Abstract
Mesoscale weather forecasting cannot be improved until a better data assimilation is obtained. Four dimensional variational analysis (4DVAR) provides the most elegant framework for data assimilation. One of the most critical issues of applying 4DVAR to weather prediction is how efficiently these variational problems can be solved. We introduce the reduced Hessian SQP algorithm for these problems and obtain an adjoint reduced Hessian SQP method, which is quadratically convergent since the exact reduced Hessian is used.
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Xie, Y.F. (1997). Atmospheric Data Assimilation Based on the Reduced Hessian Successive Quadratic Programming Algorithm. In: Biegler, L.T., Coleman, T.F., Conn, A.R., Santosa, F.N. (eds) Large-Scale Optimization with Applications. The IMA Volumes in Mathematics and its Applications, vol 92. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1962-0_11
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DOI: https://doi.org/10.1007/978-1-4612-1962-0_11
Publisher Name: Springer, New York, NY
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