five

Cloud Climatology for Land Stations Worldwide, 1971-2009 (NDP-026D)

收藏
Mendeley Data2024-06-25 更新2024-06-27 收录
下载链接:
https://www.osti.gov/servlets/purl/1394932/
下载链接
链接失效反馈
官方服务:
资源简介:
Surface synoptic weather reports for 39 years have been processed to provide a climatology of clouds for each of over 5000 land-based weather stations with long periods of record both day and night. For each station, this digital archive includes: multi-year annual, seasonal and monthly averages for day and night separately; seasonal and monthly averages by year; averages for eight times per day; and analyses of the first harmonic for the annual and diurnal cycles. Averages are given for total cloud cover, clear-sky frequency, and 9 cloud types: 5 in the low level (fog, St, Sc, Cu, Cb), 3 in the middle level (Ns, As, Ac) and one in the high level (all cirriform clouds combined). Cloud amounts and frequencies of occurrence are given for all types. In addition, non-overlapped amounts are given for middle and high cloud types, and average base heights are given for low cloud types. Nighttime averages were obtained by using only those reports that met an "illuminance criterion" (i.e., made under adequate moonlight or twilight), thus making possible the determination of diurnal cycles and nighttime trends for cloud types.The authors have also produced an online, gridded atlas of the cloud observations contained in NDP-026D. The Online Cloud Atlas containing NDP-026D data is available via the University of Washington.

本数据集基于39年的地面天气报告数据处理构建,旨在为全球超过5000个拥有昼夜长期观测序列的陆基气象站生成云气候学数据集。 针对每个气象站,该数字档案包含以下内容:分昼夜的多年年平均、季节平均与月平均统计量;按年份拆分的季节平均与月平均统计量;每日8次观测的平均结果;以及年循环与日循环的一阶谐波分析结果。 数据集涵盖总云量、晴空频率以及9类云型的平均统计:低云共5类(雾、层云(St)、层积云(Sc)、积云(Cu)、积雨云(Cb)),中云3类(雨层云(Ns)、高层云(As)、高积云(Ac)),另有1类高云(所有卷云类云合并统计)。 所有云型均提供了云量与出现频率数据。此外,数据集还给出了中、高云型的不重叠云量,以及低云型的平均云底高度。 夜间平均统计仅采用符合“照度标准”(即基于充足月光或晨昏蒙影条件下的观测报告)的数据计算得到,借此可实现云型的日循环特征与夜间变化趋势分析。 数据集制作者还基于NDP-026D中的云观测资料制作了在线网格化图集。包含NDP-026D数据的在线云图集可通过华盛顿大学获取。
创建时间:
2023-06-28
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作