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Status: Unchecked ×Keyword: extreme heat ×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
   Show 2 claims

   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

   An extension of WeatherBench 2 to binary hydroclimatic forecasts

   Zhao, Li, Tu and Chen · Geoscientific model development · 2025

   Unchecked2 claims
   Show 2 claims

   1. Unchecked“For wet extremes, the GraphCast tends to outperform the IFS HRES when using the total precipitation of ERA5 reanalysis data as the ground truth.”
   2. Unchecked“For warm extremes, Pangu-Weather, GraphCast and FuXi tend to be more skillful than the IFS HRES within 3 d lead time but become less skillful as lead time increases.”