PPBES Welcome to the Matrix - Environmental Modeling Center
NOAA/NWS/NCEP Atmospheric Constituent Prediction Capability Status, Progress, and Observational Requirements Ho-Chung Huang, Sarah Lu, Jeff McQueen and William Lapenta NOAA/NWS/NCEP/EMC Atmospheric Composition Forecasting Working Group: Aerosol Observability April 27-29, 2010, Monterey, CA 1 Outline NCEP global and regional prediction systems Air quality prediction systems Data assimilation plans and requirements Summary 2 NWS Seamless Suite of Forecast Products Spanning Weather and Climate NCEP Model Perspective Years Outlook Seasons Months Minutes -HWRF Dispersion Models for DHS
me nt iro n En v th He al on tro l Ag ri c u lt u re Re cre at i on Ec os yst em er ir C ow rvo rop
Re se nn in g Hy d Pla En erg y en cy Mg mt Co mm erc e Em erg rat ion s eW eat he r
Fir pe me Sp ace O Ma riti Av iat io n Benefits ert y Warnings & Alert Coordination rop Watches North American Ensemble Forecast System Global Ensemble Forecast System 1 Week Global Forecast System Land Surface Ocean
Days Short-Range Ensemble Forecast Waves North American Mesoscale Tropical Cyclone Hours -GFDL Rapid Update Cycle for Aviation &P Forecasts Climate Forecast System 2 Week Lif e Threats Assessments Forecast Lead Time Guidance Forecast Uncertainty 3 Global Forecast System (GFS) 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 Chous 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 4 Cycles per day T382(~35km) to 7.5 days T190(~70km) to 16 days 4 GSI 3D-VAR/GFS Plans for FY10 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 NOTE: ECMWF at T1279 (~16km) with 91 levels 5 GFS Plans for FY10 Scheduled June 2010 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 24 h accumulated precip ending 12 UTC 14 July 2009 Observed Operational GFS Upgraded Physics GFS
Updated GFS physics package eliminates grid-point precipitation bombs 6 NCEP Mesoscale Modeling for CONUS: Planned FY11 NAM NEMS 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 analysis Expanded 13 km Domain to include Alaska Experimental 3 km HRRR WRF-Rapid Refresh domain 2010 RUC-13 CONUS domain Original CONUS domain Experimental 3 km HRRR 7 Air Quality Prediction Systems Model Operational Region Products NAM-HYSPLIT CONUS ~20 km Alaska Hawaii Daily smoke forecasts (06 UTC, 48 h ) NAM-CMAQ CONUS 12 km Alaska
Hawaii ozone & PM2.5 forecasts 2x/ day (06 & 12 UTC to 48h) from anthropogenic sources smoke under development GFS-GOCART Off-line Global dust (1x1) Smoke under development 1x/day global dust (72h) for WMO & regional CMAQ LBC In-line interactive global aerosols global with interactive aerosols In-line interactive global/regional aerosols regional AQ w/ aerosol impacts on radiation Dev Para Sept 2010 NEMS/GFS Under Development GOCART Dev Para Sept 2011 NEMS/NMMBCMAQ
8 Why Include Aerosols in the Predictive Systems? Provide improve weather and air quality guidance for forecasters and researchers 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 9 Global System: Gas and Aerosol Representation and 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) 10 Spatial Evaluation of Experimental Global Dust Forecasts Observations (MODIS, OMI and MISR) used to evaluate offline GFS-GOCART Sahara Dust Trans-Atlantic simulation With NCEP T126 resolution 11 Evaluation of Vertical Distribution of Experimental Global Dust Forecasts CALIPSO BA A B B A -22.8W 12.1N, 2006072314 CALIPSO 8 -23.3W 14.2N, 2006072314
0.25 0.3 0 0.05 0.1 0.15 0.2 0.25 0.3 Aerosol Extinction (1/km ) 12 Experimental Volcanic Ash Simulation From Eyjafjallajkull Volcano, Iceland Analysis made 14 April to 20 April 2010 GFS-GOCART offline system (in development) Driven by operational GFS meteorology (T382 scaled to 1x1) Dust (5 size bins; in radius) DU1 : 0.1 1.0 m DU2 : 1.0 - 1.8 m DU3 : 1.8 3.0 m DU4 : 3.0 6.0 m
DU5 : 6.0 10.0 m Emissions: 1x106 kg/hr in a 1x1 grid box at layer 24 (~ 5 km) for each dust bib size total emission is 5x106 kg/hr (continuous release) 13 Experimental Volcanic Ash Simulation From Eyjafjallajkull Volcano, Iceland Forecast initialized 00 UTC April 14 to April 21 Total column concentration Hourly average 14 Regional System: Gas and Aerosol Representation and Data Assimilation: Ozone NCEP National Air Quality Forecasting System (NAQFC; offline operational with NAM Meteorology and CMAQ NAQFC NEMS/NMMB inline (in planning) GSI for regional ozone (in planning) Future observations 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) GSI for regional aerosol (in planning) Future observations include:
in-situ particular matters concentration (USEPS/AIRNOW) MODIS AOD (aqua, terra) GOES AOD 15 Aerosol Lateral Boundary Conditions Tests: Trans-Atlantic dust Transport Corpus Christi - Nat, TX 2006 Observed CMAQ base run CMAQ+GFS-GOCART LBC Thomas Jefferson Sch, TX 2006 Observed CMAQ base run CMAQ+GFS-GOCART LBC Karnack C85, TX 2006 Observed CMAQ base run CMAQ+GFS-GOCART LBC During Texas Air Quality Study 2006, the model inter-comparison team found all 7 regional air quality models missed some highPM 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 GFSGOCART. CONC (ug/m3)
06AUG 08AUG 10AUG Youhua Tang and Ho-Chun Huang (EMC) 16 Satellite 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 GOESR Advanced Baseline Imager (ABI) 17 Challenges Associated with the Operational 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 18 Summary 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 19
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