2001_2019_ERA5_ TotalPrecipitation_FourierProcessed_ER

Abstract: Monthly Precipitation form the ERA5 reanalysis archive supplied by the European Centre ofr Medium Range Weather Forecasting for 2001 - 2019. The original data is at 0.25 degree resolution and was downscaled by ERA extraction algroithms to 5km. This is a set of images produced by Temporal Fourier Analysis (TFA) of ERA5 data: ERA5: Total Precipitation The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for whole worldThis series of ERA5 data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2001 to 2019. Precipitation from the ERA5 reanalysis archive supplied by the European Centre ofr Medium Range Weather Forecasting for 2001 - 2019. Abstract: Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting . for 2001 - 2019 . The original data is at 0.25 degree resolution and wasdownscaled by ERA extraction algroithms. The daily data have been aggregated to dekadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets. Process: Image values were extracted from ERA5 ( Total precipitation) 5 km imagery from 2001 to 2019. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408) Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic (WGS84). The E4Warning study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility. Projection + EPSG code:Latitude-Longitude/WGS84 (EPSG: 4326) File names: The wd at the start of each file name indicates that the image covers Globally and ER refers to Europe, North Africa, Eurasia in the E4warning and is in geographic projection. 19 refers to the year timeline of 2001-2019.The next two characters identify the channel:20 - Monthly Total PrecipitationThe last two characters of each file name denote the output from Fourier processing:a0 - meanmn - minimummx - maximuma1 - amplitude of annual cyclea2 - amplitude of bi-annual cyclea3 - amplitude of tri-annual cyclep1 - phase of annual cyclep2 - phase of bi-annual cyclep3 - phase of tri-annual cycled1 - variance in annual cycled2 - variance in bi-annual cycled3 - variance in tri-annual cycleda - combined variance in annual, bi-annual, and tri-annual cyclesvr - variance in raw dataParameter Fourier Variable Image values areERA5 A0, A1, A2, A3, Min, Max, Vr Reflectance values monthly total precipitation in mmALL D1,D2,D3,Da PercentagesALL E1,E2,E3 PercentagesALL P1,P2.P3 Months*100. (Jan=100)摘要：本数据集采用欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecasts, ECMWF）提供的ERA5再分析档案中的月降水量数据，时间跨度为2001年至2019年。原始数据分辨率为0.25度，经ERA提取算法降尺度至5公里。原始日降水数据被聚合为旬、月、年尺度数据集，以匹配美国国家航空航天局（National Aeronautics and Space Administration, NASA）基于中分辨率成像光谱仪（Moderate Resolution Imaging Spectroradiometer, MODIS）影像生成的温度与植被指数数据集的输出格式与分辨率。 本数据集为基于ERA5数据经时间傅里叶分析（Temporal Fourier Analysis, TFA）生成的图像集，对应ERA5总降水量产品。该图像集涵盖全球范围的关键环境指标，包含季节动态特征。本系列数据按照Scharlemann等人2008年的研究方法处理，现已更新至2001-2019年的图像数据。 处理流程： 本数据集提取了2001-2019年ERA5总降水量5公里分辨率影像的像素值。随后，每组参数提取数据集均通过时间傅里叶处理算法进行分析。通过逐级阈值筛选与插值方法，剔除时间序列中的错误值并填补数据缺失间隙。经平滑后的时间序列以5天为间隔采样，转换为描述年周期、年双周期、年三周期波动的正弦曲线集合。针对每类曲线，傅里叶算法将生成振幅、相位与方差相关的图像。其他输出结果包括时间序列的均值、最小值、最大值，以及傅里叶变换过程中计算得到的误差值。如需了解傅里叶算法及其输出结果的详细说明，请参阅Scharlemann等人2008年发表的论文（https://doi.org/10.1371/journal.pone.0001408）。 利用可见光红外成像辐射仪套件（Visible Infrared Imaging Radiometer Suite, VIIRS）海陆掩膜图层对海洋像素进行掩膜处理，并将影像从正弦投影转换为地理坐标系（WGS84）。E4Warning研究区域为全球影像的子集。为提升数据使用者的灵活性，将Idrisi栅格格式转换为GeoTIFF格式。 投影坐标系与EPSG代码：经纬度/WGS84（EPSG: 4326） 文件名规则： 每个文件名开头的“wd”表示该影像覆盖全球范围；“ER”表示影像覆盖E4Warning项目中的欧洲、北非与欧亚大陆区域，且采用地理投影。“19”代表数据的时间跨度为2001-2019年。后续两位字符用于标识数据通道：“20”代表月总降水量。 文件名最后两位字符代表傅里叶处理的输出结果类型： a0：均值；mn：最小值；mx：最大值； a1：年周期振幅；a2：年双周期振幅；a3：年三周期振幅； p1：年周期相位；p2：年双周期相位；p3：年三周期相位； d1：年周期方差；d2：年双周期方差；d3：年三周期方差； a：年、年双周期、年三周期总方差；vr：原始数据方差。 参数说明： 傅里叶变量的图像值对应ERA5数据的A0、A1、A2、A3、最小值、最大值、Vr； 月总降水量的反射率值单位为毫米； D1、D2、D3、Da均为百分比值； E1、E2、E3均为百分比值； P1、P2、P3：相位值为月份×100（1月对应100）。