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

Transcription

1 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

2 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. BL Heights: Affects dilution and pollutant concentrations. Winds: Impacts transport/transformation 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.

3 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.

4 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: Coordinating with RSIG.

5 ADJUSTING PHOTOLYSIS RATES IN CMAQ BASED ON GOES OBSERVED CLOUDS 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 NO, NO2, O3 & JNO2 Differences (Satellite-Control) (Point A: x=38:39, y=30:31, lon=-95.3, lat=29.7) Concentration (ppb) /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) 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., Observed O3 Model (cntrl) Model (satcld) Observed O3 vs Model Predictions (South MISS., lon=-89.57, lat=30.23) OBSERVED ASSIM 60 (CNTRL-SATCLD) Ozone Concentration (ppb) CNTRL -20 Under-prediction -40 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)

6 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

7 FUNDAMENTAL APPROACH Satellite Model/Satellite comparison Underprediction W<0 W>0 0.65um VIS surface, cloud features Overprediction 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.

8 August 12 th, UTC CNTRL AI = 67.3% Assim AI = 82.6% Assimilation technique shows large gains in agreement index. Very effective at both producing and dissipating clouds.

9 36 km Results Agreement Index [36km] Agreement Index km.CNTRL 36km.Assim The assimilation technique improved agreement between model and GOES observations. AI = (A+D)/G MODEL Clear Cloud Clear A B 0.55 Cloud C D 0.50 G=(A+B+C+D) 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 Date Percent Change [36km] The daily average percentage change over the August 2006 time period was determined to be 15% % 20.00% 15.00% 10.00% 5.00% 0.00% 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 Percent Change Date

10 12-km Statistics Temperature bias is reduced. Bias CONTROL Temperature (K) ASSIMILATION TX12.cntrl TX12.assimBDY36 TX12.assim 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 Mixing ratio bias is decreased. Bias CONTROL Mixing Ratio (g/kg) ASSIMILATION TX12.cntrl TX12.assimBDY36 TX12.assim 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

11 Cloud Albedo Satellite Better agreement in cloud pattern between assimilation simulation and GOES. CNTRL Assim

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

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14 GOES Insolation Bias Increases From West to East We are working with George Diak to correct this issue.

15 Performing bias correction before converting to PAR

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

17 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.

18 Summary 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.

19 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.

20 ADDITIONAL SLIDES

21 TYPE6 TYPE6 TYPE6 TYPE4 TYPE5 TYPE1 TYPE1 TYPE2 TYPE3 albedo temp(k) TYPE1 a Low Clouds Stratocumulus TYPE2 0.4< a Low Clouds cumulus TYPE3 a > 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

22 SUN Photolysis Adjustment (CMAQ-4.7) 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. Inaccurate cloud prediction results in significant under-/overprediction of ozone. Use of satellite cloud information greatly improves O3 predictions. hν α c tr = 1. cld ( alb + abs cld cld ) BL OZONE CHEMISTRY O3 + NO NO2 + hν (λ<420 nm) VOC + NOx + hν Cloud top Determined from satellite IR temperature -----> NO2 + O > O3 + NO -----> O3 + Nitrates (HNO3, PAN, RONO2) α g α g Surface

23 Satellite Method Cloud top Determined from satellite IR temperature Photolysis Rates WRF Method Assume WRF derived cloud distribution is correct Cloud top Determined from model Transmittance = 1- reflectance - absorption Observed by satellite F(reflectance) transmittance Transmittance Determined from LWC = f(rh, T) and assumed droplet size tr cld τ cld 5 e = 4 + 3τ ( 1 f ) cld Cloud Base Cloud Base Determined from LCL J below = J clear [ 1 + cfrac( 1. 6tr cos( θ ) 1) ] cld Determined from MM5 J above = J clear [ 1 + cfrac( α ( 1 tr )cos( θ ))] i cld

24 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 Z MaxCLW can be pushed down to so that it evaporates. 1D-VAR Inputs: ww tttttttttttt = ZZ tttttttttttt ZZ pppppp_mmmmmm tt ZZ tttttttttttt = ZZ MMMMMMMMMMMM ADJ_TOP = Z ctop [m] ADJ_BOT = Z par_mod 1000 [m] Z ADJ_TOP Z ctop Z parcel_mod Z base ADJ_BOT Z target

25 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: ww tttttttttttt = ZZ SSSSSSSSSSSSSSSSSSSS ZZ pppppp_mmmmmm tt ZZ tttttttttttt = ZZ SSSSSSSSSSSSSSSSSSSS ADJ_TOP = ZZ tttttttttttt + Cloud Depth ADJ_BOT = Z par_mod 1000 [m] Z ADJ_TOP Z Saturation Z parcel_mod ADJ_BOT

26 Air Quality Modeling Systems Physical Atmosphere SCIENCE + ART Chemical Atmosphere Clouds and microphysical processes Atmospheric dynamics Boundary layer development Fluxes of heat and moisture LSM describing landatmosphere interactions Aerosol Cloud interaction Winds, temperature, moisture, surface properties and fluxes Heterogeneous chemistry, aerosol Transport and transformation of pollutants Photochemistry and oxidant formation Natural and antropogenic emissions Surface removal

27 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.

28 Insolation Satellite Better agreement in cloud pattern between assimilation simulation and GOES is also observed for insolation. CNTRL Assim