# Presentation: 522343

## NASA Applications Rapid Prototyping Capability Hampton VA April 19 & 20 2006

- Joseph.C.Coughlan@ nasa.gov
- Intelligent Systems Division
- NASA Ames Research Center

## The Vision: Turning Observations Into Knowledge Products

**The Vision: Turning Observations ****Into Knowledge Products**

## NASA Ecological Modeling, Nowcasting & Forecastinghttp://ecocast.arc.nasa.gov

## How we implement it!

**How we implement it!**

## Terrestrial Observation and Prediction System 

- Terrestrial Observation and Prediction System
- can serve many of these themes...

## ...TOPS enables Biospheric Monitoring

- ...TOPS enables Biospheric Monitoring
- Near Realtime

- Based on Running, S.W and R.R. Nemani et al., Bioscience, 2004

- TOPS - Applications

## TOPS Collaborating Scientists

**Ramakrishna Nemani, NASA Ames Research Center**
**University of Montana: Steve Running, Matt Jolly, Andy Michaelis**
**Boston University: Ranga Myneni**
**Utah State University: Michael White**
**NCAR: Peter Thornton, Dave Schimel**
**Cal State Monterey Bay: Petr Votava, Forrest Melton**
**U.S. Forest Service: Patricia Andrews**
**Scripps Inst. Of Oceanography: Charles Keeling, Dan Cayan, John Roads**
**San Jose State University: Kazuhito Itchii**
**University of Tsukuba: Kenlo Nishida**
**University of Tokyo: Hirofumi Hashimoto**
**NASA Ames Research Center: Keith Golden**
**Carnegie Mellon: Clark Glymour, David Danks**

## Carbon Management: NASA CASA / CQUEST

## Invasive species

## How to prepare for mission data that is not yet available?

## Simulation Experiment: Evaluate improved weather capability on ecosystem forecasting

- Soil water forecasting in the continental United States: Relative forcing by meteorology versus leaf area index and the effects of meteorological forecast errors
*Michael A. White (UT St. U) and Ramakrishna R. Nemani (NASA/ARC)*
*Can. J. Remote Sensing, Vol. 30, No. 5, pp. 717–730, 2004*

## TOPS Simulations

- TOPS simulations require two basic input types:
- Static
- soils,
- land cover
- Dynamic
- LAI,
- meteorology
- Coverage:
- Conterminous US at 8 km from 1982-1997:
- Meteorology data from the Daymet project from 1982 to 1997.
- Satellite data from the Pathfinder project from 1982 to present at a resolution of 8 km.

## Identifying meteorological forecast error

- Error is defined as a statistically significant difference in biome-level soil water between altered and actual soil water estimates at the end of a 1-week forecast.
- The t-test assesses whether the means of two groups are statistically different from each other.
- t-test of soil water simulated with erroneous and true meteorology.

## What is the value of improved weather forecasts on ecosystem forecasting?

**Abstract.**
- We identified the critical meteorological forecast errors in temperature and precipitation that were required to generate statistically significant differences in 1-week soil water forecasts
- Temperature critical errors approached 10 °C in winter but were only about 2–3 °C in summer.
- Precipitation critical errors were much more constant throughout the year and were usually less than 1 cm (error in weekly total precipitation).
- Geographic areas with low LAI or partially covered vegetation were more sensitive to forecast errors
*M.White & R.Nemani, Can. J. Remote Sensing, Vol. 30, No. 5, pp. 717–730, 2004*

## Adapting SMD NASA Mission Technologies for Rapid Prototyping Capabilities

- Adapting SMD NASA Mission Technologies for Rapid Prototyping Capabilities

## MER Tools

**HCC & Fatigue Countermeasures:**** Improved data understanding and Enhanced situational awareness**

**CIP:**** Customizable data navigation, search, and information management**

**Viz:**** High fidelity terrain modeling and analysis**

**MAPGEN:**** Activity plan development and analysis**

**MERBoard:**** Collaborative information analysis and sharing**

- MER science and uplink team members have estimated that** ****overall science return increased by 20 - 50%.**

## MERBoard for Science Team Ops Planning

**Shown: **MERBoard used by MER science team in ops planning
**Output:**  Graphically oriented planning tool that is easy to use under the real-time pressures of mission operations, but also enables more systematic and extensive archiving and retrieval of plan-rationales and associated documents.

**Outcome:**  1-2 orders of magnitude improvement in on-line documentation, including flexible storage and retrieval of annotated graphics, during Science Operations Working Group (SOWG) long-range and tactical planning. Key technology for future distributed mission operations.
**Beneficiaries: **MER, MSL; other NASA applications are being explored.

## MER Collaborative Information Portal (CIP)

- MER CIP is a flexible and adaptable information portal, co-designed by MER mission team, integrated, deployed, and evaluated during mission operations.
- MER CIP can flexibly integrate a variety of information for timely presentation and flexible communication among ops team members.

**Result:**  A useful tool for the MER mission; design foundation and feasibility demonstration for multi-mission data integration, schedule management, and distributed operations.

**Notes:**
MER Operations  focus on immediate task performance.
Ensuring milestones are met:
What is the health and state of the rovers ?
What shifts are on today ?
Who is SOWG Chair of team #2
When is the next uplink ?
Does a sequence plan exist yet ? – if so show it to me.
Why are they returning to the same site ? – show me the justification
What and when is the next major event ?
Where are the rover support groups in their process of preparation for the next event ?
What is the Estimated Time of Completion for the SOWG meeting ?
Where is it being held?
What were the most recent discoveries or significant findings ?
Show me the data
Who produced this result ?
Where is this person now, where are they from, and what is their phone number ?
When is the next major milestone ?
I want to post a message to the team

## Collaborative Decision Environment to Enable Decision Support

**Alignment with Earth Science Vision**

_***Collaborative Decision Environment***_*** to Enable Decision Support***
***Seamless Access to Land, Sea, Air, and Space-Based Assets***

***Many possible ROSES collaborations!***

## Intelligent Mission Operations and Rapid Prototyping

## Science-Driven Decision Support

-  - Automated Data Products
-  - Sensor Planning Service
-  - Remote Desktop Access
- Planning Tools
-  - Aircraft Tracking & Flight Planning
-  - Satellite Tracking
-  - Data Processing & Access
-  - High Resolution Features

- Currently Tracking Hazards:
-  - Fires
-  - Hurricanes
-  - Earthquakes
-  - Volcanoes
-  - Cloud Cover
- Supports MAC and PC
- Can easily subscribe to many geo-spatial data products!

## Summary

- Existing efforts with user communities publish data and customized model results on the Internet.
- Integrated data/modeling systems can be run in forecasting mode to assess the impacts of errors in static and dynamic inputs.  These results can benchmark the improvement (or degradation) in system performance by variable(s) and geographic localities.
- NASA SMD mission technologies enable rapid decision making and reliable mission operations. These can be repurposed to demonstrate an improved decision support capability including data, models and human-in-the-loop interactions.

## Commentary

- Mission data is still difficult to comprehend and access (not download; open, view and verify data takes expertise and time).
- We need examples of prototype data and access mechanisms and formats to rapid prototype.
- Software to access, visualize and understand data should be open and on sourceforge. (note: ESMF is on sourceforge)
- Frameworks and technologies for interoperability and robustness have costs that budget and time constrained PIs will be hard pressed to understand and implement.
- The most efficient sorting algorithm is not the quicksort.  It is the an algorithm specifically tailored for an unordered list.  We force PIs to be efficient so they are and build “customized” solutions to problems.
- Is the overhead worth it for a prototype given the communication of the implementation is less important than the result and ROI potential?
- Science programs can do more to require science models to ingest NASA data prior to mission launch (or maybe not).
- Is the applications program getting ahead of the science programs?