The Development of AERMOD-Ready Meteorological Data for ......AERMOD over the entire three year period 2005 – 2007 of hourly meteorology contained in these files are shown in Figure

The Development of AERMOD-Ready Meteorological Data for the South Coast

Air Basin and the Coachella Valley

Final Report

Volume I

Prepared by

EnviroComp Consulting, Inc.

2298 Ocaso Camino Fremont, CA 94539

http://www.envirocomp.com

For

South Coast Air Quality Management District 21865 Copley Drive

Diamond Bar, CA 91765-4178 (909) 396-3520

April 17, 2009

Project: EC-07-015 Report: 09-04-17 (I)

http://www.envirocomp.com/



EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________

Table of Contents

1 Introduction............................................................................................................................. 3

2 Summary of Deliverables ....................................................................................................... 6

3 AERMET Formulation ......................................................................................................... 15

4 Methodology......................................................................................................................... 29

5 Results................................................................................................................................... 54

6 Conclusion ............................................................................................................................ 75

2


1 Introduction

This is the final report submitted by Envirocomp Consulting, Inc.1 to the South Coast Air

Quality Management Division (AQMD) in partial fulfillment of the project entitled “The

Development of AERMOD-Ready Meteorological Data for the South Coast Air Basin and the

Coachella Valley”2. It describes the development of the meteorological input files required to

run AERMOD3, which replaced ISCST34 in November 2005 as the EPA recommended

regulatory dispersion model for short range applications. The AERMOD meteorological input

files, provided to AQMD on a CD accompanying this report, have been developed for 26

different regions (“sub-areas”) that cover the South Coast Air Basin and Coachella Valley. They

will fill the same role for AERMOD as those currently maintained by AQMD for use with

ISCST3, to be downloaded by modelers for regulatory applications of AERMOD within the

AQMD.

The meteorological input files for AERMOD contain hourly records of meteorological variables

needed to run the model. Regulatory applications generally require several years of data, with

five years being the standard. The hourly records include routinely measured variables such as

wind speed, wind direction and temperature, as well as variables derived from these

measurements used to characterize the mean and turbulent structure of the atmospheric boundary

layer. Examples of these derived variables are the surface friction velocity and the convective

velocity scale. The meteorological input files are the outputs of the AERMET5 processor,

developed by EPA.

The development of AERMOD input files consisted of the following steps:

1. Examine meteorological data collected throughout the Southern California area for

availability and completeness.

1 http://www.envirocomp.com2 Project Number #P2008-10 3 Cimorelli, A. J. and coauthors, 2005: “AERMOD : A dispersion model for industrial source applications. Part I:

General model formulation and boundary layer characterization”, J. Applied. Meteorol., 44, 682-693. 4 http://www.epa.gov/scram001/dispersion_alt.htm#isc3 5 http://www.epa.gov/scram001/metobsdata_procaccprogs.htm#aermet

3


http://www.epa.gov/scram001/dispersion_alt.htm#isc3

http://www.epa.gov/scram001/metobsdata_procaccprogs.htm#aermet


2. Check the quality of the data, fill in gaps, and create input files for AERMET, one for

each of the 26 AQMD sub-areas within the district.

3. Run the data through AERMET to generate the surface (.sfc) and profile (.pfl) AERMOD

inputs for AERMOD.

4. Compare AERMET output with available measurements of micrometeorological

variables.

5. Process the input files using software developed by Envirocomp to further check the

hourly entries in the *.sfc and *.pfl files to flag and correct any suspicious values.

A flowchart of this procedure is shown in Figure 1.1.

As part of this project, we also developed and provided to AQMD several post-processors that

operate on the *.sfc and .pfl files output by AERMET. These post-processors can be run at the

discretion of AQMD, and perform the following functions:

1. Adjust the mixed layer height and the associated convective velocity scale in the input

files to account for cases when the one-dimensional boundary layer model in AERMET

cannot account for the two-dimensional effects seen in coastal areas, such as Long Beach.

2. Adjust AERMOD input files to account for change in micrometeorological variables

when the AERMOD application site has different surface parameters than those at the

site at which meteorological observations were made.

3. Construct input files corresponding to specified values of surface roughness, albedo, and

Bowen ratio through adjustments of the existing input files. This processor uses a method

that provides a close approximation to the files that would be produced by re-running

AERMET with the specified values of surface parameters. The primary function of this

processor is to provide a quick assessment of the effects of changes in surface parameters

on AERMOD inputs.

In the following sections of the report, we provide details of the steps used to develop the

AERMOD input files, and describe the post-processors. We also provide a brief description of

the method used in AERMET to construct AERMOD inputs.

4


Figure 1.1: Flowchart of basic procedure to create AERMOD input files for each sub-area within the AQMD.

5


2 Summary of Deliverables

The deliverables for the project are:

• AERMOD input files for each AQMD sub-area (*.sfc and *.pfl). Two sets of files are

provided. The first set assumes values for land surface parameters (surface roughness

length, Bowen ratio, surface albedo) determined by the EPA AERSURFACE processor

for each sub-area. The second set assumes constant values of these three land-surface

parameters, which are applied to all sub-areas.

• The codes for the three post-processors to check and/or modify the AERMOD input files.

• AERMET input files for each sub-area. Among these input files are the “pre-processed”

meteorological data files, which contain the meteorological inputs to AERMET.

• Raw meteorological data files, as well as various interim files and codes, to create the

pre-processed meteorological data files for each sub-area that are input to AERMET.

• Wind roses of the hourly winds used to construct the input files for each sub-area

(Appendix A, and provided separately in electronic files on the accompanying CD)

• User’s Guide (Appendix C)

The AQMD sub-areas and district surface meteorological stations that provided the wind data are

shown in Figure 2.1.

The *.sfc and *.pfl files for each sub-area are the main deliverable. As an example of these, the

leading hourly records for the Anaheim sub-area files (anah.sfc and anah.pfl) are shown in

Figure 2-2 and Figure 2-3, respectively. The full files cover the period 2005 – 2007. An

explanation of the entries in the hourly records contained in these files is as follows, taken from

the Lakes Environmental AERMOD-View6 software help-page:

6 http://www.lakes-environmental.com/

6

http://www.lakes-environmental.com/


*.sfc file (see Figure 2.2)

7


*.pfl file (see Figure 2.3)

It is seen in Figure 2.3 that the last two columns of the anah.pfl file, which provide the hourly

values for the standard deviations of wind direction and vertical wind speed fluctuations, are

flagged as missing (99.0). This is the case for all of the *.pfl files we created. This column has

non-missing values only if measurements of these variables are read into AERMET. In absence

of these measurements, AERMOD computes these turbulence variables using methods described

in Section 3.

8

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ The period averaged concentration results obtained by running the anah.sfc and anah.pfl files in

AERMOD over the entire three year period 2005 – 2007 of hourly meteorology contained in

these files are shown in Figure 2.4 and Figure 2.5, respectively, for an elevated point source and

a surface area source. A unit emission rate of 1 g/sec was assumed in each case. The purpose of

this run was to confirm that the anah.sfc and anah.pfl files successfully port and run in

AERMOD. The *.sfc and *.pfl files developed for the other sub-areas were also tested in this

manner to be sure that they successfully port and run.

9


Figure 2.1: Map of Southern California, showing the geographical boundaries of each AQMD sub-area and the

location of district surface meteorological stations in the area.

10


Figure 2.2: Leading hourly records for the Anaheim (“ANAH”) sub-area *.sfc file output by AERMET.

11


Figure 2.3: Leading hourly records for the Anaheim (“ANAH”) sub-area *.pfl file output by AERMET.

12


Figure 2.4: Period average concentration field (μg/m3) resulting from running the Anaheim sub-area input files

(anah.sfc and anah.pfl) in AERMOD. Run assumes a unit emission rate (1 g/sec) from a point source.

13


Figure 2.5: Period average concentration field (μg/m3) resulting from running the Anaheim sub-area input files

(anah.sfc and anah.pfl) in AERMOD. Run assumes a unit emission rate (1 g/sec) from an area source.

14


3 AERMET Formulation

AERMET7 processes the raw meteorological observations (described later) to produce the

meteorological inputs for AERMOD in two files: a surface file (.sfc) and a profile file (.pfl). The

surface file contains the following variables as a function of hour of day:

1. Sensible heat flux, Hs

2. Surface friction velocity, u*

3. Convective velocity scale, w*

4. Temperature gradient above the mixed layer, γ

5. Convective boundary layer height, zic

6. Mechanical boundary layer height, zim

7. Monin-Obukhov (M-O) length, L

8. Surface roughness length, z0

9. Bowen ratio, Bo

10. Surface albedo, α

11. Wind speed

12. Wind direction

13. Temperature

The profile file contains information on the following variables as a function of height:

1. Standard deviation of the vertical velocity fluctuations, σw

2. Standard deviation of horizontal wind direction fluctuations, σθ

These variables are estimated using a one-dimensional boundary layer model that assumes

horizontally homogeneous conditions. The following sections describe relevant details of the

methods used in the model to construct the input variables.

7 http://www.epa.gov/scram001/metobsdata_procaccprogs.htm#aermet

15

http://www.epa.gov/scram001/metobsdata_procaccprogs.htm#aermet

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ Surface Energy Balance

AERMET is based on a one-dimensional boundary layer model, and assumes that Monin

Obukhov (MO) similarity holds near the surface. The boundary-layer is driven by the surface

energy balance given by

GHHRTTS LSNoi ++==−+− )1( α , (1)

where S is the incoming solar radiation, Ti is the incoming thermal radiation, and To is the

outgoing thermal radiation. The right hand side contains the sensible (Hs), latent (HL) and

ground (G) heat fluxes, respectively. In equation (1), α is the surface albedo, which is the

fraction of the incoming solar radiation that is reflected. So (1-α) is the fraction of the solar

radiation that is absorbed by the ground.

The sensible heat flux, Hs, plays a major role in the production and destruction of turbulence: it

determines the level of turbulence both during the day and the night, and governs the evolution

of the daytime boundary layer. It is the energy flux transferred from or to the ground, and is

estimated using the surface energy balance, which we now review.

The radiative input to the surface consists of two components: solar radiation, S, and thermal

radiation, T. Solar radiation refers to the wavelength region corresponding to the radiation of

from the sun, whose effective blackbody temperature is close to 6000°K. Most of the solar

energy lies in the wavelength region 0 ≤ λ ≤ 4 µm, with the peak of spectrum at around 0.5 µm.

Thermal radiation refers to energy emitted at temperatures typical of the earth's surface, about

300°K. The energy lies in the region 4 ≤ λ ≤100 µm, with the peak of the spectrum at about 10

µm. The incoming thermal radiation refers to that emitted by the component gases of the

atmosphere, such as water vapor and carbon dioxide, and other so-called greenhouse gases. The

outgoing thermal radiation is the energy emitted by the ground. Because, the ground is usually

warmer than the atmosphere, the outgoing thermal radiation usually exceeds the incoming

thermal radiation.

The sensible heat flux is the energy flux from the atmosphere to the ground driven by

temperature differences between the ground and the atmosphere. During the daytime, energy

16

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ flows away from the ground into the atmospheric boundary layer, while during the night the

boundary layer supplies energy to the ground.

The latent heat flux refers to the energy used to evaporate moisture from the ground. The soil

heat flux refers to the energy that is supplied to the ground, and which ultimately determines the

temperature of the soil layer.

By definition, the net radiation, RN , is the difference between the solar radiation absorbed at the

surface, and the net thermal radiation emitted by the surface. is the sensible heat flux

supplied to the boundary layer, is the latent heat flux related to the evaporation of water

from the surface, and G is the heat flux into the soil.

SH

LH

During the day, is usually greater than zero: heat is supplied to the atmosphere. During the

night, < 0; heat is drawn from the atmosphere and the ground to support the cooling of the

ground as

SH

SH

RN becomes negative. The cooling can be inhibited in the presence of clouds which

radiate energy towards the ground.

When the ground is moist, most of the incoming radiation can go towards evaporation. An

approximate method of accounting for energy going into evaporation is to assume that the ratio

of latent heat flux to sensible heat flux is a number, referred to as the Bowen ratio, that depends

only on the type of surface being considered. In the next section, we consider the methods used

in AERMET to calculate the components of the surface energy balance.

Solar Radiation

In AERMET, the solar radiation reaching the ground is calculated by reducing the solar flux at

the top of the atmosphere – 1350 W/m2 – through several factors that account for different

physical effects. First, the solar radiation flux normal to the ground surface is determined by the

cosine of the zenith angle, which is the angle between the normal to the surface and the direction

of the incident solar flux. The zenith angle is a function of the latitude of the receptor, the time of

day, and the declination angle. The declination angle is the angle between the normal to the plane

of rotation of the earth about the sun and the axis of rotation of the earth. The declination angle

17

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ varies between +23.5o on July 22 (summer solstice) and -23.5o on December 22 (winter solstice)

as the earth rotates about the sun.

The energy flux in the solar beam at the top of the atmosphere is reduced by absorption and

scattering by gases, particles, and clouds as it travels through the atmosphere to the ground. A

fraction of the scattered radiation reaches the ground as diffuse radiation, which adds to the

direct beam solar radiation. In AERMET, these effects are accounted for through semi-empirical

factors that depend on location and cloud cover. It turns out that the maximum solar radiation

reaching the ground is about 1000 W/m2, and this value is multiplied by the cosine of the zenith

angle (sine of the elevation angle) to give the flux per unit area normal to the ground surface.

This value is further reduced by a factor that is a function of cloud cover.

Outgoing and Incoming Thermal radiation

The earth’s surface emits long-wave radiation (4 ≤ λ ≤100 µm), which can be estimated from

the surface temperature, which is usually tens of degrees higher than the temperature at 10 m.

Because the surface temperature is not measured, AERMET, following Holtslag and Van Ulden

(1983)8, assumes that the outgoing thermal radiation is that associated with the temperature at 10

m plus a value that depends on the difference between the surface temperature and the 10 m

temperature. This additional value is then related empirically to the net radiation. Thus, during

the daytime, when the surface temperature is higher than the 10 m temperature, the correction

adds to the value computed with the 10 m temperature. During the night, when the surface is

cooler than the 10 m temperature, the correction subtracts from the radiation calculated with the

10 m temperature.

The incoming long-wave radiation is a complicated function of the vertical profiles of the

temperature and the concentrations of the gases that absorb and emit in the thermal spectrum.

Clouds are major emitters of thermal radiation; downward thermal radiation can increase

substantially in the presence of clouds. In AERMET, the incoming thermal radiation is

8 Holtslag, A.A.M., and A.P. van Ulden, 1983: A simple scheme for daytime estimates of the surface fluxes from routine weather data, J. Clim. Appl. Meteorol., 22, 517-529.

18

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ calculated using empirical expressions that depend on the sixth power of the 10 m temperature

and cloud cover.

Sensible and Ground Heat Fluxes

During the day, the ground heat flux, G , is usually small relative to the sensible heat flux. In

AERMET, it is taken to be a tenth of the net radiation on the basis of observations. This ensures

that during the day G is into the ground because the net radiation is positive; during the night, G

is directed towards the surface because the net radiation is negative.

The difference between the net radiation and the ground heat flux is partitioned between the

sensible heat flux, H, and the latent heat flux, L. AERMET assumes that the ratio of latent heat

flux to sensible heat flux is a number, referred to as the Bowen Ratio, Bo, that depends only on

the type of surface being considered. This relatively simple approximation can be improved

upon by using other techniques, such as the one proposed by Holstlag and Van Ulden (1983).

However, it should be pointed out that none of these methods is particularly reliable because

evaporation at the surface depends on a process, the transport of moisture in the soil, which is

difficult to parameterize. So the final expression for the sensible heat flux, H, becomes

GRBo

HHH NSLS −=⎟⎠⎞

⎜⎝⎛ +=+

11 , (2)

so that

( GR )Bo

BoH NS −+

=1

. (3)

When the surface is dry so that the sensible heat flux is much greater than the latent heat flux, the

Bo is large, and the heat flux is essentially 0.9RN because G=0.1RN. When the surface is

relatively wet, Bo is small. The heat flux is sensitive to the value of Bo when the ratio is about 1.

It is difficult to specify Bo because it can vary substantially at a single location. However,

variables required in air pollution modeling are not directly proportional to heat flux. So we

might be able to get away with small errors in heat flux. We next show how the daytime

boundary layer height is estimated using the surface heat flux.

19

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ Convective Boundary Layer Height (zic)

The height of the mixed layer, zic, is estimated by assuming that the sensible heat input to the

atmosphere is used to modify the potential temperature in the mixed layer. We illustrate the

procedure by considering a mixed layer that grows by eroding a layer with a stable potential

temperature gradient. Note the symbol H, rather than Hs, is used here to denote sensible heat

flux.

Height of the Convective Boundary Layer

Sensible Heat Flux

A B

C

Stable Potential Temperature Gradient

Zi

( )

TCHz

TH21zC

21

dttHzC21

2/1

p

maxic

2

max2icp

T

0icp

⎟⎟⎠

⎞⎜⎜⎝

⎛=

=

∫=

γτρ

τγρ

θΔρ

Assume that the initial potential temperature at sunrise is stable, and can be represented by the

profile AC shown in the figure. The upward surface heat flux after sunrise results in the

modification of the temperature of the boundary layer. Over a time period, T, the temperature in

the boundary layer changes from AC to BC. Note that the potential temperature (not the actual

temperature) is uniform over most of the boundary layer. The upward heat flux at the surface is

driven by a temperature difference between the surface and the mixed layer, but this temperature

gradient is confined to a shallow layer relative to the mixed layer height.

The energy change in the boundary layer caused by the sensible heating at the surface

corresponds to the area of the triangle ABC. Denoting the potential temperature gradient of AC

by γ , and the temperature change AB by Δθ , this energy change can be written as

icp zC21ABC in Energy θΔρ= . (4)

20


Noticing that iczγθΔ = , we can equate this energy to the sensible heat flux integrated over T to

obtain

( )dttHzC21 T

0

2icp ∫=γρ , (5)

where H(t) is the time-varying sensible heat flux. For simplicity, we assume that the heat flux

varies linearly with time, so that

( )τtHtH max= , (6)

where τ is a convenient time scale, whose value is not important at this point. Substituting

Equation (6) into Equation (5) and integrating we obtain

τ

γρ2THzC

21 2

max2icp = , (7)

which leads to

TCHz

2/1

p

maxic ⎟

⎟⎠

⎞⎜⎜⎝

⎛=

γτρ. (8)

In AERMET, the initial temperature profile at sunrise corresponds to the actual early morning

sounding, so that the potential temperature gradient, γ, above the mixed layer varies with time as

the mixed layer grows. In addition, the surface heat flux, computed from the surface energy

balance, generally increases from sunrise to noon and then decreases to zero at sunset.

Although the simple model considered here does not incorporate these time variations, it

provides insight into the variables that control the mixed layer height in AERMET. Notice that

the mixed layer height, zic, is proportional to the square root of the heat flux; so errors in

estimating the heat flux are dampened in this calculation. We have a similar situation with the

potential temperature gradient: . So the calculation of z2/1ic /1z γ∝ ic is forgiving of errors in

specifying the potential temperature profile in the morning. The simple formula also tells us that

zic can become very large if the potential temperature gradient is close to zero.

21

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ The background of the last two sections allows an examination of the convective velocity scale,

w*.

Convective Velocity Scale (w*)

The convective velocity scale, w*, is a measure of the turbulent velocities created by surface

heating, referred to as convection. To see this, we will examine a simple model for the motion of

an air parcel that becomes buoyant after heating at the surface. Assume that the mass of the

parcel is 1 kg, and it acquires a temperature excess of T ′ over its surroundings at temperature T.

This results in an upward buoyant force of T/Tg ′ , which accelerates the air parcel upwards. If

this force acts over a distance z, we can estimate the velocity, w, acquired by the air parcel by

equating the work done by the force to the kinetic energy of the parcel at z,

2w~zTTg ⎟⎠⎞

⎜⎝⎛ ′

. (9)

The left hand side of the equation is the product of the buoyant force and the distance, z, over

which it acts, and the right hand side of the equation is the kinetic energy of the parcel with unit

mass.

We can introduce the sensible heat flux into the equation by multiplying both sides of the

equation by w to obtain

3w~zTTwg ⎟⎠⎞

⎜⎝⎛ ′

. (10)

Now the term represents the temperature excess being carried upwards by the air parcel. In

fact, if multiply the term by ρC

Tw ′

p we find that

p

S

Sp

CH

Tw

or

HTwC

ρ

ρ

~

~

′

′

. (11)

The combination HS/ρCp is called the kinematic heat flux, and has units of velocity multiplied by

temperature, and is used instead of the sensible heat flux in constructing micrometeorological

22

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ variables. If we substitute Equation (11) into Equation (10), we can define the free convection

velocity, uf

3/1

0⎟⎟⎠

⎞⎜⎜⎝

⎛≡ z

CH

Tgu

p

Sf ρ

, (12)

where T0 is a reference temperature, taken to be the near surface value, and HS is the surface

sensible heat flux. Notice that uf depends on the 1/3 power of the heat flux, which suggests that

errors in estimating heat flux are reduced in estimating uf.

Observations indicate that the turbulent velocity, σwc, associated with buoyancy production is

given by

icfwc 0.1zz for u3.1 ≤=σ . (13)

Above z>0.1zic, σwc is relatively constant,

, (14) ∗=

==

0.6w

0.1zz at evaluated u3.1 icfwcσ

where the convective velocity scale, w*, is defined by

3/1

0⎟⎟⎠

⎞⎜⎜⎝

⎛≡∗ ic

p

S zC

HTgwρ

. (15)

So the convective velocity scale, w*, is an estimate of the turbulent velocity created by buoyancy

or free convection. Let us estimate its value for a mixed layer height of 1000 m, and a surface

heat flux of 200 W/m2. Taking ρCp=1200 J/(m3K), and T0=300 K, we find w*=1.76 m/s. So in

the upper part of the convective boundary layer, the turbulent velocities are of the order of 1 m/s.

Even at 10 m, the velocity estimated from Equation (15) is about 0.5 m/s.

It turns out that the standard deviation of the horizontal turbulent velocity fluctuations, σv, is

about 0.6w* through the depth of the boundary layer.

23

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ Surface Friction Velocity (u*) and Mechanical Mixed Layer Height (zim)

Air flowing over a surface exerts a shear stress that depends on the level of turbulence in the

boundary layer. We can establish the relationship between the turbulent velocities, σw and σv, and

the shear stress at the surface, τ0, by defining the surface friction velocity as follows:

ρτ0u ≡∗ , (16)

where ρ is the density of air.

When the boundary layer is stable or neutral, turbulent velocities close to the surface are related

to the surface friction velocity through

∗

∗

==

u2u3.1

vs

ws

σσ

, (17)

where we have included ‘s’ in the subscripts to emphasize the fact that these turbulent velocities

are generated by wind shear.

As indicated earlier, turbulence in the stable boundary layer is generated by wind shear, and

inhibited by the stable potential temperature gradient. Observations indicate that the height of

the boundary layer, which is the height to which the turbulence extends, is related to the surface

friction velocity. AERMET uses the relationship for the mechanically generated boundary layer

, (18) 2/3im Auz ∗=

where A is an empirical constant. We see that the boundary layer height is sensitive to the surface friction velocity.

The vertical turbulent velocity in the stable boundary layer decreases with height as follows:

( )2/1

imws z

z1u3.1z ⎟⎟⎠

⎞⎜⎜⎝

⎛−= ∗σ . (19)

The mean velocity during near neutral conditions is also related to the surface friction velocity

through the well-known logarithmic profile:

24


( ) ⎟⎟⎠

⎞⎜⎜⎝

⎛ −= ∗

0zhzln

kuzU , (20)

where k is the von-Karman constant=0.4, z0 is the roughness length, and h is the displacement

height. The roughness length, z0, is a function of the physical height of the obstacles on the

surface over which the air flows, and d=5z0. AERMET modifies Equation (20), described later,

to account for the effects of surface heat flux on the surface friction velocity, u*.

Monin-Obukhov Length (L)

The free convection velocity, uf, is a measure of the turbulent velocities generated by buoyancy.

Equation (12) tells us that uf increases with z. On the other hand, the turbulent velocities

generated through shear are relatively constant in the lower tenth of the boundary layer. The

height at which turbulence levels generated by buoyancy are comparable to those generated by

shear is the Monin-Obukhov length, L, obtained by equating uf evaluated at z = L to u*,

30

*

3/1

0

~

~

∗

⎟⎟⎠

⎞⎜⎜⎝

⎛

uHC

gT

L

or

uLC

HTg

S

p

p

S

ρ

ρ

. (21)

We have not used an equal sign in the expression because, for the moment, we want to

emphasize the physical meaning of the Monin-Obukhov length. We see that shear production

dominates buoyancy production of turbulence below the height L. Buoyancy production

dominates above L. So when z<<L, u* governs turbulent velocities, and the boundary layer is

essentially neutral at these heights. The boundary layer is convective for z>>L.

The M-O length, L, is formally defined as

30∗−≡ u

kHC

gT

LS

pρ, (22)

where k is the von-Karman constant. Because HS > 0 during the daytime, L is negative during the

day. During the night, the heat flux is negative, so that L is positive. The physical interpretation

discussed earlier holds for the absolute value of L.

25

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ Before we discuss how L is used in the AERMOD interface, we show how we combine the

turbulent velocities corresponding to buoyancy and shear.

Estimating turbulent velocities in the boundary layer

We computed the turbulent velocities corresponding to the situations when buoyancy or shear

dominates turbulence production. To account for the fact that both mechanisms act together in

the real boundary layer, AERMET combines the velocities using a method that we illustrate for

computing σw

, (23) ( 3/13ws

3wcw σσσ += )

If we substitute Equations (14) and (17) for σwc and σws into Equation (23), and using the

definition of L, we get

3/1

w kLz1u3.1 ⎟⎠⎞

⎜⎝⎛ −= ∗σ . (24)

We see that σw works out to be the neutral value multiplied by a function of z/L. Similar

expressions are used for the turbulent velocities in the horizontal directions. This technique of

correcting for the effects of stability using functions of z/L forms the basis of the similarity

methods used to construct the mean profiles.

Monin-Obukhov (M-O) Similarity

The AERMOD interface uses the micrometeorological variables estimated using AERMOD to

construct profiles of the mean horizontal velocity and temperature, and three components of the

turbulent velocities. These profiles are constructed using M-O similarity, which we have already

encountered in Equation (26). We will illustrate the underlying principles of the theory by

considering the mean velocity profile in the stable boundary layer.

Consider the logarithmic velocity profile in the neutral boundary layer:

( ) ⎟⎟⎠

⎞⎜⎜⎝

⎛ −= ∗

0zhzln

kuzU , (25)

which can be rewritten as

26


( )⎟⎟⎠

⎞⎜⎜⎝

⎛ −=

∗ 0zhzln

k1

uzU . (26)

Equation (26) tells us that velocity profiles in all neutral boundary layers, governed by different

roughness lengths, are similar if the wind speed is normalized by the velocity scale, u*, and the

height (z-h) by the length scale z0. This is one statement of similarity theory; the literature

provides other interpretations all of which reduce to the hypothesis that a small number of

velocity, length, time, and temperature scales can be used to summarize the mean and turbulent

structure of the atmospheric boundary layer.

When buoyancy effects are important, the additional length scale is the M-O length, L. For

example, under stable conditions, the velocity profile can be described by

( )⎥⎦

⎤⎢⎣

⎡ −+⎟⎟

⎠

⎞⎜⎜⎝

⎛ −=

∗ Lzz

zhzln

k1

uzU 0

0β . (27)

The velocity profile in the unstable boundary layer can be described using a different function of

z/L.

The temperature profile can be described in a similar manner using a temperature scale, T*,

defined by

∗

−≡uC

HT

p

S

ρ* , (28)

which can be used to describe the temperature profile in the stable boundary layer,

( )⎥⎦

⎤⎢⎣

⎡ −+⎟⎟

⎠

⎞⎜⎜⎝

⎛ −=

−

∗ Lzz

zhzln

k1

TTzT 0

0

0 β , (29)

where T0 is an effective surface temperature.

Equations such as (27) and (29) apply to heights of the order of the M-O length, L, which defines

the extent of the surface boundary layer. Above the surface layer, the relevant scale is the

boundary layer height, zic or zim, and profiles of mean and turbulent variables become functions

of z divided by the boundary height.

27

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ Analysis of data obtained from field studies conducted over the last 30 years has resulted in

semi-empirical formulations to describe the structure of the boundary layer. The AERMOD

interface uses these formulations to construct the profiles required by AERMOD. The inputs are

the parameters: z0, L, u*, w*, zim, zic, U(z), and T(z) contained in the surface file produced by

AERMET. If actual measurements of velocity, temperature, and turbulence are available at

different heights in the boundary layer, the AERMOD interface can incorporate them in

constructing these profiles.

28


4 Methodology

The general method used to create the AERMOD *.sfc and *.pfl input files is described in this

section. Appendix B provides “readme files” for each sub-area that contain further details

pertaining to the data and processing procedure used for each sub-area individually.

Data

The data used to produce the AERMOD *.sfc and *.pfl input files are the hourly near-surface

values measured at sites across Southern California for the following meteorological variables:

Wind speed and direction (near-surface)

Temperature (near-surface)

Solar radiation (Surface, Watts per Meter-Squared)

Fractional cloud coverage

These data, along with the temperature profiles from the standard 12-hourly NWS rawinsondes,

are used to produce the *.sfc and *.pfl files.

Table 4.1 summarizes the time period used to construct the input files for each sub-area. The

time period for most sub-areas is 2005 – 2007. For some sub-areas, as noted in the table, the data

are only available or of sufficient quality to allow processing for a shorter time period.

Table 4.2 lists the AQMD monitoring station coordinates, elevations and heights of their wind

and temperature measurements. The measurement heights are based on descriptions of the

towers provided to us by AQMD.

Meteorological Stations

The data for the above variables were obtained from AQMD, National Weather Service (NWS)

and California Irrigation Management Information System (CIMIS) surface meteorological

stations in the AQMD area. Table 4.3 summarizes which data sources were used for which

variable, listed in sequence according to general order of preference. A map of the available

29

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ AQMD, NWS and CIMIS stations in the AQMD area is shown in Figure 4.1. As seen, most

district sub-areas contain one or more of these meteorological stations.

The Coachella Valley was divided into two sub-areas by the arbitrarily drawn blue line shown in

Figure 4.1. One sub-area corresponds to the Palm Springs AQMD station (PLSP) and the other

to the Indio AQMD station (INDI). The San Bernadino (SNBO) was also divided into two sub-

areas, one corresponding to the Fontana AQMD station (FONT) and the other to the San

Bernadino AQMD station (SNBO). This division is denoted in Figure 4.1 by the blue line drawn

arbitrarily across the AQMD-defined SNBO sub-area.

We did not create *.sfc and *.pfl files for sub-areas that did not have an AQMD station. When

wind data was missing at a particular AQMD station, we did not fill the data from stations in

adjacent sub-areas because wind speeds and directions can vary considerably from sub-area to

sub-area. On the other hand, when temperature, solar radiation or cloud cover were missing or

unusable, data from stations in adjacent or nearby sub-areas were used because these variables

vary much less in space. As a result of this procedure, the only missing data in the hourly

meteorological data input to AERMET were missing winds.

All data used in creating the AERMET input files were inspected to make sure that they were

within reasonable bounds using procedures described in the Phase I Interim Report.

In the discussion that follows, each sub-area will be referred by the name of the AQMD station

used as the data source. For example, the sub-area containing the LAXH AQMD station will be

called the ‘LAXH sub-area’.

Data Processing

The following describes the general procedure applied for all sub-areas to process the raw

meteorological data into “pre-processed” files to be read into AERMET. Further details of the

data and processing procedure for each sub-area are given in the ‘readme’ files in Appendix B.

30

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ (i) Wind Speed and Direction

Most sub-areas contain one AQMD surface monitoring station, from where the wind data for

each sub-area were taken. The ‘AZUS’ sub-area contains two stations, AZUS and GLEN, from

which we chose AZUS to characterize the sub-area. No *.sfc and *.pfl input files were produced

for sub-areas that did not contain an AQMD monitoring station.

The wind data at each AQMD station were supplied to us by AQMD. These data were then

inspected by us to identify isolated hours or periods during which winds were obviously

inaccurate or questionable. Table 4.4 lists these time periods and describes the steps taken by us

to treat them.

The wind data were then inspected by us to separate instances of reported zero wind speed and

zero wind direction (‘zero, zero’) in the AQMD wind data files into two groups: those that we

judged “calm” and those that we judged “missing”. This was necessary since the AQMD data

indicated ‘zero, zero’ for both hours of missing wind data as well as during hours when the wind

was evidently not fast enough to register any readings (“calm”). Our judgment of what was

“missing” and what was “calm” was subjective, but followed some basic guidelines:

Isolated instances (periods of less than around 4 hours) of ‘zero, zero’ were considered

“calm” when the non-zero wind speeds reported for hours on either side of the ‘zero,

zero’ period were weak (around 1 m/s or less). This often occurred during nighttime

hours.

Isolated instances (periods of less than around 4 hours) of ‘zero, zero’ were considered

“missing” when the non-zero wind speeds reported for hours on either side of the ‘zero,

zero’ period were not weak. These values were replaced with wind speeds and wind

directions obtained through time interpolation of wind data two hours before and two

hours after the missing period. For single hours of such ‘zero, zero’ instances, the time

interpolation procedure followed the EPA “objective procedure” described in Atkinson

31


and Lee (1992)9. For more than a single hour of such ‘zero, zero’ instances, the time

interpolation followed considerations discussed in the EPA “subjective procedure”

described in Atkinson and Lee (1992). Our subjective interpolation procedure still used

the two hours before and two hours after the missing period, however the interpolation

weights assigned to each of these four values were subjectively set by us so that the final

interpolated values maintained reasonable wind direction time trends and variability10.

Long periods of ‘zero, zero’ were flagged as “missing”. While there may in fact be hours

within such ‘zero, zero’ blocks that would be better characterized as “calm”, we could not

identify them because the non-zero measurements on either side of the ‘zero, zero’ block

are too far away in time to make a reliable determination of any “calm” hours within the

‘zero, zero’ block. Furthermore, there is no reliable way to fill long periods of missing

winds since the non-zero winds on either side of the ‘zero, zero’ block are too far away in

time to allow for reliable time interpolation.

The characterization of the ‘zero, zero’ periods is presented in Excel spreadsheets, which are

provided for each AQMD station on the accompanying CD under the “WINDGAPS” directory

(Appendix D). Green shaded cells indicate ‘zero, zero’ entries judged “missing”, with the values

in these cells replaced with the missing value indicator ‘-999.0’ for wind speed and wind

direction. Yellow shaded cells indicate ‘zero, zero’ entries judged “calm”, with the values in

these cells left as ‘zero, zero’.

There were also entries in the AQMD wind files that had zero wind speed and non-zero wind

direction. Upon inspection, these entries appeared to correspond to periods of weak wind speeds.

We therefore judged these periods as “calm” and left them untreated because AERMET will

interpret a zero wind speed (regardless of wind direction) as “calm” and not process the hour.

9 Atkinson, D. and R. F. Lee, 1992: “Procedures for Substituting Values for Missing NWS Meteorological Data for Use in Regulatory Air Quality Models”, July 7, 1992; Available at http://www.epa.gov/scram001/metguidance.htm. 10 Scalar wind direction averaging was used following the procedure outlined in the EPA document “Meteorological Monitoring Guidance for Regulatory Modeling Applications”, February 2000, EPA-454/R-99-005; available at http://www.epa.gov/scram001/metguidance.htm.

32

http://www.epa.gov/scram001/metguidance.htm

http://www.epa.gov/scram001/metguidance.htm

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ The wind roses for the final wind data applied for each sub-area are shown in Appendix A, and

are provided in individual electronic files on the accompanying CD.

(ii) Temperature

The primary source for near-surface air temperature for each sub-area was the AQMD data. If

these data were not available, the temperature data were taken from a CIMIS or NWS station

within the sub-area, or from an AQMD, CIMIS or NWS station from an adjacent or nearby sub-

area.

Short periods of missing temperature data (less than around 4 hours) were filled by time-

interpolation. The interpolation technique used the average of the two non-missing temperatures

immediately before and after the missing period to replace all missing temperatures within the

period. This method is reasonable for the cases that occurred most frequently, when only one or

two hours were missing.

For long periods of missing data, temperatures were taken from an alternative AQMD or CIMIS

station within the sub-area, or from an AQMD, CIMIS or NWS station adjacent or nearby sub-

area. If no suitable station was available, time interpolation was applied.

As a result of this filling procedure, all hours of temperature are ultimately filled.

Details on the temperature data used for each sub-area are given in the ‘readme’ files. These are

printed out in Appendix B.

(iii) Solar Radiation

For most sub-areas, the primary source for surface solar radiation for each sub-area was the

CIMIS data within the sub-area. If these data were not available, the primary solar radiation data

were taken from another CIMIS station within the sub-area, or from a CIMIS station from an

adjacent or nearby sub-area.

Short periods of missing solar radiation data (less than around 4 hours) were filled by time-

interpolation. The interpolation technique used the average of the two non-missing solar

33

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ radiation values immediately before and after the missing period to replace all missing solar

radiation values within the period. This method is reasonable for the cases that occurred most

frequently, when only one or two hours were missing.

For long periods of missing data, solar radiation was taken from an alternative CIMIS station

within the sub-area, or from a CIMIS station from an adjacent or nearby sub-area. In the case of

the ‘LGBH’ sub-area, solar radiation data from the LGBH AQMD station were used to fill long

periods of missing data. Otherwise, if no suitable station to provide solar radiation data was

available, time interpolation was applied.

For the SCLR, RESE, BURK and ELSI sub-areas, we allowed AERMET to calculate solar

radiation based on time of day/year and the input value for fractional cloud coverage. This

appeared to give more realistic results for micrometeorological variables computed by AERMET

compared to what were obtained when using the local CIMIS radiation data.

As a result of this procedure, all hours of solar radiation were ultimately filled.

Details on the solar radiation data used for each sub-area are given in the ‘readme’ files. These

are printed out in Appendix B.

(iv) Cloud Coverage

The primary source for fractional cloud coverage for each sub-area was the NWS data within the

sub-area. Absent these data, the primary cloud coverage data were taken from an alternative

NWS station within the sub-area, or from a NWS station from an adjacent or nearby sub-area

NWS cloud coverage is expressed by codes “CLR” (clear), “FEW” (few), “SCT” (scattered),

“BKN” (broken), and “OVC” (overcast). The translation of these codes to fractional cloud

coverage values follows from recommended values contained in Table 3 of the ASOS User’s

Guide11

11 “Automated Surface Observing System (ASOS) User’s Guide”, NOAA, March 1998; obtainable at http://www.nws.noaa.gov/asos/pdfs/aum-toc.pdf.

34

http://www.nws.noaa.gov/asos/pdfs/aum-toc.pdf


Based on this, we assigned the following fractional values (in tenths): “CLR” = 0, “FEW” = 1.0,

“SCT” = 4.0, “BKN” = 8.0, “OVC” = 10.0.

Short periods of missing cloud coverage data (less than around 4 hours) were filled by time-

interpolation. The interpolation technique used the average of the two non-missing cloud

coverage values immediately before and after the missing period to replace all missing

temperatures within the period. This method is reasonable for the cases that occurred most

frequently, when only one or two hours were missing.

For long periods of missing data, cloud coverage values were taken from an alternative AQMD

or CIMIS station within the sub-area, or from an AQMD, CIMIS or NWS station adjacent or

nearby sub-area. If no suitable station was available, time interpolation was applied.

For the CRES sub-area, we applied a default fractional cloud coverage value of 5.0 (tenths) for

all hours since no suitable NWS station was available in this mountainous area.

As a result of this procedure, all hours of fractional cloud coverage are ultimately filled.

Details on the cloud coverage data used for each sub-area are given in the ‘readme’ files. These

are printed out in Appendix B.

35

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ (v) Land Surface Characteristics

Values for three land surface parameters – minimum (“noontime”) surface albedo, Bowen Ratio

and surface roughness length – are inputs to AERMET. Given the uncertainty in determining

these values, we generated two versions of *.sfc and *.pfl files – the first version from surface

parameter values for each sub-area computed using the EPA module AERSURFACE12, and the

second version from surface parameter values that are uniform for all sub-areas. The *.sfc and

*.pfl files for both sets are contained on the accompanying CD in file directories entitled

‘Version 1’ and ‘Version 2’ (Appendix D).

Further details of the choice in surface parameters are now discussed.

Version 1: Land Surface Parameters Determined by AERSURFACE

AERSURFACE determines the values of the three land surface parameters based on the 30-

meter gridded land-use data in the USGS NLCD92 dataset. AERSURFACE first determines the

land-use values around the primary meteorological tower. Look-up tables programmed into

AERSURFACE then map the land-use categories around this tower to values for the three land

surface parameters at each 30-meter grid square. These look-up tables are based on values from

field studies in various locations provided in the literature. The final surface parameter values for

AERMET are then determined by averaging the 30-meter gridded values over a radius of

influence around the tower. The averaged values for surface parameters can be made temporally

and directionally dependent through user-specified settings in AERSURFACE. Further details of

this procedure can be found in the EPA AERSURFACE User’s Manual.

We chose the “average” soil moisture and “arid” surface characteristic settings for running

AERSURFACE, as these seemed most appropriate to arrive at typical (climatologically

averaged) values for Southern California. For simplicity, we specified no time or directional

dependence, and therefore single values for surface parameters output by AERSURFACE were

applied in our AERMET runs for all hours and all wind directions. This simple approach seems

12 http://www.epa.gov/scram001/dispersion_related.htm#aersurface.

36

http://www.epa.gov/scram001/dispersion_related.htm#aersurface

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ justified due to the large inherent uncertainty in AERSURFACE associated with its

determination of surface parameter values at a user-specified site based on look-up tables of

“typical” values from observations at other sites.

We applied the January 2008 version of AERSURFACE, which was the most current at the time

of this writing. This was done through use of the GUI front-end for AERSURFACE provided in

the Lakes Environmental software AERMET-View13.

The resulting values applied for surface albedo and roughness length for each sub-area output

from AERSURFACE are listed in Table 4.5.

For Bowen ratio, we overrode the values output by AERSURFACE with a specified values for

each sub-area. The values we specified alongside the values output by AERSURFACE are listed

in Table 4.5. For most sub-areas we chose a value of 1.0, except for BNAP, PLSP and INDI

where we chose a value of 1.5 to be closer to the value output by AERSURFACE for these sub-

areas. As seen, AERSURFACE produced in almost all cases a higher Bowen ratio value than the

value we specified. This replacement with a lower value of Bowen ratio is justified for several

reasons. First, the uncertainty in the AERSURFACE look-up table approach is likely to be large

in the case of Bowen Ratio, since latent heat flux is difficult to measure/compute accurately.

Also, the use of NLCD92, rather than the more current 2001 or 2006 sets14, raises doubts about

whether the Bowen ratios output by AERSURFACE for Southern California account for recent

residential developments in many areas of the air basin, where Bowen ratios are likely to be

lower because of dry land with irrigated residential areas. The value of 1.0 set for most sub-areas

therefore seems like a suitable constant “place-holder” value until these issues can be resolved.

Version 2: Uniform Land Surface Parameters Across All Sub-Areas

After consultation with AQMD staff, a second version of the *.sfc and *.pfl files were

constructed using uniform values of surface parameters across all sub-areas. The values used for

each parameter are

13 http://www.lakes-environmental.com/14 http://www.epa.gov/mrlc/nlcd-2006.html

37


http://www.epa.gov/mrlc/nlcd-2006.html


• Surface Roughness Length = 0.4 m

• Bowen Ratio = 1.3

• Minimum (noontime) surface albedo = 0.2

These values were chosen by AQMD to approximately reflect the median of the values produced

by AERSURFACE for all sub-areas, which are shown in Table 4.5

Application of AERMET

By applying the procedure described above, we produced “pre-processed” meteorological input

files for each sub-area that contain the hourly values of the four meteorological variables over

the time period of interest for each sub-area (see Table 4.1). These files were then read in to

AERMET to produce the *.sfc and *.pfl files needed to run AERMOD for each sub-area.

As discussed in detail in Section 3, AERMET computes the following micrometeorological and

boundary-layer variables needed for characterizing turbulent diffusion in AERMOD:

• Surface friction velocity (u*, meters per second)

• Free convective scaling velocity (w*, meters per second)

• Monin-Obukhov length (L, meters)

• Daytime (“convective”) boundary layer height (zic, meters)

• Nighttime (“mechanical”) boundary layer height (zim, meters)

To compute these, an estimate of the morning vertical temperature gradient within the boundary

layer (γ, Kelvin per meter) is needed, which AERMET calculates based on the standard NWS

morning rawinsonde sounding. We used the Miramar AFB (San Diego area) sounding data to

AERMET for this purpose.

38

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ In addition to calculating the above variables, AERMET also passes through the hourly values of

wind speed, wind direction, temperature for use in AERMOD. Hourly values for the vertical

temperature gradient above the boundary layer (‘Field 9’ in the sample file shown in Figure 2.2)

are also output for use in the plume rise calculations in AERMOD.

To summarize, the main input and output files provided in the deliverables for running

AERMET for each sub-area are the following:

• The “pre-processed” hourly meteorological input file for the sub-area (*.prn).

• The Miramar AFB rawinsonde data (file ‘NKX.ua’)

• AERMET “control” input files (IN1, IN2 and IN3) for the sub-area, which contain

various run parameters as well as the values for surface parameters.

• The *.sfc and *.pfl output files from AERMET, which are, in turn, the meteorological

input files to AERMOD.

We also provided other intermediary and output files produced by AERSURFACE and

AERMET, which contain run-time messages and other similar content.

All of the above files are contained for each sub-area on the deliverable CD (Appendix D).

We applied AERMET version 06341, which is the most current version at the time of this

writing. AERMET was run through use of the GUI front-end provided in the Lakes

Environmental software AERMET-View15.

The meteorological input files produced by AERMET were checked through a code produced by

us to ensure that: 1) micrometeorological outputs were within plausible ranges, and 2) No errors

were made in applying AERMET. This code contains the primary equations in AERMET, and is

designed to accept NWS and AQMD onsite observations directly. The results from the code

15 http://www.lakes-environmental.com/

39


EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ were compared with those produced by AERMET. We did not find discrepancies between the

two sets to indicate errors in the application of AERMET.

Post-Processing (Optional)

We have developed three postprocessors that allow modification of the surface files (.sfc)

produced by AERMET to account for processes not included in AERMET, and to examine the

sensitivity of the outputs to surface parameters without rerunning AERMET:

• Adjust_Mxht

• Adjust_SFC_File

• Recompute_SFC_File

• Convert_Rural_to_Urban

The processor called ‘Adjust_Mxht (SFC_File, max_mxht, remark)’ reads the .sfc file, replaces

all the daytime mixed layer heights that exceed a user specified maximum value by this

maximum value, recalculates w*, and writes a new .sfc file. The inputs to the program are the

name of the SFC file (SFC_File), the user-specified maximum mixed layer height (max_mxht),

and a remark (remark). The output file is given the name of the input file plus the remark.

This processor can be used to account for the fact that the daytime boundary layer at a shoreline

location corresponds to the convective internal boundary layer that develops when stable air

from the ocean flows on to warmer land. The height of this internal boundary grows as a function

of distance from the source as the upward heat flux from the ground erodes the overlying stable

layer. Within 10 km from the shoreline, the boundary layer height can be shallow relative to the

height far inland where shoreline effects are absent. In Wilmington, for example, the internal

boundary layer height rarely exceeded 200 m at a distance of 5 km from the shoreline in

40

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ September (Yuan et al. 2006)16. AERMET would instead typically estimate a maximum

boundary layer height of around 1000 m because it does not account for two-dimensional

shoreline is effects. This processor can also be used to limit the mixed layer height to a specified

observed value, which we might be necessary in the South Coast Air Basin where large scale

subsidence can result in lower values of the mixed layer height than those estimated by

AERMET. An overestimated boundary layer height will underestimate concentrations from both

surface as well as elevated releases.

A processor called ‘Adjust_SFC_File (SFC_File, z0_new, Bo_new,remark)’ is designed to

examine the sensitivity of the .sfc information to changes in the Bowen ratio, Bo, and the surface

roughness length, z0. The processor reads the .sfc file, recalculates the heat flux, Qs, the

convective mixed layer height, zic, the surface friction velocity, u*, and the corresponding

mechanical mixed layer height, zim. The formulations used to recalculate these parameters differ

slightly from those used in AERMET; so the adjusted file is not identical to what AERMET

would produce with the new surface parameters. However, the output from this post-processor is

adequate to examine the impact of changes in Bo and z0 on AERMET output as well as on

concentrations. Figure 4.2 compares outputs from the processor with the corresponding

AERMET output for z0=1 m and Bo=2. The processor generated the output from a .sfc file for

z0=0.387 m and Bo=1. We see that there are minor differences in the two outputs especially for

w*. This is because the post-processor neglects changes in the potential temperature gradient

above the mixed layer with height, which results in different mixed layer height and hence w*.

The inputs to the program are:

1. SFC_File: the original file that is adjusted;

2. z0_new: the new roughness length;

3. Bo_new: the new Bowen ratio;

4. remark: a string descriptor of the run.

16 Yuan, J., A. Venkatram, and V. Isakov, 2006: “Dispersion from ground-level sources in a shoreline urban area”, Atmos. Environ., 40, 1361-1372.

41

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ The output file has the name of the input file plus the “remark”.

We have also developed a draft version of a postprocessor that allows examination of the

sensitivity of AERMET output to changes in surface albedo in addition to changes in surface

roughness and the Bowen ratio. The inputs to the processor called Recompute_SFC_File

1. The preprocessed (*.prn) used by AERMET;

2. SFC_File: the original file that is adjusted;

3. z0_new: the new roughness length;

4. Bo_new: the new Bowen ratio;

5. alb_new: the new albedo

6. alb_old: the old albedo

7. remark: a string descriptor of the run.

The output file has the name of the input file plus the “remark”.

As in Adjust_SFC_File, the formulations used to recalculate these parameters differ slightly

from those used in AERMET; so the adjusted file is not identical to what AERMET would

produce with the new surface parameters. Recompute_SFC_File also uses the incoming solar

radiation to estimate the thermal radiation corresponding to the old value of the albedo. Then,

the absorbed solar radiation is recalculated using the new value of the albedo, and the new

absorbed solar radiation is added to the thermal radiation (which is not affected by the change in

albedo) to estimate the net radiation. The new sensible heat flux is then estimated using the new

value of net radiation.

The new sensible heat flux is used to adjust the mixed layer height, but the method does not

account for the new value of the potential temperature gradient above the new mixed layer

height. This results in errors in the calculation of the mixed layer height, and hence errors in the

convective velocity scale, w*. These errors are evident in Figures 4.3 and 4.4, which compare

AERMET outputs with outputs from the processor based on anah.sfc for 2007. The differences

in the surface friction velocity are small, but the deviations in w* do not appear to be negligible.

The usefulness of the processor can be evaluated only be running AERMOD using the two

different input files.

42

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ A processor called ‘Convert_Rural_to_Urban (Input_File, Output_File, zou, dh, Ru, wdf)’

allows one to modify an .sfc file constructed using rural surface measurements to an .sfc file that

accounts approximately for urban effects. The inputs to the program are:

1. Input_File: the name of the original .sfc file;

2. Output_File: the name of the adjusted file;

3. zou: the roughness length in m in the urban area;

4. dh: the displacement height in m of the urban area;

5. Ru: the distance in m between the rural and urban stations;

6. wdf: a two element vector that specifies the wind directions in degrees between which the

correction is applied.

This processor uses the method described in Luhar et al. (2006)17 to account for urban effects.

AERMOD has a method (see Cimorelli et al. 200518) to partially account for urban effects

during the night when the “URBAN” option is selected in AERMOD. This method, however,

only accounts for the upward heat flux that occurs when stable air flows from a rural area over a

warmer urban area during the night. It does not account, on the other hand, for the corresponding

increase in roughness when rural air flows over an urban area. Although the surface files

provided to AQMD implicitly account for urban roughness effects because they are constructed

using data from urban measurement sites, it might still be necessary to account for additional

urban effects by (a) invoking the “URBAN” option when AERMOD is run and/or (b) running

the ‘Convert_Rural_to_Urban’ post-processor to account for changes in surface characteristics

that occur between the meteorological measurement site and the source/receptor location.

17 Luhar, A. K., A. Venkatram, and S. Lee, 2006: “On relationships between urban and rural near-surface meteorology for diffusion applications”, Atmos. Environ., 40, 6541-6553

18 Cimorelli, A. J., S. G. Perry, A. Venkatram, J. C. Weil, R. J. Paine, R. B. Wilson, R. F. Lee, W. D. Peters, and R. W. Brode, (2005): “AERMOD: A dispersion model for industrial source applications. Part I: General model formulation and boundary layer characterization”, J. App. Meteor., 44, 682-693.

43


Table 4.1: Data period considered for each AQMD sub-area

Sub-Area Data PeriodANAH 2005 - 2007AZUS 2005 - 2007BNAP 2005 - 2007BURK 2005 - 2007CELA 2006 - 2007*CRES 2005 - 2007CSTA 2005 - 2007ELSI 2005 - 2007FONT 2005 - 2007INDI 2005 - 2007LAHB 2005 - 2007LAXH 2005 - 2007LGBH 2005 - 2007LYNN 2005 - 2007MSVJ 2005 - 2007PERI 2007 **PICO 10/12/2005 - 2007***POMA 2005 - 2007PLSP 2005 - 2007RDLD 2007 ****RESE 2005 - 2007RIVR 2005 - 2007SCLR 2005 - 2007SNBO 2005 - 2007UPLA 2005 - 2007WSLA 2005 - 2007

* Questionable wind data during 2005 at the CELA station** Questionable wind data during 2005 and 2006 at the RDLD station*** Data only available since 10/12/2005 at the PICO station**** Data only available since 2007 at the PERI station

44


Table 4.2: Coordinates, elevation and measurement heights of AQMD monitoring stations

Station Latitude Longitude Elevation (m)

Height of Wind Measurement

(m)

Height of Temperature Measurement

(m)

ANAH 33o 49' 50" 117o 56' 19" 41 9.1 5.5AZUS 34o 8' 11" 117o 55' 26" 182 9.1 5.5BNAP 33o 55' 15" 116o 51' 30" 660 9.1 5.5BURK 34o 10' 33" 118o 19' 1" 175 12.2 8.5CELA 34o 3' 59" 118o 13' 36" 87 21.3 17.7CRES 34o 14' 29" 117o 16' 32" 1387 9.1 5.5CSTA 33o 40' 26" 117o 55' 33" 20 9.1 5.5ELSI 33o 40' 35" 117o 19' 51" 406 9.1 5.5FONT 34o 6' 1" 117o 29' 31" 367 9.1 5.5INDI 33o 42' 30" 116o 12' 57" -4 9.1 5.5LAHB 33o 55' 31" 117o 57' 8" 82 9.1 5.5LAXH 33o 57' 15" 118o 25' 49" 42 9.1 5.5LGBH 33o 49' 25" 118o 11' 19" 30 12.2 8.5LYNN 33o 55' 44" 118o 12' 39" 29 9.1 5.5MSVJ 33o 37' 49" 117o 40' 30" 170 9.1 5.5PERI 33o 47' 20" 117o 13' 40" 442 9.1 5.5PICO 34o 00' 37" 118o 4' 7" 58 9.1 5.5POMA 34o 4' 0" 117o 45' 0" 270 9.1 5.5PLSP 33o 51' 10" 116o 32' 28" 171 9.1 5.5RDLD 34o 3' 32" 117o 8' 52" 481 9.1 5.5RESE 34o 11' 57" 118o 31' 58" 228 12.2 8.5RIVR 34o 0' 2" 117o 24' 55" 250 9.1 5.5SCLR 34o 23' 0" 118o 31' 42" 375 9.1 5.5SNBO 34o 6' 24" 117o 16' 25" 305 9.1 5.5UPLA 34o 6' 14" 117o 37' 45" 379 9.1 5.5WSLA 34o 3' 2" 118o 27' 24" 97 9.1 5.5

45


Table 4.3: Data sources used for each meteorological variable

Variable Data Source

Wind speed and direction AQMD

Temperature AQMD, CIMIS, NWS

Solar Radiation CIMIS, AQMD, Calculated based on cloud

coverage and time of day

Fractional Cloud Coverage NWS

46


Table 4.4: Instances of potential problems in AQMD wind data.

Sub-Area Problem Course of Action

BURK Hours of unreasonably high wind speeds on

12/18-12/19/07

Replaced with missing value

indicator

CELA Frequent periods of unreasonably high wind

speed from 1/1-10/4/05

Sub-area only processed for 2006 –

2007.

CRES A few hours of slightly negative wind speed

Effectively set to zero since we

entered to wind speeds to one

decimal place into AERMET.

CSTA Frequent hours of very light winds Not treated

ELSI Unreasonable wind speeds on 1/10-1/14/05 Replaced entire period with missing

value indicator

FONT Unreasonable wind data during period 5/3-

8/2/06

Replaced entire period with missing

value indicator

INDI One hour of slightly negative wind speed

(1/24/05)

Effectively set to zero since we

entered to wind speeds to one

decimal place into AERMET.

LAHB Frequent hours of very light winds Not treated

LAXH

Hours of unreasonably high wind speeds

(9/26/05, 4/20/06, 7/29/06, 11/18/06,

11/9/07, 11/11-11/12/07)


indicator

47


LYNN Frequent hours of very light winds Not treated.

MSVJ

Frequent hours of questionably high wind

speeds often occur around sunrise and

sunset hours

Not treated

POMA

Frequent hours of questionably low wind

speeds from 1/29 – 7/10/07 (long periods of

0.3 and 0.2 mph).

Not treated

RDLD

Lots of zero and negative wind speed values

during 2005 to August 2006.

Frequent hours of very light winds.

Sub-area only processed for 2007.

Not treated.

RESE

Frequent hours of very light winds

Hours of unreasonably high wind speeds

(12/26/05, 5/22/06, 10/13/07).

Not treated


indicator

SCLR Questionably low wind speeds on 3/29 –

4/4/06 Not treated

UPLA Questionably low wind speeds on 7/20 –

8/1/06 Not treated

48


Table 4.5: Surface parameter values applied to AERMET.

Sub-Area Surface Albedo

Surface Roughness

(meters)

Bowen Ratio

Bowen Ratio from AERSURFACE

ANAH 0.17 0.524 1.0 1.18AZUS 0.19 0.421 1.0 1.68BNAP 0.22 0.157 1.5 2.32BURK 0.19 0.626 1.0 1.58CELA 0.18 0.645 1.0 1.42CRES 0.17 0.412 1.0 1.35CSTA 0.18 0.403 1.0 1.20ELSI 0.20 0.265 1.0 1.49FONT 0.19 0.273 1.0 1.30INDI 0.19 0.235 1.5 0.98LAHB 0.18 0.554 1.0 1.17LAXH 0.16 0.255 1.0 0.62LGBH 0.18 0.596 1.0 1.35LYNN 0.18 0.505 1.0 1.25MSVJ 0.18 0.351 1.0 1.32PERI 0.20 0.215 1.0 1.24PICO 0.18 0.396 1.0 1.28POMA 0.18 0.548 1.0 1.23PLSP 0.22 0.509 1.5 3.02RDLD 0.20 0.408 1.0 1.53RESE 0.18 0.611 1.0 1.17RIVR 0.19 0.387 1.0 1.34SCLR 0.21 0.291 1.0 2.16SNBO 0.18 0.361 1.0 1.20UPLA 0.18 0.399 1.0 1.15WSLA 0.18 0.451 1.0 1.37

49


Figure 4.1: Map of South Coast Air Quality District area showing locations meteorological measurement stations

across the AQMD area. Shown are the following: AQMD surface stations, AQMD profiler stations, NWS surface

stations, and CIMIS surface stations.

50


Figure 4.2: Output from AERMET for z0= 1 m and Bo=2 for Riverside for 2007 compared with output from

Adjust_SFC_File generated from the ‘rivr.sfc’ file output from AERMET for z0= 0.387 m and Bo=1. Top

panel compares variables paired in time while bottom panels compare distributions of outputs.

51


Figure 4.3: Output from AERMET for z0= 0.1 m and Bo=2, and albedo=0.32 for Anaheim for 2007 compared with

output from Recompute_SFC_File generated from the ‘anah.sfc’ file output from AERMET for z0= 0.524 m, Bo=1,

and albedo=0.19. Top panel compares variables paired in time while bottom panels compare distributions of

outputs.

52


Figure 4.4: Output from AERMET for z0= 0.4 m and Bo=0.5, and albedo=0.0.16 for Anaheim for 2007

compared with output from Recompute_SFC_File generated from the ‘anah.sfc’ file output from AERMET for

z0= 0.524 m, Bo=1, and albedo=0.19. Top panel compares variables paired in time while bottom panels

compare distributions of outputs.

53


5 Results

We present our analysis of the *.sfc and *.pfl files in two steps. First, a basic assessment to check

for typical features of average Southern California meteorology is presented. Second, we

compare results from the Riverside (RIVR) and Long Beach (LGBH) sub-area *.sfc files to

observations. Results are only shown for the ‘Version 1’ files.

Basic Assessment

(i) Wind Speed and Direction

The wind roses from each AQMD monitoring station used to construct the *.sfc files are shown

in Appendix A. A map in which these wind roses are overlaid is shown in Figure 5.1. This map

shows that the basic onshore and topographic channeling flows typical of the prevailing wind

flow pattern across the AQMD air basin appear to be successfully incorporated into the input

files.

An important difference between AERMOD and ISCST3 is that AERMOD can be run for

conditions in which the wind speed is less than 1 m/s. The frequency of wind speeds less than 1

m/s among the AQMD monitoring station data used to create the input files is therefore shown in

Figure 5.2. Relatively bigger differences in concentrations between AERMOD and ISCST3 may

be anticipated in the sub-areas where the percentage of wind speeds less than 1 m/s are relatively

high. As discussed in Section 4 (see Table 4.4), the percentage of low wind speeds at some of

these, however, may be questionable. The lower bound of wind speed in creating the *.sfc and

*.pfl files was 0.1 m/s.

(ii) Micrometeorological Variables

The average surface friction velocity (u*) over hours 0900 – 1500 LST for all sub-areas is shown

in Figure 5.3. These data depict typical daytime values of u*. By inspecting the pattern of values

across the map, it can be seen that many of the coastal and moderately inland sub-areas contain

relatively higher u* than sub-areas that are further inland and in interior valleys. This is expected

54

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ behavior due to the prevalence of sea-breeze flows closer to the coast. Of course, the highest

daytime values of u* occur at BNAP and SCLR, which are prone to very high wind speeds.

The average free convective velocity scale (w*) over hours 0900 – 1500 LST for all sub-areas is

shown in Figure 5.4. These data depict typical values of w*. By inspecting the pattern of values

across the map, it can be seen that many of the coastal and moderately inland sub-areas contain

relatively lower w* than sub-areas that are further inland or in interior valleys. This is expected

due to the stronger heat fluxes further inland due to relatively higher solar radiation and lower

cloud coverage. In the case of BNAP and PLSP, the higher w* is also a result of the relatively

higher specified value of Bowen ratio for these sub-areas. Values, however, are probably too

uniform across the air basin since daytime boundary layer heights appear to be overestimated in

coastal sub-areas, as discussed below.

The average surface friction velocity (u*) over hours 2100 – 0500 LST for all sub-areas is shown

in Figure 5.5. These data depict typical nighttime values of u*. By inspecting the pattern of

values across the map, three “clusters” of values are evident. The cluster of stations (BNAP,

SCLR, PLSP, INDI) with relatively high nighttime u* appear to exhibit the high wind speeds in

the area. Many of the stations in the second cluster of stations (CELA, LGBH, LAXH, ANAH,

PICO, BURK) with average nighttime values around 0.1 m/s are probably exhibiting the

moderate nighttime wind speed conditions in the coastal and near-inland basin due to their

relatively closer coastal proximity. Several of the stations in the third cluster (RDLD, LAHB,

WSLA, POMA, CSTA, LYNN, PERI, RESE, ELSI, SNBO) with relatively low average

nighttime values around 0.05 m/s, on the other hand, are probably exhibiting more localized

effects. This could be a characteristic of their more inland location (PERI, ELSI) or other local

effects that are unknown (WSLA). The relatively low nighttime u* at some of these stations, for

example CSTA, RDLD, POMO, LAHB, could also due to questionable wind data, as discussed

in Section 4 (see Table 4.4).

55

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ (iii) Daytime Boundary-layer heights

Average values of daytime boundary layer height over the hours of 1100 – 1500 LST for each

sub-area are listed in Table 5.1. While inland average values appear reasonable, the uniformity of

values across the basin is questionable. In particular, the coastal sub-areas (LGBH and LAXH)

should not have similar values as those far inland (RIVR, PLSP), but should have noticeably

smaller average daytime boundary layer depths due to the presence of stable internal boundary

layers from sea-breeze intrusion. AERMET does not include a mechanism to include such effects

in its formulation for computing boundary layer height, so the uniformity is therefore expected.

Corrections to include internal boundary layer effects for coastal locations must therefore be

done in post-processing.

Comparison with Observations

Because urban areas are not horizontally homogeneous, it is necessary to evaluate the

applicability of AERMET to urban areas. Here, we address this issue by comparing selected

variables from the AERMET Riverside sub-area rivr.sfc and Long Beach sub-area lgbh.sfc files

with measurements made in two urban areas in the South Coast air basin: Riverside and

Wilmington.

The roughness length, Bowen ratio and minimum surface albedo for Riverside were estimated

with AERSURFACE. The values of these parameters are based on the characterization of the

surface surrounding the AQMD sites at which surface measurements for AERMET were made.

The relationships between surface descriptors and surface parameters used in AERMET are

uncertain. Thus, the values of the surface parameters are uncertain. The Bowen ratio, which is

the ratio of the sensible to the latent heat flux, is likely to be the least reliable of the parameters

because it depends on soil moisture, which depends on soil and rainfall history that cannot be

captured in a static descriptor of the surface. In order to acknowledge this uncertainty, we have

used a nominal Bowen ratio of unity in constructing AERMET files for all the AQMD sites

except at two desert sub-areas (BNAP and PLSP) where latent heat flux is likely to be small. We

have evaluated the use of this nominal value of Bowen ratio by comparing AERMET outputs

with observations made in Riverside and Wilmington.

56

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ The meteorological observations used in the comparison were obtained from Professor Marko

Princevac, UC Riverside. His group made measurements at three sites in Riverside County,

California, in 2007. Site US (upwind suburban) is in a desert plain in Moreno Valley, located

east of Riverside. Site DS (downwind suburban) is on top of a bluff located above the Santa Ana

River in suburban Riverside, located west of Riverside. Site CU (center urban) is located on the

street corner of Arlington and Brockton in downtown Riverside. Sites US and CU are 18 km

apart and sites CU and DS are 9 km apart.

Each site was equipped with a 3 meter tower instrumented with (1) a sonic anemometer, (2) two

soil heat flux plates, (3) an infrared thermometer, (4) a krypton hygrometer, (5) two soil

temperature probes, (6) a water content reflectometer, (7) two air temperature sensors, and (8)

site US had a net radiometer.

Data were collected from early February through late April 2007 at Site CU. Sites US and DS

were run for shorter periods of time during mid-March through late April 2007 and late March to

the end of April 2007, respectively. The comparisons that follow are based on 1-hour averaged

data from the sonic anemometers.

Figure 5.6 compares measurements of the surface variables, sensible heat flux, and the surface

friction velocity measured at the US site with AERMET estimates for the same time. These

variables govern dispersion of a near surface release. The top panels, which compare the values

paired in time, show that heat fluxes and friction velocities estimated at the nominal Riverside

site differ from the measurements at the US site. This is to be expected because the surface

characteristics at the Riverside site differ from those at the US site. In this case, most of the

AERMET friction velocities and heat fluxes are within a factor of two of the observed values at

this site.

The bottom panels compare the variables after ranking them from lowest to highest value; the

values are no longer paired in time. This method compares the distributions of the surface

friction velocities and heat fluxes at the two sites. This comparison is relevant because these

distributions govern the distributions of concentrations, which are important from a regulatory

viewpoint. It answers the question: Do the paired-in-time differences in meteorological variables

translate into differences in the distributions of concentrations. In this particular case, the

57

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ differences are small except at the tails of the distributions: the observed heat fluxes and surface

friction velocities have a few high values that are not present in the AERMET distributions. This

might not have an impact on design concentrations, which are governed by low values of these

variables. Note that AERMET distribution of u* is close to the observed distribution at low

values, suggesting that design concentrations for surface releases are likely to be realistic.

Figure 5.7 compares the diurnal variation of the observed and AERMET variables for 4/26/2007,

a representative day. AERMET follows the diurnal trend of the observed values, although the

peak heat flux occurs about two hours earlier than the observed value. This is a feature that is

observed at all three sites.

The magnitudes of the AERMET surface friction velocities are similar to observed values for

this particular site. The results are similar for 3/31/07 as seen in Figure 5.8.

Figure 5.9 shows the comparison of AERMET estimates with measurements made in the

Riverside downtown area at site CU. Here we see that AERMET overestimates the surface heat

flux by almost a factor of two, but surface friction velocities from AERMET compare well with

observed values. The overestimation of heat flux could be related to the fact that the CU site was

located on a well watered lawn where the Bowen ratio might be smaller than the nominal

AERMET value of 1.

At site DS (Downwind suburban), AERMET underestimates both the surface heat and the

surface friction velocity by almost a factor of two, as seen in Figure 5.10. Note that there are

several observed values of large positive heat fluxes when AERMET values are negative. This

suggests the need for including urban effects when running AERMOD; the AERMOD interface

incorporates a positive heat flux during the night if the URBAN option is chosen in the input file.

Increasing the Bowen ratio to 2 improves the comparison for heat flux, as seen in Figure 5.11,

but the surface friction velocity is still underestimated.

AERMET outputs for Long Beach were compared with measurements made during a field study

conducted in Wilmington in 2005. The measurements were made at Los Angeles County

Sanitation District’s Joint Water Pollution Control Plant (JWPCP) during the period June 16th

and June 30th 2005. The top panel of Figure 5.12 indicates that when AERMET estimates of

heat flux are small or even negative the corresponding observations are positive and large. This

58

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ suggests that AERMET might not be capturing urban heat island effects seen in the observed

heat fluxes. However, AERMET performs well in estimating the distribution of observed surface

meteorological variables when the observed heat fluxes are positive.

These comparisons of AERMET estimates for Riverside and Long Beach with measurements

made at diverse sites in Riverside and Wilmington indicate that the discrepancies between the

measured values and AERMET estimates are generally within a factor of two of each other; such

deviations do not always translate into similar differences in the distributions of the surface

variables. The consequences of such differences need to be evaluated by running AERMOD with

differing surface files.

It is clear that AERMET files generated using data at one site will differ from those generated

using data from another nearby site. Also, AERMET estimates of surface variables such as heat

flux will differ from observations made at the same site, as seen in Figure 5.13. Here AERMET

was run using the observed wind speeds at the upwind site in Riverside. The Bowen ratio was

taken to be unity, and the roughness length was adjusted to provide the best fit between the

estimates and observed values of surface friction velocity. The cloud cover was taken to be 0.5

in the absence of observations. Even under these ideal conditions, the deviation between model

estimates and observations is a factor of two. Note that observed values of the standard deviation

of horizontal velocity fluctuations, σv, are rarely less than 0.5 m/s when the corresponding

estimates are much smaller.

We see in Figure 5.14 similar deviations between model estimates and observations at the central

urban (CU) site in Riverside where Bowen ratio is taken to be 0.5 to account for the possibility

of high latent heat fluxes.

The results presented here indicate the need to conduct sensitivity studies with AERMOD to

understand the impact on design concentrations of unavoidable differences between AERMET

estimates of meteorological variables and corresponding observations at the site of interest.

59


Table 5.1: Average daytime boundary layer heights over hours 1100 – 1500 LST for each

sub-area.

Sub-Area Average PBL height (m)

ANAH 866AZUS 837BNAP 927BURK 852CELA 861CRES 834CSTA 775ELSI 837FONT 857INDI 985LAHB 859LAXH 777LGBH 774LYNN 774MSVJ 864PERI 871PICO 839POMA 855PLSP 959RDLD 863RESE 849RIVR 861SCLR 822SNBO 866UPLA 860WSLA 768

60


Figure 5.1: Map of South Coast Air Quality District area with wind roses for AQMD monitoring station data used

to produce AERMOD input files overlaid. Wind roses overlaid on map are also shown in Appendix A.

61


0

10

20

30

40

50

60

70

ANAHAZUS

BNAPBURK

CELACRES

CSTAELSI

FONTIN

DLAHB

LAXHLGBH

LYNNMSVJ

PERIPIC

OPOMA

PSLPRDLD

RESERIVR

SCLRSNBO

UPLAWSLA

% o

f Hou

rs

< 1 m/s < 0.5 m/s

Figure 5.2: Percentage of winds less than 1 m/s and 0.5 m/s in the AQMD monitoring station wind data used to

produce the *.sfc and *.pfl files.

62


Figure 5.3: Average daytime values of surface friction velocity over the hours 0900 – 1500 LST for all sub-areas.

63


Figure 5.4: Average values of free convective scaling velocity over the hours 0900 – 1500 LST for all sub-areas.

64


Figure 5.5: Average nighttime values of surface friction velocity over the hours 2100 – 0500 LST for all sub-areas.

65


0 200 400-100

0

100

200

300

400

500

AERMET heat flux (W/m2)

Obs

erve

d he

at fl

ux

0.2 0.4 0.6 0.8 1

0.2

0.4

0.6

0.8

1

AERMET u* (m/s)

Obs

erve

d u *

0 200 400-100

0

100

200

300

400

500

Ranked AERMET heat flux (W/m2)

Obs

erve

d he

at fl

ux

0.2 0.4 0.6 0.8 1

0.2

0.4

0.6

0.8

1

Ranked AERMET u* (m/s)

Obs

erve

d u *

Figure 5.6: Comparison of measurements made at the at the US site with estimates from AERMET. The top panel compares observations paired in time, while bottom panels compare ranked observations and estimates.

66


0 5 10 15 20 25-0.1

0

0.1

0.2

0.3

Time of day

Hea

t flu

x in

K.m

/s

4/26

AERMETObserved

0 5 10 15 20 250

0.2

0.4

0.6

0.8

Time of day

u * in m

/s

0 5 10 15 20 250

1

2

3

4

Time of day

Win

d sp

eed

in m

/s

0 5 10 15 20 250

100

200

300

400

Time of day

Win

d di

rect

ion

in d

egre

es

Figure 5.7: Comparison of diurnal variation of measurements made at the at the US site with corresponding estimates from AERMET for 4/26/2007.

67


0 5 10 15 20 25-0.1

0

0.1

0.2

0.3

Time of day

Hea

t flu

x in

K.m

/s

3/31

AERMETObserved

0 5 10 15 20 250

0.1

0.2

0.3

0.4

0.5

Time of day

u * in m

/s

0 5 10 15 20 250

1

2

3

4

Time of day

Win

d sp

eed

in m

/s

0 5 10 15 20 250

100

200

300

400

Time of day

Win

d di

rect

ion

in d

egre

es

Figure 5.8: Comparison of diurnal variation of measurements made at the at the US site with corresponding estimates from AERMET for 3/31/2007.

68


0 200 400-100

0

100

200

300

400

500


Obs

erve

d he

at fl

ux

0.2 0.4 0.6 0.8 1

0.2

0.4

0.6

0.8

1

AERMET u* (m/s)

Obs

erve

d u *

0 200 400-100

0

100

200

300

400

500


Obs

erve

d he

at fl

ux

0.2 0.4 0.6 0.8 1

0.2

0.4

0.6

0.8

1


Obs

erve

d u *

Figure 5.9: Comparison of measurements made at the CU site with estimates from AERMET. The top panel compares observations paired in time, while bottom panels compare ranked observations and estimates.

69


0 200 400-100

0

100

200

300

400

500


Obs

erve

d he

at fl

ux

0.2 0.4 0.6 0.8 1

0.2

0.4

0.6

0.8

1

AERMET u* (m/s)

Obs

erve

d u *

0 200 400-100

0

100

200

300

400

500


Obs

erve

d he

at fl

ux

0.2 0.4 0.6 0.8 1

0.2

0.4

0.6

0.8

1


Obs

erve

d u *

Figure 5.10: Comparison of measurements made at the at the DS site with estimates from AERMET. The top panel compares observations paired in time, while bottom panels compare ranked observations and estimates.

70


0 200 400-100

0

100

200

300

400

500


Obs

erve

d he

at fl

ux

0.2 0.4 0.6 0.8 1

0.2

0.4

0.6

0.8

1

AERMET u* (m/s)

Obs

erve

d u *

0 200 400-100

0

100

200

300

400

500


Obs

erve

d he

at fl

ux

0.2 0.4 0.6 0.8 1

0.2

0.4

0.6

0.8

1


Obs

erve

d u *

Figure 5.11: Comparison of measurements made at the at the DS site with estimates from AERMET. Bowen ratio is taken to be 2 instead of the nominal value of 1. The top panel compares observations paired in time, while bottom

panels compare ranked observations and estimates.

71


0 200 400-100

0

100

200

300

400

500


Obs

erve

d he

at fl

ux

0.2 0.4 0.6 0.8 1

0.2

0.4

0.6

0.8

1

AERMET u* (m/s)

Obs

erve

d u *

0 200 400-100

0

100

200

300

400

500


Obs

erve

d he

at fl

ux

0.2 0.4 0.6 0.8 1

0.2

0.4

0.6

0.8

1


Obs

erve

d u *

Figure 5.12: Comparison of measurements made at Wilmington in 2005 with estimates from AERMET for Long Beach. The top panel compares observations paired in time, while bottom panels compare ranked observations and

estimates.

72


101 102 103101

102

103

Estimated heat flux in W/m2

Obs

erve

d he

at fl

ux

10-1 10010-1

100

Estimated u* in m/s

Obs

erve

d u *

10-1 100 10110-1

100

101

Estimated σw in m/s

Obs

erve

d σ

w

10-1 100 10110-1

100

101

Estimated σv in m/s

Obs

erve

d σ

v

Figure 5.13: Comparison of measurements made at the at the US site with estimates from AERMET using wind speeds and temperatures at the site.

73


Figure 5.14: Comparison of measurements made at the CU site with estimates from AERMET using wind speeds and temperatures at the site.

74


6 Conclusion

Summary

AERMOD surface (*.sfc) and profile (*.pfl) meteorological input files have been developed for

various sub-areas across the South Coast Air Quality Management District air basin. AERMET,

developed by the U.S. EPA, was used to develop these files. The preceding sections of this report

describe in detail the contents of the *.sfc and *.pfl files (Section 2), the AERMET processor that

creates them (Section 3), the methodology used to process the raw data to create the

meteorological input files for AERMET (Section 4), and the resulting mean wind and

micrometeorological turbulence fields produced by AERMET for each sub-area over the air

basin (Section 5). A comparison of the AERMET results for the Riverside and Long Beach sub-

areas with observations in Riverside and Wilmington, respectively, is also presented (Section 5).

Three post-processors were developed to allow the user to check and modify the *.sfc and *.pfl

files produced by AERMET. Application of these post-processors can allow modification of

these files to partially account for meteorological effects not accounted for in AERMET. The

post-processors are described in detail in Section 4 and Appendix C.

A ‘User’s Guide’ for the full procedure of processing raw meteorological data to produce input

files for AERMET is contained in Appendix C. The User’s Guide also gives guidance on running

AERMET and provides a description of the three post-processors mentioned above to modify the

AERMET output.

All deliverables – the *.sfc and *.pfl files, raw meteorological data, AERMET input files, post-

processing codes, and other miscellaneous files – are provided on the accompanying CD.

Appendix D describes the directory structure of this CD.

75

EC-07-015 – 09-04-17 (I) Final Report (Vol. I) _____________________________________________________________________________________________ Suggestions for Future Work

After carrying out this work and reviewing the results contained in the provided *.sfc and *.pfl

files, we have the following suggestions for future work:

1. The wind data for several of the AQMD stations should be reviewed to check that the

data are realistic. At several stations, for example CSTA, LAHB, LYNN, RESE and

POMO, a large percentage of hours (~ 50%) have winds less than 1 m/s. See discussions

in Section 4 (specifically Table 4-4 and associated discussion) and Section 5 (specifically

Figure 5.2 and associated discussion) for further details.

2. While the hourly values in the AERMET output compared with observations at Riverside

and Wilmington are generally within a factor of two, considerable variability exists in the

comparison, both in space and time. Such variability appears to be inherent to

observations made in urban areas. The impact of this variability on design concentrations

should be investigated by running AERMOD with input files that reflect this variability.

3. The daytime boundary layer depths produced by AERMET for the coastal sub-areas (for

example, WSLA, LAXH, LGBH and CSTA) are probably on average too high because

AERMET does not account for coastal internal boundary layers that result from the

prevailing onshore flow in these sub-areas or for the limiting effects of large scale

subsidence that is prevalent in the South Coast Air Basin. The post-processors produced

in this project can potentially provide suitable corrections to the daytime boundary layer

depths and convective velocity scales in the chosen sub-areas.

4. The sensitivity of design concentrations to reasonable and anticipated variations in such

meteorological variables such boundary layer depth, Bowen Ratio, and different choices

of meteorological station providing wind data for a given sub-area should be investigated

to see how important these factors are in affecting design concentrations. Such an effort

will help identify the most important improvements to be made to the *.sfc and *.pfl files

provided thus far.

76

Documents

The Development of AERMOD-Ready Meteorological Data for ......AERMOD over the entire three year period 2005 – 2007 of hourly meteorology contained in these files are shown in Figure