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

Largest Surface O3 Differences Due to Cloud Errors - August 2006 (SatCld-Cntrl)

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