Evaluation of Sub‐Hourly MRMS Quantitative Precipitation Estimates in Mountainous Terrain Using Machine Learning
In one sentence. Gradient-boosting error model maps sub-hourly MRMS radar precipitation errors across Colorado mountains, showing larger errors far from radars and at high elevation.
Abstract
Abstract 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.
Labels
- Topic
- Observations & remote sensing
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Regional
- Data
- Radar, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1029/2024wr037437 OpenAlex:W4405129143
- Added
- 2026-10-22
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