Stochastic generators are useful for estimating climate impacts on various
sectors. Projecting climate risk in various sectors, e.g. energy systems,
requires generators that are accurate (statistical resemblance to
ground-truth), reliable (do not produce erroneous examples), and efficient.
Leveraging data from the North American Land Data Assimilation System, we
introduce TemperatureGAN, a Generative Adversarial Network conditioned on
months, locations, and time periods, to generate 2m above ground atmospheric
temperatures at an hourly resolution. We propose evaluation methods and metrics
to measure the quality of generated samples. We show that TemperatureGAN
produces high-fidelity examples with good spatial representation and temporal
dynamics consistent with known diurnal cycles.

使用温度 GAN 生成高保真度、空间表现良好且时间特性与已知日变循环一致的空气温度，以在各个领域估计气候影响

TemperatureGAN：区域大气温度的生成建模

TemperatureGAN: Generative Modeling of Regional Atmospheric Temperatures

The application of models to assess the risk of the physical impacts of
weather and climate and their subsequent consequences for society and business
is of the utmost importance in our changing climate. The operation of such
models is historically bespoke and constrained to specific compute
infrastructure, driving datasets and predefined configurations. These
constraints introduce challenges with scaling model runs and putting the models
in the hands of interested users. Here we present a cloud-based modular
framework for the deployment and operation of geospatial models, initially
applied to climate impacts. The Climate Impact Modelling Frameworks (CIMF)
enables the deployment of modular workflows in a dynamic and flexible manner.
Users can specify workflow components in a streamlined manner, these components
can then be easily organised into different configurations to assess risk in
different ways and at different scales. This also enables different models
(physical simulation or machine learning models) and workflows to be connected
to produce combined risk assessment. Flood modelling is used as an end-to-end
example to demonstrate the operation of CIMF.

这篇论文介绍了一种基于云技术的模块化框架，能够实现气候影响模型的部署和操作，以评估自然灾害等气候影响对社会和企业的风险，具有互动性，可扩展性和灵活性。其中，洪水模型被用作一个示例来演示该框架的操作。