概率温度预报：近期研究成果综述

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。

概率温度预报：四种扩散-回归模型的比较

Probabilistic temperature forecasting: a comparison of four spread-regression models

扩散回归是线性回归的扩展，允许包含包含方差信息的预测因子。它可用于从天气预报集合中获取信息，并对未来温度进行概率预测。有许多方法可以详细地制定扩散回归。我们进行实证比较的四个最明显的方法，适用于校准一年的ECMWF温度预测伦敦希思罗机场。

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…

基于边缘分布的高技巧性联合概率天气预报

Skillful joint probabilistic weather forecasting from marginals

针对传统数值天气预报精度速度不足的问题，提出FGN建模方法，通过约束模型集合的学习扰动生成预报、直接优化CRPS训练，在多指标下达到SOTA，还能捕捉空间联合结构。