Comparing Two Advection Solvers in MAQSIP Shiang-Yuh Wu, Prasad Pai, Betty K. Pun AER San Ramon, CA 17 March 2000

Introduction Accurate numerical treatment of advection is important because errors can propagate in other processes (e.g., chemistry, deposition) Desired Properties –Mass conservation –Small numerical diffusion –Small phase errors –Positive definite –Monotonic

Bott Scheme and QSTSE Bott Concentrations represented by a 4th order polynomial within each cell Temporal integration by flux form discretization QSTSE Concentrations represented by quintic spline interpolators Temporal integration by Taylor series expansion (2 or 4 terms)

2-D Rotating Cone Test From Nguyen and Dabdub (2000): BottQSTSE (1) Peak (100)77101 Mass Conservation Mass Distribution Relative Time (1) 4th Order Taylor Expansion

MAQSIP Framework Model Builder Configuration File Chemical Mechanism Horizontal Advection Chemical Solver Bott Smol CB4 MQSSA other processes Compilation statements Link statements Libraries Include statements // driver module driver // generalized coordinates module sigma // chemistry module radm2 //advection module bot // diffusion modulediff //getstep modulegetstep // process analysis module pa-radm2...

Base Case Model Simulation SCAQS August 1987 Domain: 63 x 28 grid cells, consistent with previous modeling exercises Grid Resolution: 5 km MM5 used to generate input meteorology Emissions originated from UAM simulation

Upwind Simulation Results

Upwind Numerical Diffusion (Nashville Example)

Nashville Plume-in-Grid Simulation

Simulation Results at Other Sites

Effects of Courant Number Decreasing CN from 0.7 to 0.5 Increases computational time by 30% Results in a maximum difference of 25 ppb after 120 hours

Conclusions It is quite straightforward to incorporate a new advection solver into MAQSIP QSTSE shows improved performance compared to Bott solver –Reduced numerical diffusion at upwind locations –Higher concentrations at downtown and downwind locations A Courant number of 0.5 or less is recommended

Acknowledgements This work was funded by EPRI (WO ) QSTSE was obtained from Prof. Donald Dabdub of University of California, Irvine