Monthly time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2022) derived from ERA5-Land data
收藏资源简介：
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]. File naming scheme (YYYY = year; MM = month):ERA5_land_rh2m_avg_monthly_YYYY_MM.tif Projection + EPSG code:Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent:north: 82:00:30Nsouth: 18Nwest: 32:00:30Weast: 70E Spatial resolution:30 arc seconds (approx. 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.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 EU LAEA (EPSG: 3035) projection: https://doi.org/10.5281/zenodo.7427021
概述：ERA5-Land是一款再分析（reanalysis）数据集，相较于ERA5，它以更高的空间分辨率提供了数十年间地表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米气温与露点温度数据，通过与CHELSA数据（V1.2）进行图像融合，将空间分辨率从0.1°提升至30角秒（约1000米）。我们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的分辨率；2. 计算ERA5-Land与聚合后CHELSA数据的差值；3. 利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。 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2022年12月的时段，计算逐日相对湿度（rh2m）。相对湿度（rh2m）由地表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出： 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 将得到的相对湿度聚合为月均数据，并将所得结果转换为百分比×10的格式，理论取值范围为[0, 1000]。 文件命名规则（YYYY=年份；MM=月份）：ERA5_land_rh2m_avg_monthly_YYYY_MM.tif 投影与EPSG代码：经纬度/WGS84（EPSG: 4326） 空间范围：北界82°00′30″N，南界18°N，西界32°00′30″W，东界70°E 空间分辨率：30角秒（约1000米） 时间分辨率：月尺度 像素值：百分比×10（缩放为整数；示例：像素值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): 源自《地球陆面区域高分辨率气候态数据集》。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): 《地球陆面区域高分辨率气候态数据集》。《科学数据（Scientific Data）》，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）》。联邦气象服务协调员办公室与配套研究。华盛顿特区 该数据也可通过EU LAEA（EPSG: 3035）投影获取：https://doi.org/10.5281/zenodo.7427021
