1 / 57

Evaluation of real-time air quality and weather predictions using WRF-Chem

Evaluation of real-time air quality and weather predictions using WRF-Chem. Steven Peckham Georg Grell, Stu McKeen, Greg Frost and Jim Wilczak NOAA/Earth Systems Research Laboratory. Motivation. NEAQS-ITCT 2004. NEAQS-ITCT 2004 AIRNow O 3 monitors (~340) AIRNow PM2.5 monitors (~120)

kelii
Télécharger la présentation

Evaluation of real-time air quality and weather predictions using WRF-Chem

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Evaluation of real-time air quality and weather predictions using WRF-Chem Steven Peckham Georg Grell, Stu McKeen, Greg Frost and Jim Wilczak NOAA/Earth Systems Research Laboratory

  2. Motivation

  3. NEAQS-ITCT 2004 NEAQS-ITCT 2004 • AIRNow O3 monitors (~340) • AIRNow PM2.5 monitors (~120) • Speciation Trends Network (72 monitors) • NOAA P3 Aircraft • NOAA DC-3 Airborne Ozone Lidar • NOAA Research Vessel Ronald H. Brown RV Ronald H. Brown

  4. NEAQS-ITCT 2004 • Regional Air Quality research • Surface AQ with above surface transport + chemistry • Local pollution vs long-range transport impacts on local AQ 2004 surface network Pink: NOAA wind profilers Yellow: Other wind profilers Red: Chemistry sites Triangles: Vertical lidars.

  5. NEAQS-ITCT 2004 • Radiation balance • Aerosol impact upon radiation • CCN size distribution impact on cloud droplets

  6. Real-time WRF-Chem

  7. 2004 Model Domain • Real-time AQ forecasts • 12-km horizontal grid spacing

  8. Real-time AQ forecasts • 27-km horizontal grid spacing 2004 Model Domain

  9. Current Real time forecasts40-km domain

  10. Real time WRF-Chem settings • Table 1. Model configuration options and parameterizations used in the WRF-Chem real-time air quality forecasts using 27-km horizontal grid spacing. • Advection scheme 5th horizontal /3rd vertical • Microphysics NCEP 3-class simple ice • Longwave radiation RRTM • Shortwave radiation Dudhia • Surface layer Monin-Obukhov (Janjic Eta) • Land-surface model RUC_LSM (Smirnova et al. 1997) • Boundary layer scheme Mellor-Yamada-Janjic 2.5 TKE • Cumulus parameterization Grell and Devenyi, 2002 • Chemistry option RADM2 (Stockwell et al. 1990) • Dry deposition Weseley, 1989 • Biogenic emissions Gunther, 1994 and Simpson, 1995 (v. 1.3) BEIS 3.11 (v 2.03) • Anthropogenic Emissions NEI 1999 v 3.0 • Photolysis option Madronich, 1987 • Aerosol option MADE/SORGAM

  11. Biogenic emissions

  12. Real-time WRF-Chem • Visualization • RIP • IDL • ncview • Product Distribution • Web pages • FX-Net application (distributed by NOAA/ESRL)

  13. Real time WRF-Chem • Web pages - RIP • Surface fields • MP2.5 • Surface winds • Also plots of: • Ozone, CO, etc. • Precipitation, standard pressure levels

  14. Real time WRF-Chem • Web pages – NEAQS04 • Many standard meteorological fields • Some chemical fields

  15. Real time WRF-Chem • FX-Net application • Current surface observations • Ozone • Particulate matter • METAR data

  16. Real time WRF-Chem • FX-Net applications • Surface plot • WRF-Chem PM2.5 • PM2.5 observations

  17. Real-time WRF-Chem • Statistical Analysis

  18. WRF-Chem and Observed Time Series at Thompson Farm, NH

  19. CHRONOS AURAMS CMAQ/ETA-3X CMAQ/ETA-1x WRF/CHM-27km WRF/CHEM-12km BAMS -45km BAMS-15km STEM-2K3 Models Used in the ICARTT/NEAQS-2004 Evaluations Anthropogenic Emission Inventory: Model: AURAMS- 42km CHRONOS- 21km Canadian National Inv. (1990, 1995) CMAQ/ETA(1x) - 12km CMAQ/ETA(3x) - 12km(*) BAMS - 45km BAMS - 15km NEI-99, 2001, grown to 2004 WRF/CHEM-1 - 27km WRF/CHEM-2 - 27km WRF/CHEM - 12km(*) STEM(2K3) - 12 km NET-96 NEI-99 Red indicates PM2.5 forecasts available (*) Indicates a retrospective run

  20. PM2.5 Monitors within the AIRNow network (2004) • ~120 TEOM monitors • 10 am to 6 pm LDT averages • No spatial interpolation • Log-transformed statistics for PM2.5 • Only days with complete model overlap • Only 00Z forecasts analyzed so far Fraction of data Fraction of data PM2.5 (µg/m3) O3 (ppbv)

  21. ICARTT/NEAQS Model O3 statistics based on AIRNow data (valid September 2004) All models biased high: some very high Significance testing: WRF-Chem version 1.3 compares well in terms of correlation, but biased high due to biogenic emissions error

  22. ICARTT/NEAQS-2K4: Comparing equivalent PM2.5 and O3 statistics (linear stats) O3 PM2.5 (log stats) WRF-Chem version 2 improves correlation, Reduced ozone bias PM2.5 ratio bias near 1! Expected ratio bias near 0.5

  23. Summary statistics for the daytime, inland, 410-670m window Linear Scaling for O3, CO, and met. variables Blue Dashed lines: Central 2/3 of obs. Red dots and lines: Median and central 2/3 of model errors Log scaling for all other (chemistry and aerosol) PM2.5 error (Model/Obs. ratio) Median of observations Number of comparisons for each model

  24. Summary statistics for the daytime, inland, 410-670m window AIRNOW network 342 monitors AIRNOW network 118 monitors PM2.5 error (Model/Obs. ratio)

  25. Isoprene, PAN, and acetaldehyde forecast ratio error Isoprene error (Model/Obs. ratio) PAN error (Model/Obs. ratio) CH3CHO error (Model/Obs. ratio)

  26. Inland, Daytime, 410 to 670 meter comparison Window

  27. Elemental Carbon - Emissions Inventories, Model Forecasts, and Ron Brown data from ICARTT/NEAQS-2004 EPA NEI-99v3 inventory Emission rate(ng/m2/s) Model results from 7/21//04 (downwind of Boston) Observations from filter impactors - all data

  28. Use of Models in Emissions Verification

  29. Real-time WRF-Chem • Meteorological Evaluation Real-time model verification product: www.etl.noaa.gov/programs/2004/neaqs/verification

  30. Meteorological Evaluation • Winds: • Daytime: close • Night: not too good • Temperature: • MYJ parameterization appears to be too cool • Other PBL paramterizations give different results

  31. Real-time WRF-Chem • Diagnostic Evaluation

  32. NOAA DC-3 Airborne Ozone Lidar (www.etl.noaa.gov/programs/2004/neaqs/verification) Observations WRF-Chem v2

  33. NOAA DC-3 Ozone curtains; observations and models July 21, 2004 O3 ppbv Observations CMAQ/ETA-3x Model YSU PBL scheme Mellor-Yamada(2.5) PBL scheme

  34. 15-16 July 2004, forecasts starting at 1200 UTC on 15 July 2004

  35. Inland, Daytime, 410 to 670 meter comparison Window ~4.5 hours of flight data 7/25 7/20 7/15 7/22 Other 46% 23% 17% 9% 5% 19% of flight data 20% of window between 410 and 670 m

  36. Speciation Trends Network (STN) Aerosol Composition Comparisons SO4 NH4 NO3 Organic Carbon

  37. Comparing SO2 oxidation extent with P3 airborne data (410-670 meter window) WRF/Chem (no SO2 cloud oxidation) underpredicts SO2 to SO4 conversion CMAQ/ETA (with SO2 cloud oxidation) overpredicts SO2 to SO4 conversion

  38. Conclusions: AIRNow surface PM2.5 network - The benefits of O3 ensemble forecasts compared to any single model NOAA WP-3 Aircraft data - Complete analysis for each model available at http://niwot.al.noaa.gov/~stu Models show Isoprene-PAN inconsistency SO2 conversion inconsistencies with CMAQ/ETA and WRF/Chem Surface STN network Organic Carbon deficiencies NOAA Airborne O3 Lidar Illuminates the effects different PBL parameterizations

  39. 7/22/04 20:00Z to 21:20Z, O3 versus NOy minus NOx Observations CMAQ/ETA O3 (ppbv) (Observed) O3 versus NOy minus NOx slope

  40. 7/22/04 20Z to 21:20Z, CO versus NOy Observations CO (ppbv) CMAQ/ETA CO versus NOy slope (Observed)

  41. Conclusions: AIRNow surface PM2.5 network - 5 out of 6 models show skill in predicting 8-hr afternoon averages The geometric mean ensembles improve forecasts NOAA WP-3 Aircraft data - Complete analysis for each model available at http://niwot.al.noaa.gov/~stu Models show Isoprene-PAN inconsistency NOAA WP-3 Aircraft data on 7/22 - O3 production efficiency matches observed (for most models) CO to NOy ratios shows problems with “current” inventories

  42. PM2.5 Organic Carbon PM2.5 OC error (Model/Obs. ratio) PM2.5 Ammonium

  43. O3 Bias Corrections needed for models to have any forecast skill Max 8 hr avg O3 Median of 342 points Skill = fraction of 342 points with RMSE(model) < RMSE(persistence) Skill inversely related to RMSE Without Bias Correction: Only BAMS models show skill 3 models with highest RMSE require ratio-adjusted bias correction in order to provide skill

  44. O3 Photochemistry and Relevant Lifetimes O3 production is bi-catalytic in NOx, VOC Secondary PM2.5 - OH and O3 dependent SO2 + OH  H2SO4 Aerosol NO2 + OH  HNO3 Aerosol VOC + (OH, O3)  Aerosol Lifetimes against Loss O3 12 hr PM2.5 (precip, dep.) NOy (precip, dep.) NOx 3 hr CO 30 day Isoprene .5 - 2 hr SO2 2 day, or cloud HO2 .1 - 10 min OH .1 - 15 sec NOx = NO + NO2 (Source: N2 + heat) NOy = NOx + HNO3 + PAN VOC: Biogenic: Isoprene (C5H8), Terpenes (C10H16) Anthropogenic: Alkanes, Alkenes, Aromatics

  45. 2002 New England Air Quality Study (NEAQS 2K2) NOAA Ron Brown • Focus Period: 22 - 25 July 2002 • Clear skies for most of period • Frontal passage: 7/23/02 at 18Z • 7/22/02, 7/23/02: • Big local and regional plumes • 7/24/02, 7/25/02: • Maine outflow: less polluted

  46. NEAQS-ITCT 2004 • Intercontinental transport • Examine chemical and aerosol evolution and removal • Systematic examination of “same” air mass Polluted airmass path and proposed flight tracks

  47. Real-time WRF-Chem

  48. NEAQS 2002 - Example of diagnostic evaluation (Ozone production efficiency comparisons) O3 ppbv MM5-Chem MAQSIP-RT

More Related