Data set: Huai et al. (2021). JAMC. Quantifying rainfall in Greenland: a combined observational and modelling approach

Abstract Paper. This paper estimates rainfall totals at 17 Greenland meteorological stations, subjecting data from in-situ precipitation gauge measurements to seven different precipitation phase schemes to separate rain- and snowfall amounts. To correct the resulting snow/rain fractions for undercatch, we subsequently use a Dynamic Correction Model (DCM) for Automatic Weather Stations (AWS, Pluvio gauges) and a regression analysis correction method for staffed stations (Hellmann gauges). With observations ranging from 5% to 57% for cumulative totals, rainfall accounts for a considerable fraction of total annual precipitation over Greenland’s coastal regions, with the highest rain fraction in the south (Narsarsuaq). Monthly precipitation and rainfall totals are used to evaluate the regional climate model RACMO2.3. The model realistically captures monthly rainfall and total precipitation (R=0.3-0.9), with generally higher correlations for rainfall for which the undercatch correction factors (1.02-1.40) are smaller than those for snowfall (1.27-2.80), and hence the observations more robust. With a horizontal resolution of 5.5 km and simulation period from 1958-present, RACMO2.3 therefore is a useful tool to study spatial and temporal variability of rainfall in Greenland, although further statistical downscaling may be required to resolve the steep rainfall gradients. The dataset contains:<br> Automatic weather station data:<br> AWS-daily.zip: per station daily values of snowfall and rain fall derived from raw precipitation data and for 7 methods to divide between rain and snowfall<br> AWS-factork.zip: per station the factor with which the data is corrected for undercatch<br> AWS-script.zip: the scripts used for the analyses Staffed weather stations:<br> Meteo-daily.zip: per station daily values of snowfall and rain fall derived from the precipitation data and for 7 methods to divide between rain and snowfall<br> Meteo-factork.zip: per station the factor with which the data is corrected for undercatch<br> Meteo-script.zip: the scripts used for the analyses本研究论文摘要：针对格陵兰岛17个气象站点的降雨总量展开估算，针对原位降水雨量计观测数据采用7种不同的降水相态划分方案，以分离降雨量与降雪量。为校正由此得到的雪/雨占比的观测漏测偏差，后续分别针对自动气象站（Automatic Weather Stations, AWS，普卢维奥雨量计）采用动态校正模型（Dynamic Correction Model, DCM），针对有人值守气象站（赫尔曼式雨量计，Hellmann gauges）采用回归分析校正方法。 观测数据显示，格陵兰沿海地区的年降水累计总量中，降雨占比可达5%至57%，其中南部的纳萨尔苏瓦克（Narsarsuaq）降雨占比最高。研究利用月降水与月降雨总量对区域气候模型RACMO2.3进行评估。该模型可较好还原月降雨与总降水量（相关系数R=0.3~0.9），且降雨的漏测校正因子（1.02~1.40）整体小于降雪的校正因子（1.27~2.80），因此降雨观测结果更为稳健可靠。RACMO2.3的水平分辨率为5.5 km，模拟时段为1958年至今，因此该模型可作为研究格陵兰岛降雨时空变化特征的有效工具，不过若要解析陡峭的降雨空间梯度，可能仍需开展进一步的统计降尺度研究。 本数据集包含： 1. 自动气象站数据： - AWS-daily.zip：逐站逐日降雪量与降雨量数据，基于原始降水数据与7种雨雪划分方案计算得到 - AWS-factork.zip：逐站的降水漏测校正因子数据 - AWS-script.zip：本研究分析所用的脚本文件 2. 有人值守气象站数据： - Meteo-daily.zip：逐站逐日降雪量与降雨量数据，基于原始降水数据与7种雨雪划分方案计算得到 - Meteo-factork.zip：逐站的降水漏测校正因子数据 - Meteo-script.zip：本研究分析所用的脚本文件