1. The Infrastructure, Design and Applications of Observing System Simulation Experiments at NASA's Global Modeling and Assimilation Office By Ronald M. Errico (GMAO and GEST) Runhua Yang (GMAO and SSAI ) Acknowledgements: Meta Sienkiewicz, Emily Liu, Ricardo Todling, Ronald Gelaro, Joanna Joiner, Tong Zhu, Quansheng Liu, and Michele Rienecker

2. Time Analysis Real Evolving Atmosphere, with imperfect observations. Truth unknown Climate simulation, with simulated imperfect “observations.” Truth known. Observing System Simulation Experiment Data Assimilation of Real Data

3. Design of an Observation System Simulation Experiment Capability at the GMAO Goals: 1.Be able to estimate the effect of proposed instruments on analysis and forecast skill by “flying” them in a simulated environment. 2. Be able to evaluate present and proposed data assimilation techniques in a simulation where “truth” is known perfectly. 1.A self-consistent and realistic simulation of nature. One such data set has been provided to the community by ECMWF through NCEP. 2.Simulation of all presently-utilized observations, derived from the “nature run” and having simulated instrument plus representativeness errors characteristic of real observations. 3.A validated baseline assimilation of the simulated data that, for various relevant statistics, produces values similar to corresponding ones in a real DAS. Requirements:

4. Standard Deviation of the analysis increment for the u-wind in the former NCEP/ECMWF OSSE T170L42 resolution Feb. 1993 obs network

5. Quickly generate a prototype baseline set of simulated observations that is significantly “more realistic” than the set of baseline observations used for the previous NCEP/ECMWF OSSE. Immediate Goal Account for: Resources are somewhat limited The Nature Run may be unrealistic in some important ways Some issues are not very important compared to others Some important issues may still have many unknown aspects

6. New ECMWF Nature Run 1.13-month “forecast” starting 10 May 2005 2.Use analyzed SST as lower boundary condition 3.Operational model from 2006 4.T511L91 reduced linear Gaussian grid (approx 35km) 5.3 hourly output

7. Approximations and Simplifications 1.Partial thinning of radiance obs to reduce computational demand 2.Simple treatment of clouds as elevated black bodies for IR 3.No use of surface-affected MW channels over land or ice 4.Similar radiative transfer model used to simulate and assimilate 5.Locations for all “conventional” obs given by corres. real obs a. locations of “significant levels” not based on sim. soundings b. locations of CTW not based on sim. cloud cover 6. Un-biased Gaussian noise added to all observations 7. No radiance bias correction

8. Evaluation for Jan. 2006, Spin-up starts 1 Dec. 2005 Data assimilation system: NCEP/GMAO GSI (3DVAR), 6-hour periods Resolution of DAS: 2 deg lat, 2.5 deg lon, 72 levels, top at 1 Pa Conventional Obs include: raobs, aircraft, ships, vad winds, wind profilers, sfc stations, SSMI and Qkscat sfc winds, sat winds (Approx # used 1.4 M/day) Radiance Obs include: HIRS2, HIRS3, AMSUA, AMSUB, AIRS, MSU (Approx # used = 3 M/day) Experiments

9. Simulating cloud effects on IR radiances

10. a bc

11. Add Random Errors 1.Explicit random errors are drawn from a normal distribution having mean 0 and variance 0.65 R, where R is the sum of the instrument plus representativeness errors found in the GSI observation error tables. 2.No horizontal correlations of error, but for RAOBs or other “conventional” soundings, errors are vertically correlated. 3.Other implicit “errors” are present due to treatments of clouds or surface emissivity and to interpolations in space and time.

12. Standard deviations of analysis increments u field, 500 mb OSSE Real

13. mean values of analysis increments u field, 500 mb OSSE Real

14. Real OSSE Surface pressure 0.320 0.252 Temperature 2.45 1.28 Vector wind 1.11 0.79 Specific humidity 1.33 1.26 Surface wind speed 1.18 1.18 Radiance 0.259.344 January mean of Jo/n

15. Langland and Baker 2004 Gelaro et al 2007, G. and Zhu 2008 Errico 2007, Tremolet 2007

17. Adjoint-Derived Impact Estimates OSSEReal

18. NOAA-17 HIRS/3 Brightness Temperatures OSSE Real

19. Locations of Brightness Temperature accepted by the Quality-Control for NOAA-17 channel 7 HIRS-3 on 15 Jan 2006 at 0 UTC +/- 3hrs Real Data OSSE Data Ignore colors

20. Distribution of Innovations (O-F) of Brightness Temperature accepted by the Quality-Control for NOAA-17 channel 7 HIRS-3 on 15 Jan 2006 at 0 UTC +/- 3hrs Real Data OSSE Data Ignore colors

21. Locations of Brightness Temperature accepted by the Quality-Control for NOAA-17 channel 1 AMSU-A on 15 Jan 2006 at 0 UTC +/- 3hrs Real Data OSSE Data Ignore colors

22. Distribution of Innovations (O-F) of Brightness Temperature accepted by the Quality-Control for NOAA-17 channel 1 AMSU-A on 15 Jan 2006 at 0 UTC +/- 3hrs Real Data OSSE Data Ignore colors

23. What is next? 1.Finish examination of latest experiment 2.Work on improving obs simulations a. raob soundings b. MW surface emissivity c. CTW locations d. error correlations 3. Look at a wind LIDAR instrument 4.Add aerosols to the NR data (Arlindo Da Silva) 5.Improving and generalizing the software Latest version of obs. sim. software available by FTP Latest sim. obs. data available by FTP

24. End of talk