NASA Contractor Report NASAICR-2008-2 14744 

Impact of Local Sensors 

Leela R. Watson 

William H. Bauman I11 

Applied Meteorology Unit 

Kennedy Space Center, Florida 

June 2008 
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NASA Contractor Report NASAICR-2008-2 14744 

Impact of Local Sensors 

Leela R. Watson 

William H. Bauman I11 

Applied Meteorology Unit 

Kennedy Space Center, Florida 

-- 

June 2008 
Acknowledgements 

The authors thank Mr. William Roeder of the 45th Weather Squadron for his guidance on the 

methodology used to select cases, his independent review of the differing model forecasts with different 

initializing data from the operational perspective, and for his suggestion to conduct an objective analysis 

of the data for this task. 

Available from: 

NASA Center for Aerospace Information 

7 12 1 Standard Drive 

Hanover, MD 2 1 076- 1 320 

(301) 621 -0390 

This report is also available in electronic form at 

htt~://science.ksc.nasa.~ov/amul 
Executive Summary 

Forecasters at the 45th Weather Squadron (45 WS) use observations from the Kennedy Space Center (KSC) and 

Cape Canaveral Air Force Station (CCAFS) wind tower network and the CCAFS (XMR) daily rawinsonde 

observations (RAOB) to issue and verify wind advisories and warnings for operations. These observations are also 

used by the National Weather Service (NWS) Spaceflight Meteorology Group (SMG) in Houston, Texas and the 

NWS Melbourne, Florida (NWS MLB) to initialize their locally run mesoscale models. SMG also uses them to 

support shuttle landings at the KSC Shuttle Landing Facility (SLF). Due to impending budget cuts, some or all of 

the KSCICCAFS wind towers on the east-central Florida mainland and all but one of the daily XMR RAOBs may be 

eliminated. The loss of these data may impact the forecast capability of the 45 WS, SMG and NWS MLB. 

The Applied Meteorology Unit (AMU) was tasked to conduct a modeling study to determine how important 

these observations are to the accuracy of the model which is being used as a proxy for the utility of these 

observations in general forecasting to the 45 WS forecasters. To accomplish this, the AMU performed a sensitivity 

study using the Weather Research and Forecasting (WRF) model initialized with and without KSCICCAFS wind 

tower and XMR MOB observations. The AMU assessed the accuracy of model output by comparing peak wind 

forecasts with observed winds based on operationally significant wind advisories and warnings issued by the 45 WS 

to determine if the model could aid in the issuance of the advisories and warnings. To assess model performance 

when initialized with and without some of the wind tower and XMR RAOB observations, the AMU conducted 

subjective and objective analyses. In the subjective analysis, the model wind forecasts were displayed graphically 

with the observations overlaid for comparison. In the objective analysis, the maximum peak wind forecast was 

compared to the maximum peak wind observed within the KSCICCAFS wind tower network. 

The AMU collected data for 12 warm season cases from June - September 2007 and eight cool season cases 

from November - January 2008. For each case chosen, the 45 WS must have issued a wind advisory or warning for 

the KSCICCAFS area and the KSCICCAFS wind towers must have recorded significant wind events, or winds 

greater than 18 kt. These criteria would determine if the model could aid in the forecasters issuance of wind 

advisories and warnings. 

The model configuration used a "hot-start" initialization of the Weather Research and Forecasting (WRF) 

model using the Local Analysis and Prediction System (LAPS). Each run started at 0900 UTC and was integrated 12 

hours with a 1.3 krn horizontal grid spacing and 40 irregularly spaced, vertical sigma levels, with a greater number 

of levels located in the planetary boundary layer. LAPS was initialized with Level I1 Weather Surveillance Radar1988 Doppler (WSR-88D) data from the Melbourne, Florida radar, Geostationary Operational Environmental 

Satellites (GOES) visible and infrared satellite imagery, Meteorological Assimilation Data Ingest System (MADIS) 

data, and KSCICCAFS wind tower data and the XMR MOB. The AMU initialized WRF under four different 

initialization scenarios: 

With the mainland towers and without the 1000 UTC XMR MOB, 

With the mainland towers and with the 1000 UTC XMR MOB, 

Without the mainland towers and without the 1000 UTC XMR RAOB, and 

Without the mainland towers and with the 1000 UTC XMR RAOB. 

The AMU completed the subjective analysis of the WRF forecasts by comparing model output to observations 

at the time of the maximum peak wind corresponding to the 45 WS advisories and warnings for that day to 

determine if any of the four scenarios produced better results than the others. 

For the objective analysis, the AMU identified model-domain maximum peak wind speeds for each forecast 

output time and compared them to the observed maximum peak wind speed in the domain. An overall evaluation of 

how well the four model scenarios performed against each other was then conducted. 

In both the subjective and objective analyses, there was minimal difference among the four WRF model 

scenarios. The WRF model performed better in the cool season during prevailing synoptic forcing regimes. The 

model also proved to be a good indicator of the threat of advisory or warning criteria wind speeds over each 12-hr 

forecast model run. This would provide added value to the forecaster's daily planning forecast. 
Table of Contents 

Executive Summary ...................................................................................................................................................... 4 

List of Figures ............................................................................................................................................................... 6 

List of Tables ................................................................................................................................................................. 7 

1 . Introduction ............................................................................................................................................................ 8 

2 . Data and Model Configuration ............................................................................................................................. 10 

2.1 Determining Warm and Cool Season Candidate Days .............................................................................. 10 

2.2 WRF-ARW Model and LAPS Initialization .............................................................................................. 10 

2.3 Data Ingest ................................................................................................................................................. 11 

2.4 Model Configuration ................................................................................................................................. 12 

2.5 Problems Encountered ............................................................................................................................... 13 

3 . Analyses ............................................................................................................................................................... 14 

3.1 Subjective Wind Analysis ......................................................................................................................... 14 

3.1.1 Warm Season ............................................................................................................................................. 14 

3.1.2 Cool Season ............................................................................................................................................... 16 

3.2 Objective Peak Wind Analysis .................................................................................................................. 18 

4 . Summary and Conclusions ................................................................................................................................... 24 

References ................................................................................................................................................................... 25 

List of Acronyms ......................................................................................................................................................... 26 
List of Figures 

Figure 1. The KSCICCAFS wind tower network. Locations with a magenta triangle represent the mainland 

towers that may be eliminated and were removed from the some of the model initializations. The 

XMR RAOB site is located at the red triangle. ........................................................................................... 8 

Figure 2. Map of east-central Florida showing the model domain boundary (gray square) and distribution of 

surface observations. The legend indicating the observation type is located at the top right of the 

figure. County names are light gray and the KSC and CCAFS labels are black. ...................................... 12 

Figure 3. Comparison of the 2100 UTC model forecast radar reflectivity (contours) to the 2117 UTC 

observed radar reflectivity fi-om the NWS MLB WSR-88D (shaded) for the four scenarios on 20 

June 2007. The model forecast radar reflectivity contours based on (a) the mainland towers and no 

MOB , (b) mainland towers with the MOB , (c) no mainland towers and no MOB and (d) no 

mainland towers with RAOB . The reflectivity scale (dBZ) is shown at the top of each map. The 

location of the maximum peak wind speed is shown at Tower 421 by the red dot. The approximate 

broken line of observed convection is shown in blue. ............................................................................... 15 

Figure 4. Comparison of the 2 100 UTC model forecast peak winds (shaded) to the 2 1 15 UTC wind tower 

observations for the four scenarios of data withholding on 20 June 2007. The wind tower 

observations show the 5 minute average wind speed and direction (wind barb) and peak speed 

(number at upper right of the wind barb). The model forecast peak wind speeds based on the (a) 

mainland towers and no RAOB , (b) mainland towers with the RAOB , (c) no mainland towers 

and no MOB and (d) no mainland towers with RAOB . The model wind speed scale (kt) is shown 

at the top of each map. The location of the maximum peak wind speed is shown at Tower 421 by 

the red circle. ............................................................................................................................................. 16 

Figure 5. Comparison of the model forecast peak winds (shaded) to the wind tower observations for the four 

scenarios of data withholding on 17 January 2008 at 1500 UTC. The wind tower observations 

show the 5 minute average wind speed and direction (wind barb) and peak speed (number at upper 

right of the wind barb). The model forecast peak wind speeds based on the (a) mainland towers 

and no RAOB , (b) mainland towers with the RAOB , (c) no mainland towers and no RAOB and 

(d) no mainland towers with RAOB . The model wind speed scale (kt) is shown at the top of each 

map. The location of the maximum peak wind speed is shown at Tower 3 13 by the red circle ................ 17 

Figure 6. Comparison of the model forecast average winds (shaded) to the wind tower observations for the 

four scenarios of data withholding on 17 January 2008 at 1500 UTC. The model forecast average 

wind speeds based on the(a) mainland towers and no RAOB , (b) mainland towers with the 

RAOB, (c) no mainland towers and no RAOB and (d) no mainland towers with MOB . The black 

numbers are the 5 minute average wind speed observations at 54 ft. The location of the highest 

average wind speed is shown at Tower 313 by the red circle.The model wind speed scale (kt) is 

shown at the top of each map. ................................................................................................................... 18 

Figure 7. Map of east-central Florida showing the location of the model sub-domain, outlined by the reddashed rectangle, used in the objective analysis portion of this task. ........................................................ 19 

Figure 8. Chart of the observed maximum wind speed from the KSCICCAFS towers and the model forecast 

maximum peak wind speed for the four scenarios on 11 July 2007 for all 12 1-hour forecast 

periods. ...................................................................................................................................................... 20 

Figure 9. Chart of the observed maximum wind speed £rom the KSCICCAFS towers and the model forecast 

maximum peak wind speed for the four scenarios on 17 January 2008 for all 24 30-minute forecast 

periods. ...................................................................................................................................................... 20 

Figure 10. Chart showing the differences in WRF forecasts of maximum peak wind speed among the four 

scenarios for each hour of the model forecast for both the warm season (red line) and cool season 

(blue line). ............................................................................................................................................... 22 

Figure 11. Chart showing the RMSE among the four WRF forecasts of maximum peak wind speed and the 

observations for each hour of the model forecast for both the warm season (red line) and cool 

season (blue line). ...................................................................................................................................... 23 
List of Tables 

Table 1 . List of the all candidate days and observed maximum peak wind speed recorded for the day .................. 10 

Table 2 . List of the physics options used for each LAPS-WRF model run ............................................................. 13 

Table 3 . Differences in the WRF maximum peak wind speed forecasts among the four "with and without" 

data scenarios for all of the warm season and cool season cases for each forecast hour of the model ...... 21 

Table 4 . The MSE of the WRF maximum peak wind speed forecasts among the four "with and without" data 

scenarios compared to the observations for all of the warm season and cool season cases for each 

forecast hour of the model ......................................................................................................................... 23 
1. Introduction 

Forecasters at the 45th Weather Squadron (45 WS) use observations from the Kennedy Space Center (KSC) and 

Cape Canaveral Air Force Station (CCAFS) wind tower network and the CCAFS (XMR) daily rawinsonde 

observations (RAOB) to issue and verify wind advisories and warnings for operations. These observations are also 

used by the National Weather Service (NWS) Spaceflight Meteorology Group (SMG) in Houston, Texas and the 

NWS Melbourne, Florida (NWS MLB) to initialize their locally-run mesoscale models. In addition, SMG uses these 

observations to support shuttle landings at the Shuttle Landing Facility (SLF). Due to impending budget cuts, some 

or all of the KSCICCAFS wind towers on the east-central Florida mainland and the XMR RAOBs may be 

eliminated. The locations of the mainland towers and XMR RAOB site are shown in Figure I. The loss of these data 

may impact the forecast capability of the 45 WS, SMG and NWS MLB. The AMU was tasked to conduct an 

objective independent modeling study to help determine how important these observations are to the accuracy of the 

model output used by the forecasters. To accomplish this, the Applied Meteorology Unit (AMU) performed a 

sensitivity study using the Weather Research and Forecasting (WRF) model initialized with and without 

KSCJCCAFS wind tower and XMR RAOB data. 

Figure 1. The KSCJCCAFS wind tower network. Locations with a magenta tria~~~~~ ,=present 

the mainland towers that may be eliminated and were removed from the some of the model 

initializations. The XMR RAOB site is located at the red triangle. 

Twenty cases were chosen from data collected from June 2007 - January 2008. For each case chosen, the 45 

WS must have issued a wind advisory or warning for the KSCJCCAFS area and the KSCJCCAFS wind towers must 

have recorded significant wind events, i.e. winds greater than 18 kt. This criteria would determine if the model could 

aid in the forecasters issuance of wind advisories and warnings. Due to the availability of background model data 
used in the forecasts, a 0900 UTC model start time was chosen. This was the closest standard model initialization 

time to the 1000 UTC XMR RAOB which also allowed it to be included in the analysis. The AMU conducted model 

runs for each case using four different initialization scenarios: 

With the mainland towers and with the 1000 UTC XMR RAOB, 

With the mainland towers and without the 1000 UTC XMR RAOB, 

Without the mainland towers and with the 1000 UTC XMR MOB. 

Without the mainland towers and without the 1000 UTC XMR RAOB, and 

To assess model performance for each of the four runs, the AMU conducted subjective and objective analyses 

of the model wind forecasts comparing the maximum peak wind forecast to the maximum peak wind observed 

within the KSCICCAFS wind tower network. This included conducting an evaluation of how the four model 

scenarios performed against each other to determine the effect the wind tower and RAOB data have on the model 

output. Also, the model output was compared to the observations to determine if any of the four scenarios produced 

better results than the others that could help the forecasters determine if wind advisories or warnings could be 

warranted during the next 12 hours. 
2. Data and Model Configuration 

The important aspects of this study were the choice of candidate warm and cool days, the model configuration, 

and the data used to initialize the models. The candidate warm season days were chosen over the June - September 

2007 season and cools season days were chosen during November - January 2008. 

2.1 Determining Warm and Cool Season Candidate Days 

The period of record (POR) for choosing warm season candidate days was June through September 2007. At the 

request of the 45 WS, potential warm season candidate days had to meet three criteria. First, the 45 WS must have 

issued a wind advisory or warning for the KSCICCAFS area. Next, days consisting of dominant synoptic-scale 

forcing patterns were eliminated from consideration. The AMU examined daily weather maps and used them to 

eliminate days in which there was a front or low pressure system over Florida or in the immediate area. Finally, the 

KSCICCAFS wind towers must have recorded significant wind events, or winds greater than 18 kt. Twelve of the 

days in the POR met all three criteria (Table 1). 

The POR for choosing cool season candidate days was November 2007 through January 2008. The two criteria 

for selection included the issuance of a wind advisory or warning for the KSCICCAFS area by the 45 WS and the 

existence of specific cold season phenomena, such as fronts and their associated precipitation. Again, daily weather 

maps were examined to determine days in which there was a front or low pressure system over Florida or in the 

immediate area. Eight days in the POR met these criteria (Table 1). 

2.2 WRF-ARW Model and LAPS Initialization 

The AMU employed the Weather Research and Forecasting (WRF) Model Environmental Modeling System 

(EMS) software, which was developed by the National Weather Service (NWS) Science Operations Off~cer (SOO) 

Science and Training Resource Center (STRC; Iltt9;l/strc.q @. A benefit of using the 

WRF EMS is that it incorporates both dynamical cores, Advanced Research WRF (ARW) and Non-hydrostatic 

Mesoscale Model (NMM), into a single end-to-end forecasting model (Rozumalski 2006). The software consists of 

pre-compiled programs that are easy to install and run. 

For this task, the Advanced Research WRF (ARW) core was used. The ARW core was developed primarily at 

the National Center for Atmospheric Research (NCAR). It is a fully compressible, non-hydrostatic mesoscale model 

with a hydrostatic option. It consists of a mass-based hydrostatic pressure terrain following coordinate, Arakawa Cgrid staggering for the horizontal grid, time-split integration using a third order Runge-Kutta scheme with a small 

step for acoustic and gravity wave modes, and up to sixth order advection options in the horizontal and vertical 
(Skamarock et al. 2005). There are also full physics options for microphysics, planetary boundary layer, cumulus 

parameterization, radiation, and land surface schemes (Skamarock et al. 2005). 

A "hot-start" initialization of the WRF model was made using the Local Analysis and Prediction System 

(LAPS; McGinley 1995). This analysis system allows the WRF model to benefit from the addition of highresolution data sources in its initial conditions. LAPS is a data assimilation tool that uses numerous meteorological 

observations, such as satellite data, radar data, and surface observations, to generate a three-dimensional 

representation of the atmospheric forcing fields, such as wind speed and direction, surface temperature and pressure, 

relative humidity, precipitation and cloud cover (McGinley et al. 1991; Albers 1995; Albers et al. 1996; Birkenheuer 

1999; McGinley 1995). LAPS includes a wind analysis and a three-dimensional cloud analysis, which are needed 

for the WRF hot-start initialization. The LAPS cloud analysis is designed to create consistency with all data and the 

typical meteorology of clouds by combining data from infrared and visible satellite data, three-dimensional LAPS 

radar reflectivity derived from the full volume radar data, and the LAPS three-dimensional temperatures (Albers et 

al. 1996). Fields derived from the cloud analysis include cloud liquid water, cloud type, cloud droplet size, and icing 

severity (Albers et a. 1996). 

2.3 Data Ingest 

Data ingested by the model through the LAPS analysis package included Level I1 Weather Surveillance Radar1988 Doppler (WSR-88D) data from NWS MLB, Geostationary Operational Environmental Satellites (GOES) 

visible and infrared satellite imagery, Meteorological Assimilation Data Ingest System (MADIS; 

data, and wind tower and XMR RAOB data. The Level I1 WSR-88D data contained full 

volume svans VI rerlectivity at a resolution of lo by 1 km, radial velocity at lo by 0.25 km, and spectrum width data 

at a lo by 0.25 km (Fulton et al. 1998). These data were available every 4 to 6 minutes. The GOES-I2 visible 

imagery was available at a 1 km horizontal resolution every 15 minutes, and the infrared imagery was available at a 

4 km horizontal resolution also every I5 minutes. Both visible and infrared imagery provided brightness 

temperatures to the analysis packages. 

Surface observation locations for hourly surface reports (METAR), buoys, MADIS and the KSCICCAFS 

towers are shown in Figure 2. The MADIS data sets available for use in this task included mesonet, hydrological 

surface and multi-agency profiler data. Measured variables include surface wind and vertical wind profiles, 

temperature, dewpoint temperature, relative humidity, accumulated precipitation, etc., as well as types of weather 

occurrences such as hail, fog, and thunder. Local KSCICCAFS tower data (Figure 1) used in the LAPS analysis 

included average wind speed and direction, peak speed and direction, temperature, dewpoint temperature, and 

relative humidity. The daily XMR 1000 UTC RAOB was also included in the LAPS analysis and its location is 

shown in Figure 1. 

This task compared four LAPS data ingest combinations. These included all available data described above, all 

available data except mainland wind tower data, all available data except RAOB data, and all available data except 

mainland wind tower and RAOB data. These combinations were chosen to address possible model forecast 

degradation due to denial of local observations that may be lost due to budget cuts. 
Figure 2. Map of east-central Florida showing the model domain boundary (gray square) and 

distribution of surface observations. The legend indicating the observation type is located at the 

top right of the figure. County names are light gray and the KSC and CCAFS labels are black. 

2.4 Model Configuration 

Each model simulation was run at a 1.3 km horizontal grid spacing centered over the KSCICCAFS area with 40 

irregularly spaced, vertical sigma levels. Each run started at 0900 UTC and was integrated 12 hours. A 0900 UTC 

initialization time was chosen as it was the closest standard model initialization time to the 1000 UTC XMR RAOB. 

There were four runs per day, one each for the four data denial configurations. The AMU evaluated 20 cases, 

including 12 warm season and 8 cool season, resulting in a total of 80 model runs. Table 2 lists the physics options 

used in the LAPS-WRF runs. 

Boundary conditions for the WRF runs were obtained from the National Oceanic and Atmospheric 

Administration (NOAA) Centers for Environmental Prediction (NCEP) North American Mesoscale (NAM) model 

with a horizontal grid spacing of 12 km. The NAM model domain is on a Lambert conformal projection with a 

horizontal domain size of 614 by 239 grid points and 42 vertical levels. The NAM model produces an 84-hr forecast 

every six hours, or four times per day. The NAM model was chosen for boundary conditions as it has the best 

resolution of all available datasets. Initial conditions for the WRF runs were provided by the LAPS analysis. The 

background model for the LAPS analysis was a cold-start 3-km WRF model run, initialized at 0600 UTC on each of 

the candidate days. The cold-start run also used the 12 km NAM model for initial and boundary conditions and it 

covered the entire Florida peninsula and surrounding coastal waters. This output was then used to create a 1.3 km 

LAPS analysis at 0900 UTC for each candidate day. 
The WRF EMS was run as if in real-time. It ingested satellite data in Man computer Interactive Data Access 

System (McIDAS) Area format 15 minutes prior to the model initialization time; raw, full volume, radar data within 

10 minutes of the model initialization time; and surface data from 15 minutes before to 5 minutes after model 

initialization. The largest task in configuring LAPS was working with the ingest code. This code can only be used 

with raw data that have the same configuration and format as the NOAA Earth System Research Laboratory Global 

Systems Division's (GSD) raw data, which is the Network Common Data Form (NetCDF). Therefore, all data files 

were converted to NetCDF format to be used within LAPS. Software to convert the WSR-88D data and model data 

to NetCDF format was obtained from GSD. Scripts were written to convert raw wind tower and RAOB data into a 

format ingestible by LAPS. Two sets of wind tower data were created: one that included all wind tower data from 

KSCICCAFS and one that excludes the mainland tower data. Wind tower data from 15 minutes before to 5 minutes 

after the analysis time were included in the LAPS analysis. 

Table 2. List of the physics options used for each LAPS-WRF model run. 

Converting satellite data in McIDAS Area format to NetCDF required several steps. The AMU first ported the 

data to the local Meteorological Interactive Data Display System (MIDDS) system where they were remapped to the 

Lambert Conformal projection using the IMGREMAP command. Next, the remapped data were run through a 

program that converted them to NetCDF format. This program is called AreaToNetCDF and is available from the 

Space Science and Engineering Center (SSEC) at the University of Wisconsin. All of the reformatted data files were 

then ingested into LAPS to create an initialization field for the model. 

Physics Option 

Microphysical scheme 

Planetary boundary layer scheme 

Land surface option 

Surface layer scheme 

Shortwave radiation scheme 

Longwave radiation scheme 

2.5 Problems Encountered 

The AMU encountered two problems while using the LAPS software. First, in the most recent LAPS version 

available at the time of this task, version 0-32-1 5, an error in the output occurred in which values of rainwater and 

graupel mixing ratio were set to 0. The AMU determined that the problem might not be able to be remedied within a 

reasonable amount of time so a previous version of LAPS in which this error did not occur was used instead. 

Second, the AMU identified a warm bias on the order of 10-15' F in the surface temperature field in all WRF model 

0-hr output. The authors contacted Dr. John McGinley of GSD to determine the cause of the problem. After 

consultation with Dr. McGinley, the bias was traced back to the default bottom pressure level extending below the 

terrain and the observations. The default bottom pressure levels were 1050 and 1100 mb, corrected to sea level. Dr. 

McGinley indicated when LAPS has more than one level below the terrain, problems can occur since the software is 

still applying balance at fictional levels. Assumptions and errors in the extrapolation of pressure to sea level could 

cause some parts of the lowest levels to be above ground in mountainous Colorado region, where LAPS was 

originally developed and used. The default pressure levels originally worked since some part of the bottom pressure 

levels were above ground. Dr. McGinley suggested the best solution for the Florida region was to minimize the 

value and depth of the lowest pressure level. Hence, the AMU deleted the 1 100 mb pressure level and LAPS-WRF 

was rerun for all candidate days. 

LAPS-WRF 

Lin et al. (1983) 

Mellor-Yamada-Janjic (Janjic 1990, 1996,2002) 

Noah Land Surface Model (Chen and Dudhia 2001) 

Janjic Eta (Janjic 1996,2002) 

Goddard (Chou and Suarez 1994) 

RRTM (Mlawer et al. 1997) 
3. Analyses 

The AMU conducted both subjective and objective analyses of the WRF wind forecasts and compared them to 

the observed winds at the KSCICCAFS towers. As stated in the introduction, the AMU was to evaluate the impacts 

of removing the mainland wind towers and all but one recurring XMR RAOB from the initialization of a local NWP 

model to determine the impact, if any, to the model forecast. The AMU initialized the WRF model with four 

different scenarios: 

With the mainland towers and the 1000 UTC XMR RAOB, 

With the mainland towers and without the 1000 UTC XMR RAOB, 

Without the mainland towers and with the 1000 UTC XMR RAOB, and 

Without the mainland towers and without the 1000 UTC XMR MOB. 

In the subjective analysis, the AMU compared the model output of forecast radar reflectivity, peak winds and 

average winds to the corresponding observations under these four scenarios for the 12 warm season cases and eight 

cool season cases. For the objective analysis, the AMU identified the model-domain peak wind speed for each 

forecast output time using the Grid Analysis and Display System (GrADS) software. The model-domain peak wind 

speed was then compared to the observed maximum peak wind speed in the wind tower network. 

3.1 Subjective Wind Analysis 

The AMU completed a subjective analysis of the WRF forecasts for all warm season and cool season cases. 

This was accomplished by creating four-panel images displaying model output and observations at the time of the 

maximum peak wind corresponding to the 45 WS warnings and advisories for that day. The model output was 

compared to the observations to determine if any of the four scenarios produced better results than the others. The 

goals were to determine if the model could provide an indicator to the forecaster that there may be winds meeting 

advisory or warning criteria for the day and if excluding mainland wind towers and/or the XMR MOB made a 

difference in the model wind forecast. One case from the warm season and one from the cool season are discussed in 

this section. 

Warm Season 

During the warm season, WRF peak wind forecasts were highly correlated with the location and strength of the 

forecast radar reflectivity and, therefore, was the one model parameter assessed besides winds. On 20 June 2007, the 

45 WS issued a Weather Watch for winds L 50 kt, hail 2 0.75 in and/or tornadoes valid from 1830 to 2000 UTC. 

They then issued a Wind Warning for winds from the surface to 300 ft 2 35 kt for KSC after a peak wind of 38 kt 

was observed at 2 1 15 UTC on Tower 42 1 at the north end of KSC. For this event, the 45 WS had -49 minutes lead 

time and -75 minute timing error (i.e., the warning was issued after the event occurred and the event occurred after 

the watch had expired). The observed wind gust was generated by an isolated thunderstorm and was the only one 

that met the warning criteria that day. . 

The AMU first compared the 12-hr model forecast radar reflectivity (valid at 2 100 UTC) to 2 1 17 UTC observed 

radar reflectivity from the Melbourne, FL WSR-88D. As shown in Figure 3, the model forecast radar reflectivity in 

all four scenarios did a fairly good job depicting the observed broken line of convection extending northeast to 

southwest from offshore northern KSC and across the mainland. For this event, the scenarios that included the 

mainland towers and the RAOB (Figure 3b) and excluded the mainland towers and included the RAOB (Figure 3d) 

best matched the observed radar reflectivity in coverage, location and intensity. It is also interesting to note the two 

best scenarios occurred both with and without mainland wind tower data. However, there was little difference 

among all four scenarios, which raises the question, "Would any of the forecasts have helped the forecaster issue a 

better watch or warning?". 
Ne Mainland Towers Pla Mdnlant 

Figure 3. Comparison of the 2 100 UTC model forecast radar reflectivity (contours) to the 2 1 17 UTC observed 

radar reflectivity from the NWS MLB WSR-88D (shaded) for the four scenarios on 20 June 2007. The model 

forecast radar reflectivity contours based on (a) the mainland towers and no RAOB , (b) mainland towers with the 

RAOB , (c) no mainland towers and no RAOB and (d) no mainland towers with RAOB . The reflectivity scale 

(dBZ) is shown at the top of each map. The location of the maximum peak wind speed is shown at Tower 42 1 by the 

red dot. The approximate broken line of observed convection is shown in blue. 

The AMU compared the 12-hr model forecast peak wind speeds valid at 2100 UTC to the 21 15 UTC observed 

peak winds from the KSCICCAFS towers as shown in Figure 4. In all four scenarios the maximum peak winds were 

highly correlated to the model forecast radar reflectivity. The scenarios that included the mainland towers and the 

RAOB (Figure 4b) and excluded the mainland towers and included the RAOB (Figure 4d) best matched the 

observed peak winds in location and speed. As with the forecast radar reflectivity, there was little difference among 

all four scenarios. Any one of the four model runs would have given the forecaster an indication to expect 

convective winds in the 25-30 kt range during the day. Although the model did not forecast peak winds at or above 

the warning threshold, the output provided valuable information that would have allowed the forecaster to be alert 

for convective winds requiring a warning. 

The AMU also compared the model forecast average wind speeds to the KSCfCCAFS towers for each warm 

season case as was done for the peak wind speeds. The model forecast average wind speeds aligned well with the 

model forecast peak wind speeds in location and relative speed. Since every warm season wind event was 

convective in nature, the model forecast average speeds did not provide any additional information of value and, 

therefore, are not discussed here. 
Figure 4. Comparison of the 2100 UTC model forecast peak winds (shaded) to the 21 15 UTC wind tower 

observations for the four scenarios of data withholding on 20 June 2007. The wind tower observations show the 5 

minute average wind speed and direction (wind barb) and peak speed (number at upper right of the wind barb). The 

model forecast peak wind speeds based on the (a) mainland towers and no RAOB , (b) mainland towers with the 

RAOB , (c) no mainland towers and no RAOB and (d) no mainland towers with RAOB . The model wind speed 

scale (kt) is shown at the top of each map. The location of the maximum peak wind speed is shown at Tower 421 by 

the red circle. 

3.1.2 Cool Season 

During the cool season, synoptic scale gradient flow was the primary cause of high wind events that warranted 

45 WS advisories and warnings. None of the cool season cases were associated with convection. Based on the 

subjective analysis, the WRF peak wind speed forecasts were better during the cool season in both timing and 

location compared with the warm season forecasts which is expected as the model can better handle strong synoptic 

scale forcing vs. weak mesoscale forcing. On 17 January 2008, the 45 WS issued a Wind Warning for winds from 

the surface to 300 ft 2 50 kt for KSC valid from 1200 to 1700 UTC. It was downgraded to a Wind Warning for 

winds from the surface to 300 ft Z 35 kt for KSC at 1612 UTC after observing a maximum peak of 33 kt at Tower 

3 13 at 1500 UTC. Figure 5 shows the WRF peak wind speed forecast (shaded) at - 33 ft at 1500 UTC with a plot of 

the observed winds at 54 ft from the KSCICCAFS mesonet towers and 295 R from Tower 3 13 at 1500 UTC. The 

WRF forecast of peak winds indicated they would be stronger over KSCICCAFS and offshore than inland with peak 

speeds of - 20-25 kts inland, increasing to - 27-3 1 kts along the coast, and then > 35 kt offshore. The observed 

winds were lower than forecast, but the trend was the same with the strongest winds at the coastal towers. As with 

the warm season cases, there was little difference among all four scenarios in this case as well as the other seven 

cool season cases. Any one of the four model runs would have given the forecaster an indication to expect peak 

winds in the 30-35 kt range during the day with the strongest winds closer to the coast. 
m west to east 

. . . ers/ RAOB 

Figure 5. Comparison of the model forecast peak winds (shaded) to the wind tower observations for the four 

scenarios of data withholding on 17 January 2008 at 1500 UTC. The wind tower observations show the 5 minute 

average wind speed and direction (wind barb) and peak speed (number at upper right of the wind barb). The model 

forecast peak wind speeds based on the (a) mainland towers and no RAOB , (b) mainland towers with the RAOB , (c) no mainland towers and no RAOB and (d) no mainland towers with RAOB . The model wind speed scale (kt) is 

shown at the top of each map. The location of the maximum peak wind speed is shown at Tower 313 by the red 

circle. 

The AMU also compared the model forecast average wind speeds for each cool season case to the KSCICCAFS 

towers. As stated previously, none of the cool season cases were associated with convection and based on the 

subjective analysis. Like the WRF peak wind speed forecasts, the WRF average wind speed forecasts were better 

during the cool season in both timing and location compared with the warm season forecasts. Figure 6 shows the 

WRF average wind speed forecast (shaded) at - 33 ft at 1500 UTC 17 January 2008 with a plot of the observed 

average wind speeds at 54 fl from the KSC/CCAFS mesonet towers and 295 ft from Tower 3 13 at 1500 UTC. Like 

the WRF forecast of peak wind speeds for this case, the WRF forecast of average wind speeds indicated they would 

be stronger over KSCICCAFS and offshore than inland with speeds of - 13-18 kts inland, increasing to - 18-24 kts 

along the coast, and then > 25 kt offshore. The observed winds were lower than forecast, but the trend was the same 

with the strongest winds at the coastal towers. As with the warm season cases, there was little difference among all 

four scenarios in this case as well as the other seven cool season cases. Any one of the four model runs would have 

given the forecaster an indication to expect sustained winds in the 18-24 kt range during the day with the strongest 

winds closer to the coast. 
ners / No 1 RAOB 

Figure 6. Comparison of the model forecast average winds (shaded) to the wind tower observations for the four 

scenarios of data withholding on 17 January 2008 at 1500 UTC. The model forecast average wind speeds based on 

the(a) mainland towers and no RAOB , (b) mainland towers with the RAOB , (c) no mainland towers and no RAOB 

and (d) no mainland towers with RAOB . The black numbers are the 5 minute average wind speed observations at 

54 ft. The location of the highest average wind speed is shown at Tower 3 13 by the red circle.The model wind speed 

scale (kt) is shown at the top of each map. 

3.2 Objective Peak Wind Analysis 

Upon reviewing the results of the subjective analysis, the 45 WS asked the AMU to conduct an objective 

analysis of the peak wind comparisons. To do this, the AMU identified the maximum model-domain peak wind 

speed for each forecast output time using GrADS. The maxo function in GrADS allows the user to identify the 

maximum value of a variable within a user specified domain. This function was used to return the maximum peak 

wind speed within the domain pictured in Figure 7. Using the results from GrADS, the AMU compared the WRF 

forecast maximum peak wind speed to the observed maximum peak wind speed and then conducted an overall 

evaluation of how well the four model scenarios performed against each other. 

An example of the objective analysis output from a warm season case on 1 1 July 2007 is shown by the chart in 

Figure 8. The maximum observed wind speed and the model forecast maximum wind speed for the four scenarios 

are plotted over the 12-hr forecast period at 60-minute intervals. All four model runs were consistent with each other 

and forecast a steady to decreasing maximum peak wind speed between the 0- and 5-hr forecasts, and then an 

increase from the 5- to the 10-hr forecast followed by a decreasing trend. The model forecasts matched the trend of 

the observed maximum peak wind speed in the domain. The average difference between the lowest and highest 

forecast maximum peak wind speeds in the four WRF runs for all 12 hr output intervals was 1.79 kt. The average 

mean squared error (MSE) between the average of the four WRF forecasts and observed maximum peak wind 
speeds was 4.91 kt. The largest MSE of 19,67 kt occurred at the 12 hr WRF forecast interval. The observed 

maximum peak wind speed of 3 1 kt was associated with a convective thunderstorm gust. 

Figure 7. Map of east-central Florida showing the location of the model sub-domain, outlined 

by the red-dashed rectangle, used in the objective analysis portion of this task. 

An example of the objective analysis output from a cool season case on I 1 January 2008 is shown by the chart 

in Figure 9. The maximum observed wind speed and the model forecast maximum wind speed for the four scenarios 

are plotted over the 12-hr forecast period at 30-minute intervals. As with the warm season case, all four model runs 

were consistent with each other. The WRF forecasts predicted an increasing trend in the maximum peak wind 

speeds from the 0- to 2.5-hr forecast, and then a steady to decreasing trend from the 2.5- to 12-hr forecast. The 

model forecasts matched the trend of the observed maximum peak wind speed in the domain. The average 

difference between the lowest and highest forecast maximum peak wind speeds in the four WRF runs for all 12 hr 

output intervals was 1.68 kt. The average mean square error (MSE) between the average of the four WRF forecasts 

and observed maximum peak wind speeds was 4.49 kt. The largest MSE of 11.25 kt occurred at the 0-hr forecast 

interval. The observed maximum peak wind speed of 43 kt occurred at the 2-hr forecast interval. It is interesting to 

note that one of the WRF forecasts predicted a maximum peak wind speed of 41.4 kt 30 minutes after the observed 

maximum peak wind speed of 43 kt indicating the model did an excellent job forecasting the magnitude of the 

maximum peak wind speed with timing being only slightly off. 
WRF vs. Observed Maximum Wind Speed 

11 July 2007 

,All Towers/RAOB 

35 .All Towers/No RAOB I ~NoMainTowers/RAOB ,o#-\ 

30 a \ -No main Towers/No RAOB 

----- ,' - g 25 Observed Maximum I 

r. 

Y 0 

20 or--- 

15 -- 

- 

V) 

r 

10 

5 

0 

0 12 345 6789101112 

Model Forecast Hour 

Figure 8. Chart of the observed maximum wind speed fiom the KSCICCAFS towers and the model 

forecast maximum peak wind speed for the four scenarios on 11 July 2007 for all 12 1-hour forecast 

periods. 

WRF vs. Observed Maximum Wind Speed 

50 17 January 2008 

45 

40 8 - -* l 

a u ;: ; = 3 .&-* 

I ---\. ,cc--I. 

25 - - 

V, 

i 20 

l5 

I - 

- 

-All 

AllTowers/No 

TowersIRAOB - 

RAOB 

10 - No Main Towers/RAOB 

- - No main Towers/No RAOB 

----- 

0 Observed Maximum 

0 12 345 6 789101112 

Model Forecast Hour 

Figure 9. Chart of the observed maximum wind speed from the KSCICCAFS towers and the model 

forecast maximum peak wind speed for the four scenarios on 17 January 2008 for all 24 30-minute forecast 

periods. 
After comparing the WRF maximum peak wind speed forecasts to the observed maximum peak wind speed, the 

AMU computed statistics for the overall model performance for all 20 cases. The first question to answer was 

whether or not any one of the four scenarios performed better than the other three with regard to the maximum peak 

wind forecasts. To do this, the AMU computed the average difference between the maximum and minimum WRF 

forecast for each forecast hour in each case. The results are shown in Table 3. During the warm season, the four 

scenarios were within 2 kt of each other through the 7-hr forecast interval and then diverged. The cool season results 

indicate the four scenarios tracked better after the 4-hr forecast interval and remained within 1.4 kt of each other. 

The data from Table 3 is shown in the chart in Figure 10. Overall, the average difference in the four WRF scenarios 

for the entire 12-hr forecast period was 1.91 kt for the warm season and 1.38 kt for the cool season. This indicates 

the data denial scenarios performed comparably to the data rich scenarios. 

ases for each forecast hour of the model. 

Forecast Hour 
Difference of WRF Forecasts for Four Scenarios 

4 

Warm Season 

n 21;se* 

V) J - - 

1 

0 I I I 1 I I 1 I I I I I I 

0 12 3 4 5 6 7 8 9101112 

Model Forecast Hour 

Figure 10. Chart showing the differences in WRF forecasts of maximum peak wind speed among the four 

scenarios for each hour of the model forecast for both the warm season (red line) and cool season (blue 

line). 

Finally, the AMU computed the RMSE using the equations below for all cases to compare the WRF forecasts to 

the observed maximum peak wind speeds. The results are shown in Table 4 and the corresponding chart is shown in 

Figure 1 I. 

RMSE = J%?? 

Where: 

n = 12 for the warm season and 8 for the cool season, 

f= average of four WRF forecast scenario maximum peak wind speeds for each WRF forecast interval, and 

o = average of the observed maximum peak wind speeds for each WRF forecast interval. 

During the warm season, the WRF RMSE decreased from the 0- to the 3-hr forecast by just over 2 kt and then 

generally increased throughout the 12-hr forecast period to a maximum RMSE of 13.87 kt at the 1 1-hr forecast. 

During the cool season, the WRF RMSE was consistent throughout most of the forecast intervals at about 5-7 kt 

with a maximum RMSE of 7.77 kt at the 2-hr forecast. This data indicates WRF performance is worse in the warm 

season over the sub-domain. 

The MSE provides an overall indication of model performance for the cases investigated in this work. However, 

on certain days the MSE was as large as 30 kt during the warm season and 16 kt during the cool season. Sometimes 

the error was due to WRF incorrectly forecasting the magnitude of the peak wind speed for the day while at other 

times WRF forecast the magnitude correctly but error was due to timing. The bias of the model compared to the 

observations for the warm season was -3.27 kt and for the cool season -3.40 kt. This indicates a tendency for WRF 

to under forecast peak wind events in both seasons. 
WRF Performance: RMSE 

IC 

F--~arm season 

 cool Season 

12 

g 10 .- 

Y 

I Model Forecast Hour 

Figure I I. Chart showing the RMSE among the four WRF forecasts of maximum peak wind speed and the 

observations for each hour of the model forecast for both the warm season (red line) and cool season (blue 

line). 
4. Summary and Conclusions 

The 45 WS forecasters use observations from the KSCICCAFS wind tower network and the XMR MOB to 

issue and verify wind advisories and warnings for operations. SMG and NWS MLB use these observations to 

initialize their locally-run mesoscale models, and SMG also uses them to support shuttle landings at the SLF. Due to 

impending budget cuts, some or all of the KSCICCAFS wind towers on the east-central Florida mainland and some 

XMR RAOBs may be eliminated. The loss of these data may impact the forecast capability of the 45 WS, SMG and 

NWS MLB. 

The AMU conducted a modeling study to determine how important these observations are to the accuracy of the 

model output used by the forecasters as input to their forecasts. This study was done in two steps: 1) Initializing the 

WRF model with and without KSCICCAFS wind tower and XMR RAOB data, and 2) Assessing the accuracy of 

model output by comparing peak wind forecasts to observations meeting wind advisory and warning criteria as 

forecast by the 45 WS. 

To keep the study manageable yet representative of the weather in different seasons, the AMU collected data for 

12 warm season cases from June - September 2007 and eight cool season cases from November - January 2008. To 

keep the study focused, the AMU chose only cases in which the 45 WS issued a wind advisory or warning for the 

KSCICCAFS area and the KSCICCAFS wind towers recorded significant wind events, i.e. winds greater than 18 kt. 

The AMU used the WRF model with a "hot-start" initialization using LAPS. Each run started at 0900 UTC and 

was integrated 12 hours with a 1.3 km horizontal grid spacing and 40 irregularly spaced, vertical sigma levels. Data 

used to initialize LAPS included Level I1 WSR-88D data from the NWS MLB radar, GOES visible and infrared 

satellite imagery, MADIS data, and KSCICCAFS wind tower data and the XMR MOB. The AMU initialized 

LAPS using four different combinations of the local data in question: 

a With the mainland towers and without the 1000 UTC XMR MOB, 

a With the mainland towers and with the 1000 UTC XMR MOB, 

a Without the mainland towers and without the 1000 UTC XMR RAOB, and 

a Without the mainland towers and with the 1000 UTC XMR MOB. 

The AMU conducted a subjective analysis of the WRF forecasts by comparing model output to observations at 

the time of the maximum peak wind corresponding to the 45 WS advisories and warnings to determine if any of the 

four scenarios produced better results than the others. A two-part objective analysis was also conducted. In the first 

part, the model-domain maximum peak wind speeds for each forecast output time were compared to the observed 

maximum peak wind speed in the domain. The second part incorporated evaluating how well the four model 

scenarios performed against each other. 

In both the subjective and objective analyses, the AMU found little difference among the four WRF model 

scenarios. The WRF model did perform better in the cool season during prevailing synoptic forcing regimes and it 

was also a good indicator of the threat of advisory or warning criteria wind speeds over each 12-hr forecast model 

run. This would provide added value to the forecaster's daily planning forecast. 
References 

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Albers, S. C., J. A. McGinley, D. L. Birkenheuer, and J. R. Smart, 1996: The Local Analysis and Prediction System 

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VA, 19-23 August, Amer. Meteor. Soc., Boston, MA, 354-355. 

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478-485. 

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Training Resource Coordinator Forecast Decision Training Branch, 89 pp. [Available from COMETIUCAR, 

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description of the Advanced Research WRF Version 2. NCAR Technical Note NCARITN-468+STR, 88 pp. 
List of Acronyms 

45 WS 45th Weather Squadron 

AMU Applied Meteorology Unit 

CCAFS Cape Canaveral Air Force Station 

GrADS Grid Analysis and Display System 

GOES Geostationary Operational Environmental 

Satellites 

GSD Global Systems Division 

KSC Kennedy Space Center 

LAPS Local Analysis and Prediction System 

MADIS Meteorological Assimilation Data Ingest 

System 

McIDAS Man computer Interactive Data Access 

System 

MIDDS Meteorological Interactive Data Display 

System 

MSE Mean Square Error 

NAM North American Mesoscale model 

NCEP National Centers for Environmental 

Prediction 

NetCDF Network Common Data Form 

NOAA National Oceanic and Atmospheric 

Administration 

NWS National Weather Service 

POR Period of Record 

RAOB Rawinsonde observation 

SLF Shuttle Landing Facility 

SMG Spaceflight Meteorology Group 

SSEC Space Science and Engineering Center 

WRF Weather Research and Forecasting model 

WSR-88D Weather Surveillance Radar-1988 Doppler 

XMR CCAFS rawinsonde 3-letter 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.