Skillful Twelve Hour Precipitation Forecasts using Large Context Neural
Networks
- URL: http://arxiv.org/abs/2111.07470v1
- Date: Sun, 14 Nov 2021 22:53:04 GMT
- Title: Skillful Twelve Hour Precipitation Forecasts using Large Context Neural
Networks
- Authors: Lasse Espeholt, Shreya Agrawal, Casper S{\o}nderby, Manoj Kumar,
Jonathan Heek, Carla Bromberg, Cenk Gazen, Jason Hickey, Aaron Bell, Nal
Kalchbrenner
- Abstract summary: Current operational forecasting models are based on physics and use supercomputers to simulate the atmosphere.
An emerging class of weather models based on neural networks represents a paradigm shift in weather forecasting.
We present a neural network that is capable of large-scale precipitation forecasting up to twelve hours ahead.
- Score: 8.086653045816151
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The problem of forecasting weather has been scientifically studied for
centuries due to its high impact on human lives, transportation, food
production and energy management, among others. Current operational forecasting
models are based on physics and use supercomputers to simulate the atmosphere
to make forecasts hours and days in advance. Better physics-based forecasts
require improvements in the models themselves, which can be a substantial
scientific challenge, as well as improvements in the underlying resolution,
which can be computationally prohibitive. An emerging class of weather models
based on neural networks represents a paradigm shift in weather forecasting:
the models learn the required transformations from data instead of relying on
hand-coded physics and are computationally efficient. For neural models,
however, each additional hour of lead time poses a substantial challenge as it
requires capturing ever larger spatial contexts and increases the uncertainty
of the prediction. In this work, we present a neural network that is capable of
large-scale precipitation forecasting up to twelve hours ahead and, starting
from the same atmospheric state, the model achieves greater skill than the
state-of-the-art physics-based models HRRR and HREF that currently operate in
the Continental United States. Interpretability analyses reinforce the
observation that the model learns to emulate advanced physics principles. These
results represent a substantial step towards establishing a new paradigm of
efficient forecasting with neural networks.
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