From Random Forests to Flood Forecasts: A Research to Operations Success Story

Abstract

Excessive rainfall is difficult to forecast, and there is a need for tools to aid Weather Prediction Center (WPC) forecasters when generating Excessive Rainfall Outlooks (EROs), which are issued for the contiguous United States at lead times of 1–3 days. To address this need, a probabilistic forecast system for excessive rainfall, known as the Colorado State University-Machine Learning Probabilities (CSU-MLP) system, was developed based on ensemble reforecasts, precipitation observations, and machine learning algorithms, specifically random forests. The CSU-MLP forecasts were designed to emulate the EROs, with the goal being a tool that forecasters can use as a “first guess” in the ERO forecast process. Resulting from close collaboration between CSU and WPC and evaluation at the Flash Flood and Intense Rainfall experiment, iterative improvements were made to the forecast system and it was transitioned into operational use at WPC. Quantitative evaluation shows that the CSU-MLP forecasts are skillful and reliable, and they are now being used as a part of the WPC forecast process. This project represents an example of a successful research-to-operations transition, and highlights the potential for machine learning and other post-processing techniques to improve operational predictions.

Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science

“…Artificial intelligence (AI) and machine learning (ML) have recently exploded in popularity for a wide variety of environmental science applications (e.g., McGovern et al, 2019; Reichstein et al, 2019; Gagne et al, 2020; Gensini et al, 2021; Hill and Schumacher, 2021; Lagerquist et al, 2021; Schumacher et al, 2021). Like other fields, environmental scientists are seeking to use AI/ML to build a linkage from raw data, such as satellite imagery and climate models, to actionable decisions.…”

A Method for Monthly Extreme Precipitation Forecasting with Physical Explanations

“…[9]. The model has successfully emerged as an alternative forecasting method in some fields and obtained excellent results, such as daily and monthly rainfall prediction [10,11], flood prediction [12], and monthly EP indices prediction [13]. Herman et al [14] explored the RF algorithm to forecast short-term EP in America, and found that the RF-based prediction was quite reliable.…”

Kratkoročna prognoza vidljivosti određena metodom slučajne šume

“…The advantages of this method can also be used, for example, to predict ground characteristics such as soil temperature or greenhouse water content (Tsai et al, 2020). From a purely meteorological perspective, it can be used to forecast complex processes and influential phenomena such as flood forecasting (Schumacher et al, 2021) or extensive rainfall (Hill and Schumacher, 2021). However, both of the aforementioned works have also applied this method to NWP outputs.…”