Clare Flynn, Melanie Follette-Cook, Kenneth Pickering, Christopher Loughner,

James Crawford, Andrew Weinheimer, Glenn Diskin

October 6, 2015

Evaluation of Vertical Mixing in WRFChem during

DISCOVER-AQ July 2011 and Impacts on Pollutant

Profiles

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DDeriving eriving IInformation on nformation on SSurface Conditions from urface Conditions from CoColumnlumn and and VERVERtically Resolved Observations Relevant to tically Resolved Observations Relevant to AAir ir QQualityuality

A NASA Earth Venture campaign intended to improve the interpretation of satellite observations to diagnose near-surface conditions relating to air quality

Objectives: 1. Relate column observations to surface conditions for aerosols and key trace gases O3, NO2, and CH2O

2. Characterize differences in diurnal variation of surface and column observations for key trace gases and aerosols

3. Examine horizontal scales of variability affecting satellites and model calculations

NASA P-3B

NASA UC-12

NATIVE, EPA AQS, and associated Ground sites

Investigation Overview

Deployments and key collaboratorsMaryland, July 2011 (EPA, MDE, UMd, and Howard U.)SJV, California, January/February 2013 (EPA and CARB)Texas, September 2013 (EPA, TCEQ, and U. of Houston)Colorado, Summer 2014

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Deployment Strategy

Systematic and concurrent observation of column-integrated, surface, and vertically-resolved distributions of aerosols and trace gases relevant to air quality as they evolve throughout the day.

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NASA UC-12 (Remote sensing)Continuous mapping of aerosols with HSRL and trace gas columns with ACAM

NASA P-3B (in situ meas.)In situ profiling of aerosols and trace gases over surface measurement sites

Ground sitesIn situ trace gases and aerosolsRemote sensing of trace gas and aerosol columns (Pandora)OzonesondesAerosol lidar observations

Three major observational components:

Maryland Observing Strategy

Motivation

Boundary layer mixing plays an important role in the connection between column and surface data Mixing impacts the vertical distribution of pollutants

importance for profile shapes Profile shape determines which altitude layers contribute

most to the column Impacts how well column measurements relate to surface

quantities Ultimately, how well can satellite column observations

represent surface air quality?

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Motivation

Can a regional, coupled meteorology-air quality model be effectively used to understand the interplay between vertical mixing and pollutant profiles?

Objective of this study to evaluate the representation of boundary layer mixing within the WRFChem model

Important to note that WRFChem is a coupled meteorology-chemistry model!

No MCIP time averaging Chemistry and meteorology computed in same time step

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736 km 12 km

4 km

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WRFChem Simulation Options

Maryland D-AQ Campaign

Time Period June 27 through July 31, 2011

Chemical mechanism CBM-Z

Aerosols MOSAIC with 8 aerosol bins

Radiation Longwave-RRTM; Shortwave-Goddard

Meteorology and Chemical Inputs

NARR; MOZART-4 CTM

PBL Scheme YSU (non-local PBL scheme)

Surface Layer Scheme; LSM

Monin-Obukhov scheme; unified Noah LSM

Photolysis Fast-JFollette-Cook, M. B., K. Pickering, J. Crawford, B. Duncan, C. Loughner, G. Diskin, A. Fried, A. Weinheimer (2015), Spatial and temporal variability of trace gas columns derived from WRF/Chem regional model output: Planning for geostationary observations of atmospheric composition, Atmos. Environ., 118, 28-44, doi:10.1016/j.atmosenv.2015.07.024.

Evaluation of Model PBLH

Bias of YSU scheme computed relative to several observational data sets Bias = WRFChem PBLH – Observational PBLHAll comparisons during daytime (mostly 8am-5pm EDT)

Meteorological estimates of PBLH – based on potential temperature profileP-3B (available at all 6 spiral sites)Ozonesonde (available at 2 spiral sites)

Aerosol estimates of PBLH – based on aerosol backscatter profileMPL (MicroPulse Lidar; available at 3 spiral sites) 9

Comparison of PBLH Values

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Small sonde sample size!

Comparison of PBLH Values

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Average Model PBLH Biases

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Observational Dataset

ModelResolution

Mean Bias (m)

(Model-Obs.)

± 1σ (m)

P3B 12km 182.5 626.7

P3B 4km 192.1 657.4

Ozonesonde 12km 67.1 541.5

Ozonesonde 4km 9.2 575.2

MPL 12km -67.5 610.5

MPL 4km -191.8 657.2

Only MPL demonstrates a statistically significant difference between the 12km and 4km simulations!!

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PBLH Diurnal Average Behavior

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PBLH Diurnal Average Behavior

Too deep Good relative to sonde—due to fewer samples?

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PBLH Diurnal Average Behavior

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PBLH Diurnal Average Behavior

PBL too deep and collapses too early relative to MPL mixed layer heights—differences between PBLH based on stability parameters and aerosol backscatter

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Potential Temperature Profiles

Both resolutions reproduce the diurnal variation in ozonesonde theta profiles.However, some struggle with collapse of CBL during evening for both resolutions

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Potential Temperature Profiles

Same story relative to P3B as for the ozonesondes at both resolutions

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Mixing and Pollutant Median Profiles - CO

Simulated and observed profiles compare better during early afternoon than for other times of day.

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Variability of Pollutant Profiles

Error bars represent the 25th and 75th percentile values for observed median profile and simulated median profile.

Model reproduces the range of the distributions during the afternoon hours within the CBL.

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Variability of Pollutant Profiles

Error bars represent the 25th and 75th percentile values for observed median profile and simulated median profile.

Model struggles to reproduce median profile and distributions—model not extreme enough.

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Variability of Pollutant Profiles

Error bars represent the 25th and 75th percentile values for observed median profile and simulated median profile.

Model distributions not extreme enough.

Conclusions YSU PBL scheme performs differently relative to different types of

observational PBLH estimates at both resolutions Too deep relative to P3B meteorological estimates on average Too shallow relative to MPL aerosol estimates on average Reasonably well simulates average diurnal behavior of PBLH

relative to meteorological estimates!

Both resolutions also reasonably well capture the diurnal variation in theta profiles relative to the P3B and ozonesondes Some struggle to capture CBL collapse

Best captures potential temperature and CO median profile shapes during early afternoon when CBL is fully developed

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Future Work Run WRFChem with other PBL schemes, such as ACM2, MYJ and

MYNN (local schemes), to further investigate vertical mixing issues Which scheme best captures PBL mixing and height? How does vertical mixing impact pollutant profiles?

Compare observational PBLH estimate data sets among each other Also compare against the airborne High Spectral

Resolution Lidar (HSRL) PBLH data set

Investigate spatial and temporal variability in the model bias

Investigate impacts on column-surface correlation for O3 and NO2 for each PBL scheme evaluated Which scheme best captures the observed relationship?24