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The MM5 Prognostic Meteorological Model. Define the physics of the domain properly and the meteorology fields will be defined properly. Current CCOS Episodes. July 09-13, 1999 July 31, -August 02, 2000. Meteorology Field Evaluations. Objective Approaches:

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Presentation Transcript
slide1

The MM5 Prognostic

Meteorological Model

Define the physics of the domain properly

and the meteorology fields will be defined

properly.

slide2

Current CCOS Episodes

July 09-13, 1999

July 31, -August 02, 2000

slide3

Meteorology Field Evaluations

Objective Approaches:

-- statistical evaluation simulated and observed

meteorological parameter values

-- statistical evaluation of observed and simulated

air quality parameter values

Subjective Approaches:

-- spatial comparison

-- conceptual review

slide4

Air Quality Model Performance

Ozone Model : Ozone Concentration = 85 ppb

Mean Normalized Bias +/- 15 %

slide5

Alternative Wind Fields

CALMET (objective/prognostic hybrid)

MM5 Prognostic without Obs. FDDA1/

MM5 Prognostic with Obs. FDDA1/

1/ numerious itternations

slide6

Ozone Model Performance (USEPA, 1991)* for the CCOS

July/August, 2000 Episode Using CAMx/SAPRC99f

Jul 31 Aug 01 Aug 02

UPkR NB UPkR NB UPKR NB

ppb % ppb % ppb %

---------------------------------------------------

CMHb Model

SF Bay Area 0.97 +061.14 -04 1.16 -41

Sacramento 1.35 +09 0.99 00 0.99 -10

Southern SJV 1.10 -02 1.03 -10 0.88 -09

MM5_N1 Model (w/ FDDA)

SF Bay Area 0.88 +01 1.05 -11 1.04 -37

Sacramento 1.22 +02 1.00 -10 0.90 -18

Southern SJV 0.95 -07 0.88 -17 0.73 -19

MM5_N2 Model (wo/ FDDA)

SF Bay Area 0.98 +03 1.11 -23 1.16 -14

Sacramento 1.32 +08 0.93 -18 0.96 -03

Southern SJV 1.06 -03 1.03 -11 0.78 -19

---------------------------------------------------

UPkR -- Unpaired Peak Ratio

NB -- Paired Mean Normalized Bias

* simulations meeting USEPA model performance guidelines are highlighted

California Air Resources Board/PTSD April, 2005

slide7

Statistical Analysis

“There are 3 kinds of lies:

lies,

damn lies,

and statistics”

(attrib: Benjamin Disraeli)

slide11

Simulated surface winds for August 02, 2000 at 0200 PDT using

the MM5 model without observational FDDA (MM5_N2)

slide12

Simulated surface winds for August 02, 2000 at 0200 PDT using

the MM5 model with observational FDDA (MM5_N1)

slide13

Guidance on Use of Data Assimilation

USEPA. 2005. “Guidance on the Use of Models and Other

Analyses in Attainment Demonstrations for the 8-hour Ozone

NAAQS” Draft Final. USEPA. February, 2005.

“…if used improperly, FDDA can significantly degrade overall

model performance and introduce computational artifacts.

Inappropriately strong nudging coefficients can distort the

magnitude of the physical terms in the underlying … equations

and result in ‘patchwork’ meteorological fields with strong

gradients between near-site grid cells and the remainder of the

grid.”

slide14

Simulated Mixing Heights (m) for August 01, 2000 at 1700 PDT

using the MM5 model without FDDA (MM5_N2)

slide15

Simulated Mixing Heights (m) for August 01, 2000 at 1700 PDT using

the MM5 model with observational FDDA (MM5_N1)

slide16

Simulated Mixing Heights (m) for August 02, 2000 at 1700 PDT using

the MM5 model with observational FDDA (MM5_N1)

slide17

Simulated Mixing Heights (m) for July 11, 1999 at 1700 PDT using

the MM5 model with observational FDDA (MM5_N1)

slide18

Simulated Mixing Heights (m) for July 12, 1999 at 1700 PDT using

the MM5 model with observational FDDA (F02)

slide19

ABL Height Comparisons

(Colored contours are TKE, and dots indicate the observed ABL height)

slide20

Simulated 500-m winds for August 01, 2000 at 0600 PDT using

the MM5 model with without FDDA (MM5_N2)

slide21

Simulated 500-m winds for August 01, 2000 at 0600 PDT using

the MM5 model with observational FDDA (MM5_N1)

slide22

Simulated surface winds for August 01, 2000 at 1400 PDT using

the MM5 model with without FDDA (MM5_N2)

slide23

Simulated surface winds for August 01, 2000 at 1400 PDT using

the MM5 model with observational FDDA (MM5_N1)

slide24

Simulated surface winds for July 31, 2000 at 1700 PDT using

the MM5 model with without FDDA (MM5_N2)

slide25

Simulated surface winds for July 31, 2000 at 1700 PDT using

the MM5 model with observational FDDA (MM5_N1)

slide27

Simulated Surface winds for and ozone concentrations for July 31, at 1300 PDT

using the MM5 model with observational FDDA (B01) and CAMx/SAPRC99

slide28

Simulated Surface winds for and ozone concentrations for July 31, at 1400 PDT

using the MM5 model with observational FDDA (B01) and CAMx/SAPRC99

slide29

Simulated Surface winds for and ozone concentrations for July 31, at 1500 PDT

using the MM5 model with observational FDDA (B01) and CAMx/SAPRC99

slide30

Simulated Surface winds for and ozone concentrations for July 31, at 1600 PDT

using the MM5 model with observational FDDA (B01) and CAMx/SAPRC99

slide31

Simulated Surface winds for and ozone concentrations for July 31, at 1700 PDT

using the MM5 model with observational FDDA (B01) and CAMx/SAPRC99

slide33

Simulated surface winds for July 9, 1999 at 1100 PDT using

the MM5 model with observational FDDA (F02)

slide34

Simulated surface winds for July 10, 1999 at 1100 PDT using

the MM5 model with observational FDDA (F02)

slide35

Simulated surface winds for July 11, 1999 at 1100 PDT using

the MM5 model with observational FDDA (F02)

slide36

Simulated 500-m winds for July 10, 1999 at 1100 PDT using

the MM5 model with observational FDDA (F02)

slide37

Simulated 500-m winds for July 11, 1999 at 1100 PDT using

the MM5 model with observational FDDA (F02)

slide38

Simulated 500-m winds for July 12, 1999 at 1100 PDT using

the MM5 model with observational FDDA (F02)

slide39

Ozone Model Performance (USEPA, 1991)* for the CCOS

July, 1999 Episode Using CAMx/SAPRC99f

Jul 10 Jul 11 Jul 12 Jul 13

UPkR NB UPkR NB UPKR NB UPKR NB

-na- % -na- % -na- % -na- %

--------------------------------------------------------------

CMHb Model

SF Bay Area 1.11 +07 1.09 -10 0.92 -08 1.19 -24

Sacramento 1.04 -03 1.07 -12 1.20 -04 1.08 -07

Central SJV 1.09 -14 0.91 -10 1.05 +01 1.03 +09

Southern SJV 0.92 -17 0.88 -25 1.39 -02 1.29 +10

F02 Model (w/ FDDA)

SF Bay Area 0.91 -14 1.04 -07 0.99 -02 -- --

Sacramento 0.74 -25 0.93 -18 1.13 -09 -- --

Central SJV 0.81 -20 0.78 -26 0.85 -21 -- --

Southern SJV 0.72 -33 0.78 -29 1.28 -13 -- --

--------------------------------------------------------------

UPkR -- Unpaired Peak Ratio

NB -- Paired Mean Normalized Bias

* simulations meeting USEPA model performance guidelines are highlighted

California Air Resources Board/PTSD April, 2005

slide40

Hypothesis

Meteorological fields generated using MM5

will tend to be more diffusive with lower

pollutant concentrations spread over larger

areas.

slide41

Inert Tracer Analysis

Arbitrary Grid Cell in the Delta: ~ Pittsburg

~ San Francisco

Daily Inert Surface-Level Emissions: 0600-0800 PDT

Concentration Intervals: ~ * 3.1

Color Tags: CAMx/MM5

CAMx/CALMET

slide42

Concluding Remarks

Aside from the uncertainties inherent in the MM5 Prognostic

model, the use of observational FDDA distorts the simulated wind

fields leading to inconsistent flow patterns, incoherent mixing

heights, and increased mass divergence,. These effects may

misrepresent ozone formation in complex modeling domains, and

overestimate the dilution of air pollutants transported over any

significant distance.

slide43

Concluding Remarks (cont.)

Using almost any standard of objective or subjective comparison,

based on either meteorological or air quality simulation results, the

meteorological fields generated using the MM5 prognostic model

are not as satisfactory those generated using the CALMET hybrid

model.

slide44

Concluding Remarks (cont.)

The successful application of the MM5 model for the generation

of meteorological inputs required for air quality modeling in

California will not happen until a number of deficiencies are

addressed. Among them:

-- the model is too sensitive to changes in terrain elevation.

-- relatively large air temperature errors suggest poor representation

of the surface energy balance.

-- observational FDDA can not be relied upon to improve wind

field performance in a fine-scale domain with complex topography

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