perspective a thesis

FORECAST VERIFICATION: A DISPERSION MODELING

PERSPECTIVE

by

RACHEL ROGERS-VAN NICE, B.S.

A THESIS

IN

ATMOSPHERIC SCIENCE

Submitted to the Graduate Faculty of Texas Tech University in

Partial Fulfillment of the Requirements for

the Degree of

MASTER OF SCIENCES

Approved

Dr. Sukanta Basu Chairperson of the Committee

Dr. Kevin Mulligan

Dr. John Schroeder

Fred Hartmeister Dean of the Graduate School

May, 2008

Texas Tech University, Rachel Rogers-Van Nice, May 2008

ii

ACKNOWLEDGMENTS

I first must thank Dr. Sukanta Basu, the chair of my thesis advisory committee.

His patience and unending explanations have taught me more than I ever anticipated. He

has challenged and encouraged me during every step of my graduate career. I also want

to thank Dr. John Schroeder and Wes Burgett for all their help and information regarding

the West Texas Mesonet. I also thank Dr. Kevin Mulligan for allowing me to spend

countless hours in the GIS lab in addition to his input on the maps in the document. I

also need to thank Joe Touma of the NOAA Air Resources Laboratory for identifying the

need for research in this field. This work was partially funded by the Texas Advanced

Research Program (003644-0003-2006) grant.

I offer much thanks Brandon Storm, my L.C., for the insight and knowledge he

has shared with me along the way. Also, many thanks to my friends, Jennifer Huckabee

and Angela Montoya for the wise words only girlfriends can truly share.

I cannot express my thanks enough to my family for the sacrifices they have made

to allow me to attend graduate school. My husband, Chris for his loving words, support

and encouragement throughout the entire process, and my son Cameron, for his

understanding and help offered whenever needed.


iii

TABLE OF CONTENTS

ACKNOWLEDGMENTS .................................................................................................. ii

ABSTRACT...................................................................................................................... vii

LIST OF TABLES........................................................................................................... viii

LIST OF FIGURES ........................................................................................................... ix

1 INTRODUCTION .......................................................................................................... 1

1.1 Background.............................................................................................................. 1

1.2 Coupling of Mesoscale and Dispersion Models ...................................................... 2

1.3 Limitations of Present Day PBL Parameterizations ................................................ 5

1.4 Problem Statement ................................................................................................... 7

2 DESCRIPTION OF OBSERVATIONAL DATA........................................................ 18

2.1 Description of the West Texas Mesonet ................................................................ 18

2.1.1 Station Site Information.................................................................................. 18

2.1.2 Instrumentation ............................................................................................... 18

2.1.3 Quality Assurance and Control....................................................................... 20

2.1.4 Data Transfer .................................................................................................. 20

2.2 Summary ................................................................................................................ 21

3 WTM DERIVED SURFACE LAYER CHARATERISTICS...................................... 25

3.1 Location Description.............................................................................................. 25

3.2 West Texas Mesonet Data ..................................................................................... 26

3.3 Methodology.......................................................................................................... 26

3.4 ONC Observations ................................................................................................. 26

3.4.1 June 2006 Observations of Wind Speed and Direction ONC......................... 27


iv

3.4.2 December 2006 Observations of Wind Speed and Direction ONC................ 27

3.5 OFC Observations of Wind Speed and Wind Direction........................................ 27

3.5.1 June 2006 Observations of Wind Speed and Direction OFC ......................... 27

3.5.2 December 2006 Observations of Wind Speed and Direction OFC ................ 28

3.6 RTC Observations of Wind Speed and Wind Direction........................................ 28

3.6.1 June 2006 Observations of Wind Speed and Direction at RTC...................... 28

3.6.2 December 2006 Observations of Wind Speed and Direction at RTC ............ 28

3.7 2-m Temperature Observations.............................................................................. 28

3.8 Summary ................................................................................................................ 29

4 DESCRIPTION OF MODEL DATA........................................................................... 36

4.1 Description of the Weather and Research Forecast Model.................................... 36

4.1.1 Model Domain and Physics ............................................................................ 36

4.1.2 Planetary Boundary Layer Parameterizations in WRF................................... 37

4.2 Static Fields............................................................................................................ 38

4.3 Summary ................................................................................................................ 38

5 WRF SURFACE LAYER CHARACTERISTICS....................................................... 46

5.1 WRF Model Forecast............................................................................................. 46

5.2 Methodology.......................................................................................................... 46

5.3 ONC WRF Model Forecast ................................................................................... 47

5.3.1 June 2006 Model Forecast of Wind Speed and Direction ONC..................... 47

5.3.2 December 2006 Model Forecast of Wind Speed and Direction ONC............ 47

5.3.3 Monthly Statistics of Wind Speed and Direction ONC.................................. 47

5.3.4 Diurnal Statistics of Wind Speed and Direction ONC.................................... 48


v

5.4 OFC Observations of Wind Speed and Wind Direction........................................ 48

5.4.1 June 2006 Model Forecast Wind Speed and Direction OFC.......................... 48

5.4.2 December 2006 Model Forecast Wind Speed and Direction OFC................. 48

5.4.3 Monthly Statistics of Wind Speed and Direction OFC................................... 49

5.4.4 Diurnal Statistics of Wind Speed and Direction OFC .................................... 49

5.5 RTC Observations of Wind Speed and Wind Direction........................................ 49

5.5.1 June 2006 Model Forecast Wind Speed and Direction at RTC...................... 49

5.5.2 December 2006 Model Forecast Wind Speed and Direction at RTC............. 50

5.5.3 Monthly Statistics of Wind Speed and Direction at RTC............................... 50

5.5.4 Diurnal Statistics of Wind Speed and Direction at RTC ................................ 50

5.6 2-m Temperature Observations.............................................................................. 50

5.6.1 2-m Temperature Statistics ............................................................................. 51

5.7 Summary ................................................................................................................ 52

6 WIND ATLAS ANALYSIS AND APPLICATION PROGRAM ............................... 81

6.1 Description............................................................................................................. 81

6.2 Main Calculation Blocks........................................................................................ 81

6.3 Summary ................................................................................................................ 83

7 WRF-WAsP COUPLING............................................................................................. 87

7.1 Purpose................................................................................................................... 87

7.2 Macy Ranch ........................................................................................................... 88

7.3 Fluvanna................................................................................................................. 90

7.4 Reese Technology Center ...................................................................................... 90

7.5 Summary ................................................................................................................ 91


vi

8 SUMMARY AND CONCLUSIONS ......................................................................... 112

8.1 Summary .............................................................................................................. 112

8.2 Conclusions.......................................................................................................... 112

REFERENCES ............................................................................................................... 115

A WTM OBSERVATIONAL WIND ROSES.............................................................. 117

B WRF FORECAST WIND ROSES ............................................................................ 151


vii

ABSTRACT

The Environmental Protection Agency currently uses AERMOD, an air quality

dispersion model to aid in the forecasting of transport and dispersion of air pollution for

the U.S. Typically, NWS-ASOS observations (post-processed by EPA-AERMET model)

are used as input to the AERMOD model. This traditional framework of running a

dispersion model based on point observations is quite problematic from a variety of

theoretical standpoints (e.g., lack of representativeness of meteorological data). An

alternative viable framework would be to use prognostic meteorological models in

conjunction with AERMOD. Indeed, contemporary research shows that the use of

prognostic models as a substitute for NWS-ASOS observations alleviates some of the

longstanding dispersion modeling problems, but at the same time creates new concerns.

I will elaborate on several questions that need to be adequately addressed before

prognostic models can be reliably utilized in operational dispersion applications. Most of

these questions are rooted in prognostic models’ (in) ability to accurately represent the

boundary layer variables of interest to the dispersion modeling community (e.g., wind

speed, wind direction, temperature). I will compare the potential of a new generation

prognostic meteorological model called the Weather Research and Forecasting (WRF)

model in capturing wind speed variable versus data from the West Texas Mesonet by

statistical analysis for verification. One year of ARW WRF output is analyzed. The WRF

is a 36/12 km two-way nested run using the YSU PBL scheme. With use of innovative

strategies for verification of complex spatio-temporal forecast fields and novel

verification measures will make this study distinct.


viii

LIST OF TABLES

1 1: Model performance measures for surface wind speed and direction for MM5 for the 4-km grid in the Central California SARMAP domain.. ................................ 14

3.1: Air temperature lapse rate variations as a function of month. .................................. 31 7.1: Comparison of Wind Speed Observations, Forecast, WRF-WAsP Forecast, and

WRF-WAsP Forecast with roughness calculated for each sector. ............... 110 7.2: Roughness lengths for Fluvanna, Macy Ranch, and RTC...................................... 111


ix

LIST OF FIGURES

1.1: AERMOD modeling system structure........................................................................ 9 1.2: ASOS Station............................................................................................................ 10 1.3: Wind roses of simulated winds derived from NWS observations ............................ 11 1.4: Comparison of ranked hourly concentrations from AERMOD-NWS and

AERMOD-MM5...................................................................................................... 12 1.5: Average angular distribution of wind direction prevailed over Pune....................... 13 1.6: (top) Bias, (middle) RMSE, and (bottom) error standard deviation........................ 15 1.7: Time series of area-averaged horizontal wind speeds (ms-1).................................... 16 1.8: Time series of aerial averaged 2-m temperature....................................................... 17 2.1: Stations within the West Texas Mesonet as of April 2008....................................... 22 2.2: Lubbock West Texas Mesonet Station. .................................................................... 23 2.3: Elevation view of a typical West Texas Mesonet station ......................................... 24 3.1: 2-m Temperature Histogram as a Function of Month .............................................. 32 3.2: 2-m Temperature Histogram as a Function of Time................................................. 33 3.3: 10-m Wind Speed Histogram as a Function of Month ............................................. 34 3.4: 10-m Wind Speed Histogram as a Function of Time ............................................... 35 4.1: WRF system components. ........................................................................................ 39

4.2: Operational ARW domain ........................................................................................ 40

4.3: WRF Domain............................................................................................................ 41

4.4: Illustration of PBL Processes.................................................................................... 42

4.5: WRF 12-km Digital Elevation Model ...................................................................... 43

4.6: WRF 12-km Land Use.............................................................................................. 44

4.7: 30-m National Land Cover Data (2001). .................................................................. 45


x

5.1: ONC monthly mean bias of wind speed. .................................................................. 53

5.2: ONC monthly RMSE for wind speed. ...................................................................... 54

5.3: ONC diurnal mean bias for wind speed.................................................................... 55

5.4: ONC diurnal RMSE of wind speed. ........................................................................ 56

5.5: OFC monthly mean bias of wind speed.................................................................... 57

5.6: OFC monthly RMSE for wind speed........................................................................ 58

5.7: OFC diurnal mean bias of wind speed...................................................................... 59

5.8: OFC diurnal RMSE for wind speed.......................................................................... 60

5.9: RTC monthly mean bias of wind speed.................................................................... 61

5.10: RTC monthly RMSE for wind speed...................................................................... 62

5.11: RTC diurnal mean bias of wind speed.................................................................... 63

5.12: RTC diurnal RMSE for wind speed........................................................................ 64

5.13: 2-m WRF Forecast Temperature Histogram as a Function of Month .................... 65

5.14: OFC diurnal 2-m temperature bias ......................................................................... 66

5.15: ONC diurnal 2-m temperature bias......................................................................... 67

5.16: RTC diurnal 2-m temperature bias ......................................................................... 68

5.17: 2-m WRF Forecast Temperature Histogram as a Function of Time ...................... 69

5.18: OFC monthly 2-m temperature bias. ...................................................................... 70

5.19: ONC monthly 2-m temperature bias....................................................................... 71

5.20: RTC monthly 2-m temperature bias. ...................................................................... 72

5.21: OFC diurnal 2-m temperature RMSE..................................................................... 73

5.22: ONC diurnal 2-m temperature RMSE. ................................................................... 74

5.23: RTC diurnal 2-m temperature RMSE..................................................................... 75


xi

5.24: OFC monthly 2-m temperature RMSE................................................................... 76

5.25: ONC monthly 2-m temperature RMSE. ................................................................. 77

5.26: RTC monthly 2-m temperature RMSE................................................................... 78

5.27: 10-m WRF Forecast Wind Speed Histogram as a Function of Month................... 79

5.28: 10-m WRF Forecast Wind Speed Histogram as a Function of Time ..................... 80

6.1: Example of terrain corresponding to roughness class 0.. ......................................... 85 6.2: Example of terrain corresponding to roughness class 1.. ......................................... 85 6.3: Example of terrain corresponding to roughness class 2.. ......................................... 86 6.4: Example of terrain corresponding to roughness class 3.. ......................................... 86 6.5: The wind atlas method of WAsP. ............................................................................. 84 7.1: WRF-WAsP Framework........................................................................................... 93

7.2: Photo of Macy Ranch mesonet station ..................................................................... 94

7.3: April 2006 – March 2007 Macy Ranch Wind Rose ................................................. 95

7.4: Macy Ranch Wind Rose from January 2002 to Present. .......................................... 96

7.5: Annual Averaged 12-km grid spacing wind speed at Macy Ranch.......................... 97

7.6: 100-m Digital Elevation Model of Macy Ranch derived from 30-m NED.............. 98

7.7: Annual Averaged 100-m grid spacing Wind Speed at Macy Ranch. ....................... 99

7.8: April 2006 – March 2007 Fluvanna Wind Rose..................................................... 100

7.9: Fluvanna Wind Rose from July 2002 to Present. ................................................... 101

7.10: Annual Averaged 12-km grid spacing wind speed at Fluvanna. .......................... 102

7.11: 100-m Digital Elevation Model of Fluvanna derived from 30-m NED ................ 103

7.12: Annual Averaged 100-m grid spacing Wind Speed at Fluvanna........................... 104

7.13: April 2006 – March 2007 RTC Wind Rose........................................................... 105


xii

7.14: Reese Technology Center Wind Rose from June 2000 to Present. ....................... 106

7.15: Annual Averaged 12-km grid spacing wind speed at RTC .................................. 107

7.16: 100-m Digital Elevation Model of RTC derived from 30-m NED....................... 108

7.17: Annual Averaged 100-m grid spacing Wind Speed at RTC................................. 109

A.1: Wind Direction and Wind Speed for April 2006: ONC ........................................ 121

A.2: Wind Direction and Wind Speed for May 2006: ONC.......................................... 121

A.3: Wind Direction and Wind Speed for June 2006: ONC.......................................... 122

A.4: Wind Direction and Wind Speed for July 2006: ONC .......................................... 122

A.5: Wind Direction and Wind Speed for August 2006: ONC ..................................... 123

A.6: Wind Direction and Wind Speed for September 2006: ONC................................ 123

A.7: Wind Direction and Wind Speed for October 2006: ONC.................................... 124

A.8: Wind Direction and Wind Speed for November 2006: ONC ................................ 124

A.9: Wind Direction and Wind Speed for December 2006: ONC ................................ 125

A.10: Wind Direction and Wind Speed for January 2007: ONC .................................. 125

A.11: Wind Direction and Wind Speed for February 2007: ONC ................................ 126

A.12: Wind Direction and Wind Speed for March 2007: ONC .................................... 126

A.13: Wind Direction and Wind Speed for 0Z: ONC ................................................... 127




A.17: Wind Direction and Wind Speed for 12Z: ONC ................................................. 129




xiii


A.21: Wind Direction and Wind Speed for April 2006: OFC ....................................... 131

A.22: Wind Direction and Wind Speed for May 2006: OFC ........................................ 131

A.23: Wind Direction and Wind Speed for June 2006: OFC ........................................ 132

A.24: Wind Direction and Wind Speed for July 2006: OFC......................................... 132

A.25: Wind Direction and Wind Speed for August 2006: OFC.................................... 133

A.26: Wind Direction and Wind Speed for September 2006: OFC .............................. 133

A.27: Wind Direction and Wind Speed for October 2006: OFC................................... 134

A.28: Wind Direction and Wind Speed for November 2006: OFC............................... 134

A.29: Wind Direction and Wind Speed for December 2007: OFC ............................... 135

A.30: Wind Direction and Wind Speed for January 2007: OFC ................................... 135

A.31: Wind Direction and Wind Speed for February 2007: OFC ................................. 136

A.32: Wind Direction and Wind Speed for March 2007: OFC ..................................... 136

A.33: Wind Direction and Wind Speed for 0Z: OFC .................................................... 137




A.37: Wind Direction and Wind Speed for 12Z: OFC .................................................. 139




A.41: Wind Direction and Wind Speed for April 2006: RTC ....................................... 141

A.42: Wind Direction and Wind Speed for May 2006: RTC ........................................ 141


xiv

A.43: Wind Direction and Wind Speed for June 2006: RTC ........................................ 142

A.44: Wind Direction and Wind Speed for July 2006: RTC......................................... 142

A.45: Wind Direction and Wind Speed for August 2006: RTC.................................... 143

A.46: Wind Direction and Wind Speed for September 2006: RTC .............................. 143

A.47: Wind Direction and Wind Speed for October 2006: RTC................................... 144

A.48: Wind Direction and Wind Speed for November 2006: RTC............................... 144

A.49: Wind Direction and Wind Speed for December 2006: RTC ............................... 145

A.50: Wind Direction and Wind Speed for January 2007: RTC ................................... 145

A.51: Wind Direction and Wind Speed for February 2007: RTC ................................. 146

A.52: Wind Direction and Wind Speed for March 2007: RTC ..................................... 146

A.53: Wind Direction and Wind Speed for 0Z: RTC .................................................... 147




A.57: Wind Direction and Wind Speed for 12Z: RTC .................................................. 149




B.1: Wind Direction and Wind Speed for April 2006: WRF Forecast ONC ................ 156

B.2: Wind Direction and Wind Speed for May 2006: WRF Forecast ONC.................. 156

B.3: Wind Direction and Wind Speed for June 2006: WRF Forecast ONC.................. 157

B.4: Wind Direction and Wind Speed for July 2006: WRF Forecast ONC .................. 157

B.5: Wind Direction and Wind Speed for August 2006: WRF Forecast ONC ............. 158


xv

B.6: Wind Direction and Wind Speed for September 2006: WRF Forecast ONC........ 158

B.7: Wind Direction and Wind Speed for October 2006: WRF Forecast ONC............ 159

B.8: Wind Direction and Wind Speed for November 2006: WRF Forecast ONC........ 159

B.9: Wind Direction and Wind Speed for December 2006: WRF Forecast ONC ........ 160

B.10: Wind Direction and Wind Speed for January 2007: WRF Forecast ONC .......... 160

B.11: Wind Direction and Wind Speed for February 2007: WRF Forecast ONC ........ 161

B.12: Wind Direction and Wind Speed for March 2007: WRF Forecast ONC ............ 161

B.13: Wind Direction and Wind Speed for 0Z: WRF Forecast ONC ........................... 162




B.17: Wind Direction and Wind Speed for 12Z: WRF Forecast ONC ......................... 164




B.21: Wind Direction and Wind Speed for April 2006: WRF Forecast OFC ............... 166

B.22: Wind Direction and Wind Speed for May 2006: WRF Forecast OFC ................ 166

B.23: Wind Direction and Wind Speed for June 2006: WRF Forecast OFC ................ 167

B.24: Wind Direction and Wind Speed for July 2006: WRF Forecast OFC................. 167

B.25: Wind Direction and Wind Speed for August 2006: WRF Forecast OFC............ 168

B.26: Wind Direction and Wind Speed for September 2006: WRF Forecast OFC ...... 168

B.27: Wind Direction and Wind Speed for October 2006: WRF Forecast OFC........... 169

B.28: Wind Direction and Wind Speed for November 2006: WRF Forecast OFC....... 169


xvi

B.29: Wind Direction and Wind Speed for December 2006: WRF Forecast OFC ....... 170

B.30: Wind Direction and Wind Speed for January 2007: WRF Forecast OFC ........... 170

B.31: Wind Direction and Wind Speed for February 2007: WRF Forecast OFC ......... 171

B.32: Wind Direction and Wind Speed for March 2007: WRF Forecast OFC ............. 171

B.33: Wind Direction and Wind Speed for 0Z: WRF Forecast OFC ............................ 172




B.37: Wind Direction and Wind Speed for 12Z: WRF Forecast OFC .......................... 174




B.41: Wind Direction and Wind Speed for April 2006: WRF Forecast RTC ............... 176

B.42: Wind Direction and Wind Speed for May 2006: WRF Forecast RTC ................ 176

B.43: Wind Direction and Wind Speed for June 2006: WRF Forecast RTC ................ 177

B.44: Wind Direction and Wind Speed for July 2006: WRF Forecast RTC................. 177

B.45: Wind Direction and Wind Speed for August 2006: WRF Forecast RTC............ 178

B.46: Wind Direction and Wind Speed for September 2006: WRF Forecast RTC ...... 178

B.47: Wind Direction and Wind Speed for October 2006: WRF Forecast RTC........... 179

B.48: Wind Direction and Wind Speed for November 2006: WRF Forecast RTC....... 179

B.49: Wind Direction and Wind Speed for December 2006: WRF Forecast RTC ....... 180

B.50: Wind Direction and Wind Speed for January 2007: WRF Forecast RTC ........... 180

B.51: Wind Direction and Wind Speed for February 2007: WRF Forecast RTC ......... 181


xvii

B.52: Wind Direction and Wind Speed for March 2007: WRF Forecast RTC ............. 181

B.53: Wind Direction and Wind Speed for 0Z: WRF Forecast RTC ............................ 182




B.57: Wind Direction and Wind Speed for 12Z: WRF Forecast RTC .......................... 184





1

CHAPTER 1

INTRODUCTION

1.1 Background

The Environmental Protection Agency (EPA) currently uses the American

Meteorological Society (AMS) and EPA Regulatory Model (AERMOD; Figure 1.1), an

air quality dispersion model, to aid transport and dispersion forecasting of air pollution

within the United States. Typically, observations from the National Weather Service-

Automated Surface Observing Systems (NWS-ASOS; Figure 1.2) are primed by the

AERMOD meteorological preprocessor, AERMET, before input into AERMOD. This

traditional framework of running a dispersion model based on point observations is quite

problematic from a variety of theoretical and practical standpoints, and contains many

limitations (Touma et al. 2007), such as:

• NWS-ASOS data represents a single point measurement that for many situations is representative of a relatively small area around the observation site;

• observations at NWS sites are taken at a single height that is generally 10 m

above ground level;

• atmospheric turbulence measurements are not available from NWS sites; which AERMOD could directly use;

• timeliness of data is inadequate because of the need to execute time consuming

quality control programs; • and the meteorological data may not be representative due to the fact:

a) surface observations collected some distance away from emissions source, or b) upper air soundings needed to estimate the mixing height are usually not collocated with surface observation sites An alternative viable framework to reduce the limitations above would be to use

prognostic meteorological models, such as the Weather Research and Forecasting (WRF)


2

model as input for AERMOD. Contemporary research shows that the use of prognostic

models as a substitute for NWS-ASOS point observations alleviates some of the

longstanding dispersion modeling problems, but at the same time creates new concerns.

1.2 Coupling of Mesoscale and Dispersion Models

Dispersion models require turbulent characteristics of the atmosphere that,

although not a direct output, can be derived from the temperature and wind fields in

prognostic models. The use of prognostic model forecasts would also ensure

meteorological data consistency, and complete spatial coverage for the entire United

States. In this subsection, results from a few relevant works from this research arena are

summarized.

The use of prognostic mesoscale models to provide the meteorological data

necessary for air quality models in British Columbia was explored by Rowen Williams

Davies & Irwin Inc. (RWDI) in 2002. RWDI found that by combining a coarse

resolution prognostic model run with a finer scale diagnostic model run did not improve

simulation results when complex terrain was present without a large number of

observation sites incorporated into the simulation. They recommended that in the

common scenario of sparse observations, fine horizontal resolutions (less than 3-km) in

the prognostic model runs are needed in order to capture the effects of complex

topography.

Recently, Isakov et al. (2007) investigated whether or not the meteorological

inputs needed for dispersion models could be obtained from gridded outputs of mesoscale

models. The models compared in this study were the Penn State Mesoscale Model

Version 5 (MM5) and the National Centers for Environmental Prediction’s (NCEP)


3

Meso-Eta model. The model outputs were first compared to onsite measurements and

then input into a dispersion model to compare their performance with a Tracer Field

Study conducted in Wilmington, California during 2004. The MM5 contained nested

grids of 27 km, 9 km and 3 km with 36 full-sigma levels. The Meso-Eta used a 12 km

grid spacing. The surface flows in the area were largely from the south, south-east and

northwest according to NWS observations. The southerly component was attributed to a

sea breeze that occurred daily from approximately 6am to 1pm. Overall, both models had

trouble representing the wind direction (Figure 1.3), although the MM5 with a grid

resolution of 3 km outperformed the 12 km resolution of the Meso-Eta in capturing the

sea breeze patterns at Wilmington. Generally, the MM5 did fairly well in estimating the

maximum mixed layer heights at the observed site, while the Meso-Eta tended to

underestimate mixed layer heights. Encouragingly, a dispersion model (similar to

AERMOD) when coupled with either MM5 or Eta provided concentration estimates quite

comparable to the corresponding observed values.

The feasibility of using prognostic model forecasts in place of NWS-ASOS data

in the AERMOD air quality model is currently under evaluation by the EPA (Atkinson

2005, Touma et al. 2007). The use of prognostic model forecasts would provide

meteorological data consistency, complete spatial coverage for the entire U.S., and obtain

turbulence characteristics for AERMOD. AERMOD was applied using MM5 simulation

results and NWS observations to compare the hourly and annual average benzene

concentrations around the airport in Philadelphia, PA for 2001 (Touma et al. 2007). They

found that AERMOD-MM5 predicted higher concentrations of benzene by a factor of

two to three, versus those from AERMOD-NWS (Figure 1.4). This difference could be


4

attributed to the abundance of calm wind hours in the NWS data or the approach

implemented using MM5 data in AERMOD for the study. The response of AERMOD to

the different outputs available from prognostic models needs to be explored further

before comprehensive assessment of the AERMOD-MM5 modeling framework can be

made (Touma et al. 2007).

Kesarkar et al. (2007) recently compared the simulated and observed temperature

and winds fields over Pune, India by coupling the WRF with AERMOD. They found the

WRF tended to overestimate the wind speeds (mean speed 3.65 ms-1), when compared to

the observed (mean speed difference 0.65ms-1) and found the simulated wind directions

contained a bias distributed between the West and North directions when compared to

observations (Figure 1.5). The simulated temperatures showed high correlation and a

standard deviation of 2.46 °C. Most importantly, the AERMOD-WRF modeling

framework generally underestimated the PM10 concentrations over the city.

Based on these recent studies, it is perhaps fair to conclude that several questions

need to be adequately addressed before prognostic models can be reliably utilized in

operational dispersion applications. Most of these questions are rooted in prognostic

models ability to accurately represent the boundary layer variables of interest to the

dispersion modeling community (e.g., wind speed, wind direction, temperature). In the

past, a handful of modeling studies have attempted to shed some light on the strengths

and weaknesses of planetary boundary layer (PBL) parameterizations in mesoscale

models. In the next subsection, succinct summaries of their results are provided.


5

1.3 Limitations of Present Day PBL Parameterizations

Four mesoscale meteorological model simulations (MM5, Regional Atmospheric

Modeling System -RAMS, Coupled Ocean/Atmospheric Mesoscale Prediction System-

COAMPS, and the Operational Multiscale Environment Model with Grid Adaptivity-

OMEGA) over four geographic domains (Northeast U.S., Lake Michigan area, central

California, and Iraq) were compared to boundary layer observations by Hanna and Yang

(2001). Over the United States the modeled surface wind speed and direction biases were

found to be 1 ms-1 and 10° or less respectively. The calculated root mean square error

(RMSE) for wind speed was around 2 ms-1, and 60° for wind direction (Table 1. 1). In

Iraq, the bias for mean wind speed was found to be as high as a factor of 2 with RMSE as

much as 6 ms-1. The authors contributed these disparities to the complex terrain and

limited meteorological data for assimilation.

In 2003, Zhong and Fast were the first to compare the results of regional

simulations with grid spacing smaller than 1 km from the fifth-generation MM5, RAMS,

and Meso-Eta. The purpose was to determine how well each model reproduced terrain-

induced flows as well as boundary layer structure within the Salt Lake valley. It was

found that in the valley environment all three models contained the largest wind speed

and direction errors (Figure 1.6). This result could be due to the models’ inability to

accurately simulate the ambient flow and pressure gradients that occur because of the

dynamic and thermodynamic effects of the mountains. A nighttime cold bias was found

in all three models that began as cold biases in the simulated daytime boundary layer

temperatures. All three models performed relatively well in capturing the general day and

nighttime valley flows as well as near surface convergence and divergence over the


6

valley floor. Unfortunately, large timing and magnitude errors occurred in the

circulation, boundary layer temperature, and temperature gradients during the

simulations.

The ability of five boundary layer parameterization schemes of MM5 to capture

the diurnal cycles of surface winds and temperatures were compared by Zhang and Zheng

(2004). It was found that all five schemes were able to reproduce the diurnal phase of

surface temperature while they underestimated the magnitude and contained phase errors

of surface wind speed (Figure 1.7).

Berg and Zhong completed another study in 2005 of the performance of the MM5

using several turbulence parameterizations at two locations of varying topography and

climate, the southern Great Plains and the Salt Lake valley. The turbulence

parameterizations used were the Blackadar (BK), Gayno-Seaman (GS), and Medium

Range Forecast (MRF) schemes. They found that similar skills were exhibited in all

three schemes when predicting the near-surface temperature, winds and other surface

variables.

Michelson and Bao (2006), during their simulation comparison of MM5 (version

3.7) and WRF (version 2.1) over the Central California Ozone Study (CCOS) field study

area, found that both MM5 and WRF contained a warm bias for 2-m air temperature.

This bias was particularly noticeable during the night in the San Joaquin Valley, with the

WRF simulation exhibiting a greater warm bias than MM5 (Figure 1.8). The

incongruities in land use, terrain height, vegetation coverage, and soil moisture and

temperature, as well as net radiation amounts could be the cause of the larger warm bias

in the WRF simulation. The 10-m wind speeds from the WRF simulation were generally


7

faster than that of the MM5, while the diurnal phase of both model simulations differed

from the observations. A wind direction bias was also evident in simulations from both

the WRF and MM5.

1.4 Problem Statement

In summary, all mesoscale models have historically struggled to represent the

atmospheric boundary layers with any fidelity, and the traditional approach of mesoscale

model forecast verification (i.e., comparison of meteorological point observations with

spatial-averaged model output), is fundamentally flawed to some extent. The spatial

resolution of the prognostic mesoscale model can influence how realistically

meteorological variables and transport are represented. If the grid spacing is too large,

important local topographical and meteorological features can be overlooked by the

model.

In order to address these issues, a new framework to generate high-resolution

spatial wind data by coupling the WRF model with a diagnostic model (WAsP) is

proposed. This new WRF-WAsP framework makes comparison of model output and

observations more statistically meaningful. In order to establish the potential of the new

framework, WRF generated surface layer data (wind speeds, wind direction, and

temperature) is compared with high spatial resolution observations from the West Texas

Mesonet utilizing traditional forecast verification tools. The organization of this thesis is

as follows: Chapter 2 describes the West Texas Mesonet followed by the analysis of the

data in Chapter 3. The WRF model is explained in Chapter 4. The WRF model data is

analyzed in Chapter 5. Chapters 6 and 7 introduce the new framework for comparison of


8

the model output with the observations. Chapter 8 contains the summary and conclusions

of this thesis work.


9

Figure 1.1: AERMOD modeling system structure. Adapted from Touma et al (2007).


10

Figure 1.2: ASOS Station (http://www.weather.gov/om/cm/asos.html)


11

Figure 1.3: Wind roses of simulated winds derived from NWS observations (a) from the Eta Model (c) from MM5 (e) for all hours from August 26th to September 10th, and only during hours of tracer experiments (b, d, f). Adapted from Isakov et al (2006).


12

Figure 1.4: Comparison of ranked hourly concentrations from AERMOD-NWS and AERMOD-MM5. Adapted from Touma et al. (2007).


13

Figure 1.5: Average angular distribution of wind direction prevailed over Pune during the period of field campaign (12-17 April 2005): (a) as simulated by WRF (b) as observed in NCEP ADP dataset. Adapted from Kesarkar et al. (2007).


14

Table 1. 1: Model performance measures for surface wind speed and direction for MM5 for the 4-km grid in the Central California SARMAP domain. Bias is defined as simulated minus observed. Adapted from Hanna and Yang (2001).


15

Figure 1.6: (top) Bias, (middle) RMSE, and (bottom) error standard deviation of the (left) simulated temperature, (center) wind speed, and (right) wind direction at 36 stations in the Salt Lake valley and over the foothills during IHOPs 6 and 7. Gray shading denotes nighttime periods. Adapted from Zhong and Fast (2003).


16

Figure 1.7: Time series of area-averaged horizontal wind speeds (m s-1) as simulated with the BLK, BT, GS, MRF, and MYJ, PBL schemes during the 3-day period of 1200 UTC 12 Jul-1200 UTC 15 July 1997. They are taken at z = (a) 50 and (b) 243 m. The observed VSFC (solid) is superposed to help to understand the relationship between the surface and boundary layer winds. Adapted from Zhang and Zheng (2004).


17

Figure 1.8: Time series of aerial averaged 2-m temperature for the Southern San Joaquin Valley. The black line is observations, the red line is MM5 and the blue line is WRF. Adapted from Michelson and Bao (2006).


18

CHAPTER 2

DESCRIPTION OF OBSERVATIONAL DATA

2.1 Description of the West Texas Mesonet

The West Texas Mesonet (WTM) is a network of 53 automated meteorological

stations, two atmospheric profilers, and one upper-air sounding system (at Reese

Technology Center) located in the Panhandle of West Texas and New Mexico (Figure

2.1, www.mesonet.ttu.edu). Fifteen meteorological variables (e.g. temperature and wind

speed) are reported every 5minutes. Ten agricultural variables are also measured (e.g.

soil moisture, and soil temperature) and reported every 15 minutes (Schroeder et al.

2005).

2.1.1 Station Site Information

Each station is surrounded by a 10-m by 10-m enclosure fence and contains a 10-

m tall, guyed aluminum tower (Figure 2.2). All of the sensors are mounted to boom arms

at various levels of the tower, with the exception of the soil temperature and moisture

content sensors and rain gauges (Figure 2.3).

2.1.2 Instrumentation

Each mesonet station contains various meteorological instruments to measure

temperature, barometric pressure, wind speed, wind direction, solar radiation, rainfall,

barometric pressure, soil temperature and moisture, and leaf wetness. A list of the

instrumentation can be found at www.mesonet.ttu.edu. This research is focused

primarily on 10-m wind speed and direction and 2-m temperature. Detailed descriptions

of the instruments pertaining to these variables are discussed in the following.


19

a.) ANEMOMETERS

A R.M. Young 05103 Wind Monitor is located 10-m above ground level (AGL)

at each station. This instrument is a propeller type anemometer that measures both wind

speed and direction. The daily average wind speed and direction, peak gust, and standard

deviations of the wind speed and direction are calculated using data from this

anemometer. To provide information to the agricultural community about low-level wind

data necessary for crop spraying, an additional anemometer has been placed at the 2-m

level. This 2-m level anemometer is a R. M. Young 030103 Wind Sentry, but was not

used in this study.

b.) TEMPERATURE AND RELATIVE HUMIDITY

The Vaisala HMP45C (modified by Campbell Scientific) located at the 1.5-m

AGL and is mounted in a nonaspirated 12-plate radiation shield that is positioned 0.9-m

away from the tower, is the primary temperature and relative humidity sensor. Campbell

Scientific 107 temperature probes are also installed at the 9-m and 2-m levels. This

sensor is located 0.6-m away from the tower and mounted in a nonaspirated 6-plate

radiation shield.

c.) DATA LOGGERS

Campbell Scientific CR23X data loggers with one megabyte of extended memory

are used by the WTM. The 5-min observations from each meteorological sensor and 15-

min observations from the agricultural sensors are recorded by a data logger at each site.

Each data logger is equipped with two 6-V batteries as a backup in the event that power

is lost from the main marine battery. Each data logger can store 78 days of observations

before it is overwritten by new data.


20

2.1.3 Quality Assurance and Control

The WTM performs quality assurance/quality control (QA/QC) procedures in two

steps. First, a FORTRAN application was developed to test the incoming data to identify

suspicious or potentially bad data. This process is completed using five tests: the range

test, step test, persistence test, like instrument test and a spatial test. Each observation is

flagged either as: good (“0”), suspect (“1”), warning ( “2”), or as a failure (“3”) based on

the confidence level of the observation as a result of these tests. In the second step, this

data is reviewed by a “decision maker” to interpret the results of the first step. To further

identify failing instruments, communication problems, and other issues related to the data

quality, the WTM staff employs further quality checks as well. All data employed in this

research has been visually verified.

2.1.4 Data Transfer

The observations are made available in real-time by several different methods of

data communications. Recurring costs were not initially part of the WTM budget. As a

result the data transfer methods are limited. A reliable two-way communications system

was chosen using the following methods: Extended Line of Sight Radio (ELOS), cellular

phone, landline phone, and the internet.

A 73-m tower was erected at Reese Technology Center by the Texas Tech

University’s Wind Science and Engineering Research Center to accommodate the need of

a high antenna for the ELOS system. Two additional antennas were installed to serve as

the base radio station. One radio is located at each station within the radio

communication network. Because of the line-of-site requirements several other stations

serve as repeaters within the network. Due to the remote location of several station sites


21

that are beyond the radio communication network, cellular phone technology was

employed. In conjunction with the NWS Office in Lubbock, TX and Southern Regional

Headquarters of the NWS, three landline phone connections have been provided.

Because of the high cost of connecting to each station containing a landline, these

stations are called only once or twice an hour, unless a severe weather event is occurring.

An internet connection was provided free of charge from a municipality in the Texas

Panhandle along with allowing a radio antenna to be set up on the roof of their building.

This method allows for the 5-min data to be made available on a real-time basis

(Schroeder et al. 2005).

2.2 Summary

The WTM currently contains 53 surface stations located over eastern New

Mexico and the majority of the Texas Panhandle. Each station records 5-min

meteorological data and 15-min agricultural data that are relayed back to RTC in

Lubbock, Texas by radio, cell phone, land phone or internet connection. All data is

archived and available for purchase through the WTM office.


22

Figure 2.1: Stations within the West Texas Mesonet as of April 2008


23

Figure 2.2: Lubbock West Texas Mesonet Station (www.mesonet.ttu.edu).


24

Figure 2.3: Elevation view of a typical West Texas Mesonet station from Schroeder et al. 2005. The

2-m temperature probe is not easily depicted given its location and has been omitted.


25

CHAPTER 3

WTM DERIVED SUFACE LAYER CHARACTERISTICS In this Chapter the near surface meteorological variables, such as 2-m

temperature, 10-m wind speed, and 10-m wind direction are studied by utilizing West

Texas Mesonet observations.

3.1 Location Description

The study area contains an interesting topographic feature, the Llano Estacado,

known in the region as the Caprock. The Llano is a very flat, semiarid plateau that gently

slopes from the northwest to southeast at a generally uniform rate of 1.89 m km-1(10

feet/mile; Calvert 2008). The Llano straddles the New Mexico-Texas border between

Interstate 40 to the north and Interstate 20 to the south encompassing approximately half

of the study area (Figure 2.1). The Llano is dry and void of large trees, with the

prevailing wind from the southwest. This wind direction is a result of zonal flow over the

Rocky Mountains to the west of the study area. This flow induces a region of vorticity

east of the mountain range that sets up a lee trough located in eastern Colorado. The

distinguishing characteristic of the Llano is the Caprock Escarpment, a precipitous cliff,

with an average height of 91.44 m (300 ft), most prominent on the north and east sides of

the plateau. As a result of this abrupt change in topography the WTM data has been

divided into two categories: on the Caprock (ONC) and off the Caprock (OFC). These

divisions will help facilitate the evaluation of WRF performance in flat regions versus

complex terrain in a later section. ONC refers to locations west of the Caprock

Escarpment, OFC refers to locations east of the Caprock Escarpment. In addition, the

WTM station located at Reese Technology Center (RTC) was evaluated separately due to


26

its smooth and flat terrain surrounding the station site in addition to containing the most

complete dataset as a result of its location at the WTM office.

3.2 West Texas Mesonet Data

Of the 53 WTM stations currently in operation, forty-eight of them were used in

this study. The five stations that were not evaluated came online during the study period

or later, and did not contain the full year of data. All data were obtained from the WTM

archives. The data analyzed are from April 2006 through March 2007. This date range

was chosen based upon the availability of corresponding WRF model data for

comparison in Chapter 5. The surface layer characteristics considered in this study are

10-m wind speed, 10-m wind direction, and 2-m temperature. Each characteristic was

analyzed separately for the two categories ONC and OFC, as well as for RTC.

3.3 Methodology

The WTM averaged meteorological observations are recorded every five minutes

(Schroeder et al. 2005). To later compare WTM data to the WRF model outputs, the

WTM data was first averaged hourly and then down sampled every three hours. The

three hourly data were utilized to study the monthly and diurnal variations of 10-m wind

speed, 10-m wind directions, and 2-m temperature.

3.4 ONC Observations

The 10-m wind speeds and directions were analyzed as a function of month as

well as a function of time of the day for 32 WTM stations located on the Caprock. The

months of June and December are analyzed in detail in this section. The complete year

and diurnal observational data are available in Appendix A.


27

3.4.1 June 2006 Observations of Wind Speed and Direction ONC

During the month of June at locations ONC the wind was primarily from the

south and south/southeast reaching speeds of 4-6 ms-1. The south/southwest direction

also experienced wind speeds topping 4-6 ms-1, but with less frequency. The wind

directions of the north, north/northwest, west/northwest and west were virtually

nonexistent during the month of June.

3.4.2 December 2006 Observations of Wind Speed and Direction ONC

The wind direction is most prominently from the west/southwest direction in

December, with all directions containing a westerly component showing strong. The

wind speed exceeds 4-6 ms-1 in most directions. The east, east/southeast, south/southeast

and south directions are not prominent during this winter month.

3.5 OFC Observations of Wind Speed and Wind Direction

Sixteen WTM stations are located off the Caprock. The 10-m wind speeds and

directions were also broken down as a function of month and time of the day to compare

the monthly and diurnal cycles of wind observations to WRF forecasts. In this section

the months of June and December 2006 are discussed in detail. All other months and

times can be found in Appendix A.

3.5.1 June 2006 Observations of Wind Speed and Direction OFC

For locations OFC in June the predominant wind direction is from the

south/southeast with speeds of 4-6 ms-1. The southerly wind direction is also present,

though occurring less often with wind speed topping 4-6 ms-1 as well. The weakest

components of the wind in June are the north, north/northwest, west, and west/southwest

directions.


28

3.5.2 December 2006 Observations of Wind Speed and Direction OFC

In December the strongest component of the wind is from the south/southwest

direction with wind speeds predominantly of 2-4 ms-1, but wind speeds of 4-6 ms-1

occurring occasionally. The north/northeast and west/southwest directions contain the

highest occurrence of 4-6 ms-1 wind speeds.

3.6 RTC Observations of Wind Speed and Wind Direction

Reese Technology Center is located northwest of Lubbock (33° 36’ 25.75” N,

102° 02’ 54.99” W) in the center of the study area. The 10-m wind speed, 10-m wind

direction and 2-m temperature were examined for this location separately. Detailed

descriptions of the wind speeds and directions for each month, as well as time, can be

found in Appendix A.

3.6.1 June 2006 Observations of Wind Speed and Direction at RTC

The observations at RTC contain the fastest wind speeds for the month of June.

The south wind is dominant, reaching 6-8 ms-1. The south/southwest direction is the

second most often occurring wind direction with wind speeds also topping 6-8 ms-1.

3.6.2 December 2006 Observations of Wind Speed and Direction at RTC

The least common wind directions in December at RTC are the east,

east/southeast, and south/southeast. The wind is mainly from the west direction topping

6-8 ms-1. The north and north/northwest directions are strong as well at 6-8 ms-1, though

occurring less frequently.

3.7 2-m Temperature Observations

The monthly averaged 2-m temperature was found to be higher in locations OFC

when compared to stations ONC. One reason for this is the elevation difference between


29

the categories. The strong temperature dependence on elevation has been reported by

Liston and Elder (2006). Following their approach, I came up with:

Tavg(ONC) = Tavg(OFC) – Γ(zONC – zOFC) (3.1)

The average temperature (Tavg) is a function of Γ (C/m), which is the lapse rate (Table

3.1), z (m) is the elevation, and Tavg(OFC) (C) is the average temperature OFC. The

average temperature from WTM observations ONC in January is 0.69 °C, using the lapse

rate, elevation difference between ONC, OFC, and the TOFC, the average temperature

ONC was calculated as 0.98 °C. Similarly, the observed average temperature in July for

ONC is 26.73 °C, the calculated Tavg(ONC) came out to be 26.56 °C. Thus, the elevation

difference between ONC and OFC can simply explain the temperature anomaly. The

highest temperatures for all three locations can be found in the summer months of June,

July and August. The temperatures at RTC are comparable to the averaged temperature

at locations ONC (Figure 3.1).

The diurnal cycle of temperature is evident when averaging the 2-m temperature

as a function of time. Locations off the Caprock are still higher than locations on the

Caprock at all hours of the day. Reese Center and stations on the Caprock are cooler than

the stations further east, and Reese Center is continuing to be a few degrees warmer than

the averaged station on the Caprock (Figure 3.2). This difference might be attributed to

the station’s proximity to the airport runway and the urban area of Lubbock.

3.8 Summary

The WTM meteorological stations were divided into two categories: ONC, and

OFC as well as the RTC station because of the data dependability and other factors for

analysis and later comparison with the WRF model data in Chapter 5. The fastest 10-m


30

wind speeds occur in April for both categories (Figure 3.3). RTC contains higher wind

speeds at all times (Figure 3.4) and all seasons when compared to the stations averaged

ONC and OFC. The 2-m temperature average for locations OFC exceeds the 2-m

temperature averages for both RTC and stations ONC.


31

Table 3.1: Air temperature lapse rate variations as a function of month. Adapted from Liston and Elder (2006).


32

Figure 3.1: 2-m Temperature Histogram as a Function of Month


33

Figure 3.2: 2-m Temperature Histogram as a Function of Time


34

Figure 3.3: 10-m Wind Speed Histogram as a Function of Month


35

Figure 3.4: 10-m Wind Speed Histogram as a Function of Time


36

CHAPTER 4

DESCRIPTION OF MODEL DATA

4.1 Description of the Weather and Research Forecast Model The WRF model, a next generation numerical weather prediction (NWP) model,

was recently developed as a collaborative effort among the National Center for

Atmospheric Research (NCAR) Mesoscale and Microscale Meteorology (MMM)

Division, the National Oceanic and Atmospheric Administration’s (NOAA) NCEP, Earth

System Research Laboratory (ESRL), the Department of Defense Air Force Weather

Agency (AFWA) and Navel Research Laboratory (NRL), the Center for Analysis and

Prediction of Storms (CAPS) at the University of Oklahoma, the Federal Aviation

Administration (FAA), along with the participation of a number of university scientists

(Skamarock et al. 2005).

The software framework of the WRF (Figure 4.1) provides the option for two

dynamical cores to be used, the Advance Research WRF (ARW) and the Nonhydrostatic

Mesoscale Model (NMM) WRF. For this research, the ARW WRF, which was

developed primarily at NCAR, was used.

4.1.1 Model Domain and Physics

The WRF domain consists of a 36/12 km two-way nested grid (operationally ran

by NCAR; Figure 4.2). The 48-hr forecast is initialized at 0000 UTC from 40-km Eta

model forecasts. The WRF forecast is output every three hours. The Eta model is also

used as boundary conditions for the WRF. There are nine vertical grids in the lowest 2-

km of the atmosphere. The domain chosen for this research encompasses the area

covered by the West Texas Mesonet (Figure 4.3).


37

The ARW contains many physics options. The physics categories are (1)

microphysics, (2) cumulus parameterization, (3) planetary boundary layer (PBL), (4)

land-surface model, and (5) radiation (Skamarock et al. 2005). The ARW operational

forecast utilizes theYonsei University (YSU) PBL scheme, the Kain-Fritsch (KF)

cumulus parameterization, WRF Single-Moment 3-class (WSM3) scheme, Noah Land

Surface Model (LSM), Longwave Rapid Radiative Transfer Model (RRTM), and

Mesoscale Model Version 5 (MM5) (Duhdia) Shortwave. A detailed description of these

schemes can be found in Skamarock et al. (2005).

4.1.2 Planetary Boundary Layer Parameterizations in WRF

The transport of emission sources is primarily focused on the lowest 1-2-km of

the atmosphere, the PBL. Turbulence flux properties within the PBL are determined by

the PBL parameterization of the model. Three PBL parameterizations are supported in

WRF, the Yonsei University (YSU) PBL, Mellor-Yamada-Janjic (MYF) PBL and the

Medium Range Forecast Model (MRF) PBL. The YSU PBL scheme is latest version of

the MRF PBL.

To represent fluxes as a result of non-gradient terms the YSU PBL uses counter-

gradient terms. A schematic of the PBL processes represented by the YSU is available in

Figure 4.4. The YSU employs non-local mixing during the daytime and local mixing at

night. The diagram further illustrates that the surface is influenced by the strength of the

PBL scheme. A critical bulk Richardson number of zero is used to define the top of the

PBL and is only dependent on the buoyancy profile, which generally lowers the top of the

PBL compared to that of the MRF PBL which sets the bulk Richardson number to 0.5 or

1 (Skamarock et al. 2005).


38

4.2 Static Fields

For near surface flows, the two static fields of interest are terrain and land cover.

The WRF incorporates 2-min resolution United States Geological Survey (USGS)

National Elevation Datasets to represent the terrain elevation. The coarseness of this

layer at 12-km grid spacing creates a smoothing of topography in WRF (Figure 4.5) when

compared to the 30-m resolution of available National Elevation Dataset (NED)

topography. Land cover is important because surface heterogeneity and roughness affect

the variables of interest (wind speed, wind direction, and temperature). WRF’s land use

coverage is broken into 24 categories (Figure 4.6) at 12-km resolution, four of which

occur in West Texas. WRF only considers two values of roughness length for each land

use category, one for summer, another for winter. This simplification should have an

effect on all variables of interest for comparison. A finer scale (30-m resolution) land use

coverage, available from the National Land Cover Data (NLCD) is shown in Figure 4.7.

4.3 Summary

The Weather and Research Forecasting model has been developed as a

collaborative effort of many agencies and university scientists. The goal is to provide a

next generation mesoscale forecast model and data assimilation system that advances

both the understanding and prediction of mesoscale weather and accelerates the transfer

of research advances into operation (Skamarock et al. 2005). The objective in utilizing

this data is to explore the possibility of inputting the forecast into AERMOD in place of

NWS observations.


39

Figure 4.1: WRF system components. Adapted from Skamarock et al. 2005.


40

Figure 4.2: Operational ARW domain from http://wrf-model.org/plots/realtime_nest.php


41

Figure 4.3: WRF Domain


42

Figure 4.4: Illustration of PBL Processes. Adapted from Duhdia (2008).


43

Figure 4.5: WRF 12-km Digital Elevation Model


44

Figure 4.6: WRF 12-km Land Use


45

Figure 4.7: 30-m National Land Cover Data (2001).


46

CHAPTER 5

WRF MODEL DERIVED SUFACE LAYER CHARACTERISTICS

The WRF’s ability to represent surface layer characteristics is explored to assess

the accuracy of the model forecasts and the possibility of incorporating model data into

AERMOD for dispersion modeling.

5.1 WRF Model Forecast

The operational forecast data analyzed are from April 2006 through March 2007.

This date range was chosen based upon the availability of a complete year’s worth of

data. The surface layer characteristics considered in this study are 10-m wind speed, 10-

m wind direction, and 2-m temperature. Each characteristic was analyzed separately for

the two categories (ONC and OFC) and RTC.

5.2 Methodology

Each WRF grid point was paired with a corresponding WTM station. The criteria

necessary for the coupling was based upon proximity to a WTM station and elevation of

WRF grid point. The 10-m wind speed, 10-m wind direction and temperature were then

averaged for grid points ONC, OFC and RTC using the same method as previously

discussed for the WTM observations.

The mean bias and root mean square error (RMSE) were calculated as a function

of month and time for each category to further explore the performance of the model.

RMSE was calculated the following way:

( )∑ −∗ xx ofN1 2 (5.1)

The mean bias formula implemented was:


47

∑ −∗ )(1 xx ofN (5.2)

where xf and xo is WRF’s forecast and observed, respectively.

5.3 ONC WRF Model Forecast The 10-m wind speed and wind direction were analyzed as a function of month

and time for 32 WRF grid points which correspond to the WTM stations located on the

Caprock. The months of June and December 2006 are described in this section. A

complete description of the entire study period by month as well as diurnal cycle can be

found in the Appendix B.

5.3.1 June 2006 Model Forecast of Wind Speed and Direction ONC

The forecasted wind speeds in the month of June were generally underestimated

by WRF when compared to the observations. The model forecast does represent the

south wind well, but underestimates the south/southwest component.

5.3.2 December 2006 Model Forecast of Wind Speed and Direction ONC

The WRF does extremely well in forecasting the wind speeds and wind directions

in December 2006. The occurrence of the west wind is overestimated, as well as an

underestimation of the west/northwest component but the speeds are correctly forecasted

at a maximum of 4-6 ms-1.

5.3.3 Monthly Statistics of Wind Speed and Direction ONC

The monthly mean bias for wind speed is always negative for locations ONC,

implying that WRF consistently underestimates the wind speed throughout the year

(Figure 5.1). The monthly RMSE for wind speed is lower during the summer months.

Since the YSU PBL scheme is believed to perform well under unstable conditions, one

would expect the WRF model to perform better during the summer months (Figure 5.2).


48

5.3.4 Diurnal Statistics of Wind Speed and Direction ONC

The diurnal mean bias has the greatest value during the hours of 0000 and 1500

UTC, times during which the morning and evening transitions are common. The mean

bias is lowest during daytime hours, but still is negative (Figure 5.3). The RMSE is

largest during the hours of 0000, and 1500 UTC. The evening and morning transitions

occur during these hours in addition to WRF consistently underestimating the wind speed

at night (Figure 5.4).

5.4 OFC Observations of Wind Speed and Wind Direction

Sixteen of the WRF grid points that correspond to WTM stations are located

OFC. The 10-m wind speed and wind direction were also broken down as a function of

month and time to observe the seasonal and diurnal variations represented by the model.

The months of June and December are presented in this section, all other months as well

as times can be found in Appendix B.

5.4.1 June 2006 Model Forecast Wind Speed and Direction OFC

During the month of June WRF overestimates how often the wind is from the

southerly direction and underestimates the south/southeast direction. The other directions

are well reproduced in the forecast. The wind speeds are also correct at maximums of 4-

6 ms-1.

5.4.2 December 2006 Model Forecast Wind Speed and Direction OFC

The dominant wind direction in month of December is forecasted to come from

the west, unfortunately this does not appear in the observational data. The westerly wind

is also overestimated by 2-4 ms-1. WRF also underestimates the occurrence of the south

wind, but captures the wind speed of 2-4 ms-1.


49

5.4.3 Monthly Statistics of Wind Speed and Direction OFC

During January, when WRF has the most trouble capturing the true observed

value of the wind, the monthly mean bias is the highest. There is an overall positive bias

during the year for the averaged monthly data for all averaged locations OFC, but the

wind directions are consistently incorrect when compared to the observations (Figure

5.5). The RMSE is the largest in January and March. It seems the varying synoptic

conditions and thunderstorm activity in the region during the latter part of March, were

not properly captured by the model, which has lead to the increased error. The locations

OFC, during summer months are exhibiting the lowest RMSE, as previously seen in the

ONC data (Figure 5.6).

5.4.4 Diurnal Statistics of Wind Speed and Direction OFC

WRF underestimates the wind speed during the nighttime hours while

overestimates it during the day. This can be observed in the mean bias calculations

(Figure 5.7). The RMSE is constant throughout the diurnal cycle (Figure 5.8).

5.5 RTC Observations of Wind Speed and Wind Direction

RTC, is located northwest of Lubbock at latitude 33° 36’ 25.75” N, longitude

102° 02’ 54.99” W in an area of very flat, homogenous terrain and classified as grassland

in the WRF land use coverage.

5.5.1 June 2006 Model Forecast Wind Speed and Direction at RTC

During the month of June, WRF greatly underestimated the wind speeds at RTC.

The maximum wind speeds in the forecast are 4-6ms-1, while the observations reach

speeds of 6-8 ms-1. The overall wind directions are well forecasted by WRF.


50

5.5.2 December 2006 Model Forecast Wind Speed and Direction at RTC

December wind speeds are underestimated as well. The observations contain a

maximum of 6-8 ms-1 westerly wind, while WRF forecasts 4-6 ms-1 from the same

direction. WRF does manage to represent all of the directions correctly.

5.5.3 Monthly Statistics of Wind Speed and Direction at RTC

RTC contains the greatest negative mean bias and RMSE calculations compared

to those of ONC and OFC. This is due to the fact that RTC is one single station, while

ONC and OFC are averaged ensembles. The average wind speed is under predicted at

RTC during every month of the year (Figure 5.9). The RMSE is once again highest in

March due to the models inability to capture synoptic conditions which occurred during

the last week of the month (Figure 5.10).

5.5.4 Diurnal Statistics of Wind Speed and Direction at RTC

The diurnal mean bias is the most negative during the hours of 0000 and 1500

UTC, the typical hours of the morning and evening transitions (Figure 5.11). The highest

RMSE is also found during the same hours (Figure 5.12). This result is another example

of WRF’s inability to represent the diurnal transitions well.

5.6 2-m Temperature Observations

The monthly averaged 2-m temperature was found to be higher at RTC when

compared to WRF grid points ONC and OFC. The forecast temperatures for the grid

points located ONC and OFC are relatively even (Figure 5.13). In reality there should be

a difference in the temperatures due to elevation differences between ONC and OFC.

WRF fails to capture the effects of topography as a result of the large grid spacing.


51

5.6.1 2-m Temperature Statistics

The diurnal cycle of temperature is still evident in the WRF forecast when

averaging the 2-m temperature as a function of time, although WRF exhibits a warm bias

at night in the temperature forecasts for locations OFC, ONC and RTC (Figure 5.14,

Figure 5.15, and Figure 5.16). This bias is seen in all locations and does not appear to be

topography related. It has been seen in other studies that WRF has a warm bias at night,

possibly, as a result of the PBL parameterization. RTC temperatures are higher than

locations OFC and ONC at all hours of the day (Figure 5.17). The monthly bias

calculations are comparable at all locations. March contains a very low bias in locations

ONC and at RTC, even negative for OFC. After further research into the data, it was

determined that WRF showed little to no skill in forecasting temperatures at the end of

March in the region. The observations contain little diurnal cycle due to synoptic

conditions in the region, while WRF continued to forecast a large diurnal cycle in

temperature (Figure 5.18, Figure 5.19, and Figure 5.20).

The RMSE was also calculated for each category OFC, ONC and RTC. The

lowest RMSE can be found during the hours of 1500 and 1800 UTC for all locations

(Figure 5.21, Figure 5.22, and Figure 5.23). This could be attributed to the hours

identified generally being the most unstable hours of the day. The YSU parameterization

scheme is tuned for convective boundary layer conditions and performs better during

these hours (Hong et al. 2006). This thinking can also be applied when considering the

monthly RMSE for OFC, ONC and RTC. During the summer months there are a greater

number of unstable hours in each day, which leads to a lower RMSE calculation (Figure

5.24, Figure 5.25, and Figure 5.26). RMSE is not a good statistic to use because is does


52

not take into account location or timing errors associated with forecasts. This becomes

evident in the month of March, where the RMSE value is no worse than other months,

but there were extensive errors in the forecasts.

5.7 Summary

The WRF meteorological forecast was divided into two categories for analysis:

ONC and OFC for comparison with WTM observations along with the RTC station

separately. For the WRF data, the fastest 10-m wind speeds occur in April for both

categories which does agree with the observational data (Figure 5.27). The highest wind

speeds are found in locations ONC and at RTC at varying times (Figure 5.28). The 2-m

temperature average at RTC exceeds the 2-m temperature averages for both OFC and

ONC. This is contradictory to the WTM observations. The WRF model contains a warm

temperature bias. The performance of the WRF in forecasting wind speed, wind

direction, and temperature needs to be improved. In Chapter 7, a new framework is

proposed to improve the wind speed forecasts and be used as an input into AERMOD.


53

Figure 5.1: ONC monthly mean bias of wind speed.


54

Figure 5.2: ONC monthly RMSE for wind speed.


55

Figure 5.3: ONC diurnal mean bias for wind speed.


56

Figure 5.4: ONC diurnal RMSE of wind speed.


57

Figure 5.5: OFC monthly mean bias of wind speed.


58

Figure 5.6: OFC monthly RMSE for wind speed.


59

Figure 5.7: OFC diurnal mean bias of wind speed.


60

Figure 5.8: OFC diurnal RMSE for wind speed.


61

Figure 5.9: RTC monthly mean bias of wind speed.


62

Figure 5.10: RTC monthly RMSE for wind speed.


63

Figure 5.11: RTC diurnal mean bias of wind speed.


64

Figure 5.12: RTC diurnal RMSE for wind speed.


65

Figure 5.13: 2-m WRF Forecast Temperature Histogram as a Function of Month


66

Figure 5.14: OFC diurnal 2-m temperature bias


67

Figure 5.15: ONC diurnal 2-m temperature bias


68

Figure 5.16: RTC diurnal 2-m temperature bias


69

Figure 5.17: 2-m WRF Forecast Temperature Histogram as a Function of Time


70

Figure 5.18: OFC monthly 2-m temperature bias.


71

Figure 5.19: ONC monthly 2-m temperature bias.


72

Figure 5.20: RTC monthly 2-m temperature bias.


73

Figure 5.21: OFC diurnal 2-m temperature RMSE.


74

Figure 5.22: ONC diurnal 2-m temperature RMSE.


75

Figure 5.23: RTC diurnal 2-m temperature RMSE.


76

Figure 5.24: OFC monthly 2-m temperature RMSE.


77

Figure 5.25: ONC monthly 2-m temperature RMSE.


78

Figure 5.26: RTC monthly 2-m temperature RMSE.


79

Figure 5.27: 10-m WRF Forecast Wind Speed Histogram as a Function of Month


80

Figure 5.28: 10-m WRF Forecast Wind Speed Histogram as a Function of Time


81

CHAPTER 6

WIND ATLAS ANALYSIS AND APPLICATION PROGRAM

6.1 Description By comparing the WRF forecast data to the WTM meteorological observations, it

is easy to see that the WRF does not perform so well when complex topography

(locations east of the Caprock Escarpment) is present in forecasting wind speed and wind

direction. This can be attributed to the generalization of the land use, topography, and

large grid spacing of the domain, when comparing the WTM station data to the nearest

WRF grid point. To further explore the possibly of improving the WRF forecast, the

Wind Atlas Analysis and Application Program (WAsP) was explored to account for the

terrain differences across the domain as a correction factor. The WAsP is mainly used

for wind data analysis, wind atlas generation, wind climate estimation, and siting of wind

turbines (Mortensen 2004). The program was first developed at the Riso National

Laboratory in Denmark and released in 1987. The model is based on Jackson and Hunt

theory (J-H theory). Models that are based on this theory are different from other mass-

consistent models. The biggest difference is the role of momentum conservation. In the

WAsP model, mass is conserved, but also attempts to solve the Navier-Stokes momentum

equations, which are ignored in other mass-consistent models (Rohatgi and Nelson 1994).

6.2 Main Calculation Blocks

WAsP contains five main calculation blocks. The first is the analysis of raw data.

This option allows the user to receive a statistical summary of the observed wind climate,

at a specific location by analyzing any time series of wind measurements. The next

calculation block produces the generation of a numerical wind atlas of the domain. A


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regional wind climate or wind atlas data set can be produced from analyzed wind data. A

schematic diagram of the process is represented in Figure 6.1. In this step the wind

observations have been ‘cleaned’ with respect to site-specific conditions, by removing the

effects on wind by obstacles and terrain. The wind atlas data are now site independent

and standard conditions have been applied to the wind distributions. The third step

involves an estimation of the wind climate. The program estimates the wind climate at a

specific point using the wind atlas data set. This estimate is accomplished by performing

the inverse calculation as was used to generate the wind atlas. A more accurate

prediction of the wind climate at a specific location is obtained by introducing

descriptions of terrain around the location. The WAsP code considers surface roughness

and obstacles upstream during the evaluation of locations. The roughness of a surface is

determined by the size and distribution of the roughness elements it contains.

Vegetation, infrastructure, and soil surfaces are typically considered for land surfaces.

The surface roughness is divided into four classes of terrain in the wind atlas (Figure 6.2,

Figure 6.3, Figure 6.4, and Figure 6.5):

Class 0: Water areas such as seas and lakes Class 1: Open areas, usually flat or rolling with scattered windbreaks or shelters (single farms, low bushes). Class 2: Farmlands with windbreaks of mean separation distance in excess of 1000m, and some isolated trees and buildings. The terrain may be flat or strongly undulating. Class 3: Urban districts, forests, and farmland with closely spaced windbreaks, the average separation being a few hundred meters. Forest and urban areas also belong to this class (Rohatgi and Nelson 1994). The wind power potential is estimated in the next block. By providing WAsP

with the power curve of specific wind turbine, the annual mean energy production of the


83

turbine can be calculated. In the final block, an estimation of the wake losses for each

turbine in a wind farm can be calculated and the net annual energy production of each

wind turbine and the entire farm (Mortensen 2004).

6.3 Summary

The WAsP model takes into account the effects of terrain, surface roughness and

obstacles on wind speeds and directions, which are not represented in other course

models. This step is accomplished by first removing the effects of topography in a data

set to produce a wind atlas. Next the topographic effects on a specific site are calculated

and reintroduced into the data set to create a wind climate of a region. The final steps are

the estimation of wind power potential and wind farm production. Through this process

the estimated wind speed and direction of a location can be greatly improved. By

implementing the use of this program a correction factor is introduced to account for the

effects terrain and exposure have on wind variables.


84

Figure 6.1: The wind atlas method of WAsP. Meteorological models are used to calculate the regional wind climatologies from the raw data. In the reverse process – the application of wind atlas data – the wind climate at any specific site may be calculated from the regional climatology (Mortensen et al. 2005).


85

Figure 6.2: Example of terrain corresponding to roughness class 0. Adapted from Troen and Petersen (1989).



86




87

CHAPTER 7

WRF-WAsP COUPLING

7.1 Purpose WRF-based average wind forecasts can be significantly improved by utilizing

WAsP in conjunction with high-resolution local topography (digital elevation). A wind

field is influenced when a hill, obstacle or other topographic feature is present. When the

wind encounters height change in terrain, it can affectively increase the mean wind speed

as much as 5% at the top of the topographic feature. Given a 5% height increase, the

increase in wind speed can result in a 15% increase in available power (Troen and

Peterson 1989). Terrain becomes a very important variable when considering this effect.

In this work a new framework is proposed for improving the forecast of the local wind by

implementing a correction factor to account for the terrain with WAsP (Figure 7.1):

Step 1: Input the combination of the operational WRF forecasts and the 12-km WRF grid into WAsP to remove the terrain effects on the wind to obtain a numerical wind atlas of the domain.

Step 2: Next, run the numerical wind atlas with 30-m National Elevation Data

(NED) through WAsP again to add in the updated topography and obtain the local wind.

The average wind speed and power density were calculated to see if by including

the use of WAsP the model averages were improved. Three specific WTM station

locations have been chosen to compare the observational data, WRF forecast data, and

the coupling of WRF data with WAsP. The three sites chosen were Macy Ranch,

Fluvanna, and Reese Technology Center mesonet locations.

Although WAsP can account for obstacles, they were not taken into consideration

in this study. Several of the mesonet stations are affected by obstacles in the path of the


88

sensors. The stations at Plainview, Lubbock, Ralls and Memphis are affected by urban

areas. All of the stations still meet the 20 to 1 rule for distance from obstructions, but the

urban-type surroundings do have a significant impact on average wind speed readings.

The Plainview station is located one mile south of the downtown area, just off business I-

27. The station is located in an area of open exposure, but when compared to

surrounding stations the buildings and structures in the distance do impact the average

wind speeds. The Lubbock mesonet station was moved several years ago to range land

northwest of Texas Tech University, but the average wind speed is still impacted by the

urban areas of Lubbock. The town of Ralls does impact the Ralls station. Wind speeds

are higher at locations surrounding the area in the northwest wind direction. The

Memphis station is located in a broad river valley. It is located on the edge of town near

a cemetery. Urban surroundings do have some impact on wind speeds, but the

predominate influence is the temperature inversions that form at night in this region

(personal communication with Wes Burgett, WTM).

7.2 Macy Ranch

The WTM station at Macy Ranch is located approximately 10-m from the edge of

the Caprock Escarpment (Figure 7.2). As a result of its location in complex topography

the WRF model has preformed poorly when compared to the WTM observations. The

average wind speed at Macy Ranch from WTM observations for the time period April

2006 – March 2007 was 4.98 ms-1. This average for the year of data analyzed is not

unlike the overall average wind speed since January 2002, when the station came online

(Figure 7.3). The wind direction for the year analyzed is also in agreement with the

entire life period of the station as well (Figure 7.4). The WRF forecast under predicts the


89

average wind speed by 0.61ms-1. One reason for this under prediction is the averaging of

the wind speed over the 12-km grid (Figure 7.5). WRF also generalizes the terrain and

fails to capture its effects on the wind speed and wind direction (Figure 4.5). The terrain

integrated into the WAsP model is a finer resolution, a 100-m National Elevation Dataset

(Figure 7.6). Power density, a measure of the energy available from the wind is

calculated by the formula:

3

21 uE ρ= (7.1)

where ρ is the mean air density and u is the mean wind velocity (Troen and Peterson

1989). The power density at 10-m for the WTM observations was calculated by WAsP to

be 136 Wm-2, while the power density for the WRF forecast is only 86 Wm-2. By

inputting the WRF forecast into WAsP the average wind speed increases to 5.28 ms-1

with the power density increasing to 152 Wm-2 (Table 7.1). WAsP creates a numerical

wind atlas and then by adding in the terrain effects of the area, a more accurate

representation of the local wind is generated (Figure 7.7). This improves the WRF

forecast because WAsP computes power for the individual wind sectors and then takes

the average. This results in WAsP doing better in each wind sector and is responsible for

a larger power calculation.

The roughness lengths can also be manipulated in WAsP. Vega and Letchford

(2007) calculated the roughness for 16 sectors for each WTM station. The WRF-WAsP

coupling was repeated with accounting for the average annual roughness for 12 wind

sectors by following their method. The roughness lengths for each station with

corresponding sectors can be found in Table 7.2. As a result, the forecasted wind speed

at Macy Ranch increased to a greater over prediction as well as the mean power density.


90

7.3 Fluvanna

The Fluvanna WTM station is located in an area of less dramatic terrain when

compared to Macy Ranch but, is still not homogenous when compared to the area

surrounding Reese Technology Center. The average wind speed from the WTM

observations for the year is 4.43 ms-1. This average wind speed for the data range is

consistent with the average wind speed calculated for the life span of the Fluvanna

mesonet station at 4.46 ms-1 as well as the overall wind direction (Figure 7.8 and Figure

7.9). The WRF model performs well with an average wind speed of 4.42 ms-1 (Figure

7.10). The calculated power density from the observational data is 98 Wm-2, while from

the WRF forecast the power density is low at 89 Wm-2. When combing the WRF

forecast with WAsP the average wind speed is calculated to 4.44 ms-1 with the power

density increasing to 91 Wm-2 (Table 7.1). By increasing the resolution of the

surrounding terrain the wind speed forecast can be improved (Figure 7.11). Figure 7.12

represents the averaged wind speed for Fluvanna when WAsP is combined with the WRF

forecast. By repeating the process, but including the roughness lengths the average wind

speed increases to 5.15 ms-1, with the mean power density calculated at 145 Wm-2 (Table

7.2). Both of these values are over estimated like in the previous case.

7.4 Reese Technology Center

RTC is located near the center of the domain in an area of homogenous terrain on

the Llano Estacado. The average wind speed for RTC was 4.97 ms-1. The data analyzed

for the time period from April 2006 to March 2007 is representative of the overall climate

of the area (Figure 7.13). The average wind speed for the life span of the station is 5.13

ms-1 with the dominant wind direction from the south (Figure 7.14). WRF


91

underestimated the wind speed to be 4.21 ms-1 for the year (Figure 7.15). When

combining the WRF forecast data barely increases with WAsP. The average wind speed

calculation increases to 4.23 ms-1. The power density for RTC observations is 148 Wm-2

with WRF forecast power density calculated at 78 Wm-2. The WRF-WAsP power

density only increases to 79 Wm-2 (Table 7.1). Although there is a small improvement in

the forecast when WAsP is employed it is not as dramatic of an improvement as in the

Macy Ranch or Fluvanna cases. This result is likely due to the homogenous terrain in the

area (Figure 7.16). The terrain effects are minimal on the wind speed and direction at

RTC (Figure 7.17). This is a limitation of the correction factor employed, only

substantial improvements have been seen in complex terrain. By including specific

surface roughnesses for each sector at RTC the average wind speed increased to 5.31 ms-

1, with the mean power density calculated at 158 Wm-2. It seems that by taking

roughness into account for homogenous terrain the forecast is improved.

7.5 Summary

The average wind speed at three locations in the domain was calculated to

compare with each of the location’s WRF average wind speed forecast when combined

with WAsP. The locations chosen were Macy Ranch, Fluvanna and Reese Technology

Center. The forecast for Macy Ranch greatly improved when WAsP was utilized to

account for the complex topography in the area. Fluvanna’s forecast also improved but

the average wind speed forecast at Reese Technology Center showed little change due to

the homogenous terrain of the area surrounding the mesonet site.

Next, the WRF-WAsP process was repeated with the inclusion of specific surface

roughness calculations for each station’s 12 wind sectors. The yearly average surface


92

roughness lengths were used as input into WAsP. The forecasts were improved for the

RTC location, but worsened at Fluvanna and Macy Ranch. There is some controversy on

how to calculate the roughness lengths for a specific location (Vega and Letchford 2007).

This issue needs to be explored further in future work.


93

Figure 7.1: WRF-WAsP Framework


94

Figure 7.2: Photo of Macy Ranch mesonet station (www.mesonet.ttu.edu).


95

Figure 7.3: April 2006 – March 2007 Macy Ranch Wind Rose


96

Figure 7.4: Macy Ranch Wind Rose from January 2002 to Present (www.mesonet.ttu.edu).


97

Figure 7.5: Annual Averaged 12-km grid spacing wind speed at Macy Ranch.


98

Figure 7.6: 100-m Digital Elevation Model of Macy Ranch derived from 30-m NED


99

Figure 7.7: Annual Averaged 100-m grid spacing Wind Speed at Macy Ranch.


100

Figure 7.8: April 2006 – March 2007 Fluvanna Wind Rose


101

Figure 7.9: Fluvanna Wind Rose from July 2002 to Present (www.mesonet.ttu.edu).


102

Figure 7.10: Annual Averaged 12-km grid spacing wind speed at Fluvanna.


103

Figure 7.11: 100-m Digital Elevation Model of Fluvanna derived from 30-m NED


104

Figure 7.12: Annual Averaged 100-m grid spacing Wind Speed at Fluvanna.


105

Figure 7.13: April 2006 – March 2007 Reese Technology Center Wind Rose


106

Figure 7.14: Reese Technology Center Wind Rose from June 2000 to Present (www.mesonet.ttu.edu).


107

Figure 7.15: Annual Averaged 12-km grid spacing wind speed at Reese Technology Center.


108

Figure 7.16: 100-m Digital Elevation Model of Reese Technology Center derived from 30-m NED


109

Figure 7.17: Annual Averaged 100-m grid spacing Wind Speed at Reese Technology Center.


110

Table 7. 1: Comparison of Wind Speed Observations, Forecast, WRF-WAsP Forecast, and WRF-WAsP Forecast with roughness calculated for each sector with Mean Power Density Calculations for Macy Ranch, Fluvanna, and Reese Technology Center.

WTM WRF WRF-WAsP WRF-WAsP

(roughness)

MACY 4.98 ms-1 4.37 ms-1 5.28 ms-1 6.09ms-1

136 Wm-2 86 Wm-2 152 Wm-2 234 Wm-2

FLUV 4.43 ms-1 4.42 ms-2 4.44 ms-1 5.15 ms-1

98 Wm-2 89 Wm-2 91 Wm-2 145 Wm-2

REES 4.97 ms-1 4.21 ms-1 4.23 ms-1 5.31 ms-1

148 Wm-2 78 Wm-2 79 Wm-2 158Wm-2

:


111

Table 7.2: Roughness lengths for the 12 wind sectors of Fluvanna, Macy Ranch, and RTC Mesonet Station

Aerodynamic roughness Sector Angle FLUV MACY REES z01 N 0 0.022 0.017 0.010 z02 NNE 30 0.044 0.015 0.009 z03 ENE 60 0.060 0.027 0.009 z04 E 90 0.107 0.031 0.009 z05 ESE 120 0.062 0.026 0.008 z06 SSE 150 0.029 0.021 0.008 z07 S 180 0.019 0.023 0.007 z08 SSW 210 0.023 0.050 0.011 z09 WSW 240 0.024 0.085 0.014 z010 WSW 270 0.021 0.039 0.011 z011 WNW 300 0.018 0.022 0.009 z012 NNW 330 0.020 0.015 0.009


112

CHAPTER 8

SUMMARY AND CONCLUSIONS

8.1 Summary

The purpose of this research was to assess the performance of a new-generation

weather prediction model (WRF) in capturing surface layer characteristics in the hopes of

one day implementing the forecasts in dispersion modeling. West Texas Mesonet

meteorological observations for the time period April 2006 – March 2007 were compared

to WRF 12-km grid operational forecasts of the same period. The nearest WRF grid

point to a corresponding mesonet station was identified. The data were divided into two

categories, on the Caprock and off the Caprock to assess the spatial variability on wind

speed, wind direction and temperature. Observations at Reese Technology Center were

also analyzed due to the reliability of the data. The data from each category were then

analyzed as a function of time and month to further test the ability of the model in

representing the diurnal and seasonal cycles of temperature, wind speeds, and wind

direction.

WAsP was utilized to remove the effects of terrain and exposure in the wind

forecast to create a numerical wind atlas of the domain. The atlas was then coupled with

30-m NED data to determine the local wind with hopes of improving the WRF

operational forecast of wind speed and wind direction. The 2-m temperature data from

the observations and model were also compared to assess the performance of WRF.

8.2 Conclusions

By comparing the observational data to the WRF model forecast it was very

obvious that the model struggled in producing an accurate forecast of wind speed and


113

wind direction. As a result, three individual WTM stations were examined to resolve the

sub-grid orography not properly represented in WRF. By utilizing WAsP to include the

effects on wind speed and wind direction by complex terrain the model forecast was

improved. Little improvement occurred in locations of flat homogeneous topography.

This could be a result of WRF’s treatment of roughness lengths. WRF only breaks the

roughness calculations into two seasons: summer and winter. A second forecast was

made by including a roughness length in WAsP that was more representative of the

locations. This did improve the wind speed and mean power density calculations at RTC,

but only proved to worsen the Fluvanna and Macy Ranch forecasts.

The 12-km grid size is also too coarse to capture the correct land use of the area.

Specifically, Lubbock is classified as grassland which has a surface roughness of 12 cm

in the summer, and 10 cm in the winter. Lubbock should contain a higher roughness

value as a result of the urban land use that is not represented in the model. Reese

Technology Center is also categorized as grassland in the land use, but the area is very

smooth and flat. The roughness calculation should be lower for RTC. This difference

would help to increase the forecast wind speeds in the region which are presently too low.

Several steps can be taken to improve the WRF forecasts. First, the terrain

representation in the WRF model needs to be improved to account for the effects of

topography on wind variables. A solution to this problem would be to use a smaller grid

spacing. Satellite derived land use patterns at 30-m resolution, derived more frequently

throughout the year, and would result in more representative roughness length

calculations for a given area. The physical parameterizations of the PBL scheme need

improvement as well. Unfortunately this is the case with all the contemporary mesoscale


114

meteorological models. The significant temperature bias found in the model data is most

likely due to a combination of problems in the land surface and PBL parameterizations to

represent the surface and boundary layer flux values correctly.


115

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117

APPENDIX A

WTM OBSERVATIONAL WIND ROSES

This section describes the monthly and diurnal variations of wind speed and wind

directions at location ONC, OFC and RTC.

A.1 Monthly Variation of Wind Speed and Direction ONC

The ONC data from the month of April 2006 indicates predominately south and

west southwest wind directions with the west and west/southwest directions exhibiting

the strongest average wind at 6-8 ms-1 (Figure A.1). In May and June, the average wind

speed slows to 4-6 ms-1 and shifts the strongest component to the south (Figure A.2 and

Figure A.3). The wind direction in July is mainly from the south/southeast, but the

strongest winds can be found coming from the south and south/southwest (Figure A.4).

In August, September, and October the wind continues to be from the south, but the

average wind speed picks up to 4-6 ms-1 (Figure A.5, Figure A.6, and Figure A.7). In

November, the wind direction shifts to a prevailing south/southwest direction but the

fastest speeds are located the north/northwest and north directions (Figure A.8). Moving

on into December, the wind direction changes again to a predominantly west/southwest

direction with an additional strong westerly component (Figure A.9). In January 2007,

the wind direction is divided between the north and the south/southwest directions

(Figure A.10). The spring windy season begins in February, with average wind speeds

toping 6-8 ms-1 from the prevailing west/southwest direction and a peak of 8-10 ms-1

sneaking in from the west/northwest (Figure A.11). March winds are weighted towards

the south once again with speeds ranging from 4-6 ms-1 (Figure A.12).


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A.2 Diurnal Variation of Wind Speed and Direction ONC

The three hourly wind speeds and direction for the year are analyzed to assess the

timing of WRF in Chapter 5. The wind direction at 0000, 0300, and 0600 UTC are

almost identical with southerly and southeasterly components, the only difference being

the higher wind speeds at 0000 UTC (Figure A.13, Figure A.14, and Figure A.15). The

wind obtains a stronger south/southwesterly flow by 0900 UTC (Figure A.16), and

continues to gain more prominence in the westerly directions at 1200 UTC (Figure A.17).

At 1500 UTC, the averaged wind speeds have increased in strength to the south,

south/southwest, and west/southwest directions, with the strongest speed occurring from

the west at 6-8 ms-1 (Figure A.18). The 1800 and 2100 UTC wind directions agree with

the overall flow in the area. Winds are predominantly from the south, south/southwest

and west/southwest with wind speeds peaking at 6-8 ms-1 (Figure A.19 and Figure A.20).

A.3 Monthly Variation of Wind Speed and Direction OFC

Wind directions in April 2006 at locations OFC are predominately from the south.

The west and west/southwest directions feature the fastest wind speeds at 6-8 ms-1

(Figure A.21). During May and June, the prevailing wind is from the south and

south/southeast, at times reaching 4-6 ms-1 (Figure A.22 and Figure A.23). In July,

August, and September the overall speeds slow and continue to blow from the south and

south/southeast directions (Figure A.24, Figure A.25, and Figure A.26). In October, the

south/southwest direction occurs most frequently, but the strongest winds can be found

less often from directions containing a northerly component (Figure A.27). The wind

speed further increases in November from the north/northwest, to speeds as high as 6-8


119

ms-1 (Figure A.28). In December, locations OFC receive wind blowing mainly from the

south/southwest, with maximum speeds slowing to 4-6 ms-1 (Figure A.29). Wind

direction and speeds are divided between the north and south/southwest during the month

of January (Figure A.30). As the season progresses into February, maximum wind

speeds increase to 8-10 ms-1 from the west, while the prevailing wind direction is from

the south/southwest (Figure A.31). March brings winds mainly from the south and

southeast directions with speeds topping 4-6 ms-1 at times (Figure A.32).

A.4 Diurnal Variation of Wind Speed and Direction OFC

The hours of 0000, 0300, and 0600 UTC exhibit wind directions from the south

and southeast directions (Figure A.33, Figure A.34, and Figure A.35). South southwest

winds are most common at 0900 UTC, but the fastest wind speeds can be located in the

north/northwest quadrant (Figure A.36). The wind direction varies at 1200 UTC with

overall slower speeds (Figure A.37). The wind speed begins to increase at 1500 UTC,

with maximum speeds located in the west/southwest and north/northwest directions

(Figure A.38). 1800 and 2100 UTC are comparable with the prevailing wind direction

from the south and fastest speeds occurring in the west directions (Figure A.39 and

Figure A.40). The diurnal cycle of the wind speed is consistent with the stabilizing of the

surface layer at nighttime hours, as a result the 10-m wind dies down, but the upper level

wind actually speeds up at this time because the surface is decoupled.

A.5 Monthly Variation of Wind Speed and Direction at RTC

In April 2006 the wind direction is primarily from the west/southwest containing wind

speeds as high as 6-8 ms-1. The overall strongest wind speeds are from the west at 8-10

ms-1 (Figure A.41). In May the prevailing wind is from the south and south/southeast,


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with winds in excess of 6-8 ms-1 from the north (Figure A.42). A southerly wind is

dominant in June with speeds in excess of 6-8 ms-1 (Figure A.43). The southerly wind

slows to peak averages of 4-6 ms-1 in July, August, and September 2006 (Figure A.44,

Figure A.45, and Figure A.46). In October, November, and December the wind

maximums can be found in the west and west/northwest directions at 6-8 ms-1 (Figure

A.47, Figure A.48, and Figure A.49). January consists of strong northerly winds peaking

at 6-8 ms-1 but also has a reoccurring wind from the south/southwest (Figure A.50). In

February, the wind gains momentum in the west and west/southwest directions with

speeds from 8-10 ms-1 (Figure A.51). March once again shows a dominant south wind

but winds maximums in all directions of 6-8 ms-1 (Figure A.52).

A.6 Diurnal Variation of Wind Speed and Direction at RTC

During the hours of 0000, 0300, 0600, and 0900 UTC at RTC the wind

predominately blows from the south, with the fastest speeds coming from directions

containing a northerly component (Figure A.53, Figure A.54, Figure A.55, and Figure

A.56). At 1200 UTC the wind is predominately slower from 2-4 ms-1, except when it is

from the west and north/northwest directions, where it reaches 6-8 ms-1 (Figure A.57).

The times of 1500 and 1800 UTC show further intensifying wind speeds from the west to

8-10 ms-1, with strong northerly wind speeds as well (Figure A.58 and Figure A.59). By

2100 UTC the prevailing wind is from the south with wind directions from the west,

reaching maximums of 8-10 ms-1 (Figure A.60).


121

Figure A.1: Wind Direction and Wind Speed for April 2006: ONC

Figure A.2: Wind Direction and Wind Speed for May 2006: ONC


122

Figure A.3: Wind Direction and Wind Speed for June 2006: ONC

Figure A.4: Wind Direction and Wind Speed for July 2006: ONC


123

Figure A.5: Wind Direction and Wind Speed for August 2006: ONC

Figure A.6: Wind Direction and Wind Speed for September 2006: ONC


124

Figure A.7: Wind Direction and Wind Speed for October 2006: ONC

Figure A.8: Wind Direction and Wind Speed for November 2006: ONC


125

Figure A.9: Wind Direction and Wind Speed for December 2006: ONC

Figure A.10: Wind Direction and Wind Speed for January 2007: ONC


126

Figure A.11: Wind Direction and Wind Speed for February 2007: ONC

Figure A.12: Wind Direction and Wind Speed for March 2007: ONC


127

Figure A.13: Wind Direction and Wind Speed for 0Z: ONC



128




129




130




131

Figure A.21: Wind Direction and Wind Speed for April 2006: OFC

Figure A.22: Wind Direction and Wind Speed for May 2006: OFC


132

Figure A.23: Wind Direction and Wind Speed for June 2006: OFC

Figure A.24: Wind Direction and Wind Speed for July 2006: OFC


133

Figure A.25: Wind Direction and Wind Speed for August 2006: OFC

Figure A.26: Wind Direction and Wind Speed for September 2006: OFC


134

Figure A.27: Wind Direction and Wind Speed for October 2006: OFC

Figure A.28: Wind Direction and Wind Speed for November 2006: OFC


135

Figure A.29: Wind Direction and Wind Speed for December 2007: OFC

Figure A.30: Wind Direction and Wind Speed for January 2007: OFC


136

Figure A.31: Wind Direction and Wind Speed for February 2007: OFC

Figure A.32: Wind Direction and Wind Speed for March 2007: OFC


137

Figure A.33: Wind Direction and Wind Speed for 0Z: OFC



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139




140




141

Figure A.41: Wind Direction and Wind Speed for April 2006: RTC

Figure A.42: Wind Direction and Wind Speed for May 2006: RTC


142

Figure A.43: Wind Direction and Wind Speed for June 2006: RTC

Figure A.44: Wind Direction and Wind Speed for July 2006: RTC


143

Figure A.45: Wind Direction and Wind Speed for August 2006: RTC

Figure A.46: Wind Direction and Wind Speed for September 2006: RTC


144

Figure A.47: Wind Direction and Wind Speed for October 2006: RTC

Figure A.48: Wind Direction and Wind Speed for November 2006: RTC


145

Figure A.49: Wind Direction and Wind Speed for December 2006: RTC

Figure A.50: Wind Direction and Wind Speed for January 2007: RTC


146

Figure A.51: Wind Direction and Wind Speed for February 2007: RTC

Figure A.52: Wind Direction and Wind Speed for March 2007: RTC


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Figure A.53: Wind Direction and Wind Speed for 0Z: RTC



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APPENDIX B

WRF FORECAST WIND ROSES

B.1 WRF Monthly Variation of Wind Speed and Direction ONC The month of April 2006 shows predominately south and west wind directions,

with the west direction exhibiting the strongest average wind speed at 6-8 ms-1. The

model fails to capture the strong west/southwest wind as seen in the observed data. It

also overestimates the frequency of the west wind (Figure B.1). In May and June the

average wind speed slows to 4-6 ms-1 and shifts the strongest component to the south in

the observations. However, WRF again underestimates the wind speed and fails to

capture the magnitude of the wind in the south/southwest and south/southeast directions

(Figure B.2 and Figure B.3). The wind direction in July is mainly from the

south/southeast, but WRF places the highest average wind in the south/southwest

direction. The July averages are underestimated in the model as well (Figure B.4). In

August, the observed wind continues to be from the south, while WRF shifts the direction

to south/southwest (Figure B.5). During September and October, WRF captures the

overall wind direction but underestimates the average wind speed (Figure B.6 and Figure

B.7). In November, the wind direction shifts to a prevailing south/southwest direction

but the fastest speeds are located the north/northwest and north directions in the

observations. During the same time period, WRF overestimates the wind coming from

the north, as well as under predicting the wind in the south/southwest direction (Figure

B.8). The December observed wind direction is predominantly west/southwest in

direction, with an additional strong westerly component. The model forecasts capture the

correct overall direction of the wind, but the magnitude of the wind is incorrect in both


152

the west and west/southwest directions (Figure B.9). The January 2007 model forecast

shows promise with the wind direction divided between the north and the

south/southwest directions as in the observational data (Figure B.10). When the spring

windy season begins in February, the WRF wind speeds are underestimated, with average

wind speeds only reaching 4-8 ms-1 from the prevailing west/southwest direction. The

maximum observed wind of 8-10 ms-1 from the west/northwest direction is missed

completely (Figure B.11). The March observational winds are weighted towards the

south with speeds ranging from 4-6 ms-1. However, WRF places the strongest winds in

the west/southwest direction at 6-8 ms-1 which is not present in the observations. WRF

has difficulty capturing the correct wind direction (Figure B.12).

B.2 WRF Diurnal Variation of Wind Speed and Direction ONC

The wind direction from the WRF model at 0000, 0300 and 0600 UTC agree with

the observations, as both have wind directions with the south and southeasterly

components. The only difference being the model underestimates the wind speed (Figure

B.13, Figure B.14, and Figure B.15). WRF fails to capture the magnitude of the wind

speed when compared to the observations at 0900, 1200, and 1500 UTC (Figure B.16,

Figure B.17, and Figure B.18). The 1800 and 2100 UTC WRF forecasts do capture the

array wind directions, but once again WRF underestimates the wind speed (Figure B.19

and Figure B.20).

B.3 WRF Monthly Variation of Wind Speed and Direction OFC

Wind directions in the observational data during April 2006 at locations OFC are

predominately from the south. WRF splits the wind direction during this month between

the south and west directions, and fails to capture the magnitude of the wind in the


153

west/southwest direction (Figure B.21). During May and June, the observed wind is

mainly from the south and south/southeast, while the model places the wind direction

predominantly from the south/southeast direction, and again underestimates the

magnitude in those directions (Figure B.22 and Figure B.23). In July and August, WRF

underestimates the wind speed and exhibits an overestimation of the wind coming from

the south/southwest direction (Figure B.24 and Figure B.25). During September WRF

fails to represent the correct magnitude and direction of the wind (Figure B.26). In

October the observational data contains winds from the south/southwest direction, most

frequently but the model forecasts fail again to capture accurate magnitudes (Figure

B.27). The increase in the wind speed during November from the north/northwest is

underestimated in WRF. WRF not only overestimates the north wind speed in

November, but it also underestimates the frequency (Figure B.28). In December,

locations OFC receive wind blowing mainly from the south/southwest, which WRF is

able to capture, but the model overestimates the wind speed for the westerly wind (Figure

B.29). The divisions of wind direction and speeds between the north, and

south/southwest during the month of January are represented poorly in WRF (Figure

B.30). As the season progresses into February, maximum observed wind speed increases

to 8-10 ms-1 from the west while the prevailing wind direction is from the

south/southwest. WRF is unable to represent either of these conditions (Figure B.31).

WRF shows even less skill in March in capturing the wind direction or speed present in

the observational data (Figure B.32).


154

B.4 WRF Diurnal Variation of Wind Speed and Direction OFC

The hours of 0000, 0300, and 0600 UTC exhibit wind directions from the south

and southeast directions, which is in agreement with the WTM observations (Figure

B.33, Figure B.34, and Figure B.35). At 0900 UTC, the model forecast lacks skill in

determining the correct wind speed and magnitude once again (Figure B.36). The wind

direction is represented incorrectly, although the correct magnitude is being shown by

WRF at 1200 and 1500 UTC (Figure B.37 and Figure B.38). The 1800 and 2100 UTC

WRF forecasts have the predominant wind direction from the south, which is present in

the observations, but the magnitude to too low (Figure B.39 and Figure B.40).

B.5 WRF Monthly Variation of Wind Speed and Direction at RTC

In April, May, June, and July of 2006, WRF shows no skill in capturing the speed

of the wind, although the wind directions represented in the data appear to be correct

when compared to the observations (Figure B.41, Figure B.42, Figure B.43, and Figure

B.44). WRF underestimates the wind speed in August, September, October, and

November of 2006 (Figure B.45, Figure B.46, Figure B.47, and Figure B. 48). During

the months of December and January, WRF fails to capture the strong northerly winds, in

addition to underestimating the strength of the west wind in December (Figure B.49 and

Figure B.50). In February, WRF underestimates the wind speeds in the west and

west/southwest directions (Figure B.51). March once again shows a dominant south

wind in the observational data, but WRF struggles, and overestimates the wind speed in

the west/southwest as well as forecasting the wrong wind directions (Figure B.52).

B.6 WRF Diurnal Variation of Wind Speed and Direction at RTC


155

During the hours of 0000, 0300, 0600, and 0900 UTC at RTC the model forecast

does well in representing the wind directions from the south, but underestimates the wind

speed at all times (Figure B.53, Figure B.54, Figure B.55, and Figure B.56). At 1200

UTC, WRF overestimates the occurrences of the wind from the south/southwest direction

(Figure B.57). The times of 1500 and 1800 UTC show further intensifying wind speeds

from the west to 8-10 ms-1 with strong northerly wind speeds as well which is

underestimated in the model forecast (Figure B.58 and Figure B.59). At 2100 UTC, the

prevailing wind from the south is underestimated with wind directions from the west,

reaching maximums of 8-10 ms-1 in the WRF forecasts (Figure B.60).


156

Figure B.1: Wind Direction and Wind Speed for April 2006: WRF Forecast ONC

Figure B.2: Wind Direction and Wind Speed for May 2006: WRF Forecast ONC


157

Figure B.3: Wind Direction and Wind Speed for June 2006: WRF Forecast ONC

Figure B.4: Wind Direction and Wind Speed for July 2006: WRF Forecast ONC


158

Figure B.5: Wind Direction and Wind Speed for August 2006: WRF Forecast ONC

Figure B.6: Wind Direction and Wind Speed for September 2006: WRF Forecast ONC


159

Figure B.7: Wind Direction and Wind Speed for October 2006: WRF Forecast ONC

Figure B.8: Wind Direction and Wind Speed for November 2006: WRF Forecast ONC


160

Figure B.9: Wind Direction and Wind Speed for December 2006: WRF Forecast ONC

Figure B.10: Wind Direction and Wind Speed for January 2007: WRF Forecast ONC


161

Figure B.11: Wind Direction and Wind Speed for February 2007: WRF Forecast ONC

Figure B.12: Wind Direction and Wind Speed for March 2007: WRF Forecast ONC


162

Figure B.13: Wind Direction and Wind Speed for 0Z: WRF Forecast ONC



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164




165




166

Figure B.21: Wind Direction and Wind Speed for April 2006: WRF Forecast OFC

Figure B.22: Wind Direction and Wind Speed for May 2006: WRF Forecast OFC


167

Figure B.23: Wind Direction and Wind Speed for June 2006: WRF Forecast OFC

Figure B.24: Wind Direction and Wind Speed for July 2006: WRF Forecast OFC


168

Figure B.25: Wind Direction and Wind Speed for August 2006: WRF Forecast OFC

Figure B.26: Wind Direction and Wind Speed for September 2006: WRF Forecast OFC


169

Figure B.27: Wind Direction and Wind Speed for October 2006: WRF Forecast OFC

Figure B.28: Wind Direction and Wind Speed for November 2006: WRF Forecast OFC


170

Figure B.29: Wind Direction and Wind Speed for December 2006: WRF Forecast OFC

Figure B.30: Wind Direction and Wind Speed for January 2007: WRF Forecast OFC


171

Figure B.31: Wind Direction and Wind Speed for February 2007: WRF Forecast OFC

Figure B.32: Wind Direction and Wind Speed for March 2007: WRF Forecast OFC


172

Figure B.33: Wind Direction and Wind Speed for 0Z: WRF Forecast OFC



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175




176

Figure B.41: Wind Direction and Wind Speed for April 2006: WRF Forecast RTC

Figure B.42: Wind Direction and Wind Speed for May 2006: WRF Forecast RTC


177

Figure B.43: Wind Direction and Wind Speed for June 2006: WRF Forecast RTC

Figure B.44: Wind Direction and Wind Speed for July 2006: WRF Forecast RTC


178

Figure B.45: Wind Direction and Wind Speed for August 2006: WRF Forecast RTC

Figure B.46: Wind Direction and Wind Speed for September 2006: WRF Forecast RTC


179

Figure B.47: Wind Direction and Wind Speed for October 2006: WRF Forecast RTC

Figure B. 48: Wind Direction and Wind Speed for November 2006: WRF Forecast RTC


180

Figure B.49: Wind Direction and Wind Speed for December 2006: WRF Forecast RTC

Figure B.50: Wind Direction and Wind Speed for January 2007: WRF Forecast RTC


181

Figure B.51: Wind Direction and Wind Speed for February 2007: WRF Forecast RTC

Figure B.52: Wind Direction and Wind Speed for March 2007: WRF Forecast RTC


182

Figure B.53: Wind Direction and Wind Speed for 0Z: WRF Forecast RTC



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