PPT-JCSDA OSSEs for Geostationary

Hyperspectral IR and MW Sean PF Casey 12 Narges Shahroudi 12 Jack Woollen 3 Sid Boukabara 2 Kayo Ide 1 Ross Hoffman 4 and Robert Atlas 5 1 ESSICUMD 2 NOAANESDISSTARJCSDA . 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. S. . Akella. , A. da Silva, C. Draper, R. . Errico. , D. . Holdaway. , R. . Mahajan. , N. . Prive. , B. Putman. , . R. . . Riechle. , M. Sienkiewicz, M. Suarez, R. . Todling. , . R.Gelaro. Ongoing. Major contributions to GSI development. Hyperspectral. -IR and MW. Sean PF Casey. 12. , . Narges. Shahroudi. 12. , Jack Woollen. 3. , Sid . Boukabara. 2. , Kayo Ide. 1. , Ross Hoffman. 4. , and Robert Atlas. 5. 1. ESSIC/UMD . 2. NOAA/NESDIS/STAR/JCSDA . Settler. What is this picture of ?. Learning Objectives. To know what a satellite is. To know some uses of artificial satellites. To understand the differences between polar and geostationary . orbits and where they are used. 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 . Jhoon Kim. 1. , M.J. Kim. 1. , K. J. Moon. 2. GEMS Science Team. 3. , GEMS Program Office. 2.  . 1 . Department of Atmospheric Sciences, . Yonsei. University. 2. National Institute of Environmental Research, Ministry of . Using . the Navy Coastal Ocean Model . (. NCOM) 4D-VAR. Matthew J. Carrier, Hans E. Ngodock, . Scott . R. . Smith, Innocent . Souopgui. , and Brent Bartels. U.S. Naval Research Laboratory. Stennis Space Ctr., MS. 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?. Benjamin T. Johnson (UCAR @ NOAA). Tong Zhu (CIRA @ NOAA). Yingtao. Ma (AER @ NOAA). Thomas . Auligné. (Director, JCSDA). With essential contributions . from many others.. CRTM Overview JCSDA Tech/.. 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?. . SYFTET. Göteborgs universitet ska skapa en modern, lättanvänd och . effektiv webbmiljö med fokus på användarnas förväntningar.. 1. ETT UNIVERSITET – EN GEMENSAM WEBB. Innehåll som är intressant för de prioriterade målgrupperna samlas på ett ställe till exempel:. JEDI Academy . - 10-13 June 2019. The Joint . Effort for Data assimilation Integration (JEDI). Objectives. Very brief introduction to object oriented and generic programming techniques. Concepts that are the most useful to work with JEDI. 1. Potential . OSSE for . Space. -based . Lidar. winds. . Michiko . Masutani . . A Nature Run (NR, proxy true atmosphere) is produced from a free forecast run using the highest resolution operational model which is significantly different from the NWP model used in Data Assimilation Systems.. Nikki . Privé. 1 June 2023. Image: . Lahoz. , W. and P. Schneider, Front. Environ. Sci., 2014, with permission from the author. Data assimilation combines observations and a prior forecast to generate a new initial condition (“analysis”).