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15th International Symposium for the Advancement of Boundary Layer Remote Sensing 29 June 2010

Boundary layer depth verification system at NCEP M. Tsidulko, C. M. Tassone, J. McQueen, G. DiMego, and M. Ek. 15th International Symposium for the Advancement of Boundary Layer Remote Sensing 29 June 2010. Goals. Produce accurate PBL depths from routine observations

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15th International Symposium for the Advancement of Boundary Layer Remote Sensing 29 June 2010

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  1. Boundary layer depth verification system at NCEPM. Tsidulko, C. M. Tassone, J. McQueen, G. DiMego, and M. Ek 15th International Symposium for the Advancement of Boundary Layer Remote Sensing 29 June 2010

  2. Goals • Produce accurate PBL depths from routine observations • Use these estimates to evaluate model PBL depths • Provide improved estimate for AQ & Dispersion models and 1st guess analysis

  3. PBL Verification System at NCEP Observations Model output MYJ PBL scheme: 1) TKE PBL 2) Mixed layer depth Post-processing: 3) Ri number approach NAM RiCR= 0.25 (Vogelezang and Holtslag, 1996) RUC Virt. pot. temp. profile RAOB PBL depth output (internal scheme/derived in post-processing) SREF Ri number approach Aircraft PBL calculation Ri number approach CMAQ Modified Ri number approach (ACM2) Profiler Forecast Verification System Statistics

  4. OBJECTIVES • How good is the algorithm? • - Subjective verification of Radiosonde and ACARS profiles • - Comparison with other methods of PBL depth calculation • LIDAR (MPLNET,HURL) • GPS (COSMIC) • Special profilers • II. How good are model PBL forecasts? • - Use Radiosonde/ACARS estimates for “ truth” • - Subjective verification of model profiles • - Objective verification with NCEP’s verification system • Overall statistics for different domains and time periods • Statistics for individual airports • III. How do PBL depth errors impact air quality forecasts? • - compare PBL depth from NAM simulations with different resolutions • - examine PBL behavior for poor AQ episodes

  5. How good is the algorithm? - comparison with other methods Aug 2007: Lidar and GPS data COSMIC MPLNET RAOB (Sterling, VA) Sept 2009: DC PBL Variability Experiment PBL depth estimations for several locations in DC area – ACARS at BWI, radiosondes at Beltsville (Howard University) and RFK stadium. PBL depths from COSMIC data are about 300 km away from DC area.

  6. How good is the algorithm? – subjective verification of profiles Dallas-Fort Worth, Texas Wind speed θv NAM PBL: TKE,Ri,Mx Ri no TKE q ACARS PBL ANL MODEL ACARS • All ACARS PBLs are in good agreement; • Similar to Ri PBL estimates from NAM • PBL is well defined in all parameters’ profiles

  7. How good is the algorithm? – subjective verification of profiles Denver, Colorado One ACARS PBL estimate is near zero – possibly very different wind on nearby vertical levels - Inclusion of low level thermal heating Quality control issues (surface measurements, total number of levels, gap between levels)

  8. Model PBL verification: averaged over CONUS domain Diurnal cycle of ACARS PBL depth estimates NAM and RUC forecasts for Continental US area. Averaged for July – August 2009.

  9. Model PBL verification: Individual stations Houston, Texas 10 – 27 June 2009 NAM Ri PBL 1600 ACARS PBL NAM Mx depth NAM Ri PBL RUC PBL Time series Diurnal Cycle Missing ACARS reports at night Few observations some days

  10. Model PBL verification: 12 km, 4 km NAMB vs RAOBS TKE PBL RI PBL • RAOBS – twice a day, no diurnal cycle, not necessarily peak PBL • Differences between 12 km and 4 km for TKE PBL • 4 km TKE PBL lower than 12 km PBL • Almost no difference for RI PBL

  11. Model PBL verification 4 km PBL, Temperature, Dew point Temperature WEST US BIAS EAST US BIAS • 4 km TKE PBL in better agreement with RAOBS PBL for West US • No clear evidence of correlation between T, Td and PBL

  12. A B Case studies: WRF-NMM vs NMMB 17-18 Aug 2009 CT ozone overprediction WRF-NMM grid218 WRF-NMM/CMAQ NMMB • Main direction of winds is SE, potentially bringing pollutants from the NYC area • PBL is collapsing over the sea forcing the pollutant to stay near surface, which could be one of potential reasons of large ozone over-prediction in this case grid218

  13. A line B line Ozone concentrations (ppb) predicted in NCEP Air Quality Forecast system (correspondent σ-levels are shown on right axis) and PBL height from different model simulations (green and black lines). Grey lines indicate surface. Blue circles indicate PBLestimations from ACARS data at airports. Over Long Island, high-resolution (4km) NAM run has 400-500 m higher PBL than 12 km NAM PBL and 12 km ACM2 PBL (currently used in CMAQ). Potentially this may help pollutants to stay higher while travelling over water and reduce surface concentrations in Connecticut. B line

  14. SUMMARY • PBL verification system has been established at NCEP • Richardson number approach is applied to radiosonde and ACARS profiles of winds, temperature and moisture (when available) to determine and evaluate the observed PBL depth • These data are compared to boundary layer depths estimated by other methods • PBL verification for NAM and RUC models shows that they are in relatively good agreement with observations • For poor air quality ozone episode, PBL depths for two varying horizontal resolutions (12km and 4km) are verified • Further study will help to quantify the impact of meteorological model performance on air quality forecast error.

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