概率温度预报：四种扩散-回归模型的比较

概率温度预报：近期研究成果综述

Probabilistic temperature forecasting: a summary of our recent research results

我们总结了我们最近的一些文章的概率温度预报的主题的主要结果。

温度的概率预报：对贝叶斯模型平均法的评论

Probabilistic forecasting of temperature: comments on the Bayesian Model Averaging approach

最近提出了一个具体的实施贝叶斯模型平均作为一种方法，集合温度预报的校准。我们指出这种新方法和早期的方法称为内核回归之间的相似之处。我们还认为，贝叶斯模型的平均方法（如应用）有一些缺陷，将导致次优校准的平均值和不确定性的预测。

温度的概率预报：测量集合传播的效用

Probabilistic forecasts of temperature: measuring the utility of the ensemble spread

集合天气预报的传播包含关于未来可能的天气情景的传播的信息。但是它包含了多少信息，这些信息在预测未来温度的可能性方面有多大用处？对这个问题的一个传统答案是计算传播-技能相关性。我们讨论了传播技能相关性以及它如何与一些简单的校准方案相互作用。然后，我们指出为什么它不是，事实上，一个有用的措施，在合奏传播的信息量，并讨论了一些其他措施，更有用的。

Diffusion-LAM：基于扩散的概率有限区域天气预报

Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with Diffusion

本文提出Diffusion-LAM，一种以边界数据为条件的概率有限区域天气模型，并在MEPS数据集上验证了其提供准确概率预报的潜力。

WeatherBench Probability: A benchmark dataset for probabilistic medium-range weather forecasting along with deep learning baseline models

WeatherBench is a benchmark dataset for medium-range weather forecasting of geopotential, temperature and precipitation, consisting of preprocessed data, predefined evaluation metrics and a number of baseline models. WeatherBench Probability extends this to probabilistic forecasting by adding a set of established probabilistic verification metrics (continuous ranked probability score, spread-skill ratio and rank histograms) and a state-of-the-art operational baseline using the ECWMF IFS ensemble forecast. In addition, we test three different probabilistic machine learning methods -- Monte Carl…

A Deep Learning Approach to Probabilistic Forecasting of Weather

We discuss an approach to probabilistic forecasting based on two chained machine-learning steps: a dimensional reduction step that learns a reduction map of predictor information to a low-dimensional space in a manner designed to preserve information about forecast quantities; and a density estimation step that uses the probabilistic machine learning technique of normalizing flows to compute the joint probability density of reduced predictors and forecast quantities. This joint density is then renormalized to produce the conditional forecast distribution. In this method, probabilistic calibrat…