A Simulation-based Spatial Decision Support System for a New Airborne Weather Data Acquisition System

Transcription

1 A Simlation-based Satial Decision Sort System for a New Airborne Weather Data Acqisition System Erol Ozan Deartment of Engineering Management Old Dominion University Norfol, VA Pal Kaffmann Deartment of Engineering Technology Old Dominion University Norfol, VA Abstract Troosheric Airborne Meteorological Data Reorting (TAMDAR) system is envisioned as a new atomated, onboard system that will gather meteorological data as aircraft fly in the trooshere and transmit this data to the National Weather Service. The weather information that TAMDAR will generate, may be a critical element in enhancing the accracy and comleteness of weather data and reslting forecasts. This aer aims to demonstrate the feasibility of a rototye TAMDAR economic analysis tool, which will hel decision maers to determine the best strategy for system oeration. Essentially, this decision sort system is comosed of the following major comonents: a cstomized simlation-otimization engine, a data tility estimator, a GIS-based analysis layer, and the ser interface. Time correlated osition and altitde data for TAMDAR eqied aircraft are generated and entered into the decision aiding software to bild a dynamic model of the airsace occied, at any oint of time, by a fleet of indeendent TAMDAR eqied aircraft. Economic models for data costs and data tility are integrated into the decision aiding software to generate a qantifiable vale to be assigned to the TAMDAR data slied by each aircraft. This decision sort system hels decision-maers to rocess different oerational alternatives efficiently. They can also condct varios what-if analyses effectively by sing GIS-based ser interface. Keywords Geograhical Information Systems, Decision Sort Systems, Satial Mlticriteria Decision Maing, Meteorological Data Utility, Vale of Information. 1. Introdction TAMDAR (Troosheric Airborne Meteorological Data Reorts) is a new airborne weather data acqisition system, which is crrently stdied by NASA. The TAMDAR concet consists of sensor acages, information rocessors, and commnications eqiment carried aloft by articiating aircraft. As these aircraft comlete their missions, the TAMDAR system reorts weather conditions to grond-based receiving stations that rocess and distribte this data into a national system for dissemination of weather information. The Federal Aviation Administration (FAA), National Weather Service (NWS), and NASA have conclded that TAMDAR systems have otential to enhance the accracy and comleteness of weather data and the reslting weather forecasts [1]. Imroved aviation safety, along with other benefits for varios gros, is anticiated as a reslt of these imroved forecasts. The rimary objective of this research is to develo a satial decision sort system architectre that will hel sers to redce the data transmission costs of the TAMDAR system by otimizing the data collection. 2. System Architectre TAMDAR Decision Sort System (TAMDAR-DSS) has for main comonents: GIS-based User Interface and Analysis Unit, Data Point Utility Estimator, Mlticriteria Simlation-Otimization Engine, and Flight Pattern Simlator. This aer focses on the data oint tility estimator comonent of the system. The flight attern simlator comoses the TAMDAR data attern based on the daily flight schedle. The mlticriteria simlation-otimization layer finds the best data collection configration based on the tility fnctions. The GIS (Geograhical Information System) based ser interface and analysis engine rovides the data visalization fnctionality together with the data

2 storage and rocessing caabilities. It is also sed to condct satial statistical analysis, which is needed to evalate the different alternatives. 3. Data Point Utility Estimation There is no definite method to determine the vale of the collected meteorological data. Becase weather events are highly comlex henomena, which cases even more comlicated societal im acts, rendering it difficlt to derive rinciles for data valation. Meteorologists agree that aircraft based weather data resent high tility for forecasters bt there is no qantitative descrition of this vale. For examle, aircraft-based meteorological data can be very sefl in forecasting convective weather and other henomena. The availability of freqent sondings allows the forecaster to monitor the degree of instability and wind shear throghot the day, and isse imroved forecasts of convective initiation, severity, and dissiation. [1] In TAMDAR-DSS, system sers can introdce their own jdgment by tilizing decision sort system s ser interface. Dring valation rocess, assessments of a forecasters gro are entered in to the system in order to calclate the tility vales of the TAMDAR data. Utility of a meteorological data is tyically related to its following attribtes: satial coverage, temoral coverage, roximity to significant weather events, roximity to climatically significant regions, sbjective riority attribted by forecasters, and finally, altitde attribte. Each of these six attribtes is reresented by a ma layer in TAMDAR-DSS. After bilding these layers, decision maers can determine the weights of each layer deending on their jdgment. [2] The flight attern simlator modle generates the trajectories of flights together with the geograhical data oint location and acqisition time. Figre 1 shows the geograhical distribtion of tyical simlated data oint atterns for hyothetical flights between seven airorts (Chicago O Hare, Cincinnat Cleveland, Norfol, Washington Dlles, Newar, and New Yor JFK). Altitde vales for each data oint are color coded with a darer color reresenting higher altitdes. %[ %[ Cleveland %[ %[ %[ %[ Cincinnati Washington_Dlles %[ Chicago Norfol JFK Newar Figre 1. An examle of TAMDAR-DSS flight attern simlator s ott. 3.1 Satial Coverage Weather forecasters refer a homogenos and continos data coverage. Under-samling and over-samling (becase of data acqisition costs) are not desired and shold be avoided. In aircraft-based meteorological data collection, it is imossible to achieve erfectly homogenos and 100% redictable coverage characteristics. This is cased by the fact that the articiant aircraft follow their own schedle. Therefore, one shold exect to have gas in geograhical coverage. In the TAMDAR-DSS, the satial coverage attribte is integrated via a 2D ma, which is based on the data oint intensity. Conseqently, regions where data oints are abndant have lower tilities while regions and locations where data oints are scarce have higher tility vales. This layer is designed to ensre the inclsion of satial coverage concerns in the final comtation of tility vales.

3 41 Chicago Cleveland JFK Newar 17.1 lat.os 39 Cincinnati Washington long.os 8.5 Norfol Figre 2. Satial TAMDAR Data coverage estimation based on intensity calclations (S-Pls Contor Ma Grah) Satial intensity calclation is sefl for the assessment of the satial coverage of TAMDAR data. Intensity is defined as a mean nmber of data oints er nit area. Figre 2 shows a grah rendered by S-ls software ( a statistical analysis software), which shows the intensity fnction for the hyothetical flight schedle, which is introdced in Figre Temoral Coverage Forecasters also need temorally well distribted data. However, aircraft flights are scarce between 12 P.M. and 06 AM. In addition, dring weeends the nmber of flights decreases sharly. Therefore, temoral distribtion of data shold also be otimized. Temoral coverage attribte is integrated into the tility fnction by calclating oint intensity vales in a close temoral neighborhood of each data oint. 3.3 Proximity to Significant Weather Events Weather is a dynamic henomenon and some weather events may necessitate higher samling rates within their roximities. Significant weather events sch as thnderstorms, fog, snowstorms have considerable economic imacts on society. Therefore, they shold be monitored closely. Accrate rediction of the trajectory of a snow storm may redce the associated weather related costs. Other meteorological measrement methods (sch as satellites) may resent incomlete information. Timely er air data may be crcial in certain cases in order to mae an accrate forecast. Therefore, data oints, which are close to sch events shold have higher tility vales. 3.4 Proximity to Climatically Significant Regions Some geograhical locations may resent extra imortance for forecasters becase of their climatic characteristics. For examle, jet streams lay imortant role in weather events in the Continental U.S. Therefore, forecasters monitor the behavior of this atmosheric henomenon. Conseqently, geograhical locations, which are close to this henomena may have higher riority for samling. The Climatic Layer featre is designed to integrate this tye of attribtes in to the valation rocess. 3.5 Sbjective Priority Attribted by Forecasters Some weather forecasting activities reqire sbjective evalations. Therefore, TAMDAR-DSS shold inclde a layer, in which forecasters can enter their riority ratings based on their rotine assessments. In TAMDAR-DSS, once they rate the regions, they can immediately see the final stats of the tility ma and they can fine tne it. 3.6 Altitde Priorities

4 Utility of TAMDAR data oints is also related to the altitde of the data oint. As a widely acceted rle, data oints, which are collected from the Trooshere (tyically altitdes below 18,000 feet) is more valable since most of the weather events occr in this region. In addition, most of the forecasters agree that tility of data increases as altitde decreases. Therefore, a tility fnction for the altitde attribte can be develoed based on these observations. 3.7 Utility Calclations For each layer and for each flight segment, a total tility vale can be calclated. We derive a mathematical model of the concets develoed for each layer as follows: Satial Coverage Attribte: If A is defined as the maximm intensity vale, which is calclated for all the data oints identified by s, then tility for each flight segment (described by i and ) based on the satial coverage attribte can be calclated sing the following eqation: 1, = A intensity ( s ), for all. (1) In Eqation 1,, and are flight nmber, flight hase ( i.e. ascend, en rote or descend ), and index nmber to locate the individal data oints in each flight segment. Individal data oints are reresented with s,, which is a vector inclding the longitde and latitde information. Intensity (s, ) retrns the satial intensity vale (nmber of data oints er nit area) for the region, arond s,. A is comted based on the maximm intensity vale for the data set and it is sed to normalize the vale of tility estimator. Temoral Coverage Attribte: In order to calclate the temoral coverage tility vales, one shold evalate the data oint intensity vale for the region arond each individal data oint s,. Data oints, which are located close to s, and collected dring the same time frame, are selected and sed in temoral coverage calclation. In other words, close satial and temoral neighbors of the data oint ( identified by, ) are inclded for intensity calclation. Temoral neighborhood of s i,, incldes all the data oints sch that: s*i,, = { all s, sch that t*, < t i,, ± t } (2) where t, incldes the data acqisition time for data oint identified with and descritors, t*, deicts the sbset of TAMDAR data which is collected inside the temoral window arond the data oint s i,,. t defines the size of the temoral window for intensity oeration. Therefore, tility for temoral coverage layer can be calclated sing the following eqation: 2, = A intensity ( s* ), (3) Weather Events Layer: Weather events are integrated in to TAMDAR-DSS with a matrix Q, which incldes the tility ratings of a set of geograhical locations based on the available weather event information. Therefore, tility for weather events can be calclated as follows: = Q ( s, ) 3, (4) Q retrns a riging rediction of the location indicated by s, based on the linear interolation of the satial data introdced by Q. Kriging calclates the nnown vales of a satial random fnction by sing available data [3]. Climatic Layer: Inclsion of climatic attribtes is similar to weather events integration. Therefore, for C matrix containing coded

5 climatologic tility rates: = C( si,, ) 4, (5) Where C reresents the riging rediction of the tility comonent based on the climatic factors. Forecasters Sbjective Ratings: Integration of forecasters sbjective evalations are calclated as follows: = F( s, ) 5, (6) Where F reresents the riging rediction of the tility comonent based on the grid-based riority ratings of the forecasters for different geograhical regions. Since forecasters will rovide a limited nmber of rating vales, the areas between their rating oints, shold be interolated by sing riging. Altitde Attribte Layer: If we define a fnction X, which gives the tility rating of a data oint based on its altitde, which is reresented by X (h, ), we can calclate total altitde based tility of each flight as follows: Where h i,, is the altitde at which data, is collected. = X ( h, ) 6, (7) Total Utility for Segment of Flight i: One can calclate the total tility for each flight segment by sing the following eqation: Where w a reresents the weight for attribte a. U w (8) = 6 a= 1 3. Otimization Model Once each data oint is given a tility rate, the system is ready to otimize the sensor configration. The goal of the otimization engine is to maximize the total tility of daily TAMDAR data withot exceeding the bdgetary constraints. We can formlate or otimization model as follows: a a, Maximize (9) i U f sbject to: i n f N (10) <

6 0, 1 f and integer (11) i N is the limit for daily total nmber of data oints collected and U reresents the total tility rate calclated for segment of flight i. If flight i is selected for data collection f eqals to 1, meaning the sensor shold be activated dring that flight leg. If it is not selected f eqals 0 meaning the sensor shold be switched off dring that articlar flight segment. The nmber of data oint collected dring flight segment is given by n i. The ott f vector can be seen as a daily list of sensor stats (on or off) for all flights. 4. Conclsion This research develos a ractical tool for tactical and strategic decision maing in aircraft-based meteorological data collection. Althogh this stdy is based on airborne sensors, the same aroach can be adated to other weather sensors (srface and/or satellite). No model resently exists that addresses all the imortant relevant isses that have been identified [4]. The tilities of this stdy's findings are not limited to weather-related activities. The TAMDAR roblem, in essence, is a decision-maing roblem related with information acqisition system. There are varios inds of information gathering systems in today's information-oriented economies sch as maret data collection activities, environmental data acqisition systems, defense-related information gathering, secrity or safety related information collection, Internet based data-mining systems etc. The general framewor, which will be showcased in this roject, can be adated to other similar areas Acnowledgements This research is sonsored by NASA Aviation Safety Program and Federal Aviation Agency (FAA). In 2000, NASA initiated Weather Accident Prevention (WxAP) Project nder its Aviation Safety Program. TAMDAR DSS roject is one of the stdies, which are condcted nder WxAP. TAMDAR DSS concet is bilt on the findings of the NASA s TAMDAR Economic Feasibility Stdy, which was also comleted by the same gro at Old Dominion University in The views exressed in this aer are those of the athors and do not necessarily reflect official olicy or osition of the U.S. Government. Reference 1. Ozan, E., Kaffmann, P., 2001, A Technological Policy Analysis Framewor for a New Airborne Weather Data Collection System, Proc. of the 2001 American Society for Engineering Management, October 11-13, Hntsville, Alabama. 2. Malczews J., 1999, GIS and Mlticriteria Decision Analysis, John Wiley and Sons Inc. 3. Cressie, N., 1986, Kriging Non-stationary Data, Jornal of American Statistical Association, 81: Katz, R., Mrhy, A. H., 1997, Economic Vale of Weather and Climate Forecasts, Cambridge University Press.