Cloud Correction and its Impact on Air Quality Simulations Arastoo Pour Biazar 1, Richard T. McNider...
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Transcript of Cloud Correction and its Impact on Air Quality Simulations Arastoo Pour Biazar 1, Richard T. McNider...
Cloud Correction and its Impact on Air Quality Simulations
Arastoo Pour Biazar1, Richard T. McNider1, Andrew White1, Bright Dornblaser3, Kevin Doty1 , Maudood Khan2
1. University of Alabama in Huntsville2. University Space Research Association (USRA)3. Texas Commission on Environmental Quality (TCEQ)
Presented at:
The 94rd AMS Annual MeetingATlanta, GA
2-6 February 2014
Session 7.3: The Effects of Meteorology on Air Quality - Part 3, 18th Joint Conference on the Applications of Air Pollution Meteorology with the A&WMA
Background & Motivation:Background & Motivation: Clouds greatly impact tropospheric chemistry by altering dynamics as well as
atmospheric chemical processes:
Altering photochemical reaction rates and thereby impacting oxidant production.
Impacting surface insolation and temperature and thereby altering the emissions of key ozone precursors (namely biogenic hydrocarbons and nitrogen oxide.)
Impacting boundary-layer development, vertical mixing, and causing deep vertical mixing of pollutants and precursors.
Impacting the evolution and recycling of aerosols.
Impacting aqueous phase chemistry and wet removal.
Causing lightning and generating nitrogen.
Unfortunately, numerical meteorological models still have difficulty in creating clouds in the right place and time compared to observed clouds. This is especially the case when synoptic-scale forcing is weak, as often is the case during air pollution episodes.
Background & Motivation …Background & Motivation … The errors in simulated clouds is particularly important in State Implementation
Plan (SIP) modeling where the best representation of physical atmosphere is required.
Previous attempts at using satellite data to insert cloud water have met with limited success.
Studies have indicated that adjustment of the model dynamics and thermodynamics is necessary to fully support the insertion of cloud liquid water in models (Yucel, 2003).
Jones et al., 2013, assimilated cloud water path in WRF and realized that the maximum error reduction is achieved within the first 30 minutes of forecast.
Assimilation of radar observations (Dowell et al., 2010) miss the non-precipitating clouds.
Assimilation of observed cloud optical depth (Lauwaet et al., 2011) has also shown to improve model performance by improving the model surface temperatures.
UAH Approach:UAH Approach: Objective: to improve model location and timing of clouds in the Weather
Research and Forecast (WRF) model by assimilating GOES observed clouds.
Since for air quality, non-precipitating clouds are just as important as precipitating clouds, our metric for success should indicate the radiative impact of clouds.
Approach: Create an environment in the model that is conducive to clouds formation/removal through adjusting wind and moisture fields and to improve the ability of the WRF modeling system to simulate clouds through the use of observations provided by the Geostationary Operational Environmental Satellite (GOES). Observed O3 vs Model Predictions
(South MISS., lon=-89.57, lat=30.23)
-40
-20
0
20
40
60
80
100
8/30/00 0:00 8/30/00 6:00 8/30/00 12:00 8/30/00 18:00 8/31/00 0:00 8/31/00 6:00 8/31/00 12:00 8/31/00 18:00
Date/Time (GMT)
Ozo
ne C
once
ntra
tion
(ppb
)
Observed O3
Model (cntrl)
Model (satcld)
(CNTRL-SATCLD)
OBSERVED ASSIM
Under-prediction
CNTRL
Correcting for the radiative impact of clouds corrected 38 ppb under-prediction. (Pour-Biazar et al 2007)
UAH Approach …UAH Approach …
W < 0W > 0
Dynamical Adjustment
Use satellite cloud top temperatures and
cloud albedoes to estimate a TARGET
VERTICAL VELOCITY (Wmax).
Adjust divergence to comply with Wmax in a
way similar to O’Brien (1970).
Nudge model winds toward new horizontal
wind field to sustain the vertical motion.
SUN
BL OZONE CHEMISTRY
O3 + NO -----> NO2 + O2
NO2 + h (<420 nm)-----> O3 + NO
VOC + NOx + h-----> O3 + Nitrates
(HNO3, PAN, RONO2)
g
c
h
g
)(.1 cldcldcld absalbtr
Cloud albedo, surface albedo, and insolation are retrieved based on Gautier et al. (1980), Diak and Gautier (1983). From GOES visible channel centered at .65 µm.
Surface
Photolysis Adjustment (CMAQ)
Cloud top Determined
from satellite IR temperature
Implementation in WRFImplementation in WRF Focusing on daytime clouds, analytically estimate the vertical velocity needed to
create/clear clouds. Under-prediction: Lift a parcel to saturation. Over-prediction: Move the parcel
down to reduce RH and evaporate droplets. The horizontal wind components in the model are minimally adjusted (O’Brien
1970) to support the target vertical velocity. REQUIRED INPUTS FOR 1D-VAR: REQUIRED INPUTS FOR 1D-VAR: Target W: target vertical velocity (m/s); Target H: where max
vertical velocity is reached; Wadj_bot: bottom layer for adjustment; Wadj_top: top layer for adjustment.
Implementation in CMAQImplementation in CMAQCloud albedo and cloud top temperature from GOES is used to calculate cloud
transmissivity and cloud thickness
The information is fed into MCIP/CMAQ
CMAQ parameterization is bypassed and photolysis rates are then adjusted based on GOES observations:
))cos()1((1
)1)cos(6.1(1
trcfracJJ
trcfracJJ
clearabove
clearbelow
Interpolate in between.
WRFDomain 01 Domain 02 Domain 03
Running Period August, 2006
Horizontal Resolution 36 km 12 km 4 km
Time Step 90s 30s 10s
Number of Vertical Levels 42
Top Pressure of the Model 50 mb
Shortwave Radiation Dudhia
Longwave Radiation RRTM
Surface Layer Monin-Obukhov
Land Surface Layer Noah (4 – soil layer)
PBL YSU
Microphysics LIN
Cumulus physics Kain-Fritsch (with Ma and Tan
2009 trigger function)
Kain-Fritsch (with Ma and Tan 2009
trigger function)
NONE
Grid Physics Horizontal Wind
Meteorological Input Data EDAS
Analysis Nudging Yes
U, V Nudging Coefficient 3 x 10-4 1 x 10-4 3 x 10-5
T Nudging Coefficient 3 x 10-4
Q Nudging Coefficient 1 x 10-5
Nudging within PBL Yes for U and V, NO for q and T
Model Configuration:Model Configuration:
Physical Process Reference
Horizontal and vertical advection
YAMO
Horizontal diffusion MULTISCALE
Vertical diffusion ACM2
Gas-phase chemistry and solver
EBI_CB4
Gas and aqueous phase mechanism
CB4_AE3_AQ
Aerosol chemistry AERO3
Dry deposition AERO_DEPV2
Cloud dynamics CLOUD_ACM
CMAQ
36km domain
4 km
12 km
Modeling Domain
Underprediction
Overprediction
Areas of disagreement between model and satellite observation
Agreement Index for Measuring Model PerformanceAgreement Index for Measuring Model Performance
A contingency table can be constructed to explain agreement/disagreement with
observation
Clear Cloud
Clear A B
Cloud C D
MODEL
SATELLIT
E
AI = (A+D)/G
G=(A+B+C+D)
WRF Results (36-km):WRF Results (36-km):
Based on Agreement Index Model performance has
improved.
The improvements are more pronounced at times that the
model errors are larger
WRF Results (36-km) …WRF Results (36-km) …
While RMSE for temperature is reduced, cold bias has increased and dry bias has decreased. This points to an inherent problem other than clouds in the model that is making the control simulation dry and cold.
WRF Results (12-km) …WRF Results (12-km) …
Similar to 36-km simulation, for 12-km domain cloud assimilation improved Agreement Index. Using the lateral boundary condition from 36-km simulation with assimilation also improves the model performance.
WRF Results (12-km) …WRF Results (12-km) …
For 12-km domain, unlike the 36-km, temperature shows a positive bias that for some days is improved by assimilation. RMSE and bias for mixing ratio are improved by using the lateral boundary condition from 36-km with assimilation or directly assimilating GOES observations.
CMAQ Results (36-km):CMAQ Results (36-km):CONTROL SIMULATION SATCLD SIMULATION
Transmissivity
CNTRL too opaque compared to satellite
NO2 photolysis
rate
Large differences due to cloud
errors
Difference in NO2 photolysis rates for selected days
(CNTRL-SATCLD)
Difference in NO2 photolysis rates between control simulation and the simulation using observed clouds (CNTRL-SATCLD) for August 19, 21,22, and 29, 2006. Clouds in control simulation are more spread out and cover large areas (more opaque compared to observation).
Over-prediction of Clouds by CNTRL
Under-prediction of Clouds by CNTRL
Under prediction for higher ozone concentrations is slightly improved due to GOES cloud adjustment.
CNTRL SATCLD
SATCLD_ICBC
Night time over prediction is increased in some location while reduced in other locations, but generally it is slightly increased.
O3 Statistics
-6.0
-4.0
-2.0
0.0
2.0
4.0
6.0
8.0
10.0
12.0
Mean BIAS (ppb) Mean Bias O3>50 (ppb) Mean Bias O3<50 (ppb) Mean Norm. Bias O3>50 (%)
Statistic
pp
b o
r %
CNTRL SATCLD
Daytime under-prediction is improved
GOES cloud observations were assimilated in WRF/CMAQ modeling system and a month long simulation over August 2006 were performed.
Overall, the assimilation improved model cloud simulation.
Cloud correction also improved surface temperature and mixing ratio.
Cloud correction had significant impact on model ozone predictions.
While the monthly daytime ozone bias was reduced by about 2 ppb, ozone differences of up to 40 ppb can be seen at certain times and locations.
The largest errors in ozone concentration due to clouds are over urban areas and over Lake Michigan.
CONCLUSIONSCONCLUSIONS
The findings presented here were accomplished under partial support from NASA Science Mission Directorate Applied Sciences Program and the Texas Commission on Environmental Quality (TCEQ).
Note the results in this study do not necessarily reflect policy or science positions by the funding agencies.
ACKNOWLEDGMENTACKNOWLEDGMENT