# Presentation: 540485

## Applications Program

- Dr. Shahid Habib
- Assistant Director
- GSFC/Code 900
- February 3-4, 2004

**Implementation Working Group**

## Contents

- Background
- Current Environment
- National Applications
- Recent Direction/Approach
- Issues
- GSFC Efforts

## GSFC Background/Experience

- We have been producing multitude of results which are applicable to the remote sensing applications:
- Landsat imagery:  used for commodity forecasting by USDA
- TOMS Ozone mapping and UV index: skin cancer prediction
- TRMM and Quickscat data for weather prediction
- Volcanic ash monitoring for aviation safety using TOMS and MODIS data
- MODIS products for Fire detection

**Yakutsk, Russia July 19, 2001**

**Oct 30, 2000**

**Sep 28, 2000**

**Exeter, England**

## MODIS Rapid Response Project: Data Flow

- MODIS
- L0 Data

- Active Fire and
- Corrected Reflectance
**http://rapidfire.sci.gsfc.nasa.gov**

- Cumulative Fire Maps
**http://www.fs.fed.us/eng/rsac**

- Active Fire
- Locations

- Burn Severity Maps
- Handcrafted Imagery

- L1B Data

- December 2001

- Active Fire Locations
- Selected Imagery

- Web Fire Maps and Fire Feature Server
**http://rapidresponse.umd.edu**

- Backup Feed
- L1B Data

- Active Fire Locations
- GOFC Fire Partners

- NASA Earth
- Observatory
**http://earthobservatory.nasa.gov**

**MODIS Rapid Response Project: Data Flow**

**T+2-5hrs**

**T+30min**

- Updated twice daily

**T+5hrs**

**T+5hrs**

- MODIS home page
**http://modis.gsfc.nasa.gov**

## Environment

- Curiosity Science

- Curiosity Science

- Free Problem

- Science

- Results

- Additional Questions

- Science

- Results

- Applications

- Benefits

**Environment**

***Then ..................and..............................................................Now***

- _Discovery_

- Science

- Societal
-  Needs

- Technology

- Optimize

- Balancing our Investments

- Applications

## Science to Applications Transition

**Curiosity  or Entrepreneurial Science Pipeline**

**Science Drivers**

**Results/Findings**

**Adjust**

_**Discovery Process**_

**Technology  Pipeline**

**System, Subsystem and Component Products**

**Measurements/Missions**

_**Applications Process**_

**Proof of Concept**

**Users**

**Societal, Economic Benefits and Policy Decisions**

**Observations, Data and Products**

***Time t***

***t >> 0 ***

***t << 0 ***

***t = 0 ***

**Science to Applications Transition**

**Models**

**Validate**

## Science to Applications to Decision Making Framework

***Value & benefits to citizens and society***

**Data**

**Policy **
**Decisions**
**Management**
**Decisions**

**Predictions**

**Decision**
**Support**
_**Tools**_

** ****Assessments**
** ****Decision**
**    ****Support**
**       ****Systems**

**Models &**_** **_**Data Assimilation**

**Oceans**
**Land**
**Atmosphere**
**Ice **
**Coupled**

**Observations**

**Measurements & Monitoring**

**Satellites**
**Airborne**
**Ground**

**Inputs**

**Outputs**

**Outcomes**

**Impacts**

- Stakeholders

*Applications Domain*

## Decision Making Levels

**Decision Making Levels**

**National**

**State**

**County**

**Local**

- Individual

**Integrated Process**

**Regionalized Process**

**National**

**State**

**County**

**Local**

**Individual**

## GSFC Role in the National Applications

| National Application | GSFC Role | GSFC Expertise | Funding Source | Funding Type | GSFC Interested |
| --- | --- | --- | --- | --- | --- |
| Public Health | Leading | Exists | YO | Directed | Yes |
| Invasive Species | Leading | Exists | YS & YF | Competed | Yes |
| Water Management | Leading | Exists | YS & YO | Competed & Directed | Yes |
| Homeland Security | Involved | Exists | YS | Competed | Yes |
| Disaster Management | Involved | Exists | YS & YO | Competed & Directed | Yes |
| Air Quality | Involved | Exists | YS | Competed | Yes |
| Coastal Management | Not Involved | Exists | YS | Competed | Yes |
| Aviation Safety | Not Involved | Exists | None | N/A | Yes |
| Ecological Forecasting | Not Involved | Exists | YS | Competed | Yes |
| Carbon Management | Not Involved | Exists | YS & Internal | Directed & Competed | Yes |
| Energy Management | Not Involved | Exists | None | N/A | Yes |
| Agriculture Efficiency | Not Involved | Exists | YS & Others | Competed | Yes |

**GSFC Role in the National Applications**

## GSFC Applications Tasks Detail

## GSFC Code 900 Organization

** ****Computing Division**
**Richard Rood, Chief**
**Code 930**

**Global Change Data Center**
**Stephen Wharton, Chief**
**Code 902**

**Laboratory for **
**Atmospheres**
**William Lau, Chief**
**Code 910**

**Laboratory for **
**Terrestrial Physics**
**David Smith, Chief**
**Code 920**

**Goddard Institute for**
**Space Studies**
**James Hansen, Chief**
**Code 940**

**Laboratory for **
**Hydrospheric Processes**
**Peter Hildebrand, Chief**
**Code 970**

**CHIEF SCIENTIST****Mark Schoeberl**

**CHIEF ENGINEER**
**Bob Connerton**

**DIRECTOR -**** ****Franco Einaudi**
**DEPUTY DIRECTOR**** ****-**** ****Dorothy Zukor**

**GSFC Code 900 Organization**

**EOS PROJECT SCIENTIST**
**Michael King**

**ASSISTANT DIRECTOR FOR**
** ****PLANNING AND APPLICATIONS**
**Shahid Habib**

**ASSISTANT DIRECTOR FOR **
**OPERATIONS**** **
**Jack Richards**

**Global Modeling and**
** ****Assimilation Office**
**Michele Rienecker, Head**
**Code 900.3**

**RESOURCES MANAGER**
**Kelly Pecnick**

## Applications Activities Distribution within Code 900

**Earth Sciences Directorate**
**Code 900**

**Applications Activities Distribution within Code 900**

**Code 910**

**Code 920**

**Code 930**

**Code 940**

**Code 970**

**Air Quality**
**Disaster**

**Disaster**

**Invasive Species**

**Agriculture**
**Air Quality**

**Water**
**Coastal**

**Code 902**

**Code 900.3**

**Air Quality**
**Disaster**

**Public Health**

**Code 900.3**
**Geospatial **

## GSFC Applications Management Team 

**Code 900**
**Applications Manager**

**HQ/YO**

**Other NASA**
**Centers**

**External**
**(CCRI, Conferences, Agencies, etc.)**

**Code 900**
**Application**
**Leads**

**1**

**2**

**3**

**11**

**12**

**GSFC Applications Management Team **

## Systems Engineering Approach

**Landsat-7**

**Terra**

**Aqua**

**TRMM**

**Aura**

**QuikScat**

**User**

**Systems Engineering Approach**

- MODELS

- MISSIONS

- Applied Research Domain

*Science and Research Products*

*User Specific Operational Products*

***Engineering***

- Partnership

***Management***

***Science/Research***

## Agency

- Center Implementation Plan

- Center Implementation Plan

- MSFC

- Center Implementation Plan

- LaRC

- Center Implementation Plan

- GSFC

- Other
- Project
- Plan

- Other
- Project
- Plan

- Health
- Project
- Plan

- Health
- Project
- Plan

- Health
- Project
- Plan

- Health
- Project
- Plan

- Health
- Project
- Plan

- Cross Cut
- Solutions
- Project Plan

- NASA
- Strategic
- Plan

- ESE
- Strategic
- Plan

- Earth Science
- Technology
- Plan

- Earth Science
- Applications
- Plan

- Earth Science
- Education
- Plan

- Earth Science
- Outreach
- Plan

- Cross Cut
- Solutions
- Project Plan

- Agriculture
- Project
- Plan

- Energy
- Project
- Plan

- Air quality
- Project
- Plan

- Aviation
- Project
- Plan

- Health
- Project
- Plan

**Agency**

**Enterprise**

**Division**

**Centers**

- Earth Science
- Research
- Plan

- Other
- Project
- Plan

- Data Distribution
- and Archive
- Plan

- OMB

*Strategic*

*Programmatic*

*Project*

- JPL

**IBPD**

**PART**

## Systems Engineering Life Cycle

- Establish Partnership with the respective Agency (MoU)

- Jointly Define User Requirements (e.g., DSS)

- Develop Joint Implementation Plan

- Design and Development Phase

- Test and Verification Phase

- Deliver for User Validation

**CMAQ**

**Select  National Applications**

**Systems Engineering Life Cycle**

*Science/Research*

*Engineering*

*Management*

- Partnership

## GSFC REASoN Awards – continued

| ESD PI | Subject | Description | Linkage to National Application |
| --- | --- | --- | --- |
| Robert Atlas | A Cross-Calibrated, Multi-Platform Ocean Surface Wind Velocity Product for Meteorological and Oceanographic Applications | This activity focuses on producing cross-calibrating multi-instrument ocean surface wind velocity data set for the period of 1987-2007.  This information will be used in developing consistent climatological record of ocean surface winds and further assist in numerical weather predictions and El Niño/Southern Oscillations (ENSO) related studies. | Aviation, Energy Management |
| Michael Bosilovich | The Development of Multiyear Global Assimilated Dataset for Weather and Climate Variability and Prediction Studies with a Focus on the Hydrological Cycle | This activity will produce a global dataset, called MERRA. This will be a long term, global gridded data set providing information such as meteorological and weather data to support climatology research, applications and education communities. One of our primary activities will be validating the global and regional water cycle. | Agriculture Efficiency, Water and Energy Management |
| George Collatz | Synthesizing, Validating and Distributing Science Community-Driven Carbon, Water and Energy Cycling Data Products for Research | Consolidating and distributing multi-year (1986-1995) interdisciplinary global data sets to improve our understanding of land surfaces, climate interactions and their variability at annual and decadal time scale.  Particularly includes meteorological, vegetation, and static data such as biome and soil characteristics.  This will also include evaluation of these data products. | Carbon Management, Water Management |
| Watson Gregg | Development and Maintenance of An Ocean Color Time Series | To develop and maintain a seamless ocean color time series from 1978 to 2008 using compatible and consistent processing algorithms. The mission-specific approach has led to individual data access portals, with different user interfaces, levels of complexity and formats. | Coastal Zone |
| Brent Holben | The REASoN NASA CAN Support BAMGOMAS-Back trajectories, AERONET, Micropulse Lidar, GOcart, and MODIS for Aerosol Synergism | BAMGOMAS will provide aerosol and aerosol related data through the www for the ~150 globally distributed AERONET sites.  The data sets will include: ground based AERONET, MODIS data from Terra and Aqua, lidar profiles from MPLNet (Micro Pulse Lidar Network), Back trajectories and modeled results from the Chemical Transport Model, GOCART. | Public Health, Air Quality |

**GSFC REASoN Awards – *****continued***

## GSFC REASoN Awards

| ESD PI | Subject | Description | Linkage to National Application |
| --- | --- | --- | --- |
| Steven Kempler | Integrating NASA Earth Sciences Enterprise Data into Global Agricultural Decision Support Systems | The project aims to provide operational solutions to USDA Foreign Agricultural Service (FAS) and U.N. World Food Programme (WFP), as well as other agricultural users of ESE-derived data products.  GSFC DAAC developed prototype TRMM Online Visualization and Analysis System will form the basis for a proposed Agricultural Information System (AIS). | Agriculture Efficiency |
| Edward Masuoka | A 0.05 Degree Global Climate/Interdisciplinary Long Term Data Set from AVHRR, MODIS and VIIRS Instruments | The goal is to create a consistent record of data products from AVHRR through NPP VIIRS. This will involve creating an improved AVHRR data set for the full record of AVHRR from 1981 onward by adapting atmospheric and Bidirectional Reflectance Distribution Function (BRDF) corrections used in processing MODIS. | Agriculture Efficiency, Carbon Management |
| William Rossow | Variability of Global Cloud Property Distributions from Diurnal to Decadal Time Scales:  Continuation of Data Analysis for the International Satellite Cloud Climatology Project (ISCCP) | The proposed work is to extend and enhance the use of satellite observations of clouds for research into the causes of weather and climate variability through systematic measurements of the global distribution of cloud properties and mergers of these with other satellite measurements of the atmosphere and precipitation to study cloud processes in different dynamic regimes. | Weather/Seasonal Forecasting, Aviation, Energy Management, Water Management |
| John Schnase | The Invasive Species Data Service: Towards Operational Use of ESE Data in the USGS Invasive Species Decision Support System | The Invasive Species Data Service (ISDS) will provide customized, easily accessible data products and tools to support invasive species management and policy decision-making. The ISDS will be a networked service that integrates a suite of NASA ESE data providers with the data resources of the National Biological Information Infrastructure (NBII). | Invasive Species |

**GSFC REASoN Awards**

## Issues

***Roles and Responsibilities***
- How can we take advantage of SSC’s contributions in this process
- Should understand their role
- Improve bilateral communication
- Center to Center responsibilities should be clarified and recognized by the joint teams
- Center Task managers need to follow a formal systems engineering process by developing the project plans and the necessary requirements documents to initiate the tasks
***Communications***
- Center to HQ communication
- Need to improve the Program Executives interactions with the Center Applications Manager (main PoC).
- Center Task/Project managers need to improve their communication with the Center Applications Manager
***Resources***
- Is there any directed funds available for these Applications, if yes then how can these funds be made available at the Centers
- If the 85% of the budget is to be competed then what is the overall process and philosophy for doing this
- Center Applications Manager need to have some support (travel etc.) from HQ to carry out the Center plus Enterprise functions

## Examples

## Public Health - Mekong Malaria and Filariasis

_**Public Health**_** - Mekong Malaria and Filariasis**

- Advance warning
- Optimized pesticide and chemoprophylaxis usage
- Reduced damage to environment
- Reduced morbidity and mortality

***Vector Habitat Identification: ***when/where to apply larvicide   and insecticide
***Identification of Key Factors: ***Curtail ongoing Transmission
***Risk Prediction:***
- Predict Transmission

- Ikonos
- ASTER
- Landsat
- MODIS

- Vector Habitat
- Transmission
- Risk Prediction

- Surface Hydrology
- Climate Prediction

-  Malaria Cases

*Satellite Data*

*Models*

*Decision Support*

*Benefits*

_**BACKGROUND**_
- The Greater Mekong Sub-region is the world’s epicenter of multi-drug resistant *falciparum *malaria
- While less deadly, lymphatic filariasis has incapacitated more than 50 million people worldwide

_**PARTNERS**_
- Armed Forces Res. Inst. for Med. Sciences
- US Army Med. Res. Inst. for Infec. Disesease
- Uniformed Services Univ
- Naval Dis. Vec. Ecology & Control Cente)
- Mahidol University, Thailand

## Public Health - Mekong Malaria in Tak, Thailand

- 2-Year Prediction of Malaria Cases Based on Environmental Parameters (temperature, precipitation, humidity, vegetation index)

- Temperature--satellite
- Rainfall--satellite
- Pf Parasite—field observations

- Satellite Vegetation Data used for Insecticide Planning

_**Public Health**_** - Mekong Malaria in Tak, Thailand**

## time series). 

_**Air Quality (**__***Regional***__**) **_**– Adapting MODIS Aerosol Data for EPA **

***NASA-LaRC/USEPA ***
***Air Quality***
***Applications Group***

- Relate MODIS AOD
- observations (red dots)
- to EPA ground station PM
- measurements (black
- time series).

- Evaluate current EPA
- observational network
- (posts) as to its ability
- to show regional aerosol
- events as resolved by
- MODIS (colored
- background).

- Evaluating EPA network for regional areas
- Finding correlation between MODIS aerosol optical thickness and EPA ground based measurements
- Developing finer resolution MODIS products for variable urban regions

## Air Quality Applications (Global)

- Plans are to produce global air quality analysis forecast using multiple space based sensors such as MODIS, TOMS, OMI and TES
- Ozone and aerosol analyses and predictions from the GMAO atmospheric analysis and Models
- Provide necessary boundary conditions for EPA/NOAA regional models for air quality forecast
- Conduct benchmarking of CMAQ with and without satellite and model data

**RO****2******** + NO ******** RO******** + NO****2**
**NO****2**** + *****hv ********* NO + O**
**O + O****2**** + M ******** O****3**** + M**

- TOMS Satellite Tracked Pollution Plumes Around the World

- Moroccan dust over Spain June 23, 2003

## Air Quality Decision Support System

- Observations
- Aerosol and Ozone

- Community
- Multi-Scale
-  Air Quality Model
- (Regional Model)

**Global MODELS**

- Run CMAQ without BCs
- Run CMAQ with BCs
- Compare the difference

**Region**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**x**

**Scenarios**

**x – Boundary Conditions**

**If positive, then infuse satellite data into CMAQ on a regular basis**

**Air Quality Decision Support System**

**Data Analysis**

- Global
-  Forecast

- Global Analysis

## Weather - fvGCM Modeling

- 10-day forecast: *precipitation* and *clouds*

## Home Land Security – Using fvGCM Modeling 

***Experimental prediction***** of the global transport of *****radioactive gases***

## Invasive Species Management

- NASA and USGS developing a National Invasive Species Forecasting System.  This uses MODIS data, aerial imagery, and ground data of various resolutions to map biological resources.
- Strong partnership with DoI, USDA and USGS

_***Cheat Grass  (***__***Bromis tectorum***__*)*_
***Reduces soil moisture***
***Increase fire loading***
***Re-establishes early in burnt areas***

- Trending & modeling

- Hot spots of native biodiversity
- Distribution of non-native species
- Potential spread of invasive species

- Economic and environmental risk assessment
- Priorities for control and Containment

- Data fromMODIS, LandSat, EO-1

- Decision Support

## Disaster Management-Fire Burn Information

- MODIS Active Fire
- Radiative Energy

- Rate of emission
- of smoke aerosol  (1)

- Rate of emission
- of carbon monoxide  (2)

- Regional and global
- Smoke aerosol fields
- Prediction

- Regional and global
- carbon monoxide fields
- Prediction

- Assimilation into
- chemical transport models
- (USFS Smoke/ CO model)

**1. GSFC-provided algorithm developed from correlation of MODIS Aerosol Optical Thickness data with MODIS Radiative Energy Data**
**2. GSFC-provided algorithm developed from correlation of MOPPIT Carbon Monoxide data with MODIS Radiative Energy Data**

- Using MODIS data to derive smoke/CO information to forecast smoke/CO advancement and hazards
- Partnership with US Forest Service

## Decision Support Systems (DSS)

| National Application | DSS Name | Partner Agencies | Primary Supporting Organizations |
| --- | --- | --- | --- |
| Public Health | EPHTN: Environmental Public Health Tracking Network | HHS / CDC | CDC / EPA / State & Local Health Departments |
| Air Quality | CMAQ: Community Multiscale Air Quality modeling system | EPA | NOAA / EPA |
| Disaster Management | HAZUS:  Natural Hazard Loss Estimation model | FEMA | N/A |
| Aviation Safety | AWIN: Aviation Weather Information  | FAA | LaRC |
|  | SVS: Synthetic Vision Systems |  |  |
| Water Management | RiverWare  | USBR / TVA | U. of Colorado |
|  | AWARDS: Agricultural Water Resources Decision Support | USBR |  |
| Coastal Management | HAB Mapping System and Bulletin | NOAA | NOAA Coastal Services Center |