1

Seasonal Modeling: Comparison of Phases 1 and 2

Emission inputs to CMAQ

Shaheen R. Tonse

Lawrence Berkeley National Laboratory

CCOS Technical Committee MeetingSacramento, 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

CCOS4km res.190 x 190

SARMAP4km res.96 x 117

Vertical resolution: 27 layers. Lowest layers: 20m thickUppermost 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

Area emissions: NOx

0.0E+00

4.0E+05

8.0E+05

1.2E+06

1.6E+06

2.0E+06

0 12 0 12 0 12 0 12 0 12 0 12

hour/day

sum

ove

r th

e S

AR

MA

P d

om

ain

(m

ole

s/h

ou

r)

former emissions new emissions

211 212 213 214 215 216

9

Area Emissions

Area emissions: VOC

0.0E+00

2.0E+05

4.0E+05

6.0E+05

8.0E+05

1.0E+06

1.2E+06

0 12 0 12 0 12 0 12 0 12 0 12

hour/day

sum

ove

r th

e S

AR

MA

P d

om

ain

(m

ole

s/h

ou

r)

former emissions new emissions

211 212 213 214 215 216

10

Area Emissions

Double counting of fires intwo counties.

11

Motor Vehicle Emissions

MV emissions: NOx

0.0E+00

4.0E+05

8.0E+05

1.2E+06

1.6E+06

2.0E+06

0 12 0 12 0 12 0 12 0 12

hours/day

sum

ove

r S

AR

MA

P d

om

ain

(m

ole

s/h

ou

r)

former emissions new emissions new-light duty new-heavy duty

211

12

Biogenic Emissions

Biogenic emissions: VOC

0.0E+00

1.0E+06

2.0E+06

3.0E+06

4.0E+06

0 12 0 12 0 12 0 12 0 12 0 12

hour/day

sum

ove

r S

AR

MA

P d

om

ain

(m

ol/

ho

ur)

former emissions new emissions

13

Point Emissions

Point sources: VOC

0.00E+00

5.00E+04

1.00E+05

1.50E+05

2.00E+05

2.50E+05

0 12 0 12 0 12 0 12 0 12 0 12

day/hour

sum

ove

r C

CO

S d

om

ain

(m

ole

s/h

r)

former emissions new emissions

213 214 215212211 216

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

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(%)

CHEM 238K (94%) 7K (32%)

HADV 7K (2.7%) 7K (33%)

HDIFF 634 (-) 633 (2.7%)

VDIF 7K (2.7%) 7K (29%)

ZADV 733 (-) 733 (3%)

(Modules) 253K 23K

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

# PE Time (s)Scalability

(%) Imbalance CHEM (%)

1 255K 100 ---

4 70K 91 11

9 33K 86 16

18 18K 78 20

25 14K 73 20

• 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)