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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

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:

-- statistical evaluation simulated and observed

meteorological parameter values

-- statistical evaluation of observed and simulated

air quality parameter values

Subjective Approaches:

-- spatial comparison

-- conceptual review


Air Quality Model Performance

Ozone Model : Ozone Concentration = 85 ppb

Mean Normalized Bias +/- 15 %


Alternative Wind Fields

CALMET (objective/prognostic hybrid)

MM5 Prognostic without Obs. FDDA1/

MM5 Prognostic with Obs. FDDA1/

1/ numerious itternations


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


Statistical Analysis

“There are 3 kinds of lies:

lies,

damn lies,

and statistics”

(attrib: Benjamin Disraeli)


Observed surface winds for July 31, 2000 at 0200 PDT. Wind

vectors are shown as 1-hour wind run.




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

the MM5 model without observational FDDA (MM5_N2)


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

the MM5 model with observational FDDA (MM5_N1)


Guidance on Use of Data Assimilation using

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.”


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

using the MM5 model without FDDA (MM5_N2)


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

the MM5 model with observational FDDA (MM5_N1)


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

the MM5 model with observational FDDA (MM5_N1)


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

the MM5 model with observational FDDA (MM5_N1)


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

the MM5 model with observational FDDA (F02)


ABL Height Comparisons using

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


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

the MM5 model with without FDDA (MM5_N2)


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

the MM5 model with observational FDDA (MM5_N1)


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

the MM5 model with without FDDA (MM5_N2)


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

the MM5 model with observational FDDA (MM5_N1)


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

the MM5 model with without FDDA (MM5_N2)


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

the MM5 model with observational FDDA (MM5_N1)


CAMx/MM5 (w/FDDA)/SAPRC99 using

July 31, 2000


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

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


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

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


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

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


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

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


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

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


July, 1990 Episode July 31, at 1700 PDT


Simulated surface winds for July 9, 1999 at 1100 PDT using July 31, at 1700 PDT

the MM5 model with observational FDDA (F02)


Simulated surface winds for July 10, 1999 at 1100 PDT using July 31, at 1700 PDT

the MM5 model with observational FDDA (F02)


Simulated surface winds for July 11, 1999 at 1100 PDT using July 31, at 1700 PDT

the MM5 model with observational FDDA (F02)


Simulated 500-m winds for July 10, 1999 at 1100 PDT using July 31, at 1700 PDT

the MM5 model with observational FDDA (F02)


Simulated 500-m winds for July 11, 1999 at 1100 PDT using July 31, at 1700 PDT

the MM5 model with observational FDDA (F02)


Simulated 500-m winds for July 12, 1999 at 1100 PDT using July 31, at 1700 PDT

the MM5 model with observational FDDA (F02)


Ozone Model Performance (USEPA, 1991)* for the CCOS July 31, at 1700 PDT

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


Hypothesis July 31, at 1700 PDT

Meteorological fields generated using MM5

will tend to be more diffusive with lower

pollutant concentrations spread over larger

areas.


Inert Tracer Analysis July 31, at 1700 PDT

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


Concluding Remarks July 31, at 1700 PDT

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.


Concluding Remarks (cont.) July 31, at 1700 PDT

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.


Concluding Remarks (cont.) July 31, at 1700 PDT

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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