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[Data Assimilation] L5: Optimal interpolation and variational (3D/4D) methods

The Imperial College London (UK) and INPE (National Institute for Space Research, Brazil) organized a short course on data assimilation in the period July 11th-15th, 2022. The course consists of two parts: theory/methods and applications, including 15 lectures. Lecture 1: What is data assimilation?    • [Data Assimilation] L1: What is data ...   Lecture 2: Nudging and backward-forward approach for data assimilation    • [Data Assimilation] L2: Nudging and b...   Lecture 3: From least squares to Kalman filter, particle filter, and beyond    • [Data Assimilation] L3: From least sq...   Lecture 4: Ensemble Kalman filter    • [Data Assimilation] L4: Ensemble Kalm...   Lecture 5: Optimal interpolation and variational (3D/4D) methods    • [Data Assimilation] L5: Optimal inter...   Lecture 6: Adjoint-free approach to 4D variational data assimilation    • [Data Assimilation] L6: Adjoint-free ...   Lecture 7: Hybrid methods: The best of ensemble Kalman filters and variational methods    • [Data Assimilation] L7: Hybrid method...   Lecture 8: On particle filters and particle flow filters and smoother: towards fully nonlinear data assimilation    • [Data Assimilation] L8: On particle f...   Lecture 9: Data assimilation by neural networks on ocean circulation model    • [Data Assimilation] L9: Data assimila...   Lecture 10: Data assimilation on space weather models    • [Data Assimilation] L10: Data assimil...   Lecture 11: Data assimilation in hydrology by neural network    • [Data Assimilation] L11: Data assimil...   Lecture 12: WRF atmospheric model and data assimilation by neural network    • [Data Assimilation] L12: WRF atmosphe...   Lecture 13: Data assimilation: big data and exascale computing    • [Data Assimilation] L13: Data assimil...   Lecture 14: Data learning: integrating data assimilation and machine learning – Applications to the COVID-19 pandemic    • [Data Assimilation] L14: Data learnin...   Lecture 15: Real-time predictive modelling machine learning and data assimilation in environmental problems    • [Data Assimilation] L15: Real-time pr...   World experts on data assimilation gave several talks on many techniques for data assimilation. Data assimilation for physics-based models representing several applications was discussed during the course, including meteorology, air pollution, oceanography, hydrology, and Covid-19 pandemic dynamics. Theoretical and practical challenges were addressed in the course, dealing with mathematical computer techniques, Big Data, and machine learning schemes, with green and exascale computing.