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The overview of the NCEP Air Quality Forecasting (AQF) and the global dust and aerosol model. Ho-Chun Huang, Jeff McQueen, Pius Lee, Marina Tsidulko, Youhua Tang, Sarah Lu, Shrinivas Moorthi, Mark Iredell, Geoff DiMego, Steve Lord, and Paula Davidson 1 NOAA/NWS/NCEP/EMC 1 NOAA/ARL. Outline.
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The overview of the NCEP Air Quality Forecasting (AQF) and the global dust and aerosol model Ho-Chun Huang, Jeff McQueen, Pius Lee, Marina Tsidulko, Youhua Tang, Sarah Lu, Shrinivas Moorthi, Mark Iredell, Geoff DiMego, Steve Lord, and Paula Davidson1 NOAA/NWS/NCEP/EMC 1NOAA/ARL
Outline • Quick overview of AQF • Operational status • Future development
NCEP Air Quality Forecasting System (AQF) • WRF-NMM • NCEP Weather Research and Forecasting Mesoscale Model Non-hydrostatic Mesoscale Model • CMAQ • US EPA Community Multiscale Air Quality modeling system
GFS MET Fields WRF-NMM CMAQ CHEM Fields AQ users
NCEP Global Forecasting System (GFS) • Spectral triangular 382 (approximately 35 km resolution) • Hybrid σ-p coordinate, 64 layers from surface to ~ 0.2 mb • Spectral computation for horizontal discretization, finite difference for vertical discretization with conservations of momentum, mass, potential temperature, and total energy. • Leapfrog time scheme with semi-implicit in linear forcing. • Spectral higher order horizontal diffusion. • Strong wind damping in linear spectral forcing. • Navy Research Lab ozone physics algorithm (production and destruction are parameterized from monthly and zonal mean dataset derived from NRL 2D ozone chemistry model) • Non-local boundary layer diffusion scheme (Hong and Pan, 1996) • Noah Land Surface Model • Simplified Arakawa and Schubert scheme for convective cumulus parameterization • SBUV/2 assimilation for stratospheric ozone (above 250 mb). • 66 min wall-clock time for 7.5 days T382L64 simulation using 13 nodes (32 CPU per node) • 3D VAR Global Data Assimilation System (GDAS) http://www.emc.ncep.noaa.gov/gmb/moorthi/gam.html http://wwwt.emc.ncep.noaa.gov/gmb/gdas
WRF Non-hydrostatic Mesoscale Model • Hybrid sigma-pressure coordinate. • The grid staggering is the Arakawa E-grid. • The model uses a forward-backward scheme for horizontally propagating fast waves, implicit scheme for vertically propagating sound waves, Adams-Bashforth scheme for horizontal advection, and Crank-Nicholson scheme for vertical advection. • RRTM radiation or GFDL longwave radiation,MM5 short wave • Noah Land Surface Model • Mellor-Yamada-Janjic PBL scheme • Kain-Fritsch cumulus scheme • Ferrier micro-physics scheme • 12 km 60 NMM hybrid sigma - pressure levels • June 2007: Changes to landuse & roughness to address moist biases in Pac NW (http://www.dtcenter.org/wrf-nmm/users/docs/user_guide/WPS)
Air Quality Forecast System • CONUS (ozone) became operational model on September 18, 2007 • Developmental model; operational* + PM Chemistry • CMAQ v4.5 driven by the WRF/NMM at 12 km • NEI (2001), BEIS3, Mobile 6 • AERO3: Aerosol Module with SOA (no sea salt) • Updated ISORROPIA for numerical stability at low relative humidity • Euler Backward Iterative (EBI) Solver for CB4 • Asymmetric Convective Model (ACM-2) PBL parameterizations (http://www.cmascenter.org/index.cfm)
Operational Requirements • Driven by NCEP Operational Meteorological Model ( WRF/NMM) • I/O Formats: • Only machine binary, GRIB and BUFR, disk space limitations • Time Requirement: • 12 Z 48 hour forecast available by 17:25 Z (1:25 pm EDT) • 06 Z 48 hour forecast available by 13:00 Z ( 9 am EDT) • 65 IBM Power 4+ processors available • 12 Z start after WRF/NMM is complete (14:30 Z) • Robustness: • Thoroughly tested & evaluated with retrospective and real-time experimental runs • Available to NWS Gateway, NDGD: 99% reliability, 24x7 NCEP support • Accuracy: 90% exceedence hit rate
NAM GFS-O3 CMAQ AQM-PRDGEN PREMAQ SMOKE MOBLE5 IC & BC Convert NetCDF to Grib Verification Tools Data flow of AQFS
00 0612 18 00 06 12 18 00 06 12 18 00 06 12 18 CMAQ Forecast Products to users 6h 48h 48h 6h 6h Time (Z) 48h 48h 6h 6h Meteorology (WRF/NMM, Premaq): 6 hr cycling
Expanded domain 04/01/08 Current NAM modeling domain
Operational domain – Eastern US (3X) 12km 259X269 grids
Operational domain – Continental US (CONUS 5X) 12km; 442X265 grids
Proposed O-CONUS Domains Alaska 2.5X Hawaii 0.5X
EMC AQ Products • Communications with the AQ community and most important the general public
1h Ozone Bias EMC AQFWeb Products Daily Max of 8h Ozone http://www.emc.ncep.noaa.gov/mmb/aq
1hr, 24hr average hourly & Max PM & Profiles EMC AQFWeb Products
Verification • Continuous verification, especially in real-time, is important to AQF performance. • It allows one to discover the weakness of the model and implement the solution. • It identifies the episodes for detail analyses of the performance changes of AQF.
Daily 8hr max Ozone BiasesOp vs Exp over Eastern U.S. 8/1/07 7/1/07 Summer 2006 Summer 2007 • Both Op and Experimental Runs improved in 2007 • For Operational run, NAM improvements partially responsible
(2) (1) • Type • Daily Average Time series of a Month • Monthly Average of Score by Threshold • Daily Averages of Score by Threshold • Score • CSI – Threat Score • Equitable Threat Score • BIAS • Probability of Detection • False Alarm Ratio • Forecast Accuracy Rate (3)
NAM –RTMA: T2m Mid-Atlantic 8h Max Performance July 9, 2007 NAM is slightly cooler than observed in PA and W. NJ Continued overprediction along CT coastal regions
Mid-Atlantic 8 h Max Performance July 10, 2007 Very Similar performance between operational and experimental
NAM 3h Precipitation Prediction Precipitation Analysis Mid-Atlantic 8 h Max Performance July 10, 2007 Convective precipitation started earlier than predicted in Mid-Atlantic
8h Max Ozone Regional PerformanceAugust 7, 2007 Over-prediction in both runs due to cloud cover timing
Cloud already move in on 15Z Cloud cover most of the area earlier than the model prediction 15z 18z 21z
Future Development of AQF • EMC models under ESMF framework • Provide PM forecast • Real-time fire events • dust • CBM5 chemical mechanism • Provide Alaska and Hawaii forecast • Global dust and aerosol modeling to provide lateral boundary conditions for PM forecasting
Why real-time wild fire data is important to PM forecast skill Here is the evidence
CBM4 CBM5 CBM5 generally produce higher ozone, could be up to 10 ppb
CBM4 2005 EMIS CBM5 2005 EMIS Courtesy of Rohit et al. (NOAA, RTP NC)
CHEM+PM LBCs CHEM+PM Fields GOCART CHEM+PM LBCs PM Fields GFS MET Fields WRF-NMM CMAQ CHEM Fields AQ users
GFS-GOCART Offline System • GFS • NCEP/EMC Global Forecast System • GOCART • NASA Goddard Global Ozone Chemistry Aerosol Radiation and Transport Model • Steps • (1) dust modeling • (2) aerosol modeling
GSI 1 The aerosol modeling will be added later
GFS-GOCART Simulations • Dust (5 size bins) • 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
GFS-GOCART T62 and T126 Results • T62 : 1.93°x1.93°; ~210km; 192x94 grids • T126 : 0.95°x0.95°; ~105km; 384x190 grids • Simulation from 12/03/2007 to present • Initial Condition • cold start began at 12/03/2007 • DU2 : 1.0 - 1.8 µm • Surface layer and ~ 700mb • Daily Average • Periods • 12/25/07 to 01/06/08 • 02/26/08 to 03/xx/08
Dust Storm Event • 時間:2007/12/31 下午 04:59:01 • 主旨:沙塵遠離,環保署解除沙塵警告 • 內容:依據環保署空氣品 質監測結果,本波東亞沙塵自29日下午7時起隨著大陸冷氣團影響我國,造成空氣中懸浮微粒濃度上升,空氣品質轉差。12月31日下午沙塵已逐漸遠離,但中 南部空氣品質仍差,……30日各地最高濃度如北部萬里測站懸浮微粒濃 度(小時值)為每立方公尺219微克(14時);東部宜蘭站為200微克(16時);中部沙鹿站達232微克(12時);雲嘉南朴子站達471微克(16 時),台南安南站達433微克(18時);林園站達302微克(22時)。 ……. Source : 行政院環境保護署網站 http://taqm.epa.gov.tw/emc
T62 T126
Dust Storm Events 時間:2008/3/4上午 11:00:07 主旨:沙塵影響說明 內容:中國北方自2月 29日起持續出現沙塵暴天氣現象,隨著大陸高壓的移動向東移動,部分沙塵隨東北季風影響到台灣,影響最顯著的地區為我國的外島,金門測站小時濃度每立方公 尺高達302微克(3日9時),馬祖測站也達239微克(3日10時),澎湖測站達163微克(3日16時),約為背景值4-6倍。預計本波沙塵自今日入 夜後影響開始減弱,4日後可望脫離沙塵的影響。 本波沙塵於今(3)日影響最大,自上午11時起台灣西部由北向南懸浮微粒濃度開始上升,北部萬里測站最高小時濃度為253微克(3日 15時);陽明測站最高小時濃度為152微克(3日12時);宜蘭測站為208微克(3日16時),約為背景值4-6倍。全台懸浮微粒濃度超過200微克 測站達43個。目前沙塵開始向南影響到我國中南部地區,在原本較高背景濃度條件下,台南市的安南測站最高小時濃度已達355微克,空氣品質達不良等級。環 保署仍持續監控沙塵對我國空氣品質的影響情形。 Source : 行政院環境保護署網站 http://taqm.epa.gov.tw/emc
Surface T62 T126 700mb
Future Development • Verification with observations • AERONET, MODIS, GOES, OMI • Lidar, CALIPSO, OMI-profile