PPT-Non-Gaussian Based Data Assimilation

Lecture 2 Applications Steven J Fletcher Cooperative Institute for Research in the Atmosphere Colorado State University Overview of Lecture Do we linearize the Bayesian problem or do we find the Bayesian Problem for the linear increment. Jason Sippel. . - NCEP EMC. . / IMSG. (. work completed at NASA GSFC). . Fuqing. Zhang, . Yonghui. . Weng. – Penn State. Scott Braun – NASA GSFC. Dan Cecil – NASA MSFC. NASA unmanned GH recently used in GRIP and HS3 experiments. 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. 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. Benjamin Ruston. Steve Swadley, Nancy Baker, . Rolf . Langland. NRL, . Monterey, . CA. Outline. Gather information about instrument: . What are previous sensors or instruments, how have they been used?. 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 . Statistical . Sig. nificance of . Clust. ers. in . HDLSS. Data. When is a cluster “really there”?. Liu et al (2007), Huang et al (2014). Simple Gaussian Example. Clearly only 1 Cluster in this Example. Compiled by Henrik . Vedel. Center for Meteorological Models (CMM). Danish Meteorological Institute. NWP is an” initial . value. problem”. . We. . need. a NWP model . field. to start . an NWP model simulation from. 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 . Lecture . 2: Applications. Steven J. Fletcher. Cooperative Institute for Research in the Atmosphere. Colorado State University. Overview of Lecture. Do we linearize the Bayesian problem or do we find the Bayesian Problem for the linear increment?. 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. Workshop. Evaluating a Satellite in Assimilation. Benjamin Ruston. Steve Swadley, Nancy Baker, . Rolf . Langland, Bill Campbell and Elizabeth Satterfield. NRL, . Monterey, . CA. | . 2. U.S. Naval Research Laboratory.