Development funds are essential to finance climate change adaptation and are
thus an important part of international climate policy. % However, the absence
of a common reporting practice makes it difficult to assess the amount and
distribution of such funds. Research has questioned the credibility of reported
figures, indicating that adaptation financing is in fact lower than published
figures suggest. Projects claiming a greater relevance to climate change
adaptation than they target are referred to as "overreported". To estimate
realistic rates of overreporting in large data sets over times, we propose an
approach based on state-of-the-art text classification. To date, assessments of
credibility have relied on small, manually evaluated samples. We use such a
sample data set to train a classifier with an accuracy of $89.81\% \pm 0.83\%$
(tenfold cross-validation) and extrapolate to larger data sets to identify
overreporting. Additionally, we propose a method that incorporates evidence of
smaller, higher-quality data to correct predicted rates using Bayes' theorem.
This enables a comparison of different annotation schemes to estimate the
degree of overreporting in climate change adaptation. Our results support
findings that indicate extensive overreporting of $32.03\%$ with a credible
interval of $[19.81\%;48.34\%]$.

使用文本分类技术来估算发展基金中关于气候变化适应性的过度报告率的方法，以及使用贝叶斯定理将证据融合到预测中，结果表明过度报告率为 32.03％，可信区间为 [19.81％;48.34％]。

使用贝叶斯校正和文本分类技术估算气候适应融资收报情况

Using Text Classification with a Bayesian Correction for Estimating Overreporting in the Creditor Reporting System on Climate Adaptation Finance

High-resolution nowcasting is an essential tool needed for effective
adaptation to climate change, particularly for extreme weather. As Deep
Learning (DL) techniques have shown dramatic promise in many domains, including
the geosciences, we present an application of DL to the problem of
precipitation nowcasting, i.e., high-resolution (1 km x 1 km) short-term (1
hour) predictions of precipitation. We treat forecasting as an image-to-image
translation problem and leverage the power of the ubiquitous UNET convolutional
neural network. We find this performs favorably when compared to three commonly
used models: optical flow, persistence and NOAA's numerical one-hour HRRR
nowcasting prediction.

采用深度学习技术中的 UNET 卷积神经网络，将高分辨率（1km × 1km）短期（1 小时）降水预测视为图像翻译问题，用于气候变化适应的高分辨率实时预测，表现优于光流、持续性和 NOAA 的数值一小时 HRRR 气象预测模型。