Dataset for "Uncertainty-Aware Methods for Enhancing Rainfall Prediction with deep-learning based Post-Processing Segmentation"
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Title: Dataset for "Uncertainty-Aware Methods for Enhancing Rainfall Prediction with Deep-Learning Based Post-Processing Segmentation" Authors: Simone Monaco, Luca Monaco, Daniele Apiletti Description:This dataset supports the study presented in the paper "Uncertainty-Aware Methods for Enhancing Rainfall Prediction with Deep-Learning Based Post-Processing Segmentation". The work focuses on improving daily quantitative precipitation forecasts and uncertainty estimates over the Piedmont and Aosta Valley regions in Italy by blending outputs from four Numerical Weather Prediction (NWP) models using uncertainty-aware deep learning methods and NWIOI observational data (Turco et al., 2013). NWPs forecasts can be obtained on request, observational data is provided in this repository. The NWPs include: BOLAM-ISAC-CNR (Buzzi et al., 1994): data requests are possibile at ISAC-CNR, at this link COSMO-2I (Baldauf et al., 2011): data requests are possible at the COSMO Consortium, at this link COSMO-5M (Doms & Baldauf, 2018): data requests are possible at the COSMO Consortium, at this link ECMWF-IFS-HRES (ECMWF, 2016): data download is possible at this link Observational data from NWIOI serve as the ground truth for model training. The dataset contains 420 gridded precipitation events from 2018 to 2024. Dataset contents: obs.zip: NWIOI observed precipitation data (.csv format, one file per event) domain_mask.csv: Binary mask (1 for grid points in the study area, 0 otherwise) allevents_dates.csv: Classification of all events by type and intensity, used for n-fold cross-validation and dataset splits Citations: BOLAM-ISAC-CNR: Buzzi, A., Fantini, M., Malguzzi, P., & Nerozzi, F. (1994). Validation of a limited area model in cases of Mediterranean cyclogenesis: surface fields and precipitation scores. Meteorology and Atmospheric Physics, 53(3), 137–153. Springer. COSMO-2I: Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., & Reinhardt, T. (2011). Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities. Monthly Weather Review, 139(12), 3887–3905. American Meteorological Society. COSMO-5M: Doms, G., & Baldauf, M. (2018). A Description of the Nonhydrostatic Regional COSMO Model. Part I: Dynamics and Numerics. COSMO Technical Report, Deutscher Wetterdienst. ECMWF-IFS-HRES: ECMWF. (2016). IFS Documentation CY43R1. ECMWF Technical Documentation. NWIOI: Turco, M., Zollo, A. L., Ronchi, C., De Luigi, C., & Mercogliano, P. (2013). Assessing gridded observations for daily precipitation extremes in the Alps with a focus on northwest Italy. Natural Hazards and Earth System Sciences, 13(6), 1457–1468. Related Repository: https://github.com/simone7monaco/probabilistic-rainprediction
标题：用于《基于深度学习后处理分割的不确定性感知降雨预测增强方法》研究的数据集 作者：西蒙尼·莫纳科（Simone Monaco）、卢卡·莫纳科（Luca Monaco）、达尼埃莱·阿皮莱蒂（Daniele Apiletti） 数据集说明：本数据集支撑了论文《基于深度学习后处理分割的不确定性感知降雨预测增强方法》中的相关研究。该研究聚焦于融合四类数值天气预报（Numerical Weather Prediction, NWP）模型的输出结果，结合NWIOI观测数据（Turco等，2013），以提升意大利皮埃蒙特与瓦莱达奥斯塔地区的逐日定量降水预报精度及不确定性评估效果。用户可按需申请获取数值天气预报模型的预报产物，本仓库仅提供观测数据集。本次研究涉及的四类数值天气预报模型如下： 1. BOLAM-ISAC-CNR（Buzzi等，1994）：数据申请可通过ISAC-CNR机构，访问此链接提交申请 2. COSMO-2I（Baldauf等，2011）：数据申请可通过COSMO联盟，访问此链接提交申请 3. COSMO-5M（Doms与Baldauf，2018）：数据申请可通过COSMO联盟，访问此链接提交申请 4. ECMWF-IFS-HRES（ECMWF，2016）：数据下载可通过此链接获取 NWIOI观测数据将作为模型训练的地面真值基准。本数据集涵盖2018年至2024年间的420个网格化降水事件。 数据集内容： - "obs.zip"：NWIOI观测降水数据（格式为.csv，每个降水事件对应一个独立文件） - "domain_mask.csv"：二进制掩码文件（研究区域内的网格点取值为1，区域外网格点取值为0） - "allevents_dates.csv"：所有降水事件的类型与强度分类表，用于n折交叉验证及数据集划分操作 引用文献： 1. BOLAM-ISAC-CNR：Buzzi, A., Fantini, M., Malguzzi, P., & Nerozzi, F. (1994). 地中海气旋生成事件中有限区域模式的验证：地面场与降水评分. 《气象学与大气物理学》, 53(3), 137–153. Springer出版社. 2. COSMO-2I：Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., & Reinhardt, T. (2011). 基于COSMO模式的业务对流尺度数值天气预报：模型描述与敏感性分析. 《月度天气评论》, 139(12), 3887–3905. 美国气象学会. 3. COSMO-5M：Doms, G., & Baldauf, M. (2018). 非静力区域COSMO模式描述 第一部分：动力学与数值方法. COSMO技术报告, 德国气象局. 4. ECMWF-IFS-HRES：ECMWF. (2016). IFS文档CY43R1. ECMWF技术文档. 5. NWIOI：Turco, M., Zollo, A. L., Ronchi, C., De Luigi, C., & Mercogliano, P. (2013). 意大利西北部阿尔卑斯山区逐日极端降水的网格化观测评估. 《自然灾害与地球系统科学》, 13(6), 1457–1468. 相关仓库：https://github.com/simone7monaco/probabilistic-rainprediction
