ERA5-Land weekly: Total precipitation, weekly time series for Europe at 1 km resolution (2016 - 2020)

Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Total precipitation: Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. Processing steps: The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate proportion of ERA5-Land / aggregated CHELSA 3. interpolate proportion with a Gaussian filter to 30 arc seconds 4. multiply the interpolated proportions with CHELSA Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA. The spatially enhanced daily ERA5-Land data has been aggregated on a weekly basis starting from Saturday for the time period 2016 - 2020. Data available is the weekly average of daily sums and the weekly sum of daily sums of total precipitation. File naming: Average of daily sum: era5_land_prectot_avg_weekly_YYYY_MM_DD.tif Sum of daily sum: era5_land_prectot_sum_weekly_YYYY_MM_DD.tif The date in the file name determines the start day of the week (Saturday). Pixel values: mm * 10 Example: Value 218 = 21.8 mm Coordinate reference system: ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035) Spatial extent: north: 82:00:30N south: 18N west: 32:00:30W east: 70E Spatial resolution: 1km Temporal resolution: weekly Period: 01/01/2016 - 12/31/2020 Lineage: Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used. Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief) Original ERA5-Land dataset license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4 Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122 Other resources: https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b Format: GeoTIFF Representation type: Grid Processed by: mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/) Contact: mundialis GmbH & Co. KG, [e-mail] Acknowledgements: This study was partially funded by EU grant 874850 MOOD. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the European Commission.概述： ERA5-Land是一款再分析数据集，相较ERA5，它以更高的空间分辨率完整呈现了数十年间陆面变量的演变过程。该数据集通过重演欧洲中期天气预报中心（ECMWF）ERA5气候再分析的陆面分量生成。再分析技术依据物理定律，将数值模式数据与全球各地的观测数据融合，构建出一套全球覆盖且一致性良好的数据集。再分析可回溯数十年的历史气候数据，精准刻画过去的气候状态。 总降水量： 指降落到地球表面的液态与固态凝结水总量，涵盖降雨与降雪。其为大尺度降水（由槽、冷锋等大尺度天气系统生成的降水）与对流降水（由低层大气暖湿空气密度低于上层空气，从而抬升形成的对流活动生成的降水）的总和。该降水变量不包含雾、露，以及在抵达地表前便在大气中蒸发的降水。此变量的累积时段为预报起始时刻至预报步长结束时刻。降水量的单位为米（m），即若将网格单元内的降水均匀铺开，对应的水深。在对比模式数据与观测数据时需格外谨慎，因为观测数据通常仅对应特定时空点，而非模式网格单元与模式时间步长内的平均值。 处理流程： 原始逐小时ERA5-Land数据集已通过与CHELSA数据（V1.2）（https://chelsa-climate.org/）进行图像融合，将空间分辨率从0.1度提升至30角秒（约1000米）。我们每日均采用CHELSA对应的长期月平均值。本处理的目标是，既保留CHELSA的精细空间细节，又同时保留ERA5-Land的区域整体格局与精细时间细节。具体处理步骤包括聚合与增强，详情如下： 1. 将CHELSA数据空间聚合至ERA5-Land的分辨率 2. 计算ERA5-Land与聚合后CHELSA数据的比例 3. 使用高斯滤波器将该比例插值至30角秒分辨率 4. 将插值后的比例与CHELSA数据相乘 采用比例计算可确保无降水区域始终保持无降水状态。仅当特定区域存在实际降水时，才会依据CHELSA的空间细节对降水量进行重新分配。 经空间增强后的逐日ERA5-Land数据集已按周为单位进行聚合，周起始日为周六，时间范围为2016年至2020年。可用数据为每日总降水量的周平均值，以及每日总降水量的周累加值。 文件命名规则： 每日总和的周平均值：era5_land_prectot_avg_weekly_YYYY_MM_DD.tif 每日总和的周累加值：era5_land_prectot_sum_weekly_YYYY_MM_DD.tif 文件名中的日期代表该周的起始日（周六）。 像素值： mm * 10 示例：像素值218对应21.8毫米降水量 坐标参考系统： ETRS89 / LAEA Europe（EPSG:3035）（EPSG:3035） 空间范围： 北：82°00′30″N 南：18°N 西：32°00′30″W 东：70°E 空间分辨率： 1千米 时间分辨率： 周度 时间范围： 2016年1月1日 — 2020年12月31日 数据溯源： 本数据集源自哥白尼气候数据服务中心（CDS）的原始ERA5-Land数据源，辅助数据采用了CHELSA气候数据集。 所用软件： GDAL 3.2.2与GRASS GIS 8.0.0（r.resamp.stats -w；r.relief） 原始ERA5-Land数据集使用许可： https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf CHELSA气候数据集（V1.2）： 所用数据：Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): 来自《地球陆表区域高分辨率气候数据集》。Dryad数字仓储。http://dx.doi.org/doi:10.5061/dryad.kd1d4 原始同行评议论文：Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): 《地球陆表区域高分辨率气候数据集》。《科学数据》4 170122. https://doi.org/10.1038/sdata.2017.122 其他资源： https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b 数据格式： GeoTIFF 表征类型： 网格 处理方： 德国mundialis GmbH & Co. KG（https://www.mundialis.de/） 联系方式： mundialis GmbH & Co. KG，[e-mail] 致谢： 本研究部分由欧盟资助项目编号874850 MOOD资助。本文内容仅代表作者观点，不必然反映欧盟委员会的立场。