1. 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

2. 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 1 st guess analysis

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

4. 4 I.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 OBJECTIVES

5. 5 MPLNET COSMIC RAOB (Sterling, VA) 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. Sept 2009: DC PBL Variability Experiment Aug 2007: Lidar and GPS data How good is the algorithm? - comparison with other methods

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

7. 7 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) How good is the algorithm? – subjective verification of profiles Denver, Colorado

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

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

10. 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

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12. 12 Model PBL verification 4 km PBL, Temperature, Dew point Temperature WEST US BIASEAST 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

13. 13 17-18 Aug 2009 CT ozone overprediction WRF-NMM NMMB WRF-NMM/CMAQ grid218 Case studies: WRF-NMM vs NMMB AB 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

14. 14 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 PBL estimations 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. A line B line

15. 15 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.