ERA5-Land daily: Total precipitation, daily time series for Europe at 30 arc seconds (ca. 1000 meter) resolution (2000 - 2020)

ERA5-Land daily: Total precipitation, daily time series for Europe at 30 arc seconds (ca. 1000 meter) resolution (2000 - 2020)

Source data: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 (period 2000 - 2020) 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-Land2. calculate proportion of ERA5-Land / aggregated CHELSA3. interpolate proportion with a Gaussian filter to 30 arc seconds4. multiply the interpolated proportions with CHELSAUsing 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.

Data available is the daily sum of precipitation.File naming:era5_land_daily_prectot_YYYYMMDD_sum_30sec.tif e.g.:era5_land_daily_prectot_20200418_sum_30sec.tif

The date within the filename is Year, Month and Day of timestamp.

Pixel values:mm * 10Scaled to Integer, example: value 218 = 21.8 mm

Projection + EPSG code:Latitude-Longitude/WGS84 (EPSG: 4326)

Spatial extent:north: 82:00:30Nsouth: 18:00:00Nwest: 32:00:30Weast: 70:00:00E

Temporal extent:01.01.2000 - 31.12.2020NOTE: Due to file size, only 2020 data are available here. Data for other years are available on request.

Spatial resolution:30 arc seconds (approx. 1000 m)

Temporal resolution:daily

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)

Format: GeoTIFF

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.kd1d4Original 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

Representation type: Grid

Processed by:mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/)

Contact: mundialis GmbH & Co. KG, [e-mail]

ERA5-Land 逐日数据集：欧洲区域30角秒（约1000米）分辨率的总降水量逐日时间序列（2000年-2020年）

源数据：ERA5-Land是一套再分析数据集，相较于ERA5，其空间分辨率更高，可连贯展现数十年来陆面变量的演变过程。该数据集通过再分析欧洲中期天气预报中心（ECMWF）ERA5气候再分析的陆面分量生成。再分析技术借助物理定律，将全球模式数据与全球观测数据整合为一套全域完整且一致的数据集。再分析可回溯数十年的历史数据，精准刻画过往气候状态。

总降水量：指降落到地球表面的液态和固态凝结水（包括降雨、降雪）总量，由大尺度降水（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、产生垂直对流而生成的降水）共同组成。该变量不包含雾、露，以及在抵达地表前便在大气中蒸发的降水。其数值为预报时段起始至结束的累计总量，单位为米，表征若将网格盒内的降水均匀铺开时的水层厚度。在将模式数据与实测观测数据对比时需注意：实测观测数据通常对应特定时空单点，而非模式网格盒与模式时间步长内的平均值。

处理流程：原始ERA5-Land逐小时数据（2000年-2020年）已通过与CHELSA数据（V1.2版，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）。本数据集每日采用对应月份的CHELSA长期平均数据，旨在兼顾CHELSA的精细空间细节与ERA5-Land的区域整体格局及精细时间分辨率。具体处理步骤如下：1. 将CHELSA数据空间聚合至ERA5-Land的原始分辨率；2. 计算ERA5-Land与聚合后CHELSA数据的比例因子；3. 使用高斯滤波器将比例因子插值至30角秒分辨率；4. 将插值后的比例因子与CHELSA数据相乘。采用比例因子可确保无降水区域始终保持无降水状态，仅在存在实际降水的区域，才依据CHELSA的精细空间细节重新分配降水量。

本数据集提供逐日累计降水量。
文件命名规则：era5_land_daily_prectot_YYYYMMDD_sum_30sec.tif，示例：era5_land_daily_prectot_20200418_sum_30sec.tif。文件名中的日期为时间戳对应的年、月、日。像素值为缩放后的整数型数据，实际为降水量（毫米）乘以10，例如像素值218对应21.8毫米降水量。

投影及EPSG代码：纬度-经度坐标系/WGS84（EPSG: 4326）

空间范围：北纬82°00′30″，南纬18°00′00″，西经32°00′30″，东经70°00′00″

时间范围：2000年1月1日至2020年12月31日。注：受文件大小限制，此处仅提供2020年数据，其他年份数据可申请获取。

空间分辨率：30角秒（约1000米）
时间分辨率：逐日

数据谱系：本数据集源自哥白尼气候数据服务中心（Copernicus Climate Data Store）的原始ERA5-Land数据源，辅助数据采用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）

数据格式：GeoTIFF

原始ERA5-Land数据集许可协议：https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

CHELSA气候数据集（V1.2版）引用信息：所用数据来源：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): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122

数据表示类型：格网数据

数据加工方：德国mundialis GmbH & Co. KG（https://www.mundialis.de/）
联系方式：mundialis GmbH & Co. KG，邮箱[e-mail]