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Update on GOES Radiative ProductsRichard T. McNider, Arastoo Pour Biazar, Andrew White
University of Alabama in Huntsville
Daniel Cohan, Rui Zhang
Rice University
Presented at:
AQAST St. Louis
Visible Radiative Forcing Has a Major Impact on the Chemical Atmosphere
• Models have difficulty getting clouds at the right place and right time
• Satellite observations have the potential to correct cloud errors
LSM describing land-atmosphere interactions
Physical Atmosphere
Boundary layer developmentFluxes of heat and
moisture
Chemical Atmosphere
Atmospheric dynamics and microphysics
Natural and antropogenic emissionsSurface removal
Photochemistry and oxidant formation
Transport and transformation of pollutants
Aerosol Cloud
interaction
Winds, temperature, moisture, surface
properties and fluxes
hv
Biogenic Volatile Organic Compounds (BVOC) Emissions
BVOC is a function of radiation and temperature
NOx + VOC + hv O3
BVOC estimates depend on the amount of radiation reaching the canopy (i.e. Photosynthetically Active Radiation (PAR)) and temperature.
Large uncertainty is caused by the model insolation estimates that can be corrected by using satellite-based PAR in biogenic emission models (Guenther et al. 2012)
T & R
SUN
BL OZONE CHEMISTRY
O3 + NO
-----> NO2 + O2
NO2 + h (<420 nm) -----> O3 + NOVOC + NOx + h
-----> O3 + Nitrates
(HNO3, PAN, RONO2)
g
c
h
g
)(. 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 model cloud prediction results in significant under-/over-prediction of BVOCs. Use of satellite cloud information greatly improves BVOC Emission estimates.
Satellite-Derived Insolation
Cloud top Determined from
satellite IR temperature
Obs Pyranometer
Sat
elli
te
GOES Insolation Bias Increases From West to East The clear sky bias is partly due to the lack of a dynamic precipitable water in retrieval
algorithm. The retrievals will be re-processed to correct this issue.
VA
KS
TN
New GOEs Insolation Product
1. Includes automatic checks on sensor calibration
2. Includes new variable water vapor product
Figure 1. Station locations: red triangles indicate SURFRAD sites and black squares indicate SCAN sites.
New Water Vapor Dependent Product Original UAH Product
September 2013 Discovery AQ
New NOAA ProductWRF
With variable precipitable
water
Original UAH Product
Satellite-Derived Photosynthetically Active Radiation (PAR)
Based on Stephens (1978), Joseph (1976), Pinker and Laszlo (1992), Frouin and
Pinker (1995)
WRF uses 0.5 CF
Satellite-derived insolation and PAR for September 14, 2013, at 19:45 GMT.
GOES Insolation Bias Increases From West to East The clear sky bias is partly due to the lack of a dynamic precipitable water in retrieval
algorithm. The retrievals will be re-processed to correct this issue.
Performing bias correction before converting to PAR
PAR evaluation
against SURFRAD
stations for August 2006
PAR WRF PAR Cloud Corr
PAR UMDPAR UAH
Improving Cloud Simulation in WRF Through Assimilation of GOES Satellite Observations
Andrew White1, Arastoo Pour Biazar1, Richard McNider1, Kevin Doty1, Bright Dornblaser2
1. University of Alabama in Huntsville
2. Texas Commission on Environmental Quality (TCEQ)
Assimilation Technique
Approach: Create a dynamic environment in the WRF that is supportive of cloud formation and removal through the use of GOES observations.
Makes use of GOES derived cloud albedos to determine where WRF under-predicts and over-predicts clouds.
Developed an analytical technique for determining maximum vertical velocities necessary to create and dissipate clouds within WRF.
Use a 1D-VAR technique similar to O’Brien (1970) to minimally adjust divergence fields to support the determined maximum vertical velocity. Inputs for 1D-VAR: target maximum vertical velocity (Wtarget), target height
for the maximum vertical velocity (Ztarget), bottom adjustment height (ADJ_BOT), top adjustment height (ADJ_TOP)
Agreement Index for Determining Model Performance
𝐴𝐼=[ 𝐴+𝐷 ]
[𝐴+𝐵+𝐶+𝐷 ]
CLOUDY CLEARCLOUDY A BCLEAR C D
Model
GOES
August 12th, 2006 at 17UTC
Underprediction
Overprediction
August 12th, 2006 – 17UTC
CNTRL AI = 66.8% Assim AI = 82.0%
Assimilation technique shows large gains in agreement index. Very effective at both producing and dissipating
clouds.
InsolationCNTRL GOES
Assim
Better pattern agreement between
assimilation simulation and GOES is also observed for insolation.
Radiative Impacts
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100
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180 Insolation Gross Mean Error [36km]
36km.CNTRL
36km.Assim
Date
Err
or [W
/m2]
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180Insolation Gross Mean Error [12km]
12km.CNTRL
12km.Assim
Date
Err
or [W
/m2]
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0
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250 Insolation Gross Mean Error [4km]
4km.CNTRL
4km.Assim
Date
Err
or [W
/m2]
Status of UAH GOES Radiative Archive
1. Data is being reprocessed using calibration technique based on pyranometer comparisons
2. Data being reprocessed with new water vapor product (new pyranometer calibration may be developed)
3. PAR product will be provided in archive4. Improved process to provide model gridded data5. Working with Jim Szykman to connect to EPA
Satellite Portal
Acknowledgment
The findings presented here were accomplished under partial support from NASA Science Mission Directorate Applied Sciences Program and the Texas Air Quality Research Program (T-AQRP).
Note the results in this study do not necessarily reflect policy or science positions by the funding agencies.
