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1,634 claims from 1,009 papers are on the record. 46 have been checked so far; the other 1,588 have no check with a result yet.

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Keyword: weather forecasting ×Clear all

12 claims from 7 papers

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Toward Data‐Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning

   Scher · Geophysical Research Letters · 2018

   A deep neural network trained on a simple general circulation model can forecast the model's state ahead and, run repeatedly on its own output, produce a climate with similar statistics.

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   1. Unchecked“Additionally, after being initialized with an arbitrary model state, the network can through repeatedly feeding back its predictions into its inputs create a climate run, which has similar climate statistics to the climate of the general circulation model.”
   2. UncheckedA neural-network climate run, made by feeding its predictions back in, shows no long-term drift despite no built-in conservation properties.“This network climate run shows no long‐term drift, even though no conservation properties were explicitly designed into the network.”
   3. UncheckedA deep neural network can emulate the dynamics of a simple general circulation model, according to this 2018 paper.“It is shown that it is possible to emulate the dynamics of a simple general circulation model with a deep neural network.”

2. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500‐hPa Geopotential Height From Historical Weather Data

   Weyn, Durran and Caruana · Journal of Advances in Modeling Earth Systems · 2019

   The authors trained deep convolutional neural networks on past weather data to forecast 500-hPa height on a Northern Hemisphere grid, and compared them with simple baselines and physics-based models.

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   1. UncheckedFor forecasts up to 3 days, CNNs predicting only 500-hPa height beat simple and barotropic baselines but not an operational full-physics model.“At forecast lead times up to 3 days, CNNs trained to predict only 500‐hPa geopotential height easily outperform persistence, climatology, and the dynamics‐based barotropic vorticity model, but do not beat an operational full‐physics weather prediction model.”
   2. UncheckedThe authors' best convolutional neural network reproduces 500-hPa height climatology and annual variability and forecasts realistic atmospheric states 14 days ahead.“Our best performing CNN does a good job of capturing the climatology and annual variability of 500‐hPa heights and is capable of forecasting realistic atmospheric states at lead times of 14 days.”

3. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground

   Scher and Messori · Geoscientific model development · 2019

   The authors trained deep neural networks on two simple climate models of differing complexity to test whether they can forecast weather a few days ahead and reproduce model climate.

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   1. UncheckedNeural networks reproducing the climate of simple climate models with a seasonal cycle remains challenging, unlike earlier results for a model without one.“Additionally, we show that using the neural networks to reproduce the climate of general circulation models including a seasonal cycle remains challenging – in contrast to earlier promising results on a model without seasonal cycle.”

4. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   ClimaX: A foundation model for weather and climate

   Nguyen, Brandstetter, Kapoor, Gupta and Grover · arXiv (Cornell University) · 2023

   The authors built ClimaX, a Transformer-based deep learning model pre-trained on CMIP6 climate data, and report that it outperforms existing data-driven baselines on weather forecasting and climate projection benchmarks.

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   1. UncheckedThe pre-trained ClimaX model can be fine-tuned for many weather and climate tasks, including ones using variables and scales not seen in pretraining.“The pre-trained ClimaX can then be fine-tuned to address a breadth of climate and weather tasks, including those that involve atmospheric variables and spatio-temporal scales unseen during pretraining.”

5. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Machine Learning Methods in Weather and Climate Applications: A Survey

   Chen, Han, Wang, Zhao, Yang and Yang · Applied Sciences · 2023

   A survey of over 20 machine learning methods for weather and climate prediction, picking out eight promising techniques and noting that ML is more capable for short-term weather than for long-term climate.

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   1. UncheckedThe paper says existing studies usually cover either short-term weather or longer-term climate forecasting, rarely linking them or examining model structure.“Current literature tends to focus narrowly on either short-term weather or medium-to-long-term climate forecasting, often neglecting the relationship between the two, as well as general neglect of modelling structure and recent advances.”

6. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh

   Karlbauer, Cresswell‐Clay, Durran et al. · arXiv (Cornell University) · 2023

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   1. Unchecked“Without any loss of spectral power after the first two days, the model can be unrolled autoregressively for hundreds of steps into the future to generate realistic states of the atmosphere that respect seasonal trends, as showcased in one-year simulations.”
   2. Unchecked“Yet, at one-week lead times, its skill is only about one day behind both SOTA ML forecast models and the SOTA numerical weather prediction model from the European Centre for Medium-Range Weather Forecasts.”

7. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Boosting weather forecast via generative superensemble

   Nai, Chen, Yang, Yuan, Xiao and Pan · arXiv (Cornell University) · 2024

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   1. Unchecked“Integration of the ECMWF ensemble mean further improves the ACC to 0.683.”
   2. Unchecked“The framework also enhances extreme event representation and produces energy spectra more consistent with ERA5 reanalysis.”