A Hybrid Framework for Drought Forecasting using Land Data Assimilation and Multivariate Copula Modeling
Abstract
Drought is the most costly hazard among all natural disasters. Despite the significant improvements in drought modeling over the last decade, accurate provisions of drought conditions in a timely manner is still one of the major research challenges. In order to improve drought forecasting skills, this study presents a hybrid system with a combination of land data assimilation (DA) system based on particle filtering (PF) and a probabilistic drought forecasting model. One major novelty of this system is to seek the use of DA to quantify land initial condition uncertainty rather than relying entirely on the land surface model to generate single deterministic initial condition. The initial conditions at each forecast starting date are sampled from the probability distribution characterized by DA for forecast initialization. Therefore, seasonal drought forecasting products are generated using the updated initial conditions. To cope with the large computational demand, a modular parallel particle filtering modular framework (PPFF) is developed which allows a large ensemble size in PF applications. The implementation of the hybrid system is demonstrated over the Western US basins. Results from both synthetic and real case studies suggest that the DA system significantly improves the drought forecasting skills. Also, it is shown how sensitive the seasonal drought forecasting skill is to the initial conditions, which can better facilitate the state drought preparation and declaration.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2016
- Bibcode:
- 2016AGUFM.H23L..04Y
- Keywords:
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- 1807 Climate impacts;
- HYDROLOGYDE: 1812 Drought;
- HYDROLOGYDE: 1817 Extreme events;
- HYDROLOGYDE: 1833 Hydroclimatology;
- HYDROLOGY