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Evaluation of Pleim-Xiu Land Surface Model and Asymmetric Convective Model in WRF

Evaluation of Pleim-Xiu Land Surface Model and Asymmetric Convective Model in WRF. Jonathan Pleim 1 , Robert Gilliam 1 , and Aijun Xiu 2 1 Atmospheric Sciences Modeling Division, NOAA, Research Triangle Park, NC (In partnership with the U.S. EPA)

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Evaluation of Pleim-Xiu Land Surface Model and Asymmetric Convective Model in WRF

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  1. Evaluation of Pleim-Xiu Land Surface Model and Asymmetric Convective Model in WRF Jonathan Pleim1, Robert Gilliam1, and Aijun Xiu2 1Atmospheric Sciences Modeling Division, NOAA, Research Triangle Park, NC (In partnership with the U.S. EPA) 2Institute for the Environment, UNC-Chapel Hill

  2. Outline • Addition of new physics to WRF • Asymmetric Convective Model – version 2 (ACM2) • Pleim-Xiu Land Surface Model (PX-LSM) • Preliminary evaluation of WRF-PX-ACM2 and WRF-NOAH-YSU • Surface observations and analyses • PBL heights derived from radar wind profilers

  3. New Physics • ACM2 • Combined non-local and local closure • Similar to eddy diffusion w/ counter-gradient adjustment (e.g. MRF, YSU) but better for chemistry • Produces realistic profiles in convective boundary layers (CBL) and accurate PBL heights (Pleim 2007 parts 1&2 JAMC) • In CMAQ, MM5, and WRF • PX LSM • Three pathways for evaporation • Ground evaporation - f(sfc soil moisture) • Wet canopies - f(cwc) • Evapotranspiration - f(stomatal resistance) • Detailed Vegetation and Soil Data • USGS and STATSGO (1 km) soon NLCD-30m • Grid cell aggregate parameters from fractional area LU and soil type data. • Indirect Soil Moisture Nudging • Model-obs surface temperature and humidity

  4. WRF PBL/Surface Fluxes • PBL/Surface model components are in three separate, but interdependent modules in WRF • Land surface model – computes soil moisture and temperature and moisture fluxes from ground, wet leaves, evapotranspiration. Added PX LSM • Surface layer – Solves flux-profile relationships. Outputs: L, u*, Ra. Added Pleim (2006) surface layer scheme • PBL – Computes subgrid turbulent vertical transport. Added ACM2

  5. Evaluation of WRF-PX-ACM2 and WRF-NOAH-YSU • Grid configuration: • Horizontal grid resolution = 12 km • 34 vertical layer • Physics: • RRTM long wave radiation • Dudhia SW • WSM6 microphysics • KF2 convective cloud scheme • FDDA • 3-D analysis nudging for winds (all levels), T, qv (above PBL only) • Indirect soil moisture nudging using NCEP surface analysis of T and RH for PX LSM

  6. 2006 WRF 12km T-2m Stats

  7. WRF PX 2-m Temperature Statistics

  8. WRF NOAH 2-m Temperature Statistics

  9. T-2m, WRF PX – Analysis, August 2006

  10. T-2m, WRF NOAH – Analysis, August 2006

  11. WRF Analysis 2-m Temperature Statistics

  12. WRF PX 2-m MixR Statistics

  13. WRF NOAH 2-m MixR Statistics

  14. PBL Heights from TexAQS II • PBL heights derived from 10 radar wind profilers in Texas area by Jim Wilczak and Laura Bianco NOAA/ESRL • Observations and models averaged by hour of the day for August 1-31, 2006

  15. Summary • WRF-PX-ACM2 and WRF-NOAH-YSU show similar performance for surface statistics • Less humidity bias for PX • Low wind speed bias less for PX • T-2m error and bias better for PX in eastern portion of domain but worse in western plains • Preliminary PBL height analysis shows that PX-ACM2 has generally less high bias than NOAH-YSU

  16. Next • Finish the study outlined in the abstract • Run WRF-PX-MYJ • Run CMAQ-ACM2 using met from WRF-PX-ACM2 • Run CMAQ-MYJ using met from WRF-PX-MYJ • Evaluate both met/aq combinations against TexAQSII Field experiment

  17. Further WRF Development • Implement snow model in PX LSM • Implement new, high resolution, more accurate 2002 National Land Cover Data • Based on 30 m Landsat-7 ETM • WRF-CMAQ 2-way “On-line” system under development • Beta release September 2008

  18. Acknowledgements Tanya Otte (ASMD), Lara Reynolds (CSC) Disclaimer - The research presented here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Commerce’s National Oceanic and Atmospheric Administration (NOAA) and under agreement number DW 13921548. This work constitutes a contribution to the NOAA Air Quality Program. Although it has been reviewed by EPA and NOAA and approved for publication, it does not necessarily reflect their policies or views.

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