use of satellite radiative properties to improve air...
TRANSCRIPT
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
10
15
20
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)
-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
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
0.60
0.65
0.70
0.75
0.80
0.85
8/1
8/3
8/5
8/7
8/9
8/11
8/13
8/15
8/17
8/19
8/21
8/23
8/25
8/27
8/29
8/31
Agre
emen
t Ind
ex
Date
Agreement Index [36km] 36km.CNTRL 36km.Assim
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
8/1
8/3
8/5
8/7
8/9
8/11
8/13
8/15
8/17
8/19
8/21
8/23
8/25
8/27
8/29
8/31
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
0.4
0.6
0.8
1
1.2
1.4
8/04
8/05
8/06
8/07
8/08
8/09
8/10
8/11
8/12
8/13
8/14
8/15
8/16
8/17
8/18
8/19
8/20
8/21
8/22
8/23
8/24
8/25
8/26
8/27
8/28
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
8/04
8/05
8/06
8/07
8/08
8/09
8/10
8/11
8/12
8/13
8/14
8/15
8/16
8/17
8/18
8/19
8/20
8/21
8/22
8/23
8/24
8/25
8/26
8/27
8/28
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.