Suspended in the atmosphere are millions of tonnes of mineral dust which
interacts with weather and climate. Accurate representation of mineral dust in
weather models is vital, yet remains challenging. Large scale weather models
use high power supercomputers and take hours to complete the forecast. Such
computational burden allows them to only include monthly climatological means
of mineral dust as input states inhibiting their forecasting accuracy. Here, we
introduce DustNet a simple, accurate and super fast forecasting model for
24-hours ahead predictions of aerosol optical depth AOD. DustNet trains in less
than 8 minutes and creates predictions in 2 seconds on a desktop computer.
Created by DustNet predictions outperform the state-of-the-art physics-based
model on coarse 1 x 1 degree resolution at 95% of grid locations when compared
to ground truth satellite data. Our results show DustNet has a potential for
fast and accurate AOD forecasting which could transform our understanding of
dust impacts on weather patterns.

DustNet 是一个快速而准确的模型，使用纳秒级训练和秒级预测，在粗糙的 1x1 度分辨率情况下，超过了基于物理模型的最先进模型的 95% 格点位置上的地面真实卫星数据，具有潜力改变我们对天气模式影响的理解。

DustNet：萨赫勒地区沙尘的技巧性神经网络预测

DustNet: skillful neural network predictions of Saharan dust

Since the weather is chaotic, forecasts aim to predict the distribution of
future states rather than make a single prediction. Recently, multiple data
driven weather models have emerged claiming breakthroughs in skill. However,
these have mostly been benchmarked using deterministic skill scores, and little
is known about their probabilistic skill. Unfortunately, it is hard to fairly
compare AI weather models in a probabilistic sense, since variations in choice
of ensemble initialization, definition of state, and noise injection
methodology become confounding. Moreover, even obtaining ensemble forecast
baselines is a substantial engineering challenge given the data volumes
involved. We sidestep both problems by applying a decades-old idea -- lagged
ensembles -- whereby an ensemble can be constructed from a moderately-sized
library of deterministic forecasts. This allows the first parameter-free
intercomparison of leading AI weather models' probabilistic skill against an
operational baseline. The results reveal that two leading AI weather models,
i.e. GraphCast and Pangu, are tied on the probabilistic CRPS metric even though
the former outperforms the latter in deterministic scoring. We also reveal how
multiple time-step loss functions, which many data-driven weather models have
employed, are counter-productive: they improve deterministic metrics at the
cost of increased dissipation, deteriorating probabilistic skill. This is
confirmed through ablations applied to a spherical Fourier Neural Operator
(SFNO) approach to AI weather forecasting. Separate SFNO ablations modulating
effective resolution reveal it has a useful effect on ensemble dispersion
relevant to achieving good ensemble calibration. We hope these and forthcoming
insights from lagged ensembles can help guide the development of AI weather
forecasts and have thus shared the diagnostic code.

天气模型的概率技能与延迟集合相比较，发现 GraphCast 和 Pangu 是领先的 AI 天气模型，在确定性评分方面前者优于后者，但在概率 CRPS 指标上两者得分相同。

一种实用的用于人工智能天气模型的概率基准

A Practical Probabilistic Benchmark for AI Weather Models

Machine learning (ML)-based weather models have recently undergone rapid
improvements. These models are typically trained on gridded reanalysis data
from numerical data assimilation systems. However, reanalysis data comes with
limitations, such as assumptions about physical laws and low spatiotemporal
resolution. The gap between reanalysis and reality has sparked growing interest
in training ML models directly on observations such as weather stations.
Modelling scattered and sparse environmental observations requires scalable and
flexible ML architectures, one of which is the convolutional conditional neural
process (ConvCNP). ConvCNPs can learn to condition on both gridded and
off-the-grid context data to make uncertainty-aware predictions at target
locations. However, the sparsity of real observations presents a challenge for
data-hungry deep learning models like the ConvCNP. One potential solution is
'Sim2Real': pre-training on reanalysis and fine-tuning on observational data.
We analyse Sim2Real with a ConvCNP trained to interpolate surface air
temperature over Germany, using varying numbers of weather stations for
fine-tuning. On held-out weather stations, Sim2Real training substantially
outperforms the same model architecture trained only with reanalysis data or
only with station data, showing that reanalysis data can serve as a stepping
stone for learning from real observations. Sim2Real could thus enable more
accurate models for weather prediction and climate monitoring.

在这篇论文中，研究者分析了一种基于机器学习的天气模型，通过在德国使用 ConvCNP 模型和不同数量的气象站进行 fine-tuning，论证了通过 Sim2Real 方法，使用重新分析数据进行预训练并利用实测数据进行精调，可以显著提高模型的性能，从而为天气预测和气候监测提供更准确的模型。