Decadal time series of spatially enhanced relative humidity for Europe at 1000 m resolution (2000 - 2021) 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 - 07/2021. 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 decadal averages. Each month is divided into three decades: the first decade of a month covers days 1-10, the second decade covers days 11-20, and the third decade covers days 21-last day of the month. 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; dD = number of decade): ERA5_land_rh2m_avg_decadal_YYYY_MM_dD.tif Projection + EPSG code: EU LAEA (EPSG: 3035) Spatial extent: north: 6874000 south: -485000 west: 869000 east: 8712000 Spatial resolution: 1000 m Temporal resolution: Decadal 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 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近数十年间陆面变量演变的一致性刻画。该数据集通过重演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观测资料相结合，基于物理定律构建出一套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态。 处理步骤： 原始的逐小时ERA5-Land 2米气温与2米露点温度数据，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对应的月尺度长期平均值。本处理的目标是兼顾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流程，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，对气温时间序列进行日尺度聚合。基于2000年1月至2021年7月的时段，从聚合后的气温数据中计算得到每日相对湿度。 相对湿度（rh2m）通过2米气温（Ta）与2米露点温度（Td），采用Wright（1997）提出的饱和水汽压公式计算得到： 最大水汽压 = 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 / 最大水汽压 将得到的相对湿度聚合为旬平均值。每个月被划分为三个旬段：每月上旬对应1-10日，中旬对应11-20日，下旬对应21日至当月最后一日。 最终结果被转换为“百分比×10”的形式，理论取值范围为[0, 1000]。 数据已重投影至欧盟Lambert等面积投影（EU LAEA）。 文件命名规则（YYYY = 年份；MM = 月份；dD = 年代段编号）： ERA5_land_rh2m_avg_decadal_YYYY_MM_dD.tif 投影与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： 旬度 像素值： 百分比×10（缩放为整数；示例：数值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： 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): 来自《地球陆面区域高分辨率气候图集》的数据。Dryad数字知识库。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 致谢： 本研究部分由欧盟项目MOOD（项目编号874850）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。