Areal reduction factors (ARFs) convert a point estimate of extreme precipitation to an estimate of extreme precipitation over a spatial domain, and are commonly used in flood risk estimation. The fixed-area approach to ARF estimation considers an area of a certain size and constructs the ratio of extremes with the same exceedance probability for areal average precipitation and point precipitation at a representative location. In regions with sparse observation networks, estimates of areal average precipitation are highly uncertain if based on rain gauge data only. We construct and compare regional and seasonal ARF estimates for Norway using different gridded data products, both observation-based products (SURFdat, seNorge) and reanalysis products (NORA3). For data products that cover a sufficiently long time period, the extremes are estimated using the generalised extreme value (GEV) distribution, while the metastatistical extreme value (MEV) formulation is applied to data products with short records. The results indicate that the NORA3 reanalysis, available at a temporal resolution of 1 h and a spatial resolution of 3km, provides a good overall adequacy for the purpose of obtaining robust and reliable ARF estimates.

中文翻译：

---

网格数据产品的面积缩减因子

面积折减因子 (ARF) 将极端降水的点估计转换为空间域内极端降水的估计，通常用于洪水风险估计。固定区域 ARF 估计方法考虑一定大小的区域，并针对代表性位置的区域平均降水量和点降水量构建具有相同超标概率的极值比率。在观测网络稀疏的地区，如果仅基于雨量计数据，区域平均降水量的估计具有很大的不确定性。我们使用不同的网格数据产品（基于观测的产品（SURFdat、seNorge）和再分析产品（NORA3））构建并比较挪威的区域和季节性 ARF 估计。对于覆盖足够长时间段的数据产品，使用广义极值（GEV）分布来估计极值，而对于记录较短的数据产品则应用转移统计极值（MEV）公式。结果表明，NORA3 再分析可在 1 小时的时间分辨率和 3 公里的空间分辨率下进行，为获得稳健且可靠的 ARF 估计提供了良好的整体充分性。