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Evaluation of sub-hourly MRMS quantitative precipitation estimates in mountainous terrain using machine learning
  • Phoebe White,
  • Peter A. Nelson
Phoebe White
Colorado State University

Corresponding Author:[email protected]

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Peter A. Nelson
Colorado State University
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The Multi-Radar Multi-Sensor (MRMS) product incorporates radar, climate model, and gage data at a high spatiotemporal resolution for the contiguous United States. 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-hour time series samples of MRMS 15-minute 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-minute 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 dataset 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.
08 Mar 2024Submitted to ESS Open Archive
15 Mar 2024Published in ESS Open Archive