# Presentation: 231616

## AWARE

- NASA Weather Accident Prevention Project Annual Review
- May 23-25, 2000
- David Kil
- Rockwell Science Center
- Palo Alto, CA

## Outline

- Program background
- Objectives
- Technical accomplishments
- Information-fusion architecture
- Information visualization
- Leveraging other programs
- Technical trade-off issues
- Plans for the next year
- Aviation weather-information portal with easy-to-use decision analysis/situational awareness functions
- Summary

## Program Background

- Supported by the NASA under Contract No. NCC1-290
-  NASA Program Manager: Anthony Busquets
- Funded as a topical R&D project under the NASA AWIN program
- Rockwell Science Center, Rockwell Collins, and NASA
- Developing a proof-of-concept prototype, not a commercial system
- September 1998 - September 2002

## Objectives

- Weather information filtering and situational awareness using advanced decision-support algorithms coupled with human factors analysis

## Technical Accomplishments

- Official project Web site: _http://www.awin.org_
- AWARE system architecture design (11/30/98)
- NEXRAD kinetic analysis (2/15/99)
- METAR interpretation and decision analysis (4/30/99)
- Icing products integration (9/30/00)
- VFR preflight briefing design (12/31/00)
- VFR summary display (3/31/00)

## Current Status

**Capabilities**

- FY’99

- FY’00

- FY’01

- FY’02

*Interpretation*

*Visualization and Integration*

- Current

## Weather Information Fusion

-          _**Inputs:**_
- Onboard sensor data
- Uplinked weather data

-                 _**Processing:**_
- Hazard object detection/clustering
- Attribute extraction
- Severity of hazard calculation
-    -Hazard analysis
-    -Pattern classification
-    -Fusion (Bayesian, Fuzzy, D-S)
- Display formatting

-    _**Output:**_
- Hazard index
- as a function
- of time

## Information-Fusion Architecture for Decision-Support Analysis

- Data
- projection

- Classifier
- performance
- analysis

- Mapping

- Attribute
- ranking

- Attribute
- extraction

- Reporting &
- visualization

- Good
- perf?

- Extracted
- everything
- useful?

- Classifier
- DB

- Raw
- data

- Y

- N

- N

- Y

- More
- data
- collection

- Data fusion
- New data source
- New sensor design

- Parametric
- Nonparametric
- Bayesian networks
- ANNs, DBNs

- Test for
- sufficient
- statistics

- Image/signal
- processing

## System Architecture

## AWARE Weather Briefing

## VFR Summary Display (3/31/00)

## Information Visualization

-  Turbulence overlay
- from NCAR forecasts

- Vertical flight profile using METAR and flight data plan

- Flight path

- Direction &
- velocity of
- winds aloft

- Cloud
- cover

## Data Processing

- AIRMET ICE...WA OR CA

- FROM TVL TO FAT TO BIH TO RNO TO TVL

- LGT OCNL MOD RIME ICGIC BTN 070

- AND FL200.  CONDS SPRDG SLOLY

- SEWD AND CONTG BYD 09Z THRU 15Z.

- AIRMET TURB...WA OR CA ID MT

- FROM EHF TO SMX TO SBA TO VTU TO PMD

- TO EHF

- OCNL MOD TURB BLW 120 DUE TO MOD WLY

- FLOW.  CONDS SPRDG EWD AND

- CONTG BYD 09Z THRU 15Z.

**Weather Event**
**Database**

- Icing_Event
-  Unique_ID:
-  Spatial_Extent: ...
-  Temporal_Extent: ...
-  Type: Rime
-  Severity:
-  Direction:
-  Velocity:
-  Supercedes:

- Turbulence_Event
-  Unique_ID:
-  Spatial_Extent: ...
-  Temporal_Extent: ...
-  Type: Clear air
- Severity: Moderate
-  Direction:
-  Velocity:
-  Supercedes:

- Parsing from text data

- Image/signal processing

- Sufficient statistics analysis &
- Information filtering for planning

- Flight planning assistance &
- Decision-support analysis

- Data models

- XML

## Key Trade-off Issues

- Server- vs. client-side programming
- Migration from Informix to Oracle
- Spatial cartridge vs. Datablade
- Data storage: sufficient statistics?
- Bayesian network implementation using Hugin
- Where is data coming from? Data mining to assign hard numbers to various convective weather events
- Complement BN with classical hazard analysis and pattern classification preprocessing
- Automation vs. trust
- XML eliminating or reducing the need for text parsing (Weather Observation Definition Format at SPAWAR)
- Military TAF vs. civilian TAF

## Leveraging Other Programs

- ONR mine countermeasure: Data-mining toolbox along with various preprocessing algorithms (wavelet, image processing, and transformation) for information filtering and fusion
- DARPA programs on the use of Bayesian networks for information fusion and resource allocation
- IR&D programs on creating scalable, database-backed Web sites

## Data-Mining Toolbox

- _http://www.__killmine__.com_

## Information Fusion

- Pre-flight
- danger
- level

- E-PIREPS

- Turbulence

- Radar
- shadow

- Convective
- weather
- attributes*

- HazardSA

-  CAT LIDAR
- & other sensors

- S.Temp

- LAS

- Dew P.

- DP
- spread

- Move

- EWxR (WxR+NEXRAD)

- Uplinked data

- A/C location
- FMS data
- Constraints

- Height
-  Gradient
-  Shape
-  Reflectivity
-  Wavelet coefficients
-  Velocity vector
- All extracted from
- image/signal
- processing algorithms

## Financial Status: Spent vs. Actual

- Slightly over budget
- due to one-time
- NCAR subcontract
- which will be
- spread over
- multiple milestones

## Roadmap

- SIGMETS/AIRMETS
- VFR preflight briefing integration
- PIREP integration
- IFR preflight briefing/display
- In-flight architecture
- In-flight decision support
- EWxR integration
- Usability study
- Integration in NASA 757
- IFR in-flight briefing integrated demo

## Summary

- Identification of suitable databases & data models
- Information filtering and fusion
- How to find the right spectrum between raw data presentation and decision making for pilots (*The Future of Air Traffic Control* by NAP)
- Automated data collection with the totally connected world
- How to identify the point of diminishing returns with sufficient statistics analysis