PPT-Adjoint

models Examples ATM 569 Fovell Fall 2015 See course notes Chapter 16 1 Integrating the adjoint 2 1 Run the control model to time N and save C n every time step 2 Initialize . S. . Akella. , A. da Silva, C. Draper, R. . Errico. , D. . Holdaway. , R. . Mahajan. , N. . Prive. , B. Putman. , . R. . . Riechle. , M. Sienkiewicz, M. Suarez, R. . Todling. , . R.Gelaro. Ongoing. Major contributions to GSI development. by Debbie Konkowski (USNA). and. Tom . Helliwell. (HMC). MG12 July 12 -18, 2009. T.M. . Helliwell. and D.A. Konkowski,. . “. Quantum . healing of classical singularities in. power-law . spacetimes. Rolf Langland. Data Assimilation Section. Naval Research Laboratory. Monterey, CA . langland@nrlmry.navy.mil. Including . Material Provided by. Dr. Ronald M. Errico (NASA-UMBC). Santa . Fe, N.M., . 31 July . Conjugate Gradient . 1) CG is a numerical method to solve a linear system of equations . 2) CG is used when A is Symmetric and Positive definite matrix (SPD). 3) CG of . Hestenes. and . For Use in . Adjoint. Inverse Modeling. Kevin J. Wecht. 17 June 2010. TES Science Team Meeting. Special thanks to:. . Annmarie. . Eldering. Daniel Jacob Steve . Wofsy. Susan . Kulawik. Lin Zhang Eric . Local search algorithms benefit from derivatives even when they are calculated by finite differences. Often derivatives can be calculated at fraction of cost of finite-difference derivatives. Goal of today’s lecture is to show why this is usually true for static response. Continuous Problems. . Fr. é. chet. Derivatives. Syllabus. Lecture 01 Describing Inverse Problems. Lecture 02 Probability and Measurement Error, Part 1. Lecture 03 Probability and Measurement Error, Part 2 . in September. -October 2015: . A new . tool for fire management to reduce downwind smoke . exposure. Shannon. N. . Koplitz. Miriam . E. . Marlier. Jonathan . J. . . Buonocore. Patrick . S. . . Kim. Tianjia Liu. Conseil de laboratoire. Conseil scientifique. Comité de Direction. Equipe de direction. Directeur : Etienne . Ruellan. Directeur adjoint : Olivier . Ribolzi. Directeur adjoint : Sylvain . Bonvalot. Directeur adjoint : Olivier . . - inverse model . components (structure . / data flow. ). - building process (code versioning/sync. .). Model development. . - develop based on v11-02. . - get GCHP to run . backwards (could be trivial). Daniel Holdaway, . Yannick . Trémolet. , Anna . Shlyaeva. , Mark . Miesch. , . Stephen . Herbener. , Guillaume . Vernieres. , Rahul Mahajan and Jong Kim . Joint Center for Satellite Data Assimilation (JCSDA). Lecture 21 Continuous Problems Fr é chet Derivatives Syllabus Lecture 01 Describing Inverse Problems Lecture 02 Probability and Measurement Error, Part 1 Lecture 03 Probability and Measurement Error, Part 2 A cofactor matrix . C. of a matrix . A. is the square matrix of the same order as . A. in which each element a. ij. is replaced by its cofactor c. ij. . . Example:. If. The cofactor C of A is. Matrices - Operations. cyclone intensity . change. : Irma (2017). Michael C. Morgan and . Zhaoxiangrui. He. University of Wisconsin - Madison. Motivation. Three key questions regarding TC intensity:. What is the maximum intensity a TC may achieve in a given environment? .