The Multi-Radar Multi-Sensor (MRMS) product incorporates radar, quantitative precipitation forecasts, and gage data at a high spatiotemporal resolution for the United States and southern Canada. MRMS is subject to various sources of measurement error, especially in complex terrain. The goal of this study is to provide a framework for understanding the uncertainty of MRMS in mountainous areas with limited observations. We evaluate 8-hr time series samples of MRMS 15-min intensity through a comparison to 204 gages located in the mountains of Colorado. This analysis shows that the MRMS surface precipitation rate product tends to overestimate rainfall with a median normalized root mean squared error (RMSE) of 42% of the maximum MRMS 15-min intensity. For each time series sample, various features related to the physical characteristics influencing MRMS performance are calculated from the topography, surrounding storms, and rainfall observed at the gage location. A gradient-boosting regressor is trained on these features and is optimized with quantile loss, using the RMSE as a target, to model nonlinear patterns in the features that relate to a range of error. This model was used to predict a range of error throughout the mountains of Colorado during warm months, spanning 6 years, resulting in a spatiotemporally varying error model of MRMS for sub-hourly precipitation rates. Mapping of this data set by aggregating normalized RMSE over time reveals that areas further from radar sites in higher elevation terrain show consistently greater error. However, the model predicts larger performance variability in these regions compared to alternative error assessments.

中文翻译：

---

使用机器学习评估山区地形中的亚小时级 MRMS 定量降水估计


多雷达多传感器 （MRMS） 产品包含美国和加拿大南部的高时空分辨率的雷达、定量降水预报和测量数据。MRMS 会受到各种测量误差来源的影响，尤其是在复杂地形中。本研究的目的是为理解观测有限的山区 MRMS 的不确定性提供一个框架。我们通过与位于科罗拉多州山区的 204 个量具进行比较，评估了 MRMS 15 分钟强度的 8 小时时间序列样品。该分析表明，MRMS 地表降水率积往往高估降雨量，归一化均方根误差 （RMSE） 的中位数为 MRMS 15 分钟最大强度的 42%。对于每个时间序列样本，根据地形、周围风暴和在量具位置观察到的降雨量计算出与影响 MRMS 性能的物理特性相关的各种特征。梯度提升回归器根据这些特征进行训练，并使用 RMSE 作为目标，使用分位数损失进行优化，以对与一系列误差相关的特征中的非线性模式进行建模。该模型用于预测整个科罗拉多州山区在温暖月份的误差范围，跨越 6 年，导致亚小时降水率的 MRMS 误差模型时空变化。通过随时间聚合归一化 RMSE 来绘制此数据集的地图表明，在高海拔地形中，距离雷达站点较远的区域始终显示出更大的误差。但是，与其他错误评估相比，该模型预测这些区域的性能变化更大。