Wind and Precipitation Extremes in Great Britain (1979-2019) to apply the methodology for Spatiotemporal Identification of Compound Hazards

The data used in this study is extracted from ERA5. ERA5 is a climate reanalysis product which was released in 2019 by ECMWF and benefits from the latest improvements in the field (Hersbach et al., 2020). ERA5 data (ECMWF, 2020) is available 1979 to present (we use up to September 2019), with a spatial resolution of 0.25deg x 0.25deg and an hourly temporal resolution. The data resolves the atmosphere using 137 levels from the surface up to a height of 80 km (ECMWF, 2020). ERA5 data are generated with a short forecast of 18 h twice a day (06:00 and 18:00 UTC) and assimilated with observed data (ECMWF, 2020). more information about ERA5 can be found here. The two following variables are extracted from the product: Extreme precipitation (p): accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth’s in one hour (mm). This value is averaged over a grid cell. Extreme wind (w): hourly maximum wind gust at a height of 10 m above the surface of the Earth (m s-1). The WMO (2021) defines a wind gust as the maximum of the wind averaged over 3 s intervals. As this duration is shorter than a model time step, this value is deduced from other parameters such as surface stress, surface friction, wind shear and stability. This value is averaged over a grid cell. Importation of the raw data Input data is divided into 4 files for each variables representing 4 periods: 1979-1986 1987-1997 1998-2008 2009-2019 <pre>library(ncdf4) filer=c(paste0(getwd(),"/data/in/raindat_7986.nc"), paste0(getwd(),"/data/in/raindat_8797.nc"), paste0(getwd(),"/data/in/raindat_9808.nc"), paste0(getwd(),"/data/in/raindat_0919.nc")) filew=c(paste0(getwd(),"/data/in/windat_7986.nc"), paste0(getwd(),"/data/in/windat_8797.nc"), paste0(getwd(),"/data/in/windat_9808.nc"), paste0(getwd(),"/data/in/windat_0919.nc")) Startdate=as.POSIXct("1979-01-01 10:00:00") Enddate=as.POSIXct("1986-12-31 23:00:00") # ncr = nc_open(filer) # ncw = nc_open(filew)</pre> Intermediary data Intermediary data are stored in the “data/interdat” folder which contains the following files in Rdata format: <pre><code>## [1] "allraininclusters1.Rdata" "allraininclusters2.Rdata" ## [3] "allraininclusters3.Rdata" "allraininclusters4.Rdata" ## [5] "extremEventsWind.Rdata" "interclustRain.Rdata" ## [7] "interclustWind.Rdata" "metaclustRain.Rdata" ## [9] "metaclustWind.Rdata" "Rain_99_AllP.Rdata" ## [11] "rainP1.Rdata" "rainP2.Rdata" ## [13] "rainP3.Rdata" "rainP4.Rdata" ## [15] "rawclustRain.Rdata" "rawclustWind.Rdata" ## [17] "timeP1.Rdata" "timeP2.Rdata" ## [19] "timeP3.Rdata" "timeP4.Rdata" ## [21] "windP1.Rdata" "windP2.Rdata" ## [23] "windP3.Rdata" "windP4.Rdata" ## [25] "Wnd_99_AllP.Rdata" </code></pre> allraininclustersX: [data.frame] files are used to assess more accurately the accumulated precipitation during events by collecting precipitations from timesteps in which precipitation is above and below the threshold for every grid cell and the whole duration of the cluster. 99_allp: [matrix] value of extreme precipitation and extreme wind gust threshold over the whole domain (one value per grid cell) interclust: [list] files contain a list of data from wind and precipitation clusters divided in the 4 periods aggregated over space and clusters (1 value per grid cell per cluster). These files are uses to create the files “RainEv_ldat” and “Windev_ldat”. metaclust: [list] files contain a list of metadata from wind and precipitation clusters divided in the 4 periods . These files are uses to create the files “RainEv_meta” and “Windev_meta”. rainPX: [matrix] files contain a 3D matrix of dimension long<em>lat</em>time containing precipitation data for the period X. rawclust: [list] files contain a list of data.frame from wind and precipitation clusters divided in the 4 periods. These files are uses to create the files “RainEv_hdat” and “Windev_hdat”. timePX: [vector] contain vectors of time for the 4 periods. windPX: [matrix] files contain a 3D matrix of dimension long<em>lat</em>time containing wind gust data for the period X. Output data Output data contains metadata and raw data of single and compound hazard clusters are stored in the “data/out” folder which contains the following files in Rdata format: <pre><code>## [1] "compoundclusters.csv" "CompoundRW_79-19.v3x.Rdata" ## [3] "extremEvents_Rain.Rdata" "extremEvents_Wind.Rdata" ## [5] "Rain_stfprint.Rdata" "rainclusters.csv" ## [7] "RainEv_hdat_1979-2019.Rdata" "RainEv_ldat_1979-2019.Rdata" ## [9] "Rainev_ldatp_1979-2019.Rdata" "RainEv_meta_1979-2019.Rdata" ## [11] "RainEv_metap_1979-2019.Rdata" "Wind_stfprint.Rdata" ## [13] "windcluster.csv" "WindEv_hdat_1979-2019.Rdata" ## [15] "WindEv_ldat_1979-2019.Rdata" "WindEv_meta_1979-2019.Rdata" </code></pre> CompoundRW: [data.frame] contains metadata for the compound hazard clusters identified _hdat: [data.frame] hourly data of precipitation and wind gust clusters. _ldat: [data.frame] aggregated data over space and clusters (1 value per grid cell per cluster) for wind gust and precipitation clusters.Rain_ldatp contains aggregated values including non-extreme timesteps. Created from allraininclustersX. _meta:[data.frame] metadata for wind gust and precipitation clusters stfprint: [data.frame] files containing duration*footprint of each hazard clusters during all clusters sptdf: [data.frame] data.frame containing spatial, temporal, cluster and intensity information Codes assiciated to the method are availaible here: https://github.com/Alowis/SI-CH本研究所用数据提取自ERA5。ERA5是欧洲中期天气预报中心（ECMWF）于2019年发布的气候再分析产品，依托该领域最新技术进展开发（Hersbach等，2020）。ERA5数据集（ECMWF，2020）的时间覆盖范围为1979年至今（本研究使用至2019年9月），空间分辨率为0.25°×0.25°，时间分辨率为逐小时。该数据集通过从地表至80 km高空的137层大气垂直分层实现大气状态解析（ECMWF，2020）。ERA5数据通过每日两次（协调世界时06:00与18:00）启动的18小时短期预报生成，并融合观测数据进行同化（ECMWF，2020）。更多ERA5相关信息可查阅此处。 本研究从该产品中提取了两类变量： 1. 极端降水（p）：1小时内降落到地表的液态与固态水（包括降雨与降雪）的累计量（单位：毫米），该数值为单个网格格点的平均值。 2. 极端风速（w）：地球地表10 m高度处的逐小时最大阵风风速（单位：m·s⁻¹）。世界气象组织（WMO，2021）将阵风定义为3秒滑动平均风速的最大值。由于该时长小于模式时间步长，此类阵风数值需通过地表应力、地表摩擦、风切变与大气稳定性等其他参数推导得到，该数值同样为单个网格格点的平均值。 ### 原始数据导入 本研究按4个时段将两类变量的原始数据分别拆分为4个文件，时段划分如下：1979-1986年、1987-1997年、1998-2008年、2009-2019年。对应的R代码示例如下： r library(ncdf4) filer <- c(paste0(getwd(), "/data/in/raindat_7986.nc"), paste0(getwd(), "/data/in/raindat_8797.nc"), paste0(getwd(), "/data/in/raindat_9808.nc"), paste0(getwd(), "/data/in/raindat_0919.nc")) filew <- c(paste0(getwd(), "/data/in/windat_7986.nc"), paste0(getwd(), "/data/in/windat_8797.nc"), paste0(getwd(), "/data/in/windat_9808.nc"), paste0(getwd(), "/data/in/windat_0919.nc")) Startdate <- as.POSIXct("1979-01-01 10:00:00") Enddate <- as.POSIXct("1986-12-31 23:00:00") # ncr = nc_open(filer) # ncw = nc_open(filew) ### 中间数据 中间数据存储于"data/interdat"文件夹中，均为Rdata格式文件，具体列表如下： r ## [1] "allraininclusters1.Rdata" "allraininclusters2.Rdata" ## [3] "allraininclusters3.Rdata" "allraininclusters4.Rdata" ## [5] "extremEventsWind.Rdata" "interclustRain.Rdata" ## [7] "interclustWind.Rdata" "metaclustRain.Rdata" ## [9] "metaclustWind.Rdata" "Rain_99_AllP.Rdata" ## [11] "rainP1.Rdata" "rainP2.Rdata" ## [13] "rainP3.Rdata" "rainP4.Rdata" ## [15] "rawclustRain.Rdata" "rawclustWind.Rdata" ## [17] "timeP1.Rdata" "timeP2.Rdata" ## [19] "timeP3.Rdata" "timeP4.Rdata" ## [21] "windP1.Rdata" "windP2.Rdata" ## [23] "windP3.Rdata" "windP4.Rdata" ## [25] "Wnd_99_AllP.Rdata" 各中间文件的含义说明如下： - `allraininclustersX`：数据框格式，用于精准评估降水事件过程中的累计降水量，其整合了每个网格格点在聚类事件全时段内所有时间步的降水数据（无论是否超过阈值）。 - `99_allp`：矩阵格式，存储全域范围内极端降水与极端阵风的阈值（每个网格格点对应一个阈值）。 - `interclust`：列表格式，包含按4个时段划分的降水与风场聚类数据，已完成空间与聚类维度的聚合（每个聚类在每个网格格点对应一个数值），用于生成`RainEv_ldat`与`Windev_ldat`文件。 - `metaclust`：列表格式，包含按4个时段划分的降水与风场聚类的元数据，用于生成`RainEv_meta`与`Windev_meta`文件。 - `rainPX`：矩阵格式，为三维矩阵（维度为经度×纬度×时间），存储对应时段X的降水数据。 - `rawclust`：列表格式，包含按4个时段划分的降水与风场聚类的原始数据框，用于生成`RainEv_hdat`与`Windev_hdat`文件。 - `timePX`：向量格式，存储对应时段X的时间序列数据。 - `windPX`：矩阵格式，为三维矩阵（维度为经度×纬度×时间），存储对应时段X的阵风风速数据。 ### 输出数据 单灾种与复合灾种聚类的元数据及原始数据存储于"data/out"文件夹中，均为Rdata或CSV格式文件，具体列表如下： r ## [1] "compoundclusters.csv" "CompoundRW_79-19.v3x.Rdata" ## [3] "extremEvents_Rain.Rdata" "extremEvents_Wind.Rdata" ## [5] "Rain_stfprint.Rdata" "rainclusters.csv" ## [7] "RainEv_hdat_1979-2019.Rdata" "RainEv_ldat_1979-2019.Rdata" ## [9] "Rainev_ldatp_1979-2019.Rdata" "RainEv_meta_1979-2019.Rdata" ## [11] "RainEv_metap_1979-2019.Rdata" "Wind_stfprint.Rdata" ## [13] "windcluster.csv" "WindEv_hdat_1979-2019.Rdata" ## [15] "WindEv_ldat_1979-2019.Rdata" "WindEv_meta_1979-2019.Rdata" 各输出文件的含义说明如下： - `CompoundRW`：数据框格式，存储已识别的复合灾种聚类的元数据。 - `_hdat`：数据框格式，存储降水与阵风聚类的逐小时原始数据。 - `_ldat`：数据框格式，存储降水与阵风聚类在空间与聚类维度的聚合数据（每个聚类在每个网格格点对应一个数值）。其中`Rain_ldatp`包含了非极端时间步的聚合数值，由`allraininclustersX`文件生成。 - `_meta`：数据框格式，存储降水与阵风聚类的元数据。 - `stfprint`：数据框格式，存储所有灾种聚类的持续时长×影响范围数值。 - `sptdf`：数据框格式，存储空间、时间、聚类与强度相关的信息。 本研究方法对应的代码可于以下网址获取：https://github.com/Alowis/SI-CH