A Simulation-based Spatial Decision Support System ...

A Simulation-based Spatial Decision Support System for a New Airborne Weather Data Acquisition System Erol Ozan Department of Engineering Management Old Dominion University Norfolk, VA 23529 Paul Kauffmann Department of Engineering Technology Old Dominion University Norfolk, VA 23529 Abstract Tropospheric Airborne Meteorological Data Reporting (TAMDAR) system is envisioned as a new automated, onboard system that will gather meteorological data as aircraft fly in the troposphere and transmit this data to the National Weather Service. The weather information that TAMDAR will generate, may be a critical element in enhancing the accuracy and completeness of weather data and resulting forecasts. This paper aims to demonstrate the feasibility of a prototype TAMDAR economic analysis tool, which will help decision makers to determine the best strategy for system operation. Essentially, this decision support system is composed of the following major components: a customized simulation-optimization engine, a data utility estimator, a GIS-based analysis layer, and the user interface. Time correlated position and altitude data for TAMDAR equipped aircraft are generated and entered into the decision aiding software to build a dynamic model of the airspace occupied, at any point of time, by a fleet of independent TAMDAR equipped aircraft. Economic models for data costs and data utility are integrated into the decision aiding software to generate a quantifiable value to be assigned to the TAMDAR data supplied by each aircraft. This decision support system helps decision-makers to process different operational alternatives efficiently. They can also conduct various what-if analyses effectively by using GIS-based user interface.

Keywords Geographical Information Systems , Decision Support Systems, Spatial Multicriteria Decision Making, Meteorological Data Utility, Value of Information.

1. Introduction TAMDAR (Tropospheric Airborne Meteorological Data Reports) is a new airborne weather data acquisition system, which is currently studied by NASA. The TAMDAR concept consists of sensor packages, information processors, and communications equipment carried aloft by participating aircraft. As these aircraft complete their missions, the TAMDAR system reports weather conditions to ground-based receiving stations that process and distribute this data into a national system for dissemination of weather information. The Federal Aviation Administration (FAA), National Weather Service (NWS), and NASA have concluded that TAMDAR systems have potential to enhance the accuracy and completeness of weather data and the resulting weather forecasts [1]. Improved aviation safety, along with other benefits for various groups, is anticipated as a result of these improved forecasts. The primary objective of this research is to develop a spatial decision support system architecture that will help users to reduce the data transmission costs of the TAMDAR system by optimizing the data collection.

2. System Architecture TAMDAR Decision Support System (TAMDAR-DSS) has four main components: GIS-based User Interface and Analysis Unit, Data Point Utility Estimator, Multicriteria Simulation-Optimization Engine, and Flight Pattern Simulator. This paper focuses on the data point utility estimator component of the system. The flight pattern simulator composes the TAMDAR data pattern based on the daily flight schedule. The multicriteria simulation-optimization layer finds the best data collection configuration based on the utility functions. The GIS (Geographical Information System) based user interface and analysis engine provides the data visualization functionality together with the data

storage and processing capabilities. It is also used to conduct spatial statistical analysis, which is needed to evaluate the different alternatives.

3. Data Point Utility Estimation There is no definite method to determine the value of the collected meteorological data. Because weather events are highly complex phenomena, which causes even more complicated societal imp acts, rendering it difficult to derive principles for data valuation. Meteorologists agree that aircraft based weather data present high utility for forecasters but there is no quantitative description of this value. For example, aircraft-based meteorological data can be very useful in forecasting convective weather and other phenomena. The availability of frequent soundings allows the forecaster to monitor the degree of instability and wind shear throughout the day, and issue improved forecasts of convective initiation, severity, and dissipation. [1] In TAMDAR-DSS, system users can introduce their own judgment by utilizing decision support system’s user interface. During valuation process, assessments of a forecasters group are entered in to the system in order to calculate the utility values of the TAMDAR data. Utility of a meteorological data is typically related to its following attributes: spatial coverage, temporal coverage, proximity to significant weather events, proximity to climatically significant regions, subjective priority attributed by forecasters, and finally, altitude attribute. Each of these six attributes is represented by a map layer in TAMDAR-DSS. After building these layers, decision makers can determine the weights of each layer depending on their judgment. [2] The flight pattern simulator module generates the trajectories of flights together with the geographical data point location and acquisition time. Figure 1 shows the geographical distribution of typical simulated data point patterns for hypothetical flights between seven airports (Chicago O’Hare, Cincinnati, Cleveland, Norfolk, Washington Dulles, Newark, and New York JFK). Altitude values for each data point are color coded with a darker color representing higher altitudes.

## [# % ## ## ### # # # ## ### # # # # ### # # # # # ### # # # # # ### ##### [### % # ## #Cleveland ## # # # # # ## # # ## #

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# #

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N ew ark

Cinc innati

W ashington_Dulles

N orfol k

Figure 1. An example of TAMDAR-DSS flight pattern simulator’s output.

3.1 Spatial Coverage Weather forecasters prefer a homogenous and continuous data coverage. Under-sampling and over-sampling (because of data acquisition costs) are not desired and should be avoided. In aircraft-based meteorological data collection, it is impossible to achieve perfectly homogenous and 100% predictable coverage characteristics. This is caused by the fact that the participant aircraft follow their own schedule. Therefore, one should expect to have gaps in geographical coverage. In the TAMDAR-DSS, the spatial coverage attribute is integrated via a 2D map, which is based on the data point intensity. Consequently, regions where data points are abundant have lower utilities while regions and locations where data points are scarce have higher utility values. This layer is designed to ensure the inclusion of spatial coverage concerns in the final computation of utility values.

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Figure 2. Spatial TAMDAR Data coverage estimation based on intensity calculations (S-Plus Contour Map Graph) Spatial intensity calculation is useful for the assessment of the spatial coverage of TAMDAR data. Intensity is defined as a mean number of data points per unit area. Figure 2 shows a graph rendered by S-plus software ( a statistical analysis software) , which shows the intensity function for the hypothetical flight schedule, which is introduced in Figure 1. 3.2 Temporal Coverage Forecasters also need temporally well distributed data. However, aircraft flights are scarce between 12 P.M. and 06 AM. In addition, during weekends the number of flights decreases sharply. Therefore, temporal distribution of data should also be optimized. Temporal coverage attribute is integrated into the utility function by calculating point intensity values in a close temporal neighborhood of each data point. 3.3 Proximity to Significant Weather Events Weather is a dynamic phenomenon and some weather events may necessitate higher sampling rates within their proximities. Significant weather events such as thunderstorms, fog, snowstorms have considerable economic impacts on society. Therefore, they should be monitored closely. Accurate prediction of the trajectory of a snow storm may reduce the associated weather related costs. Other meteorological measurement methods (such as satellites) may present incomplete information. Timely upper air data may be crucial in certain cases in order to make an accurate forecast. Therefore, data points, which are close to such events should have higher utility values. 3.4 Proximity to Climatically Significant Regions Some geographical locations may present extra importance for forecasters because of their climatic characteristics. For example, jet streams play important role in weather events in the Continental U.S. Therefore, forecasters monitor the behavior of this atmospheric phenomenon. Consequently, geographical locations, which are close to this phenomena may have higher priority for sampling. The Climatic Layer feature is designed to integrate this type of attributes in to the valuation process. 3.5 Subjective Priority Attributed by Forecasters Some weather forecasting activities require subjective evaluations. Therefore, TAMDAR-DSS should include a layer, in which forecasters can enter their priority ratings based on their routine assessments. In TAMDAR-DSS, once they rate the regions, they can immediately see the final status of the utility map and they can fine tune it. 3.6 Altitude Priorities

Utility of TAMDAR data points is also related to the altitude of the data point. As a widely accepted rule, data points, which are collected from the Troposphere (typically altitudes below 18,000 feet) is more valuable since most of the weather events occur in this region. In addition, most of the forecasters agree that utility of data increases as altitude decreases. Therefore, a utility function for the altitude attribute can be developed based on these observations. 3.7 Utility Calculations For each layer and for each flight segment, a total utility value can be calculated. We derive a mathematical model of the concepts developed for each layer as follows: Spatial Coverage Attribute: If A is defined as the maximum intensity value, which is calculated for all the data points identified by s i,p,k then utility for each flight segment (described by i and p) based on the spatial coverage attribute can be calculated using the following equation:

u1, i , p =

A ∑ intensity (s i, p, k )

for all k.

(1)

k

In Equation 1, i, p, and k are flight number, flight phase ( i.e. ascend, en route or descend ), and index number to locate the individual data points in each flight segment. Individual data points are represented with s i,p,k, which is a vector including the longitude and latitude information. Intensity (s i,p,k ) returns the spatial intensity value (number of data points per unit area) for the region, around s i,p,k. A is computed based on the maximum intensity value for the data set and it is used to normalize the value of utility estimator. Temporal Coverage Attribute: In order to calculate the temporal coverage utility values, one should evaluate the data point intensity value for the region around each individual data point s i,p,k. Data points, which are located close to s i,p,k and collected during the same time frame, are selected and used in temporal coverage calculation. In other words, close spatial and temporal neighbors of the data point ( identified by i, p, k ) are included for intensity calculation. Temporal neighborhood of s i’, p’, k’ includes all the data points such that: s* i’, p’, k’ = { all s i, p, k such that t* i, p, k < t i’, p’, k’ ± ∆t }

(2)

where t i, p, k includes the data acquisition time for data point identified with i, p and k descriptors, t* i, p, k depicts the subset of TAMDAR data which is collected inside the temporal window around the data point s i’, p’, k’. ∆t defines the size of the temporal window for intensity operation. Therefore, utility for temporal coverage layer can be calculated using the following equation:

u 2 ,i , p =

A ∑ intensity (s *i , p, k )

(3)

k

Weather Events Layer: Weather events are integrated in to TAMDAR-DSS with a matrix Q, which includes the utility ratings of a set of geographical locations based on the available weather event information. Therefore, utility for weather events can be calculated as follows: ∧ u 3, i , p = ∑ Q ( s i, p ,k )

(4)

k

∧ Q returns a kriging prediction of the location indicated by s i,p,k based on the linear interpolation of the spatial data introduced by Q. Kriging calculates the unknown values of a spatial random function by using available data [3]. Climatic Layer: Inclusion of climatic attributes is similar to weather events integration. Therefore, for C matrix containing coded

climatologic utility rates:

u 4 ,i , p = ∑ ∧C( si , p , k )

(5)

k

∧

Where C represents the kriging prediction of the utility component based on the climatic factors. Forecasters’ Subjective Ratings: Integration of forecasters’ subjective evaluations are calculated as follows:

u 5 ,i , p = ∑ ∧F ( s i , p , k )

(6)

k

∧

Where F represents the kriging prediction of the utility component based on the grid-based priority ratings of the forecasters for different geographical regions. Since forecasters will provide a limited number of rating values, the areas between their rating points, should be interpolated by using kriging. Altitude Attribute Layer: If we define a function X, which gives the utility rating of a data point based on its altitude, which is represented by X (h i, p, k), we can calculate total altitude based utility of each flight as follows:

u 6 ,i , p = ∑ X∧ ( h i , p ,k )

(7)

k

Where

hi , p , k is the altitude at which data i,p,k is collected.

Total Utility for Segment p of Flight i: One can calculate the total utility for each flight segment by using the following equation: 6

U i , p = ∑ wa u a , i , p

(8)

a =1

Where

wa represents the weight for attribute a.

3. Optimization Model Once each data point is given a utility rate, the system is ready to optimize the sensor configuration. The goal of the optimization engine is to maximize the total utility of daily TAMDAR data without exceeding the budgetary constraints. We can formulate our optimization model as follows:

Maximize

∑∑ U i

i, p

f i, p

(9)

p

subject to:

∑∑ i

p

ni , p f i , p < N

(10)

0 ≤ f i , p ≤ 1 and integer

(11)

N is the limit for daily total number of data points collected and Ui,p represents the total utility rate calculated for segment p of flight i. If flight i is selected for data collection fi,p equals to 1, meaning the sensor should be activated during that flight leg. If it is not selected fi,p equals 0 meaning the sensor should be switched off during that particular flight segment. The number of data point collected during flight segment i,p is given by n i. The output f vector can be seen as a daily list of sensor status (on or off) for all flights.

4. Conclusion This research develops a practical tool for tactical and strategic decision making in aircraft-based meteorological data collection. Although this study is based on airborne sensors, the same approach can be adapted to other weather sensors (surface and/or satellite). No model presently exists that addresses all the important relevant issues that have been identified [4]. The utilities of this study's findings are not limited to weather-related activities. The TAMDAR problem, in essence, is a decision-making problem related with information acquisition system. There are various kinds of information gathering systems in today's information-oriented economies such as market data collection activities, environmental data acquisition systems, defense-related information gathering, security or safety related information collection, Internet based data-mining systems etc. The general framework, which will be showcased in this project, can be adapted to other similar areas

Acknowledgements This research is sponsored by NASA Aviation Safety Program and Federal Aviation Agency (FAA). In 2000, NASA initiated Weather Accident Prevention (WxAP) Project under its Aviation Safety Program. TAMDAR DSS project is one of the studies, which are conducted under WxAP. TAMDAR DSS concept is built on the findings of the NASA’s TAMDAR Economic Feasibility Study, which was also completed by the same group at Old Dominion University in 2001. The views expressed in this paper are those of the authors and do not necessarily reflect official policy or position of the U.S. Government.

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