From Random Forests to Flood Forecasts: A Research to Operations Success Story
نویسندگان
چکیده
Abstract Excessive rainfall is difficult to forecast, and there a need for tools aid Weather Prediction Center (WPC) forecasters when generating Rainfall Outlooks (EROs), which are issued the contiguous United States at lead times of 1–3 days. To address this need, probabilistic forecast system excessive rainfall, known as Colorado State University Machine Learning Probabilities (CSU-MLP) system, was developed based on ensemble reforecasts, precipitation observations, machine-learning algorithms, specifically random forests. The CSU-MLP forecasts were designed emulate EROs, with goal being tool that can use “first guess” in ERO process. Resulting from close collaboration between CSU WPC evaluation Flash Flood Intense Experiment, iterative improvements made it transitioned into operational WPC. Quantitative shows skillful reliable, they now used part This project represents an example successful research-to-operations transition, highlights potential machine learning other postprocessing techniques improve predictions.
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ژورنال
عنوان ژورنال: Bulletin of the American Meteorological Society
سال: 2021
ISSN: ['1520-0477', '0003-0007']
DOI: https://doi.org/10.1175/bams-d-20-0186.1