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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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boundary layer depth verification system at ncep m tsidulko c m tassone j mcqueen g dimego and m ek

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

goals
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
slide3

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

slide4

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
slide5

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.

slide6

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
slide7

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)

slide8

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.

slide9

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

slide10

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
slide12

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
slide13

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

slide14

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

slide15

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.