PPT-Model Adjoint Sensitivity Impacts

1 2 Operational ECMWF system September to December 2008 Averaged over all model layers and entire global atmosphere contribution of different observations to reduction in forecast error Courtesy Carla Cardinali. 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 . (The 10. th. . Adjoint. Workshop). Roanoke. , West Virginia. June . 1. -5, . 2015. The Use of Ensemble-Based Sensitivity with Observations to Improve Predictability of Severe Convective Events. Brian . 1. Sensitivity Analysis. Basic theory. Understanding optimum solution. Sensitivity analysis. Summer 2013. LP: Sensitivity Analysis. 2. Introduction to Sensitivity Analysis. Sensitivity analysis . means determining effects of changes in parameters on the solution. It is also called What if analysis, Parametric analysis, Post optimality analysis, etc,. It is not restricted to LP problems. Here is an example using Data Table.. 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. with . HDDM. Sensitivity . Analysis . Chris Emery, Tasko Olevski, Ralph Morris. CMAS: Fine Scale Modeling and Applications. October 25, 2016. Investigate ozone sensitivity to single source emissions. A tour through models, interpretations, analogies, and laws . Gil Kalai. Einstein Institute of Mathematics. Hebrew University of Jerusalem. ICM 2018, beautiful Rio. Outline: two puzzles, four parts, six theorems, eight models. 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 . . - 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). Fovell. Fall 2018. (See course notes, Chapter 16). 1. Motivation. We often look at a forecast and wonder “where did this come from” and “how could this be changed”, especially if it involves model error. Daniel Holdaway, . Yannick . Trémolet. , Anna . Shlyaeva. , Mark . Miesch. , . Stephen . Herbener. , Guillaume . Vernieres. , Rahul Mahajan and Jong Kim . Joint Center for Satellite Data Assimilation (JCSDA). 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. 2. nd. Adjoint. Patrice VERON. 1. er. Adjoint. Conseillers municipaux. Conseil Municipal de Chatillon La Borde. Sylvie. BACH. J-Christophe. BOURBON. Bruce. GUYON. Elise. DELMOTTE. P-Louis. DELMOTTE. 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? .