Status of the Regional OSSE for Space-Based LIDAR Winds – July 2002

1. Status of the Regional OSSE for Space-Based LIDAR Winds – July 2002 FSL ETL NCAR

2. Collaborators • NOAA/ETL • Lidar Simulation/ Data • Atmospheric propagation • NOAA/FSL • Regional Nature Run • Data assimilation • Assessment • NCAR • Quality assessment of model results • Validation • NOAA/NWS/NCEP • Global model background grids • Advice

3. Goals of Regional OSSE • Compare to the NCEP global lidar OSSE. Regional OSSEs should benefit from lateral boundary conditions obtained from the global model that has incorporated lidar data. • Assess impact of space-borne lidar winds on regional (mesoscale) weather forecasts. Mesoscale models can resolve local forcing from surface conditions. • Demonstrate advantages of assimilation over shorter time intervals than for global OSSE. • Incorporate high-frequency, high-density observations that a global OSSE might ignore.

4. Nature runs are the source for simulated lidar and conventional observations from a regional model. These observations are assimilated into an independent regional model. Global models provide lateral boundary conditions for the regional models.

5. Regional OSSE Methodology • Perform the regional nature run (MM5) using the global nature run (ECMWF) for lateral boundary conditions. • Extract a suite of simulated observations from the MM5. • Extract lidar line-of-sight winds from satellite scanning positions in 4D space from MM5 including representativeness error and instrument error. • Conduct tests with the mesoscale data assimilation system (RUC) with and without the lidar data, and with various combinations of boundary conditions from the MRF model. • Evaluate the separate effects of the simulated lidar data and boundary conditions on regional weather forecasts.

6. Flow of Data between Models MM5 Simulated Observations ECMWF Background MM5 Nature Run Standard Data ACARS Data LIDAR Data RUC Forecast Fields RUC Data Assimilation Validation Results MRF boundary & initial conditions

7. FSL MM5 REGIONAL NATURE RUN • Model Physics • Schultz microphysics • Kain-Fritsch cumulus parameterization • Burk-Thompson planetary boundary layer • RRTM radiation formulation • RUC Land Surface Model • Model Grid and Domain • 10-km horizontal grid spacing (740 x 520) • 43 sigma levels (model top at 20 km with level spacing ~800 m) • Most of North America (includes RUC domain) • Model Datasets • Initial and boundary data taken from ECMWF nature run • No soil moisture was available in ECMWF data provided by NCEP • Soil moisture initialized with climatology (related to USGS landuse) • Hourly MM5 output (235 GB total) using 36 processors on FSL Jet computer

8. ECMWF Nature Run Relative HumidityInitial Analysis – 21 Feb 1993

9. MM5 Nature Run Relative Humidity10 day Forecast – 21 Feb 1993 RUC RH

10. ECMWF Nature Run 5-km Temperature Analysis – 21 Feb 1993

11. MM5 Regional Nature RunTemperature 10-day forecast – 21 Feb 1993

12. MM5 - ECMWF Nature Run Comparisons:Forecast areal-averaged MSLP Trajectories Forecast MSLP trajectories diverge, particularly for days 9 – 11. However, this drift is understandable, as shown next…

13. ECMWF MSLP 00Z 22 Feb 93 MM5 MSLP 00Z 22 Feb 93 MM5 – ECMWF MSLP Drift in forecast MSLP is concentrated near the eastern seaboard and is synoptic-scaleThe drift results from phase displacements of the cyclone center and downstream ridge.

14. There is no significant drift in precipitable water.Drying occurs in the MM5 relative humidity field at 850 hPa. This RH drift is due to warming relative to ECMWF nature run. In summary: neither model stands out as “better” than the other. A period of < 24 h is sufficient to “spin up” cloud hydrometeors to realistic values in the MM5 nature run. Cloud liquid water and ice (both important for lidar simulations) exhibit realistic spatial structures (not shown here).

15. Assimilation Timeline ECMWF Nature Run: 5 February - 7 March 1993 MM5 Nature Run: 11 - 22 February MM5 Obs Extraction, RUC Assim & Forecasts Verification Feb 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

16. Generation of a Simulated Observation from the MM5 Nature Run involves: • Interpolation from the MM5 grid to the appropriate time and location of the measurement assuming 2001 distribution of conventional data (e.g., VAD, ACARS, Profiler) instead of the year for the nature run (1993) • A forward model to compute the observed quantity from the model variables if not explicitly carried in the model (e.g., lidar LOS radial winds) • Specification of error characteristics appropriate for the obs

17. Std OBS Metadata Satellite Track Info ACARS Track Info Simulated Random Errors in Observations Extractor Standard OBS Extractor LIDAR OBS Simulator ACARS Simulator Simulated Random Errors MM5 Nature Run Hourly Output STD OBS Data Files ACARS Data Files LIDAR Data Files

18. REGIONAL LIDAR ASSIMILATION • Use 40-km version of operational RUC20 with 3h assimilation cycle • Assimilate extracted conventional observations plus idealized lidar observations • Boundary conditions (BC) from NCEP MRF model • Experimental design: • BC only (no lidar or conventional obs) • BC + conventional obs • Ideal lidar, no clouds, BC + conventional obs • Ideal lidar in cloudy atmosphere, BC + conventional obs • Opaque water clouds • Opaque water clouds + ice clouds

19. RUC 151 X 113 gridpoint 40-km domain and topography

20. Cycled variables in RUC assimilation cycle • u, v wind components • Virtual potential temperature (v) • Water vapor mixing ratio (qv) • Pressure • Height (diagnostically determined) • Cloud, land-surface variables • Control variables in 3DVAR •  - stream function •  - velocity potential • Zunb - unbalanced height • v • ln (qv )

21. Observation Errors (sO) and their Ratio to Background Errors (sO / sB) used in RUC Data Assimilation

22. MM5 RH RUC 550 hPa Relative Humidity48h Forecast - 21 February 1993

23. Instrument model Direct detection (molecular) Doppler lidar employing a Fabry Perot interferometer (“fringe imaging system”) Low satellite orbit (450 km) as proposed by industry Lidar characteristics: 20-W transmitter at 355 nm, 1-m telescope, 5-sec averaging Simulated cloud effects use forecast ice and liquid water mixing ratios. Assume water clouds are opaque. For ice clouds, we employ a relation of ice water content to backscatter and extinction coefficients Assume an “ideal” lidar for this initial study, so as to duplicate the strategy of global OSSE run at NCEP Each circular scan is interrupted by 8 “stares” per 1-sec period, consisting of 5 shots averaged over 35 km path Dual-look scanning of the same atmospheric volume

24. Ice cloud backscatter and extinction

25. LIDAR FORWARD MODEL GEOMETRY (schematic only, actual model includes earth curvature) Lidar LOS wind components: 3DVAR analyzes horizontal wind: Neglect vertical velocity in vr :

26. Nadir 30º / 930440303

27. Nadir 30º / 930441448

28. 8-POINT SCAN Nadir Angle: 30° Azimuth Angles: 55.05°, 117.67°, 17.83°, 154.88°, -25.12°, -162.17°, -62.34°, -124.95° 0 1 -0.5 2 -1 5 3 7 4 -1.5 3 8 Latitude [degrees] 1 6 -2 4 2 5 -2.5 6 -3 7 8 -3.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 Longitude [degrees]

29. 0 -2 -4 -6 -8 Latitude [degrees] -10 -12 -14 -16 -18 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 Longitude [degrees]

30. Remaining Tasks • Continuous assimilation and forecasts for test period - compare RUC to MM5 nature run • Comparison of LIDAR, no-LIDAR runs with control run, including precipitation impacts • Final Report due 30 September 2002