NASAICR-20 1 0-216289 

Peak Wind Tool for General Forecasting Phase II 

Joe H Barrett III 

ENSCO, Inc., Cocoa Beach, Florida 

NASA Applied Meteorology Unit, Kennedy Space Center, Florida 

September 2010 
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NASN CR-20 10-216289 

Peak Wind Tool for General Forecasting Phase II 

Joe H. Barrel/ Ill 

ENSCO, Inc., Cocoa Beach, Florida 

NASA Applied Meteorology Unit, Kennedy Space Center, Florida 

National Aeronautics and 

Space Administration 

Kennedy Space Center 

Kennedy Space Center, FL 32899-0001 

September 2010 
Acknowledgments 

The author thanks Mr. Mark Wheeler of the NASA Applied Meteorology Unit for providing expenise 

necessary to develop the MIDDS version of the tool. Personnel from the 45th Weather Squadron provided 

valuable feedback on the tool's user interface. 

Available from: 

NASA Center for AeroSpace Information 

71 15 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 

This repon is also available in electronic form at 

hltp:/lscience.ksc.nasa.gov/amul 
Executive Summary 

The expected peak wind speed of the day is an imponant forecast element in the 45th Weather 

Squadron's (45 WS) daily 24-Hour and Weekly Planning Forecasts. The forecasts are used for ground 

and space launch operations at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station 

(CCAFS). The 45 WS also issues wind advisories for KSC/CCAFS when they expect wind gusts to meet 

or exceed 25 kt, 35 kt and 50 kt thresholds at any level from the surface to 300 ft. The 45 WS forecasters 

have indicated peak wind speeds are challenging to forecast, panicularly in the cool season months of 

October - April. In Phase I of this task, the Applied Meteorology Unit (AMU) developed a tool to help the 

45 WS forecast non-convective winds at KSC/CCAFS for the 24-hour period of 0800 to 0800 local time. 

The tool was delivered as a Microsoft Excel graphical user interface (GUI). The GUI displayed the 

forecast of peak wind speed, 5-minute average wi nd speed at the time of the peak wind, timing of the 

peak wind and probability the peak speed would meet or exceed 25 kt, 35 kt and 50 kt. 

For the current task (Phase II), the 45 WS requested additional observations be used for the creation 

of the forecast equations by expanding the period of record (POR). Additional parameters were evaluated 

as predictors, including wind speeds between 500 ft and 3000 ft, static stability classification, Bulk 

Richardson Number, mixing depth, venical wind shear, temperature inversion strength and depth and 

wind direction. Using a verification data set, the AMU compared the performance of the Phase I and II 

prediction methods. Just as in Phase I, the tool was delivered as a Microsoft Excel GUI. The 45 WS 

requested the tool also be available in the Meteorological Interactive Data Display System (MIDDS). 

The AMU first expanded the POR by two years by adding tower observations, surface observations 

and CCAFS (XMR) soundings for the cool season months of March 2007 to April 2009. The POR was 

expanded again by six years, from October 1996 to April 2002, by interpolating 1000-ft sounding data to 

100-ft increments. The Phase II developmental data set included observations for the cool season months 

of October 1996 to February 2007. The AMU calculated 68 candidate predictors from the XMR 

soundings, to include 19 stability parameters, 48 wind speed parameters and one wind shear parameter. 

Each day in the data set was stratified by synoptic weather pattern, low-leve l wind direction, precipitation 

and Richardson Number, for a total of 60 stratification methods. Linear regression equations, using the 68 

predictors and 60 stratification methods, were created for the tool's three forecast parameters: the highest 

peak wind speed of the day (PWSD), 5-minute average speed at the same time (A WSD), and timing of 

the PWSD. For PWSD and A WSD, 30 Phase II methods were selected for evaluation in the verification 

data set. For timing of the PWSD, 12 Phase \I methods were selected for evaluation. 

The verification data set contained observations for the cool season months of March 2007 to April 

2009. The data set was used to compare the Phase I and II forecast methods to climatology, model 

forecast winds and wind advisories issued by the 45 WS. The model forecast winds were derived from the 

0000 and 1200 UTC runs of the 12-km Nonh American Mesoscale (MesoNAM) model. The forecast 

methods that performed the best in the verification data set were selected for the Phase II version of the 

tool. For PWSD and A WSD, linear regression equations based on MesoNAM forecasts performed 

significantly bener than the Phase I and II methods. For timing of the PWSD, none of the methods 

performed significantly bener than climatology. 

The AMU then developed the Microsoft Excel and MIDDS GUls. The GUls display the forecasts for 

PWSD, A WSD and the probability the PWSD will meet or exceed 25 kt, 35 kt and 50 kt. Since none of 

the prediction methods for timing of the PWSD performed significantly better than climatology, the tool 

no longer displays this predictand. The Excel and MIDDS GU ls display forecasts for Day-I to Day-3 and 

Day-I to Day-5, respectively. The Excel GUI uses MesoNAM forecasts as input, while the MIDDS GU I 

uses input from the MesoNAM and Global Forecast System model. Based on feedback from the 45 WS, 

the AMU added the daily average wind speed from 30 ft to 60 ft to the tool, which is one of the 

parameters in the 24-Hour and Weekly Planning Forecasts issued by the 45 WS. In addition, the AMU 

expanded the MIDDS GUI to include forecasts out to Day-7. 
Table of Contents 

Executive Summary .............................................................. ........................................................................ I 

Introduction ............................................................................................................... ............................ 7 

2 Data ............................................................................................................................ ............................ 8 

2. 1 Tower Data .................................................................................................................................... 8 

2.2 Surface Observations .............................. ...................................................................................... 9 

2.3 Sound ing Data .......................................... ..................................................................................... 9 

3 Development of Phase II Prediction Equations ................................................................................... 14 

3. 1 Synoptic Weather Pattern ................................................................................. ........................... 14 

3.2 Phase II Predictors ...................... ......... .......... ........... .................................................................. 14 

3.3 Prediction Equations fo r PWSD and A WSD .............................................................................. 18 

3.4 Timing of PWSD ........................................ ................................................................................ 20 

4 Independent Verification of Pred iction Methods ........................... ...................................................... 22 

4.1 Peak and Average Wind Speed .............................. ............. ............................. ........................... 27 

4.1.1 Comparing Phase 1 and JJ Methods to Climatology ............... ........................................ ........ 27 

4.1.2 Comparing Phase 1 and JJ Methods 10 Model Forecast Winds ............................................... 31 

4.1.3 Comparison of Phase I and JJ Peak Wind Speed Predictions to 45 WS Wind Advisories ...... 35 

4.2 Timing of the Peak Wind .... ............................ .... ...... ............. ..................................................... 35 

5 Peak Wind Tool GUls .............. ..................... ...................................................................................... 38 

5. 1 Microsoft Excel GU I ........ ........................................................................................................... 38 

5.1.1 Equation Development ......... .............................................................. ..................................... 38 

5.1.2 Using the Excel GUI ............................................................................................................... 42 

5.2 MIDDS GUI .............................. ................... ............. ............................ ...................................... 45 

5.2.1 Equation Development ....................................................... ..................................................... 46 

5.2.2 Point Model Data .................................................................................................................... 47 

5.2. 3 Using the MIDDS GUJ ........................................................... ................................................. 47 

6 Summary and Future Work ................................................................................................................. 49 

6.1 Summary .................................................................. ................................................................... 49 

6.2 Future Work ...................................................... .......................................................................... 50 

References ................ .. ................................................................................................................................. 5 I 

List of Acronyms .......... .......................................... .................................................................................... 52 

2 
List of Figures 

Figure I. A map showing the towers used in the task. Except for tower 0300 (lower-right, in blue), 

only the yellow- and red-colored towers were used .................................................................. 9 

Figure 2. Comparison of I OO-ft sounding data on I January 2003 to interpolated data using Method I 

(left) and Method 2 (right) ...................................................................................................... 10 

Figure 3. Monthly averages of height versus u-wind component, using interpolated (dashed) and 100-ft 

data (solid). The interpolated data used Method I (left) and Method 2 (right). The data are 

from the cool season months in the period October 2002 - April 2008 ................................. I I 

Figure 4. Monthly averages of height versus MAE in u-wind component, using interpolated and 100-ft 

data. The data were interpolated using Method I (left) and Method 2 (right) . .... ................... I I 

Figure 5. The December plots of height versus MAE in u-wind component, using interpolated and 100-

ft data. The data were interpolated using Method I (left) and Method 2 (right). The 2002-

2007 December average is displayed by the black lines ......................................................... 12 

Figure 6. Height versus MAE in u- (left) and v-wind (right) components, using Method I (red) and 

Method 2 (green) interpolated data. The interpolated data were averaged over all months in 

the POR .......................................................................... .. ................................... .................... 12 

Figure 7. Height versus MAE in temperature (left) and dew point (right), using Method I (red) and 

Method 2 (green) interpolated data. The interpolated data were averaged over all months in 

the POR ........ ........................................................................................................................... 13 

Figure 8. MAE values for least-squares single linear regression equations for PWSD (left) and A WSD 

(right). The MAE is shown for the stability parameters (S I -S 19), wind speed parameters 

(W 1-W48) and wind shear parameter (H I). Mean MAE (blue) is the average of all 

stratification methods, while Minimum MAE (red) is the lowest of all stratification 

categories ................................................................................................................................ 18 

Figure 9. MAE values for robust single linear regression equations for PWSD (left) and A WSD (right). 

The MAE is shown for the stability parameters (S I -S 19), wind speed parameters (WI -W48) 

and wind shear parameter (H I). Mean MAE (blue) is the average of all stratification 

categories, while Minimum MAE (red) is the lowest of all stratification categories .............. 18 

Figure 10. Mean MAE values for least-sq uares (blue) and robust (red) single linear regression equations 

for PWSD (left) and A WSD (right). The MA E is shown for the 60 stratifications (x-axis) ... 19 

Figure I I. Mean MAE values for least-squares (blue), robust (red) and least-trimmed squares (green) 

multiple linear regression equations for PWSD (left) and A WSD (right). The MAE is shown 

for the 60 stratifications (x-axis) ..................................... .. ...................................................... 20 

Figure 12. MAE values for least-squares (left) and robust (right) single linear regression equations for 

timing of the PWSD. The values are shown for the stability parameters (S I -S I 9), wind speed 

parameters (W 1-W48) and wind shear parameter (H I). Mean MAE (blue) is the average of 

all stratification methods. Minimum MAE (red) is the lowest of all stratification categories.20 

Figure 13 . Mean MAE values for single (left) and multiple (right) linear regression equations for timing 

of the peak wind. The least squares and robust regressions are in blue and red, while the leasttrimmed regressions are in green. The values are shown for the 60 stratifications (x-axis) ... 2 I 

Figure 14. Mean Error for PWSD (left) and A WSD (right) on non-precipitation days For the Phase II 

methods, points I -6 (on x-axis) correspond to stratification methods SM I -SM6, respectively. 

The legend describes the colors corresponding to the Phase II linear regression types. Refer to 

3 
Table 3 for a description of the stratification methods and linear regression types. The 

cl imatology methods are plotted on points 1-4. The Phase I method is plotted on point I. ... 27 

Figure 15. Mean Error for PWSD (left) and A WSD (right) on precipitation days, for the climatology and 

Phase I and II methods. Refer to Figure 14 for a description of the x-axis ............................. 28 

Figure 16. MAE fo r PWSD (left) and A WSD (right) on non-precipitation days, for the climatology and 

Phase I and II methods. Refer to Figure 14 for a description of the x-axis ............................. 28 

Figure 17. MAE for PWSD (left) and A WSD (right) on precipitation days, for the climatology and 

Phase I and II methods. Refer to Figure 14 for a description of the x-axis ............................. 29 

Figure 18. Mean Error for PWSD on days in which the forecast 's absolute error is within 5 kt (left) and 

greater than 5 kt (right). Refer to Figure 14 for a description of the x-axis ............................ 30 

Figure 19. Mean Error for A WSD on days in which the forecast's absolute error is within 5 kt (left) and 

greater than 5 kt (right). Refer to Figure 14 for a description of the x-axis . .. .... ..................... 30 

Figure 20. Average difference between the PWSD and the tower-average peak speed, on days in which 

the forecast's absolute error is within 5 kt (left) and greater than 5 kt (right). Refer to Figure 

14 for a description of the x-axis ...................................................... ... .................................... 3 I 

Figure 2 1. Mean Error for PWSD and A WSD. The strongest 24-hour 0000 UTe MesoNAM forecast 

winds are plotted on points 1-20, in black. The 0000 UTe MesoNAM linear regression 

forecasts are plotted on points 1-20, in light blue. The climatology methods are plotted on 

points 1-4, in dark blue. The Phase I method is plotted on point I (in red), and the Phase II 

methods are plotted on points 1-30 (in yellow). Refer to Table 3 for a description of the Phase 

II methods .................................................... ........................................................................... 32 

Figure 22. MAE for PWSD and A WSD. Refer to Figure 21 for a description of the x-axis . .................. 33 

Figure 23. Mean Error for PWSD on prec ipitation (left) and non-precipitation days (right). Refer to 

Figure 2 1 for a description of the x-axis .............................................................. ................... 33 

Figure 24. Mean Error for A WSD on precipitation (left) and non-precipitation days (right). Refer to 

Figure 2 1 for a description of the x-axis . ................................................................................ 33 

Figure 25. MAE for PWSD on precipitation (left) and non-precipitation days (right). Refer to Figure 21 

for a description of the x-axis ........................................................ .......................................... 34 

Figure 26. MAE for A WSD on precipitation (left) and non-precipitation days (right). Refer to Figure 2 1 

for a description of the data points .......................................................................................... 34 

Figure 27. MAE for PWSD (left) and AWSD (right), for 0000 and 1200 UTe MesoNAM regression 

forecasts. Points 1-1 7 depict model levels 2- 18, and points 18-20 depict the strongest winds 

in the lowest 1000-, 2000-, and 3000-ft of the model. The legend shows the color 

corresponding to each method . .. ............ ................................................................................. 34 

Figure 28. Mean Error (diamonds) and MAE (circles) for timing of the peak wind. The Phase I method 

is plotted on point I, Phase II methods on points 1-12, climatology methods on points 1-4 and 

0000 UTe MesoNAM winds on points 1-20 . ... ................................................................. ..... 37 

Figure 29. The Mean Error (diamonds) and MAE (triangles) fo r the timing of the peak wind by the 0000 

and 1200 UTe MesoNAM winds ........................................................................................... 37 

Figure 30. MAE for PWSD on precipitation (left) and non-precipitation (right) days, for Day-I to Day-3 

forecasts. Forecasts are from linear regression equations using the 0000 UTe MesoNAM 

forecast winds. Points 1-1 7 depict model levels 2-18, and points 18-20 depict the strongest 

winds in the lowest 1000-,2000- and 3000-ft of the mode!.. .................................................. 39 

4 
Figure 31. MAE for PWSD on precipitation (left) and non-precipitation (right) days, for Day-I to Day-3 

forecasts. Forecasts are ITom linear regression equations using the 1200 UTe MesoNAM 

forecast winds. Points 1-17 depict model leve ls 2-18, and points 18-20 depict the strongest 

winds in the lowest 1000-, 2000- and 3000-ft of the model.. ...................... ............................ 39 

Figure 32. MAE for A WSD on prec ipitation (left) and non-precipitation (right) days, for Day-I to Day-3 

forecasts. Forecasts are from linear regression equations using the 0000 UTe MesoNAM 

forecast winds. Points 1-1 7 depict model levels 2-1 8, and points 18-20 depict the strongest 

winds in the lowest 1000-, 2000- and 3000-ft of the model.. .................................................. 40 

Figure 33. MAE for A WSD on precipitation (left) and non-precipitation (right) days, for Day-I to Day-3 

forecasts. Forecasts are from linear regression equations using the 1200 UTe MesoNAM 

forecast wi nds. Points 1-1 7 depict model leve ls 2-18, and points 18-20 depict the strongest 

winds in the lowest 1000-,2000- and 3000-ft of the model.. .................................................. 40 

Figure 34. Intro worksheet to start the Excel version of the Peak Wind Tool.. ........................................ 42 

Figure 35. Dialog box used to select a MesoNAM input file ................................................................... 43 

Figure 36. Dialog box used to select the forecast day . ............................................................................. 44 

Figure 37. GUI displaying the output from the Excel version of the Peak Wind tool. The top section 

shows the predicted PWSD, the middle section shows the predicted A WSD, and the booom 

section shows the probability the PWSD will meet or exceed 25 kt, 35 kt and 50 kt. ............ 45 

Figure 38. MAE for daily average speed for forecasts from 0000 (left) and 1200 UTe (right) 

MesoNAM. Forecasts for precipitation days are plooed on poi nts I, 4 and 7 of the x-axis. 

Forecasts for non-precipitation days are plooed on points 2, 5 and 8, and forecasts for all days 

are plooed on points 3, 6 and 9 ............................................................................................... 47 

Figure 39. MIDDS version of the Peak Wind Tool showing forecasts from the 7 July 2010 model runs. 

Forecasts for precipitation days have the header "Precip", while non-precipitation days have 

the header "NoPrecip". The header "All" includes days with and without precipitation ....... 48 

5 
List of Tables 

Table I . ... ......................................................... ... ........................................................ ................................ 16 

Table 2 . ............................................... .............................................................. .............. ............................ 17 

Table 3. Phase II methods verified for PWSD, A WSD and timing of the PWSD. Methods in red were not 

verified for the timing of the PWSD ........................................................................................... 23 

Table 4. Stratification categories used in Phase II methods. Wind direction was based on the vector 

average wind in the lowest 300 ft of XMR sounding. The stratification methods are described in 

Table 3 ........................................................................................................................................ 24 

Table 5. Stratification category and predictors for each PWSD equation using single linear regression. 

The stratification categories are described in Table 4 ................................................................. 25 

Table 6. Stratification category and predictors for each PWSD equation using multiple linear regression. 

The stratification categories are described in Table 4 ................................................................. 26 

Table 7. Average heights and standard deviations of the MesoNAM venicallevels used in the 

verification data .. ...... ................................................................. .................................................. 32 

Table 8. Verification for days in which the highest 45 WS wind warning/advisory was 25-34 kt (left) and 

35-49 kt (right). Phase II methods are shown in blue ............................................................ ..... 35 

Table 9. Climatology methods for predicting timing of the peak wind, based on the mean peak winds in 

a previous AMU task (Lambert 2002). The "average wind speed" is the average of all the 

towers used in the previous AMU task, and the "maximum wind speed" is the maximum of all 

the towers .................................................................................................................................... 36 

Table 10. Prediction equations for PWSD, based on linear regressions in which the predictor is the 

strongest 24-hour wind speed at the MesoNAM model level, and the predictand is the PWSD. 

Refer to Table 7 for the height of each modelleve!... ................................................................. 41 

Table II . Prediction equations for A WSD, based on linear regressions in which the predictor is the 

strongest 24-hour wind speed at the MesoNAM model level, and the predictand is the A WSD. 

Refer to Table 7 for the height of each model leve!... ................................................................. 41 

Table 12. MAE (kt) for daily average speed. "All Days" is all days in the verification data set. "W. Avg" 

is the weighted average of precipitation and non-precipitation days. "00 UTC" is the 0000 UTC 

MesoNAM and "12 UTC" is the 1200 UTC MesoNAM .............................. .. ............ ................ 47 

6 
1 Introduction 

The expected peak wind speed of the day is an important element in the 45th Weather Squadron's 

(45 WS) daily 24,Hour and Weekly Planning Forecasts. The forecasts are used for ground and space 

launch operations at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). 

The 24-Hour Forecast is valid from 0800 to 0800 local time the next day, and it is broken into six 4-hour 

time blocks. The Weekly Forecast is for the next six days starting with the next day and is divided into 

12-hour time blocks. The 45 WS also issues wind advisories for KSC/CCAFS and Patrick Air Force Base 

(PAFB) when they expect peak gusts to meet or exceed 25 kt, 35 kt and 50 kt thresholds at any level from 

the surface to 300 fl. 

The 45 WS forecasters have indicated peak wind speeds are challenging to forecast, particularly in 

the cool season months of October - April. In Phase I of this task (Barrett and Short 2008), the Applied 

Meteorology Unit (AMU) developed a tool to help them forecast non-convective winds during the cool 

season for the 24-Hour Forecast. The tool forecasts the highest peak wind speed, the timing of the peak 

speed and the 5-minute average wind speed at the time of the peak wind from the surface to 300 fl on 

KSC/CCAFS. In addition, it provides the probability the peak speed will meet or exceed the wind 

advisoty thresholds. The tool calculates its forecasts based on the morning CCAFS sounding (XMR), the 

synoptic weather pattern and whether or not precipitation is expected over KSC/CCAFS during the 24-

hour period. 

For Phase II, the 45 WS requested additional observations be used in the creation of the forecast 

equations by expanding the period of record (POR). Additional parameters were evaluated, including 

wind speeds between 500 ft and 3000 fl, static stability classification, Bulk Richardson Number, mixing 

depth, vertical wind shear, inversion strength and depth and wind direction. Using a verification data set, 

the AMU compared the Phase I and II prediction methods to climatology and model forecasts. The 

methods that performed the best in the verification data set were selected for the Phase II tool. As in 

Phase I, the tool was delivered as a Microsoft Excel graphical user interface (GUI). The 45 WS requested 

the AMU also make the tool available in the Meteorological Interactive Data Display System (MIDDS), 

their main weather display system, to provide the ability to produce 5-day forecasts quickly. 

7 
2 Data 

In Phase I and Phase II, three types of observations were used: 5-minute observations from the 

KSC/CCAFS tower network, hourly and special surface observations from the Shuttle Landing Facility 

(SLF) and XMR soundings. The tower observations and soundings were obtained from the Range 

Technical Services Contractor, Computer Sciences Raytheon (CSR). Surface observations were obtained 

from the 14th Weather Squadron Strategic Climatic Information Service. In Phase I, the data set incl uded 

observations from the cool season months of October 2002 to February 2007. In Phase II, cool season 

data from March 2007 to April 2009 were added to the existing data set. Days in which east-central 

Florida was under the influence of a tropical cyclone were removed from the data set, si nce only coolseason weather phenomena were of interest. 

The prediction equations in the Phase I tool required sounding data in 100-ft increments. To increase 

the size of the Phase II data set even further, the AMU evaluated whether data prior to October 2002 

could be added by interpolating 1000-ft sounding data down to 100-ft increments. Sounding data between 

October 1996 and April 2002 are only available in 1000-ft increments, while data between October 2002 

and April 2009 are available in both 1000-ft and 100-ft increments. The AMU interpolated the 1000-ft 

data from October 1996 to April 2008 to 100-ft increments, and then compared the interpolated data to 

the 100-ft data for the period October 2002 to April 2008. Since no significant differences were found 

between the interpolated and 100-ft data, the interpolated data from October 1996 to April 2002 were 

included in the Phase II data set. The 100-ft data were used for the period October 2002 to April 2009. 

The tool predicts the peak wind in the 24-hour period 0800 to 0800 local time. The tool's prediction 

eq uations used observati ons from the 24-hour period 1300 to 1300 UTC, since the majority of days in the 

cool season were in Eastern Standard Time (EST). 

2. 1 Tower Data 

Data were collected from all 32 towers used to verify wind advisories issued by the 45 WS. Their 

locations are shown in Figure I. Tower 0300, which is not used to verify wind advisories, was also used 

in order to provide better coverage of the southern portion of KSC. The meteorological variables in the 

tower data set included temperature and relative humidity, 5-minute average and peak wind speeds, 5-

minute average and peak wind directions, and the standard deviation of the 5-minute average wind 

direction over a 30-minute period. Wind speed and direction data were sampled every second. The peak 

wind was the maximum I-second speed in the 5-minute period. Case and Bauman (2004) provides a 

detailed description of the KSC/CCAFS tower network instrumentation. Since 45 WS wind advisories 

apply to winds up to 300 ft, only tower wind observations up to 300 ft were used in this task. 

Lambert (2002) describes the automated quality control (QC) algorithms and associated Fortran 

software to flag bad tower data prior to analysis. The AMU rewrote the QC software in the Java 

programming language to make it more portable and easier to maintain. For example, Java software does 

not need to be recompiled in order to run on multiple computer operating systems. The Java version uses 

a configuration text fil e to store values previously hard-coded in the software, such as the years in the 

POR and the list of wind tower identifiers. This allows the user to easily change the parameters by 

modifying the configuration file . Previously, the user had to edit the source code and recompile the 

software. The AMU also fixed a bug in the Fortran software, which did not properly handle the case in 

which a sensor's height changed during the POR. In addition, a supplementary QC algorithm was added 

to check for repeating observations that can occur during a sensor outage. 

After the QC software was run, the peak wind speed of the day (PWSD) was determined from the 33 

towers at all levels up to 300 ft. The 5-minute average wind speed at the tower reporting the PWSD was 

also recorded. The AMU manually examined each PWSD of 60 kt or greater to check for erroneous 

outliers. If the observations from the tower appeared erroneous, then the highest daily peak wind speed 

from one of the other towers was used. 

8 
,-

.6.2202 

.6.'0'2 

.6."" 

~"" 

.6."" 

Woo Tower locations 

KSC WomingiAdvisoly Verfic-. 

IIl22 .6. CCAFS _glActMory VOIIicalion 

.6. Non-_glActMory Towers 

.. " 

.... 

-'" 

Figure I. A map showing the towers used in the task. Except for tower 0300 

(lower-right, in blue), only the yellow- and red-colored towers were used. 

2.2 Surface Observations 

The SLF hourly and special observations were used to determine if precipitation occurred at or within 

5 NM (the vici nity) of the SLF at least once during each 24-hour period. Each day was then classified as a 

precipitation- or non-precipitation day. 

2.3 Sounding Data 

The XMR soundings were used to create the candidate predictors for PWSD, timing of the PWSD 

and the 5-minute average wind at the time of the PWSD (herealler referred to as A WSD). The soundings 

included wind speed and direction, pressure, temperature and dew point. Only soundings between 0930 

UTe and 1230 UTe were used, since soundings outside this time period may be unrepresentative due to 

diurnal changes in temperature, humidity and wind. If a sounding did not extend to at least 15,000 II mean 

sea level (MSL), the sounding was eliminated from the data set. If there were multiple soundings between 

0930 UTe and 1230 UTe, the latest one was used. 

All of the sounding data needed to be in 100-11 increments to calculate the predictors. While data 

from October 2002 to April 2009 were already available in 100-11 increments, data between October 1996 

and April 2002 were only available in 1000-11 increments. If the 1000-11 sounding data could be 

interpolated to 100-11 increments without a significant loss in accuracy, the interpolated sounding data 

would be used in the Phase II data set. 

The 1000-11 sounding data from October 1996 to April 2008 were interpolated to 100-11 increments 

up to 15,000 II MSL, using two different methods. In Method I, data at the 1000-11, sign ificant and 

9 
mandatory levels were linearly interpolated to 100-ft increments. In Method 2, only the significant and 

mandatory level data were linearly interpolated to 100-ft increments. A significant level occurs when 

there is a significant change in temperature or wind with height. Therefore, the number and heights of the 

significant levels were variable. Up through 15,000 ft MSL, mandatory levels in the XMR soundings 

were at 1000 mb, 950 mb, 900 mb, 850 mb, 800 mb, 750 mb, 700 mb, 650 mb and 600 mb. In July 2002, 

another mandatory level was added at 925 mb. 

The AMU compared the interpolated data to the 100-ft sounding data for the period October 2002 to 

April 2008. First, the AMU compared individual soundings from a small sample of seven days in the 

2002/2003 cool season (October 2002 to April 2003). As an example, Figure 2 compares the 100-ft data 

in the I January 2003 XMR sounding to the interpolated data usi ng Method I and 2. The Method I and 2 

wind speeds are nearly identical, except around 6000 ft MSL. The following conclusions were reached 

after analyzing data from the seven soundings: 

70 

I ~ 60 -so 

-g 40 

~ 30 ... 

~ 20 

~ 10 

0 

• The 100-ft data were usually similar to the interpolated data, 

• The interpolated data were usually closer to each other than to the 100-ft data, 

• The differences between the Method I and Method 2 interpolated data were largest at 

multiples of 1000 ft MSL, 

• At multiples of 1000 ft MSL, the Method I interpolated and 100-ft data were usually exactly 

the same, especially for temperature, dew point, and relative humidity, 

• Due to the high vertical variability in dew point, there were occasionally large (on the order 

of 5 0c) differences between the interpolated and I OO-fi data, and 

• There were occasionally large differences between the interpolated and 100-fi data in wind 

speed and direction (on the order of 5 kt and 90°). ~~-

1/1/2003 XMR 

" 

Sounding, Methodl 1/1/2003 XMR Sounding, Method2 

1,-4 70 

I I lr' r 1 1 ~60 r '\. 

I 1 :;so 

~ 40 

- ~ -jJ 1-+ 30 t- l-Ii I I I 'E 20 v.--l 1 , 

1 1 ~ 10 I 0 

" '" ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ !2 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ... 

~ ~ 001 ~ .......... ~ ~ 

Altitude inlt (MSLI ~ lDO-ft data Altitude in It (MSL) ~ lDO-ft data 

et hodl Method2 I 

Figure 2. Comparison of 100-ft sounding data on I January 2003 to interpolated data using Method I 

(left) and Method 2 (right). 

Next, the monthly averages for the interpolated and 100-ft data were compared for the cool season 

months in the period October 2002 to April 2008. The averages were practically equal for each month, 

indicating no significant biases in the interpolated data. As an example, Figure 3 compares the u-wind 

component in the I OO-ft and interpolated data for each month. The figure shows no significant differences 

between the interpolated (dashed lines) and 100-ft (solid lines) data in the monthly averages. 

10 
All data (100-1t solid, Method1 dashed) 

40 - Jan-IOO 

- Feb-IOO 

B: 30 Mar-IOO B: ~ ~1·100 c 1: .. 20 Oct-IOO .. c c 

&. Nov-IOO &. E E 0 De c-IOO 0 u 10 u .., .., c ... _- Jan-Ml c ~ 

--_ .. Feb-Ml ~ 

• 0 • ... _- Mar-Ml 

.J ____ . Apr-Ml 

-10 ._--- Oct-M1 

~gg g 88 8 88 g 88 gg gg ..... Nov-Ml 

"""NM~II\ID""OOcnO"'NM~U'!I 

L ... """ """...-I", """ ----- Oe c-Ml 

-- ---

40 

30 

20 

10 

0 

-10 

All data (100-1t solid, Method2 dashed) Tn 

iii 

I I 

m- i'\T: 

c·- r r 

/ 

v V' :::::1 v. ;- -

t:t; ':f / ;4;:f~~:r ·· .' 

v.:/v .- ' " .' " 

:y ".-" , .. - - " " .' .-

I I 

- Jan-IOO 

- Feb-IOO 

Mar-lOO 

- Apr-loo 

Oct-IOO 

Nov-lOO 

Dec-lOO 

----- Jan-M2 

----- Feb-M2 

----- Mar-M2 

____ A Apr-M2 

-._-- Oct-M2 

IDOOOOOOOOOOOOOOO "':5:5:5:5:5:5:5:5:5 0:5:5:5:5:5 ----. Nov·M2 .... N M oot U'!IID"" GO en ~::=:::: ~ ~ ~ ... __ Dec-M2 

Figure 3. Monthly averages of height versus u-wind component, using interpolated (dashed) and 100-ft 

data (solid). The interpolated data used Method I (left) and Method 2 (right). The data are from the cool 

season months in the period October 2002 - April 2008. 

The AMU compared the interpolated and 100-ft data usi ng mean absolute error (MAE). The MAE is 

the mean of the absolute value of the differences between the interpolated and 100-ft data. The MAE 

monthly averages for wind varied by height, but were generally between 0.5 and 1.0 kt. As an example, 

Figure 4 shows the MAE monthly averages of the u-wind component versus height. The figure shows 

very little scatter in the monthly averages, and the MAE of the Method I data was smaller than the 

Method 2 data. The MAE minimums correlated to the mandatory pressure levels. There were also 

secondary MAE min imums for Method I at multiples of 1000-ft MSL. While the monthly averages of 

MAE contained little scatter, MAE for ind ividual months had more scatter. For example, Figure 5 shows 

a fairly large amount of scatter in the MAE for the u-wind component in December. 

MAE of u-wind component in Methodl 

2,5 I I I I I I I I I - Jan 

r----- ,I --+-t • -,---, - Feb 

Mar 

- Apr 

1A-tJi!+-+-- -hif.. 

2 

1 

Oct 

0.5 - Nov 

o - Dec ~ooooooooooooooo 

.... ggggggggggggggg 

.... NM'ltU'l\.D,...COC'lC.-4Nrt'I'ltU'l .-4.-4.-4.-4.-4.-4 

2.5 

2 

g 1.5 w 

~ 1 

0.5 

MAE of u-wind component in Method2 

I I I I I - Jan 

- feb 

.c-- ~ >/A 

~~M \; 8. 

~ 

- Mar I - Apr 

Oct 

- Nov 

Figure 4. Monthly averages of height versus MAE in u-wind component, using interpolated and 100-ft 

data. The data were interpolated using Method I (left) and Method 2 (right). 

I I 
2.5 

2 

B:1.S 

"' 

~ 1 

0.5 

o 

December MAE of u-wind component, Methodl 

- Oec2002 

H+-H++H~-~-I--1 --Oec2003 

Oec2004 

- Oec2005 

Oec2006 

,-+++-H++-H++-H+-i --Oec2007 

... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 o-OecAil .... 000000000000000 

000000000000000 

.... NMOIt"'''O,...OOcnO .... NMOIt''' ........................ 

2.5 

2 

_ ~ 1.5 

"' " :Ii 1 

0.5 

December MAE of u-wind component, Method2 

- Oea002 

- Oea003 

Dec2004 

, - DeaOOS 

Oea006 

- Oec2007 

~gg ggggg g ggg gggg -OecAll 

o '-'-'--'-'---.L.C-'---L.L--'---'-'--L..J 

000000000000000 

.... NM'ltIl'l"O,...OOcnO .... NMOIt'" ......-1 ................ 

Figure 5. The December plots of height versus MAE in u-wind component, using interpolated and 100-ft 

data. The data were interpolated using Method I (left) and Method 2 (right). The 2002-2007 December 

average is displayed by the black lines. 

When averaging the MAE over the cool-season months from 2002 to 2008, comparing the MAE of 

Method I and 2 showed a clearer pattern. Figure 6 compares the MA E of the u- and v-wind components 

using the Method I and 2 interpolated data. The MAE pattern was practically the same between the uwind (left) and v-wind (right) components, and the MAE of Method I was lower than Method 2. At the 

mandatory levels the MAE was the same in the two interpolation methods, which was expected since both 

methods used mandatory level data. The difference in MAE between the two methods was maximized 

near multiples of 1000-ft MSL, unless a mandatory level happened to be nearby. 

Figure 7 compares the MAE of the temperature and dew point data. Method I had the lowest MA E 

for temperature and dew point. The MAE values were small for Method I and 2, on the order of 0.1 °C 

and 0.2-0.6 °C for temperature and dew point, respectively. 

o 

MAE of u-wind, averaged over all months MAE of v-wind, averaged over all months 

2.5 , 

Method~ 1 I I I- Methodl l 

Method2 2 I I Method2 11 

-------1-1- B: 1.5 '" 'if ,/ r"'1 l 'j Ii "' r'l i If'" '" r f\ '" " 1 f'o 

IV-"'V\ .r\ NY 'V ~ V r--Y :Ii 

I~ v'V\. 1\. rw (\ V W V V \J fr- 0.5 

I I I 0 I I 

2.5 

I B: 1.: 

"' 

~ 1 

0.5 

... ~ g g 00 g 0 0 0 §§§§§§ ... 0 0 g 0 0 0 0 0 g g g g g 0 g ... 00 0 0 0 ... 0 0 0 00 0 0 0 

00 00 0 0 0 0 0 0 0 0 00 0 0 i 8 ~ ~ g 0 0 

N '" .. '" ... .... 00 '" o .... N "" 'It U"I ... N '" .. '" ... .... 00 .. .... .... .... .... .... .... '" .... .... .... .... ... ... -----

Figure 6. Height versus MAE in u- (left) and v-wind (right) components, using Method I (red) and 

Method 2 (green) interpolated data. The interpolated data were averaged over all months in the POR. 

12 
j: MAE of temperature averaged over all months MAE of dew point averaged over all months 

1 1 1 I I I 

1 I I I Methodl ~ Methodl 

v-.. 11'\ 1(\1 

0.8 - Method2 

~O 6 

- Method2 '\ J r, p- f'" 

~O.l r' w r' (" /', r f\ 

:; N i\ IA ~ ~ ~ ~ y ~ ~ 0.4 

'A . Irl ~ tJ rlI 

0.2 IfV' VV IV 

I 0 '---'- __ L- 0 

OD 0 0 0 000 0 0 0 §§§§§§ OD 0 g 0 0 0 g 0 0 0 0 0 0 0 0 0 

~ g 0 0 g g g 0 g 0 ~ 0 0 0 0 0 0 0 0 0 0 0 0 0 

0 0 0 0 0000 0 0 0 0 0 0 0 0 0 0 0 

~ N '" '" '" OD r- oo '" o r4 N 1'1"1 o::t U"I .-I N f'I'I o::t '" OD r- oo '" 0 ~ N 

.... .... .-4 .... """ .... '" '" '" ~ ~ ~ ~ ~ ~ 

Figure 7. Height versus MAE in temperature (left) and dew point (right), using Method I (red) and 

Method 2 (green) interpolated data. The interpolated data were averaged over all months in the POR. 

For temperature and dew point, the MAE of Method I was near 0 °C at mUltiples of 1000 ft MSL. On 

the other hand, for u- and v-wind components, the MAE of Method I was between 0.5 and 1.0 kt at 

multiples of 1000 ft MSL. The AMU investigated the reasoning behind this. According to CSR staff, the 

1000-ft winds are smoothed by averaging over a 1000-ft interval (Mr. Rick Kulow of CSR, personal 

communication). For example, the 3000 ft wi nd is derived by smoothing the raw wind data from 2500 ft 

to 3500 ft. The other meteorological variables, such as temperature or dew point, are not smoothed in the 

1000-ft data. For all of the meteorological variables, raw data (i.e. not smoothed) are used for the 

mandatory, significant and 100-ft data. 

The AMU made the following conclusions after analyzing the monthly averages of MAE for the 

interpolated data: 

• For all of the meteorological variables except pressure, a minimum in MAE occurred at the 

mandatory levels and a maximum in MAE occurred halfway between mandatory levels, 

• A secondary minimum in MAE occurred in the Method I interpolated data at mUltiples of 

1000 ft MSL, 

• For pressure, the maximum in MAE occurred at mandatory levels and the minimum occurred 

hal fway between the mandatory levels, and 

• For all meteorological variables, the Method I interpolated data's MAE was lower than the 

Method 2 interpolated data. 

Since no significant differences were found between the interpolated and 100-ft sounding data, the 

AMU used interpolated sounding data for the October 1996 to April 2002 period and 100-ft data for the 

October 2002 to April 2009 period. Method I was used to interpolate the sounding data since it had the 

lowest MAE values. 

13 
3 Development of Phase II Prediction Equations 

The QC'd observation data were used to develop prediction equations for each forecast parameter. 

The developmental data set, consisting of cool-season observations from October 1996 to February 2007, 

was used to develop the prediction equations. The verification data set, contai ning cool-season 

observations from March 2007 to April 2009, was later used to independently verify the performance of 

the Phase 11 equations. 

3.1 Synoptic Weather Pattern 

The AMU stratified each day in the data set by synoptic weather pattern to develop separate 

prediction equations for each weather pattern. The surface synoptic weather pattern at 1200 UTC was 

analyzed for each day using surface charts obtained from the National Weather Service (NWS) 

Hydrometeorological Prediction Center's short-term and long-term archives 

(http://docs.lib.noaa.gov/rescue/dwm/datarescuedailyweathermaps.htm!). Each day was categorized 

into one of the following: 

• High pressure over Florida, with light and variable winds across east-central Florida, 

• High pressure to the north or west of central Florida, with northwest, north, northeast, or east 

winds across east-central Florida, 

• High pressure to the south or east of central Florida, with southeast, south, southwest, or west 

winds across east-central Florida, 

• Cold front over north Florida, 

• Cold or stationary front over central Florida, 

• Cold or stationary front over south Florida or Florida Keys, 

• Tropical storm or hurricane affecting Florida, and 

• Surface weather map unavailable. 

Only one day had a missing surface weather map. The days in which a tropical storm or hurricane 

affected Florida were removed from the data set to make the analysis more representative of the cool 

season. 

3.2 Pbase II Predictors 

The AMU wrote and executed Perl scripts to calculate the candidate predictors from the XMR 

soundings. The predictors were evaluated for their skill in predicting the PWSD, A WSD and timing of the 

PWSD. The predictors included 68 wind shear, stability and wind speed parameters. There were 19 

stability parameters (identifiers in parentheses): 

• Temperature inversion depth and strength (S I - S2), 

• Differences in temperature between 1000 ft and 16, 100, 200, 300,400 and 500 ft (S3 - S8), 

• Differences in temperature between 2000 ft and 16, 100, 200, 300, 400 and 500 ft (S9 - S 14), 

• A fternoon mixing height (S 15 - S 17), and 

• Gradient Richardson Number (S 18 - S 19). 

There were 48 wind speed parameters: 

• Maximum wind speeds from the surface to 500 ft, 1000 ft, 2000 ft and 3000 ft (W I - W4), 

• Maximum wind speed between 1000 ft and 2000 ft (W5), 

• Maximum wind speed between 2000 ft and 3000 ft (W6), 

• A verage wind speed from the surface to 500 ft and 1000 ft (W7 - W8), 

14 
• A verage wind speed between 500 ft and 1000 ft (W9), 

• A verage wind speed between 1000 ft and 2000 ft (W I 0), 

• A verage wind speed between 2000 ft and 3000 ft (W I I), 

• Wind speeds at 16 ft through 3000 ft, by 100-ft increments (W 12 - W42), 

• Wind speed at the afternoon mixing height (W43 - W45), and 

• Maximum wind speed from the surface to afternoon mixing height (W46 - W48). 

There was one wind shear parameter: wind shear between the surface and 1000 ft (H I). Table I describes 

the stability and wind shear parameters, while Table 2 describes the wind speed parameters. 

15 
Table I. Stability and wind shear parameters calculated from XMR soundings. Heights in MSL. 

Parameter Description 

name 

iov.depth Depth of the surface-based inversion in ft. The top of the inversion was defined as the 

(SI) first level in which the temperature did not increase with height. 

inv.str.C (S2) Strength of the surface-based inversion in °C, defined as the difference between the 

temperatures at the surface and top of the inversion. 

XI6 ... IOOO to The difference between the temperature (0C) at 16 ft (or 100 ft, 200 ft, 300 ft, 400 ft, and 

X500 ... I OOO 500 ft) and 1000 ft. 

(S3 - SS) 

XI6 ... 2000 to The difference between the temperature CC) at 16 ft (or 100 ft, 200 ft, 300 ft, 400 ft, and 

X500 ... 2000 500 ft) and 2000 ft. 

(S9 - S14) 

mixhgtLI Morning mixing height (in ft), defined as the height at which the temperature lapse rate 

(SIS) first meets or exceeds 4 °C/km. This was based on an average saturated adiabatic lapse 

rate of 4-5 °Clkm near the surface In temperature above freezing 

htl~: lIen. wi ki~ed a.orglwiki La~se rate). 

mixhgtL2 Morning mixing height (in ft) calculated by the same method as mixhghtLl, except data 

(S16) was in 200-ft increments instead of I ~O-ft. 

mixhgtT Afternoon mixing height based on the forecast maximum temperature, in ft. The forecast 

(S17) maximum was derived by adding 2 degrees Kelv in to the average boundary-layer 

potential temperature. 

brl (SIS) Bulk Richardson Number (BRN) for the lowest 1000 ft in the sounding. The BRN was 

calculated by averaging the gradient Richardson number between the surface and 1000 ft 

htl~ :i/amsglossa!), .al l~n~re ss com glossa!}' searc i d=bul k -richardson-num ber I ). 

brlmod (SI9) The BRN was calculated by the same method as brl, except values greater than 5.0 were 

set to 5.0 in order to improve the linear relationship to peak wind speed. 

diff.sCc.1 k Difference between 500- I 000 ft and surface-SO~ ft layer-average wind speeds. 

(HI) 

16 
Table 2. Wind speed parameters calculated from XMR soundings. Heights in MSL. 

Parameter Description 

name 

max.sfc.Sh Maximum wind speed from the surface to 500 ft. (WI) 

max.sfc.lk Maximum wind speed from the surface to 1000 ft. (W2) 

max.sfc.2k Maximum wind speed from the surface to 2000 ft. (W3) 

max.sfc.3k Maximum wind speed from the surface to 3000 ft. (W4) 

max.lk.2k Maximum wind speed between 1000 ft and 2000 ft. (WS) 

max.2k.3k Maximum wind speed between 2000 ft and 3000 ft. (W6) 

ave.sfc.Sb A verage wind speed from the surface to 500 ft. (W7) 

ave.Sh.lk A verage wind speed from 500 ft to 1000 ft. (WS) 

ave.sfc.lk A verage wind speed from the surface to 1000 ft. (W9) 

ave.1.2k A verage wind speed from 1000 ft to 2000 ft. (WIO) 

ave.2.3k Average wind speed from 2000 ft to 3000 ft. (Wll) 

Xl6.ft to Wind speeds at 16 ft through 3000 ft. (W12 - W42) 

X3000.ft 

spdmixLl Wind speed at the height defined by mixhgtLl. (W43) 

spdmixL2 Wind speed at the height defined by mixhgtL2. (W44) 

spdmixT Wind speed at the height defined by mixhgtT. (W4S) 

maxspdLl Maximum wind speed from the surface to the height defined by mixhgtLl. (W46) 

maxspdL2 Maximum wind speed from the surface to the height defined by mixhgtL2. (W47) 

maxspdT Maximum wind speed from the surface to the height defined by mixhgtT. (W4S) 

The data set was stratified using the synoptic weather pattern, low-level wind direction, precipitation, 

Richardson Number and Gradient Richardson Number, for a total of 60 stratification methods. The total 

number of observation days in each stratification method varied due to missing or undefi ned data. For 

example, if the wind shear was zero, the Richardson and Gradient Richardson numbers could not be 

calculated. 

The AMU wrote scripts in the S-PLUS programming language to calculate the least-sq uares single 

linear regression equations for the PWSD, A WSD and the timing of the PWSD. Based on the lowest 

MAE of the equations, the best predictors were selected to create multiple linear regression equations. 

Single and multiple linear regression equations were also created using robust functions in S-PLUS. 

Unlike least-squares functions, robust functions reduce the infl uence of data outliers, while still providing 

a good fit to most of the data points (Insightful, 2007). 

17 
3.3 Prediction Equations fo r PWSD and A WSD 

Figure 8 shows the MAE values fo r predicting the PWSD and A WSD, usi ng least-squares single 

linear regression equations. The predictors shown on the x-axis incl ude the stability parameters (S I -

S 19), wind speed parameters (W I - W48) and the wind shear parameter (H I). The mean MAE val ues (in 

blue) were calculated for each stratification method, and then all 60 stratification methods were averaged 

together. The min imum MAE values (in red) were the lowest values from all stratification methods. The 

wind speed parameters performed the best, since they had the lowest MAE values. 

I Peak Speed MAE (kt), least-squares regression Ave. Speed MAE (Id), least-squares regression 8 I111111111 8 

7 11 1111111 111 1 7 

6 ~ 6 

5 I 5 

4 4 

3 3 

2 2 

1 , 1 

0 0 

~ ~ ~ '" ~ ~ ~g~ ~ ~ U ~ g g g ~ ~ ~ ~ ~ ~ :z: ~~"'~~~~~ ~~~~ gg~~~~~ :z: 

~W~~~ b:~~N~~~~~ae ~ ~OW~~ A b:~~N~~ ~A e .. ~ 

'" • Mean MAE • Minimum MAE • Mean MAE • Minimum MAE 

Figure 8. MAE val ues for least-squares single linear regression equations for PWSD (left) and A WSD 

(right). The MAE is shown for the stability parameters (S I-S I9), wind speed parameters (WI-W48) and 

wind shear parameter (H I). Mean MAE (blue) is the average of all stratification methods, while 

Minimum MAE (red) is the lowest of all stratification categories. 

Figure 9 shows the MAE values for predicting PWSD and A WSD, using robust single linear 

regression equations. The robust method is the " lmRobMM" function in the S-PLUS language. The 

ImRobMM function creates an equation with the fo llowing features: 

• The data fit to the equation are minimally infl uenced by outliers and 

• The equation min imizes the maximum possible bias of the coefficient estimate (i.e. the slope 

of the li ne). 

The wind speed parameters performed the best, with the lowest MAE values. The minimum MAE values 

(in red) were similar for PWSD and A WSD, but the mean MAE values (in blue) for A WSD were lower. 

,- I PeakSpeed MAE (kt), robust regression Ave. Speed M AE tkt), robust regression 

I 8 8 I 7 , 7 , 6 ...I. 6 

5 5 I ii' 4 

~!m ~ .... jIo'i 4 

3 3 i, 2 ~ , 2 

1 , 1 

0 0 

~ ~ ~ ~ ~ ~ ~g~ ~ ~ ~ ~ ~ g ~ g ~ ~ ~ ~ ~ ~ i!i ~ ~ ~ ~ ~ ~ ~g~ ~ ~ ~ ~ H g g ~ ~ ~ ~ ~ :z: 

I O WG'lIDA .... .... ...... N ..... AAA .. ~ 

O WO'I IDNVlOO .......... OWG'l CWG'I O Wa'lIDNVl OO .... ..... IoU '" • Mean MAE • M inimum MAE • Mean MAE • Minimum MAE 

Figure 9. MAE values for robust single linear regression eq uations for PWSD (left) and A WSD (right). 

The MAE is shown for the stability parameters (S I-S 19), wind speed parameters (W 1-W48) and wind 

shear parameter (H I). Mean MAE (blue) is the average of all stratification categories, while Minimum 

MAE (red) is the lowest of all stratification categories. 

18 
The MAE value for each stratification method was calculated by averaging the MAE values from all 

68 stability, wind speed and wind shear parameters. The MAE values were weighted by the size of the 

categories in the stratification methods. 

Figure 10 shows the least-squares method (in blue) performed better than the robust method (in red), 

with MAE values between 0.5 and 1.0 kt less than the robust method. 

Peak Speed MAE (kt), single linear regression Ave. Speed MAE (kt), single linear regression 

8 8 

7 ~I I -- I I 7 T ,-I-- ~ - ---t6 I II 6 • ~ ... ••••••• ·. I; ~:.:..; .' •• • +: •• ::: .. I .. ... ....... .1 ••• ot!...! " . •• •• •••• .. . ... ." .. • • .. 5 " .. -f,,~ . J 5 ... .. 4 4 .. ....... • • ••• ••••• ... ... .. . .. . ." 

3 I I 3 - I 

2 H-i- 1 2 " - 1 .. .. -

1 I 1 I I 0 0 

0 .. .. ... ... N N N ... ... .. .. .. '" '" '" 0 .. .. .... ... N N N ... ... .. .. .. '" '" '" N '" 0 .. .. N '" 0 .. .. N '" 0 N '" 0 .. .. N '" 0 .. .. N '" 0 

• MAE ~ast·sq ares • MAE robust • MAE least-squares • MAE robust 

Figure 10. Mean MAE values for least-squares (blue) and robust (red) single linear regression 

equations for PWSD (left) and A WSD (right). The MAE is shown for the 60 stratifications (x-axis). 

Three different multiple linear regression methods were evaluated: least-squares, robust and leasttrimmed squares. The least-squares method uses the "step" function in S-PLUS. The step function 

performs a stepwise multiple linear regression to determine which predictors to use in the regression 

equation. Due to limitations of the S-PLUS software, only 26 predictors (out of68) were evaluated by the 

step function: SI-S6, S9-S12, SI9, W2-W6, W8-WII, W22, W27, W32, W37, W48 and HI. The 26 

predictors were chosen based on their performance in the single linear regression equations. 

The robust method combined the step function with the "1m Rob" function in S-PLUS. The ImRob 

function is similar to the ImRobMM function, and is available from the Robust library in S-PLUS. The SPLUS 6 Robust Library User's Guide explains the use of the ImRob function (Insightful, 2002). The 

ImRob function only used 10 predictors (S I-S4, SI9, W4, W6, WI I, W37 and W48), due to the 

limitations of the S-PLUS software. The 10 predictors were chosen based on their performance in the 

single linear regression equations. 

The least-trimmed squares method is similar to the least squares regression, and it is implemented by 

the " Itsreg" function in S-PLUS. The Itsreg function first removes observations corresponding to large 

errors to the linear fit. It then calculates a least squares regression equation using the remaining 

observations. The Itsreg function used the same 10 predictors as the robust method. Unlike the leastsquares and robust methods, the Itsreg function does not use stepwise regression to determine which 

predictors to use in the multiple regression equations. Instead, 10 predictors were used in each leasttrimmed squares regression equation. 

Figure II shows the MAE values of PWSD and A WSD for the three multiple linear regression 

methods. The values from the least-squares (in blue) and least-trimmed squares (in green) methods were 

almost identical and overlap in the chart, while the robust (in red) method had MAE values higher than 

the other two methods. 

19 
8 

7 

6 

5 

4 

3 

2 

1 

o 

Peak Speed MAE (kt), multiple linear regression 

,- , 

--- -+ I n . ,.. ....... .. i...-'t'· • •• ,1. " .. • .. ~ ' .~ '. .. ' • ••••••••• , • •• • • ... ,., ... _1 

I I 

I I I 

" .. ., .. .. .... .... .... ... ... .. .. .. '" '" '" .... '" " .. ., .... '" " .. ., .... '" " • MAE least·squares • MAE robust • MAE leasHrimmed 

,---

8 

7 

6 

5 

4 

3 

2 

1 

0 

" 

Ave. Speed MAE (let), multiple linear regression 

• .• ••••• • "t'. 

. ....... " 

A ~ ~ ~ N N N ~ W A A A ~ ~ ~ 

N ~ 0 A ~ N ~ 0 A ~ N ~ 0 

• MAE least-squares • MAE robust • MAE least-trimmed 

Figure II. Mean MAE values for least-squares (blue), robust (red) and least-trimmed squares (green) 

multiple linear regression equations for PWSD (left) and A WSD (right). The MAE is shown for the 60 

stratifications (x-axis). 

3.4 Timing of PWSD 

The prediction equations for the timing of the PWSD predict the number of hours elapsed since the 

beginning of the forecast period at 1300 UTe on the current day. The number of hours that have elapsed 

can then be converted to clock time, in UTe. For example, if an equation predicts 17 hours have elapsed, 

then the PWSD is expected to occur at 0600 UTe (or 0 I 00 EST). 

Figure 12 shows the MAE (in hours) for predicting the timing of the PWSD, using least-squares (left) 

and robust (right) single linear regression equations. The mean MAE values (in blue) were the average 

from all stratification methods, and the minimum MAE values (in red) were the lowest values from all 

stratifications. There was little difference in MAE among the 68 predictors. The mean coefficient of 

determination (R') values were below 0.1 , indicating the regression equations provided little skill in 

predicting the timing of the PWSD (figures not shown). 

7 

6 

5 

4 

3 

2 

1 

o 

-

Timing of Peak Wind MAE (hr), least-sq. reg. 

~ II ~ ~ -

111111 

1 

1 1 

1 I I 

~ \:: ~ ~ ow~~~ ~ ~ ~i!l:li ~ :Ii 

~~~~NNNWW :Ii :Ii :Ii :Ii :Ii :Ii :Ii :Ii ~ :Ii :Ii :Ii % A_A~ OWO'IIDNVlO:O ......... OWO'l 

• Mean MAE • Minimum MAE 

TIming of Peak Wind MAE (hr), robust 

7 I 1 1 

6 , , 

11 5 

4 

3 

2 1 !1 

1 1 , 0 , 

~ \:: ~ ~ ~ ~ ~i!l:li ~ :Ii :Ii :Ii :Ii :Ii :Ii :Ii :Ii :Ii :Ii :Ii :Ii :Ii % ow~~. ~~~~NNNWWW.A .. ~ 

OWO'IIDNllloo ......... O'"'" .. • Mean MAE • Minimum MAE 

Figure 12. MAE values for least-squares (left) and robust (right) single linear regression eq uations for 

timing of the PWSD. The values are shown for the stability parameters (S I-S 19), wind speed parameters 

(W 1-W48) and wind shear parameter (H I). Mean MAE (blue) is the average of all stratification methods. 

Minimum MAE (red) is the lowest of all stratification categories. 

Figure 13 shows the MAE values for the 60 stratification methods, for single (left) and multiple 

(right) linear regressions. All of the linear regressions performed about the same, with MAE values 

mostly between 5.5 and 6.0 hours. 

20 
7 

6 

5 

4 

3 

2 

1 

o 

Timing of Peak Wind MAE (hr), single linear reg. 

~+l;~~ i I I ' I I 

I I 1 

0 .. co ... ... .... .... .... '" '" .. .. .. '" '" '" .... '" 0 .. co .... '" 0 .. co .... '" 0 

• MAE least-squares • MAE robust 

7 

6 

5 

4 

3 

2 

1 

o 

- Timing of Peak Wind MAE (hr), multiple lin. reg. 

:II T1Tr- , ,,'t""" r'" IIIIIIt .. ft' "li:1 mll.1 ft. , " ,.If f-+---+-

T f--- -'---'-I- .-.-f-. ~-

i 

I ! I 

i 

0 .. co ... ... .... .... .... '" '" .. .. .. '" '" '" .... '" 0 .. co .... '" 0 .. co .... '" 0 

• MAE least-squares • MAE robust • MAE least-trimmed 

Figure 13. Mean MAE values for single (left) and multiple (right) linear regression equations for 

timing of the peak wind. The least squares and robust regressions are in blue and red, while the leasttrimmed regressions are in green. The val ues are shown for the 60 stratifications (x-axis). 

21 
4 Independent Verification of Prediction Methods 

The Phase II developmental data set used observations from October 1996 to February 2007, while 

the Phase I data set used observations from October 2002 to February 2007. An independent verification 

data set was used to compare the Phase I and II prediction methods to climatology, model forecast winds 

and wind advisories issued by the 45 WS. The verification data set used observations from March 2007 to 

April 2009. The prediction methods showing the greatest skill in the verification data set were chosen for 

the Phase II version of the tool. 

Several Phase II prediction methods for the three forecast parameters (PWSD, A WSD and timing of 

the PWSD) were selected for the independent verification based on the lowest MAE val ues in the 

developmental data set. The difficulty or subjectivity in how the predictors were calculated also affected 

the selection, to make it easier to automate the tool. For example, the prediction methods usi ng the 

synoptic weather pattern performed well, however the classification of the weather pattern is somewhat 

subjective. Two trained, experienced forecasters could classifY the weather pattern on a given day 

differently. In addition, stratification methods with more than 10 categories were not selected in order for 

each category to have a sufficie ntly large sample size. The stratification methods fo r precipitation and 

low-level wind direction performed well in the Phase II methods, so they were se lected for the 

verificati on. The stratification '·none", which included all days in the Phase II data set, was used as a 

control in the verification. 

Table 3 shows the 30 Phase II prediction methods selected for the verification of PWSD, A WSD and 

timing of the PWSD, based on five linear regression types and six stratification methods. For predicting 

the timing of the PWSD, the multiple linear regression equations did not perform significantly better than 

the single li near regression equations. Therefore, the mUltiple linear regressions were not independently 

verified for the timing of the PWSD. These regressions are indicated in red in the left column of Table 3. 

Table 4 describes the stratification categories for each stratification method listed in Table 3. Table 5 lists 

the predictor for each single linear regression equation for PWSD, and Table 6 lists the predictors for the 

multiple linear regression equations. 

22 
Table 3. Phase II methods verified for PWSD. A WSD and timing of the PWSD. Methods in red were not 

verified for the timing of the PWSD. 

Method Name Regression Type Stratification Method 

precipt.best Least-squares single linear By precipitation and wind direction: NE. 

SE. SW and NW (SMl) 

precipl.best Least-squares single linear By precipitation and wind direction: N. E. 

Sand W (SM2) 

preeip3.best Least-squares single linear By precipitation and wind direction: NE. 

SE. SW. NW and wind speed ~ 6.0 kt 

(SM3) 

precip4.best Least-squares single linear By precipitation and wi nd direction: N. E. 

S. Wand wind speed < 6.0 kt (SM4) 

none.best Least-squares single linear No stratification (SM5) 

precipcon.best Least-squares single linear Mean of the first four stratifications (SM6) 

~ precipl.bestrob Robust single linear SMI 

precipl.bestrob Robust single linear SM2 

precip3.bestrob Robust single linear SM3 

preci04.bestrob Robust single linear SM4 

none. best rob Robust single linear SM5 

preeipeon.bestrob Robust single linear SM6 

precip1.step Stepwise least-squares multiple linear SMI 

preeipl.step Stepwise least-squares multiple linear SM2 

precip}.step Stepwise least-squares multiple linear SM3 

preci04.step Stepwise least-squares multiple linear SM4 

none.step Stepwise least-squares multiple linear SM5 

preeipeon.step Stepwise least-squares multiple linear SM6 

precipt.steprob Stepwise robust multiple li near SM I 

precipl.steprob Stepwise robust multiple linear SM2 

precip3.steprob Stepwise robust multiple linear SM3 

preeip4.steprob Stepwise robust multiple linear SM4 

Done.steprob Stepwise robust multiple li near SM5 

precipeon.steprob Stepwise robust multiple linear SM6 

preeipl.ItsrOl! Least-trimmed multiple linear SMI 

precipl.1tsrOl! Least-trimmed multiple linear SM2 

precip3.1tsre2 Least-trimmed multiple linear SM3 

preeip4.1tsreg Least-trimmed multiple linear SM4 

none.ltsrOl! Least-trimmed multiple linear SM5 

precipeon.ltsre2 Least-trimmed multiple linear SM6 

23 
Table 4. Stratification categories used in Phase II methods. Wind direction was based on the vector 

average wind in the lowest 300 ft of XMR sounding. The stratification methods are described in Table 3. 

Category / Category Description Category / Category Description Stratification Stratification 

CO / SMS all days in data set C I8 I SM3 precipitation, SE wind direction, 

wind speed > 6 kt 

CI / SMI precipitation, NE wind direction CI9 I SM3 precipitation, SW wind direction, 

wind speed > 6 kt 

C2 / SMI precipitation, SE wind direction C20 / SM3 precipitation, NW wind direction, 

wind speed > 6 kt 

C3 / SMI precipitation, SW wind direction C2 11 precipitation, wind speed ~ 6 kt 

SM3&SM4 

C4 I SM I precipitation, NW wind direction C22 I SM3 no precipitation, NE wind 

direction, wind speed > 6 kt 

CS / SMI no precipitation, NE wind C23 I SM3 no precipitation, SE wind 

direction direction, wind speed > 6 kt 

C6 / SMI no precipitation, SE wind direction C24 I SM3 no precipitation, SW wind 

direction, wind speed > 6 kt 

C7 / SMI no precipitation, SW wind C2S I SM3 no precipitation, NW wind 

direction direction, wind speed > 6 kt 

C8 / SMI no precipitation, NW wind C26 I no precipitation, wind speed ~ 6 kt 

direction SM3&SM4 

C9 / SM2 precipitation, N wind direction C27 /SM4 precipitation, N wind direction, 

wind speed > 6 kt 

C IO I SM2 precipitation, E wind direction C28 I SM4 precipitation, E wind direction, 

wind speed > 6 kt 

CII I SM2 precipitation, S wind direction C29 I SM4 precipitation, S wind direction, 

wind speed > 6 kt 

CI2 I SM2 precipitation, W wind direction C30 I SM4 precipitation, W wind direction, 

wind speed > 6 kt 

CI3 / SM2 no precipitation, N wind direction C3 1 I SM4 no precipitation, N wind direction, 

wind speed > 6 kt 

CI4 I SM2 no precipitation, E wind direction C32 I SM4 no precipitation, E wind direction, 

wind speed > 6 kt 

CIS I SM2 no precipitation, S wind direction C33 I SM4 no precipitation, S wind direction, 

wind speed > 6 kt 

C I6 I SM2 no precipitation, W wind direction C34 I SM4 no precipitation, W wind direction, 

wind speed > 6 kt 

CI7 / SM3 precipitation, NE wind direction, 

wind speed > 6 kt 

24 
Table 5. Stratification category and predictors for each PWSD equation using single linear regression. 

The stratification categories are described in Table 4. 

Method Nome Stratification Category / Method Name Stratification Category / 

Predictor Predictor 

precip I.best CII W42 precip I.bestrob CI / W42 

precip I.best C2 / W39 precip I.bestrob C2 / W38 

preci pl. best C3 1 WI8 precip I.bestrob C3 1 SI9 

precip I.best C4 1W4 precip I.bestrob C4 1W3 

precip I.best C5 / W25 precip I.bestrob C5 / W25 

precip I.best C6 / W24 precip I.bestrob C6 1W4 

precip I.best C7 / W39 precip I.bestrob C7 / W35 

precip I.best C8 1 W9 precip I.bestrob C8 / W3 

precip2.best C9 / WI2 precip2.bestrob C9 / WI2 

precip2.best CIO 1 W42 precip2.bestrob C IO/ W42 

precip2.best CII I W48 precip2.bestrob C II I WI8 

precip2.best CI2 / W37 precip2.bestrob C 12 / W23 

prec i p2. best CI3 / W23 precip2.bestrob C I3 1 W3 

precip2.best C14 / W25 precip2.bestrob C I4 1 W24 

precip2.best C15 / W4 precip2.bestrob CI5 1 WIO 

precip2.best C16 / W48 precip2.bestrob C I6 1WIO 

precip3.best C I7 1 W7 precip3.bestrob C I7 1WI3 

precip3.best C18 / W5 precip3.bestrob C I8 1 W2 

prec i p3. best C19 / SI8 precip3.bestrob CI9 1S19 

precip3.best C20 / W30 precip3.bestrob C20 1 W3 

prec i p3. best C21 / W37 precip3.bestrob C2 1 1 W38 

]Jrec ij>3. best C22 / W25 precip3.bestrob C22 / W25 

precip3.best C23 1 W24 precip3.bestrob C23 1 W4 

precip3.best C24 1 W38 precip3 .bestrob C24 / W38 

precip3.best C25 1 W9 precip3.bestrob C25 / W9 

precip3.best C26 1 W3 precip3.bestrob C26 / W28 

precip4.best C27 / W48 precip4.bestrob C27 1 W3 

precip4.best C28 / W42 precip4.bestrob C28 1 W13 

prec i p4. best C29 1WI precip4.bestrob C29 / WI8 

prec i p4. best C30 / W42 precip4.bestrob C30 1 W42 

precip4.best C2 11W37 precip4.bestrob C2 1 1 W38 

precip4.best C32 I W8 precip4.bestrob C32 1 W8 

prec i p4. best C33 / W25 precip4.bestrob C33 1 W9 

precip4.best C34 / W24 precip4.bestrob C34 1 W24 

precip4.best C35 1 W48 . Qfecio4.bestrob C35 / W48 

precip4.best C26 1 W3 orecip4.bestrob C26 / W28 

none.best CO I W4 none.bestrob CO l W3 

25 
Table 6. Stratification category and predictors for each PWSD equation using multiple linear regression. 

The stratification categories are described in Table 4. 

Method Name Stratification Category / Method Name Stratification Category / 

Predictor(s) . Predictor(s) 

precip i.step C I / S2,W4 W5 , WII precip I.steprob CI / SI,W4, WI I 

precip i.step C2 / W6 precip I.steprob C2 / SI , W37 

precip i.step C3 / S19, W9 precip i.steprob C3 I S3, S4, S19, W6, WII 

precip i.step C4 I W37 precip i.steprob C4 / S19, W4 

precip i.step C5 I WIO precip I.steprob C5 / S19, WI I 

precip i.step C6 / S9, W8 prec ip I.steprob C6 / W4, W6, WII 

precip i.step C7 / SII,WII precip i.sleprob C7 I S2, S3, W II 

precip i.slep C8 / S10, W9, W48 precip i.sleprob C8 I S3, W6, W48 

precip2.step C9 I S5, W3, W48 precip2.sleprob C9 / SI,SI9,W4 

precip2.slep C IO I S2, WII precip2.sleprob C IO/ SI , WII 

precip2.slep CI I I SI9, W9, W22, W48 precip2.sleprob CII / SI9,W48 

precip2.slep CI2 I W37 precip2.steprob CI2 I S2, S3, S4, W6 

precip2.slep CI3 / S4, SI9, W22 precip2.steprob CI3 I SI S2, S3, W48 

precip2.slep C I4 I HI , S12, S19, W2, W5, precip2.sleprob CI4 I S4, S19, W4, W6, WI I, 

W22, W32, W37 W37 

precip2.step CI5 I S2, W4, W9 precip2.steprob CI5 / S2, S3, SI9, W4, W6, 

WI I 

precip2.step C I6 1 W9, W48 precip2.sleprob C 16 / S4, W6, W37, W48 

precip3.slep CI7 l SI , W2, W3, W4 precip3.sleprob CI7 / S4,519, W4 

precip3.slep CI8 / W5 precip3.sleprob C I8 / SI , W4, W6, W37 

precip3.slep CI9 I W48 precip3 .sleprob CI9 I 53, S4 S19, W6 

precip3 .slep C20 I S4, S5, W37 precip3.sleprob C20 / S4, S19, W4, W6, WI I 

precip3.step C21 1519, WIO, W37 precip3.sleprob C2 1 I S2, WII , W37 

precip3.slep C22 I W5 precip3.sleprob C22 / SI , 52,S3, WI I, W48 

j)recip3.sle~ C23 I W8 precip3.steprob C23 I W4 W6, WI I 

precip3.step C24 I S12, WII . jlrecij)3.steprob C24 I S2, S3, WI I 

precip3.step C25 I S9, W6, W9, W48 precip3.steprob C25 l SI, S3, W6, W48 

precip3.step C26 I S5, S 12, W II precip3.steprob C26 l SI , S3 S4, W4, W6 W37 

prec ip4.step C27 I S5, S19, W48 precip4.sleprob C27 I S4, S19, W48 

precip4.step C28 I S4, S 10, W II precip4.steprob C28 I S4, W6, WI I, W48 

precip4.step C29 I W9, W22, W48 precip4.sleprob C29 I S2, S3 W48 

precip4.slep C30 I S9, W32, W37 precip4.sleprob C30 I S2, S3, S4, W4 

precip4.step C3 1 1519, WIO, W37 precip4.sleprob C31 I S2, WII, W37 

precip4.step C32 I S9, W3, W6, W8 precip4.steprob C32 I S3, W37, W48 

precip4.slep C33 I W2, W3 , W9, W32, W3 7 precip4.sleprob C33 l SI , S2, S3, S19, W4, W6, 

WI I, W37 

precip4.step C34 I S2, S12, W2 precip4.steprob C34 I S2, S3, S19, W4 

precip4.slep C35 I SI I, W48 eci~.ste rob C3S / SI, W48 

precip4.step C36 / S5,512, WII precip4.steprob C36 I S 1, 53,54, W4, W6, W37 

none. step CO I SIO, S12, W4, W6, W9, W37 none.sleprob CO l SI , S2, S3, W4, W6, WI I 

26 
4.1 Peak and Average Wind Speed 

The Phase I and II prediction methods for PWSD and A WSD were compared 10 climatology and the 

0000 and 1200 UTC runs of the 12-km Nonh American Mesoscale (MesoNAM) model. Comparisons 

were done for precipitation and non-precipitation days in the verification data set. Then, the Phase I and II 

prediction methods for PWSD were compared to wind advisories issued by the 45 WS. 

4.1.1 Comparing Phase I and II Methods to Climatology 

The Phase I and II prediction methods for PWSD and A WSD were first compared to four climatology 

methods. The prediction methods for PWSD were compared to the climatological PWSD (surface to 300 

ft), while the prediction methods for A WSD were compared to the climatological A WSD (surface to 300 

ft). The climatological peak/average wind speeds were also based on the mean peak/average wind speeds 

at 54 ft, 90 ft and 204 ft calculated in a previous AMU task (Lamben 2002). 

Figure 14 shows the mean error for PWSD (left) and A WSD (right) for days without precipitation. 

The Phase II climatological winds (point 4, in dark blue) had a positive bias around 2 kt, while the 

climatological winds at 54 ft, 90 ft and 204 ft (points 1-3, in dark blue) had large negative biases. The 

Phase I and II methods generally had a slight negative bias. Figure 15 shows the mean error for PWSD 

(left) and A WSD (right) for days with precipitation. There was a negative bias in the Phase I and II 

methods, indicating the methods under-predict the PWSD and A WSD on precipitation days. 

Peak Speed Mean Error (let). No Precip 

4 

~ 

• 

.. 

• -: ; ---

o Climatolo8Y 

i X Phase I 

Least-squares sln81e 

.... 

Average Speed Mun Error (kt), No Preclp 

4] 1 0 I 0 elimatol08Y 

~ j--..-_- "'.- ----.:j,c----'==---.. X Ph.se I 

o - Least·squares sinlle 

o rei . 

o 

-8 0 o - Stepwise least-squares -8 - Stepwise least-squares 

multiple re,. 

o 

-12 

-16 

-20 

1 2 3 4 5 

multiple res. 

- Robust sin lie res. 

- Least-trimmed squares 

multiple rea . 

- Stepwise robust 

6 multlple,el· 

-12 I 

-16 t- ~ 

20 ~ 1 2 3 

- Robust sinlle ,el. 

, - leist-trimmed squares 

multiple rea. 

....L_~_~ - Stepwise robust 

4 5 6 multiple,e • . 

Figure 14. Mean Error for PWSD (left) and A WSD (right) on non-precipitation days For the Phase II 

methods, points 1-6 (on x-axis) correspond to stratification methods SM I-SM6, respectively_ The legend 

describes the colors corresponding to the Phase II linear regression types. Refer to Table 3 for a 

description of the stratification methods and linear regression types. The climatology methods are plotted 

on points 1-4_ The Phase I method is plotted on point I. 

27 
Peak Speed Mean Error Ikt), Predp o Climatology Average Speed Mean Error lid). Precip 

• • 

I 

1-- I ~ 0 Climatology 

0 ,---- X Phase l 0 

~ • T " ~ ! .. i X Phasel 

- " -4 1< i Least-squaressingle 

-4 1 0 -i least-squares single 

- "'g. 

0 ',". 

I 

·8 - Stepwise least-squares .. 1 ---- - Stepwise least-squares muttlple reg. multiple reg. I 

0 

·12 - Robust single reg. ·12 0 I - Robust single reg. 

·1. T- - Least-trimmed squares ·1. - Least-trimmed squares 

0 <D multiple reg. I multiple reg. 

0 

I .20 ·20 - Stepwise robust - Stepwise robust 

multiple reg. multiplereg . 

1 2 3 • 5 • 1 2 3 • 5 

---

• J 

Figure IS . Mean Error for PWSD (left) and AWSD (right) on precipitation days, for the climatology 

and Phase I and \I methods. Refer to Figure 14 for a description of the x-axis. 

Figure 16 shows the MAE for PWSD (left) and A WSD (right) for days witho ut precipitation. The 

Phase I and \I methods had very similar MAE values. Figure 17 shows the MAE for PWSD (left) and 

A WSD (right) for days with precipitation. Similar to Figure 16, the MAE values from the Phase I and II 

methods were similar, with the Phase \I "SM5" stratification (poi nt 5) having the hi ghest MA E values. 

Comparing Figure 16 and Figure 17, the MAE values were higher on precipitation days. 

Peak Speed MAE lid), No Preclp o Climatology Average Speed MAE lid), No Precip 0 Climatology 

20 20 ~ -" I 

18 "f --I. X Phase I X Phase I I. ,. i 

,. L_ least-squares single I. I least-squares single 

reg. reg. 

12 

t-o. 

12 +- - Stepwise least-squares - Stepwise least-squares 

multiple reg. -l multiple reg. 

10 10 

CD 0 - Robust single reg. 

+- J - Robust single reg. 8 8 

0 I 

• • 0 

J 0 - least-trimmed squares 

.* - Least-trimmed squares 

; multiple reB· 11 I 0 multiple reg. 

• • • • • • • • 

I - Stepwise robust 2 I ! - Stepwise robust 

2 multiple reg. multiple res. 

2 3 4 5 , I L 1 2 3 • 5 • 

I 

Figure 16. MAE for PWSD (left) and A WSD (right) on non-precipitation days, for the climatology 

and Phase I and \I methods. Refer to Figure 14 for a description of the x-axis. 

28 
Peak Speed MAE (letl, Precip o Climatology 120 1 

Average Speed MAE (ktl, Precip 0 r I 

20 r I ~_ C unato ogy 

18 

1 

0 118 I --t 

0 0 x Phase I X Phase I 

16 

11

'6 I Least-squaresslngle tRast-sq uares single " ,. I 'eg. 'eg. 

12 ~ -- 12 <p 

1 - Stepwise least-squares II _~---+ 

- Stepwise least-squares

l 

10 multiple res. 10 multiple reg. 

T 

8 I 0 - Robust single reg. 8 j. - --~ -~ - Rob\.lst single res. 

6)( • • = ~ 6 I 0 • - tRut-trlmmed sq\.lares 

~ • • • • - tRast-trimmed squares 

I multiple res. • multiple res. 

• • 

[ 

1 

2 , - Stepwise robust 2 - Stepwise robust 

multiple reg. multiple reg. 

1 2 3 • 5 6 1 2 3 • 5 6 

Figure 17. MAE for PWSD (left) and A WSD (right) on precipitation days, for the climatology and 

Phase I and II methods. Refer to Figure 14 for a description of the x-axis. 

The relationship between mean error, MAE and precipitation was investigated further. Most of the 

days with large absolute errors had precipitation (figures not shown). The large errors could be due to 

convective wind gusts, since the Wind Tower QC program did not filter out all of the convective wind 

gusts. Some of the non-precipitation days with large errors in PWSD and A WSD mi ght actually have had 

rainfall over the forecast area. A day with precipitation was defined as having at least one observation at 

SLF with precipitation, thunder, or a shower within 5 NM. It is possible some days had precipitation 

across KSC or CCAFS, but not within 5 NM of the SLF. 

Figure 18 shows the mean error when the absolute error for PWSD was within 5 kt (left) and greater 

than 5 kt (right). The figure shows a bias near zero when the absolute error was ~ 5 kt, but large negative 

bias when the absolute error was > 5 kt. This indicates the negative bias in PWSD becomes greater as the 

absolute error increases. There was also a much greater spread between the Phase I and II methods when 

the absol ute error was greater than 5 kt. Figure 19 shows the mean error when the absolute error for 

A WSD was ~ 5 kt (left) and > 5 kt (right). Similar to Figure 18, the negative bias in A WSD was greater 

when the absolute error was> 5 kt. However, the spread between the Phase I and II methods was not as 

large. The negative bias was even greater for PWSD and A WSD when the absolute error was > 8 kt 

(figure not shown). 

29 
Peak Wind, Mean Error Ikt) when Abs. Error S 5 kt 

4 I o Climatology 

-- --.i i.-~; X Phase I 

':*1- ~- .-,~-- "- ;.:..~ 

o 0 ... X Phase I 

o o o 

-8 

·12 . 

·16 . 

1'20 1 

2 3 4 5 6 

Least-squares single 

.... -4 i _ 

- Stepwise least-squares -8 

multiple reg. 

- Robust slnsle reg. -12 

_ LeasHrimmed squares -16 CD) 

multiple reg. 

- Stepwise robust 

[

-20

, multiple res . 

-~ 

: 

to o 

1 

2 3 4 

-

J. 

5 6 

Lust-squares single 

- Stepwise least-squares 

multiple reg. 

- Robust single rea. 

- ~ast-trimmed squares 

rTKJltiple reg. 

- Stepwise robust 

nwltiplereg. 

Figure 18. Mean Error for PWSD on days in which the forecast 's absol ute error is within 5 kt (left) 

and greater than 5 kt (right). Refer to Figure 14 for a description of the x-axis. 

Averall! Wind, Mean Error (Id) when Abs. Error S S kt 

4 I - 0 Climato'oBY 

Averas'!! Wind, Mean Error (Id) when Abs. Error> 5 kt 

4 

o ~ "--- ~ X Ph ... , 

-4 ' Least-squares sinele 

o 

-4 f 

J 

r 

-8 -

-12 +-

-16 f 

-20 I 

1 2 3 4 5 6 

reg. 

- Stepwise least-squares 

multiple rea . 

- Robust single reg. -12 

- least-trimmed squares -16 

multlplereg. 

- Stepwise robust 

multiple reg. 

.20 L 

1 

! 

o 

l 

r---

2 3 4 ~~--

o Climatology 

X Phasel 

Least-squares single 

reg. 

- Stepwise least-squares 

multiple reg. 

r 1-Oobu ... ',.'".,. 

----1 - Least·trimmed squares 

5 

I multiple reg. 

6 

- Stepwise robust 

multiple reg . 

Figure 19. Mean Error for A WSD on days in which the forecast's absolute error is within 5 kt (left) 

and greater than 5 kt (right). Refer to Figure 14 for a description of the x-axis. 

A relationship was found between the absolute error of the Phase I and II equations for PWSD, and 

the difference between PWSD and the tower-average peak speed. As noted in Section 2.1 , the PWSD was 

the highest peak speed (surface to 300 ft) of all the KSC/CCAFS towers used in the task, during a 24-hour 

period. The tower-average peak speed is the average of the 24-hour peak speeds (surface to 300 ft) for all 

of the towers. Figure 20 shows the average difference between the PWSD and the tower-average peak 

speed, for days in which the absolute error was :s 5 kt (left) and > 5 kt (right). As the absolute error 

increased, the difference between the PWSD and the tower-average peak speed increased. The increase 

continued when the absolute error was > 8 kt (figure not shown). In other words, the difference between 

the PWSD and the tower-average peak speed was related to the predictability of the PWSD. The cause of 

30 
this effect may be due to wind gusts that only affect a portion of the KSC/CCAFS tower network. 

Measurement errors in the tower network could also contribute to the difference between the PWSD and 

tower-average peak speed. It is possible the absolute error also increases with the magnitude of the 

PWSD, but this was not investigated. 

Peak Speed, Ava. Twr Diff. lid) when Abs. Error:S 5 kt I \ :~ak Speed, Avg. Twr Dltt. (kt) :hen Abs. Error> 5 kt I 10 

o Climatology o Climatology 

• I 

• X Phase I XPhasel 

8 8 

7 --,- +-- - least-squares single II 7 _ - Least-squaresslngle 

reg, : t - reg. 

- • " 

:1 - St •• w,··' ...... '''' .. 11 

6 <D 0 0 - - Stepwise ~art-squares 

multiple rei. multJplereg. 

~ !!! a • 5 

4f----

~ - - Robust single reg . - Robustsing1erel· 

I 4 I 

0 0 - least-trimmed squares - Least-trimmed squares 

3 

L 

multiple !'eg. 3 multiple reg. 

2 --L-J. _ - Stepwise robust 2 - Stepwise robust 

1 2 3 4 5 6 multiple reg. 

1 2 3 4 5 6 

multiple reg. 

- Figure 20. Average difference between the PWSD and the tower-average peak speed, on days in 

which the forecast's absolute error is within 5 kt (left) and greater than 5 kt (right). Refer to Figure 14 for 

a description of the x-axis. 

4.1.2 Comparing Phase 1 and II Methods to Model Forecast Winds 

The Phase I and II methods for predicting PWSD and A WSD were then compared to model forecast 

winds. The model forecast winds were derived from 0000 and 1200 UTC runs of the MesoNAM. The 

MesoNAM contained hourly forecasts out to 84 hours, although the independent verification only used 

the Day-I (1300 UTC to 1300 UTC) forecasts. The comparison used the MesoNAM wind data for the 

grid point closest to the XMR sounding. Levels 2to 18 of the MesoNAM were evaluated, along with the 

strongest winds in the lowest 1000-, 2000- and 3000-ft of the model. The height of each model level 

varied by forecast hour and model run, due to changes in surface pressure and temperatures aloft. Table 7 

shows the average and standard deviation of the heights for the MesoNAM levels 2 - 18, based on the 

verification data set. Two sets of MesoNAM forecasts were used in the comparison. The first set included 

the strongest wind at each model level during the 24-hour period. The second set used least-squares single 

linear regression equations, in which the predictor was the model level's highest 24-hour peak speed and 

the predictand was the PWSD or A WSD. 

31 
Table 7. Average heights and standard deviations of the MesoNAM venicallevels 

used in the verification data. 

Average Standard Average Standard 

Level Height Deviation of Level Height Deviation of 

(MSL) Hei£ht (MSLJ Hei£ht 

2 207 ft 4ft 11 157 1 ft 23 ft 

3 344 ft 5ft 12 175 1 ft 25 ft 

4 483 ft 7ft 13 1940 ft 28 ft 

5 626 ft 9ft 14 2 140 ft 31 ft 

6 772 ft 12 ft 15 2352 ft 34 ft 

7 922 ft 14 ft 16 2583 ft 37 ft 

8 1076 ft 16 ft 17 2842 ft 41ft 

9 1234 ft 18 ft 18 3145 ft 45 ft 

10 1399 ft 20 ft 

Figure 2 1 shows the mean error for PWSD (left) and A WSD (right) of the Phase I and II, 

climatology, and MesoNAM forecasts. Only the 0000 UTe MesoNAM is shown, since there were only 

minor differences between the 0000 and 1200 UTe runs of the model. The Phase I and II methods 

generally had a weak negative bias. The strongest 24-hour MesoNAM forecast winds at each level are 

shown in black, and their bias varied by model level. The MesoNAM linear regression forecasts are 

shown in light blue, and their bias was zero. Figure 22 shows the MAE for PWSD (left) and A WSD 

(right). The MesoNAM linear regression forecasts were the most accurate, especially at the lower model 

levels. 

Mean Error (ktl - Peak Speed Mean Error (ktJ - Avera,!!! Speed 

8 8 

•'2 f' -+------ +- r--- 4 6 .--------t 0 QG_oDQ 0 0 ~ 0 ~ 0 

2 00 9 -

o lB-oae-Elso-ooO·se-ee e-o·e -SO-EJrc---- -- 0 8 o-§-ee-e·e-e·e-o-e-a-eGO e-e-eSC<Sr...-===,......,,,"", 

-..,.--

~ I OOOOIlJDOOCGOOo ~ ~ -6 000 , . r. "'lim""C7to IO Y ----, I -6 0 0-

-8 f aD O Phasel -8 0 

·1. ~ 1 Phlsell 1 '• -12 0 0 0002 MesoNAM 12 -14 0 0 OOZ MesoNAM Unear Relt. -14 

• 5 ,. 15 2. 25 30 • 5 

~ 

==:::---_--jl I 

o Cl1matolo,y 

C Phasel 

Phase II 

[J DOZ MesoNAM 

______ 0 002 MesoNAM Unear ReI:. 

, . 15 2. 25 30 

Figure 21. Mean Error for PWSD and A WSD. The strongest 24-hour 0000 UTe MesoNAM forecast 

winds are plotted on points 1-20, in black. The 0000 UTe MesoNAM linear regression forecasts are 

plotted on points 1-20, in light blue. The climatology methods are plotted on points 1-4, in dark blue. The 

Phase I method is plotted on point I (in red), and the Phase II methods are plotted on points 1-30 (in 

yellow). Refer to Table 3 for a description of the Phase II methods 

32 
MAE (Ilt) · Pellk Speed --rl --------------------------~ 

MAE (kt)· Ave~le Speed 

o 

12 0 0 

o ClimatololY 

o Phase I 

Phue ll 

o OOZ MesoNAM 

,. 10 o OOZ MesoNAM Une., Rei:. 

• 0 . \ 0 80 0 0 

0000000000 0 - 00 : I COOOOOOO"'j oooeeo"'Eloe -

• 5 ,. 15 2. 2S 30 

14 r 

12 

,. 

o 

• 0 0 

I • 

• 5 ,. 

o CllmitololY 

O Phasel 

Phase II 

- __ DOO2MesoNAM ~ 

D OOZ MesoNAM Une., Rea. 

o 

15 20 2S 30 

Figure 22. MAE for PWSD and A WSD. Refer to Figure 2 I for a description of the x-axis. 

Figure 23 shows the mean error for PWSD for days with (left) and without precipitation (right). For 

the MesoNAM data, separate regression equations were not developed for precipitation and nonprecipitation days. The MesoNAM regression forecasts (i n light blue) had a negative bias around 2 kt on 

precipitation days and a positive bias around I kt on non-precipitation days. Figure 24 shows the mean 

error for A WSD for days with (left) and without (right) preci pitation. Similar to PWSD, the MesoNAM 

regression forecasts had a negative bias on precipitation days and a positive bias on non-precipitation 

days. 

Meln Error (kt) · Peak Speed. Predp 

~ +--- ~ 1 8 000000000000000000 0 

·4 0000000 a 

-6 0 ocoo ~ 0 

-. 't 0 000 - - .- -1. """=='=:-__ --., .12 0 0 DCllmatololY 

·14 

-1' 

-18 

-20 

• 

o 0 

C 

5 ,. 15 

O Phue l 

Phuell 

OOOZMHONAM 

a OOZ MesoNAM Unur Rei. 

2. 2S 30 

4 

2 

• 

-2 

.. 

.. 

-8 

-1. 

·12 

-14 

·1. 

·18 

·20 

• 

Mean Error (Ilt) · Pelk Speed, No Predp 

OE 'EE ooaOO~90c?OCO ~ ~ I 

000 

0 0 t 

o 0 

o 

5 

0 0 

000_0000000000 ~I 

o ClimatololY 

o Phase I 

Ph.se II 

_ 0 OOZ MesoNAM 

a OOZ MesoNAM Unear R ••. 

,. 15 20 2S 30 

Figure 23. Mean Error for PWSD on precipitation (left) and non-precipitation days (right). Refer to 

Figure 2 I for a description of the x-axis. 

• Mun Error (kt) · Avera,e SpHd, Predp 

o 6 ' oooo e oo 0 

4 ~ 0000 0 . 2 0 0 ] , 

o 0 0 a ---l-..- +---_ .2 BOoooooooooooooooooo 

• • 

-2 

·4 0 , - I .. 

.. "o.,c"";:: ... =,;:.;:,.=,,:-----., .. ·8 0 Phlse I -I 

-1. 

·12 

·14 .... 

• 

o ° o 

5 ,. 15 

Phase II -1. o OOZ Me50NAM -12 o OOZ MesoNAM Une., RH. -14 

20 2S 30 • 

o ° ° 

5 

Mean Error (kt) - Avel1lle $pHd, No Predp 

C ClllnltololV 

C Phase I 

Phllsell 

COOl Me50NAM 

::J I 

C OOl MHONAM Unur ReI:. 

,. 15 20 2S 30 

Figure 24. Mean Error for AWSD on precipitation (left) and non-precipitation days (right). Refer to 

Figure 2 I for a description of the x-axis. 

Figure 25 and Figure 26 show the MAE fo r PWSD and A WSD for days with (left) and without 

precipitation (right). The MesoNAM regression fo recasts performed better than the Phase I and II 

methods, especially for precipitation days. 

33 
2. 

18 i 0 

,. , , Cl " 

,. 

MAE (ktl- Peak Speed, Precl p 

o Ctlmatology 

o Phase I 

Phase II 

a OOZ MesoNAM 

12 Cl 0 002 MesoNAM Untar Rei. 

,. 

• 

• 

• 

2 

• 

Cl 

ClCl 

Cl o 00 a 

= OOOOOO~OO 00 

00000000000000000000 

5 ,. 15 2. 2S 30 

2. 

,. 

,. 

MAE (ktJ· Peak Speed, No Pre<:ip 

_ 0 Climatology 

o Phase I 

Phase II 

._ 0 OOZ MesoNAM " 12 o OOZMesoNAM Unear ReI:. 

10 0 tCl Cl 1 : 0 00 t----- -00 ----. _ ~ooooooooooo 00 

• OClClClOClOOClOOClClClClaaOClB 

2 

• 5 ,. 15 2. 2S 

1 

3. 

Figure 25. MAE for PWSD on precipitation (left) and non-precipitation days (right). Refer to Figure 

2 1 for a descri ption of the x-axis. 

" 12 r 0 

a a 1. 

• 5 

MAE {ktJ - Averalfl Speed, Preclp 

Cl II mo=' olo'~V~C===-,l 

, . 15 

o Phase I 

Phase II 

a OOZ Me50NAM 

o OOZ MesoNAM Unear ReI:. 

Cl 

2. 2S 30 

,. 

12 

10 

• Cl 

MAE (kt) · Average Spe~. No Predp 

o Climatology 

o Phase I 

Ph.sell 

a DOl MesoNAM 

o OOZ MesoNAM Unur Re . 

o 6 0 0 00 0 0000000 0 

ClCl " 

• 0 8 800 ,

~ 0000 - f 0000000000 - ~ ~ ~ -~-

2 ~---+----~--------~------~ 

o 5 10 15 20 2S 30 

Figure 26. MAE for A WSD on precipitation (left) and non-precipitation days (right). Refer to Figure 

21 for a description of the data points. 

Figure 27 shows the MAE for PWSD (left) and A WSD (right) from the MesoNAM regression 

forecasts. On precipitation days, the 1200 UTe model runs (in purple) performed just slightly better than 

the 0000 UTe model runs (in red). On non-precipitation days, there was virtually no difference between 

the 0000 UTe (in light blue) and 1200 UTe (in orange) model runs. 

MAE (ktJ - Peak Speed 

• o DOl MesoNAM Unear Reg., Precip 

0 122 MesoNAM Linear Rei., Precip 

• I 

o OOZ MesoNAM linear Reg ., No Precip 

122 MesoNAM Unear Rei ., No Precip 

~CI§CI CI !l !l , !l 8 Ia § EI 8 !l III CI EI CI a 

4 I _ 

- - .... - - . - - - - - = = f ---- 2 

- - - -1-- - ~~ 

0 • • 12 1. 20 

• 

• 

• 

1 2 

0 

0 

MAE (ktl' Averale Speed 

O OOlMesoNAM Unear Rei., Precip 

o 12ZMesoNAM Unear Rei., Precip 

D OOlMesoNAM Unear Rei" No Precip 

12l MesoNAM Unear Rei., No Preclp 

Cl Cl CIllC Cl§§§§CI ~68C1~E1E1E1 1 

- - -I " _ r ", 

• • 12 , . 20 

Figure 27. MAE for PWSD (left) and AWSD (right), for 0000 and 1200 UTe MesoNAM regression 

forecasts. Points 1-17 depict model levels 2-18, and points 18-20 depict the strongest winds in the lowest 

1000-, 2000-, and 3000-ft of the model. The legend shows the color corresponding to each method. 

The MesoNAM linear regression forecasts were clearly the most accurate in the verificati on data set. 

The differences between the 0000 and 1200 UTe model runs were not significant. The Phase I and II 

34 
forecasts were similar, although the best Phase II methods were slightly more accurate than the Phase I 

forecasts. The climatology forecasts performed the worst. 

4.1.3 Comparison of Phase I and /I Peak Wind Speed Predictions to 45 WS Wind Advisories 

The Phase I and II forecasts of PWSD were compared to the 45 WS wind advisories on days in which 

the 45 WS issued at least one non-convective wind advisory. In addition to KSC and CCAFS, the 

comparison also included wind advisories for PAFS. There were three types of wind advisories for nonconvective peak winds: 25-34 kt, 35-49 kt and 50 kt or greater. The valid time periods for the wind 

advisories were variable in length, ranging from around one hour to close to two days. Some of the wind 

advisories extended across multiple 24-hour forecast periods (0800 to 0800 local time). Multiple wind 

advisories could also be issued during one 24-hour forecast period. For this comparison, if a wind 

advisory extended across two 24-hour forecast periods, then the comparison analyzed the two days 

separately. If a 24-hour forecast period included multiple wind advisories, then they were counted as one 

wind advisory. The comparison used the strongest wind advisory for the forecast period. Wind advisories 

of the same type and forecast period, but different areas (such as KSC and CCAFS), were merged for the 

comparison. Also, the comparison did not include wind advisories with a time period of less than four 

hours, in order to avoid small-scale weather systems. 

Table 8 shows the comparison for days in which the strongest wind advisory was for 25-34 kt (left) 

and 35-49 kt (right). The wind advisories for winds of 50 kt or greater are not shown, since only two 

warnings meeting the criteria for this comparison (see previous paragraph) were issued during the 

verification period. A "hit" was defined as an observed PWSD in the correct forecast interval (25-34 kt or 

35-49 kt). An "over-forecast" was defined as an observed PWSD weaker than the forecast interval. An 

"under-forecast" was defined as an observed PWSD stronger than the forecast interval. Table 8 shows the 

45 WS out-performed the Phase I and II methods, because the 45 WS had the most hits. On days in which 

the 45 WS issued a wind advisory for 35-49 kt, they tended to over-forecast more often than underforecast, while the Phase I and II methods under-forecast more often than they over-forecast. 

Table 8. Verification for days in which the highest 45 WS wind warning/advisory was 25-34 kt (left) 

and 35-49 kt (right). Phase II methods are shown in blue. 

25 - 34 kt 35 - 49 kt 

Method Under- Over- Under- Over- Hits forecast forecast 

Hits forecast forecast 

least-squares single regression 35 13 4 35 29 3 

robust single regression 32 19 I 29 36 I 

stepwise least-squares regression 36 13 4 33 30 3 

stepwise robust regression 34 15 3 34 30 3 

least-trimmed squares regression 31 17 4 31 33 3 

Phase I 34 16 2 34 31 I 

45WS 45 6 2 41 2 28 

4.2 Timing of the Peak Wind 

As described in the first paragraph of Section 3.4, the timing of the peak wind was defined as the 

number of hours elapsed since the beginning of the forecast period (1300 UTC). The Phase I and II 

methods for predicting the timing of the peak wind were compared to six climatology values. The first 

three climatology values were based on the mean peak winds at 54-, 90- and 204-ft (Lambert 2002). For 

35 
each of the three levels, the cool season mean peak wind speed was calculated for each hour of the day. 

The timing was then defined as the hour in which the highest mean peak wi nd speed occurred. Table 9 

shows the climatological timing of the peak wind. The hours used for this comparison are highlighted in 

yellow. 

Table 9. Climatology methods for predicting timing of the peak wind, based on the mean peak winds in a 

previous AMU task (Lambert 2002). The "average wind speed" is the average of all the towers used in 

the previous AMU task, and the "maximum wind speed" is the maximum of all the towers. 

Hour 54 ft / 60ft 54 ft / 60ft 90 ft average 90ft 204ft 204ft 

in average wind maximum maximum average wind maximum 

UTC speed wind speed wind speed wind weed speed wind speed 

0000 11.03 12.69 11.71 11.73 14.14 14.1 9 

0100 10.94 12.53 11.66 11.68 14. 13 14.18 

0200 10.88 12.45 11.62 11 .63 14. 11 14.18 

0300 10.76 12.28 11.56 11 .57 13.98 14.06 

0400 10.62 12.07 11.5 1 11.53 13.91 14.03 

0500 10.47 11.87 11.42 11.43 13.71 13.85 

0600 10.39 11.76 11.33 11.33 13.6 1 13.75 

0700 10.24 11.57 11.18 11.18 13.41 13.56 

0800 10.1 4 11.50 11.05 11.06 13.22 13.39 

0900 10.16 11.58 10.98 10.99 13.19 13.37 

1000 10.14 11.58 10.94 10.96 13.18 13.37 

1100 10. 10 11.51 10.88 10.89 13.07 13.26 

1200 10.36 11.71 10.96 10.98 13 .13 13.36 

1300 11.21 12.40 11 .48 11.48 13.25 13.42 

1400 12.3 1 13.42 12.37 12.38 13.67 13.79 

1500 13.18 14.38 13 .08 13. 11 14.26 14.33 

1600 13.75 15.07 13.47 13.51 14.74 14.79 

1700 14.09 15.37 13 .77 13.8 1 15.07 15.09 

1800 14.24 15.50 13 .98 14.04 15.28 15.28 

1900 14.10 15.35 13.99 14.06 15.22 15.22 

2000 13.76 15.11 13 .7 1 13.79 15.05 15.05 

2100 13.06 14.51 13.20 13.26 14.65 14.66 

2200 12.03 13 .63 12.30 12.35 14.26 14.28 

2300 11.27 12.97 11.77 11.80 14.09 14.12 

The last three climatology values were based on the average timing of the peak wind in the 

verification data set and the Phase 1 and 11 developmental data sets. The average timing for the 

verification data set was 9.8 hours, while the Phase 1 and 11 data sets had an average timing of 9.7 hours. 

The MesoNAM forecasts for the timing of the peak wind were derived by determining when the peak 

wind occurred at model levels 2-18, as well as the strongest wind in the lowest 1000-, 2000- and 3000-ft 

of the model. 

Figure 28 compares the mean error (diamonds) and MAE (circles) of the timing of the peak wind, in 

hours. The Phase 1 (point 1) method had a bias near zero. The Phase 11 methods using least-squares single 

linear regressions (points 1-6, in yellow) also had a bias near zero, while the robust single linear 

regressions (points 7- 12, in yellow) had a large negative bias, around 4 hours. The 0000 UTC MesoNAM 

winds (points 1-20, in blue) had a positive bias around 2 hours. The first three climatology values had a 

negative bias of 4-5 hours, while the last three climatology values had a bias near zero. None of the 

methods performed significantly bener than climatology. 

36 
Figure 29 compares the mean error (diamonds) and MAE (triangles) for the 0000 and 1200 UTe runs 

of the MesoNAM. There was little diffe rence in MAE between the 0000 and 1200 UTe runs. All of the 

model winds had a positive bias, although the bias was larger in the 1200 UTe mode l runs. Since none of 

the methods improved significantly upon climatology, the 45 WS forecasters should use climatology for 

the timing of the peak wind until a more accurate method can be developed. 

Mean Error and MAE (hr) for Peak Wind TIming 

8 

6 

4 

0 

·2 

-4 [ 00.0 

-6 

0 

000 

; 

T 

5 

M E· Phase II 

MAE - Phase II 

<> M E· Climatology 

~ o M AE - Climatology 

~-- .. () ME - OOZ model 

10 15 20 0 MAE - OOZ model 

Figure 28. Mean Error (diamonds) and MAE 

(circles) for timi ng of the peak wind. The Phase I 

method is plotted on point I, Phase \I methods on 

points 1- 12, climatology methods on points 1-4 

and 0000 UTe MesoNAM wi nds on points 1-20. 

8 

6 

4 

2 

o 

Mean Error and MAE (hr) for Peak Wind TIming 

0000 and 1200 UTe Model Runs ~ 

[ . . ~ 

;..~ ... ' ····r ~ + ME-OOZ 

.. ······t:·S···.·: :-.... MAE- 12Z 

• ME - 12Z 

o 5 10 15 20 I 

Figure 29. The Mean Error (diamonds) and 

MAE (triangles) for the timing of the peak wind 

by the 0000 and 1200 UTe MesoNAM winds. 

37 
5 Peak Wind Tool GUls 

The AMU delivered the Phase II tool as a Microsoft Excel GUI. The 45 WS also requested a tool for 

MIDDS, their main weather display system. The Excel and MIDDS tools display the same forecast 

parameters: PWSD, A WSD, and the probability the peak wind will meet or exceed 25 kt, 35 kt and 50 kt. 

Unlike Phase I, the Phase II tool does not display the timing of the peak wind since none of the methods 

improved upon climatology. The Excel GUI uses the 0000 and 1200 UTC MesoNAM winds as input, 

therefore it can display predictions for the Day-I to Day-3 forecast periods. The MIDDS tool uses winds 

from the 0000 and 1200 UTC MesoNAM and Global Forecast System (GFS) models. The MIDDS tool 

can use the MesoNAM winds to display predictions for the Day-I to Day-3 forecast periods, and use the 

GFS winds to display predictions for the Day-I to Day-7 forecast periods. 

5.1 Microsoft Excel GUI 

The Phase I version of the Excel GUI used the moming XMR sounding as input to the prediction 

equations. The 45 WS forecaster manually entered sounding data, and then the GU I calculated and 

displayed the predicted PWSD, A WSD, timing of the PWSD and the probability the peak speed will meet 

or exceed 35 kt, 50 kt and 60 kt. The 45 WS no longer issues wind advisories for peak winds of 60 kt or 

greater, therefore, the Phase II version does not display the probability the peak speed will meet or exceed 

60 kt. However, the 45 WS did request the AMU add the probability the peak speed will meet or exceed 

25 kt, therefore the Phase II version displays the probability the peak speed will meet or exceed 25 kt, 35 

kt and 50 kt. Since the MesoNAM linear regression forecasts performed the best for PWSD and A WSD 

in the verification, the Phase II version uses MesoNAM forecast winds as input. MesoNAM forecasts are 

provided to the 45 WS by ACTA, Inc. and include hourly forecasts from 0 to 84 hours based on the 

model runs at 0000, 0600, 1200 and 1800 UTe. The 45 WS receives the MesoNAM forecasts via e-mail 

after each model run, and they can be stored on a computer hard drive as a text file . 

5.1.1 Equation Development 

The AMU created single linear regression equations for the Day-I to Day-3 forecasts of PWSD and 

A WSD, using both the 0000 and 1200 UTC MesoNAM forecast winds. The equations were developed 

with MesoNAM forecasts from the same POR as the verification data set, March 2007 to April 2009. The 

data were first stratified by precipitation and non-precipitation days. Equations were then developed for 

each model level, from level 2 to level 18, as well as the lowest 1000-,2000- and 3000-ft. 

Figure 30 and Figure 31 show the MAE values for PWSD from the 0000 UTC and 1200 UTC 

MesoNAM regression forecasts, respectively. As expected, the Day-I forecasts were the most accurate, 

followed by the Day-2 and Day-3 forecasts. The forecasts were significantly more accurate on nonprecipitation days. On precipitation days, the 1200 UTC runs were more accurate than the 0000 UTC 

runs, especially for Day-3. On non-precipitation days, the forecasts from the 0000 and 1200 UTC runs 

were very similar. 

38 
6 

5 

4 

3 

2 

o 

MAE (kt) - PeakSpeed, Precip 

0000 lITe MesoNAM 

0000000 0 a 00 

aooomoo OJ 

888g~ S-8 88 1g9-BS P 8 g~ 

t a Oay-l 

a Oay-2 

---!---- - a Oay-3 

5 10 15 20 

6 

5 

4 

3 

2 

~ 

MAE (kt) - Peak Speed, No Precip 

0000 UTe MesoNAM 

I , a Oay-l 

a Oay-2 

O Oay-3 

[ 00.0000 - 0 0 -

0000 8 8 00 

o .on 1'1 88 8 88BB !jl:l 

a a 0 -0-

o 5 10 15 20 

Figure 30. MAE for PWSD on precipitation (left) and non-precipitation (right) days, for Day-I to 

Day-3 forecasts. Forecasts are from linear regression equations using the 0000 UTe MesoNAM forecast 

winds. Points 1-1 7 depict model levels 2- 18, and points 18-20 depict the strongest winds in the lowest 

1000-, 2000- and 3000-ft of the model. 

6 

5 

4 

3 

MAE (kt) - Peak Speed, Precip 

1200 lITe M esoNAM -,--

o I i I 

0oomoooo ooo ~ 00 0 

6 

MAE (kt) - Peak Speed, No Preclp 

1200 lITe M esoNAM 

o Oay-l 

o Oay-2 

a Oay-3 9 B g-g-to'C-c-h -Cl-C1j1=l a a IJ ~ 5 

I a Oay-l 4 'P 

o Oay-2 0 a a a m 0 ~ 8 B 8 8 B 8 8 ~ 8 R 8 oom oooo 00 om CI 

--+ L-='-0 ~a 3 1 3 - 81l-Cl-s-a-B1-"-'''-=--t--=--'---+---''Y 

I I 

2 ------ ------ ------- ------~0 II 2 -------~------ ------~ ------~0 '----" _-----=-_______ ....J 

Figure 3 1. MAE for PWSD on precipitation (left) and non-precipitation (right) days, for Day-I to 

Day-3 forecasts. Forecasts are from linear regression equations using the 1200 UTe MesoNAM forecast 

winds. Points 1-17 depict model levels 2- 18, and points 18-20 depict the strongest winds in the lowest 

1000-, 2000- and 3000-ft of the model. 

Figure 32 and Figure 33 show the MAE values for A WSD. The Day- I and Day-2 forecasts had 

similar MAE values. The Day-3 forecasts were worse than the Day- I and Day-2 forecasts, except the 

Day-2 and Day-3 forecasts from the 0000 UTe runs were similar on non-precipitation days. On 

precipitation days, the 1200 UTe forecasts were slightly more accurate than the 0000 UTe forecasts. 

39 
MAE (kt) - Average Speed, Predp 

0000 UTe MesoNAM 

6 

O Oay-1 

O Oay-2 

5 O Oay-3 

0000 0000 1il 0 0000 

88-8 E1'-g-~ -8-8 8 EI 

O OElEI j- -- - -

4 

3 

2 

o 5 10 15 20 

6 

S 

4 

3 

2 

I 

+-

MAE (kt) - Average Speed, No Precip 

0000 UTe M esoNAM 

o Oay-1 

o Day-2 

o Oay-3 

---f- - - --

ElCCI:! 8BBB 00 

U§ ~ 00 ~8 -.D_0-g.J:L' 

o 5 10 15 20 

Figure 32_ MAE for A WSD on prec ipitation (left) and non-prec ipitation (right) days, for Day-I to 

Day-3 forecasts. Forecasts are from linear regression equations using the 0000 UTC MesoNAM forecast 

winds. Points 1- 17 depict model levels 2-18, and points 18-20 depict the strongest winds in the lowest 

1000-, 2000- and 3000-ft of the model. 

6 

5 

4 

3 

2 

o 

MAE (kt) - Average Speed, Precip 

1200 UTe MesoNAM 

I I 

o _'Oay-2 ~ I 

o Oay-3 

0000 ';' 000 0 0000 

I [J°B°'il 8"OF_r " BB CI -

5 10 15 20 

6 

5 

4 

3 

2 

-

MAE (kt) - Average Speed, No Precip 

1200 UTe MesoNAM 

o Day-1 

o Day-2 

o Oay-3 

- -

fBo 888 8 00 00 gB 8 OOOCI B 

6-B-tI 

o 5 10 15 20 

Figure 33. MAE for A WSD on precipitation (left) and non-precipitation (right) days, for Day-I to 

Day-3 forecasts. Forecasts are from li near regression equations using the 1200 UTC MesoNAM forecast 

winds_ Points 1-1 7 depict model levels 2- 18, and points 18-20 depict the strongest winds in the lowest 

1000-, 2000- and 3000-ft of the model. 

For each forecast parameter, the tool uses the model level with the most accurate equation, based on 

the lowest MAE values. Since the tool uses separate equations for precipitation and non-precipitation 

days, as well as each forecast day, 12 prediction equations are used for both PWSD and A WSD. Table 10 

and Table II describe the PWSD and A WSD equations used in the Excel GU L 

40 
Table 10. Prediction equations for PWSD, based on linear regressions in which the 

predictor is the strongest 24-hour wind speed at the MesoNAM model level, and 

the predictand is the PWSD. Refer to Table 7 for the height of each model level. 

Foreeasl Preeipilalion Model Run Model MAE Slope Intercept Day Occurrence (UTe) Level (kO 

Day-I Yes 0000 5 4.6 1 1.05 7.99 

Day-I No 0000 2 2.76 1.08 5.55 

Day-I Yes 1200 4 4.53 1.10 7.87 

Day-I No 1200 2 2.73 1.13 5.41 

Day-2 Yes 0000 4 4.78 1.08 8.76 

Day-2 No 0000 3 3.07 0.98 6.72 

Day-2 Yes 1200 18 4.56 0.58 16.24 

Day-2 No 1200 4 2.88 0.99 5.84 

Day-3 Yes 0000 15 5. 17 0.58 17.04 

Day-3 No 0000 3 3.42 0.95 7.54 

Day-3 Yes 1200 17 4.89 0.65 14.45 

Day-3 No 1200 4 3.43 0.90 7.60 

Table II . Prediction equations for A WSD, based on linear regressions in which the 

predictor is the strongest 24-hour wind speed at the MesoNAM model level, and 

the predictand is the A WSD. Refer to Table 7 for the height of each model level. 

Forecast Preeipilalion Model Model MAE Slope Intereepl Day Occurrence Run Level (kO 

Day-I Yes 0000 4 3.68 0.78 4.76 

Day-I No 0000 4 3.02 0.75 2.25 

Day-I Yes 1200 4 3.64 0.8 1 4.31 

Day-I No 1200 4 2.98 0.76 2.59 

Day-2 Yes 0000 4 3.61 0.79 4.76 

Day-2 No 0000 5 3.36 0.63 4.20 

Day-2 Yes 1200 2 3.5 1 0.82 5.37 

Day-2 No 1200 5 3.16 0.67 3.53 

Day-3 Yes 0000 15 4.0 1 0.40 11.62 

Day-3 No 0000 5 3.30 0.64 4.28 

Day-3 Yes 1200 5 3.94 0.71 5.9 1 

Day-3 No 1200 4 3.36 0.66 4. 17 

In Phase I, the 45 WS developed a statistical method for estimating the probability the PWSD will 

meet or exceed the thresholds for wind advisories (Barrett and Short 2008). The method is based on the 

error bars of the linear regression equations. The equation for calculating the probabilities gives the area 

under the right-side of the Gaussian curve: 

1-[0.5 * (I ± ~I- J2 /".*((x - Y)/ Zf))] 

In the equation, x is the threshold value (25, 35 and 50), Y is the predicted PWSD and z is the predicted 

sigma (estimated error of the linear regression equation). The + sign before the radical is used for y ~ x, 

and the - sign is used for y > x. 

41 
5. J.2 Using Ihe Excel GU I 

The AMU developed the Excel GUI using the Visual Basic for Applications programming language. 

To use the tool, the forecaster opens the Excel file to the " Intro" worksheet (Figure 34). The Intro 

worksheet contains instructions on how to use the tool. Common user questions are answered in the 

" FAQs" (Frequently Asked Questions) worksheet. To start the tool, the forecaster selects the "Start CoolSeason Peak Wind Calculation" button. The tool then displays a "Browse" dialog box containing a list of 

files. The dialog box opens to the file directory last used. The forecaster needs to navigate to the directory 

containing the MesoNAM files and select one of the files (Figure 35). 

42 

, 

• • ,

, • 

6 ' 

.. 

I 

I , P .. k WInd Tool lor Gene'" FOfK .. tIII9. ION .. I; v .... !on 1 .• ,"'-Y 2010) 

12 o...IopM .,. ~,,",~u... 

U ANd ......... Uta."""_ ......... IMI ............... 

• 

.. " 

.. " 

, 

~ ~ofPMkWlndTooIfofco.-elFor.ea."", : 

11 n.s ,s 'IP~ use. tnte1&ee IGlIjlOOlh, ~ Ihe U5e1loliMd anN:.TA MesoNAM leld" 

22 In 0II1er 10 CIb.ftIe me 'Ipeed and mq d lie 0IIi,. (800 am tw.y to 8'00 ~ IOmOfTOfIj peak WIne! 

2J speed The 1001 can be used rOf !he Giy. 1, dlJ.2 Of OIJ.3 forecasts 

I" 2S IMtrvctIoft. on h_ to II •• the i00i: 

1

21 I SIan Ihe I0OI by ckking on1he "Su.rI Peak WIIId C81a8tion" bll!0I\ 

~ 2 SeIed in ACT'" I.4esoNAAIIeId fI/IIlfom me &_ cbIog 00., by Ieft.cllcklng on. fl/elwme 

.., n. fileNrnes IIOITIlIII¥ SWtvdl"slr, net off' SeIed. 'IetOlll' 00 or 12 UTe modeIlIJI 

1I After W'IkIng .1\IefWrIIt. (ido; on"OK"I000re....OI "CanteI" 10 SIOO .. IOOI 

31 The I0OI ihenwnfles !he Me IS 110m. 00 or 12 UTe model l1l'i, .ndrtmllkes Sin !he file ~II\ 

1

u12 Ihe _ tomId IonnM f!he Me IS ntI m tie correa rom.~ IIf\ error message IS !i_~ and !he I0OI i$ 

: 3 The "Petk Wind CaIOJanon" dialog bol lS eIIspllyed 5eIect. roreuSl dly II1d IhQn seied!he 

l6 "Calculet. P .. ~ W1r.r' bIAIorI 

" .. . The pl"edIc*I...ues I re dt~yed ItI IM "Peak WInd Pf~· GlI • dispNYS .,. dlifit 

~ peak .ncI • ......age 'Mfodspeed IMJOS:ti KSC 100 CCAFS (14110 300. Sl ~ lor <It)'S \IlIMtI 

.0 PfeopItaIIOn !1lrIhbrn rain, showeB eIC. .ndWltl'lolfPfeopnMOn ThelorecaSl 

, I*IOCIIOO ~ rJ lie mocttI ,.., 15 chp/llyed .t !he bI:*m d IhI GlJ 

" U 5 5eIecI fle "Choose ~ ForK." Dey From Slme Model AI"II" bUIon 10 selecllIlOIhef 

.u forea,51 pertOd SeIed IhB "Pnnl 0\fpI.t' bt.CIonlo Pllnl1he GUI S8llcl1Ie "end rod' bUIorl tG slop 

. S hloo1 

Figure 34. Intro worksheet to start the Excel version 

of the Peak Wind Tool. 
It r OW'ioC ? 'x 

look~ , 10 Ap<2OO9NAMsn 1:' 1 ~ r.a X r:;" Ii] -

llill ~""'''' sn 7 .. 79"0.e32..Q9040 1-QOOJ I sn" ,9<0. ,32-0901lJ5-1200 sn7 .. mo.e32-09Q"10-0000 I sn7-41910.e32..Q904 1"-I 200 

Docuner<s sn 7 .. mo.e32.()9(MQ 1 {)600 sn717940.e32...Q90105-18OO $07 .. 79'40 .e32..Q90410{)600 $0717910.e32-090111-18OO 

"""'OP sn 7" mo.e32..()9(MQ I-I ZOO ' sn717940 ,e32...Q9(H{)6-{X(( sn7 .. 7940 .e32-09011 1)-1200 ~ sn717910.e32-o901Is-<DXJ 

sn71791O.e32...Q901O I-I BOO I sn7 .. 7910 .e32-Q904(l6.-{)6OO sn717910 .e32-09011 1)-1800 ~ sn71mo.e32-090115-0600 

iJ ~s sn71791O.e32...()900W2-QOOJ . sn7"7910,e32-09IJ106.-1200 sn71791O.e32-090111-QOOJ . $0717910.e32-090115-1200 

sn717910.e32-090102{)600 ~ $07179i0 .e32-090406- J 800 sn71791O, e32-09011 1-0600 ~ $071791O.e32-09011S-IBOO 

et sn717940.e32-090402-1200 !gj sn717910 . e32.()9()4()7-QOOJ sn74791O.e32-Q90111 -12oo ~ sn717910 .e32-09(K 16-0c00 

~Network sn71791O.e32-Q90102- 1800 ~sn717910 e32.()9()4()7.Q600 sn74791O,e32-09Q111-1600 ~ sn71791O.e32-o901I6-()600 

Places sn717910.e32-Q9Oot03-QOOJ sn71791O.e32-09M07- 1ZOO I sn"7940. 032-II904I2-(XOI I $0717910.1132-090116-t 200 

$0717910,e32...(}9(K()3{)60 sn717940.e32.()9(MQ7-1BOO sn 747910.e32-0901 12-Q600 . sn71791O.e32-090116-1 BOO 

sn71791O.e32-Q90403.1 ZOO sn717940.e32-Q90106-OCOJ sn71791O,e32-090112·1ZOO ~ sn71791O,e32-Q9Q117-OOX1 

sn 7 .. 7910. e32-090403-1600 sn717940.e32..Q90108-06O() sn71791O,e32-D90112-1 600 ~ $07"7910.e32-090417-0600 

sn7''7910,e32-o90101-QOOJ sn717910.e32-Q901CJ6-.1 ZOO r--'

-

I sn717910.e32-()90117-1200 

sn7"79"0.e32-Q9OotOH )600 sn71791O.e32-o90108-1800 sn717910 .e32-090113{)600 sn7 .. 7910. e32-Q9Oot 17-1800 

sn 717910.e32-Q904()04-1 ZOO sn7"7910.e32-090109-00C(J sn71791O.e32-0904 13-1200 ~ sn71791O.e32-Q901 18-oo:xJ 

sn71791O.e32-{)9(M(M-I 800 ~sn"7940.032_ sn71791O.e32-090413-1eoo !fj sn7-4 7910.e32-{)91)1 t 8-0600 

~ sn 7"7940.e32-090405-ocoJ ~ sn7 .. 7910 .1132-090109-1 ZOO sn717910 .e32-0904 1 .. -ocoJ ~ sn 7"7940.e32.Q9IM 16-1200 

~ 50717910 .e32..Q901()5{)600 ~ sn7 .. 7940. e32-090409-18OO [il sn71791O.e32-09041 H.6oo ~ sn H7940 .e32..Q90118-1800 

< I ~ 

File [}MOe: 1 L .... I 

_of typo, IAI '1M (0.0) vi 

1 Tools -I "" ~ C«><~ l 

Figure 35. Dialog box used to select a MesoNAM input fil e_ 

The tool veri ties the fil e chosen by the forecaster is in the correct format and is from a 0000 or 1200 

UTC run of the MesoNAM. If the tile is invalid, the tool displays an error message and exits. Otherwise, 

the " Peak Wind Calculation" dialog box is displayed (Figure 36)_ The dialog box shows the date and time 

of the model run. In this example, it is the 0000 UTC run from I April 2009. The forecaster selects a 

forecast day and then selects the "Calculate Peak Wind" bunon. The Peak Wind Prediction GU I with the 

desired output is then displayed (Figure 37). The GUI shows the forecasts for PWSD, A WSD and the 

probability the peak wind will meet or exceed 25 kt, 35 kt and 50 kt. The lef\Jright side of the GU I shows 

the forecasts for precipitation days/non-precipitation days. Since no methods for predicting the timing of 

the PWSD performed bener than climatology, the GUI contains the fo llowing note: "The peak wind speed 

of the day usually occurs during the afternoon or evening. The climatological timing of the peak speed is 

2248 UTe. Adjust the time of the peak wind, based on expected movement of fronts, wi nd surges, 

changes in pressure gradient, etc_" The forecast period is displayed at the bonom of the GU I. In this 

example, the forecast period is from I April 2009 (0800 local) to 2 April 2009 (0800 local). The 

forecaster can then select one of three options: "Print Output", "Choose Another Forecast Day From 

Same Model Run" and "End Tool ". 

43 
44 

Calculations use the 00 UTe IUl of I>CTA 

Meso'IAM on 4/1~ 

_t Fon!cast Day (void 13Z - 13Z) 

(0'00 

r 0ay-2 

r Oay-3 

I calcula .. Peak WI"d I cancel I 

Figure 36. Dialog box used to select the forecast 

day. 
$ Peak WhI Speed (13Z-l3Z) fnm ~f_ to 3DO It Aa. 

'''IrF-"~l~UUfl Ul...UM"a:.,.;~ K5C 

or CCAFS: 

Pl:I.ot WiaJ r::-:- knots 

Speed I 29.9 

If IJrt!UpllMlUI Uu~ IILI' uu;uacross kSC or CCAFS: 

~--------------------------~ 

I • ~--~~'~·'-----·-·:· If preciPtaUon ()t(U'S across KSC If preciPtation does not occur 1 or CCAFS: across rsc or CCNS: 

'_Wrd Speed I r-:;;"7 193 <naG ''''aoeWrd Speed I ~ 163 knots 

• If precidtation occurs across rsc or CCAFS: 

If PI ec:lpltatki i does not oca.r a:ross KSC (I" CCAf'S: 

25 knolS I 78.7 percarr. 

~ knots f19.9 percen: 

50 knos ~ pElfcen: 

251crml I 10.0 pe-cent 

~ kno:s f"OD percert 

so kro:s f"OD percert 

Print nrtput I 1a-_~ForeoastDay I 

From &ome Model Run EI1d Tool I 

+ on days With thunderstorms, ttB tool wei ~tty l.a'KIerestimate the pe_ 

,.;00 ..-I and the"..-ty the peak ..-I wi! meet ... """oed 2S kL 3S Irt. and 

5Okt, 

$ The peak wInd..-l of the day usuaIy oca.n rumg the aft""""", ... -*'9. 

1l1e tlmatoklgKal ~ of the pede speed ts 2248 UTe. ~t the tme of peak WInd, 

based on expected IOOYefTlE!f'It of frunh, wnt swges. changes In pre5SU'e gradent. 

etc. 

Based on day-l forecast fl"Oftl MesoNMt 00 UTe run on 4/1/2009 

Valid forecast period; 4/ l/ 2009 (ill) to 4/ 2/ 2009 (13Z) 

Figure 37. GUI displaying the output from the Excel version of the 

Peak Wind tool. The top section shows the predicted PWSD, the 

middle section shows the predicted A WSD, and the bonom section 

shows the probability the PWSD will meet or exceed 25 kt, 35 kt and 

50 kt. 

5.2 MIDDS GUI 

The AMU initially developed the MIDDS tool to use gridded model data as input because the ACTA 

MesoNAM files are not available in MIDDS. The tool was created using the Tool Command 

Languagerrool Kit (TcIlTk) programming language. It used the model data for the closest grid point to 

XMR. The MIDDS gridded data contains output from the 0000 and 1200 UTC model runs of the North 

American Mesoscale (NAM) and GFS models. Both models have a horizontal grid spacing of 80 km in 

45 
MIDDS. After the 45 WS was given access to receive point model data from Johnson Space Center, the 

tool was modified to use these data. 

5.2.1 Equation Development 

While the MesoNAM contains hourly forecasts out to 84 hours, the NAM and GFS gridded data in 

MIDDS contain 6-hourly forecasts out to 60 hours and 240 hours, respectively. The MIDDS tool used 

NAM data to generate Day-I and Day-2 forecasts and GFS data to generate Day- I to Day-5 forecasts. 

The Day- I, Day-2 and Day-3 forecast equations developed for the Excel tool were also used for the Day1,2 and 3 forecasts in the MIDDS tool. The Day-3 forecast equations were used for the Day-4 and Day-5 

forecasts. The forecasts based on the GFS gridded model data used the MesoNAM linear regression 

equations developed for the Excel tool, since the verification data set did not contain GFS model data. 

Since the MIDDS gridded model forecasts are only available every six hours, the AMU updated the 

linear regression equations to take the forecast interval into account. Otherwise, the MIDDS tool would 

have a low bias in predicting wind speeds. The MIDDS tool calculates the probability the PWSD will 

meet or exceed 25 kt, 35 kt and 50 kt, using the same statistical method as the Excel tool (Section 5.1.1). 

Based on feedback from the 45 WS, least-squares single linear regression equations were developed 

for the daily average wind speed at 30-60 ft, which is one of the forecast parameters in the 24-Hour and 

Weekly Planning Forecasts issued by the 45 WS. The predictands were calculated by averaging the 5-

minute average wind speeds at 30 ft and 54/60 ft from all of the towers used in this task through a 24-hour 

period (1300 UTC to 1300 UTC). The predictors were calculated by averaging the hourly MesoNAM 

winds for the 24-hour period. The predictors were evaluated for model levels I and 2 of the MesoNAM, 

which have average heights of70 ft and 207 ft MSL, respectively. 

Figure 38 shows the MAE for daily average wind speed from the 0000 (left) and 1200 UTC (right) 

model runs. The level-I (in blue) and level-2 (in red) forecasts are compared to climatology (in green) for 

Day- I to Day-3, including precipitation days, non-precipitation days and "all days". The "all days" 

forecasts included precipitation and non-precipitation days. Separate climatology val ues were calculated 

for precipitation days (8.4 kt), non-precipitation days (6.8 kt) and "all days" (7.3 kt). The level-2 forecasts 

performed slightly better than level-I forecasts and significantly better than climatology. As expected, the 

MAE values were lowest on non-precipitation days and highest on precipitation days. The MAE values 

increased slightly from Day-I to Day-3. There was not much difference between the 0000 and 1200 UTC 

runs, except the level-I and level-2 forecasts from the 1200 UTC runs did better than the 0000 UTC runs 

for Day-3. 

The forecasts for "all days" were then compared to the average of the precipitation and nonprecipitation days, weighted by the number of precipitation and non-precipitation days. Table 12 shows 

the differences between the "all days" forecasts and weighted average forecasts were not significant. 

Therefore, unlike for PWSD and A WSD, the prediction equations for daily average wind speed did not 

stratify by precipitation. 

46 
MAE (kll- Dally Average Speed 

0000 UTC M esoNAM 

1 f-t-I 

i Ii! r o level-l 

0.5 C- o level-Z 

o ------'--~-

o Oimatology 

~ ·S "9- ~ ... ~ ",'" " If' t- o.' .. .:. 

Q 7> :t Q'" 

t- Q 7> t- Q 

2.5 

2 ~ 

1.5 

1 

0.5 

0 

t- ·S ,~ ~ .. o.' 

;.. :t t- ... Q .:. 

Q'" 

MAE (kll- Daily Average Speed 

1200 UTC MesoNAM 

I 0 

1 8 '-- r 

o level.1 

o Level-2 

o dimatology 

~ ·S ·S ~ ·S .,s. ~ ... ~ ~ "v'" ~ IlIw ,.,V' ;.. 

t- o.' o.' .:. o.' 4,,4;', ;t.. 

Q " ~o Q'" 7> .-I <:J'fI t- " t- Q 7> t- Q t- Q Q 

Figure 38. MAE for daily average speed for forecasts from 0000 (left) and 1200 UTC (right) 

MesoNAM. Forecasts for precipitation days are plotted on points 1, 4 and 7 of the x-axis. Forecasts for 

non-precipitation days are plotted on points 2, 5 and 8, and forecasts for all days are plotted on points 3, 6 

and 9. 

Table 12. MAE (kt) for daily average speed. "All Days" is all days in the verification data set. "W. 

Avg" is the weighted average of precipitation and non-precipitation days. "00 UTC" is the 0000 UTC 

MesoNAM and " 12 UTC" is the 1200 UTC MesoNAM. 

Forecast 

Leve/-I Leve/-I Leve/-2 Leve/-2 Leve/-I Leve/-I Level-2 Leve/-2 

00 UTe 00 UTe 00 UTe 00 UTe 12 UTe 12 UTe 12 UTe 12 UTe Day All Days W. Av-,! All Days W. Av-,! All Days W. Av-,! All Days W. Av-,! 

Day-I 1.00 0.99 0.96 0.95 0.98 0.97 0.94 0.93 

Day-2 1.1 5 1.14 1.11 1.10 1.08 1.07 1.05 1.04 

Day-3 1.39 1.37 1.36 1.34 1.22 1.19 1.17 1.16 

5.2.2 Point Mode/ Data 

The 45 WS MIDDS workstations were given access to point model data from a MIDDS server at 

Johnson Space Center. The MIDDS point model data are available for the MesoNAM and GFS models. 

The MIDDS point model data have a higher vertical resolution than the gridded model data. For example, 

the vertical levels in the gridded model data are primarily confined to the NWS mandatory sounding 

levels (surface, 1000 mb, 925 mb, 850 mb, etc.). On the other hand, the point model data include all of the 

model's vertical levels. The MesoNAM contains 60 vertical levels from the surface to 14.5 mb, while the 

GFS model contains 64 vertical levels from the surface to 0.29 mb. The point model data also have a 

higher temporal resolution. While MIDDS gridded model forecasts are available every six hours, point 

model forecasts are available every three hours from the GFS and every hour from the MesoNAM. In 

order to increase the accuracy of the tool, the AMU updated the tool to use the higher resolution point 

model data. The tool's source code and installation instructions were then delivered to the 45 WS for 

operational use. 

5.2.3 Using the MIDDS GUJ 

To use the MIDDS tool, the forecaster first opens it from the MIDDS Weather Menu. The tool reads 

in MesoNAM and GFS data from the latest 0000 and 1200 UTC model runs. After the tool has finished 

reading the model data, it displays the message " Initialization Complete". The user selects a model name 

47 
(NAM or GFS) and model run ("OOl" or" 12l") and then selects the "Calculate Peak Wind" bUlton. The 

tool calculates and displays the forecasts for PWSD, A WSD, daily average wind speed, and the 

probability the peak wind will meet or exceed 25 kt, 35 kt and 50 kt. Except for the daily average wind 

speed, separate forecasts are made for days with and without precipitation. Based on feedback from the 45 

WS, the tool was updated to provide forecasts out to Day-7. Therefore, the MesoNAM forecasts are for 

Day- I to Day-3, while the GFS forecasts are for Day-I to Day-7. Figure 39 shows the tool's forecasts 

from the model runs on 7 July 20 I O. The forecaster has the option of printing out the displayed forecasts 

by selecting the "Print Display" bulton. The user closes the tool by selecting the "Exit Tool" bulton. 

....... : ...., HAM • GFS ....... rw.: " ooz • 

~':;~;:dt;:r~o:~;!:=o:-~:r:..~:" unit 

•• P; ,. i4 hou.r peW<. i-•• cenci wind ep •• d. tt .... aw:fac:. to 300 tt AO!. 5-11i.n ,. S-.. inu.t.e __ ",ag. rind .p •• d at th. tt ... ot th. paN:. rind, f,,_ .urf~. to 300 ft AOL Dly. __ 24_hour --""!if& rind ap •• 6 hoa 30 ft to 60 't MIL 

INti.u .. in9 )IAJII d.au tr_ 00 l1l"C run .... l"itialicin'l"" ute froa 12 ~ run on Initial..iain'l )lAX data IT .... DO tn'C run on :tnitiallain9 DX dee.. tr .... 12 l1T'C run on I.n.itia1icing .,.. data tr .... 00 l1l"C .. un .... lnitial..1c>.J\9 IiUIX dau tr .... 12 OTC run O<'l 

tN.t.i.u.inV MItJII dee. fr .... 00 UTe run on lnitial.l.c:r.t\9 IIAX au fr .... 12 ore nan on lnitial.1cin9 IO\X data fr .... DO UTC run on :tniti.u.it\9 )IAJII elata It .... 12 urc r\WI on %nitial.l.I;:I.n9 JfAX ate fro .. DO UTe run on Xn.i.tia.l..1O::I.n9 MAlI data froa 12 UTC run on 

:1nitiilici0\9 MM choe. fr .... 00 UTe run on Xnitial..iC1ll9 If AX data frca 12 vrc run on :rn.itial..i.in9 Dr s dar.. tr .... 00 UTC run on lnitialiZ1n9 ars dau fro. 12 urc run on :1nitial.iC1n9 01'1 dac.a tr .... DO UTC run on :bI.itial.i"int O. S data fro& 12 uro c ...... 0>'> 

Zrtitial.iKi"9 OI'S clae.. tco. 00 UTe c ...... on tr.itialio:inq 01'1 dae.. rc .... 12 UTe 

:1rI.itial.io:in9 OI'S clae.. teo. 00 U'1"O t'UI\ 0>'> 

Znitialilli"9 01'1 data tco. 12 uro t'UI\ on :lI\itialiunq O1'S dae.. tc .... 00 vrc IWI on :Initialillil\9 01'1 clata. tco. 12 U'1"O nm 0>'> 

1nitialio::u.9' 01'1 data. fco. 00 In'C IWI on Xftitial..1O:Lnf 01'1 d .. ta fco. 12 U'1"O l'W\ on Irtitial.ill .. tl0n COIlJlLe t. IlIO/CeAI'l 24- 8our (BAJI _8AJ1) ronc a . t 

HAX 00.1 nlft I'r.e ip lIeqin Du1 Puk(kt.) ::'.i(i~f S~~~t.) Oy- l 07 / 07 07/0B 27 . 4 22 . 7 lB. 2 Oy-2 07/OB 071051 25. 22 . 3 17. 3 11J'- 3 01/051 07/ 10 22 . 2 U . S 15. 2 

IfU 12.1 cun Pn e i p ::'..k(i~f ~~~t.) Oy-l ~;m 0f't88 r.ak(kt.) 

25. ' 22. 3 17. 3 Oy-2 0710B 011051 22, 7 14. 7 11. 7 Oy- 3 07/051 01/10 n , 16 , 6 13. 1 

OpS ooz cun 1'recip Ho1'r.oi f 1'r.ci~ 

Oy- l ~;~1 O~gB "~~t) P.ak(ltt. Ilin~ t ) 19 . 1 lS. 11J'- 2 07108 071051 23 6 n . l >S. ' Oy- 3 071051 07/10 ZS . O 18. 3 17. 1 0.,-4 07 / 10 07/11 ZS 5 110. 51 17. 5 0.,- 5 07/ 11 07112 25 . 4 21 . 7 17. 4 0.,- ' 07112 07113 26 . " 21. , lS. 4 0.,_7 07/ 13 07/ 14 2B. 5I 23 . 6 151 . B 

on 12'1 run rrec ip lIagift End reak (kt.) ;::k(k~f ~~~~t.) Oy- l 01/07 0710B 23 . 6 n . l >S . • Oy-2 07/oS 071051 '" 20 . 0 >S. , 

0.,_3 07/051 07 / 10 '" 15 , 7 12 . 3 Oy-4 07110 07 / 11 21 . 5 17 . 1 13. 6 Oy- s 07/11 01 / 12 ". 18. 7 14 . , 

0.,-' 07112 07113 255 21 . 1 16. 5 0.,- 1 07113 07114 26, 4 23. 11 18. 8 

at .i9"Oa a-l. II .•• at .i9*" l.ev.l. 2 . .t .:i.ga .. a-l. 3 .. .t. lIill'l'a l.ev.l. 3 . at .i9"Oa Lev.l. 4 .. . .t .ill'l'. t.v.l. 4 .. . .t ai~ I.e .. al. 5 .. . .t .i9M< l..vel 5 .. . .t aip. laval. 1.5 .•. .t .ipa 1.eval. 15 ... at .i9"Oa l.aval. 17 •.. at ai9"O. laval. 17. .t aipa l.ev.l. 18 .. .t .i~ Lev.l 18 •.. at ai~ laval. 2 .. . at. aip_ l.ev.l. 2 .. . at. .ipa lav.l. 3 .. . .t .i9"O. l. .... l. 3 .. .t .ip_ l.ev.l. 4 .. . .t .i ... lav.1. 4 . . . .t .ipa l..,.1. 10 .. .. t .:i.gaa 1.e.,.1. 10. at. .igaa 1.8 ... e1. 11 .. . .. t dgaa 1. .... 1. 11 .. . at. .i ..... 1.8v.1. 1 2 .. . at. ei,... 1.8v.1 12 .. , 

1f000ncip All S- lf.in Olt) Dly"". Olt) I'r: ~i~t p~:~i:e~{ >S. , ... 65. 1_ IS. I-" 14 . 5 ' .3 55 . 511t 24 . 71t 10 . 6 ... 34 . 2_ 3. 0_ 

lI'oPr.cip All S-lf.in(k.t.) D1.yA.,. {kt.) pr:~:.;i~t. p~:~i;C~{ 14. 8 ... 5 3 . B_ 21 , n ... 3 . , 35 . 0_ 0 . 2_ 10 . 7 .., 26 . 6 .. 2 . ' • 

No1'ncip AU 5-MinOlt) DlyAv. Olt) 1'r:~2Si~t 1'~:1'2Sc~~ 12. 6 ... :W . 21t 4 . 7_ 12. 51 5 . ' 41 . lit 6 . 5I1t 12. 1 5 . 5 451 . 81t 7 . 01t 13. 6 5 . ' 5 2 . ,_ U . 4" 14 . 1 ' . 5 52 . 4. 23 . Sit 14 . 1 ,., 60 . Bit 24 . 11 .. 15. 6 .., 71 . ,.- 37 , 11'-

Ifol'r.c ip All S · lfin(kt.) DlyAv. {kt.) pr:~2Si~t. 1'~:.'2Sc~{ 13. 5 5.' 41 . n 4 . 2' 13. 1 ... 3 3. o. a . 7' 10. 1 ' . 5 2a . e. 1 . a. 11. 3 .., 2B. B. 4 . 3. 12. 3 S . 3 31 . 1111 B. O. 14. 0 •. ,5 3 . 1 .. UI . 3. 16 . 1 ' . S .511 , lit 40, 4 .. 

pcacip HoPreeip Prb . 35 kt. Prb . 35 kt 10. 3'- 0 . 01t 7 . 0'- 0 . 01t 2 . li lt 0 . 01t 

pc-c ip HoPrec ip rcb . 35 kt Prb 3S kt 5 . BIt 0 . 01t 1 . 81t 0 . 0'- 1 . lit 0 . 0 .. 

rrec ip MoPr.cip Prb . JS kt P rb . 3S kt. 1. . Bit ,a. 3 . n ,a. 6 . Bit 0 . 01t 8 . O. ,a 7 . S. 0 . 11t 11 . 5' 0 .1' 18.4' 0 .5' 

1'rec ip )JoPncip rrb. 35 kt. rcb . 35 Itt. 2 . B. 0 , 0' 1 . 6. ,a. 1. 4. 0 , 0. 1 . 41t 0 . 0111 1 . 6" ,a. ' . 2' 8 . 4111 '" ,o. 

- . 

Pr. cip lfoPceeip I' rb . SO k t pcb. SO kt. 0 . 0_ 0 . 0_ 0 . 0'- 0 . 0 .. 0 . 01t 0 . 01t 

rr.cip HoI'recip Prb . 50 kt. r rb, SO kt. 0 . 0'- 0 . 0_ 0 . 0 .. 0 . 0 .. 0 . 0 .. 0 . 0 .. 

:b~~~ ltt 1'~:~~~o~t 0 . 01t 0 . 0 .. 0 . 0 1t 0 . 01t 0 . 01t 0 . 01t 0 . 01t 0 . 01t 0 . 01t 0 , 0. 0 . 01t 0 . 0' o . n 0 . 0' 

PnOlP NoPreoip pcb. SO kt. prb. SO kt 

0 . 0 ' 0 . 0'- 0 . 0. 0 , 0. 0 . 0. 0 . 0111 0 . 0 . 0 , Dill 0 . 0' 0 . 0" D. O. 0 . 0' 0 . 0111 0 . 0. 

Figure 39. MIDDS version of the Peak Wind Tool showing forecasts from the 7 July 2010 model 

runs. Forecasts for precipitation days have the header "Precip", while non-precipitation days have the 

header "NoPrecip". The header "All" includes days with and without precipitation. 

48 
6 Summary and Future Work 

The expected peak wind speed is an important element in the 45 WS daily 24-Hour and Weekly 

Planning Forecasts. The forecasts are used for ground and space launch operations at KSC and CCAFS. 

The 45 WS forecasters have indicated peak wind speeds are challenging to forecast, especially in the cool 

season months of October - April. In Phase I of this task (Barrett and Short 2008), the AMU developed a 

Microsoft Excel tool to help the 45 WS forecast non-convective winds at KSCICCAFS for the 24-hour 

period of 0800 to 0800 local time. The tool displayed forecasts for the PWSD, A WSD, timing of the 

PWSD and the probability the PWSD will meet or exceed the 45 WS wind advisory thresholds of 35 kt, 

50 kt and 60 kt. The wind advisory threshold for 60 kt wind gusts was later dropped and a threshold for 

25 kt added. For Phase II, the 45 WS requested the AMU expand the POR to increase the number of 

observations used in the creation of the prediction equations. In addition to an Excel tool, the 45 WS also 

requested a MIDDS tool to provide forecasts out to seven days. 

6.1 Summary 

The AMU first expanded the POR by two years, by adding KSCICCAFS tower observations, SLF 

surface observations and XMR soundings for the cool season months of March 2007 to April 2009. The 

prediction equations required sounding data in 100-ft increments. The AMU evaluated whether the POR 

could be expanded even more by interpolating 1000-ft sounding data down to 100-ft increments. A 

comparison showed no significant differences between the interpolated and 100-ft sounding data. 

Therefore, the POR was expanded again by six years after adding interpolated sounding data for the cool 

season months of October 1996 to April 2002. 

The Phase II developmental data set contained tower observations, surface observations and 

soundings for the cool season months of October 1996 to February 2007. The AMU calculated candidate 

predictors from the XMR soundings. The predictors included 19 stability parameters, 48 wind speed 

parameters and one wind shear parameter. The data set was stratified by synoptic weather pattern, lowlevel wind direction, precipitation, Richardson Number and Gradient Richardson Number, for a total of 

60 stratification methods. Linear regression equations, using the 68 predictors and 60 stratification 

methods, were created for the tool's three forecast parameters (PWSD, A WSD, and timing of the PWSD). 

Instead of selecting the most accurate Phase II method for each forecast parameter, several wellperforming methods were selected for evaluation in the verification data set. 

The verification data set contained observations for the cool season months of March 2007 to April 

2009. The verification data set was used to compare the Phase I and II prediction methods to climatology, 

model forecasts and wind advisories issued by the 45 WS. The comparison was first performed for PWSD 

and A WSD. The Phase II methods performed slightly bener than Phase I and significantly better than 

climatology. The Phase I and II methods were then compared to the 0000 and 1200 UTC runs of the 

MesoNAM. The MesoNAM data contained wind forecasts for model levels 2 - 18 (between 207 ft and 

3145 ft MSL), as well as the strongest winds in the lowest 1000-, 2000- and 3000-ft of the model. Linear 

regression equations were created, in which the predictor was the 24-hour peak speed in the MesoNAM 

data and the predictand was the PWSD or A WSD. The MesoNAM linear regression equations performed 

significantly better than the Phase I and II methods. The Phase I and II forecasts of PWSD were compared 

to wind advisories issued by the 45 WS. On days in which the 45 WS issued at least one non-convective 

wind advisory, the 45 WS out-performed the Phase I and II methods. 

The verification data set was then used to evaluate the Phase I and 11 methods for the timing of the 

PWSD. The Phase I and II methods were compared to climatological winds and the 0000 and 1200 UTC 

runs of the MesoNAM. The MesoNAM forecasts were derived by determining when the 24-hour peak 

wind occurred at each model level. The Phase I and 11 methods, as well as the MesoNAM forecasts, did 

not perform significantly bener than climatology. 

49 
The Microsoft Excel and MIDDS versions of the tool were then developed by the AMU. The tool 

displays forecasts for the PWSD, A WSD and the probability the PWSD will meet or exceed 25 kt, 35 kt 

and 50 kt. The probabilities, based on the PWSD, were calculated using a statistical method developed by 

the 45 WS (Barren and Short 2008). For PWSD and A WSD, the tool uses linear regression equations 

based on the 0000 and 1200 UTe MesoNAM forecast winds. Since none of the prediction methods for 

the timing of the peak wind performed significantly bener than climatology, the tool does not provide 

forecasts for the timing of the peak wind. Instead, the forecaster should use climatology to forecast the 

timing of the peak wind. The forecaster can then adjust the timing based on the movement of weather 

features. The Excel and MIDDS GUls display forecasts for Day-I to Day-3 and Day-I to Day-7, 

respectively. The Excel GUI uses MesoNAM point forecasts as input, while the MIDDS GUI uses point 

forecasts from the MesoNAM and GFS models. The AMU also updated the Microsoft Excel and MIDDS 

tools to provide the daily average wind speed from 30 ft to 60 ft AGL, which is one of the forecast 

parameters in the 24-Hour and Weekly Planning Forecasts issued by the 45 WS. 

6.2 Future Work 

The 45 WS proposed a follow-on task (Phase III) to the Peak Wind tool. Instead of providing 24-hour 

forecasts from Day-I to Day-7, the Phase III tool would display 4-hour forecasts for Day-I, then 12-hour 

forecasts for Day-2 to Day-7. The Phase III tool would not provide forecasts for AWSD or timing of the 

PWSD. Instead, the tool would provide forecasts for PWSD, daily average wind speed and the probability 

the PWSD will meet or exceed 25 kt, 35 kt and 50 kt. Phase III would also expand the POR for the 

MesoNAM forecasts to October 2006 to April 2010. 

50 
References 

Barrett, J. H. and D. A. Short, 2008: Peak Wind Tool for General Forecasting Final Report. NASA 

Contractor Report CR-2008-214743, Kennedy Space Center, FL, 59 pp. [Available from ENSCO, 

Inc., 1980 N. Atlantic Ave. , Suite 230, Cocoa Beach, FL 3293 I and 

http://science. ksc.nasa.gov/am u/fi nal .htm I) 

Case, J. L. and W. H. Bauman III, 2004: Tower Mesonetwork Climatology and Interactive Display Tool 

Final Report. NASA Contractor Report CR-2004-21 1526, Kennedy Space Center, FL, 80 pp. 

[Available from ENSCO, Inc., 1980 N. Atlantic Ave., Suite 230, Cocoa Beach, FL 32931 and 

http://science.ksc.nasa.gov/amulfinal .htmJ) 

Insightful, 2002: S-PLUS 6 Robust Library User's Guide, Insightful Corporation, Seattle, WA, 188 pp. 

Insightful, 2007: S-PLUS 8 Guide to Statistics, Volume I, Insightful Corporation, Seattle, WA, 7 18 pp. 

Lambert, W. C., 2002: Statistical Short-Range Guidance for Peak Wind Speed Forecasts on Kennedy 

Space Center/Cape Canaveral Air Force Station: Phase I Results Final Report. NASA Contractor 

Report CR-2002-21 I 180, Kennedy Space Center, FL, 33 pp. [Available from ENSCO, Inc., 1980 N. 

Atlantic Ave., Suite 230, Cocoa Beach, FL 3293 I and hnp:llscience.ksc.nasa.gov/amulfinaJ.html) 

51 
Term 

45 WS 

AGL 

AMU 

AWSD 

CCAFS 

CSR 

EST 

FAQ 

GFS 

GUI 

KSC 

MAE 

MesoNAM 

MIDDS 

MSL 

NAM 

NWS 

PAFB 

POR 

PWSD 

QC 

SLF 

Tclrrk 

XMR 

52 

List of Acronyms 

Description 

45th Weather Squadron 

Above Ground Level 

Applied Meteorology Unit 

5-minute average wind speed at the time of the PWSD 

Cape Canaveral Air Force Station 

Computer Sciences Raytheon 

Eastern Standard Time 

Frequently Asked Questions 

Global Forecast System 

Graphical User Interface 

Kennedy Space Center 

Mean Absolute Error 

12-krn North American Mesoscale Model 

Meteorological Interactive Data Display System 

Mean Sea Level 

40-km or 80-km North American Mesoscale Model 

National Weather Service 

Patrick Air Force Base 

Period Of Record 

Peak Wind Speed of the Day 

Quality Control 

Shunle Landing Facility 

Tool Command Languagerrool Kit 

CCAFS rawinsonde 3-lener identifier 
NOTICE 

Mention of a copyrighted, trademarked or proprietary product, service, or document does not constitute 

endorsement thereof by the author, ENSCO Inc., the AMU, the National Aeronautics and Space 

Administration, or the United States Government. Any such mention is solely for the purpose of fully 

informing the reader of the resources used to conduct the work reported herein. 

53