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Andrew P. Osborne
,
Jian Zhang
,
Micheal J. Simpson
,
Kenneth W. Howard
, and
Stephen B. Cocks

Abstract

The Multi-Radar Multi-Sensor (MRMS) system produces a suite of hydrometeorological products that are widely used for applications such as flash flood warning operations, water resource management, and climatological studies. The MRMS radar-based quantitative precipitation estimation (QPE) products have greater challenges in the western United States compared to the eastern two-thirds of the CONUS due to terrain-related blockages and gaps in radar coverage. Further, orographic enhancement of precipitation often occurs, which is highly variable in space and time and difficult to accurately capture with physically based approaches. A deep learning approach was applied in this study to understand the correlations between several interacting variables and to obtain a more accurate precipitation estimation in these scenarios. The model presented here is a convolutional neural network (CNN), which uses spatial information from small grids of several radar variables to predict an estimated precipitation value at the central grid point. Several case analyses are presented along with a yearlong statistical evaluation. The CNN model 24-h QPE shows higher accuracy than the MRMS radar QPE for several cool season atmospheric river events. Areas of consistent improvement from the CNN model are highlighted in the discussion along with areas where the model can be further improved. The initial findings from this work help set the foundation for further exploration of machine learning techniques and products for precipitation estimation as part of the MRMS operational system.

Significance Statement

This study explores the development and use of a deep learning model to generate precipitation fields in the complex terrain of the western United States. Generally, the model is able to improve on the statistical performance of existing radar-based precipitation estimation methods for several case studies and over a long-term period in 2021. We explore the patterns associated with certain areas of strong performance and suggest potential means of improving areas with weaker performance. These initial results indicate the potential of deep learning to supplement radar-based approaches in areas with observational limitations.

Open access