精确的天气预报是社会发展、城市安全运行、人民生活以及水旱灾害防御的重要保障。近年来，尽管数值预报降水技术取得显著进展，然而，其预报精度仍受到初始场不确定性、模式结构限制及参数化方法局限性的影响，较粗的空间分辨率也制约了其在气象水文领域的广泛应用。\n
因此，本研究基于U-net对CMA、ECMWF和NCEP三者的多模式超级集合数据CNE开展了统计后处理研究，研制了2021-2022年长江下游和里下河地区集合预报降水数据集。经U-net订正后，空间分辨率由0.5°细化到0.1°，确定性精度和概率性精度指标得到全面提升，且预见期得到有效延长，为项目示范区模型构建提供了精细化数据支撑。", "ds_source": "
本研究使用TIGGE数据库中CMA、NCEP和ECMWF三个天气预报中心的全球中期集合预报降水数据，时空分辨率均为0.5°/6h，集合成员数量依次为30、30和50，预见期依次为360h、360h和384h。\n
CMA和ECMWF的预报作业时间为世界时（UTC）的00和12点，NCEP为00、06、12和18点，三者的数值模式均为随机物理倾向与随机动能补偿的组合扰动方案。数据下载地址为：https://apps.ecmwf.int/datasets/data/tigge/levtype=sfc/type=cf/。", "ds_process_way": "
主要采用python工具，步骤：\n
（1）通过等权重集成方式将CMA、NCEP和ECMWF的所有集合成员汇总到一起，形成多模式超级集合预报数据CNE；\n
（2）采用双线性插值方法，将CNE的0.5°空间分辨率细化到0.1°；\n
（3）针对降尺度后的CNE，构建基于U-net深度神经网络的集合预报统计后处理模型；\n
（4）基于构建的模型研制2021-2022年长江下游和里下河地区的数据预报统计后处理数据集。", "ds_quality": "
经U-net订正后，分类指标误报率（FAR）由0.70左右降到了0.55左右，公平威胁评分（ETS）由0.15左右提高到0.3左右，预见期的平均增益分别达到0.17和0.14。定量指标KGE'在大部分预见期下均得到改善，并且有效保持了数据的空间结构。综合精度指标CAS得到有效改善，预见期得到有效延长，最高延长到54h。因此，本数据可为长江下游和里下河地区提供高质量的预报降水数据集。", "ds_acq_start_time": "2021-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "长江下游和里下河地区", "ds_acq_lon_east": 123.0, "ds_acq_lat_south": 29.0, "ds_acq_lon_west": 114.0, "ds_acq_lat_north": 35.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 21555373680, "ds_files_count": 25, "ds_format": "*.npy", "ds_space_res": "0.1°", "ds_time_res": "6时", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "a663c2cd-0c05-4e12-98dc-397f31314722.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "37eb642a-c117-47e4-a677-07ecffb4b8b7", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2025-03-27 21:17:04", "last_updated": "2025-06-30 11:40:11", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NHRI.DB6789.2025", "i18n": { "en": { "title": "U-net-based ensemble forecast precipitation dataset for the lower Yangtze River and Lower Rivers (2021-2022)", "ds_format": "*.npy", "ds_source": "
In this study, we use the global medium-term ensemble forecast precipitation data from three weather forecast centers, CMA, NCEP and ECMWF, in the TIGGE database, all with a spatial and temporal resolution of 0.5°/6h, ensemble memberships of 30, 30, and 50, and foresight periods of 360h, 360h, and 384h, in that order.\n
The forecast operation times of CMA and ECMWF are at Universal Time (UTC) of 00 and 12 o'clock, and NCEP at 00, 06, 12 and 18 o'clock, and the numerical model of all three is a combined perturbation scheme of stochastic physical tendency and stochastic kinetic energy compensation. The data can be downloaded at https://apps.ecmwf.int/datasets/data/tigge/levtype=sfc/type=cf/.", "ds_quality": "
After the U-net revision, the categorical indicator False Alarm Rate (FAR) is reduced from about 0.70 to about 0.55, and the Equitable Threat Score (ETS) is improved from about 0.15 to about 0.3, and the average gain of the foresight period reaches 0.17 and 0.14, respectively. the quantitative indicator KGE' improves under most of the foresight periods, and the effective maintains the spatial structure of the data. The comprehensive accuracy index CAS is effectively improved, and the foresight period is effectively extended up to 54 h. Therefore, the present data can provide a high-quality forecast precipitation dataset for the lower reaches of the Yangtze River and the Lower Lower River region.", "ds_ref_way": "", "ds_abstract": "
Accurate weather forecasting is an important guarantee for social development, safe operation of cities, people's lives, and defense against water and drought disasters. In recent years, despite the significant progress in numerical precipitation forecasting, the accuracy of the forecast is still affected by the uncertainty of the initial field, the limitations of the model structure and the limitations of the parameterization method, and the coarser spatial resolution also restricts its wide application in the field of meteorology and hydrology.\n
Therefore, in this study, a statistical post-processing study was carried out based on U-net on the multi-model super ensemble data CNE of CMA, ECMWF and NCEP, and an ensemble forecast precipitation dataset was developed for the downstream of the Yangtze River and the Lower Rivers for the period of 2021-2022. After U-net revision, the spatial resolution was refined from 0.5° to 0.1°, the deterministic and probabilistic accuracy indexes were comprehensively improved, and the forecast period was effectively extended, which provided refined data support for the model construction in the demonstration area of the project.
", "ds_time_res": "6时", "ds_acq_place": "The lower reaches of the Yangtze River and the Lixia River region", "ds_space_res": "0.1°", "ds_projection": "", "ds_process_way": "Mainly using python tools, the steps are: \n
(1) Aggregate all the ensemble members of CMA, NCEP, and ECMWF together by equal weight integration to form a multi-model super-ensemble forecast data CNE;\n
(2) Adopt the bilinear interpolation method to refine the 0.5° spatial resolution of CNE to 0.1°;\n
(3) Construct an ensemble forecast statistical post-processing model based on U-net deep neural network for the downscaled CNE;\n
Development of statistical post-processing datasets for data forecasting in the lower Yangtze River and Lower Rivers for 2021-2022 based on the constructed model.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "数值预报", "降水", "统计后处理", "深度学习" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "长江下游", "里下河地区" ], "ds_time_tags": [ 2021, 2022 ], "ds_contributors": [ { "true_name": "李伶杰", "email": "ljli@nhri.cn", "work_for": "南京水利科学研究院", "country": "中国" }, { "true_name": "高锐", "email": "ruigao_o@163.com", "work_for": "南京水利科学研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李伶杰", "email": "ljli@nhri.cn", "work_for": "南京水利科学研究院", "country": "中国" }, { "true_name": "高锐", "email": "ruigao_o@163.com", "work_for": "南京水利科学研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "李伶杰", "email": "ljli@nhri.cn", "work_for": "南京水利科学研究院", "country": "中国" }, { "true_name": "高锐", "email": "ruigao_o@163.com", "work_for": "南京水利科学研究院", "country": "中国" } ], "category": "气象" }