Simulation of Observation Simulation of Conventional Observations Jack Woollen (NCEP/EMC) Considerations Data distribution depends on atmospheric conditions Cloud and Jet location, Surface orography, RAOB drift Precursor run with Conventional Data Yuanfu Xie (NOAA/ESRL) T62L64 or T126 L64 is used in the experiment for entire period for T511 NR using perfect observation without quality control. This will to test the OSSE system and provide initial condition for other OSSEs. Sat wind was included to provide reasonable fields for SH Radiation data are not included. Initial data will have no error added and quality control is not necessary.

1)Run OSSE assimilation of new conv sim obs from 1may z 2)Run free forecast from same IC 3)Compare fits of 1) and 2) to the nature run truth Global 850mb T fits to NR Free forecast OSSE conv obs After about a week things looked pretty good! Checkout New Conventional Simulated Observations

After two weeks it went sideways

What happened? Antarctica got very very cold. Day 13Day 15 Day 17 Day mb T (nr-osse) plotted !!50 deg differences!!

EC 1x1 T 80S section NC 1x1 T 80S section EC–NC T 80S section 80S topography Comparison of EC and NC interpolation of t from the model level data at latitude 80S showed a systematic difference under the ground Maybe the observations are incorrectly created from model level data?

Comparing EC(white) NC (green) and ML(red) profiles at 00E 80S shows NC near surface interpolation reproduces ML values better (for data simulation) than the EC method.

Resolution of the situation is in progress Additional developments include: Upgrade the system to use the latest GSI version. Model overeaction to cold surface data circumstantially caused by a moisture constraint parameter. Turning the constraint off avoids the problem. Need to examine the variational QC reaction to unrepresentative data. Higher resolution (t126 vs t62) also seems to avoid the problem.

For development purposes, 91-level ML variables are processed at NCEP and interpolated to observational locations with all the information need to simulate radiance data (OBS91L). OBS91L made for all foot prints of HIRS, AMSU, GOES are produced for a few weeks of the T799 period in October 2005 As well as for the month of May OBS91L are produced for all radiance foot prints assimilated in operational GDAS as recorded in the archived “radstat” files. The OBS91L are also available for development of a Radiative Transfer Model (RTM) for development of other forward model. OBS91L Nature Run Model level profiles for simulating radiance obs

Radiance Simulation System for OSSE GMAO, NESDIS, NCEP Tong Zhu, Haibing Sun, Fuzhong Weng (NOAA/NESDIS) Jack Woollen(NOAA/NCEP) Ron Errico, Runhua Yang, Emily Liu, Lars Peter Riishojgaard, Ravi Govindaraju (NASA/GSFC/GMAO) Existing instruments experiments must be simulated for control and calibration and development of DAS and RTM Test GOESR,NPOESS, and other future satellite data Other resources and/or advisors David Groff, Paul Van Delst (NCEP) Yong Han, Walter Wolf, Cris Bernet,, Mark Liu, M.-J. Kim, Tom Kleespies, (NESDIS) Erik Andersson (ECMWF); Roger Saunders (Met Office) OBS91L is produced by Jack Woollen at NCEP NESDIS and NCEP are working on thinned data to perform precursor run for entire period. NASA/GMAO is developing best strategies to simulate and work on more complete foot prints. This include development of cloud clearing algorithm. Full resolution data for GOES for cloudy radiance are also produced by Tong Zhu.

Simulation of GOES-R ABI radiances for OSSE Tong Zhu et al. : 5GOESR P1.31 at AMS annual meeting Simulated from T511 NR. GOES data will be simulated to investigate its data impact