Atmospheric Impacts Routing (AIR) 

by Jeffrey O. Johnson 

ARL-TR-5792 November 2011 

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Army Research Laboratory 

White Sands Missile Range, NM 88002-5501 

ARL-TR-5792 November 2011 

Atmospheric Impacts Routing (AIR)

Jeffrey O. Johnson 

Computational and Information Sciences Directorate, ARL 

Approved for public release; distribution is unlimited. 
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Atmospheric Impacts Routing (AIR) 

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Jeffrey O. Johnson 

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U.S. Army Research Laboratory 

Battlefield Environment Division 

Computational and Information Sciences Directorate (ATTN: RDRL-CIE-M) 

White Sands Missile Range, NM 88002-5501

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ARL-TR-5792 

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14. ABSTRACT 

Atmospheric impacts on platforms and alternative routing options which consider environmental factors along a planned path 

of movement are of high importance during combat operations. Such options serve to improve survivability and movement 

efficiency of air and ground platforms and systems. Environmental factors which may adversely affect systems during combat 

operations along a projected path include adverse weather, threat activity, conflicting friendly operations, and other obstacles. 

The U.S. Army Research Laboratory’s Battlefield Environment Division has developed the Atmospheric Impacts Routing 

(AIR) application, which calculates optimized routes in 3D space, avoiding adverse atmospheric conditions and other obstacles 

during mission execution. ARL’s AIR web service will supplement the Air Force Weather Agency (AFWA) web services 

(AFW-WEBS) as well as the U.S. Army Tactical Airspace Integration System (TAIS) web service developments, incorporating 

TAIS airspace conflict detection services as input for route optimization. 

15. SUBJECT TERMS

Automated routing, web services 

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Jeffrey O. Johnson 

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iii 

Contents 

List of Figures iv

1. Introduction 1

2. Overview 1

3. AIR Functionality 2

4. A Source of Weather Impacts for AIR 6

5. Conclusions 7

6. References 8

Bibliography 9

List of Symbols, Abbreviations, and Acronyms 10

Distribution 11 
iv 

List of Figures 

Figure 1. Notional example of a 3D grid of “impacts.” The data from this 30×30×30 grid were used 

during some of the testing phases of the AIR application. ..................................................................... 3

Figure 2. AIR output of two paths in Google Earth KML format: high risk and lower risk. The 

impacts used when the paths were calculated are shown as additional overlays. .................................. 5

Figure 3. Example output from AIRView (Windows application). Image shows both the Java 2D 

plot (foreground) and the Google Earth KML output (background). ..................................................... 5

Figure 4. Prototype MyWIDA Version 2 reference graphical user interface. ............................................. 7
1 

1. Introduction 

Atmospheric impacts on platforms and alternative routing options, which consider environmental 

factors along a planned path of movement, are of high importance during combat operations. 

Such options serve to improve survivability and movement efficiency of air and ground 

platforms and systems. Environmental factors that may adversely affect systems during combat 

operations along a projected path include adverse weather, threat activity, conflicting friendly 

operations, and other obstacles. The U.S. Army Research Laboratory’s (ARL) Battlefield 

Environment Division has developed the Atmospheric Impacts Routing (AIR) application and 

web service, which calculates optimized routes in 3D space, avoiding adverse atmospheric 

conditions and other obstacles during mission execution. ARL’s AIR web service will 

supplement the U.S. Air Force Weather Agency’s (AFWA) Air Force Weather Web Services 

(AFW-WEBS), as well as the U.S. Army Tactical Airspace Integration System (TAIS) web 

service developments, incorporating TAIS airspace conflict detection services as input for route 

optimization. 

2. Overview 

The AIR application is a replacement of the Army’s earlier technology Aviation Weather 

Routing Tool (AWRT) described by Knapp et al. (2006). Developed from scratch, AIR’s design 

allows it to be applied to both air and ground routing applications by its use of 4D data grids (3D 

grids over time) of “impacts.” The impacts data may be areas of adverse weather or other 

environmental parameters that may be used to identify areas to be avoided by an air or ground 

platform. AIR has been developed as both a web service and as a standalone desktop computer 

application. 

The primary research objective of AIR is to “Improve survivability and movement efficiency of 

air and ground platforms and systems.” This highest level objective is met by addressing the 

following sub-objectives: 

• Develop routing technologies that allow avoidance of adverse weather and other obstacles 

during mission execution. 

• Develop technologies that facilitate decision-making for air and ground platform/system 

movements. 
2 

This research results in the following warfighter value/impacts: 

• Optimized air and ground movement. 

• Decreased friendly force losses and increased mission success rates. 

• More efficient use of platforms, weapon systems, and personnel. 

Ongoing AIR developments include coordination with: 

• AFWA “AFW-WEBS” web services integration. 

• TAIS (PM-Aviation) web services integration. 

• Coalition Attack Guidance Experiment II (CAGEII), which is a networked experiment 

comprised of modeling and simulation (M&S) systems and tactical systems from multiple 

countries, including U.S., Canada, U.K., and Australia. 

3. AIR Functionality 

AIR is capable of ingesting any data type as a 3D grid of “impacts” to be considered when the 

application executes. Each cell in the 3D grid is assigned a value which represents a level of 

severity or “impact” at that particular cell location. One example of such data are areas of 

adverse weather, or weather impacts. For example, “If windspeed is greater than <some value>, 

then condition is UNFAVORABLE,” and the “UNFAVORABLE” condition is assigned a value 

(typically this value is an integer, but not necessarily). My Weather Impacts Decision Aid 

(MyWIDA), described in section 4, can generate 3D weather impacts grids for various platforms, 

coupling the operational characteristics of the platform with the weather conditions at every data 

point in a 3D grid (e.g., forecast data). The resulting 3D grid of impacts can then be fed into 

AIR, with additional user inputs. A notional example of what a 3D impacts grid may look like is 

seen as represented in Google Earth in figure 1. 
3 

Figure 1. Notional example of a 3D grid of “impacts.” The data from this 30×30×30 grid were used 

during some of the testing phases of the AIR application. 

For best route determination, AIR implements the A* algorithm1 in 3D space. AIR addresses 

AWRT deficiencies and adds increased capabilities that were not possible and/or present in 

AWRT, such as varied airspeeds in different, user-defined route segments, and 3D volume 

avoidance (e.g., areas of known threat activity, areas of conflicting friendly activity, or other 

obstacles). The data structures in AIR also lend the application to additional search algorithms, 

such as D* Lite (Koenig, 2002), the description of which is beyond the scope of this paper. 

The AIR implementation of A* is only briefly described in this report. Further details of the A* 

algorithm are described in Dechter and Pearl (1985). Additionally, improvements to A* are 

described by Koenig and Likhachev (2005) and Hernandez et al. (2011). 

In general, the A* algorithm uses a best-first search and finds a least-cost path from a given 

“start node” to a “goal node.” It also uses a heuristic, which is an estimate from a node to the 

goal node, and which allows A* to consider the fewest nodes possible while finding a solution 

path. The function used in the A* algorithm is as follows: 

f(n) = g(n) + h(n) (1)

 

1 A derivative of the A* algorithm has been used by high visibility projects, such as NASA’s Jet Propulsion Laboratory Mars 

Exploration Rovers “Spirit” and “Opportunity,” as described by Singh (1999). 
4 

where 

f(n)= the total cost evaluated at the node n 

g(n)= the sum of the costs along the current path to the node n from the start node 

h(n)= the heuristic function, which is an estimate of the least-cost path remaining between the 

node n and the goal node 

In the AIR implementation of A*, impact costs, described previously, are combined with the 

traditional movement costs (i.e., costs for distance only to node n) for a total g(n) result. 

Additionally, while most implementations of A* are describing movement over a single 2D 

plane (surface movement), the implementation of A* in AIR is executing in 3D space with 

impacts (and other obstacles) varying over time. 

When AIR is executed, either by a web service operation request or directly from an interface in 

the standalone application, the user request includes 3D-gridded “impacts” data (with or without 

3D volumes of avoidance); waypoints required along the path of movement (number of 

waypoints in the request is not bounded, and only limited by hardware); speed at each waypoint 

(identifying speed along segment between required waypoints); and the level of “risk” for the 

resulting path. For the level of risk, there are currently two options: a path of least risk, or a path 

of higher risk. For the “least risk” option, the impacts data are weighted more heavily, while for 

the higher risk option, the movement costs are higher than other factors. As an example, if one is 

willing to traverse higher-risk areas, one can possibly have a shorter path, both spatially and 

temporally, to arrive at the goal node in a route. 

Figure 2 shows AIR output of two paths in Google Earth Keyhole Markup Language (KML) 

format: high risk and lower risk. The impacts used when the paths were calculated are shown as 

additional overlays. Though only a single altitude is shown (effectively, a 2D path), this is only 

shown for visual clarity. In addition, the sample grids shown contain identical impact values for 

each of the two levels seen. However, AIR has been demonstrated calculating paths through 3D 

grids, traversing 50 levels of varying 2D impact grids with computation times on the order of 

1000 potential paths per second as the final path is calculated. As seen on the left side of the 

image, each node in the paths contain additional information, such as time at location. 
5 

Figure 2. AIR output of two paths in Google Earth KML format: high risk and lower risk. 

The impacts used when the paths were calculated are shown as additional overlays. 

An example of the output from the AIR Java Windows standalone/desktop application (called 

AIRView) is shown in figure 3. 

Figure 3. Example output from AIRView (Windows application). Image shows both the Java 2D plot 

(foreground) and the Google Earth KML output (background). 
6 

Figure 3 shows both the Java 2D plot (foreground) as well as the Google Earth KML 4D output 

(background), both of which were generated simultaneously when AIR was executed. Note that 

the Java 2D plot shows impact values for the bottom grid layer, only (top-down view), though 

the path traversed multiple levels. 

4. A Source of Weather Impacts for AIR 

Regional mesoscale model and nowcast forecast grids supply pertinent data to populate a 4D 

weather data volume with required raw and post-processed parameters. These forecast data 

parameters are applied to critical aircraft thresholds such as icing, turbulence, convection, 

instrument flight rules (IFR) conditions, winds, crosswinds, etc., along a flight path. When 

critical weather thresholds are identified, the specific points are labeled to show favorable, 

marginal, and unfavorable (or adverse) hazardous flight weather, depending on the exceeded 

threshold. Thus, a tailored weather effects field is created for each aircraft based on the aircraft’s 

specific weather sensitivity thresholds (Knapp et al., 2006). 

MyWIDA, developed by ARL’s Battlefield Environment Division as part of the Tri-Service 

Integrated Weather Effects Decision Aid (T-IWEDA) program, is a tactical decision aid for 

automating the prediction and display of forecast weather impacts on military systems and 

operations based on live data acquisition and user-defined critical thresholds in conjunction with 

customized databases of thresholds/impacts. 

A prototype of the MyWIDA Version 2 web service application reference client is shown in 

figure 4. 
7 

Figure 4. Prototype MyWIDA Version 2 reference graphical user interface. 

5. Conclusions 

Alternative routing options that consider environmental factors along a planned path of 

movement are of high importance during combat operations. Such environmental conditions 

may consist of adverse weather, areas of known threat, areas of conflicting friendly activity, or 

other obstacles. 

Designed and developed from scratch, the AIR application allows the determination of a leastcost path across multiple waypoints and will determine this path based on a 3D grid of impacts, 

regardless of the impact type. Additionally, AIR provides a mechanism to identify 3D volumes 

to avoid when calculating a path, allowing identification of exclusion zones due to buildings, 

terrain, friendly/threat operations, or other user-defined conditions. Due to its design, AIR is a 

highly-flexible and efficient route planning software package applicable to multiple domains, 

including both air and surface operations, and may be executed either as a standalone desktop 

application or deployed as a web service. 
8 

6. References 

Knapp, D.; Raby, J.; Measure, E.; Brown, R.; Gupta,V. A weather decision aid for unmanned 

aerial vehicle missions. Twelfth Conf. on Aviation, Range, and Aerospace Meteorology, 

Atlanta, GA . Amer. Meteor. Soc., [Online] 2006. 

Singh, S. The Mars Autonomy Project. http://www.frc.ri.cmu.edu/projects/mars/(accessed 2010). 

Koenig, S.; Likhachev, M. D* Lite. American Association for Artificial Intelligence (AAAI),

[Online] 2002, 476−483. 

Dechter, R.; Pearl, J. Generalized Best-First Search Strategies and the Optimality of A*. Journal 

of the Association of Computing Machinery 1985, 32, (3), 505−536. 

Koenig, S.; Likhachev M. Adaptive A*. 4th International Joint Conference on Autonomous 

Agents and Multiagent Systems, Utrecht, Netherlands, July 25−29, 2005. 

Hernandez, C.; Sun, X.; Koenig, S.; Meseguer, P. Tree Adaptive A*. Proceedings of the 

International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 

Taipei, Taiwan, May 2−6, 2011. 
9 

Bibliography 

Patel, A.J. Amit's thoughts on path-finding and A-Star. 

http://theory.stanford.edu/~amitp/GameProgramming/(accessed 2010). 
10 

List of Symbols, Abbreviations, and Acronyms 

AFWA U.S. Air Force Weather Agency 

AFW-WEBS Air Force Weather Web Services 

AIR Atmospheric Impacts Routing 

ARL U.S. Army Research Laboratory 

AWRT Aviation Weather Routing Tool 

CAGEII Coalition Attack Guidance Experiment II 

IFR Instrument flight rules 

KML Keyhole Markup Language 

M&S modeling and simulation 

MyWIDA My Weather Impacts Decision Aid 

TAIS Tactical Airspace Integration System 

T-IWEDA Tri-Service Integrated Weather Effects Decision Aid 
11 

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