update on goes radiative products richard t. mcnider, arastoo pour biazar, andrew white university...

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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36km.CNTRL

36km.Assim

Date

Err

or [W

/m2]

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Date

Err

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/m2]

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/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.