PPT-Hybrid Variational/Ensemble Data Assimilation

for the NCEP GFS Tom Hamill for Jeff Whitaker NOAA Earth System Research Lab Boulder CO USA jeffreyswhitakernoaagov Daryl Kleist Dave Parrish and John Derber National Centers for Environmental Prediction Camp Springs MD USA. . 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”. 4D-Ensemble-Var – a development path for data assimilation at the Met Office. Probabilistic Approaches to Data Assimilation for Earth Systems. , . BIRS, Banff, February 2013.. Andrew Lorenc. Outline of Talk. Andrew Collard, Daryl . Keist. , David Parrish, Ed Safford, Emily Liu, Manuel . Pondeca. , . Miodrag. . Rancic. , Lidia . Cucurull. , . Haixia. Liu, . XiuJuan. Su, Shun Liu, Wan-. Shu. Wu, Paul van . 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 . Applying data assimilation for rapid forecast updates in global weather models. Luke E. Madaus --- Greg Hakim; Cliff Mass. University of Washington. In Revision -- QJRMS. Outline. Brief introduction. 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). Kalman. filter. Part I: The Big Idea. Alison Fowler. Intensive course on advanced data-assimilation methods. 3-4. th. March 2016, University of Reading. Recap of problem we wish to solve. Given . prior knowledge . system: implementation and test for hurricane prediction. Xuguang Wang, . Xu. Lu, . Yongzuo. . Li, Ting Lei. University of Oklahoma, Norman, OK. In collaboration with . Mingjing. Tong , Vijay . Tallapragada. (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 . 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. 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.