1. The Environmental Data Cube Distributing Realistic Weather
Steve Lowe
ESG/EDC Program Manager
AER, Inc.
Maj Jim Everitt and Mark Webb (DRC)
ASNE / MSEA Office

2. ASNE MSEA Organization
Air & Space Natural Environment Modeling and Simulation Executive Agent
Director of AF Weather
Sponsors
The MSEA
M&S CO
XCOM
AFWA
Col Zettlemoyer
LtCol Rabayda
Developers
AFCCC
Maj Everitt
AER
SAIC
NGDC
Program Office
(DRCx3)

3. ASNE/MSEA is not in the visual simulation business
Why are we here?
ASNE/MSEA is not in the visual simulation business
Virtual (cockpit) simulators are beginning to require/utilize environment representations behind the imagery
Sensor Prediction / Lighting Models
Embedded Dynamic Effects
Vehicle Performance Behaviors
Virtual simulators are being linked into distributed LVC simulation architectures
Standalone “dial-a-weather” environment is no longer acceptable!
ASNE/MSEA offers technology and support services providing realistic natural environment representations ideal for use in distributed LVC simulation architectures.
FREE to DoD

4. Providing Realistic Context

5. Past: How we got Weather into an Exercise
Manpower intensive process focused on graphic products Rather than DATA in the simulations.
Customer Sets
Weather Objectives
Looks clear
to me…
…you won’t
see targets…
Run the
Simulation
Provide
Weather
Determine Effects
& Make Briefing
Retrieve
Products =
In the past, weather charts were archived hap-hazardly. When an exercise was being prepared for, the weather scenario was limited to what had been archived in it’s choice of visualization products. Also, it took lots of man hours to create the entire weather scenario that was going to be used and it had to be distributed by hand. Often the simulations didn’t even use the scenario because there was no data that could be ingested into the models. This meant that there was disagreement between what was being played in the simulation and what was being briefed.
100% Accuracy
Archive Products
The military was NOT really training like it fought!
In isolation

6. ASNE/MSEA Vision for Weather Support
Customer Sets
Weather Objectives
Simulations
Environmental Data Cube
Briefing Products
Weather Effects
Focused Data Sets
System Impacts
White Cell
Common
Distribution
ASNE MSEA SME
Now we can create the data that the models can ingest and match it to the available archived visualizations. The simulations match what is being briefed. The data and products still have to be distributed by hand though.
Develop
Scenario
COP

7. Conditions, Place/Time, Typical Application of ESG
M&S Support with ESG
Customer Request
Conditions, Place/Time,
Content, Format
ASNE
SME
Customer
HTTP / FTP
Pickup
Custom
Database
Typical Application of ESG
Find the Event
Generate Base Representation
Generate Customer
Representation
Custom
Packaging
NCEP/NCAR 50-Year
Reanalysis
NWP
Model
Transforms
Format
Encoder

8. Ocean Content (Tentative)
ESG Content Sample
Absolute Humidity
Best Lifted index
Blowing Sand
Blowing Snow
Bulk Richardson Number
Cloud Base
Cloud Ceiling
Density Altitude
Dewpoint Temperature
Evaporative Duct Height
Fog
Freezing Level Height
Frozen Ground
Geopotential Height
Ground Wetness
Heat Index
High/Mid/Low Cloud Base
High/Mid/Low Cloud Cover
High/Mid/Low Cloud Top
High/Mid/Low Cloud Type
High/Mid/Low Icing Intensity
High/Mid/Low Turbulence Intensity
Illumination
Inversion Height
Land Cover
Land Use Category
Modified Refractive Index Pasquill Stabilty Index
Precipitation Intensity
Precipitation Rate
Precipitation Type
Pressure
Pressure (Reduced to MSL)
Pressure Altitude
Relative Humidity
Skin Temperature
Snow Depth
Soil Moisture
Soil Temperature
Specific Humidity
Surface Duct Height
Temperature
Thunderstorm Probability
Total Cloud Cover
Total Precipitation
Visibility
Water Vapour Mixing Ratio
Wind Chill
Wind Direction
Wind Gust Speed
Wind Speed
Wind U-Component
Wind V-Component
Ocean Content (Tentative)
Primary Wave Direction
Primary Wave Period
Significant Wave Height
Sea State
Critical Depth
Current U-Component
Current V-Component
Deep Sound Channel Axis Depth
Depth Excess
Mixed Layer Depth
Salinity
Shallow Sound Channel Axis Depth
Sonic Layer Depth
Sound Speed
Surface Duct Cutoff Frequency
Water Temperature

9. The Payoff – Runtime Access in Milliseconds !!
Hypercube Concept
Example: IR Sensor modeling using Target Acquisition Weapons Software (TAWS)
Sensor
Properties
Atmospheric
Transmission
4 Target Types
4 Sensors
4 Target Orientations
8 Sensor-Target Azimuths
5 Sensor-Target Ranges
3 Sensor Altitudes
3 Background Types
24 Times per day
400 Lat/Lon locations
=> 220 million
TAWS computations
Target
Thermal
Properties
Background
Thermal
Properties
The Payoff – Runtime Access in Milliseconds !!

10. Hypercube Concept
Provide pre-computed performance data for simulations that can’t afford the computational burden at run-time
Use a physics solution to a slightly different problem
Compute a desired metric for an n-dimensional parameterized space
Pd = f (Sensor, Target, Background, Weather, Tactics)
Provide a simple API to allow applications to rapidly sample that space for the closest match to the run-time situation
Provides for multi-dimensional interpolation, as appropriate

11. ASNE/MSEA Vision for Weather Support
Customer Sets
Weather Objectives
Simulations
Environmental Data Cube
Briefing Products
Weather Effects
Focused Data Sets
System Impacts
White Cell
Common
Distribution
ASNE MSEA SME
Now we can create the data that the models can ingest and match it to the available archived visualizations. The simulations match what is being briefed. The data and products still have to be distributed by hand though.
Develop
Scenario
COP

12. Environmental Data Cube
ASNE/MSEA FY-07 New Start
Provides for production of full-suite of environment representation products required to support simulation events
Customized data representations or effects
Derived visual representations to support decision makers
Simulated Operational METOC Product Feeds
Provides for coordinated distribution of all products
The right product to the right consumer at the right time
Manages changes/branches in scenario
Provides embeddable technology to improve use of environment representation within the simulations
Pre-configured for use with EDC dynamic data distributions
Focused on embedding the impact without the data volume

13. EDC Conceptual Architecture
EDC Production
Data
Product-based
Web Services
(C2 Feeds, HITL)
Hypercubes
Scenario
Data
Graphics
Text (Obs)
Customer Simulation
DATA file
Data Cache
EDC Runtime Component
API
(Custom) Encoder
Data, Effects
HLA
DIS ??
EDC Pack
EDC Service
“Sim Net”
Pack A
EDC
Sim X
HLA/RTI
DIS
XMSF
???
EDC Distributor
Pack B
EDC
Sim Y
Scenario Management
Simulation Package Mgr
Exercise V&V
EDC
Sim Z
Pack C
An EDC Simulation Package is a complete set of Data, Hypercube, Image, Text products mapped to simulation requirements.

14. EDC Process for Blue Flag - 07
Blue Flag NIPR Site
GRIB
(AFWA/MM5)
JWIS
METAR
(JMIBL)
NITES
CSV
Spatial Shift
Temporal Shift
Derived Content
Custom Formats
“Pacifica”
June 2007
AFCCC
Data
505th
Base
Atmospheric
Representation
50km / 1-hr
Califon
Feb 94
IMAGES
GrADS
Hypercubes
AWSIM
HyperTAWS

15. WX CHARTS and METAR’s
METAR KBOI Z 18006G15KT 10SM TS SCT011 02/M03 Q0856 RMK SLP145 LAT351N LON1764E ESGACMES
METAR KBAM Z 18005G12KT 10SM CLR 03/M09 Q0821 RMK SLP100 LAT322N LON1757E ESGACMES
METAR KWMC Z 18005G08KT 10SM CLR 04/M08 Q0842 RMK SLP120 LAT325N LON1748E ESGACMES

16. Summary Existing ASNE Capabilities for Environment Representation
Environmental Scenario Generator
Environmental Hypercube
Auxiliary Products (Graphics, Text, etc.)
Environmental Data Cube is an FY-07 New Start to formalize many existing processes
Unique “weather server” in its modularity / flexible use
Consistent Environmental Representation is the foundation
Broad range of derived products and delivery mechanisms

17. Questions ? Steve.Lowe@aer.com 757.348.9997 Mark.Webb.CTR@afccc.af.mil