“Earthly Machine Learning (EML)” offers AI-generated insights into cutting-edge machine learning research in weather and climate sciences. Powered by Google Not...
ACE2 - Accurately learning subseasonal to decadal atmospheric variability and forced responses
DOI:https://doi.org/10.48550/arXiv.2411.11268Abstract:Existing machine learning models of weather variability are not formulated to enable assessment of their response to varying external boundary conditions such as sea surface temperature and greenhouse gases. Here we present ACE2 (Ai2 Climate Emulator version 2) and its application to reproducing atmospheric variability over the past 80 years on timescales from days to decades. ACE2 is a 450M-parameter autoregressive machine learning emulator, operating with 6-hour temporal resolution, 1° horizontal resolution and eight vertical layers. It exactly conserves global dry air mass and moisture and can be stepped forward stably for arbitrarily many steps with a throughput of about 1500 simulated years per wall clock day....Citation:Watt-Meyer, Oliver, et al. "ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses." arXiv preprint arXiv:2411.11268 (2024).
--------
11:35
AURORA — A Foundation Model of the Atmosphere
DOI:https://doi.org/10.48550/arXiv.2405.13063
Abstract:Reliable forecasts of the Earth system are crucial for human progress and safety from natural disasters. Artificial intelligence offers substantial potential to improve prediction accuracy and computational efficiency in this field, however this remains underexplored in many domains. Here we introduce Aurora, a large-scale foundation model for the Earth system trained on over a million hours of diverse data...
Citation: Bodnar, Cristian, et al. "Aurora: A foundation model of the atmosphere." arXiv preprint arXiv:2405.13063 (2024).
--------
15:33
ACE - A Fast, Skillful Learned Global Atmospheric Model for Climate Prediction
Abstract:
Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a 200M-parameter, autoregressive machine learning emulator of an existing comprehensive 100-km resolution global atmospheric model. The formulation of ACE allows evaluation of physical laws such as the conservation of mass and moisture...
Citation: Watt-Meyer, Oliver, et al. "ACE: A fast, skillful learned global atmospheric model for climate prediction." arXiv preprint arXiv:2310.02074 (2023).
DOI:https://doi.org/10.48550/arXiv.2310.02074
--------
13:24
WeatherBench 2 - A benchmark for the next generation of data-driven global weather models
DOI:https://doi.org/10.48550/arXiv.2308.15560Abstract:
WeatherBench 2 is an update to the global, medium-range (1-14 day) weather forecasting benchmark proposed by Rasp et al. (2020), designed with the aim to accelerate progress in data-driven weather modeling. WeatherBench 2 consists of an open-source evaluation framework, publicly available training, ground truth and baseline data as well as a continuously updated website with the latest metrics and state-of-the-art model...
Citation: Rasp, Stephan, et al. "WeatherBench 2: A benchmark for the next generation of data‐driven global weather models." Journal of Advances in Modeling Earth Systems 16.6 (2024): e2023MS004019.
--------
25:34
FuXi-ENS - A machine learning model for medium-range ensemble weather forecasting
Abstract:
Ensemble forecasting is crucial for improving weather predictions, especially for forecasts of extreme events. Constructing an ensemble prediction system (EPS) based on conventional NWP models is highly computationally expensive. ML models have emerged as valuable tools for deterministic weather forecasts, providing forecasts with significantly reduced computational requirements and even surpassing the forecast performance of traditional NWP models. However, challenges arise when applying ML models to ensemble forecasting. Recent ML models, such as GenCast and SEEDS model, rely on the ERA5 EDA or operational NWP ensemble members for forecast generation. Their spatial resolution is also considered too coarse for many applications. To overcome these limitations, we introduce FuXi-ENS, an advanced ML model designed to deliver 6-hourly global ensemble weather forecasts up to 15 days. This model runs at a significantly increased spatial resolution of 0.25\textdegree, incorporating 5 atmospheric variables at 13 pressure levels, along with 13 surface variables. By leveraging the inherent probabilistic nature of Variational AutoEncoder (VAE), FuXi-ENS optimizes a loss function that combines the CRPS and the KL divergence between the predicted and target distribution, facilitating the incorporation of flow-dependent perturbations in both initial conditions and forecast. This innovative approach makes FuXi-ENS an advancement over the traditional ones that use L1 loss combined with the KL loss in standard VAE models for ensemble weather forecasting. Results demonstrate that FuXi-ENS outperforms ensemble forecasts from the ECMWF, a world leading NWP model, in the CRPS of 98.1% of 360 variable and forecast lead time combinations. This achievement underscores the potential of the FuXi-ENS model to enhance ensemble weather forecasts, offering a promising direction for further development in this field.
Zhong, Xiaohui, et al. "FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting." arXiv preprint arXiv:2405.05925 (2024).
arXiv:2405.05925
“Earthly Machine Learning (EML)” offers AI-generated insights into cutting-edge machine learning research in weather and climate sciences. Powered by Google NotebookLM, each episode distils the essence of a standout paper, helping you decide if it’s worth a deeper look. Stay updated on the ML innovations shaping our understanding of Earth.
It may contain hallucinations.