4DVar is capable of generating reasonable analysis fields.
arXiv Detail & Related papers (2023-12-16T02:07:56Z) - Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance.
We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach.
Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
arXiv Detail & Related papers (2023-09-12T13:28:06Z) - 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) - Forecasting large-scale circulation regimes using deformable
convolutional neural networks and global spatiotemporal climate data [86.1450118623908]
We investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs)
We forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future.
Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days.
arXiv Detail & Related papers (2022-02-10T11:37:00Z) - 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.