# Presentation: 608289

## Profiler Observations, Applications, and Analysis: Techniques That Use Profiler/RASS Data to Characterize Meteorological Phenomena for Use in Air Quality Data Analysis and Forecasting

- Timothy S. Dye
- Mark R. Lilly
- Sonoma Technology, Inc.
- Petaluma, CA
-  American Meteorological Society
-  Short Course on the Fundamentals of Boundary Layer Wind and Temperature Profiling using Radar and Acoustic Techniques
- Long Beach, CA, February 9-13, 2003

- Profiler Observations, Applications, and Analysis: *Techniques That Use Profiler/RASS Data to Characterize Meteorological Phenomena for Use in Air Quality Data Analysis and Forecasting*

- 2320

## Outline

- Background
- Mixing Depth
- Transport/Flow Patterns
- Meteorological Models

## Background - Regional Air Quality Study Objectives

- NARSTO (North American Research Strategy for Tropospheric Ozone)
- Understand the fundamental physical, chemical, and meteorological processes responsible for ozone accumulation on local and regional scales in North America
- Southern Oxidant Study
- Determine the links among the chemical, biological, and meteorological processes that govern formation and accumulation of ozone and fine particulate aerosols
- California Regional Particulate Air Quality Study
- Provide an improved understanding of emissions, PM10 and PM2.5 composition, and dynamic atmospheric processes

## Shasta County, California
Investigate the contribution of transported pollutants to local ozone exceedances
Minnesota Pollution Control Agency 
Characterize the ground-level ozone air quality of the Minneapolis/St. Paul region, including contributing factors of local emissions, meteorology, and regional transport to support air quality forecasting

- Investigate the contribution of transported pollutants to local ozone exceedances
- Minnesota Pollution Control Agency
- Characterize the ground-level ozone air quality of the Minneapolis/St. Paul region, including contributing factors of local emissions, meteorology, and regional transport to support air quality forecasting

- Background - Local Air Quality Study Objectives

## Background - Common Thread

- Meteorological processes that influence air quality
- Sunlight
- Horizontal dispersion
- Vertical mixing
- Transport
- Clouds and precipitation

## Background - Instruments and Use

**RWP = radar wind profiler****	****RASS = Radio Acoustic Sounding System**

## Background - Rawinsonde Winds

## Background – RWP Winds

## Background – RASS Tv

- 4 a.m. and 4 p.m.
- rawinsonde soundings do not capture the
- onset of the sea breeze

- RASS virtual temperature (Tv) data at Simi Valley on August 6, 1997,
- depicting the onset of the sea breeze

## Background – Mixing Derived by Rawinsondes

## Background - Mixing Derived by RWP and RASS

## Background – Transport Derived by RWP

- Background – Transport Derived by RWP

## Mixing Depth – Data and Methods

- Mixing depth is the maximum height to which pollutants emitted near the surface will be mixed.
- Mixing depths can be determined using a variety of RWP, RASS and Sodar data:
- RWP
- Reflectivity (Cn2,SNR)
- Vertical velocity (*w*)
- Spectral width
- Horizontal winds
- RASS
- Tv profiles
- Sodar
- Reflectivity
- Winds

## Mixing Depth – Data and Methods

**CBL****	**** = Convective Boundary Layer**
**NBL****	**** = Nocturnal Boundary Layer**

## Mixing Depth – Data and MethodsRWP (1 of 3)

- Reflectivity
- Measures variations in the refractive index of the atmosphere
- Turbulence produces variations in atmospheric temperature, humidity, and pressure, which in turn cause variations in the refractive index.
- Reflectivity largest at inversion capping convective boundary
- CBL height-finding algorithms – (Angevine et al., 1994; Dye et al., 1995; Bianco and Wilczak, 2002)

## Vertical Velocity
The radial velocity measured by the RWP vertical beam is a direct measure of vertical velocity
Convective thermals within the CBL act to vertically redistribute heat, moisture, and momentum
Identify the vertical extent of convective thermals to obtain an estimate of mixing depth
Spectral Width
Illustrates the range of radial velocities measured within the scatter volume
Locating the gradient in the spectral width (variability in the scales of turbulence) can be used as a reasonable estimate of mixing depth

- The radial velocity measured by the RWP vertical beam is a direct measure of vertical velocity
- Convective thermals within the CBL act to vertically redistribute heat, moisture, and momentum
- Identify the vertical extent of convective thermals to obtain an estimate of mixing depth
- Spectral Width
- Illustrates the range of radial velocities measured within the scatter volume
- Locating the gradient in the spectral width (variability in the scales of turbulence) can be used as a reasonable estimate of mixing depth

- Mixing Depth – Data and MethodsRWP (2 of 3)

## Mixing Depth – Data and MethodsRWP (3 of 3)

- Estimating Mixing Depths

- Cn2

- Vertical
- Velocity

- Spectral
- Width

## Mixing Depth – Data and MethodsRASS (1 of 2)

- Nighttime
- Inflection point on morning Tv profiles corresponds with maximum height of nocturnal boundary layer (may be higher than true extent of mixing)
- Daytime
- Point where the surface temperature lifted dry adiabatically intersects with the Tv profile is typically a reasonable estimate of daytime mixed layer height
- Inversion Strength
- Strength of nocturnal inversion influences the time at which the evolving CBL mixes with the residual layer
- Strength of the subsidence inversion controls CBL growth

## Mixing Depth – Data and MethodsRASS (2 of 2)

- Mixing Depth – Data and MethodsRASS (2 of 2)

## Mixing Depth – Data and MethodsRWP Winds (1 of 2)

- During the daytime (CBL), wind profiles will  increase with height throughout the surface layer (below first profiler gate), before showing little change throughout the depth of the ABL and, finally, an increase in wind speed above the ABL
- At night (NBL), an increase in wind speed is typically observed near the top of the NBL as air above the NBL decouples from the surface

## Mixing Depth – Data and MethodsRWP Winds (2 of 2)

- Radar profiler wind data at Ventura on August 6, 1997 showing the sea breeze (MacDonald et. al., 1999)

- Radar profiler wind data at Visalia on August 9, 1998, showing the nocturnal jet, CBL, and eddy flow.  This wind pattern was observed on the majority of the episode days (MacDonald et. al., 1999).

## Mixing Depth – Data and MethodsSodar (1 of 2)

- Sodar reflectivity data at Brighton on January 14, 1997, showing the onset of daytime convective mixing (Watson et. al., 1998)** **

- Reflectivity – measures variations in the refractive index of the atmosphere (CT2)

## Mixing Depth – Data and MethodsSodar (2 of 2)

- Mixing Depth – Data and MethodsSodar (2 of 2)

- Time series of inversions at Brighton and effective stack heights for the Cherokee and Pawnee stacks on January 13 and 14, 1997 (Watson et. al., 1998)

## Mixing Depth – Applications

- Mixing Depth Growth Rate (MGR)
- Peak mixing depth
- Time of peak mixing depth
- Averaging techniques
- Spatial views (profiler networks)

## Mixing Depth – ApplicationsMGR (1 of 5)

- The mixing depth growth rate (MGR) is the rate of change of boundary layer height through time
-    				MGR =  h/t
-     where h is the height of the boundary layer and t is time.
- Rapid CBL evolution occurs between 0600 and 1200 local time and is a good choice for t
- MGR is important because it indicates the rate of change in the volume of air in which pollutants mix and indicates rate of entrainment of aloft air
- MGR is controlled by aloft stability and the partitioning of solar radiation between the sensible and latent heat flux at the surface

## Mixing Depth – ApplicationsMGR (2 of 5)

- 1996 Paso del Norte Summer Ozone Study

- August 13, 1996

- August 12, 1996

- August 14, 1996

- 500-mb Heights at 1700 MST for August 12 through August 14, 1996 (MacDonald et al., 2001a)

## Mixing Depth – ApplicationsMGR (3 of 5)

- RASS virtual temperature on August 12 through 14, 1996, at 0600 MST (MacDonald et al., 2001a)

- Morning inversion increased from 6.5oC on August 12 to 8.7oC on August** **13, to 9.7oC on August** **14

- 1996 Paso del Norte Summer Ozone Study

## Mixing Depth – ApplicationsMGR (4 of 5)

- 1996 Paso del Norte Summer Ozone Study

- Mixing depths on August 12 through 14, 1996 (MacDonald et al., 2001a)

## Mixing Depth – ApplicationsMGR (5 of 5)

- A slower MGR and light winds lead to a higher peak ozone value on August 13 (MacDonald et al., 2001a)

| Parameter | August 12 | August 13 | August 14 |
| --- | --- | --- | --- |
| MGR (m/hr) | 380 | 150 | 120 |
| Peak Mixing Depth. (m) | 3800 | 3700 | 3600 |
| Avg. Surface Wind Speed (m/s) (0600-1000, local time) | 1.3 | 0.9 | 2.0 |
| Peak Ozone (ppb) | 77 | 137 | 79 |

- 1996 Paso del Norte Summer Ozone Study

- Summary of Results

## Mixing Depth – ApplicationsPeak Mixing (1 of 6)

- The peak mixing depth is the maximum vertical extent of the atmospheric boundary layer
- The peak mixing depth influences air quality concentrations by
- defining the volume in which pollutants mix
- influencing chemistry rates
- The timing of this peak depth is also critical
- The same peak but different time will result in different air quality concentrations

## Mixing Depth – ApplicationsPeak Mixing (2 of 6)

- Sacramento, July 16, 1998

- RASS virtual temperature data at Franklin Field on July 16, 1998 showing a subsidence inversion at about 800 m (MacDonald et al., 1999)

- Subsidence Inversion

## Mixing Depth – ApplicationsPeak Mixing (3 of 6)

- Time (PST)

- Time/height cross-section of radar profiler (a) vertical velocity and (b) Cn2 data at Franklin Field on July 16, 1998.  The top of the CBL is shown as a white line (MacDonald et al., 1999)

- Sacramento, July 16, 1998

## Mixing Depth – ApplicationsPeak Mixing (4 of 6)

- RASS virtual temperature data at Franklin Field on July 17, 1998, showing a subsidence inversion at about 500 m (MacDonald et al. 1999)

- Sacramento, July 17, 1998

## Mixing Depth – ApplicationsPeak Mixing (5 of 6)

- Time/height cross-section of radar profiler (a) vertical velocity and (b) Cn2 data at Franklin Field on July 17, 1998.  The top of the CBL is shown as a white line (MacDonald et al., 1999).

- Sacramento July 17, 1998

## Mixing Depth – ApplicationsPeak Mixing (6 of 6)

- Broad upper-level high over California
- Warm aloft temperatures
- Shallower mixing depths on the July 17 compared to July 16

- Sacramento, July 16-17, 1998

| Ozone | July 16 | July 17 |
| --- | --- | --- |
| 1-hour max
(ppb) | 95 | 137 |
| 8-hour avg
(ppb) | 120 | 152 |

## Mixing Depth – ApplicationsAveraging Techniques (1 of 6)

- Average mixing depth, winds, and Tv
- Helps to view the “forest through the trees”
- Group by
- Flow types
- Episode vs. non-episode
- Synoptic classification (ridge, trough, cut-off low, etc.)
- Seasons
- Years
- Months

## Mixing Depth – ApplicationsAveraging Techniques (2 of 6)

- Mean Seasonal Tv Profiles

- Average daytime and nighttime RASS Tv data over the Gulf of Mexico at Vermillion during the summer (top) and winter (bottom) (MacDonald et al., 2002)

- Average each gate for nighttime and daytime
- Set minimum percent needed (e.g., 50%)

## Mixing Depth – ApplicationsAveraging Techniques (3 of 6)

- Annual Wind Profiles

- 1998 average radar profiler wind data over the Gulf of Mexico at South Marsh Island (top) and Vermillion (bottom) (MacDonald et al., 2002)

- Average each   gate for each hour
- Set minimum percent needed (e.g., 50%)

## Mixing Depth – ApplicationsAveraging Techniques (4 of 6)

- Mean Winds by Synoptic Pattern

- Average radar profiler wind data over the Gulf of Mexico at Vermillion during a trough pattern (top) and ridge pattern (bottom), (MacDonald et al., 2002)

## Mixing Depth – ApplicationsAveraging Techniques (5 of 6)

- PROPHET July and August 2001 hourly running mean mixing depths categorized by synoptic transport (13Z-22Z)

- PROPHET 2001 Mean MGR categorized by synoptic transport (13Z-20Z)

-  CBL heights measured during northerly flow are ~200 m higher than CBL heights measured during southerly flow
- MGRs measured during northerly flow are twice as great as MGRs during southerly flow from 0900 to 1000 EDT (1300 to1400 UTC)

- (Lilly, 2003)

## Mixing Depth – ApplicationsAveraging Techniques (6 of 6)

- PROPHET 2001 mean ozone time series categorized by synoptic transport, 0900–1800 EDT (13Z-22Z)

- At University of Michigan, little local production of ozone occurs; however, the site is dominated by ozone transport.
- A polluted air mass from south is transported into the region and is entrained into the evolving CBL, increasing surface ozone.
- Through the same process, a clean air mass to the north can    act to dilute surface ozone (offset any local production).

- (Lilly, 2003, in progress)

- PROPHET 2001 mean hourly ozone change categorized by synoptic transport, 0900–1600 EDT (13Z-20Z)

## Mixing Depth – ApplicationsSpatial Views (1 of 6)

- Use network of sites to assess the temporal and spatial variations in mixing, winds, and Tv profiles
- Important because many areas have strong temporal and spatial variations in meteorology, which has a large impact on air quality

## Mixing Depth – ApplicationsSpatial Views (2 of 6)

- SCOS-97 radar wind profiler and RASS sites (MacDonald et al., 2001b)

- SCOS-97 cross-section of sites

## Mixing Depth – ApplicationsSpatial Views (3 of 6)

- East-to-west cross-section of mixing depths from San Clemente Island (SCE) to Hesperia (HPA) on September 4, 1997, at 1400 PST (MacDonald et al., 2001b)

- SCOS-97 cross-section of mixing depths, September 4, 1997

## Mixing Depth – ApplicationsSpatial Views (4 of 6)

- Diurnal cycle of mixing depths on September 4, 1997, at Santa Catalina Island (SCL), Los Angeles Int. Airport (LAX), Ontario (ONT), and Hesperia (HPA) (MacDonald et al., 2001b)

- SCOS-97 mixing depths, September 4, 1997

## Mixing Depth – ApplicationsSpatial Views (5 of 6)

- Spatial distribution of mixing depths on September 4, 1997, at 0300 PST (top) and 1400 PST (bottom) (MacDonald et al., 2001b)

- SCOS-97 mixing depths, September 4, 1997

## Mixing Depth – ApplicationsSpatial Views (6 of 6)

- Mixing depths at coastal sites were typically about 400 to 600 m agl
- Daily maximum mixing depths at basin sites such as Ontario and Riverside were typically about 1000 to 2000 m agl
- Daily maximum mixing depths at desert sites were typically about 3000 to 4000 m agl but ranged from 1400 to 4000 m agl
- There were significant spatial gradients in mixing depths between the coastal and inland sites in the afternoon hours

- (MacDonald et al., 2001b)

- SCOS-97 Summary Results

## Mixing Depth Summary

- Mixing depth controls vertical dispersion of pollutants
- Same peak mixing depth can have different MGR, allowing for dissimilar pollutant concentrations
- Mixing-depth evolution is important in understanding the contribution/influence of residual-layer air on pollutant concentrations within the ABL

## Transport/Flow PatternsBackground

- Radar profiler wind data with boundary layers at Franklin Field on July 17, 1998

- July 23, 2000 MSLP

**L**

**L**

**H**

- Synoptic scale
- Winds driven by large high-  and low-pressure systems
- Meso-scale
- Land/sea or lake breeze
- Mountain/valley
- Terrain-forced
- Surface vs. boundary layer
- Transport at different levels
- Daytime coupling

**Notes:**
Meso-scale important to local aq

## Transport/Flow PatternsData and Methods

- Data
- RWP and Sodar provide continuous time-height cross-sections of winds
- Methods
- Trajectories
- Transport, ventilation, and recirculation statistics
- Ventilation index
- Flux planes
- Spatial views

## Large-scale NWP models (NGM, ETA, AVN, MRF, etc.) cannot resolve sub-grid scale processes
Incorporating the RWP measurements allows for some of the sub-grid scale processes to be incorporated in the trajectory calculation
“Trajectories” can either be forward or backward calculations and can be run at multiple heights  

- Incorporating the RWP measurements allows for some of the sub-grid scale processes to be incorporated in the trajectory calculation
- “Trajectories” can either be forward or backward calculations and can be run at multiple heights

- Transport/Flow Patterns – Data and Methods Trajectories (1 of 3)

## Use hourly wind speed and direction derived by the RWP to calculate local forward trajectories
Work in Universal Transverse Mercator (UTM)
Step 1: Convert hourly wind speed (ws) and direction (wd) to U and V
    U = -ws x sin(wd), V = -ws x cos(wd)
Step 2: For wind at a given altitude, calculate hourly (i) parcel position (UTMx and UTMy):
   UTMx (i+1)= (U x time step) + UTMx (i)
   UTMy (i+1)= (V x time step) + UTMy (i)
Step 3: Plot UTMx and UTMy at each time

- Work in Universal Transverse Mercator (UTM)
- Step 1: Convert hourly wind speed (ws) and direction (wd) to U and V
*    **U = -ws** **x sin(wd), V = -ws** **x cos(wd)*
- Step 2: For wind at a given altitude, calculate hourly (i) parcel position (UTMx and UTMy):
*   **UTMx (i+1)= (U x time step) + UTMx (i)*
*   **UTMy (i+1)= (V x time step) + UTMy (i)*
- Step 3: Plot UTMx and UTMy at each time

- Transport/Flow Patterns – Data and Methods Trajectories (2 of 3)

## Transport/Flow Patterns – Data and Methods Trajectories (3 of 3)

- Trajectories associated with land/sea breeze circulation

- Transport/Flow Patterns – Data and Methods Trajectories (3 of 3)

## Transport/Flow Patterns – Data and MethodsTransport, Ventilation, and Recirculation Statistics (1 of 4)

- The procedure discussed here is based on integral quantities computed from profiler data following the work of Allwine and Whiteman (1994).
- Parameters are typically calculated using 12 or 24 hrs of hourly wind data.
- Parameters include:
- 		S	Scalar wind run (km)
- 		L	Resultant (vector) transport distance (km)
- 			Resultant wind directions in degrees from true N adjusted 		to the proper quadrant
- 		R	Recirculation factor (*L/S*)

- Transport/Flow Patterns – Data and MethodsTransport, Ventilation, and Recirculation Statistics (1 of 4)

## Transport/Flow Patterns – Data and MethodsTransport, Ventilation, and Recirculation Statistics (2 of 4)

- 		R	=	1	Straight-line, steady transport occurred 				during integration period
- 		R	=	0	No net transport
- 		R	≈	1	Good ventilation conditions (for *L* = few 				hundred km)
- 		R	< ~0.3	Recirculation

- Transport/Flow Patterns – Data and MethodsTransport, Ventilation, and Recirculation Statistics (2 of 4)

- Transport/Flow Patterns – Data and MethodsTransport, Ventilation, and Recirculation Statistics (3 of 4)

## Transport/Flow Patterns – Data and MethodsTransport, Ventilation, and Recirculation Statistics (4 of 4)

- The transport distances in the first few hundred meters were about 175 km with the recirculation factor indicating good ventilation conditions.
- On this day, low ozone concentrations were observed, and the aloft winds showed little recirculation.

- (Main & Roberts, 2000)

- Transport/Flow Patterns – Data and MethodsTransport, Ventilation, and Recirculation Statistics (4 of 4)

## Ventilation Index = Mixing Depth x Boundary Layer Wind Speed
Good indicator of the amount the ventilation or transport of pollutants

- Good indicator of the amount the ventilation or transport of pollutants

- Surface winds at Azusa, morning mixing depths at El Monte,
-  and calculated ventilation indices (Reissell et al., 2003)

- Transport/Flow Patterns – Data and MethodsVentilation Index

## Flux calculations can be used to understand the amount of pollution transported from upwind to downwind areas
Flux, for air quality purposes, is the rate of transfer of pollutant mass per unit area per unit time (ug/m-2  sec-1)
Flux = Wind speed (m/sec) x Concentration (ug/m3)
Total amount of mass through a plane = Flux x Area x Time 

- Flux, for air quality purposes, is the rate of transfer of pollutant mass per unit area per unit time (ug/m-2  sec-1)
- Flux = Wind speed (m/sec) x Concentration (ug/m3)
- Total amount of mass through a plane = Flux x Area x Time

- Transport/Flow Patterns – Data and MethodsFlux (1 of 3)

## Transport/Flow Patterns – Data and MethodsFlux (2 of 3)

- Transport/Flow Patterns – Data and MethodsFlux (2 of 3)

## Transport/Flow Patterns – Data and MethodsFlux (3 of 3)

- of August 6, 1990 (Blumenthal et al., 1997)

- Finding:  Amount of NOx flowing into the San Joaquin Valley (SJV) was equivalent to NOx emissions in the northern SJV for this day

- Transport/Flow Patterns – Data and MethodsFlux (3 of 3)

## 		Regional extent of low-level jet

- Upper-air winds on July 14, 1995, at 0300 EST, used to locate the low-level jet during an air pollution episode

- Transport/Flow Patterns – Data and MethodsSpatial Views

## Transport/Flow Patterns – Summary

- Continuous aloft wind data are useful in understanding the processes that influence local air quality
- Assessment techniques include
- Trajectories
- Transport, ventilation, and recirculation statistics
- Ventilation index
- Flux planes
- Spatial views

## Meteorological Models

- Meteorological models are used to estimate transport and dispersion parameters that affect air quality
- Meteorological and emission models provide inputs to air quality models
- Air quality models simulate physical and chemical processes in the atmosphere and are used to
- Assess impacts from pollutant sources
- Develop an understanding of air pollution problems
- Design and evaluate pollution control strategies
- Estimate the impact of emission growth and controls on future air quality

## Meteorological Models – Types

- Objective Model
- Uses profiler observations
- Utilizes interpolation and extrapolation of observations
- Diagnostic Model
- Uses profiler/RASS observations to provide physical relationships and parameterizations
- Is not predictive in time (observationally limited)
- Produces output that can be combination of objective and diagnostic analysis
- Prognostic Model
- Predicts meteorological parameters based on time-dependent equations of atmospheric physics
- Is predictive in time
- Uses profiler/RASS data to
- Set initial conditions
- Account for sub-grid scale phenomena that is not resolved by model
- Constrain/limit accumulation of model errors with time (model drift)
- Quality assure model results

## Meteorological Models – CALMET (1 of 3)

- The CALMET diagnostic meteorological model is a software tool that interpolates observations to a gridded field and diagnoses winds based on terrain, stability, and large-scale flow patterns
- Inputs
- 1-km surface-terrain height
- 1-km land-use data
- Surface, rawinsonde, RWP, RASS, buoy, C-MAN, and EDAS data

## Meteorological Models – CALMET (2 of 3)

- CALMET 40-m agl winds at 0300 PST on August 6, 1997 (MacDonald et al., 2000)

- SCOS-97 CALMET (Diagnostic Model)

## Meteorological Models – CALMET (3 of 3)

- Twenty-four-hour 158-m agl back trajectory beginning at Azusa, California, at 0600 PDT on August 5, 1997.  UTM E and UTM N are Universal Transverse Mercator East and North coordinates, respectively (Reissell et al., 2003).

- Modeling domain showing location of 26 RWP sites during the SCOS-97 field study and the locations of Azusa, Pine Mountain, and Glendora monitoring sites (Reissell et al., 2003).

## Meteorological Models – Verification (1 of 2)

- Naval Postgraduate School MM5 Real-Time Forecast
- http://www.weather.nps.navy.mil/~dkmiller/MM5/

## Meteorological Models – Verification (2 of 2)

- Observed 400-m agl winds on August 5, 1990, at 0900 PST

- MM5 400-m agl 12-km grid model winds on August 5, 1990, at 0900 PST

- SJVAQS/AUSPEX

- (Blumenthal et al., 1997)

- Meteorological Models – Verification (2 of 2)

## Meteorological Models – Summary

-    Continuous upper-air meteorological data are a key component of accurate diagnostic and prognostic meteorological and air quality modeling

## Summary

- RWP/RASS and Sodar data are important components of solving many air quality issues
- Data supports
- Boundary-layer characterization
- Winds/transport assessment
- Model inputs and evaluation

## References (1 of 3)

- Allwine K.J. and Whiteman C.D. (1994) Single-station integral measures of atmospheric stagnation, recirculation, and ventilation.  *Atmos. Environ*. **28**, 713-721.
- Angevine W.M. (1994) Boundary-layer depth and entrainment zone characterization with a
-      boundary-layer profiler.  *Boundary-Layer Meteorology.* **68**, 375-385.
- Bianco L., and Wilczak J.M., (2002) Convective boundary layer depth: Improved measurement by Doppler radar wind profiler using fuzzy logic methods.  *J. Atmos. Oceanic Tech. ***19***, *1745-1758.
- Blumenthal D.L., Lurmann F.W., Roberts P.T., Main H.H., MacDonald C.P., Knuth W.R., and Niccum E.M. (1997) Three-dimensional distribution and transport analyses for SJVAQS/AUSPEX. Final report prepared for San Joaquin Valley Air Pollution Study Agency, Sacramento, CA, by Sonoma Technology, Inc., Santa Rosa, CA, Technical & Business Systems, Santa Rosa, CA, and California Air Resources Board, Sacramento, CA, STI-91060-1705-FR, February.
- Dye T.S., Lindsey C.G., and Anderson J.A. (1995) Estimates of mixing depth from "boundary layer" radar profilers. Preprints from the *9th Symposium on Meteorological Observations and Instrumentation, Charlotte, NC, March 27-31*, pp. 156-160 (STI-94212-1451).
- Lilly M.A.R. (2003) The influence of synoptic meteorology on convective boundary layer characteristics and the observed chemical response during PROPHET 2000 and 2001 summer intensives.  Department of Environmental Sciences, University of Virginia, Charlottesville, VA (in progress).

## References (2 of 3)

- MacDonald C.P., Roberts P.T., Knoderer C.A., Miller D.S., and Hanna S.R. (2002) Boundary layer study in the western and central Gulf of Mexico.  Report prepared for the Minerals Management Service, New Orleans, LA, by Sonoma Technology, Inc., Petaluma, CA, STI-998555-2266-DIR, October.
- MacDonald C.P., Roberts P.T., Main H.H., Dye T.S., Coe D.L., and Yarbrough J. (2001a) The 1996 Paso del Norte Ozone Study: analysis of meteorological and air quality data that influence local ozone concentrations. *Sci. Total Environ.* (Special Issue: U.S.-Mexico Trans-boundary Air Pollution Studies) **276, Nos. 1-3**, pp. 93-110 (STI-1949).
- MacDonald C.P., Dye T.S., and Roberts P.T. (2001b) Spatial and temporal observations of the planetary boundary layer during ozone episodes in Southern California. Presented at *SCOS97-NARSTO Data Analysis Conference, Los Angeles, CA, February 13-15* (STI-2057).
- MacDonald C.P., Blumenthal D.L., Roberts P.T., and Crews J.M. (2001c) Analysis of 1997 through 1999 air quality data collected by the Baylor University aircraft. Report prepared for the Texas Natural Resource Conservation Commission, Austin, TX, by Sonoma Technology, Inc., Petaluma, CA, STI-900510-2033-FR, January.
- MacDonald C.P., Knoderer C.A., Arndt R.L., Roberts P.T., Emery C., Stoeckenius T., and Tai E. (2000) PAMS data analysis for Southern California volume III:  three-dimensional wind fields and trajectories during three SCOS97 episodes. Report prepared for South Coast Air Quality Management District, Diamond Bar, CA, by Sonoma Technology, Inc., Petaluma, CA, STI-997526-1960-DFR, March.

## References (3 of 3)

- MacDonald C.P., Chinkin L.R., Dye T.S., and Anderson C.B. (1999) Analysis of PAMS data in California Volume I:  The use of PAMS radar profiler and RASS data to understand the meteorological processes that influence air quality in selected regions of California. Report prepared for the U.S. Environmental Protection Agency, Research Triangle Park, NC, by Sonoma Technology, Inc., Petaluma, CA, STI-998391-1888-FR, May.
- Main H.H. and Roberts P.T. (2000) PAMS Data Analysis Workbook: Illustrating the use of PAMS data to support ozone control programs. Prepared for the U.S. Environmental Protection Agency, Research Triangle Park, NC, by Sonoma Technology, Inc., Petaluma, CA, STI-900243-1987-FWB, September.
- Reissell A., MacDonald C., Roberts P., and Arey J. (2002) Characterization of BVOC'S and meterology at Azusa during the 1997 Southern California Ozone Study. Atmos. Environ. (submitted for publication).
- Watson J.G., Fujita E.M., Chow J.C., Zielinska B., Richards L.W., Neff W., and Dietrich D. (1998) Northern front range air quality study. Final report prepared for Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO by Desert Research Institute, Reno, NV, June.