Machine Learning for Postprocessing Ensemble Streamflow Forecasts
Skillful streamflow forecasts can inform decisions in various areas of water policy and management. We integrate numerical weather prediction ensembles, distributed hydrological model and machine learning to generate ensemble streamflow forecasts at medium-range lead times (1 - 7 days). We demonstrate a case study for machine learning applications in postprocessing ensemble streamflow forecasts in the Upper Susquehanna River basin in the eastern United States. Our results show that the machine learning postprocessor can improve streamflow forecasts relative to low complexity forecasts (e.g., climatological and temporal persistence) as well as standalone hydrometeorological modeling and neural network. The relative gain in forecast skill from postprocessor is generally higher at medium-range timescales compared to shorter lead times; high flows compared to low-moderate flows, and warm-season compared to cool ones. Overall, our results highlight the benefits of machine learning in many aspects for improving both the skill and reliability of streamflow forecasts.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningManagementMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison
Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations. However, only few recent studies have focused on ensemble postprocessing of wind gust fo…
BIG-bench Machine Learningquantile regressionregressionPostprocessing of Ensemble Weather Forecasts Using Permutation-invariant Neural Networks
Statistical postprocessing is used to translate ensembles of raw numerical weather forecasts into reliable probabilistic forecast distributions. In this study, we examine the use of permutation-invariant neural networks …
Ensemble quantile-based deep learning framework for streamflow and flood prediction in Australian catchments
In recent years, climate extremes such as floods have created significant environmental and economic hazards for Australia. Deep learning methods have been promising for predicting extreme climate events; however, large …
Deep LearningPredictionquantile regressionTime Series+1Distributional Regression U-Nets for the Postprocessing of Precipitation Ensemble Forecasts
Accurate precipitation forecasts have a high socio-economic value due to their role in decision-making in various fields such as transport networks and farming. We propose a global statistical postprocessing method for g…
Decision Makingquantile regressionregressionscoring ruleSTIPP: Space-time in situ postprocessing over the French Alps using proper scoring rules
We propose Space-time in situ postprocessing (STIPP), a machine learning model that generates spatio-temporally consistent weather forecasts for a network of station locations. Gridded forecasts from classical numerical …