1 / 19

Towards an Ensemble Forecast Air Quality System for New York State

Towards an Ensemble Forecast Air Quality System for New York State Michael Erickson 1 , Brian A. Colle 1 , Christian Hogrefe 2,3 , Prakash Doraiswamy 3 , Kenneth Demerjian 3 , Winston Hao 2 , Mark Beauharnois 3 , Jia-Yeong Ku 2 , and Gopal Sistla 2

audra
Télécharger la présentation

Towards an Ensemble Forecast Air Quality System for New York State

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. Towards an Ensemble Forecast Air Quality System for New York State Michael Erickson1, Brian A. Colle1, Christian Hogrefe2,3, Prakash Doraiswamy3, Kenneth Demerjian3, Winston Hao2, Mark Beauharnois3, Jia-Yeong Ku2, and Gopal Sistla2 1 School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY 2 New York State Department of Environmental Conservation, Albany, NY 3 Atmospheric Sciences Research Center, State University of New York at Albany, Albany, NY

  2. Motivations and Goals • Project Goal: Develop an air quality ensemble forecast system to aid operational forecasters for New York State. • Motivation: Could errors in the atmospheric models impact air quality forecast simulations? Can these errors be corrected via post-processing? • Goal of this talk: Evaluate the air quality models (AQM) and Stony Brook (SBU) ensemble with a focus on similar biases and errors within each ensemble. • Future: Use a post-processing technique called Bayesian Model Averaging (BMA) to improve the deterministic and probabilistic forecasts within the ensembles.

  3. Ensemble Air Quality Model Flowchart Atmospheric Model Ensemble: SBU, NCEP NAM, ASRC, NYSDEC Air Quality Model (AQM) Ensemble: CAMx, CAMQ Emissions Inventory: NYSDEC, EPA Ensemble of Air Quality Model Forecasts

  4. AQM Operational Ensemble Members Member Name Met. Emis. Inv. AQM Grid Res Initial-ize Start Date NCEP_12z WRF-NMM EPA CMAQv4.6 12-km 12z Summer 2004; Winter 2004-2005; everyday since June 2005 NCEP_00z WRF-NMM EPA CMAQv4.6 12-km 00z May 2008 SBU* MM5/WRF NYSDEC CMAQv4.6 36-km, 12-km 00z June 2008 NYSDEC_3x WRF-NMM NYSDEC CMAQv4.6 12-km 00z November 2008 ASRC** WRF-ARW NYSDEC CAMxv4.5.1 12-km 00z March 2009 *Currently two SBU members are run in the operational AQM ensemble. Retrospective simulations used all SBU members except those with the Ferrier microphysics. **ASRC model was not run in the retrospective simulations.

  5. Operational AQM Example - 8/18/2009 ASRC Member - http://asrc.albany.edu/research/aqf/aqfms/camx/mfb.php Synoptic Setup Daily Total PM 2.5 8-hr Max. Ozone 150 100 70 40 30 20 10 0 100 80 60 40 20 0 AQI Categories 1-hr max PM 2.5 1-hr Max. Ozone 130 115 100 85 70 55 30 50 40 30 20 10 0

  6. Data and Methods • Retrospective simulations of particulate matter 2.5 and ozone were verified over following time periods: - June 4, 2008 – July 22, 2008 - December 1, 2008 – February 28, 2009 • Regions 1, 2, and 7 were selected to represent coastal, urban and inland New York, respectively. • AQM output was compared against daily 8-hr maximum ozone and 24-hr average PM 2.5 model predictions from the AIRNOW database and official NYSDEC forecasts. • To elucidate potential error sources in the AQM ensemble, the SBU 10-m wind speed and 2-m temperature were verified with ASOS observations over the same time period. SBU Ensemble Domain NYSERDA Regions

  7. Name Model Cloud PBL Radiation Microphysics Initialization F1 MM5 BM MY CCM2 Simple Ice GFS F2 MM5 Grell MRF CloudRad Simple Ice WRF-NMM F3 MM5 Grell MY CloudRad Reisner2 WRF-NMM F5 MM5 Grell Blackadar CCM2 Simple Ice NOGAPS F6 MM5 KF2 MY CCM2 Simple Ice CMC F7 MM5 KF2 MRF CloudRad Reisner2 GFS F8 WRF KF2 MY RRTM WSM3 CMC F9 WRF BM MY RRTM WSM3 WRF-NMM F10 WRF KF2 MY RRTM WSM3 GFS F13 MM5 Grell Blackadar CCM2 Simple Ice GFS F14 WRF BM YSU RRTM WSM3 NOGAPS F15 WRF KFE MY RRTM Thompson GFS AQM Retrospective Simulations SBU Ensemble Members • F2 and F9 were used to drive CMAQ forecasts each day since June 1, 2008. They were selected based on temperature and wind verification results for summer 2007 and operational considerations. • Two additional SBU members use the Ferrier microphysics scheme that is currently not compatible with CMAQ.

  8. Ozone Retrospective Simulations Time Series – 6/4/08 to 7/22/08 SBU WRF KFMY-CMC SBU WRF BMMY-NAM SBU WRF KFMY-GFS SBU MM5 GRBK-GFS SBU WRF BMYSU-NGPS SBU WRF KFMY-GFS Ensemble Avg Ensemble Median DEC Forecast NCEP NAM 12z NCEP NAM 00z NYSDEC 3x SBU MM5 BMMY-GFS SBU MM5 GRMRF-NAM SBU MM5 GRMY-NAM SBU MM5 GRBK-NGPS SBU MM5 KFMY-CMC SBU MM5 KFMRF-GFS 100 80 60 40 20 0 100 80 60 40 20 0 100 80 60 40 20 0 • Model simulations generally track observations (in red) well.

  9. Ozone Retrospective Simulations Bias and RMSE – 6/4/08 to 7/22/08 SBU WRF KFMY-CMC SBU WRF BMMY-NAM SBU WRF KFMY-GFS SBU MM5 GRBK-GFS SBU WRF BMYSU-NGPS SBU WRF KFMY-GFS Ensemble Avg Ensemble Median DEC Forecast NCEP NAM 12z NCEP NAM 00z NYSDEC 3x SBU MM5 BMMY-GFS SBU MM5 GRMRF-NAM SBU MM5 GRMY-NAM SBU MM5 GRBK-NGPS SBU MM5 KFMY-CMC SBU MM5 KFMRF-GFS WRF Mean 12 8 4 0 • Ozone is underpredicted by SBU MM5 members and overpredicted by most remaining models. • RMSE varies between members, with the ensemble mean/median outperforming individual members. 4 0 -4 -8 MM5 12 8 4 0 4 0 -4 -8 12 8 4 0 4 0 -4 -8

  10. SBU WRF KFMY-CMC SBU WRF BMMY-NAM SBU WRF KFMY-GFS SBU MM5 GRBK-GFS SBU WRF BMYSU-NGPS SBU WRF KFMY-GFS Ensemble Avg Ensemble Median DEC Forecast NCEP NAM 12z NCEP NAM 00z NYSDEC 3x SBU MM5 BMMY-GFS SBU MM5 GRMRF-NAM SBU MM5 GRMY-NAM SBU MM5 GRBK-NGPS SBU MM5 KFMY-CMC SBU MM5 KFMRF-GFS PM 2.5 Retrospective Simulations Time Series – 12/1/08 to 2/28/09 100 80 60 40 20 0 100 80 60 40 20 0 100 80 60 40 20 0 • Model simulations generally track observations (in red) well.

  11. SBU WRF KFMY-CMC SBU WRF BMMY-NAM SBU WRF KFMY-GFS SBU MM5 GRBK-GFS SBU WRF BMYSU-NGPS SBU WRF KFMY-GFS Ensemble Avg Ensemble Median DEC Forecast NCEP NAM 12z NCEP NAM 00z NYSDEC 3x SBU MM5 BMMY-GFS SBU MM5 GRMRF-NAM SBU MM5 GRMY-NAM SBU MM5 GRBK-NGPS SBU MM5 KFMY-CMC SBU MM5 KFMRF-GFS PM 2.5 Retrospective Simulations Bias and RMSE – 12/1/08 to 2/28/09 Mean 10 5 0 -5 15 10 5 0 MM5 • PM is overpredicted for region 2 but underpredicted for region 7 and all other inland stations (not shown). • NCEP members exhibit the least amount of bias overall. • The WRF SBU members exhibit greater negative bias than the MM5 SBU. WRF 15 10 5 0 10 5 0 -5 15 10 5 0 10 5 0 -5

  12. Retrospective Simulations - Rank Histograms Winter Particulate Matter Summer Ozone • Ozone and PM forecasts are “L” shaped (biased) or “U” shaped (underdispersed). • Biases and dispersion issues have also been noted in the SBU ensemble and may be negatively affecting the AQM. • Therefore it is important to verify the SBU ensemble in juxtaposition with the AQM.

  13. SBU/AQM Ensemble Comparison – Temperature Ozone and Bias – 6/4/08 to 7/22/08 AQI Ensemble SBU Ensemble • The cooler, shallower and cloudier simulated PBL in the MM5 MY scheme is likely resulting in lower model ozone. • This affect may be offset in one MY member by the KF convective scheme, which has been shown to decrease cloudiness and increase simulated ozone. (Tao et al. 2008). • The MYJ WRF members have greater ozone concentrations than MY MM5, which could be the result of a higher PBL growth within the MYJ scheme. (Zielonka et al. 2008). oC oC oC

  14. SBU/AQM Ensemble Comparison – Temperature PM 2.5 and Bias – 12/1/08 to 2/28/09 SBU Ensemble AQI Ensemble • The MM5 members using the Reisner microphysics have more PM than those using Simple Ice. PM sensitivity to cloud microphysics schemes have also been noted in Meij et al. 2009. • Lower WRF PM concentrations have been noted compared to MM5 (Meij et al 2009) due to the increase of vertical mixing within WRF caused by warmer surface temperatures. oC oC oC

  15. SBU/AQM Ensemble Comparison – Rank Histogram • After bias correction, the SBU ensemble is underdispersed for temperature and wind speed in all regions. • The AQI ensemble also appears to be underdispersed in the absence of biases, suggesting that a lack of variability in atmospheric forecasts could affect the air quality models. • Post-processing techniques, such as Bayesian Model Averaging (BMA), could help correct this lack of variability in ensemble forecasting. Winter PM 2.5 AQM Region 7 Winter Wind SBU Region 7 Summer Temp. SBU Region 7 Summer Ozone AQM Region 7

  16. Post-Processing - Bayesian Model Averaging • Bayesian Model Averaging (BMA, Raftery et al. 2005) has been shown to correct some model deficiencies associated with reliability and dispersion. • BMA creates a probability density function (PDF) for each ensemble member depending on the uncertainty in the model forecast and weights the result based on its performance and uniqueness in the recent past. • The main advantages of BMA appear to be with probabilistic skill, although deterministic skill is also increased. • An example using the 24 hour temperature forecast from the SBU ensemble will be presented. PDF for Temperature PDF for Wind Speed BMA weights each member based on past performance and assigns an uncertainty.

  17. BMA Example – Temperature Hour 24 Rank Histogram- Warm Season 2007-2009 BMA Bias Corrected BMA Region 1 Region 1 Region 2 Region 2 Region 7 Region 7

  18. BMA Example – Temperature Hour 24 Reliability > 295 K- Warm Season 2007-2009 Region 7 Region 2 Region 1

  19. Conclusions • An operational air quality forecast ensemble is currently being run using a variety of atmospheric models, air quality models (AQM) and pollutant emission inventories. • Particulate matter and ozone simulations track observations reasonably well in the warm and cool seasons, although the ensemble exhibits systematic biases and underdispersion. • Ensemble biases may be sensitive to the PBL parameterization, with the decreased (increased) vertical mixing within the MY (YSU) scheme resulting in lower (higher) ozone and higher (lower) PM forecasts. • Bayesian model averaging (BMA) has been shown to correct dispersion and improve reliability for 2-m temperature and 10-m wind speed within the SBU ensemble. Therefore BMA could improve AQM forecasts through direct application or insertion of the post-processed SBU forecasts into the AQM ensemble.

More Related