1. Update on GOES Radiative Products Richard T. McNider, Arastoo Pour Biazar, Andrew White University of Alabama in Huntsville Daniel Cohan, Rui Zhang Rice University Presented at: AQAST St. Louis

2. 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 development Fluxes of heat and moisture Chemical Atmosphere Atmospheric dynamics and microphysics Natural and antropogenic emissions Surface removal Photochemistry and oxidant formation Transport and transformation of pollutants Aerosol Cloud interaction Winds, temperature, moisture, surface properties and fluxes

3. hv Biogenic Volatile Organic Compounds (BVOC) Emissions BVOC is a function of radiation and temperature NOx + VOC + hv  O 3  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

4. SUN BL OZONE CHEMISTRY O3 + NO----->NO2 + O2 NO2 + h ( O3 + NO VOC + NOx + h -----> O3 + Nitrates (HNO3, PAN, RONO2) gg cc h gg 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

5. Obs Pyranometer Satellite

6. 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

7. New GOEs Insolation Product 1.Includes automatic checks on sensor calibration 2.Includes new variable water vapor product

8. Figure 1. Station locations: red triangles indicate SURFRAD sites and black squares indicate SCAN sites.

9. New Water Vapor Dependent Product Original UAH Product September 2013 Discovery AQ New NOAA Product WRF

10. With variable precipitable water Original UAH Product

11. Satellite-Derived Photosynthetically Active Radiation (PAR) Based on Stephens (1978), Joseph (1976), Pinker and Laszlo (1992), Frouin and Pinker (1995)

12. WRF uses 0.5 CF

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

14. 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.

15. Performing bias correction before converting to PAR

16. PAR evaluation against SURFRAD stations for August 2006 PAR WRF PAR Cloud Corr PAR UMD PAR UAH

17. Improving Cloud Simulation in WRF Through Assimilation of GOES Satellite Observations Andrew White 1, Arastoo Pour Biazar 1, Richard McNider 1, Kevin Doty 1, Bright Dornblaser 2 1.University of Alabama in Huntsville 2.Texas Commission on Environmental Quality (TCEQ)

18. 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 (W target ), target height for the maximum vertical velocity (Z target ), bottom adjustment height (ADJ_BOT), top adjustment height (ADJ_TOP)

19. Agreement Index for Determining Model Performance August 12 th, 2006 at 17UTC Underprediction Overprediction

20. August 12 th, 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.

21. Insolation CNTRL GOES Assim Better pattern agreement between assimilation simulation and GOES is also observed for insolation.

22. Radiative Impacts

23. 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 archive 4.Improved process to provide model gridded data 5.Working with Jim Szykman to connect to EPA Satellite Portal

24. 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.