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, 29 th 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.A _00.RF964.arb 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 29 th to Wed August 2 nd 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 CCOS 4km res. 190 x 190 SARMAP 4km res. 96 x 117 Vertical resolution: 27 layers. Lowest layers: 20m thick Uppermost layer at P=100 mbar, (16km) is 2km thick

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

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 O 3 and very concentrated effects More scattered longer-lasting effects at surface layer

16 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

17 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.

18 Parallel Performance In general, for parallel codes, improvement in performance scales worse than linearly with number of PEs. 1.Parts of the code simply not parallelizable. Execute redundantly on all the PEs 2.Load imbalance between PEs: those with lighter loads wait for others until they have finished 3.Increased inter-PE communication costs relative to actual computation 4.Latency: Operations whose cost is dominated by startup costs eg. disk file accesses

19 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.

Single PE Benchmark Times Module Name SMVGear time in seconds(%) EBI time in seconds(%) CHEM238K (94%)7K (32%) HADV7K (2.7%)7K (33%) HDIFF634 (-)633 (2.7%) VDIF7K (2.7%)7K (29%) ZADV733 (-)733 (3%)  (Modules) 253K23K SMVGear: dominated by CHEM only EBI: HADV, CHEM and VDIF all contribute

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

SMVGear Parallel Performance # PETime (s) Scalability (%) Imbalance CHEM (%) 1255K  K K K K7320 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)