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Observing System Simulation Experiments (OSSEs)

Observing System Simulation Experiments (OSSEs). Georg Grell (NOAA/FSL/CIRES, soon to be GSD within ESRL) With slides from Dezso Devenyi and Steve Weygandt. OSE, OSSE, which one was it?. OSE : Observing System Experiment

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Observing System Simulation Experiments (OSSEs)

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  1. Observing System Simulation Experiments (OSSEs) Georg Grell (NOAA/FSL/CIRES, soon to be GSD within ESRL) With slides from Dezso Devenyi and Steve Weygandt

  2. OSE, OSSE, which one was it? • OSE : Observing System Experiment • estimation and evaluation of an existing observing system and/or modeling system • OSSE : Observing System Simulation Experiment • Design new observing systems

  3. OSE: What do you do • Control run (atmosphere and its composition) over extended period • Same model and assimilation system, leave out or perturb certain observations • Statistical evaluation control/experiments

  4. OSE Use of data denial experiments, statistical methods • Effective data density estimation (effective degree of freedom of the signal) • Do the observing stations need to be distributed differently? Can resources be saved by taking some out? • Test of data assimilation system and forecast system • How well can the system use the data (how sensitive is it to the amount and quality of data, how good is the background error correlation scale, etc.)

  5. OSSE – Why? • Which data – or observing system - would have an impact on model forecast (with most bang for the buck) • What measurement accuracy may be required for “this” observing system • What accuracy may be required in forward model • Optimal design of observing system

  6. OSSE: What do you do • Nature run: produce high resolution reasonably realistic simulation of the “nature” (atmosphere and its composition) over extended period • Extract simulated observations from this nature run (also add observation errors) • Run data assimilation and forecast experiments

  7. OSSE Tools requirements for global-to-local: • Four models (must be different for nature runs and assimilation runs) • Two data assimilation systems • Observation generator • Verification system • Usually also calibration system for nature run

  8. Relationship between Global and Regional OSSEs Nature Run Assimilation Run Global Assimilation Run Global Nature Run Simulated Observations Global Boundary Conditions Boundary Conditions Regional–to-local Nature Run Regional-to-local Regional-to-local Assimilation Run Simulated Observations

  9. Why regional-to-local only? • Significantly simpler/cheaper • Different resolution • Different observing systems Problem: boundary conditions, somewhat dirty

  10. OSSE Tool requirements for regional-to-local: • Minimum of two models (must be different for nature run and assimilation run) • A data assimilation system • Observation generator • Verification system • Usually also calibration system for nature run

  11. OSSE – some problems • Models need to be different (no identical twins), but: how different can they be? Calibration? Climate of global/regional nature runs should be “about” the same. • Simulated observation and forward model errors should be as realistic as possible (danger to overestimate the impact) • Very tedious work

  12. OSSEs – some examples

  13. 15-20 Feb 1993 Simulated Data Verify against nature run • Does simulated-data impact (OSSE) • match real-data impact (OSE) • for an existing observation type? Compare real-data and simulated-data ACARS denial Regional OSSE Calibration 4-16 Feb 2001 Real Data Verify against raobs

  14. CNTLerror – EXPerrorCNTLerror Impact of denying ACARS obs on 6-h fcst vector wind RMSE Normalize Errors NEGATIVE VALUE  % degradation POSITIVE VALUE  % improvement % degradation ACARS denial yields similar % degradation for real-data and OSSE simulated-data

  15. What is the upper bound on lidar obs • impact for regional models? • Assume lidar obs contain no errors • Assume no loss of lidar obs due to clouds • (lidar shots provide full coverage from 2 to 17 km) Idealized lidar obs experiments

  16. Assimilation of lidar observations(but no lidar obs in boundary conditions) • ~5% improvement in 6-h forecast from lidar obs • Positive impact greater for non-raob init times • Positive impact decreases with forecast length Non-raob init time (06z,18z) % improvement Raob init time (00z,12z)

  17. Assimilation of lidar observations+ lidar obs in boundary conditions • > 6% improvement for all forecast times • Positive impact greater for non-raob init times • Contributions from lidar assim and LBC nearly additive Total lidar impact (assim + BC) on 500 mb fcst vector wind RMSE % improvement 500 mb vector wind RMSE from lidar obs in assimilation and boundary conditions Raob init time (00z,12z) Non-raob init time (06z,18z) % improvement

  18. IDEAS? • What data assimilation system should we use? • What models should we use? • What data should we test? (must be observable and should be likely to have impact) • Minimum hope must exist that observing system sooner or later should be financed

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