Utilizing JavaSpaces for Distributing Weather Research and 

Forecasting Model Output and Meteorological Assimilation 

Data Ingest System Data and Their Use in the Local 

Analysis and Prediction System

by Stephen Kirby 

ARL-TR-3963 November 2006 

Approved for public release; distribution is unlimited. 
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Army Research Laboratory 

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ARL-TR-3963 November 2006 

Utilizing JavaSpaces for Distributing Weather Research and 

Forecasting Model Output and Meteorological Assimilation 

Data Ingest System Data and Their Use in the Local 

Analysis and Prediction System

Stephen Kirby 

Computational and Information Sciences Directorate 

Approved for public release; distribution is unlimited. 
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Utilizing JavaSpaces for Distributing Weather Research and Forecasting Model Output and 

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U.S. Army Research Laboratory 

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14. ABSTRACT 

The Future Force can benefit from a means for inhomogeneous computer systems to access data objects through the network. A platform that 

supports this paradigm is JavaSpaces, because they are based on the Java Virtual Machine; the concept “write once, run anywhere” applies. 

This report illustrates how a Weather Research and Forecasting (WRF) model output file can be split into objects based on the dimension of the 

variable and then placed in a JavaSpace, where the data objects are accessible to clients and can be reassembled into their original WRF output 

file format. A methodology, whereby WRF data is used as the “background” model for the Local Analysis and Prediction System (LAPS), is 

discussed; the converse, of utilizing LAPS output to initialize a fine-scale WRF model run is also presented. 

15. SUBJECT TERMS

JavaSpaces, Weather Research and Forecasting Model, WRF, Local Analysis and Prediction System, LAPS 

16. SECURITY CLASSIFICATION OF:

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Stephen Kirby 

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Contents 

List of Figures iv

Summary 1

1. Introduction 3

2. JavaSpaces and WRF Variable Objects 3

3. Initializing LAPS with WRF 10

4. LAPS Execution 10

5. Executing WRF Utilizing LAPS as Background 19

6. Conclusions 19

7. Future Considerations 19

References 21

Acronyms 22

Distribution List 24

iii
List of Figures 

Figure 1. A schematic illustrating the placement of WRF output objects into a JavaSpace and 

clients accessing these objects. ................................................................................................. 6 

Figure 2. A screen capture showing the server (upper left hand window) placing a WRF 

variable entry into a JavaSpace and the client Java agent (lower right hand window) 

finding a 1-D entry and processing it........................................................................................ 9 

Figure 3. N-S cross section of wind speed valid time (VT) 30 May 2006, 0300 UT.................. 12 

Figure 4. N-S cross section of wind speed VT 30 May 2006, 0600 UT...................................... 12 

Figure 5. N-S cross section of wind speed VT 30 May 2006, 0900 UT...................................... 13 

Figure 6. N-S cross section of wind speed VT 30 May 2006, 1200 UT...................................... 13 

Figure 7. N-S cross section of wind speed VT 30 May 2006, 1500 UT...................................... 14 

Figure 8. N-S cross section of wind speed VT 30 May 2006, 1800 UT...................................... 14 

Figure 9. N-S cross section of wind VT 30 May 2006, 0900 UT................................................ 15 

Figure 10. A sounding within the LAPS domain......................................................................... 16 

Figure 11. Total precipitable water (in cm). ................................................................................ 17 

Figure 12. The 850 hPa temperatures (°C) for the 30 May 2006, 0900 UT LAPS case. ............ 18 

iv
Summary 

The Future Force will face challenges in terms of making data accessible to clients in a seamless 

manner. Clients, running on computers likely featuring differing operating systems (OS), will 

need to access inhomogeneous datasets on servers with different OSs. An intermediate storage 

area accessible to these widely varying clients is required. Such a storage area can be 

constructed utilizing JavaSpaces, an interface developed by Sun Microsystems, Inc. Because 

JavaSpaces are built based on the Java Virtual Machine (VM), any client who can run the Java 

VM will be able to access data objects within a JavaSpace. Similarly, any server that can run the 

Java VM will be able to place objects into a JavaSpace. 

With the JavaSpaces interface, applications may be developed that allow the user to do the 

following: 1) create templates to represent data “objects,” 2) build objects based on these 

templates, 3) place these objects in a JavaSpace, 4) take these objects from a JavaSpace, and if 

desired, 5) notify a client when a certain type of object has been inserted into the JavaSpace. 

One Future Force application, the Nowcast, is a weather forecast that is to be updated frequently 

with new observations and large-scale model background data. The first step in this process is to 

obtain large-scale Global Forecast System (GFS) model initialization data to initialize a 

relatively coarser scale (6-km horizontal resolution) Weather Research and Forecasting (WRF) 

model run. This “outer” WRF run data will, in turn, be used to initialize a Local Analysis and 

Prediction System (LAPS) model run. 

At a remote computer, a client wishing to run LAPS will require this “outer” WRF model output 

data for initialization. Obtaining an entire WRF output file for this purpose would require 

significant bandwidth. Also, if the WRF output file were in a database, the client would require 

the database server to be up, as well as need to know the protocols for database access. To 

circumvent these hindrances, this report shows how a WRF output file can be split into objects, 

each representing one WRF output variable. Each object will contain the variable name, its 

dimensions, and the data block tied to that variable. 

One of the methods specified in the JavaSpaces interface is “notify.” With this method, Java 

agents can continuously poll a JavaSpace searching for a particular object. For this application, 

three Java agents can run on the client that search for one-dimensional, two-dimensional, or 

three-dimensional WRF object variables in the JavaSpace. When such an object is found, the 

agent performs a take on the space and proceeds to extract the data portion from the entry and 

write it to a file on the client, still in its original serialized format. The agent then converts the 

serialized format to a readable text format. Another agent on the client monitors for the presence 

of all the files needed to regenerate the WRF output file into its original network Common Data 

format (netCDF). When all of the required files are found, the agent runs the program that 

recreates the WRF output netCDF file. The client now has a file (WRF netCDF) that is suitable 

for input to a model such as LAPS. Once the LAPS model has been executed, the client can 

generate plots using the LAPS-supplied graphics program, LAPSPLOT. Sample LAPSPLOT 

output graphics are provided showing both cross sections and planar views of various LAPS 

output parameters. 

1
2

This report also briefly covers how the LAPS model output can be used as input to a finer scale 

(2-km horizontal resolution) WRF model run. This cascade of a coarse scale WRF run used to 

initialize LAPS and then LAPS used to initialize a finer scale WRF run, performed cyclically, 

comprises one Nowcast scenario. 

For this report, Internet data sources, such as the Meteorological Data Ingest System (MADIS), 

were employed. Such Internet sources may not be available to the Future Force. Thus, alternate 

data sources, such as the Air Force Weather Agency, may need to be examined as sources for 

entities such as large-scale model background data and satellite data. 
3

1. Introduction 

The net-centric Future Force will require a means for heterogeneous (in terms of operating 

system (OS)) clients to asynchronously query heterogeneous server(s) for a wide variety of 

datasets, including meteorological. Fault tolerance must be inherent, i.e., the loss of a server 

should not obstruct a client from obtaining the requisite data. The JavaSpace paradigm addresses 

these issues: a JavaSpace can be described as “a shared, network-accessible repository for 

objects” and a JavaSpace application as “a collection of processes cooperating via the flow of 

objects into and out of one or more spaces (1).” One Future Force application is a “Nowcast,” 

which will be comprised of short (1–3 h), frequently updated forecasts based on the Weather 

Research and Forecasting (WRF) model. WRF is first run at a coarser scale, comprising a 

domain approximately 8 km on a side. The output from this model run serves as initialization 

data for the Local Analysis and Prediction System (LAPS) model, which also assimilates a 

multitude of datasets including surface, upper air, and satellite. The boundary conditions from 

the LAPS model run then serve to initialize the finer-scale WRF Nowcast run, which will be 

nested inside both the coarser-scale WRF domain and the LAPS domain. A demonstration of 

how WRF model output can be converted into objects and made accessible to a generic WRF 

client through a JavaSpace and then reconstituted into a format amenable for use as a 

background field for LAPS will be presented in this report, along with methods for generating 

objects out of Meteorological Assimilation Data Ingest System (MADIS) data and passing them 

through a JavaSpace. A look at how LAPS can be used as a means to initialize and set up WRF 

runs on a continuing basis, thus yielding a Nowcast, is also covered.

2. JavaSpaces and WRF Variable Objects 

JavaSpaces can be utilized in various ways. Two ways that apply here are as a means for either 

shared distributed communication or object storage. JavaSpaces are elegantly simple as 

evidenced by the diminutiveness of its specification (2): 

public interface JavaSpace{ 

 Lease write (Entry entry, Transaction txn, long lease) 

 throws TransactionException, RemoteException; 

 long NO_WAIT=0; 

 Entry read (Entry tmpl, Transaction txn, long timeout) 

Throws UnusableEntryException,TransactionException, 

InterruptedException,RemoteException; 

 

 Entry take (Entry tmpl, Transaction txn, long timeout) 

Throws UnusableEntryException, 

TransactionException,InterruptedException, 

RemoteException; 
4

Entry takeIfExists (Entry tmpl, Transaction txn, long 

timeout) 

Throws UnusableEntryException, 

TransactionException,InterruptedException, 

RemoteException; 

EventRegistration notify (Entry tmpl, Transaction txn, 

RemoteEventListener listener, long lease, MarshalledObject 

handback) 

 Throws TransactionException, RemoteException; 

Entry snapshot (Entry e) throws RemoteException; 

 } 

As seen in the aforementioned specification, there are four main methods: read, write, take, and 

notify. For this effort, entry classes for one-dimensional (1-D), two-dimensional (2-D), and 

three-dimensional (3-D) variables extracted from WRF output files have been designed. For 

example, the 2-D entry class is as follows: 

public class WrfEntry_2d implements Entry{ 

 

 public String variable; 

 public byte[] arry; 

 public String central_latitude; 

 public String central_longitude; 

 public String time_stamp; 

 public Integer Time; 

 public Integer s_n; 

 public Integer w_e; 

 

 public WrfEntry_2d(){ 

 } 

public WrfEntry_2d(String name, byte[] arr, String lat,String lon,String 

ts,Integer dim1,Integer dim2,Integer dim3){ 

 variable=name; 

 arry=arr; 

 central_latitude=lat; 

 central_longitude=lon; 

 time_stamp=ts; 

 Time=dim1; 

 s_n=dim2; 

 w_e=dim3; 

 } 

Each field of an entry must be declared public. Here the variable name; data array; grid center 

latitude and longitude; number of forecast periods; time the WRF output was generated; and 

x- and y-grid dimensions have all been defined. Note also the required empty constructor as well 

as a constructor to set each of the fields. 
5

JavaSpaces are designed to overcome some of the deficiencies in distributed communication 

between computers. For example, a Remote Procedure Call (RPC) requires the following (3): 

1. The client to know who it is calling 

2. The client to know where the server is 

3. The client and server to be active at the same time 

4. The client and server to agree in detail on what form the call will take 

5. The client and server to agree in detail on how the call will be made 

JavaSpaces surmount these potential bottlenecks by virtue of “loose coupling,” that is, 

“processes interact indirectly through a space and not directly with other processes (2).” Thus, 

for example, a server can write an object to a JavaSpace with some Lease time (i.e., how long it 

is to remain in the space). A client can then search this space by virtue of a take (which removes 

the entry) or a read (which obtains a copy of the entry), either of which requires a (entry) 

template as an argument. This template can be very specific by defining all of the fields or quite 

generic by defining only one of the fields and wildcarding (set to null) all others. The use of a 

generic template greatly increases the chance of matching an entry in the space. 

In this application, the 1-, 2-, and 3-D variables within the WRF network Common Data Format 

(netCDF) file are extracted using netCDF file operators, the data serialized, and the objects then 

written to the JavaSpace (see figure 1.). The generic server process, which writes the objects to 

the space, takes arguments consisting of the relevant variable, its dimensions, the number of 

forecast periods, and integers defining each dimension size. Three client agents monitor the 

JavaSpace, using the JavaSpace notify method, searching for 1-, 2-, or 3-D WRF output variable 

entries. Each agent blocks until it finds its object type, at which point it performs a take on that 

object with the agent, and then deserializes the data and writes it to a file. Another agent blocks 

until all of the requisite data files have been garnered by the client. Upon discovery of the 

required files, it launches a program to reconstruct the separate 1-, 2-, and 3-D variable data files 

back to a single netCDF format file. Finally, when an adequate number of netCDF files are 

present, LAPS can be executed. 
6

Figure 1. A schematic illustrating the placement of WRF output objects into a JavaSpace and clients 

accessing these objects. 

The WRF objects written to the JavaSpace include the following: 

1. 1-D objects 

• model run time 

• upper weight for vertical stretching 

• lower weight for vertical stretching 

• inverse d(eta) values between full (w) levels 

• inverse d(eta) values between half (mass) levels 

• d(eta) values between full (w) levels 

• d(eta) values between half (mass) levels 

• eta values on half (mass) levels 

• eta values on full (w) levels 

• two extrapolation constants 

• leapfrog time filter constant 

• inverse x grid length 

• inverse y grid length 

• time weight constant for small steps 

• zeta at model top 

• three 2nd order extrapolation constants 

linux OS 

computer 

solaris OS 

computer 

WxSpace 

server 

client 2 

client 3 

client n 

client 1 

write WRF 

objects poll/block 

take WRF 

objects 

ftp WRF 

model 

output 

linux 

cluster 
7

• model time step 

• depths of centers of soil layers 

• thicknesses of soil layers 

• pressure top of the model 

2. 2-D objects 

• land use category 

• perturbation dry air mass in column 

• base state dry air mass in column 

• nest position 

• water vapor mixing ratio at 2 m 

• temperature at 2 m 

• potential temp at 2 m 

• surface pressure 

• u-component at 10 m 

• v-component at 10 m 

• land mask—1 for land; 0 for water 

• sea ice flag 

• surface runoff (in mm) 

• underground runoff 

• dominant vegetation category 

• dominant soil category 

• vegetation fraction 

• ground heat flux 

• snow water equivalent 

• physical snow depth 

• canopy water 

• sea surface temperature 

• map scale factor on mass grid 

• map scale factor on u-grid 

• map scale factor on v-grid 

• Coriolis sine latitude term 

• Coriolis cosine latitude term 

• local sine of map rotation 
8

• local cosine of map rotation 

• terrain height 

• surface skin temperature 

• accumulated grid scale accumulation 

• accumulated cumulus scale accumulation 

• downward shortwave flux at ground surface 

• downward longwave flux at ground surface 

• latitude, south is negative 

• longitude, west is negative 

• albedo 

• soil temperature 

• landmask—1 for land, 2 for water 

• reciprocal of Monin-Obhukov length 

• planetary boundary layer (PBL) height 

• upward heat flux at the surface 

• upward moisture flux at surface 

• latent heat flux at surface 

• flag indicating snow coverage—1 for snow coverage 

3. 3-D objects 

• U—x wind component 

• V—y wind component 

• W—z wind component 

• perturbation geopotential 

• base state geopotential 

• perturbation potential temperature 

• perturbation pressure 

• base state pressure 

• water vapor mixing ratio 

• cloud water mixing ratio 

• rain water mixing ratio 

• ice mixing ratio 

• snow mixing ratio 

• graupel mixing ratio 
• soil temperature 

• soil moisture 

• soil liquid water 

Figure 2 is a screen capture of activity involving WRF objects and a JavaSpace, in which the 

window at the upper left displays activity on the server and the window at the lower right 

displays activity on the client. In this instance, the server is processing each of the 1-D data 

blocks by serializing each variable entry, placing it in the JavaSpace, and then blocking to give 

the client discovery and processing time. On the client side, a Java agent, “notify_1d,” 

continuously scans the JavaSpace; as soon as a 1-D entry is found, a take is performed and the 

data is written in its current serialized form to the client machine. The agent then converts the 

serialized (binary) file into a text file in preparation for the reconstitution into a WRF netCDF 

file later. 

Figure 2. A screen capture showing the server (upper left hand window) placing a WRF variable entry into a 

JavaSpace and the client Java agent (lower right hand window) finding a 1-D entry and processing it. 

The process to place MADIS data into the JavaSpace is quite similar to that described for WRF. 

For this portion, the netCDF operator, ncdump, is also utilized and generates text files of data 

from MADIS mesonet netCDF data for latitude, longitude, report time, 2-m temperature, dew 

9
10

point, relative humidity, wind direction, wind speed, visibility, and elevation data. Because each 

of these file sizes are relatively small, all of the data types for one time period can be combined 

into one serialized data file prior to placement into the JavaSpace. 

3. Initializing LAPS with WRF 

LAPS requires a coarser scale background model for initialization. Among those that have been 

used in LAPS are the Rapid Update Cycle (RUC) (4), GFS (5), Navy Operational Global 

Atmospheric Prediction System (NOGAPS) (6), Mesoscale Model Version 5 (MM5) (7), 

Regional Atmospheric Modeling System (RAMS) (8), and WRF (9) models. The WRF model 

was chosen as the model to initialize LAPS, because it is considered the next generation of the 

MM5 and also because of the author’s prior experience executing WRF in parallel on a local 

Linux cluster. 

The large-scale model background utilized for this effort was the Global Forecast System (GFS) 

model. This “outer” WRF domain contained 144 by 144 grid points with 6-km grid spacing and 

31 levels in the vertical. For the 0000 universal time (UT) model run, the analysis fields and the 

3-h, 6-h, 9-h, 12-h, 15-h, 18-h, 21-h, and 24-h forecasts were obtained from the National Center 

for Environmental Prediction (NCEP). In order to mesh with the 3-h LAPS cycle time 

employed, WRF was run for seven 3-h forecast periods: 0000–0300 UT, 0300–0600 UT, 0600–

0900 UT, 0900–1200 UT, 1200–1500 UT, 1500–1800 UT, and 1800–2100 UT on a 16-node 

dual processor Linux cluster. 

Due to the Linux cluster being located on a private network, it was not possible to communicate 

from it to the JavaSpace, thus the WRF output was transferred to a Linux computer, which acted 

as a server (see figure 1). In fact, the server and client were deliberately chosen to be systems 

with differing OSs to further illustrate the concept of loose coupling. JavaSpaces are built on top 

of the Java Virtual Machine; thus, the concept of “write once, run anywhere” applies. 

4. LAPS Execution 

The essence of LAPS is its ability to assimilate a very large number of different data types in 

order to provide the most realistic depiction of an atmospheric volume over short (1–3 h) time 

periods. A non-exhaustive list of data that LAPS can ingest includes radio acoustic sounding 

system (RASS), high density winds (HDW) (derived from Geostationary Operational 

Environmental Satellite (GOES) data), buoy, Meteorological Aerodrome Report (METAR) 

(surface reports), satellite sounder, satellite imagery, radiosonde observation (RAOB), aircraft 

automated reports (ACAR), pilot reports (PIREP), Next Generation Weather Radar (NEXRAD), 

global positioning system (GPS) (from which moisture profiles can be inferred), profiler, 

radiometer data, as well as data from the background model mentioned in section 2. For this 

application, MADIS (managed by National Oceanic and Atmospheric Administration (NOAA) 

Global Systems Division (GSD)) provided ACAR, HDW, mesonet, METAR, polar orbiting 

satellite (POES), profiler, radiometer, RAOB, and satellite radiance data. To insure that local 

effects were accounted for, a utility was built to convert White Sands Missile Range mesonet 
“Surface Automated Measurement System” (SAMS) data into an intermediate format, known as 

the LAPS surface observation (LSO) format. The ability to convert to a text intermediate format 

obviated the need to convert the data into a netCDF format. 

The LAPS domain was nested inside the WRF domain, with 400 by 400 grid points at 2-km grid 

spacing and 21 vertical levels. This domain was also centered at the Las Cruces, NM, Airport. 

The fields produced by LAPS were output at 0300 UT, 0600 UT, 0900 UT, 1200 UT, 1500 UT, 

and 1800 UT. 

LAPS output can be visualized with a National Center for Atmospheric Research (NCAR) 

graphics-based utility, LAPSPLOT. Some sample plots from a LAPS run for 30 May 2006 are 

shown in figures 3–12. Figures 3–8 are cross sections, oriented north (N) to south (S) across the 

domain displaying isotachs. Note the development of a low-level jet by 0600 UT and its 

dissipation by 1800 UT.

11
Figure 3. N-S cross section of wind speed valid time (VT) 30 May 2006, 0300 UT. 

Figure 4. N-S cross section of wind speed VT 30 May 2006, 0600 UT. 

12
Figure 5. N-S cross section of wind speed VT 30 May 2006, 0900 UT. 

Figure 6. N-S cross section of wind speed VT 30 May 2006, 1200 UT. 

13
Figure 7. N-S cross section of wind speed VT 30 May 2006, 1500 UT. 

Figure 8. N-S cross section of wind speed VT 30 May 2006, 1800 UT. 

14
Figure 9 is a N-S oriented cross section of winds. Note the veering of the winds between 

450 hPa and 700 hPa at the southern end of the domain. 

Figure 9. N-S cross section of wind VT 30 May 2006, 0900 UT. 

15
LAPSPLOT can also produce sounding diagrams, as shown in figure 10. 

Figure 10. A sounding within the LAPS domain. 

NOTE: The x-axis represents temperature (°C) and the y-axis represents pressure (hPa). 

16
Figure 11 illustrates LAPS output showing the integrated total precipitable water over the entire 

domain. This LAPS run preceded the monsoonal flow typical to the region in July and August; 

thus, the deepest moisture is restricted to the extreme eastern portions of the domain. 

Figure 11. Total precipitable water (in cm). 

17
Figure 12 depicts the 850 hPa (~1500 m level) temperatures for the 30 May 2006, 0900 UT 

LAPS case. 

Figure 12. The 850 hPa temperatures (°C) for the 30 May 2006, 0900 UT LAPS case. 

18
5. Executing WRF Utilizing LAPS as Background 

Next, the resultant LAPS output files served as large-scale background initialization data for yet 

another WRF run, whose domain was set to be inside the LAPS domain. Specifically, the size 

was 350 by 350 grid points and 2-km grid spacing with 31 vertical levels. North American 

Model (NAM) model data was also introduced to provide three additional soil temperature layers 

required for the NCEP, Oregon State University, Air Force, Hydrologic Research Lab (NOAH) 

Land Surface Model (LSM), one component of WRF, to successfully execute. Again 3-h 

forecast windows were used and Advanced Research WRF (ARW) output times were 

0300–0600 UT, 0600–0900 UT, 0900–1200 UT, 1200–1500 UT, and 1500–1800 UT. 

6. Conclusions 

As a result of this study, an application has been developed whereby the following are possible: 

1. A WRF output netCDF file can be split into separate files, each representing one WRF 

variable that has been serialized and then placed into a JavaSpace. 

2. Agents running on a client machine can monitor the JavaSpace for 1-, 2-, or 3-D variables. 

When found, the variable can be taken from the JavaSpace, written first to a file in its 

original (binary) format, and then converted to a text format. 

3. The text files can be reassembled into a netCDF file suitable for input to a model such as 

LAPS. 

Although the finer-scale WRF run will provide the greatest fidelity in terms of horizontal 

resolution, it was instructive to visualize the LAPS data, which will serve as input to the WRF, 

by means of LAPSPLOT, with some of its capabilities illustrated in section 3. 

7. Future Considerations 

For this effort MADIS data from NOAA GSD supplied all of the upper air and satellite 

observations and the majority of the surface observations. For the Future Force, this Internet 

source will likely not be available; thus, alternate sources for data will need to be pursued. For 

example, the Air Force Weather Agency can supply the WRF large-scale initialization files 

needed for input to LAPS; however, their Gridded Binary (GRIB) format will require conversion 

to the netCDF format first. Candidates for satellite data include the SeaSpace TeraScan 

hardware/software system or the NOAAport data feed available from Unidata (10). Although 

not tested in this effort, radar data can be processed by LAPS and should be examined; however, 

its applicability in a Future Force scenario is unclear. 

19
Also, a Future Force Nowcast application is anticipated to have an outer domain model with 

80 by 80 grid points at a 5-km grid resolution with an inner nest of 100 by 100 points at a 1-km 

resolution. Execution time for a model such as WRF, run at such a fine horizontal scale, needs 

to be investigated to determine what size cluster will be required for “reasonable” run times. 

20
References 

1. Freeman, E.; Hupfer, S.; Arnold, K. JavaSpaces Principles, Patterns, and Practice; 

Addison-Wesley Longman Ltd.: Essex, UK, 1999. 

2. Halter, S.L. JavaSpaces Example by Example; Prentice Hall PTR: Upper Saddle River, NJ, 

2002. 

3. McClain, J. (staff engineer) Introduction to JavaSpaces Technology. Sun Microsystems, Inc., 

PowerPoint presentation (http://www.javasig.com/Archive/lectures/JavaSIG-JiniIntroToJavaSpaces.pdf). 

4. Rapid Update Cycle (RUC) model. http://maps.fsl.noaa.gov/ (accessed 5 Oct 2006). 

5. Global Forecast Model (GFS). http://www.emc.ncep.noaa.gov/modelinfo/index.html

(accessed 5 Oct 2006). 

6. Navy Operational Global Atmospheric Prediction System (NOGAPS). 

http://www.nrlmry.navy.mil/sawmill/index.html (accessed 5 Oct 2006). 

7. Mesoscale Model Version 5 (MM5). http://www.mmm.ucar.edu/mm5/ (accessed 5 Oct 

2006). 

8. Regional Atmospheric Modeling System (RAMS). 

http://www.srh.noaa.gov/ssd/nwpmodel/html/rams.htm (accessed 5 Oct 2006). 

9. Weather Research and Forecasting (WRF). http://www.wrf-model.org/index.php (accessed 5 

Oct 2006). 

10. Dumais, R., research meteorologist, U.S. Army Research Laboratory, Computational and 

Information Sciences Directorate, Battlefield Environment Division: White Sands Missile 

Range, NM. Personal communication, September 2006.

21
Acronyms 

1-D one-dimensional 

2-D two-dimensional 

3-D three-dimensional 

ACAR aircraft automated reports 

ARW Advanced Research WRF 

GFS Global Forecast System 

GOES Geostationary Operational Environmental Satellite 

GPS global positioning system 

GRIB Gridded Binary 

GSD Global Systems Division 

HDW high density winds 

LAPS Local Analysis and Prediction System 

LSM Land Surface Model 

LSO LAPS surface observation 

MADIS Meteorological Assimilation Data Ingest System 

METAR Meteorological Aerodrome Report 

MM5 Mesoscale Model Version 5 

N north 

NAM North American Model 

NCAR National Center for Atmospheric Research 

NCEP National Center for Environmental Prediction 

netCDF Network Common Data Format 

NEXRAD Next Generation Weather Radar 

NOAA National Oceanic and Atmospheric Administration 

NOAH NCEP, Oregon State University, Air Force, Hydrologic Research Lab 

NOGAPS Navy Operational Global Atmospheric Prediction System 

OS operating system 

PBL planetary boundary layer 

PIREP pilot reports 

22
POES polar orbiting satellite 

RAMS Regional Atmospheric Modeling System 

RAOB radiosonde observation 

RASS radio acoustic sounding system 

RPC Remote Procedure Call 

RUC Rapid Update Cycle 

S south 

SAMS Surface Automated Measurement System 

UT universal time 

VM Virtual Machine 

VT valid time 

WRF Weather Research and Forecasting 

23
Distribution List 

 Copies 

DARPA 1 

ATTN IXO S WELBY 

3701 N FAIRFAX DR 

ARLINGTON VA 22203-1714 

MIL ASST FOR ENV SCI OFC OF THE 1 

UNDERSEC OF DEFNS FOR RSRCH & ENGRG 

R&AT E LS 

PENTAGON RM 3D129 

WASHINGTON DC 20301-3080 

OFC OF THE SECY OF DEFNS 1 

ATTN ODDRE (R&AT) 

THE PENTAGON 

WASHINGTON DC 20301-3080 

SCI & TECHNLGY CORP 1 

10 BASIL SAWYER DR 

HAMPTON VA 23666-1340 

NATL CTR FOR ATMOS RSRCH 1 

ATTN NCAR LIBRARY SERIALS 

PO BOX 3000 

BOULDER CO 80307-3000 

US ARMY TRADOC 1 

BATTLE LAB INTEGRATION & TECHL 

DIRCTRT 

ATTN ATCD B 

10 WHISTLER LANE 

FT MONROE VA 23651-5850 

TECOM 1 

ATTN CSTE DTC CL 

APG MD 21005-5057 

COMMANDING GENERAL 1 

US ARMY AVN & MIS CMND 

ATTN AMSTM RD W C MCCORKLE 

REDSTONE ARSENAL AL 35898-5000 

US ARMY CRREL 3 

ATTN CRREL GP R DETSCH 

ATTN CECRL GP M MORAN 

ATTN CERCL SI E L ANDREAS 

72 LYME RD 

HANOVER NH 03755-1290 

 Copies 

US ARMY DUGWAY PROVING GROUND 1 

DPG METEOROLOGY DIV 

ATTN J BOWERS 

WEST DESERT TEST CENTER 

DUGWAY UT 84022-5000 

US ARMY INFO SYS ENGRG CMND 1 

ATTN AMSEL IE TD F JENIA 

FT HUACHUCA AZ 85613-5300 

US ARMY MATERIEL SYS ANAL ACTVTY 1 

ATTN AMXSY CS BRADLEY 

APG MD 21005-5071 

US ARMY NATICK RDEC 1 

ACTING TECHL DIR 

ATTN SBCN TP P BRANDLER 

KANSAS ST BLDG 78 

NATICK MA 01760-5056 

US ARMY OEC 1 

ATTN CSTE AEC FSE 

4501 FORD AVE PARK CENTER IV 

ALEXANDRIA VA 22302-1458 

US ARMY SIMULATION TRAIN & INSTRMNTN 

CMND 1 

ATTN AMSTI CG M MACEDONIA 

12350 RESEARCH PKWY 

ORLANDO FL 32826-3726 

US ARMY TACOM ARDEC 1 

ATTN AMSTA AR WEL TL 

BLDG 59 PHILLIPS RD 

PICATINNY ARSENAL NJ 07806-5000 

US ARMY TRADOC ANAL CMND WSMR 1 

ATTN ATRC WSS R 

WSMR NM 88002-5502 

NAV POSTGRADUATE SCHL DEPT OF 1 

METEOROLOGY 

ATTN P FREDERICKSON 

1 UNIVERSITY CIR 

MONTEREY CA 93943-5001 

24
 Copies 

NAV AIR WAR CTR WPN DIV 1 

ATTN CMD 420000D C0245 A SHLANTA 

1 ADMIN CIR 

CHINA LAKE CA 93555-6001 

AFCCC/DOC 1 

ATTN GLAUBER 

151 PATTON AVE RM 120 

ASHEVILLE NC 28801-5002 

HDQTRS AFWA/DNX 1 

106 PEACEKEEPER DR STE 2N3 

OFFUTT AFB NE 68113-4039 

USAF ROME LAB TECH 1 

ATTN CORRIDOR W STE 262 RL SUL 

26 ELECTR PKWY BLDG 106 

GRIFFISS AFB NY 13441-4514 

AIR FORCE 1 

ATTN WEATHER TECHL LIB 

151 PATTON AVE RM 120 

ASHEVILLE NC 28801-5002 

COLORADO STATE UNIV DEPT OF ATMOS SCI 1 

ATTN R A PIELKE 

200 WEST LAKE STREET 

FT COLLINS CO 80523-1371 

DUKE UNIV PRATT SCHL OF ENGRG 1 

DEPT OF CIVIL & ENVIRON ENGRG 

ATTN R AVISSAR 

HUDSON HALL BOX 90287 

DURHAM NC 27708 

THE CITY COLLEGE OF NEW YORK 1 

DEPT OF EARTH & ATMOS SC 

ATTN S D GEDZELMAN 

J106 MARSHAK BLDG 

137TH AND CONVENT AVE 

NEW YORK CITY NY 10031 

UNIV OF ALABAMA AT HUNTSVILLE 1 

DEPT OF ATMOS SCI 

ATTN R T MCNIDER 

HUNTSVILLE AL 35899 

DIRECTOR 1 

US ARMY RSRCH LAB 

ATTN AMSRD ARL RO EV W D BACH 

PO BOX 12211 

RESEARCH TRIANGLE PARK NC 27709 

 Copies 

US ARMY RSRCH LAB 12 

ATTN AMSRD ARL D J M MILLER 

ATTN AMSRD ARL CI OK T TECHL PUB (2 

copies) 

ATTN AMSRD ARL CI OK TL TECHL LIB (2 

copies) 

ATTN IMNE ALC IMS MAIL & RECORDS 

MGMT 

ATTN AMSRD ARL CI CS R MEYERS 

ATTN AMSRD ARL CI CS D GARVEY 

ATTN AMSRD ARL CI P CLARK 

ATTN AMSRD ARL CI CN A TUNICK 

ATTN AMSRD ARL SE EE Z G SZTANKAY 

ATTN AMSRD ARL CI ES J M NOBLE 

ATTN AMSRD ARL SE SA N SROUR 

ADELPHI MD 20783-1197 

COMMANDING GENERAL 1 

US ARMY MATERIAL CMD 

ATTN AMSRD SS T 

60006 TH ST STE 100 

FT BELVOIR VA 22060 

IMETS PROJECT OFFICE 1 

ATTN SFAE C3T IE MET BLDG 

1622 RM 131 

WSMR NM 88002-5501 

NCSU 1 

ATTN DR JERRY DAVIS 

PO BOX 8208 

RALEIGH NC 27650-8208 

US ARMY FIELD ARTILLERY SCHOOL 1 

ATTN ATSF TSM TA 

FT SILL OK 73503-5600 

NAVAL SURFACE WEAPONS CTR 1 

ATTN CODE G63 

DAHLGREN VA 22448-5000 

US ARMY CORPS OF ENGRS 1 

ENGR TOPOGRAPHICS LAB 

ATTN CETEC TR G P F KRAUSE 

FT BELVOIR VA 22060 

US ARMY TOPO ENGR CTR 1 

ATTN CETEC ZC 1 

FT BELVOIR VA 22060-5546 

US ARMY RESEARCH LAB 1 

ATTN AMSRD ARL CI 

COMP & INFO SCI DIR 

WSMR NM 88002-5501 

25
 Copies 

US ARMY MISSILE CMND 1 

ATTN AMSMI 

REDSTONE ARSENAL AL 35898-5243 

WSMR TECH LIBRARY BR 1 

ATTN STEWS IM IT 

WSMR NM 88002 

US ARMY CECOM 1 

INFORMATION & INTELLIGENCE 

WARFARE DIRECTORATE 

ATTN AMSEL RD IW IP 

FORT MONMOUTH NJ 07703-5211 

 Copies 

DEFENSE TECHNICAL INFO CTR 1 ELEC 

ATTN DTIC OA MS CORA SANTOS 

8725 JOHN J KINGMAN RD STE 0944 

FORT BELVOIR VA 22060-6218 

US ARMY RESEARCH LAB 2 

AMSRD CI OK TP TECHL LIB 

APG MD 21005 

US ARMY RESEARCH LAB 2 HCs + 1 CD 

AMSRD ARL CI EM 

ATTN S KIRBY (2 hard copies) 

T HENMI (1 CD) 

WSMR NM 88002-5501 

TOTAL 59 (2 HCs + 56 CDs + 1 elec)

26