Monthly time series of spatially enhanced relative humidity for Europe at 1000 m resolution (2000 - 2023) derived from ERA5-Land data
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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 - 12/2023. 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: 6874000south: -485000west: 869000east: 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 8.0.0/8.3.2 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.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 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://doi.org/10.5281/zenodo.6146383
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观测视角。该数据集通过重演欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecasts，简称ECMWF）ERA5气候再分析的陆面分量生成。再分析技术将模式数据与全球观测数据相结合，依托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。 处理流程：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/）进行影像融合，将空间分辨率从0.1度提升至30角秒（约1000米）。每日均采用对应月份的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分辨率。具体处理步骤包括聚合与增强流程：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. 将插值后的差值叠加至CHELSA数据。 后续处理：随后，将气温时间序列聚合为日尺度数据，并基于2000年1月至2023年12月的时段，计算得到逐日相对湿度。 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月平均值，并将结果转换为“百分比×10”的格式，理论取值范围为[0, 1000]。 数据已重投影至欧洲Lambert等面积投影（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：数值738对应73.8%） 所用软件：GDAL 3.2.2与GRASS GIS 8.0.0/8.3.2 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): 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 处理方：德国mundialis GmbH & Co. KG（https://www.mundialis.de/） 参考文献：Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC 该数据也可通过WGS84经纬度投影（EPSG: 4326）获取：https://doi.org/10.5281/zenodo.6146383
