With the continuous expansion of the scale of air transport, the demand for
aviation meteorological support also continues to grow. The impact of hazardous
weather on flight safety is critical. How to effectively use meteorological
data to improve the early warning capability of flight dangerous weather and
ensure the safe flight of aircraft is the primary task of aviation
meteorological services. In this work, support vector machine (SVM) models are
used to predict hazardous flight weather, especially for meteorological
conditions with high uncertainty such as storms and turbulence. SVM is a
supervised learning method that distinguishes between different classes of data
by finding optimal decision boundaries in a high-dimensional space. In order to
meet the needs of this study, we chose the radial basis function (RBF) as the
kernel function, which helps to deal with nonlinear problems and enables the
model to better capture complex meteorological data structures. During the
model training phase, we used historical meteorological observations from
multiple weather stations, including temperature, humidity, wind speed, wind
direction, and other meteorological indicators closely related to flight
safety. From this data, the SVM model learns how to distinguish between normal
and dangerous flight weather conditions.

使用支持向量机（SVM）模型来预测危险的飞行天气，尤其是对于暴风雨和湍流等高度不确定的气象条件，通过找到高维空间中的最优决策边界，以更好地捕捉复杂的气象数据结构并区分正常和危险的飞行天气条件。

基于机器学习的危险飞行天气预测研究

Research on Dangerous Flight Weather Prediction based on Machine  Learning

Federated weather forecasting is a promising collaborative learning framework
for analyzing meteorological data across participants from different countries
and regions, thus embodying a global-scale real-time weather data predictive
analytics platform to tackle climate change. This paper is to model the
meteorological data in a federated setting where many distributed low-resourced
sensors are deployed in different locations. Specifically, we model the
spatial-temporal weather data into a federated prompt learning framework that
leverages lightweight prompts to share meaningful representation and structural
knowledge among participants. Prompts-based communication allows the server to
establish the structural topology relationships among participants and further
explore the complex spatial-temporal correlations without transmitting private
data while mitigating communication overhead. Moreover, in addition to a
globally shared large model at the server, our proposed method enables each
participant to acquire a personalized model that is highly customized to tackle
climate changes in a specific geographic area. We have demonstrated the
effectiveness of our method on classical weather forecasting tasks by utilizing
three spatial-temporal multivariate time-series weather data.

通过搭建一个基于联邦学习的时空气象数据预测分析框架，本研究在一个去中心化的环境下建立了一个空间 - 时间的气象数据模型，实现了低成本、高效率、安全共享的监督学习和知识融合。在共享大型的融合模型同时，每个参与者还可根据不同的地理位置获取个性化的定制模型来应对气候变化。实验结果表明，该方法可以有效地优化经典气象预测任务。

联邦气象预测的时空提示学习

Spatial-temporal Prompt Learning for Federated Weather Forecasting

The Weather4cast 2021 competition gave the participants a task of predicting
the time evolution of two-dimensional fields of satellite-based meteorological
data. This paper describes the author's efforts, after initial success in the
first stage of the competition, to improve the model further in the second
stage. The improvements consisted of a shallower model variant that is
competitive against the deeper version, adoption of the AdaBelief optimizer,
improved handling of one of the predicted variables where the training set was
found not to represent the validation set well, and ensembling multiple models
to improve the results further. The largest quantitative improvements to the
competition metrics can be attributed to the increased amount of training data
available in the second stage of the competition, followed by the effects of
model ensembling. Qualitative results show that the model can predict the time
evolution of the fields, including the motion of the fields over time, starting
with sharp predictions for the immediate future and blurring of the outputs in
later frames to account for the increased uncertainty.

该研究承担了预测卫星气象数据的二维场的时间演变的任务，使其成为一个更加具有竞争力的浅层模型版本，并采用了 AdaBelief 优化器和改进处理，其中预测变量训练集与验证集不匹配时，通过使用多个模型来提高表现。