Use of Satellite Radiative Properties to Improve Air Quality Models and Emission Estimates

Arastoo Pour Biazar Richard T. McNider

Kevin Doty Andrew White

University of Alabama in Huntsville

Presented at:

Air Quality Applied Sciences Team 8th Semi-Annual Meeting (AQAST 8) December 2-4, 2014

Georgia Institute of Technology

Clouds:

• Impact all of the above.

• Impact photolysis rates (impacting photochemical reactions for ozone and fine particle formation). Also impacting biogenic HC emissions.

• Impact transport/vertical mixing, LNOx, aqueous chemistry, wet removal, aerosol growth/recycling and indirect effects.

BL Heights: • Affects dilution and pollutant concentrations.

THE ROLE OF PHYSICAL ATMOSPHERE IN AIR QUALITY CHEMISTRY Insolation/Temperature:

• impacts biogenic emissions (soil NO, isoprene) as well as anthropogenic evaporative loses.

• Affects chemical reaction rates and thermal decomposition of nitrates.

Moisture:

• Impacts gas/aerosol chemistry, as well as aerosol formation and growth.

Winds:

• Impacts transport/transformation

Use of Satellite Observation to Improve the Performance of Meteorological / Air Quality Models in Support of Regulatory AQ

Community

Targeting the needs of regulatory air quality community (SIP and SIP related activities).

Utilizing satellite observation for improved representation of clouds and their radiative impact: photolysis rates, biogenic emissions, vertical mixing, etc.

Activities Within AQAST

Improved representation of chemical reactions:

Satellite-derived photolysis rate (TT projects: AQ Reanalysis and DYNAMO)

Improved estimate of biogenic emissions:

Satellite-derived PAR (Photosynthetically Active Radiation) (TT projects: AQ Reanalysis and DYNAMO)

Improved near surface temperature (TT projects: AQ Reanalysis and DYNAMO).

Better data archiving/delivery:

Improving website: http://satdas.nsstc.nasa.gov/.

Coordinating with RSIG.

ADJUSTING PHOTOLYSIS RATES IN CMAQ BASED ON GOES OBSERVED CLOUDS

NO, NO2, O3 & JNO2 Differences (Satellite-Control)(Point A: x=38:39, y=30:31, lon=-95.3, lat=29.7)

-25

-20

-15

-10

-5

0

5

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25

8/24/00 0:00 8/25/00 0:00 8/26/00 0:00 8/27/00 0:00 8/28/00 0:00 8/29/00 0:00 8/30/00 0:00 8/31/00 0:00 9/1/00 0:00

Date/Time (GMT)

Conc

entra

tion

(ppb

)

NO NO2 O3 JNO2 (/min) The differences between NO, NO2, O3 (ppb) and JNO2 from satellite cloud assimilation and control simulations for a selected grid cell over Houston-Galveston area.

Adapted from: Pour-Biazar et al., 2007

This technique is included in the standard release of CMAQ

Cloud 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 cloud information

Observed O3 vs Model Predictions(South MISS., lon=-89.57, lat=30.23)

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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

Clouds at the Right Place and Time

• Current Method for insolation and photolysis while improving ozone predictions, physical atmosphere is inconsistent with model dynamics and cloud fields

• What if we can create an environment that is conducive to cloud formation/removal.

W>0

W<0

0.65um VIS surface, cloud features

FUNDAMENTAL APPROACH

Use satellite cloud top temperatures and cloud albedos 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.

W<0

W>0

Underprediction

Overprediction

Satellite Model/Satellite comparison

August 12th, 2006 – 17UTC

CNTRL AI = 67.3% Assim AI = 82.6%

Assimilation technique shows large gains in agreement index. Very effective at both producing and dissipating clouds.

36 km Results

0.50

0.55

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Agre

emen

t Ind

ex

Date

Agreement Index [36km] 36km.CNTRL 36km.Assim

0.00%

5.00%

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15.00%

20.00%

25.00%

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Perc

ent C

hang

e

Date

Percent Change [36km]

The assimilation technique improved agreement between model and GOES observations.

The daily average percentage change over the August 2006 time period was determined to be 15%.

Clear Cloud

Clear A B

Cloud C D

MODELAI = (A+D)/G

G=(A+B+C+D)

12-km Statistics

Temperature (K)

0

0.2

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1

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Days

Bias

TX12.cntrl TX12.assimBDY36 TX12.assim

Mixing Ratio (g/kg)

-1.4-1.2

-1-0.8-0.6-0.4-0.2

0

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Days

Bias

TX12.cntrl TX12.assimBDY36 TX12.assim

CONTROL

ASSIMILATION

CONTROL ASSIMILATION

Temperature bias is reduced.

Mixing ratio bias is decreased.

Cloud Albedo

CNTRL

Satellite

Assim

Better agreement in cloud pattern between assimilation simulation and GOES.

Satellite-Derived Photosynthetically Active Radiation (PAR)

Based on Stephens (1978), Joseph (1976),

Pinker and Laszlo (1992), Frouin and Pinker (1995)

GOES Insolation Bias Increases From West to East

We are working with George Diak to correct this issue.

Performing bias correction before converting to PAR

Satellite-derived insolation and PAR for September 14, 2013, at 19:45 GMT.

PAR Requests and Status of Deliverables

2013: Requests: DYNAMO, Texas Discover-AQ studies Status: Processed and delivered

2012: Requests: The Texas Commission on Environmental Quality (TCEQ) Status: Being processed.

2011: Requests: Wisconsin Department of Natural Resources (DNR),

DYNAMO Status: June-July 2011 has been processed and delivered. Rest of

the year being processed. 2006:

Requests: For evaluation purposes. Status: Being processed.

We continue to support AQAST activities regarding improved AQ model performance.

Currently there is a high demand for archived PAR product and we are trying to produce a reasonable product to be used as a preliminary version of data.

We are hoping to include our WRF cloud correction technique in other AQAST modeling activities.

Summary

Acknowledgment

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.

ADDITIONAL SLIDES

TYPE1

TYPE2

TYPE4

TYPE3

TYPE5

TYPE6

TYPE6

TYPE1

TYPE6

albedo temp(K)

TYPE1 a ≤ 0.4 ≥ 260 Low Clouds Stratocumulus

TYPE2 0.4< a ≤ 0.8 ≥ 260 Low Clouds cumulus

TYPE3 a > 0.8 260 Low Clouds Cumulunimbus

TYPE4 a > 0.8 < 260 High Clouds Nimbostratus

TYPE5 0.4< a ≤ 0.8 < 260 High Clouds Altostratus

TYPR6 a ≤ 0.4 < 260 High Clouds Cirrus

SUN

BL OZONE CHEMISTRY O3 + NO -----> NO2 + O2 NO2 + hν (λ<420 nm) -----> O3 + NO VOC + NOx + hν -----> O3 + Nitrates (HNO3, PAN, RONO2)

αg

αc

hν

αg

)(. cldcldcld absalb1tr +−=

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

Inaccurate cloud prediction results in significant under-/over-prediction of ozone. Use of satellite cloud information greatly improves O3 predictions.

Photolysis Adjustment (CMAQ-4.7)

Cloud top Determined from

satellite IR temperature

Cloud Base

Determined from LCL

Satellite Method WRF Method Photolysis Rates

Transmittance =

1- reflectance - absorption

Observed by satellite F(reflectance)

Cloud top

Determined from satellite IR temperature

Cloud top

Transmittance

Determined from LWC = f(RH, T) and

assumed droplet size

Determined from model

Determined from MM5

Cloud Base

Assume WRF derived cloud distribution is correct

[ ]

[ ]))cos()((

))cos(.(

θα

θ

cldiclearabove

cldclearbelow

tr1cfrac1JJ

1tr61cfrac1JJ

−+=

−+=

)( f134e5tr

cldcld

cld

−+−

=−

τ

τ

transmittance

Correcting Over-Prediction Objective: Create subsidence within the

model to evaporate cloud droplets. Determine the model layer with the

maximum amount of cloud liquid water (CLW).

Determine the location that a parcel located at ZMaxCLW can be pushed down to so that it evaporates.

1D-VAR Inputs:

𝑤𝑤𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = −𝑍𝑍𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡−𝑍𝑍𝑝𝑝𝑡𝑡𝑡𝑡_𝑚𝑚𝑚𝑚𝑚𝑚

∆𝑡𝑡

𝑍𝑍𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 𝑍𝑍𝑀𝑀𝑡𝑡𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 ADJ_TOP = Zctop + 1000. [m] ADJ_BOT = Zpar_mod – 1000 [m]

Zctop

Zbase

Zparcel_mod

ADJ_TOP

ADJ_BOT

Ztarget

∆Z

Correcting Under-Prediction Objective: Lift a parcel to saturation. Use GOES derived cloud top

temperature and cloud albedo to estimate the location and thickness of the observed cloud.

Use this estimated cloud thickness to determine the minimum height a parcel at a given model location needs to be lifted to reach saturation.

1D-VAR Inputs: 𝑤𝑤𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 𝑍𝑍𝑆𝑆𝑡𝑡𝑡𝑡𝑆𝑆𝑡𝑡𝑡𝑡𝑡𝑡𝑆𝑆𝑚𝑚𝑆𝑆−𝑍𝑍𝑝𝑝𝑡𝑡𝑡𝑡_𝑚𝑚𝑚𝑚𝑚𝑚

∆𝑡𝑡

𝑍𝑍𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 𝑍𝑍𝑆𝑆𝑡𝑡𝑡𝑡𝑆𝑆𝑡𝑡𝑡𝑡𝑡𝑡𝑆𝑆𝑆𝑆𝑆𝑆 ADJ_TOP = 𝑍𝑍𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 + Cloud

Depth ADJ_BOT = Zpar_mod – 1000 [m]

ZSaturation

Zparcel_mod

ADJ_TOP

ADJ_BOT

∆Z

Air Quality Modeling Systems

LSM describing land-atmosphere interactions

Physical Atmosphere

Boundary layer development Fluxes of heat and

moisture

Clouds and microphysical

processes

Chemical Atmosphere

Atmospheric dynamics

Natural and antropogenic emissions Surface removal

Photochemistry and oxidant formation

Heterogeneous chemistry,

aerosol

Transport and transformation of pollutants

Aerosol Cloud

interaction

Winds, temperature, moisture, surface

properties and fluxes

SCIENCE +

ART

Use of Satellite Observation to Improve the Performance of Meteorological / Air Quality Models in Support of Regulatory AQ

Community Motivation: To improve the fidelity of the physical atmosphere in air quality modeling

systems such as WRF/CMAQ.

Models are too smooth and do not maintain as much energy at higher frequencies as observations. Surface properties and clouds are among major model uncertainties causing this problem. NWS stations are too sparse for model spatial resolution and are not representative of the grid averaged quantity. On the other hand, satellite data provide pixel integral quantity compatible with model grid.

Relevant Activities: Targeting the needs of regulatory air quality community (SIP and SIP related

activities).

Utilizing satellite observation for improved representation of clouds and their radiative impact: photolysis rates, biogenic emissions, vertical mixing, etc.

Improving representation of surface energy budget and boundary layer evolution by utilizing satellite observation: Insolation, albedo, Moisture availability, and bulk heat capacity.

Insolation

CNTRL

Satellite

Assim

Better agreement in cloud pattern between assimilation simulation and GOES is also observed for insolation.