HEPEX-SIP Topic: Post-processing (3/3)
Contributed by Nathalie Voisin, Jan Verkade and Maria-Helena Ramos
So, what are the current challenges and research needs in post-processing?
At the HEPEX meetings and workshops, several challenges related to the use of statistical post-processors in hydrological ensemble prediction were identified:
- How to select suitable / best predictors to make an efficient use of prior knowledge and information available at the moment of forecasting?
- ‘Stationarity is dead; whither postprocessing?’ How can existing postprocessors adapt their modeling approach to non-stationarities in hydro-climatic data and hydrological processes?
- How can postprocessors contribute to improve the probabilistic forecasting of hydrological extremes? How to calibrate and assess performance for extreme events?
- How can postprocessing techniques be combined with data assimilation and preprocessing without compromising each other benefits? How to guide users to the best bias correcting strategies to implement in operational systems?
- How to generate plausible bias corrected ensemble traces to be use in water resources applications? How to evaluate them with a focus on users and their decision making contexts, i.e., how to evaluate bias correction methods with comparable skill according to their capacity to provide useful information in decision-making?
- How to prevent postprocessing to become black-boxes in operational forecasting systems? How to involve operational forecasters in their conception and validation?
- How to evaluate existing approaches on the basis of objective and comparable calibration/validation settings? How to go beyond validation under particular settings or catchments? How to promote intercomparison studies for multiple applications (e.g., short- to medium-range forecasting, monthly reservoir operations) and using multiple verification metrics to better assess postprocessors’ strengths and limitations?
- How can users know which approach might better suit their specific applications and decision-making problems? How to find a balance among different crucial issues in the implementation of postprocessors in operational hydrologic ensemble forecast systems (complexity of the model, parsimony in model parameterization across lead times and catchments, data requirements, good performance)?
What else do you think should be considered?
Part 1: What is hydrologic post processing? (https://hepex.org.au/hepex-sip-topic-post-processing-13/)
Part 2: Literature review on post processing (https://hepex.org.au/hepex-sip-topic-post-processing-23/)