PPT-A comparison of hybrid variational data assimilation methods in the Met Office

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 wwwmetofficegovuk Crown Copyright 2018 Met Office. . 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 . for the NCEP GFS. Tom Hamill, for . Jeff . Whitaker. NOAA Earth System Research Lab, Boulder, CO, USA. jeffrey.s.whitaker@noaa.gov. Daryl Kleist, Dave Parrish and John . Derber. National Centers for Environmental Prediction, Camp Springs, MD, USA. 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”. Brendan Griffin. Me.About. (). http. ://. aka.ms/fromthefield. @. brendankarl. . Senior Premier Field Engineer . @ Microsoft. Microsoft Certified Master: . SharePoint 2010. 10 years . experience with SharePoint. 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 . 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 . 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). 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. 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. 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.