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Charles E. Skupniewicz 1 Torsten Duffy 1 Douglas L. Westphal 2 Cynthia A. Curtis 2 Ming Liu 2

Fleet Numerical Meteorology & Oceanography Center F NMOC Operational Aerosol Modeling and Derived Products 23WAP/19NWP June 2009. Charles E. Skupniewicz 1 Torsten Duffy 1 Douglas L. Westphal 2 Cynthia A. Curtis 2 Ming Liu 2

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Charles E. Skupniewicz 1 Torsten Duffy 1 Douglas L. Westphal 2 Cynthia A. Curtis 2 Ming Liu 2

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  1. Fleet Numerical Meteorology & Oceanography Center FNMOC Operational Aerosol Modeling and Derived Products 23WAP/19NWP June 2009 Charles E. Skupniewicz1 Torsten Duffy1 Douglas L. Westphal2 Cynthia A. Curtis2 Ming Liu2 1 Operations Department, Fleet Numerical Meteorology and Oceanography Center Monterey, California, USA 2 Marine Meteorology Division, Naval Research Laboratory Monterey, California, USA Fleet Numerical… Supercomputing Excellence for Fleet Safety and Warfighter Decision Superiority…

  2. FNMOC Models and Applications Ocean Acoustic Forecasting Aircraft Routing Automated High Seas / Wind Warnings Visibility/Dust Forecasts Aerosol Models Optimum Track Ship Routing Electro-Optical Forecasts Global Model Mesoscale Models Ocean Models Search and Rescue Target Weapon Systems Ice Forecasts CEEMS Ensemble Models Tropical Cyclone Forecasts Long-Range Planning WRIP Ballistic Wind Computations

  3. Impact of Aerosol Plumes on Navy Activities Chinese Dust and Korean Smoke, 8 April, 2000 Korea

  4. Navy Aerosol Modeling: Different Goals / Different Approaches • Climate Approach: Utilize first principles • Concerned with climate change and drift • Low-resolution weather • Theoretically based • Trace gasses, chemistry • Aerosol direct, indirect, and semi-direct effects • Produce monthly or seasonal averages of column integrated properties, e.g. AOD • Derive sensitivities • Navy Forecasting Approach: Pragmatic • Concerned with onset and cessation of events • High-resolution weather • More diagnostic and empirically based • Aerosol direct effects • Produce instantaneous forecasts of visibility • Surface-centric

  5. Navy Aerosol Forecasting Approach • - Predict events as weather phenomena emphasizing sources and transport • - Simulate aerosols that impact visibility: • dust • smoke • sea salt • sulfate • - Develop operational capability (practical) • - Utilize real-time data streams • - Use nested models to cover the large range of scales

  6. NAAPS: Navy Aerosol Analysis and Prediction System Purpose: Forecasts aerosol concentrations Status: Operational, 4X day Input: NOGAPS, dust source DB, FLAMBE (smoke), MODIS Aerosol Optical Depth (AOD) Species: Dust, Smoke, Sulfate, SO2, Sea salt Units: Mass concentration Horizontal resolution: 1 degree, 360 X 180 grid Vertical resolution: 20 m, 200 m inc. to 2 km, 1 km inc. to 16 km Output Filter: FAROP (Forecast of Aerosol Radiative and Optical Properties) Output: Visibility, AOD, extinction, scattering, asymmetry parameter, phase function, species partition for extinction Distribution: Ocean data analysis (SST), tactical decision aids, forecaster web products, customer download (GRIB) 2007, Witek, M. L., P. J. Flatau, P. K. Quinn, and D. L. Westphal, Global sea-salt modeling: Results and validation against multicampaign shipboard measurements, J. Geophys. Res., 112, D08215, doi:10.1029/2006JD007779.

  7. 6 May, 2003 Smoke Flux, 3 May, 2003 MODIS Fires, 3 May ,2003 FLAMBE: Fire Locating and Modeling of Burning Emissions Purpose: Determine real-time smoke fluxes Input: GOES, MODIS Output: Fire parameters: Location (lat, lon) Smoke flux, g m -2 s -1 Horizontal res.:GOES: 4 km; MODIS: 1 km Temporal res.: GOES: 30 min., MODIS: 2X Day Next step: use foreign geostationary satellites Fire detections for 2006092012 2004, Reid, J. S., E. M. Prins, D. L. Westphal, C. C. Schmidt, K. A. Richardson, S. A. Christopher, T. F. Eck, E. A. Reid, C. A. Curtis, and J. P. Hoffman: Real-time monitoring of South American smoke particle emissions and transport using a coupled remote sensing/box-model approach, Geophys. Res. Lett., 31, L06107, doi:10.1029/2003GL018845.

  8. Dust Source Database (DSD) Version Area Data sources Status NAAPS Global USGS FY99 NAAPS Global USGS, TOMS AI, and surface wx reports FY00 DSD v0.1 East Asia USGS, maps, reports, and sfc. wx. reports FY04 DSD v1.1 East Asia DSD including DEP 4Q FY09 DSD v1.2 SW Asia DSD including DEP FY03 DSD v1.2.8 SW Asia Updates based on field reports and DEP FY08 DSD v1.3 N. Africa DSD including DEP FY10 DSD v0.1 DSD v1.1

  9. NAVDAS-AOD: NRL Atmospheric Variational Data Assimilation System – Aerosol Optical Depth Purpose: Data assimilation for aerosol optical depth (3-d Var) Status: Operational 3Q09, 4x daily Input: NRL Level 3 MODIS Over-Ocean AOD (6-h data window) Next step: Over-land and CALIPSO Future input: NPP, NPOESS, AVHRR, MetOp, MSG, MTSAT, AATSR, GOES-R Output: Aerosol analysis and: 3-d distribution of four species error statistics Temporal resolution: 3 hourly Distribution: NAAPS and FAROP; web 2008, Zhang, J., J. S. Reid, D. L. Westphal, N. L. Baker, and E. J. Hyer, A system for operational aerosol optical depth data assimilation over global oceans, J. Geophys. Res., 113, doi:10.1029/2007JD009065.

  10. Data Assimilation Methodology 1) Convert NAAPS mass concentration to aerosol optical depth 2) Two-D variational assimilation of the optical depth field 3) Convert optical depth to NAAPS three-D mass concentration (ill-posed; simple conditional scaling scheme used) r =.83 r =.69 NAAPS MODIS MODIS Next step: 4D-VAR NAAPS AOD (no assimilation) NAAPS AOD (w/ assimilation)

  11. NAAPS Validation against AERONET • (a) AERONET versus NAAPS for 5-month (January –May 2006) NAAPS without data assimilation • (b) AERONET versus NAAPS for 5-month (January–May 2006) NAAPS run with AOD assimilation 2008, Zhang, J., J. S. Reid, D. L. Westphal, N. L. Baker, and E. J. Hyer, A system for operational aerosol optical depth data assimilation over global oceans, J. Geophys. Res., 113, doi:10.1029/2007JD009065.

  12. Current Real-Time Verification of NAAPS Sede Boker, Israel, February 13 – March 4, 2007 Optical Depth →

  13. 1 . 06 µm Extinction Extinction (1/km) 0.012 0.008 0.004 200 500 900 0.000 0 3 6 9 1000 12 15 18 21 24 27 Pressure (mb) Forecast Time FAROP: Forecast of Aerosol Radiative and Optical Properties Purpose: Calculates Optical Properties Status: Operational, 4X day Input: NOGAPS, NAAPS Physics Extinction: Mass extinction efficiencies with RH effects for sulfate, smoke, and salt Scattering: Mass scattering efficiencies Asymmetry parameter: Measurements and theory Phase function: Heney-Greenstein function Optical depth: Vertical integral of extinction Slant path range: Contrast transmittance Output 3D: visibility, extinction (km-1), scattering (km-1), asymmetry parameter, phase function, species partition for extinction on pressure/flight levels Column: AOD (visible) for each species Frequencies: 19 wavelengths, 5 bands in UV, Vis, NIR, MWR and IR Work in progress: performance surfaces - slant path visual range (nm)

  14. February 2007 Optical Depth NAAPS Forecast Example

  15. Surface Visibility Example

  16. MCSST Screening with NAAPS

  17. Tactical Mission Support • Extinction, scattering, asymmetry parameter, phase function, species partitioning used to calculate slant path transmissivity, as a function of • - Altitude /Sensor/Target • - Field Of View • - Probability of Detection Detection Ranges / Best Attack Axis ( FOVs) Thermal Crossover Times / Polarity (for multiple targets) Uses realistic target models and backgrounds

  18. Regional Model (COAMPS) Dust Example COAMPS 31-h forecast of dust mass load (µg m-2) 0700 UTC 10 October, 2001 MODIS DEP 0634 UTC 10 October, 2001 DSD allows prediction of individual plumes

  19. FNMOC Operational Aerosol Modeling and Derived Products Questions?

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