Randomized Preconditioned Solvers for Strong Constraint 4D-Var Data Assimilation
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arXiv
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| Natura: | Preprint |
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2024
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| author | Subrahmanya, Amit N. Rao, Vishwas Saibaba, Arvind K. |
| author_facet | Subrahmanya, Amit N. Rao, Vishwas Saibaba, Arvind K. |
| contents | The Strong Constraint 4D Variational (SC-4DVAR) data assimilation method is widely used in climate and weather applications. SC-4DVAR involves solving a minimization problem to compute the maximum a posteriori estimate, which we tackle using the Gauss-Newton method. The computation of the descent direction is expensive since it involves the solution of a large-scale and potentially ill-conditioned linear system, solved using the preconditioned conjugate gradient (PCG) method. To address this cost, we efficiently construct scalable preconditioners using three different randomization techniques, which all rely on a certain low-rank structure involving the Gauss-Newton Hessian. The proposed techniques come with theoretical guarantees on the condition number, and at the same time, are amenable to parallelization. We also develop an adaptive approach to estimate the sketch size and choose between the reuse or recomputation of the preconditioner. We demonstrate the performance and effectiveness of our methodology on two representative model problems--the Burgers and barotropic vorticity equation--showing a drastic reduction in both the number of PCG iterations and the number of Gauss-Newton Hessian products after including the preconditioner construction cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15758 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Randomized Preconditioned Solvers for Strong Constraint 4D-Var Data Assimilation Subrahmanya, Amit N. Rao, Vishwas Saibaba, Arvind K. Numerical Analysis 35R30 (primary), 65F99 (secondary) The Strong Constraint 4D Variational (SC-4DVAR) data assimilation method is widely used in climate and weather applications. SC-4DVAR involves solving a minimization problem to compute the maximum a posteriori estimate, which we tackle using the Gauss-Newton method. The computation of the descent direction is expensive since it involves the solution of a large-scale and potentially ill-conditioned linear system, solved using the preconditioned conjugate gradient (PCG) method. To address this cost, we efficiently construct scalable preconditioners using three different randomization techniques, which all rely on a certain low-rank structure involving the Gauss-Newton Hessian. The proposed techniques come with theoretical guarantees on the condition number, and at the same time, are amenable to parallelization. We also develop an adaptive approach to estimate the sketch size and choose between the reuse or recomputation of the preconditioner. We demonstrate the performance and effectiveness of our methodology on two representative model problems--the Burgers and barotropic vorticity equation--showing a drastic reduction in both the number of PCG iterations and the number of Gauss-Newton Hessian products after including the preconditioner construction cost. |
| title | Randomized Preconditioned Solvers for Strong Constraint 4D-Var Data Assimilation |
| topic | Numerical Analysis 35R30 (primary), 65F99 (secondary) |
| url | https://arxiv.org/abs/2401.15758 |