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1,584 claims from 981 papers are on the record. 46 have been checked so far; the other 1,538 have no check with a result yet.

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Status: Unchecked ×Keyword: cubed sphere ×Clear all

4 claims from 2 papers

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Global Extreme Heat Forecasting Using Neural Weather Models

   Lopez‐Gomez, McGovern, Agrawal and Hickey · Artificial Intelligence for the Earth Systems · 2022

   Neural weather models trained to forecast global surface temperature anomalies 1 to 28 days ahead did better on heat waves when trained with losses that emphasise extremes than with mean squared error.

   Unchecked2 claims
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   1. Unchecked“We find that training models to minimize custom losses tailored to emphasize extremes leads to significant skill improvements in the heat wave prediction task, compared to NWMs trained on the mean squared error loss.”
   2. UncheckedTraining with a symmetric exponential loss reduces how much neural weather model forecasts blur out as the forecast lead time grows.“In addition, we find that the use of a symmetric exponential loss reduces the smoothing of NWM forecasts with lead time.”

2. 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

   Unchecked2 claims
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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.”