PPT-Mean Field Variational Bayesian Data Assimilation

EGU 2012 Vienna Michail Vrettas 1 Dan Cornford 1 Manfred Opper 2 1 NCRG Computer Science Aston University UK 2 Technical University of Berlin Germany Why do data assimilation Aim of data assimilation is to estimate the posterior distribution of the state of a dynamical model X given observations Y. 1. Min-Jeong Kim. JCSDA 9th Workshop on Satellite Data Assimilation, May 24-25, 2011, M-J. Kim. 2. Fuzhong Weng, . 3. Emily Liu, . 4. Will McCarty, . 3. Yanqiu Zhu, . 3. John Derber, and . 3. Andrew Collard. Michele . Rienecker. Global Modeling and Assimilation Office. NASA/GSFC. WMO CAS Workshop. Sub-seasonal to Seasonal Prediction. Met Office, Exeter. 1 to 3 December 2010 . 2. What do we mean by “coupled data assimilation”?. BigData. Jay Gu. Feb 7 2013. MapReduce. Homework 1 Review. Logistic Regression. Linear separable case, how many solutions?. Suppose . wx. = 0 is the decision boundary,. (a * w)x = 0 will have the same boundary, but more compact level set.. . CRF Inference Problem. CRF over variables: . CRF distribution:. MAP inference:. MPM (maximum posterior . marginals. ) inference:. Other notation. Unnormalized. distribution. Variational. distribution. the Upper Troposphere - Lower Stratosphere. K. . Wargan, . S. . Pawson. , . M. . Olsen, . J. . Witte, . A. . Douglass. Global Modeling and Assimilation Office (GMAO). Chemistry and Dynamics Branch. NASA GSFC. Lecture 1: Theory. Steven J. Fletcher. Cooperative Institute for Research in the Atmosphere. Colorado State University. Overview of Lecture. Motivation. Evidence for non-Gaussian . Behaviour. Distributions and Descriptive Statistics . . Autoencoders. Theory and Extensions. Xiao Yang. Deep learning Journal Club. March 29. Variational. Inference. Use a simple distribution to approximate a complex distribution. Variational. parameter:. Inference. Dave Moore, UC Berkeley. Advances in Approximate Bayesian Inference, NIPS 2016. Parameter Symmetries. . Model. Symmetry. Matrix factorization. Orthogonal. transforms. Variational. . a. Kevin Garrett. 1,2,3. , Sid Boukabara. 1,2. , . and Erin Jones. 1,2,3. 1. NOAA/NESDIS/STAR. 2. Joint Center for Satellite Data Assimilation. 3.. Riverside Technology, Inc.. Preparation for GPM GMI . June 5-7, 2013, NCWCP, College Park, MD. Utility of . GOES. -R . ABI . and GLM instruments in . regional . data assimilation . for . high-. impact weather. Milija Zupanski. Cooperative . Institute for Research in the Atmosphere. . VIIRS Snow . and . Ice Product Provisional Maturity Review November 14, 2013. Mike . Ek. , . Jiarui. Dong . and EMC Land Team. NOAA/NCEP/EMC. •. Unified Noah LSM in all NCEP NWP and climate systems, plus in NLDAS/GLDAS.. Polly Smith, Alison . Fowler & Amos . Lawless. School of Mathematical and Physical . Sciences, . University of . Reading, UK.. Introduction (1). Typically, . initial conditions for coupled atmosphere-ocean model forecasts . Mihail. Codrescu. 1. , Stefan Codrescu. 1,2. , . Mariangel. Fedrizzi. 1,2. , and Claudia Borries. 3. 1. Space Weather Prediction Center, Boulder, United States of America (. mihail.codrescu@noaa.gov. the 2018 Hurricane Season. Maria . Aristizabal. Scott Glenn. Travis Miles . Benjamin . LaCour. Pat Hogan . MTS Oceans Meeting . 2019. Roy Watlington. Doug Wilson (OCOVI). Improve the intensity forecast of the operational hurricane models.