Monthly time series of spatially enhanced relative humidity for Europe at 1000 m resolution (2000 - 2022) derived from ERA5-Land data

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. Processing steps: The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m 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 difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds. 4. add the interpolated differences to CHELSA Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 12/2022. Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997): maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta)) actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td)) relative humidity = actual water pressure / maximum water pressure The resulting relative humidity has been aggregated to monthly averages. Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000]. The data have been reprojected to EU LAEA. File naming scheme (YYYY = year; MM = month): ERA5_land_rh2m_avg_monthly_YYYY_MM.tif Projection + EPSG code: EU LAEA (EPSG: 3035) Spatial extent: north: 6874000 south: -485000 west: 869000 east: 8712000 Spatial resolution: 1000 m Temporal resolution: Monthly Pixel values: Percent * 10 (scaled to Integer; example: value 738 = 73.8 %) Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 Original ERA5-Land dataset license: https://apps.ecmwf.int/datasets/licences/copernicus/ 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 Processed by: mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/) Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC Data is also available in Latitude-Longitude/WGS84 (EPSG: 4326) projection: https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/b9ce7dba-4130-428d-96f0-9089d8b9f4a5 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，相较于ERA5，它以更高的分辨率提供了数十年间陆面变量演变的一致观测视图。ERA5-Land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。再分析将模式数据与全球各地的观测数据相结合，依据物理定律构建出全球完整且一致的数据集。再分析可回溯数十年的历史数据，精准刻画过去的气候状况。 处理流程： 原始逐小时ERA5-Land地表2米气温与地表2米露点温度数据，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）。每日处理时，我们采用对应月份的CHELSA长期平均数据。本次处理的目标是融合CHELSA的精细空间细节，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，详细如下： 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数据的差值 3. 使用高斯滤波器将差值插值至30角秒分辨率 4. 将插值后的差值添加至CHELSA数据 随后，将气温时间序列按日进行聚合。基于此，我们计算了2000年1月至2022年12月期间的逐日相对湿度。 相对湿度（rh2m）通过地表2米气温（Ta）与地表2米露点温度（Td），依据Wright（1997）提出的饱和水汽压公式计算得到： 饱和水汽压 = 611.21 * exp(17.502 * Ta / (240.97 + Ta)) 实际水汽压 = 611.21 * exp(17.502 * Td / (240.97 + Td)) 相对湿度 = 实际水汽压 / 饱和水汽压 将得到的相对湿度数据聚合为月平均值。 最终结果值按“百分比×10”进行转换，理论取值范围为[0, 1000]。 数据已重投影至欧洲等面积圆柱投影（EU LAEA）。 文件命名规则（YYYY=年份；MM=月份）： ERA5_land_rh2m_avg_monthly_YYYY_MM.tif 投影及EPSG代码： EU LAEA（EPSG: 3035） 空间范围： 北：6874000 南：-485000 西：869000 东：8712000 空间分辨率： 1000米 时间分辨率： 月度 像素值： 百分比×10（缩放为整数；示例：值738对应73.8%） 所用软件： GDAL 3.2.2与GRASS GIS 8.0.0 原始ERA5-Land数据集许可证： https://apps.ecmwf.int/datasets/licences/copernicus/ 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): 源自《地球陆面区域高分辨率气候图集》，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): 《地球陆面区域高分辨率气候图集》，《科学数据》，4卷，170122。https://doi.org/10.1038/sdata.2017.122 处理方： 德国mundialis GmbH & Co. KG（https://www.mundialis.de/） 参考文献：Wright, J.M. (1997): 《联邦气象手册第3号》(FCM-H3-1997)，联邦气象服务协调办公室与配套研究，华盛顿特区。 该数据也可通过经纬度/WGS84（EPSG: 4326）投影获取：https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/b9ce7dba-4130-428d-96f0-9089d8b9f4a5 致谢： 本研究部分受欧盟项目资助编号874850 MOOD。本文内容仅代表作者观点，不一定反映欧盟委员会的立场。