Detection of Atmospheric Rivers in the Northern Hemisphere based on ERA5 reanalysis data and the IPART algorithm, 1979-2020
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# 1. Overview This is a catalogue of atmospheric river (AR) detections over the Northern Hemisphere, based on 6-hourly ERA5 reanalysis dataset and the Image-Processing based Atmospheric River Tracking (IPART) algorithm. Time domain of the data: From 1979-Jan-01 to 2020-Dec-31 Temporal resolution is 6-hourly Spatial domain of the data: Northern Hemisphere, land and ocean Spatial resolution is 0.25 * 0.25 degrees latitude/longitude Input data from ERA5 include: Vertical integral of northward water vapour flux, in kg/(m s). Vertical integral of eastward water vapour flux, in kg/(m s). Data in the Northern Hemisphere domain (0 - 90 N), at 0.25 * 0.25 degrees latitude/longitude resolution are obtained from https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. Version v3.0.8 of the IPART Python module used for detection and tracking of atmospheric rivers is preserved at <strong>10.5281/zenodo.4164826</strong>, available via Creative Commons Attribution 4.0 International license and developed openly at the Github repository https://github.com/ihesp/IPART. # 2. File naming convention The data files are named using the following convention: ar_YYYYMM.nc where: YYYY: 4-digit year number MM: 2-digit month number E.g. `ar_199902.nc` means detections in Feb of 1999. Months are calendar months, including Feb-29th in leap-years. # 3. Data format Data are saved in netCDF format. Each data file contains one 3-dimensional array, of a shape `(t, 360, 1440)`, where: `t`: length of the time dimension. Since data are 6-hourly, t equals 4 * num_of_days_in_month. `360`: latitude dimension, from 0 - 90N, with a 0.25-degree step. `1440`: longitude dimension, from 80 - 440 E (shifted eastward by 80 degrees to put both the Pacific and Atlantic oceans within the domain), with a 0.25-degree step. Each time slice of the data contains maps of the Northern Hemisphere, with integer values in grid cells. Possible values are: 0: meaning no AR is detected in the grid cell. 1, 2, ... ,n: integer labels, each corresponding to the region of an AR entity. # 4. Important parameters in the IPART algorithm Here are the most important parameters used when detecting ARs from ERA5 data using the IPART python module: THR filtering kernel: `[16, 13, 13]`. `16` means 16 time slices, or equivalently 4 days given 6-hourly input data. `13` means 13 grid cells, or equivalently ~325 km, given 0.25 degrees latitude/longitude input data. Note that both of these temporal and spacial lengths are half of the sizes of the filtering kernel. minimum area: `50 * 1e4`, in km^2, minimum size of AR region candidates. maximum area: `1800 * 1e4`, in km^2, maximum size of AR region candidates. minimum L/W: `2.0`, minimum length/width ratio of AR region candiates. minimum length: `2000`, in km, minimum length of AR region candidates. minimum latitude: `20`, minimum latitude of the geometrical centroid of an AR region candidate. maximum latitude: `80`, maximum latitude of the geometrical centroid of an AR region candidate. For more details regarding these parameters, as well as the IPART algorithm, please refer to our published works: Xu, G., Ma, X., Chang, P., and Wang, L.: Image-processing-based atmospheric river tracking method version 1 (IPART-1), Geosci. Model Dev., 13, 4639–4662, https://doi.org/10.5194/gmd-13-4639-2020, 2020. Or the Github repository that houses the IPART module: https://github.com/ihesp/IPART
# 1. 概述 本数据集为北半球大气河（Atmospheric River, AR）探测编目，基于6小时分辨率的ERA5再分析数据集与基于图像处理的大气河追踪（Image-Processing based Atmospheric River Tracking, IPART）算法构建。 数据时间覆盖范围：1979年1月1日至2020年12月31日，时间分辨率为6小时。 数据空间覆盖范围：北半球陆地与洋面区域，空间分辨率为0.25°×0.25°经纬度。 ERA5输入数据包含：北向水汽通量垂直积分（单位：kg/(m·s)）、东向水汽通量垂直积分（单位：kg/(m·s)）。 北半球区域（0°~90°N）0.25°×0.25°经纬度分辨率的数据来源于https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5。 用于大气河探测与追踪的IPART Python模块v3.0.8版本存档于<strong>10.5281/zenodo.4164826</strong>，采用知识共享署名4.0国际许可协议发布，源代码可在GitHub仓库https://github.com/ihesp/IPART公开获取。 # 2. 文件命名规范 数据文件遵循以下命名格式：ar_YYYYMM.nc，其中： YYYY：4位年份数字； MM：2位月份数字。 示例：`ar_199902.nc`代表1999年2月的大气河探测结果。月份为自然月，闰年包含2月29日。 # 3. 数据格式 数据以netCDF格式存储。每个数据文件包含一个三维数组，形状为`(t, 360, 1440)`，其中： `t`：时间维度长度。由于数据为6小时分辨率，因此`t`等于当月天数×4； `360`：纬度维度，覆盖0°~90°N，步长为0.25°； `1440`：经度维度，覆盖80°E~440°E（向东偏移80°以将太平洋与大西洋纳入同一区域），步长为0.25°。 每个时间切片对应北半球全域地图，网格单元内为整数值，可能的取值包括： 0：表示该网格单元未探测到大气河； 1, 2, …, n：整数标签，分别对应一个大气河实体的区域。 # 4. IPART算法关键参数 使用IPART Python模块从ERA5数据中探测大气河时，采用的核心参数如下： 1. 阈值滤波核：`[16, 13, 13]`。其中`16`代表16个时间切片，对应6小时分辨率下的4天；`13`代表13个网格单元，对应0.25°经纬度分辨率下的约325 km。需注意，该时间与空间长度均为滤波核尺寸的一半。 2. 最小面积：`50 × 10^4 km²`，为大气河候选区域的最小面积阈值。 3. 最大面积：`1800 × 10^4 km²`，为大气河候选区域的最大面积阈值。 4. 最小长宽比：`2.0`，为大气河候选区域的最小长宽比阈值。 5. 最小长度：`2000 km`，为大气河候选区域的最小长度阈值。 6. 最小纬度：`20`，为大气河候选区域几何质心的最小纬度阈值。 7. 最大纬度：`80`，为大气河候选区域几何质心的最大纬度阈值。 如需了解更多参数细节及IPART算法内容，请参阅已发表论文：Xu, G., Ma, X., Chang, P., and Wang, L.: Image-processing-based atmospheric river tracking method version 1 (IPART-1), Geosci. Model Dev., 13, 4639–4662, https://doi.org/10.5194/gmd-13-4639-2020, 2020。或IPART模块的GitHub仓库：https://github.com/ihesp/IPART
