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Comparison of Emission Inputs to CMAQ Phases 1 and 2

This study compares the gridded emission inputs for CMAQ Phases 1 and 2, analyzing area, biogenic, motor vehicle, point and fire sources. Detailed comparisons include temporal profiles and summation of emissions by category. Additionally, a parallel performance analysis of CMAQ v4.5 on a Beowulf Linux Cluster is conducted to evaluate scalability and timing aspects. Results show improvements in the new emissions inventory, particularly in biogenic and motor vehicle emissions. Recommendations are made for refining fire emissions input. This research contributes insights for optimizing air quality modeling processes.

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Comparison of Emission Inputs to CMAQ Phases 1 and 2

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  1. Seasonal Modeling: Comparison of Phases 1 and 2 Emission inputs to CMAQ Shaheen R. Tonse Lawrence Berkeley National Laboratory CCOS Technical Committee Meeting Sacramento, 29th November, 2006

  2. Gridded Emission Comparisons • Compare the sums and temporal profiles of Area, Biogenic, Motor Vehicle and Point sources • Former emissions: Obtained Fall 2004 for Phase 1. For reference purposes: CCAQS4k_Ep000729_AR_rf934_V042104_R0003_SAPRCV5_CAMX • New emissions: Obtained Summer 2006 for Phase 2. For reference purposes: cc.A20000729_00.RF964.arb.20060629.CAMX.SAPRC_V1 (Claire Agnoux, visiting student from France, in summer 2006)

  3. Gridded Emission Comparisons • Emissions summed by hour over: •  SARMAP domain (96 ×117 grid) for area, biogenic and motor vehicle • CCOS domain (190 × 190 grid) for point sources emissions. • Sat July 29th to Wed August 2nd 2000 (Days 211 to 215) • Times on plots are in PDT • Units are either moles/hour or moles/s

  4. SARMAP domain within CCOS domain Vertical resolution: 27 layers. Lowest layers: 20m thick Uppermost layer at P=100 mbar, (16km) is 2km thick CCOS 4km res. 190 x 190 SARMAP 4km res. 96 x 117

  5. NOx emissions by category(next 3 figures from Phase 1 report)

  6. VOC emissions by category

  7. CO emissions by category

  8. Area Emissions

  9. Area Emissions

  10. Area Emissions Double counting of fires in two counties.

  11. Motor Vehicle Emissions

  12. Biogenic Emissions

  13. Point Emissions

  14. Fire Emissions • Xiao Ling Mao (visiting researcher at) and Ling Jin (UCB) compiled fires during episode, by location, duration, acreage. • Summary of sensitivity study to fire emissions: • Very high local influence on ozone and its precursor concentrations. • At upper layers large percentage change in O3 and very concentrated effects • More scattered longer-lasting effects at surface layer

  15. Summary of comparisons Area: Wildfires need to be removed from Tuolumne and Northern Fresno counties Biogenic: VOC emissions have more than doubled in the new emissions. NOx emissions are zero Motor Vehicle: Good improvement in weekend NOx time profile. We do not see any obvious problems. Point: VOCs are down by half in the new inventory Fire: LBNL can provide useful input to improve the fire inventory

  16. A Timing and Scalability Analysis of the Parallel Performance of CMAQ v4.5 on a Beowulf Linux Cluster Shaheen Tonse Lawrence Berkeley National Laboratory Berkeley, CA, USA.

  17. Parallel Performance In general, for parallel codes, improvement in performance scales worse than linearly with number of PEs. • Parts of the code simply not parallelizable. Execute redundantly on all the PEs • Load imbalance between PEs: those with lighter loads wait for others until they have finished • Increased inter-PE communication costs relative to actual computation • Latency: Operations whose cost is dominated by startup costs eg. disk file accesses

  18. Method • Inserted timing calls into CMAQ to measure time spent in various portions of code. • Most timing calls placed in the scientific processes subroutine (SCIPROC) or its daughter subroutines, which calculate the chemistry, horizontal/vertical diffusion, and horizontal/vertical advection. • Measurement of times spent for pure calculation, inter-PE communication, and disk access.

  19. Single PE Benchmark Times SMVGear: dominated by CHEM only EBI: HADV, CHEM and VDIF all contribute

  20. EBI Parallel Performance HADV: scales poorly and expensive CHEM: scales ~100% cost mid-level VDIF: scale and cost both mid-level HDIF: scales poorly but cheap ZADV: scales ~100% and cheap

  21. SMVGear Parallel Performance • Imbalance even for 25PEs is ~20%. Scalability of the overall code good even for 25 PEs. • Chemistry imbalance accounts for much of the scalability loss (since chemistry dominates). (Also note: 100-Scalability  Imbalance)

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