Cloud Correction and its Impact on Air Quality Simulations

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1 Cloud Correction and its Impact on Air Quality Simulations Arastoo Pour Biazar 1, Richard T. McNider 1, Andrew White 1, Bright Dornblaser 3, Kevin Doty 1, Maudood Khan 2 1. University of Alabama in Huntsville 2. University Space Research Association (USRA) 3. Texas Commission on Environmental Quality (TCEQ) Presented at: The 94 rd AMS Annual Meeting ATlanta, GA 2-6 February 2014 Session 7.3: The Effects of Meteorology on Air Quality - Part 3, 18th Joint Conference on the Applications of Air Pollution Meteorology with the A&WMA

2 Background & Motivation: Clouds greatly impact tropospheric chemistry by altering dynamics as well as atmospheric chemical processes: Altering photochemical reaction rates and thereby impacting oxidant production. Impacting surface insolation and temperature and thereby altering the emissions of key ozone precursors (namely biogenic hydrocarbons and nitrogen oxide.) Impacting boundary-layer development, vertical mixing, and causing deep vertical mixing of pollutants and precursors. Impacting the evolution and recycling of aerosols. Impacting aqueous phase chemistry and wet removal. Causing lightning and generating nitrogen. Unfortunately, numerical meteorological models still have difficulty in creating clouds in the right place and time compared to observed clouds. This is especially the case when synoptic-scale forcing is weak, as often is the case during air pollution episodes.

3 Background & Motivation The errors in simulated clouds is particularly important in State Implementation Plan (SIP) modeling where the best representation of physical atmosphere is required. Previous attempts at using satellite data to insert cloud water have met with limited success. Studies have indicated that adjustment of the model dynamics and thermodynamics is necessary to fully support the insertion of cloud liquid water in models (Yucel, 2003). Jones et al., 2013, assimilated cloud water path in WRF and realized that the maximum error reduction is achieved within the first 30 minutes of forecast. Assimilation of radar observations (Dowell et al., 2010) miss the non precipitating clouds. Assimilation of observed cloud optical depth (Lauwaet et al., 2011) has also shown to improve model performance by improving the model surface temperatures.

4 UAH Approach: Objective: to improve model location and timing of clouds in the Weather Research and Forecast (WRF) model by assimilating GOES observed clouds. Since for air quality, non-precipitating clouds are just as important as precipitating clouds, our metric for success should indicate the radiative impact of clouds. Approach: Create an environment in the model that is conducive to clouds formation/removal through adjusting wind and moisture fields and to improve the ability of the WRF modeling system to simulate clouds through the use of observations provided by the Geostationary Operational Environmental Satellite (GOES). Observed O3 vs Model Predictions (South MISS., lon=-89.57, lat=30.23) 100 Observed O3 Correcting for the radiative impact of clouds corrected 38 ppb underprediction. (Pour Biazar et al 2007) Ozone Concentration (ppb) Model (cntrl) Model (satcld) (CNTRL-SATCLD) CNTRL OBSERVED ASSIM 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)

5 UAH Approach Dynamical Adjustment Photolysis Adjustment (CMAQ) W < 0 W > 0 SUN Cloud top Determined from satellite IR temperature α c hν tr =. ( alb+ abs) cld 1 cld cld BL OZONE CHEMISTRY Use satellite cloud top temperatures and cloud albedoes to estimate a TARGET VERTICAL VELOCITY (Wmax). Adjust divergence to comply with Wmax in a way similar to O Brien (1970). 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. O3 + NO -----> NO2 + O2 NO2 + hν (λ<420 nm) -----> O3 + NO VOC + NOx + hν -----> O3 + Nitrates (HNO3, PAN, RONO2) Nudge model winds toward new horizontal wind field to sustain the vertical motion. α g α g Surface

6 Implementation in WRF Focusing on daytime clouds, analytically estimate the vertical velocity needed to create/clear clouds. Under prediction: Lift a parcel to saturation. Over prediction: Move the parcel down to reduce RH and evaporate droplets. The horizontal wind components in the model are minimally adjusted (O Brien 1970) to support the target vertical velocity. REQUIRED INPUTS FOR 1D VAR: Target W: target vertical velocity (m/s); Target H: where max vertical velocity is reached; Wadj_bot: bottom layer for adjustment; Wadj_top: top layer for adjustment. Implementation in 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 observations: J [ ] below = Jclear 1+ cfrac(1.6tr cos( θ ) 1) J = J [ 1+ cfrac((1 tr)cos( θ ))] above clear Interpolate in between.

7 Model Configuration: WRF Running Period Horizontal Resolution Time Step Number of Vertical Levels Top Pressure of the Model Shortwave Radiation Longwave Radiation Surface Layer Land Surface Layer PBL Microphysics Cumulus physics Grid Physics Meteorological Input Data Analysis Nudging U, V Nudging Coefficient T Nudging Coefficient Q Nudging Coefficient Nudging within PBL Domain km 90s Kain Fritsch (with Ma and Tan 2009 trigger function) 3 x 10 4 Domain 02 August, km 30s mb Dudhia RRTM Monin Obukhov Noah (4 soil layer) YSU LIN Kain Fritsch (with Ma and Tan 2009 trigger function) Horizontal Wind EDAS Yes 1 x x x 10 5 Yes for U and V, NO for q and T Domain 03 4 km 10s NONE 3 x 10 5 Physical Process Vertical diffusion Gas-phase chemistry and solver Aerosol chemistry Dry deposition Cloud dynamics Modeling Domain 36km domain Horizontal and vertical advection Horizontal diffusion Gas and aqueous phase mechanism YAMO MULTISCALE ACM2 4 km CMAQ Reference EBI_CB4 CB4_AE3_AQ AERO3 AERO_DEPV2 CLOUD_ACM 12 km

8 Agreement Index for Measuring Model Performance Areas of disagreement between model and satellite observation A contingency table can be constructed to explain agreement/disagreement with observation Underprediction AI = (A+D)/G Clear MODEL Cloud Clear A B Cloud C D G=(A+B+C+D) Overprediction

9 WRF Results (36 km): Based on Agreement Index Model performance has improved. The improvements are more pronounced at times that the model errors are larger

10 WRF Results (36 km) While RMSE for temperature is reduced, cold bias has increased and dry bias has decreased. This points to an inherent problem other than clouds in the model that is making the control simulation dry and cold.

11 WRF Results (12 km) Similar to 36 km simulation, for 12 km domain cloud assimilation improved Agreement Index. Using the lateral boundary condition from 36 km simulation with assimilation also improves the model performance.

12 WRF Results (12 km) For 12 km domain, unlike the 36 km, temperature shows a positive bias that for some days is improved by assimilation. RMSE and bias for mixing ratio are improved by using the lateral boundary condition from 36 km with assimilation or directly assimilating GOES observations.

13 CMAQ Results (36 km): CONTROL SIMULATION SATCLD SIMULATION Transmissivity CNTRL too opaque compared to satellite NO2 photolysis rate Large differences due to cloud errors

14 Difference in NO2 photolysis rates for selected days (CNTRL-SATCLD) Difference in NO2 photolysis rates between control simulation and the simulation using observed clouds (CNTRL-SATCLD) for August 19, 21,22, and 29, Clouds in control simulation are more spread out and cover large areas (more opaque compared to observation). Over-prediction of Clouds by CNTRL Under-prediction of Clouds by CNTRL

15

16 CNTRL SATCLD Under prediction for higher ozone concentrations is slightly improved due to GOES cloud adjustment. SATCLD_ICBC Night time over prediction is increased in some location while reduced in other locations, but generally it is slightly increased.

17 O3 Statistics 12.0 CNTRL SATCLD ppb or % Mean BIAS (ppb) Mean Bias O3>50 (ppb) Mean Bias O3<50 (ppb) Mean Norm. Bias O3>50 (%) Daytime underprediction is improved -6.0 Statistic

18 Largest Surface O3 Differences Due to Cloud Errors - August 2006 (SatCld-Cntrl)

19 CONCLUSIONS GOES cloud observations were assimilated in WRF/CMAQ modeling system and a month long simulation over August 2006 were performed. Overall, the assimilation improved model cloud simulation. Cloud correction also improved surface temperature and mixing ratio. Cloud correction had significant impact on model ozone predictions. While the monthly daytime ozone bias was reduced by about 2 ppb, ozone differences of up to 40 ppb can be seen at certain times and locations. The largest errors in ozone concentration due to clouds are over urban areas and over Lake Michigan.

20 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.

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