We present an operations-ready multi-model ensemble weather forecasting
system which uses hybrid data-driven weather prediction models coupled with the
European Centre for Medium-range Weather Forecasts (ECMWF) ocean model to
predict global weather at 1-degree resolution for 4 weeks of lead time. For
predictions of 2-meter temperature, our ensemble on average outperforms the raw
ECMWF extended-range ensemble by 4-17%, depending on the lead time. However,
after applying statistical bias corrections, the ECMWF ensemble is about 3%
better at 4 weeks. For other surface parameters, our ensemble is also within a
few percentage points of ECMWF's ensemble. We demonstrate that it is possible
to achieve near-state-of-the-art subseasonal-to-seasonal forecasts using a
multi-model ensembling approach with data-driven weather prediction models.

我们提出了一个实用的多模式集成天气预报系统，该系统使用混合数据驱动的天气预测模型与欧洲中期天气预报中心（ECMWF）海洋模型相结合，在 4 周的前期预测全球天气，分辨率为 1 度。对于 2 米温度的预测，我们的集成模型相比于原始的 ECMWF 扩展范围集成模型，在平均性能上提高了 4-17％（根据预测提前期而定）。然而，在应用统计偏差修正之后，ECMWF 集成模型在 4 周时相比较好，优势约为 3％。对于其他地表参数，我们的集成模型与 ECMWF 集成模型的性能差距也在几个百分点以内。我们证明了使用基于数据驱动的天气预测模型的多模式集成方法可以实现接近最新技术水平的次季节到季节性天气预报。

用于运营的数据驱动气象预测模型集合的亚季节预测

An ensemble of data-driven weather prediction models for operational  sub-seasonal forecasting

Given multi-model ensemble climate projections, the goal is to accurately and
reliably predict future sea-level rise while lowering the uncertainty. This
problem is important because sea-level rise affects millions of people in
coastal communities and beyond due to climate change's impacts on polar ice
sheets and the ocean. This problem is challenging due to spatial variability
and unknowns such as possible tipping points (e.g., collapse of Greenland or
West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost
thawing), future policy decisions, and human actions. Most existing climate
modeling approaches use the same set of weights globally, during either
regression or deep learning to combine different climate projections. Such
approaches are inadequate when different regions require different weighting
schemes for accurate and reliable sea-level rise predictions. This paper
proposes a zonal regression model which addresses spatial variability and model
inter-dependency. Experimental results show more reliable predictions using the
weights learned via this approach on a regional scale.

利用多模型集合气候预测，通过降低不确定性精确可靠地预测未来海平面上升是一个重要问题，本文提出了一个区域回归模型来解决空间变异性和模型相互依赖性，并通过实验结果表明该方法在区域尺度上能给出更可靠的预测。

减少海平面上升预测中的不确定性：一种考虑空间变异性的方法

Reducing Uncertainty in Sea-level Rise Prediction: A  Spatial-variability-aware Approach

The cold start problem is a common challenge in various domains, including
media use cases such as predicting viewership for newly launched shows on
Over-The-Top (OTT) platforms. In this study, we propose a generic approach to
tackle cold start problems by leveraging metadata and employing multi-model
ensemble techniques. Our methodology includes feature engineering, model
selection, and an ensemble approach based on a weighted average of predictions.
The performance of our proposed method is evaluated using various performance
metrics. Our results indicate that the multi-model ensemble approach
significantly improves prediction accuracy compared to individual models.

本研究提出了一种通用方法，利用元数据和多模型集成技术来解决冷启动问题，并通过加权平均预测的集成方法来提高预测准确性，实验结果表明，与单个模型相比，多模型集成方法显著提高了预测准确性。