Improving Cloud Simulation in Weather Research and Forecasting (WRF) Through Assimilation of GOES Satellite Observations Andrew White Advisor: Dr. Arastoo Pour Biazar Dr. Richard McNider, Dr. Kevin Doty, Dr. Bright Dornblaser

Motivation  Goal: To improve the simulated clouds fields in the Air Quality model system.  WRF, Sparse Matrix Operator Kernel Emissions (SMOKE), Community Multi-scale Air Quality (CMAQ).  Clouds greatly impact tropospheric chemistry by altering dynamics and chemical processes.  Regulate photochemical reaction rates  Impact boundary-layer development and vertical mixing  Impact surface insolation and temperature leading to changes in biogenic emissions  Wet removal  Generation of NO x by lightning

Background  Errors in simulated clouds is a particular area of concern in State Implementation Plan (SIP) modeling where the best representation of the physical atmosphere is necessary.  Model how an emission control strategy will lead to attainment of the National Ambient Air Quality Standards (NAAQS)  Previous attempts at using satellite data to insert cloud water have had limited success.  Studies have indicated that adjustment of dynamics and thermodynamics is necessary to support 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 the forecast.  Assimilation of radar observations miss non-precipitating clouds.

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)

Description of Over-Prediction Method Z ctop Z base Z parcel_mod ADJ_TOP ADJ_BOT Z target ∆Z

Description of Under-Prediction Method Z Saturation Z parcel_mod ADJ_TOP ADJ_BOT ∆Z

WRF Configuration Domain 1Domain 2 Running PeriodAugust, 2006 Horizontal Resolution36 km12 km Time Step90s30s Number of Vertical Levels 42 Top Pressure of the Model 50 hPa Shortwave RadiationDudhia Longwave RadiationRRTM Surface LayerMonin-Obukhov Land Surface LayerNoah (4-soil layer) PBLYSU MicrophysicsLIN Cumulus physics Kain-Fritsch (with Ma and Tan 2009 trigger function) Grid PhysicsHorizontal Wind Meteorological Input Data EDAS Analysis NudgingYes U, V Nudging Coefficient 3 x T Nudging Coefficient3 x Q Nudging Coefficient1 x Nudging within PBLYes for U and V, NO for q and T

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

36 km Results Based on agreement index, the assimilation technique improved agreement between model and GOES observations. The daily average percentage change over the August 2006 time period was determined to be 14.79%.

36 km Results

August 12 th, 2006 – 17UTC CNTRL AI = 67.3%Assim AI = 82.6% Assimilation technique shows large gains in agreement index. Very effective at both producing and dissipating clouds.

Cloud Albedo CNTRL GOES Assim Better pattern agreement between assimilation simulation and GOES.

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

12 km Results Based on agreement index, the assimilation technique improved agreement between model and GOES observations. The daily average percentage change over the August 2006 time period was determined to be 14.12%.

12 km Results

August 12 th, 2006 – 17UTC CNTRL AI = 67.8%Assim AI = 78.6% Assimilation technique shows large gains in agreement index. Very effective at both producing and dissipating clouds.

Cloud Albedo CNTRL GOES Assim Better pattern agreement between assimilation simulation and GOES.

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

Radiative Impacts

Summary & Future Work  GOES cloud observations were assimilated into WRF for a simulation over the August 2006 time period.  Overall, the assimilation improved model cloud simulation.  Improved the agreement index between the model and GOES observed clouds.  Improved or maintain model statistics with respect to surface observations of wind speed, temperature and mixing ratio.  Improved insolation statistics with respect to GOES observations.  Assess the usefulness of this technique with respect to air quality forecasting.