In recent years, there has been significant progress in the development of
fully data-driven global numerical weather prediction models. These machine
learning weather prediction models have their strength, notably accuracy and
low computational requirements, but also their weakness: they struggle to
represent fundamental dynamical balances, and they are far from being suitable
for data assimilation experiments. Hybrid modelling emerges as a promising
approach to address these limitations. Hybrid models integrate a physics-based
core component with a statistical component, typically a neural network, to
enhance prediction capabilities. In this article, we propose to develop a model
error correction for the operational Integrated Forecasting System (IFS) of the
European Centre for Medium-Range Weather Forecasts using a neural network. The
neural network is initially pre-trained offline using a large dataset of
operational analyses and analysis increments. Subsequently, the trained network
is integrated into the IFS within the Object-Oriented Prediction System (OOPS)
so as to be used in data assimilation and forecast experiments. It is then
further trained online using a recently developed variant of weak-constraint
4D-Var. The results show that the pre-trained neural network already provides a
reliable model error correction, which translates into reduced forecast errors
in many conditions and that the online training further improves the accuracy
of the hybrid model in many conditions.

通过使用神经网络在欧洲中程天气预报中的操作性集成预测系统（IFS）中开发模型误差校正来改进天气预报的准确性，并通过在线训练进一步提高混合模型的准确性。

在线模型误差校正与神经网络：应用于综合预报系统

Online model error correction with neural networks: application to the  Integrated Forecasting System

Over the past few years, due to the rapid development of machine learning
(ML) models for weather forecasting, state-of-the-art ML models have shown
superior performance compared to the European Centre for Medium-Range Weather
Forecasts (ECMWF)'s high-resolution forecast (HRES) in 10-day forecasts at a
spatial resolution of 0.25 degree. However, the challenge remains to perform
comparably to the ECMWF ensemble mean (EM) in 15-day forecasts. Previous
studies have demonstrated the importance of mitigating the accumulation of
forecast errors for effective long-term forecasts. Despite numerous efforts to
reduce accumulation errors, including autoregressive multi-time step loss,
using a single model is found to be insufficient to achieve optimal performance
in both short and long lead times. Therefore, we present FuXi, a cascaded ML
weather forecasting system that provides 15-day global forecasts with a
temporal resolution of 6 hours and a spatial resolution of 0.25 degree. FuXi is
developed using 39 years of the ECMWF ERA5 reanalysis dataset. The performance
evaluation, based on latitude-weighted root mean square error (RMSE) and
anomaly correlation coefficient (ACC), demonstrates that FuXi has comparable
forecast performance to ECMWF EM in 15-day forecasts, making FuXi the first
ML-based weather forecasting system to accomplish this achievement.

使用机器学习技术开发的 FuXi 天气预报系统，在全球 15 天天气预报方面表现与 ECMWF EM 相当，是第一个取得这一成就的 ML-based 天气预报系统。

FuXi：一种用于 15 天全球天气预报的级联机器学习预测系统

FuXi: A cascade machine learning forecasting system for 15-day global  weather forecast

Short-term load forecasting (STLF) is vital for the daily operation of power
grids. However, the non-linearity, non-stationarity, and randomness
characterizing electricity demand time series renders STLF a challenging task.
To that end, different forecasting methods have been proposed in the literature
for day-ahead load forecasting, including a variety of deep learning models
that are currently considered to achieve state-of-the-art performance. In order
to compare the accuracy of such models, we focus on national net aggregated
STLF and examine well-established autoregressive neural networks of indicative
architectures, namely multi-layer perceptrons, N-BEATS, long short-term memory
neural networks, and temporal convolutional networks, for the case of Portugal.
To investigate the factors that affect the performance of each model and
identify the most appropriate per case, we also conduct a post-hoc analysis,
correlating forecast errors with key calendar and weather features. Our results
indicate that N-BEATS consistently outperforms the rest of the examined deep
learning models. Additionally, we find that external factors can significantly
impact accuracy, affecting both the actual and relative performance of the
models.

本研究旨在比较不同深度学习模型在短期负荷预测方面的准确性，并针对葡萄牙的国家网联合短期负荷预测，进行多层感知器、N-BEATS、长短期记忆神经网络和时间卷积网络等一些具有代表性的自回归神经网络的评估，并进行后续分析以确定最合适的模型。结果表明，N-BEATS 模型始终优于其它深度学习模型，并且外部因素显著影响准确性，从而影响模型的实际和相对性能。