charles e. skupniewicz 1 torsten duffy 1 douglas l. westphal 2 cynthia a. curtis 2 ming liu 2
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Charles E. Skupniewicz1 Torsten Duffy1
Douglas L. Westphal2Cynthia A. Curtis2
Ming Liu2
1 Operations Department, Fleet Numerical Meteorology and Oceanography Center Monterey, California, USA2 Marine Meteorology Division, Naval Research Laboratory Monterey, California, USA
Fleet Numerical Meteorology & Oceanography Center
FNMOC Operational Aerosol Modeling and Derived Products
23WAP/19NWPJune 2009
Fleet Numerical…Supercomputing Excellence for Fleet Safety and Warfighter Decision Superiority…
Fleet Numerical…Supercomputing Excellence for Fleet Safety and Warfighter Decision Superiority…
2
GlobalGlobalModelModel
Aerosol Models
MesoscaleModels
EnsembleModels
Ocean Models
Tropical Cyclone Forecasts
Optimum TrackShip Routing
Automated High Seas / Wind Warnings
Ballistic Wind Computations
Electro-Optical Forecasts
Aircraft RoutingOcean Acoustic
Forecasting
Long-Range Planning
Ice Forecasts
Target WeaponSystems
Visibility/DustForecasts
WRIP
CEEMS
Search and Rescue
FNMOC Models and Applications
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Chinese Dust and Korean Smoke, 8 April, 2000Impact of Aerosol Plumes on Navy Activities
Korea
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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
Navy Aerosol Modeling: Different Goals / Different Approaches
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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
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NAAPS: Navy Aerosol Analysis and Prediction System
Purpose: Forecasts aerosol concentrationsStatus: Operational, 4X dayInput: NOGAPS, dust source DB, FLAMBE (smoke),
MODIS Aerosol Optical Depth (AOD)Species: Dust, Smoke, Sulfate, SO2, Sea salt
Units: Mass concentrationHorizontal resolution: 1 degree, 360 X 180 gridVertical resolution: 20 m, 200 m inc. to 2 km, 1 km inc. to 16 kmOutput 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.
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6 May, 2003
FLAMBE: Fire Locating and Modeling of Burning Emissions
Purpose: Determine real-time smoke fluxesInput: GOES, MODISOutput: 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.
MODIS Fires, 3 May ,2003 Smoke Flux, 3 May, 2003
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Version Area Data sources Status NAAPS Global USGS FY99NAAPS Global USGS, TOMS AI, and surface wx reports FY00
DSD v0.1 East Asia USGS, maps, reports, and sfc. wx. reports FY04DSD v1.1 East Asia DSD including DEP 4Q FY09
DSD v1.2 SW Asia DSD including DEP FY03DSD 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
Dust Source Database (DSD)
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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.
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Data Assimilation Methodology
Next step: 4D-VAR
r =.83r =.69
NA
AP
S
MODIS MODIS
NAAPS AOD (no assimilation)
NAAPS AOD(w/ assimilation)
1) Convert NAAPS massconcentration 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)
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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.
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Opt
ical
Dep
th →
Current Real-Time Verification of NAAPS
Sede Boker, Israel, February 13 – March 4, 2007
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FAROP: Forecast of Aerosol Radiative and Optical Properties
Purpose: Calculates Optical PropertiesStatus: Operational, 4X dayInput: NOGAPS, NAAPS
Physics Extinction: Mass extinction efficiencies
with RH effects for sulfate, smoke, and saltScattering: Mass scattering efficiencies Asymmetry parameter: Measurements and theoryPhase function: Heney-Greenstein function Optical depth: Vertical integral of extinctionSlant path range: Contrast transmittanceOutput3D: 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)
0 3 6 9 12 15 18 21 24 271000
900
500
200
0.000
0.004
0.008
0.012
Extinction (1/km)
Forecast Time Pressure (mb)
1 .06 µm Extinction
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NAAPS Forecast Example
14
February 2007 Optical Depth
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Surface Visibility Example
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MCSST Screening with NAAPS
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Tactical Mission Support
Thermal Crossover Times / Polarity(for multiple targets)
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)
Uses realistictarget models andbackgrounds
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MODIS DEP 0634 UTC 10 October, 2001
COAMPS 31-h forecast of dust mass load (µg m-2)
0700 UTC 10 October, 2001
DSD allows prediction of individual plumes
Regional Model (COAMPS) Dust Example
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FNMOC Operational Aerosol Modeling and Derived Products
Questions?