1 noaa/nws/ncep atmospheric constituent prediction capability – status, progress, and...
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NOAA/NWS/NCEP Atmospheric Constituent NOAA/NWS/NCEP Atmospheric Constituent Prediction Capability – Status, Progress, and Prediction Capability – Status, Progress, and
Observational RequirementsObservational Requirements
Ho-Chung Huang, Sarah Lu, Jeff McQueenand William Lapenta
NOAA/NWS/NCEP/EMC
Atmospheric Composition Forecasting Working Group: Aerosol ObservabilityApril 27-29, 2010, Monterey, CA
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OutlineOutline
– NCEP global and regional prediction systems
– Air quality prediction systems
– Data assimilation plans and requirements
– Summary
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Forecast Forecast UncertaintyUncertaintyForecast Forecast UncertaintyUncertainty
MinutesMinutes
HoursHours
DaysDays
1 Week1 Week
2 Week2 Week
MonthsMonths
SeasonsSeasons
YearsYears
NWS Seamless Suite of ForecastNWS Seamless Suite of ForecastProducts Spanning Weather and ClimateProducts Spanning Weather and Climate
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Warnings & Alert Warnings & Alert CoordinationCoordination
WatchesWatches
ForecastsForecasts
Threats Assessments
GuidanceGuidance
OutlookOutlook
Benefits
NCEP Model Perspective
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Fire
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Fire
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Avi
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Avi
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•North American Ensemble Forecast System
•Climate Forecast System
•Short-Range Ensemble Forecast
•Land Surface•Ocean•Waves•Tropical Cyclone
•Global Forecast System
•North American Mesoscale
•Rapid Update Cycle for Aviation
•Dispersion Models for DHS
-GFDL -HWRF
•Global Ensemble Forecast System
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Global Forecast System (GFS)
• 4 Cycles per day• T382(~35km) to 7.5 days• T190(~70km) to 16 days
RESOLUTION • T382 horizontal resolution (~ 37 km)• 64 vertical levels (from surface to 0.2 mb)
MODEL PHYSICS AND DYNAMICS• Vertical coordinate changed from sigma to
hybrid sigma-pressure• Non-local vertical diffusion• Simplified Arakawa-Schubert convection
scheme• RRTM longwave radiation• NCEP shortwave radiation scheme based on
MD Chou’s scheme• Explicit cloud microphysics• Noah LSM (4 soil layers: 10, 40, 100, 200 cm
depth)
INITIAL CONDITIONS (both atmosphere and land states)
• NCEP Global Data Assimilation System
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• Data Assimilation (Implemented 17 December 2009)– Assimilate:
• NOAA-19 AMSU-A/B, HIRS• RARS 1b data• NOAA-18 SBUV/2 and OMI
– Improved use of GPS RO observations• Refractivity forward operator • Allow more observations, in particular in the tropical latitudes, due to better QC
checks for COSMIC data• Better QC procedures Metop/GRAS, GRACE-A and CHAMP
• Modify GFS shallow/deep convection and PBL (17 June 2010)– Detrainment from all levels (deep convection)– Testing at low resolution shows reduction in high precipitation bias– PBL diffusion in inversion layers reduced (decrease erosion of marine stratus)
• GSI/GFS Resolution (17 June 2010)– Working towards T574 (~28km) & 64 L (Operational Parallel Running)– T190 (~70km) from 7.5 to 16 days
GSI 3D-VAR/GFS Plans for FY10GSI 3D-VAR/GFS Plans for FY10
NOTE: ECMWF at T1279 (~16km) with 91 levels
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• Modify GFS shallow/deep convection and PBL– Detrainment from all levels (deep convection)– PBL diffusion in inversion layers reduced (decrease erosion of marine stratus)
• GSI/GFS Resolution– T382 (~35km) to T574 (~28km) & 64L
GFS Plans for FY10GFS Plans for FY10 Scheduled June 2010Scheduled June 2010
Updated GFS physics package eliminates grid-point precipitation “bombs”
Observed Operational GFS Upgraded Physics GFS
24 h accumulated precip ending 12 UTC 14 July 2009
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NAM• NEMS based NMMNEMS based NMM• Bgrid replaces Egrid• Parent remains at 12 km• Multiple Nests Run to ~48hr
– ~4 km CONUS nest– ~6 km Alaska nest– ~3 km HI & PR nests– ~~1.5-2km DHS/FireWeather/IMET
possible
Rapid Refresh• WRF-based ARW• Use of GSI analysisUse of GSI analysis• Expanded 13 km Domain to
include Alaska• Experimental 3 km HRRR
RUC-13 CONUS domain
WRF-Rapid Refresh domain – 2010
Original CONUS domain
Experimental 3 km HRRR
NCEP Mesoscale Modeling for CONUS: NCEP Mesoscale Modeling for CONUS: Planned FY11Planned FY11
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Air Quality Prediction SystemsAir Quality Prediction Systems
Model Region ProductsSmoke
NAM-HYSPLIT
CONUS- 12 km
Alaska
Hawaii
Daily smoke forecasts
(06 UTC, 48 h )
NAM-CMAQ CONUS 12 km
Hawaii (Sept 2010)
Alaska (Sept 2010)
ozone forecasts 2x/day (06 & 12 UTC to 48h) from anthropogenic sources
NAM-HYSPLIT-CMAQ
CONUS 12 km dust & total fine particulate matter, under development
GFS-GOCART Dev Para Sept 2010
Off-line Global dust (1x1°)
Smoke under development
1x/day global dust (72h) for WMO & regional CMAQ LBC
NEMS/GFS
GOCARTDev Para Sept 2011
In-line interactive global aerosols
global with interactive aerosols
NEMS/NMMB-CMAQ
In-line interactive global/regional aerosols
regional AQ w/ aerosol impacts on radiation
Operational
Under Development
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Why Include Aerosols in the Why Include Aerosols in the Predictive Systems?Predictive Systems?
Provide improve weather and air quality guidance for forecasters and researchers
Fine particulate matter (PM2.5) is the leading contributor to premature deaths from poor air quality
Improved satellite radiance assimilation in the Community Radiative Transfer Model (CRTM) allowing realistic atmospheric constituents loading
Improve SST retrievals
Provide aerosol lateral boundary conditions for regional air quality forecasting systems, e.g., NAQFC.
Meet NWS and WMO global dust forecasting goals
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Global System:Global System: Gas and Aerosol Representation Gas and Aerosol Representation
and Data Assimilationand Data Assimilation• Ozone
– GFS ozone climatology w/ monthly production and loss– GSI with SBUV2 profile ozone (noaa-17, noaa-18) and OMI total
column ozone (aura)– Future observations for GSI includes:
• SBUV2 (noaa-19)• GOME-2 (METTOP)
• Aerosol– GFS with NASA/GOCART aerosol modules (in progress)– GSI with MODIS AOD (aqua, terra; in progress)– Future observations for GSI includes:
• OMI AI• Geostationary AOD (GOES-11, GOES-12)• MetoSAT-9, and MTSAT• GOME-2 OMI-like aerosol retrievals, AIRS, MLS, ABI (GOES-
R), VIIRS (NPOESS)
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Observations (MODIS, OMI and MISR) used to evaluate offline GFS-GOCART Sahara Dust Trans-Atlantic simulation With NCEP T126 resolution
Spatial Evaluation of Experimental Global Dust Forecasts
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-23.3W 14.2N, 2006072314
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Aerosol Extinction (1/km)
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MODEL
-22.8W 12.1N, 2006072314
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Aerosol Extinction (1/km)
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-23.3W 14.2N, 2006072314
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Aerosol Extinction (1/km)
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MODEL
-22.8W 12.1N, 2006072314
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Aerosol Extinction (1/km)
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-23.3W 14.2N, 2006072314
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Aerosol Extinction (1/km)
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-22.8W 12.1N, 2006072314
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Aerosol Extinction (1/km)
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B A
Evaluation of Vertical Distribution of Experimental Global Dust Forecasts
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Experimental Volcanic Ash Simulation Experimental Volcanic Ash Simulation From Eyjafjallajökull Volcano, IcelandFrom Eyjafjallajökull Volcano, Iceland
• Analysis made 14 April to 20 April 2010• GFS-GOCART offline system (in development)
– Driven by operational GFS meteorology (T382 scaled to 1˚x1˚)– Dust (5 size bins; in radius)
• DU1 : 0.1 – 1.0 µm• DU2 : 1.0 - 1.8 µm• DU3 : 1.8 – 3.0 µm• DU4 : 3.0 – 6.0 µm• DU5 : 6.0 – 10.0 µm
– Emissions:• 1x106 kg/hr in a 1˚x1˚ grid box at layer 24 (~ 5 km) for each dust bib size
• total emission is 5x106 kg/hr (continuous release)
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Forecasts initialized 00 UTC April 14 to April 21 Total column concentration Hourly average
Experimental Volcanic Ash Simulation Experimental Volcanic Ash Simulation From Eyjafjallajökull Volcano, IcelandFrom Eyjafjallajökull Volcano, Iceland
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Regional System:Regional System:Gas and Aerosol RepresentationGas and Aerosol Representation
and Data Assimilation:and Data Assimilation:• Ozone
– NCEP National Air Quality Forecasting Capability (NAQFC; offline operational with NAM Meteorology and CMAQ
– Verification, ground-level predictions: EPA in-situ monitoring– NAQFC NEMS/NMMB inline (in planning)– GSI for regional ozone (in planning)– Future observations for data assimilation include
• Total column ozone (GOES-11, GOES-12)• in-situ ozone concentration (USEPA/AIRNOW)
• Aerosol– NAQFC (offline in progress; NEMS/NMMB-NAQFC inline in planning)– Verification, developmental PM2.5 predictions: EPA in-situ monitoring– GSI for regional aerosol (in planning)– Future observations for data assimilation include:
• in-situ particulate matter concentration (USEPA/AIRNOW)• MODIS AOD (aqua, terra)• GOES AOD
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Aerosol Lateral Boundary Conditions Tests: Aerosol Lateral Boundary Conditions Tests: Trans-Atlantic dust TransportTrans-Atlantic dust Transport
During Texas Air Quality Study 2006, the model inter-comparison team found all 7 regional air quality models missed some high-PM events, due to trans-Atlantic Saharan dust storms.
These events are re-visited here, using dynamic lateral aerosol boundary conditions provided from dust-only off-line GFS-GOCART.
Corpus Christi - Nat, TX 2006 Observed
CO
NC
(ug/
m3)
60 50 40 30 20 10 0
Corpus Christi - Nat, TX 2006CMAQ base run
CO
NC
(ug/
m3)
60 50 40 30 20 10 0
Corpus Christi - Nat, TX 2006
CMAQ+GFS-GOCART LBC
CO
NC
(ug/
m3)
60 50 40 30 20 10 0
Thomas Jefferson Sch, TX 2006 Observed
CO
NC
(ug/
m3)
80 70 60 50 40 30 20 10 0
Thomas Jefferson Sch, TX 2006 Observed
CO
NC
(ug/
m3)
80 70 60 50 40 30 20 10 0
Thomas Jefferson Sch, TX 2006CMAQ base run
CO
NC
(ug/
m3)
80 70 60 50 40 30 20 10 0
Thomas Jefferson Sch, TX 2006
CMAQ+GFS-GOCART LBC
CO
NC
(ug/
m3)
80 70 60 50 40 30 20 10 0
Karnack C85, TX 2006 Observed
CO
NC
(ug/
m3)
60 50 40 30 20 10 0
29JUL 31JUL 02AUG 04AUG 06AUG 08AUG 10AUG
Karnack C85, TX 2006 Observed
CO
NC
(ug/
m3)
60 50 40 30 20 10 0
29JUL 31JUL 02AUG 04AUG 06AUG 08AUG 10AUG
Karnack C85, TX 2006CMAQ base run
CO
NC
(ug/
m3)
60 50 40 30 20 10 0
29JUL 31JUL 02AUG 04AUG 06AUG 08AUG 10AUG
Karnack C85, TX 2006
CMAQ+GFS-GOCART LBCC
ON
C (u
g/m
3)
60 50 40 30 20 10 0
29JUL 31JUL 02AUG 04AUG 06AUG 08AUG 10AUG
Youhua Tang and Ho-Chun Huang (EMC)
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Satellite Data AvailabilitySatellite Data Availability
• NCEP is receiving MODIS level 1 product and OMI AI in real time
• GOES column integrated AOD product is available (regional)
• Future potential data sources– OMI aerosol product and radiance– OMI-like aerosol retrievals produced by the GOME-2 – MODIS AOD similar products produced by the GOES-
R Advanced Baseline Imager (ABI)
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Challenges Associated with the Challenges Associated with the Operational Use of Satellite ProductsOperational Use of Satellite Products
– Requirements in operational environment• Bring observations into operational data stream (WMO BUFR
format)
• Shorter data delivery time
– Global coverage and higher temporal resolution (mixed orbital and geostationary constellation products)
– Need profile observations for speciated aerosols as well as ozone precursor species (NO, NO2, Hydrocarbon species).
– Forward model also needs global satellite product to improve model first guess, e.g., need near-real time global emissions derived from satellite observations (Fire emissions, Volcanic eruption)
– Critical information to improve and/or project near-real time global fire emissions in forward model simulation, e.g., injection height and fire intensity tendency
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SummarySummary
– NCEP commits to improve weather and air quality forecasts with atmospheric constituents data assimilations
– NCEP GSI is going to evolve from 3DVar to 4DVar
– Aerosol data assimilation is in development and ozone data assimilation continues to improve its DA with incorporated additional observations
– Satellite data are not only critical for data assimilation it is also important to improve forward model guess fields
– Near real-time satellite data flow is critical to operational data assimilations