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. . Radar Data Assimilation for 0-12 hour severe weather forecasting. Juanzhen. Sun . National Center for Atmospheric Research. Boulder, Colorado. sunj@ucar.edu. Outline. . Background. - . Motivation . P. Lewis. What is Data Assimilation?. Optimal merging of models and data. Models. Expression of current understanding about process. E.g. terrestrial C model. Data. Observations. E.g. EO. . Some basic stats. data assimilation. and forecast error statistics. Ross Bannister, 11. th. July 2011. University of Reading, r.n.bannister@reading.ac.uk. “All models are wrong …” . (George Box). “All models are wrong and all observations are inaccurate”. 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.. on. Bayesian. . Techniques. for. . Inference. Asensio Ramos. Instituto de Astrofísica de Canarias. Outline. General . introduction. . . The. . Bayesian. . approach. . to. . inference. . Examples. 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. Yudong. Gao. 1 observational data analysis. Pictures are from P. W. Chan et al. (2014). Left figure is the reconnaissance data from 9:06 to 9:59 UTC, which is thinning at 3km. . data from 12:00 to 12:12 UTC. The direction of wind was changing as the flight direction.. 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 . 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 . A comparison of hybrid variational data assimilation methods in the Met Office global NWP system Andrew Lorenc 11 th Adjoint Workshop, Aveiro Portugal, July 2018 www.metoffice.gov.uk © Crown Copyright 2018, Met Office 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. in the NCEP Global Forecast System. 1. Ting-Chi Wu. 1. , Lewis Grasso. 1. , . Milija. Zupanski. 1. , Heather Cronk. 1. , . James Fluke. 1. , Richard Schulte. 2. , Wesley Berg. 2. , Anton Kliewer. 1. Jeremy Berman. University at Albany. Thursday 03 May 2018. ATM 419/563. Outline. Data Assimilation (DA). At the end, you should know:. What is it?. How it works?. Why is it important?. Basic DA techniques. Tom Hamill. NOAA Earth System Research Lab, Physical Sciences Division, Boulder CO USA. tom.hamill@noaa.gov. +1 (303) 497-3060. Presentation to US-UK-Canada postprocessing group, 1 May 2019. 1. Problem statement.