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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Short summary
Accurate prediction of the Planetary Boundary layer is essential to both numerical weather prediction (NWP) and pollution forecasting. This paper presents a methodology to combine these measurements with the models through a statistical data assimilation approach that calculates the correlation between the PBLH and variables like temperature and moisture in the model. The model estimates of these variables can be improved via this method, and this will enable increased model accuracy.
Preprints
https://doi.org/10.5194/amt-2020-238
https://doi.org/10.5194/amt-2020-238

  07 Jul 2020

07 Jul 2020

Review status: a revised version of this preprint was accepted for the journal AMT and is expected to appear here in due course.

Assimilation of lidar planetary boundary layer height observations

Andrew Tangborn1, Belay Demoz1,2, Brian J. Carroll2, Joseph Santanello3, and Jeffrey L. Anderson4 Andrew Tangborn et al.
  • 1JCET, UMBC, Baltimore, MD, USA
  • 2Dept. of Physics, UMBC, Baltimore, MD, USA
  • 3Laboratory for Hydrology, NASA GSFC, Greenbelt, MD, USA
  • 4National Center for Atmospheric Research, Boulder, CO, USA

Abstract. Lidar backscatter and wind retrievals of the planetary boundary layer height (PBLH) are assimilated into 22 hourly forecasts from the NASA Unified – Weather and Research Forecast (NU-WRF) model during the Plains Elevated Convection Convection at Night (PECAN) campaign on 11 July 2015 in Greensburg, Kansas, using error statistics collected from the model profiles to compute the necessary covariance matrices. Two separate forecast runs using different PBL physics schemes were employed, and comparisons with 5 independent sonde profiles were made for each run. Both of the forecast runs accurately predicted the PBLH and the state variable profiles within the planetary boundary layer during the early morning, and the assimilation had little impact during this time. In the late afternoon, the forecast runs showed decreased accuracy as the convective boundary layer developed. However, assimilation of the doppler lidar PBLH observations were found to improve the temperature, water vapor and velocity profiles relative to independent sonde profiles. The computed forecast error covariances between the PBLH and state variables were found to rise in the late afternoon, leading to the larger improvements in the afternoon. This work represents the first effort to assimilate PBLH into forecast states using ensemble methods.

Andrew Tangborn et al.

 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Andrew Tangborn et al.

Andrew Tangborn et al.

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Short summary
Accurate prediction of the Planetary Boundary layer is essential to both numerical weather prediction (NWP) and pollution forecasting. This paper presents a methodology to combine these measurements with the models through a statistical data assimilation approach that calculates the correlation between the PBLH and variables like temperature and moisture in the model. The model estimates of these variables can be improved via this method, and this will enable increased model accuracy.
Citation