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- W3199896535 startingPage "147" @default.
- W3199896535 abstract "An agricultural drought forecasting system that leverages dynamical land surface modeling, statistical Bayesian multivariate analysis, satellite soil moisture data assimilation, and high-performance computing is proposed. The system is designed to generate deterministic and probabilistic drought forecasts at various lead-times including 1, 2, and 3 months. Since the land surface condition plays an important role in forecast skill, the drought forecasting model is reliant on soil moisture memory, and therefore is initialized at each forecast time using the particle filter (PF) data assimilation. The PF technique is utilized to assimilate satellite soil moisture observations into a land surface model. Results show that the use of updated initial conditions helps quantify the uncertainty of land surface states and improves drought prediction skill. By assessing the performance of the model in hindcasting two recent drought events in the United States, we show that the proposed model leads to earlier and more accurate drought detection. Additionally, probabilistic forecasts are beneficial for strategic planning, risk management, and decision-making." @default.
- W3199896535 created "2021-09-27" @default.
- W3199896535 creator A5012028982 @default.
- W3199896535 creator A5037342105 @default.
- W3199896535 creator A5045624160 @default.
- W3199896535 creator A5090638636 @default.
- W3199896535 date "2021-08-04" @default.
- W3199896535 modified "2023-09-23" @default.
- W3199896535 title "A Probabilistic Framework for Agricultural Drought Forecasting Using the Ensemble Data Assimilation and Bayesian Multivariate Modeling" @default.
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