**[NASA Earth Science Enterprise]{.smallcaps}**

**[Applications Division]{.smallcaps}**

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

*Ecological Forecasting Project on*

*An Agent-Based Interface to Terrestrial Ecological Forecasting*

***Terrestrial Observation Prediction System (TOPS) Project Plan***

*(v. 3.6.5)*

*05/01/06*

![](media/image1.pct){width="1.1901574803149606in"
height="0.9838582677165354in"}![](media/image2.png){width="1.3200787401574803in"
height="1.1456692913385826in"}

*Earth Science for Society*

> *Accelerating the realization of economic*
>
> *and societal benefits from Earth science,*
>
> *information, and technology ...*
>
> **Table of Contents**

1 Purpose [4](#purpose)

**1.1** **Document Purpose** [4](#document-purpose)

**1.2** **Project Goals & Objectives** [4](#project-goals-objectives)

**1.3** **Project Scope** [5](#project-scope)

**1.4** **Project History** [7](#project-history)

**1.5** **Background** [8](#background)

**1.5.1** **TOPS Background** [8](#tops-background)

**1.5.2** **Yosemite National Park Management Background**
[9](#yosemite-national-park-management-background)

**1.6** **Outside of Scope** [10](#outside-of-scope)

2 Project TOPS Team [11](#project-tops-team)

3 Roles and Responsibilities [12](#roles-and-responsibilities)

4 Project Implementation [14](#project-implementation)

**4.1** **System Design** [14](#system-design)

**4.1.1** **System Interactions** [14](#system-interactions)

**4.1.2** **Applicability to ESE Missions**
[16](#applicability-to-ese-missions)

**4.2** **Implementation Approach** [17](#implementation-approach)

**4.2.1** **Technology and approach** [17](#technology-and-approach)

**4.2.2** **User Support** [18](#user-support)

**4.2.3** **SEEDS participation** [18](#seeds-participation)

**4.2.4** **Other Topics** [19](#other-topics)

**4.2.5** **Archiving and distribution**
[19](#archiving-and-distribution)

**4.2.6** **TOPS Data Sources and Integrated Models**
[20](#tops-data-sources-and-integrated-models)

**4.2.7** **TOPS Data Outputs** [21](#tops-data-outputs)

**4.3** **Technology Development** [22](#technology-development)

**4.3.1** **Description of TOPS technology**
[22](#description-of-tops-technology)

**4.3.2** **Current technology status** [23](#current-technology-status)

4.3.2.1 AI Planner and Scheduler [23](#ai-planner-and-scheduler)

4.3.2.2 JDAF: Java Distributed Application Framework
[24](#jdaf-java-distributed-application-framework)

4.3.2.3 SOGS [25](#sogs)

4.3.2.4 Metadata [26](#metadata)

4.3.2.5 QA/QC [26](#qaqc)

4.3.2.6 Parallel Processing [26](#parallel-processing)

**4.3.3** **TOPS improvements** [27](#tops-improvements)

4.3.3.1 AI Planner and Scheduler [27](#ai-planner-and-scheduler-1)

4.3.3.2 JDAF: Java Distributed Application Framework
[27](#jdaf-java-distributed-application-framework-1)

4.3.3.3 SOGS [28](#sogs-1)

4.3.3.4 Metadata, QA/QC, Parallel Processing
[28](#metadata-qaqc-parallel-processing)

4.3.3.5 User Interface [28](#user-interface)

**4.3.4** **Technology Readiness Level (TRL) Assessment**
[29](#technology-readiness-level-trl-assessment)

4.3.4.1 Current Status [29](#current-status)

4.3.4.2 Year 1 (FY06): TRL 5 [30](#year-1-fy06-trl-5)

4.3.4.3 Year 2 (FY07): TRL 6 [30](#year-2-fy07-trl-6)

4.3.4.4 Year 3 (FY08): TRL 7 [30](#year-3-fy08-trl-7)

**4.4** **Deliverables** [30](#deliverables)

**4.5** **TOPS Schedule & Milestones** [33](#tops-schedule-milestones)

**4.5.1** **Schedule** [33](#schedule)

**4.5.2** **Milestones** [34](#milestones)

4.5.2.1 FY06 [34](#fy06)

4.5.2.2 FY07 [35](#fy07)

4.5.2.3 FY08 [36](#fy08)

**4.6** **Risk Management** [36](#risk-management)

**4.7** **Budget** [37](#budget)

**4.8** **Exit Strategy/Project Termination/Transferability**
[38](#exit-strategyproject-terminationtransferability)

**4.9** **Performance Measures, Metrics and Success Criteria**
[39](#performance-measures-metrics-and-success-criteria)

5 References [41](#references)

# Purpose

## **Document Purpose**

This document defines the project plan for "*An Agent-Based Interface to
Terrestrial Ecological Forecasting"* project, which extends the
capabilities of the Terrestrial Observation and Prediction System
(TOPS)\[6,7\]. As such, it defines the scope, goals and objectives,
personnel, budget, risk, deliverables, technical approach, and
transition approach associated with the project. This document serves as
a formal statement of the work that will be performed on the project as
a recipient of funds from the Earth Science REASoN program. In addition,
it outlines both the scientific and technology background of the various
components of the project and serves as the primary reference document
for anyone interested in the details of the project. This project plan
represents a modification to the scope and deliverables associated with
this project, which were contained in the original proposal and revised
work plan. Details regarding the history of this project and
justification for the adjustment in project scope are provided in
Section 1.4 of this plan.

## **Project Goals & Objectives** 

The overall goal of this project is to create a flexible ecological
nowcasting and forecasting system with applications for ecosystem
monitoring and protected area management. This system will combine
multiple distributed data sources and models to provide near-real-time
answers to questions about the state of the Earth system and provide
additional information to aid land managers in decision-making. By
providing a framework for ecological forecasting as well as the
post-processing of model outputs needed to visualize the results, TOPS
will facilitate rapid data exploration and what-if analysis. In order
for these results to be accurate, TOPS will account for quality,
resolution, and availability of the input data and provide the means of
obtaining, processing and fusing data from a wide range of sensors,
encompassing satellite observations, ground observations, and other
modeled data.

The objectives of the project are as follows:

1.  Develop software for automated production of monitoring and
    forecasting data and information products to assist the National
    Park Service (NPS) in management of U.S. National Parks by project's
    end in FY08. Under this objective we will provide capabilities for:

    a.  Real-time monitoring of ecosystem conditions within the park at
        moderate resolution (500m -- 1000m). The datasets produced will
        include solar radiation, air temperature, humidity, rainfall;
        and satellite and model-derived data on ecosystem variables
        including soil moisture, vegetation water stress, phenological
        stage, and biomass.

    b.  Short-term forecasting for a suite of parameters related to
        ecosystem function (snow pack, soil water content,
        evapotranspiration, stream flow, net primary productivity,
        phenological state, and vegetation) at moderate resolution (500m
        -- 1000m).

    c.  Processing of historic climate and satellite data for the areas
        of NPS interest going back to 1982.

    d.  Detection of anomalies from long-term normals for requested
        environmental variables. These anomalies may be indicative of
        ecosystem change or potential threats to the park.

    e.  On-demand acquisition and delivery of high-resolution (30m --
        250m) satellite data from MODIS, ASTER, or LANDSAT/TM
        instruments depending on availability.

2.  Extend TOPS software for automated production and delivery of
    climate and satellite data to be used by the SERVIR project by the
    end of 2006.

3.  Extend the current TOPS modeling framework to implement the RHESSyS
    \[30\] model to improve high-resolution modeling and to aid in
    scenario evaluation of forecasting of potential impacts of
    management decisions within the NPS.

4.  Provide access to the data produced by the system in the following
    ways:

    a.  Provide a user-friendly web interface to the TOPS data holdings
        using the following strategies:

        i.  Expansion of the existing data browsing tools contained
            within the TOPS website (http://ecocast.arc.nasa.gov) to
            allow browsing of the full data archive, data subsetting,
            data query, and data retrieval;

        ii. Implementation of data web-based data visualization tool
            (e.g., ArcIMS) to distribute TOPS data products related to
            U.S. National Parks and the SERVIR project's areas of
            interest in Central America, and facilitate data
            visualization and query;

        iii. Distribution of data via a Web Map Server (WMS) for
             visualization using the NASA WorldWind and/or Google Earth
             applications.

    b.  Automated integration of data products from TOPS into the NPS
        and into SERVIR systems by:

        i.  Automating the extraction of regional subsets of data (e.g.,
            Yosemite National Park plus a buffer zone) from existing
            data archives using shapefiles provided by NPS and SERVIR
            collaborators;

        ii. Implement tools to automate the conversion of data from
            binary to GIS compatible formats (e.g., GeoTIFF) and convert
            existing TOPS metadata structures into formats compatible
            with the NPS Vital Signs Inventory & Monitoring (I&M) system
            database templates;

        iii. Design and build client-side tools that will reside on I&M
             servers and will query and retrieve data from TOPS.

## **Project Scope**

This project will provide ecosystem forecasts and \"nowcasts\" from the
TOPS system for use in protected area management. The products focus on
terrestrial vegetation, hydrology, and fire occurrence within the
regions of interest of the NPS in the western United States, and the
geographic scope of the SERVIR project in Central America. We will first
develop a prototype system that focuses on two main watersheds in
Yosemite National Park, followed by expansion to the entire park and
conversion of the prototype into an operational system. We will then
expand the system for use in other national parks and I&M monitoring
networks.

This project focuses on 1) gathering and combining data, information and
services from numerous sources to determine the values of parameters
that cannot be directly observed on a routine basis for the area of
interest, and 2) making access and use of this information easy for the
end-user. The ultimate goal is to provide NPS managers with easy-to-find
and interpret information that is relevant to their decision making
process, such as the risk of fire, estimates of snow melt and runoff,
flood forecasting, and tracking of anomalies in climate and ecosystem
conditions in order to provide early warning of potential threats to
critical biological resources within the park. This activity will
require rectification of temporal and spatial differences in data,
fusion of multiple data products using ecosystem models, and integration
of component models to facilitate automation. We will build on previous
work with TOPS, with an initial focus on vegetation, hydrology, and fire
models, but the long-term goal is to include as many parameters as
possible, in order to provide a more comprehensive, integrated picture
of the ecosystem conditions within U.S. national parks and other
protected areas world wide.

Developing the tools needed to integrate and process heterogeneous data
sets and make them accessible to users will require technology
development. The main technology components of this project are remote
sensing, modeling, forecasting, distributed computing, and planning and
scheduling. This project will also create an interface that enables
users to easily access ecosystem information. The interface relies on a
distributed framework for data access, delivery and visualization which
is built on top of an AI planning and scheduling component. This
framework supports interoperability at its core by building on a
declarative, modular representation of data and data-processing
operations, in which a new component can be introduced by providing or
importing a description of that component. The planning and scheduling
component will consist of a planner-based system for automating data
processing and metadata generation, which was developed during the first
two years of this project. This component will be completed in FY06.

This project will support operational decision making for protected area
management through the delivery of four primary types of information and
services -- continuous awareness, anomaly detection, local nowcasting
and forecasting, and dynamic what-if scenarios simulation. To support
continuous awareness, we will provide both near-real-time and daily
monitoring capabilities of selected environmental variables using NASA's
remote sensing satellites. This capability will be supported through the
acquisition of Direct Broadcast data from MODIS \[10\] instruments on
both Terra and Aqua satellites \[8\]. Variables available through this
data stream will be fire occurrence \[43\], NDVI/EVI \[41\], snow extent
\[44\], FPAR/LAI \[13\], GPP/NPP \[12\], and land surface temperature
\[42\]. These variables will be available within a few hours of the
satellite overpass. We will also add data from other satellite-based
instruments to our data sources in order to achieve better temporal
coverage for monitoring of rapid environmental changes and extreme
events. The additional sources of data that we will add to our system
will come from: Advanced Spaceborne Thermal Emission and Reflection
Radiometer - ASTER \[40\], Atmospheric Infrared Sounder - AIRS \[45\],
Ozone Monitoring Instrument - OMI \[46\], GOES \[48\], SSMI \[47\], and
AMSR-E \[49\]. This should enable us not only to improve the temporal
coverage over the region of interest, but also to improve the quality of
the data that we can produce and deliver to the NPS, due to additional
information and the higher spatial and/or temporal resolution of data
from these instruments. We will use this information together with
additional climate datasets to model other variables of interest to the
NPS in both monitoring and forecasting modes. We will also combine the
above variables with historic data records to identify anomalies in
ecosystem conditions that could be indicative of changes in protected
ecosystems requiring action on the part of NPS managers. Finally, we
will provide what-if scenario simulations to assist the NPS in the
evaluation of how specific changes in park infrastructure, such as the
construction of a new road, may affect the hydrologic and vegetation
patterns within a park.

In order to support operational decision making by the NPS, we will
provide tools and software to automate integration of our data and
services with existing protected area management decision support
systems (DSSs), such as the NPS I&M system and the SERVIR system for
Central America. To accomplish this goal, we will first extend the
existing TOPS capabilities to automate the extraction of regional
subsets of data (e.g., Yosemite National Park plus a buffer zone) from
existing data archives using shapefiles provided by NPS collaborators.
We will also implement tools to automate the conversion of data from
binary to GIS compatible formats (e.g., GeoTIFF) and convert existing
TOPS metadata structures into formats compatible with the Yosemite and
I&M database templates. Concurrent with verification of these tools, we
will work with NPS staff to design and build client-side tools that will
reside on NPS servers and will query the TOPS system to retrieve data.

In addition to these tools for automated machine to machine transfer and
integration of data products into the Yosemite and I&M systems, we are
currently evaluating additional web-based interfaces to facilitate the
use of TOPS data for NPS-related uses outside of the I&M system and for
use by other potential customers. Current options include: 1) expansion
of the existing data browsing tools contained within the TOPS website
(http://ecocast.arc.nasa.gov) to allow browsing of the full data
archive, data subsetting, data query, and automated data retrieval; 2)
implementation of a web-based data visualization interface (e.g.,
ArcIMS) to distribute TOPS data products related to U.S. National Parks
and facilitate data visualization and query; 3) distribution of
park-related data via a WMS server for visualization using the NASA
WorldWind and/or Google Earth applications. All of these interfaces are
outside the scope of the current I&M framework. As such, the primary
purpose of these interfaces would be to facilitate use of data developed
for NPS decision making for outreach and education, and to support use
of the system by non-NPS users. While important, the primary purpose of
this project is to support operational decision making by the NPS.
Extension of the data for use by the general public using the interfaces
described above will be further evaluated in 2006 upon completion of the
integration of TOPS data with the Yosemite system, and based on our
experience working with the SERVIR project.

## **Project History**

As referenced in Section 1.1, this project plan represents a
modification to the scope and deliverables associated with the project,
which were contained in the original proposal and revised work plan. The
reason for this correction is two-fold. First, due to the departure of
the project's PI, Keith Golden, who played a crucial role in the AI
technology development, we are starting to focus our efforts on the
application of technology developed to date. Second, as the technology
evolved, it became clear that fire danger forecasting, which was our
original goal, was only a narrow component of the overall capabilities
of system. This document is an amendment to the original work plan,
which provides an updated project scope. In order to provide a more
comprehensive demonstration of our technology, we have established a new
partnership with the U.S. National Park Service (NPS) to apply this
technology to assist in the management of national parks and other
protected areas in the western United States. This partnership has been
established pursuant to the Memorandum of Agreement (MOA) signed by NASA
and NPS in January 2005. Under this project we will also demonstrate
this technology in collaboration with NASA's SERVIR project in Central
America.

## **Background**

### **TOPS Background** 

The latest generation of NASA Earth Observing System \[8\] satellites
has brought a new dimension to continuous monitoring of the living part
of the Earth system, the biosphere. EOS data can now provide weekly
global measures of vegetation productivity and ocean chlorophyll, and
many related biophysical factors such as land cover changes or snowmelt
rates. However, information with the highest economic value would be
forecasts of impending conditions of the biosphere that would allow
advanced decision-making to mitigate dangers, or exploit positive
trends. NASA's strategic planning efforts identify ecological
forecasting as a focus for future research. Ecological forecasting
***predicts*** the effects of changes in the physical, chemical and
biological, environment on ecosystem activity. Imagine if we could
accurately predict shortfalls or bumper crops of agricultural
production, or wildfire danger 3-6 months in advance, allowing improved
preparation and logistical efficiency. The climate forecasting skills of
many coupled Ocean-Atmosphere general circulation models (GCMs) \[9\]
have steadily improved over the past decade. Given observed anomalies in
sea-surface temperatures from satellite data, GCMs are able to forecast
general climatic conditions 6-12 months into the future, trends of
hotter/colder temperatures and wetter/drier precipitation than normal,
with reasonable accuracy. While such climatic forecasts are useful
alone, the advances in ecosystem modeling allow us to explore
specifically the impacts of these future climate trends on the ecosystem
directly.![](media/image4.png){width="4.125694444444444in"
height="4.476388888888889in"}

One of the key problems in adapting climate forecasts to natural
ecosystems is the \"memory\" that these systems carry from one season to
the next (e.g. soil moisture, plant seed banks, fire fuel accumulation
etc.). Simulation models are often the best tools to carry forward the
spatio-temporal 'memory' information. The power of models that can
describe and predict ecosystem behavior has advanced dramatically over
the last two decades, driven by major improvements in process-level
understanding, computing technology, and the availability of a
wide-range of satellite- and ground-based sensors.

In order to estimate possible future states of the biosphere, TOPS is a
flexible system that integrates ecosystem models with frequent satellite
observations, that can be forced by weather or climate forecasts, and
downscaled to resolutions appropriate to resolve surface processes. The
Terrestrial Observation & Prediction System \[6\] (TOPS, Figure 1) is
a modeling software system that integrates and pre-processes EOS data
fields so that land surface models can be run in near-real-time with
minimal intervention, facilitating accurate and timely interpretation of
Earth Observing System data. Such a system allows us to determine the
vulnerabilities of different socio-economic and resource management
approaches to fluctuations within our biosphere, and will help in
mitigating potential negative impacts. Many U.S. natural resources,
including many of our national forests and national parks are vulnerable
to changes in Earth's biosphere.

### **Yosemite National Park Management Background**

Yosemite National Park encompasses over 760,000 acres (3,000 sq. km) and
receives over 3.2 million visitors each year. Managing this national
treasure is a complex task requiring a large staff of scientists, park
rangers, and resource specialists.

Resource managers in Yosemite National Park currently rely on
ground-based observations of weather and ecosystem conditions to make
management decisions pertaining to issues including wildfire, prescribed
burns, invasive species, trail closures and visitor management, and
detection of long term ecosystem changes. Park scientists collect data
on a range of factors including weather, stream flow, snow pack,
biodiversity, tree condition and growth, species distribution, and
animal behavior. Work conducted under the I&M effort is currently
compiling an extensive catalog of the biologic resources within
Yosemite. Much of this data is collated, stored, and analyzed using an
extensive library of GIS-based maps and analysis tools for tracking
biodiversity and ecosystem conditions within the park.

To date, use of remote sensing data has been limited to single data-set
acquisitions to study a particular problem of interest, e.g., an
analysis of Landsat scenes to identify trends in land-use change
following major burn events. Individual scientists working within the
park have made occasional use of instruments such as AVHRR, but routine
retrieval and analysis of satellite data have not been integrated into
operational decision making systems for the park. Weather data are
currently collected from a network of stations, but are not regularly
projected over the land surface or regridded to provide spatially
continuous maps of meteorological conditions. Even the use of models has
been limited to manual runs of models pertaining to wildfire (FlamMap &
Farsite), forest growth, and impacts of long-term climate change (GAP
models). As of July 2005, within Yosemite the NPS does not operate any
type of automated monitoring and modeling system.

Automated delivery of satellite and model output data from TOPS will
greatly aid park managers in making decisions pertaining to:
infrastructure management and placement, fire, prescribed natural fire
events, detection of other large scale disturbance events (e.g.,
blowdown and insect infestations), snowcover and runoff monitoring,
flood forecasting, and tracking of anomalies in climate and ecosystem
conditions to provide early warning of potential threats to critical
biological resources within the park.

## **Outside of Scope**

This project's main focus is collaboration with the NPS and the SERVIR
project and thus we will not be directly supporting any additional
customers as a part of this project. We will provide subsets of our data
to the general public, but the main emphasis will be on delivery of data
for regions of interest to our collaborators in the NPS and the SERVIR
projects, namely Yosemite National Park, Sequoia-Kings Canyon National
Park, Yellowstone National Park, Glacier National Park, and Central
America. We will also provide lower resolution data products for
California and the continental United States for use by the general
public, but we will not provide any support outside of product
documentation for these datasets. We will deliver a software package to
the NPS that will serve as a client application and will interact with
the TOPS system to automate data retrieval and integration with existing
DSSs. Additionally, we will provide an application-programming interface
(API) to extend this client, and documentation for the API. We will also
deliver a set of libraries that will provide functionality for anomaly
detection and continuous awareness using data streams from TOPS. We will
not provide packaged software for local nowcasts/forecasts and what-if
scenario analysis. These components are custom components that will be
built for the NPS and run at NASA Ames. These datasets will be
accessible to the NPS through one of the interfaces described
previously.

In addition, this project will not include the implementation of custom
models developed by the NPS within our system. The integration of new
NPS models will be done in terms of data integration only, i.e., we will
provide interfaces and descriptions of the dataset that can be accessed
directly using our API and NPS personnel will be able to use this to
interface their models with our data system. This can be done in an
automated way through a set of function calls. We will not, however,
provide support for integration of NPS developed models into the TOPS
software package. The reasons for this are poor scalability and lack of
resources. We will release a subset of tools that we develop to the
general public and Earth science research community so that they may
also interact with our system and integrate our datasets with their
models.

# Project TOPS Team 

Key personnel on the TOPS project include:

-   Dr. Ramakrishna Nemani, Principal Investigator

```{=html}
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```
-   Mr. Petr Votava, Software development management

-   Mr. Andy Michaelis, Climate component and security development

-   Mr. Forrest Melton, Applications development

-   Dr. Wanlin Pang, QSS Group, AI planning and scheduling

Other collaborators include:

[Yosemite National Park Staff]{.underline}

-   Niki Nicholas, Chief, Resource Mgmt. & Science

-   Bill Kuhn, GIS, Project Point of Contact

-   Samantha Weber, Resource Information for Plans & Projects

-   Andi Heard, Inventory and Monitoring

-   Meryl Rose, Inventory and Monitoring

-   Lisa Acree, Park Botanist

-   Kent Van Wagtendonk, GIS/Fire

[Other NPS Staff:]{.underline}

-   John Gross, Inventory & Monitoring Program, NPS Ft. Collins

-   Linda Mutch, Sierra Nevada Network I&M Coordinator

[Component Models and Tools:]{.underline}

-   Dr. Steve Running, University of Montana, ecosystem model
    development (ecological forecasting);

-   Dr. Michael White, Utah State University, modeling integration into
    TOPS.

-   Dr. Christina Tague, San Diego State University, RHESSyS model
    development

The percentage of commitment of each person is reflected in the attached
budget, and the specific contributions detailed in Section 4.6

The project has ongoing collaborations with multiple NPS staff including
Dr. John Gross of the NPS Colorado office; Dr. Niki Nicholas, Chief of
Resource Management and Science in Yosemite National Park; Bill Kuhn,
Yosemite GIS Specialist; and Dr. Linda Mutch of Sequioa-Kings Canyon
National Park. In addition, the project is currently renewing previous
collaborations with park managers at Yellowstone and Glacier National
Parks. These and other NPS staff will review prototype products, assist
with benchmark evaluations, and provide guidance with regard to the
application of TOPS products to park management. TOPS project results
will help enhance a suite of decision support systems in place at the
parks.

# Roles and Responsibilities 

This project will require the coordination of personnel at multiple
sites. Coordination will be conducted via periodic phone meetings, with
more frequent communication via email.

Code will be shared using a centralized CVS repository. Principal
Investigator Dr. Ramakrishna Nemani will manage the overall project, Mr.
Petr Votava will manage the software development, and Mr. Forrest Melton
will manage the application development and NPS interactions. The code
will be integrated in a distributed fashion using Web services and Java
RMI. Documentation of application programming interfaces (APIs) will be
shared using the Web. The Principal Investigator and the Project
Managers will participate equally in SEEDS, for a total contribution of
at least .25 FTE.

The TOPS project operates under the following Work Breakdown Structure
(WBS):

1.0 TOPS Project Management (Lead -- Nemani, R. with Votava, P. and
Melton, F.)

2.0 SEEDS Participation (Lead -- Nemani, R. with Votava, P., Melton, F.)

3.0 Baseline System Documentation (Lead -- Melton, F., with Nemani, R.,
Votava, P.)

4.0 NPS Coordination Management (Lead -- Melton, F. with Nemani, R.)

4.1 Meeting and teleconference scheduling and coordination

4.2 Target location selection and coordination

5.0 NPS Data Requirements (Lead -- Melton, F. with Votava, P., Nemani,
R.)

5.1 Product/variable requirements

5.2 Format requirements

5.3 Volumes constraints

5.4 Metadata requirements

6.0 NPS Service Requirements (Lead -- Melton, F. with Votava, P.,
Nemani, R.)

6.1 Anomaly and warning system requirements

6.2 Monitoring/Continuous awareness system requirements

7.0 AI Technology Integration (Lead -- Votava, P. with Pang, W.)

7.1 Temporal composition

7.2 Data acquisition

8.0 Remote Sensing and Climate Integration (Lead -- Votava, P. with
Michaelis, A.)

8.1 Direct Broadcast

8.1.1 EDC dataset integration

8.1.2 OSU dataset integration

8.2 GOES data integration

8.3 AMSR-E data integration

8.4 SSMI data integration

8.5 AIRS data integration

8.6 OMI data integration

8.6 DMSP data integration

9.0 TOPS core system redesign/refactoring (Lead -- Votava, P. with
Michaelis, A.)

10.0 Climate Module Development/Enhancement (Lead -- Michaelis, A., with
Votava, P.)

10.1 Algorithm Improvements

10.2 Parallelization

11.0 QA/QC (Lead -- Votava, P. with Michaelis, A., Nemani, R.)

11.1 Approach development

11.2 QA/QC implementation

11.3 Automation

12.0 TOPS/NPS System Integration (Lead -- Votava, P. with Michaelis, A.)

12.1 Data Converter Development

12.2 NPS Client Development

12.3 Anomaly Module Integration

12.4 Monitoring Module Integration

12.5 Data Visualization

12.6 Integration with other NPS I&M Networks (TBD)

13.0 Subsystem Integration (Lead -- Votava, P. with Michaelis, A.)

13.1 JDAF/AI planner integration

13.2 JDAF/SOGS Integration

14.0 Documentation (Lead -- Melton, F. with Votava, P., Michaelis, A.)

15.0 Field Tests/Demonstration (Lead -- Votava, P. with Melton, F.,
Michaelis, A., Pang, W.)

> 15.1 NPS Interface demonstration

16.0 Testing and Validation (Lead -- Votava, P. with Michaelis, A.,
Melton, F.)

16.1 End-to-end Test Design

16.2 Climate Validation

16.3 TOPS Model Output Validation

16.4 Climate Software Test

16.5 TOPS Software Test

17.0 Security (Lead -- Michaelis, A., with Votava, P.)

18.0 SERVIR Project Integration (Lead -- Melton, F., with Votava, P.)

18.1 Climate Data Integration

18.2 Satellite Data Integration

18.3 Automation

19.0 Final Demonstration (Lead -- Nemani, R. with Votava, P., Michaelis,
A., Melton, F.)

# Project Implementation

## **System Design**

### **System Interactions**

TOPS is designed as a distributed system with components located at NASA
Ames, NPS, and the University of Montana. Data produced by TOPS will
also be available to the general public. This section describes the
system architecture (Figure 2) and the interactions between the main
components of TOPS.

The core of the system is located and maintained by the Ecocast group at
NASA Ames Research Center. We have develop a layered approach where at
the lowest level the system interacts with the numerous data providers
such as the Distributed Active Archive Centers (DAAC), the National
Center for Environmental Predictions (NCEP), the National Weather
Service (NWS), NOAA, and others. Climate and Satellite Acquisition
Agents handle data acquisition. This subsystem is fully autonomous,
gathering data through periodic query to the requesting centers
sometimes as often as every 15-minutes. A subset of our input datasets
are also available by subscriptions and are being pushed to us as they
become available (Oregon State University direct broadcast feed is an
example of such interaction). When the data are obtained, they are
archived in the database and then further processed to fit our needs.
Due to significant differences in climate and satellite data processing,
we have two separate subsystems to accomplish the task. On the climate
side, we merge ground station data with model data and grid these to
required resolution together with generation of basic statistics and
QA/QC information. The satellite processing subsystem performs spatial
data tiling and subsetting, as well as composition of data sets based on
the QA/QC information supplied in order to obtain the highest quality
inputs. After the climate and satellite data are processed, they are
inserted into the database.

The model framework contains the models that are currently integrated
into the system as well as a set of utilities that access, pre-process
and post-process the inputs and outputs of these models. It frequently
communicates with the database to obtain and query data for regions of
interest. All of the described components are accessible through the
Java Distributed Application Framework (JDAF). JDAF provides a unified
view of the system and its capabilities and enables easier data, model
and utility integration. It can be used to build applications by
combining together existing components or as an interface to the system
for the AI planner. JDAF can be accessed in two main ways - using java's
Remote Method Invocation (RMI), and through SOAP and Web Services
mechanisms. As mentioned previously, an AI planner uses JDAF to access
the internals

![](media/image5.png){width="6.6402777777777775in"
height="8.093055555555555in"}

Figure 2 - TOPS System Diagram

of the system and create plans for optimal execution of our models with
respect to quality and data availability. There are currently two
interfaces to the planner that are used internally -- first, there is a
graphical user interface that is an extension to jEDIT software package,
and second there is a command line interface that enables us to use the
planner as a standalone UNIX tool.

On the external side of our system, public users will be able to access
our data via FTP and/or WMS servers. Our partners in NPS will also be
able to access our data through a password-protected Web Services
interface. Users will be able to subset our datasets on demand as well
as perform temporal compositing.

We will supply a set of tools to the NPS that will automate the process
of obtaining data from TOPS as well as produce anomaly detection and
ecosystem monitoring products. We will further provide a module that
will convert our datasets so that they can be easily integrated with the
NPS system based on their specifications, allowing NPS to continue to
use their existing tools (e.g., I&M data frameworks and ArcGIS) for data
analysis. Finally, in order to provide a robust interface to our system,
we will use an off-site database server mirror at the NTSG lab at the
University of Montana. We will push our data to NTSG servers
automatically and will provide a tool to the NPS that will seamlessly
switch between the data servers based on speed and availability at that
moment.

### **Applicability to ESE Missions**

The management and processing of Earth science data has been gaining
importance over the last decade due to higher data volumes generated by
a larger number of instruments and ground stations, and due to the
increase in complexity of Earth science models that use these data. This
growth in volume and complexity is just the beginning, because in order
for TOPS to be able to understand and predict the processes within the
regions of interest, TOPS needs a far more comprehensive data set
spanning many years and possibly even decades. Additionally, because the
data will come from a vast number of platforms and instruments, TOPS
needs the ability to seamlessly merge data from different sources within
the models. With the launch of NASA\'s Terra and Aqua missions during
the last 6 years, the need for more efficient and scalable data systems
is even more apparent. The volume of data itself is often a limiting
factor in obtaining the information needed by the scientists; without
more sophisticated automation technologies, possibly key information may
not be discovered. TOPS will address many of the issues facing the NASA
Earth Science missions by developing an efficient and scalable
technology that will incorporate automation in access, transport,
translation, distributed processing and analysis of the Earth science
data. Moreover, the technology will provide a capability for fusion of
data from multiple satellites and ground stations. This will enable TOPS
to process more data more efficiently and in an intelligent way, and
thus give the Earth scientists better foundations for their research
and, by doing so, improve our ability to understand and predict Earth
system processes. Due to the higher volumes of data we are able to
process with TOPS, the addition of new data sets is possible because of
better scalability. This in turn will help us improve the continuous
awareness aspect of this project, by enabling additional data
acquisition at different times of the day and eliminates the reliance on
only one overpass every 24-36 hours, which is the current situation.

Important aspects of the above system that will be addressed during the
course of this project are the user interface and data presentation.
Even the best system will fall short of its true capabilities if the
data are presented to the users in an incomprehensible way and if the
users have, in general, problems interacting with the system. To prevent
a long learning curve for deploying TOPS within the NPS, it is very
important that there is a bridge between the users and the system. We
will provide the user interface in three ways. First, we will expand the
existing data browsing tools contained within the TOPS website
(http://ecocast.arc.nasa.gov) to allow browsing of the full data
archive, data subsetting, data query, and data retrieval. Second, we
will implement a web-based data visualization tool (e.g., ArcIMS) to
distribute TOPS data products related to NPS and the SERVIR project's
areas of interest. Finally, we will provide our datasets in WMS
compliant format for integration with NASA WorldWind and/or Google Earth
applications. This interface will be also available to the general
public. Additionally, we will provide a password-protected web-services
interface to NPS that will allow them to interact directly with our
database servers and will improve automation of the NASA-NPS
interactions.

This project will first develop a prototype system whose goal is to
provide nowcasts and forecasts of several biospheric variables for
Yosemite National Park and for Central America. The main inputs required
by TOPS are landcover, daily temperatures, humidity, radiation, and
FPAR/LAI. These datasets currently come from a variety of sources: MODIS
Terra (FPAR/LAI -- MOD15A2 and landcover -- MOD12Q1), NCEP\'s Rapid
Update Cycle \[23\] (temperatures, wind vectors, humidity, radiation),
CPC (weather stations providing precipitation and additional temperature
information), NEXRAD (precipitation), and CIMIS. The project is planning
additions of other sources of data, for example FPAR/LAI products from
Aqua MODIS and Soil Water Content from AMSR-E \[49\], so that the
reliability of project inputs is improved and thus provides better
forecasts generated by TOPS.

[]{.mark}

## **Implementation Approach**

### **Technology and approach**

In order to achieve the scientific goals of the TOPS project, existing
technologies developed by commercial and government organizations and
open source projects will be deployed. Additionally, new technologies in
the areas of automation, data fusion, data reduction and ecosystem
forecasting will be developed. To fully utilize both existing and
developed technologies, a flexible and generic framework will be created
that will enable the project to combine these technologies.

Existing technologies to be deployed by TOPS fall into several
categories -- remote sensing, modeling, and distributed computing. TOPS
will use the latest in remote sensing technologies, as many inputs to
its system will be datasets produced by the NASA's Terra \[11\] and Aqua
\[26\] satellites. Furthermore TOPS will re-use some of the algorithms
used to process Terra and Aqua MODIS \[10\] data, including the PSN/NPP
\[12\] or FPAR/LAI \[13\] algorithms. This will benefit the NASA
community, because it will provide a new option for MODIS data
validation. Other models that TOPS will be using are BGC, Biome-BGC, and
RHESSyS \[14,15,30\], which are existing technologies developed by the
modeling community. To provide flexibility and efficiency in TOPS, the
project will utilize already proven technologies. For example, Java RMI
\[16\] together with JNI \[16\] will enable TOPS to easily merge new
code written in C and C++ with legacy code and the rest of the system,
and it will also provide a basis for distributed execution transparent
to the user. Metadata descriptors, such as the Earth Science Markup
Language (ESML) \[17\] will provide metadata support for data used
within the system and distributed to the user community.

### **User Support**

As described in sections 1.3 and 1.5, the main users of our project at
this time are the NPS and the SERVIR project. We will work with them
closely to develop data requirements and specifications, and protocols
for system interactions. We will provide user interfaces to our data
systems so that the users can easily obtain TOPS datasets and perform
data transformations, such as subsetting and temporal/spatial
compositing in a process that will be easy to automate. We will also
deliver documentation for the datasets and the interface itself and will
work closely with NPS and SERVIR users to assist them with integration
efforts. While we will not directly support other users, we will publish
our datasets together with their metadata in NASA's Global Change Master
Directory (GCMD) \[22\], so that other users can access and manipulate
them. TOPS products will be compliant with the Federal Geographic Data
Committee (FGDC) \[19\] metadata standards.

### **SEEDS participation**

The main goal of SEEDS (The Strategic Evolution of ESE Data Systems) is
to establish evolution strategy and coordinating activities to assure
continued effectiveness of ESE data management systems and services.
SEEDS objectives are:

1.  Ensure timely delivery of Earth Science information at an affordable
    cost.

2.  Maximize availability and utility of ESE products.

3.  Engage community on data management issues, objectives, and
    solutions.

4.  Enable the development of flexible systems to readily accommodate
    evolving products and services.

TOPS supports the SEEDS objectives in the following ways:

-   The framework is designed to help cope with high data volumes, while
    providing greater ease of use to accommodate broad use by the
    general public and custom data product generation to meet the demand
    for a larger variety of data and data products.

-   The project provides a flexible architecture that can rapidly
    accommodate changes in data formats and processes.

-   TOPS aims to provide Principal Investigators with the software that
    will make it easier for them to process and manage their own data.

-   TOPS addresses the archiving of data that will be produced.
    Additionally, the automatic metadata generation will facilitate
    archiving and location of data.

-   By providing a system that strives for ease of users interaction,
    TOPS will help move beyond data and information and towards a
    knowledge-based system.

-   Science questions and priorities are fundamental drivers of the
    project.

The TOPS project plans to participate in the Technology Infusion Working
Group, or alternatively the Standards and Interfaces Working Group. The
project team members collectively have broad experience in Earth science
and information technology -- and years of first-hand experience
applying IT to Earth science applications.

The TOPS project team will also contribute substantially to standards
and interfaces. Mr. Petr Votava has been involved with data formats and
metadata applications in the Earth science domain since 1996 and he has
participated at the IEEE GRSS Technical Committee on Data Archiving and
Distribution. This experience will prove valuable in evaluating new
standards and interfaces.

### **Other Topics**

Format. The data format that TOPS will deliver will mainly be determined
by the needs of our customers within the NPS. We currently have the
ability to provide data in number of different formats including HDF,
HDF-EOS, HDF5 \[18\], GeoTIFF, binary and text.

Metadata. Apart from FGDC-compliant metadata, we will provide metadata
descriptors for our datasets using ESML or other emerging metadata
technology, which should provide flexibility in terms of both data
descriptions and software reuse. Metadata provided by TOPS will be
consistent with NPS I&M metadata standards. Large parts of today's Earth
science systems, especially ones merging data from different sources,
contain special components to deal with each new dataset that is added
to the system, which is not very flexible. The approach used in TOPS is
to describe the dataset externally to the system through the use of
appropriate metadata, and thus minimize the need to modify and/or add
additional modules that deal only with the new format. The added benefit
of this approach is that, by intercepting the data transformation early
in the process, we can also reuse large parts of scientific algorithms
without any modifications. The overall project's goal is software reuse
of both existing technologies and new ones as we strive to provide a
component-based architecture with high level of flexibility. TOPS will
share research, products and technology with the SEEDS community.

### **Archiving and distribution**

One of the main aspects of Earth science research is the vast amount of
data that are generated. TOPS will perform significant data volume
reductions and, given its dynamic nature, will be able to
"intelligently" re-use many existing data products for further analysis.
Because much of the data processing components are computationally
expensive and because we strive to provide continuous awareness without
temporal gaps, we will provide reduced data set replication. We will
have two sites that will mirror each other for data produced by the TOPS
system. One of the archive and distribution sites will be at NASA Ames
and the other at the Numerical Terradynamic Simulation Group (NTSG) at
the University of Montana. These sites will provide failover capability
for one another in both archiving and distributions of the products
produced by the TOPS system. The TOPS client delivered to NPS will
switch seamlessly between the two mirror sites depending on data
availability and network latencies so that our users have highly
reliable inputs into their system. Our partners in NTSG are our
collaborators on other projects including improvements to existing
models used by TOPS. It is thus important for them to have full access
to the data for their research purposes that will ultimately benefit our
users and us.

Due to the flexible and scalable nature of TOPS, the project will
incorporate additional new data products from the Earth science
community as they become available, together with new and enhanced
production algorithms. One of the goals of the project is to utilize new
and improved technologies and data products with very little integration
effort, thus making the development process more efficient. This is
accomplished through software re-use and component-based architecture.

### **TOPS Data Sources and Integrated Models**

TOPS is a general-purpose system, easily extensible to incorporate new
models and data sources. The following is a list of the current and
planned data sources and models that TOPS will support in order to
achieve its objectives in the protected area management / NPS domain.

[Data Source Agency Status]{.underline}

MODIS/Terra NASA Integrated

MODIS/Aqua NASA Partially Integrated

ASTER NASA Future

AVHRR NOAA Integrated

RUC2 NOAA (NCEP) Integrated

GCIP NOAA, UMD Partially Integrated

Snotel USDA Integrated

CPC NOAA Integrated

NEXRAD DoD, DoC, DoT Integrated

SOGS UofM/NTSG Partially Integrated

DayMET NCAR Partially Integrated

TRMM NASA, NASDA Future

SeaWIFS NASA Future

OMI NASA Future

GOES NOAA Future

SSMI NASA Future

AMSR-E NASA Future

CIMIS State of California Integrated

**[Model Description Status]{.underline}**

BGC Basic biogeochemical model Integrated

RHESSyS Hydro-ecological modeling framework Future

MOD17 Daily PSN and NPP model Partially Integrated

MOD16 Daily Evapotranspiration model Future

SOGS Daily met data gridding/processing Partially Integrated

### **TOPS Data Outputs**

Routine, near real-time, park-wide monitoring data products and
information from TOPS will include: high resolution weather data
including solar radiation, air temperature, humidity, rainfall; and
satellite and model-derived data on ecosystem variables including soil
moisture, vegetation water stress, phenological stage, and biomass.
These products are collectively referred to as the TOPS-30 products
(Table 1). TOPS will be able to provide these products at scales ranging
from 30m to 1000m for both park-wide monitoring and targeted monitoring
for areas of special concern to the NPS. In support of the I&M
objectives to establish baseline conditions and identify trends, the
system will also provide historical information from 1982 to the
present. This historical data will provide an important baseline for
interpreting current monitoring data on park conditions, allowing for
the identification of anomalies in ecosystem conditions that may be
indicative of ecosystem change or a potential threat to the park. In
addition to near-real time monitoring of park conditions, TOPS will be
also be used to provide short-term forecasts for these data products.

+------------------------------------------+---------------------------+
| **TOPS-30 Data Products to be            |                           |
| Incorporated**                           |                           |
+==========================================+===========================+
| **MODIS (8 day and annual products)**    | **Meteorology (Daily)**   |
|                                          |                           |
| 1.  LAI (Leaf Area Index)                | 11. Maximum Temperature   |
|                                          |                           |
| 2.  FPAR (Fraction of absorbed           | 12. Minimum Temperature   |
|     Photosynthetically Active Radiation) |                           |
|                                          | 13. Rainfall              |
| 3.  GPP / NPP (Gross / Net Primary       |                           |
|     Production)                          | 14. Solar Radiation       |
|                                          |                           |
| 4.  LST (Land Surface Temperature)       | 15. Dew Point / VPD       |
|                                          |     (Vapor Pressure       |
| 5.  NDVI (Normalize Difference           |     Deficit)              |
|     Vegetation Index)                    |                           |
|                                          | 16. Degree Days           |
| 6.  EVI (Enhanced Vegetation Index)      |                           |
|                                          |                           |
| 7.  Landcover (Annual)                   |                           |
|                                          |                           |
| 8.  Albedo                               |                           |
|                                          |                           |
| 9.  Snow                                 |                           |
|                                          |                           |
| 10. Fire                                 |                           |
+------------------------------------------+---------------------------+
| **TOPS Ecosystem**                       | **TOPS-BGC Forecasts**    |
|                                          |                           |
| 17. Snow                                 | 24. LAI/Phenology         |
|                                          |                           |
| 18. Soil Moisture                        | 25. Soil Moisture         |
|                                          |                           |
| 19. ET (Evapotranspiration)              | 26. Outflow               |
|                                          |                           |
| 20. Outflow                              | 27. ET                    |
|                                          |                           |
| 21. GPP / NPP                            | 28. Vegetation Stress     |
|                                          |                           |
| 22. Phenology                            | 29. Snow                  |
|                                          |                           |
| 23. Vegetation Stress                    | 30. GPP / NPP             |
+------------------------------------------+---------------------------+

Table 1. Summary of TOPS data outputs.

Another key feature of the system will be the ability to acquire
high-resolution imagery to aid in detailed monitoring of areas where
anomalies have been detected using MODIS data and ecosystem models.
Acquisitions of high-resolution data will also be made to verify the
utility of this data for periodic monitoring of areas of particular
interest to park managers, such as riparian corridors, restoration
sites, and habitat for endangered species. This high-resolution
information will be from sensors such as ASTER, LANDSAT/TM, or SPOT,
depending upon availability.

This project is primarily focused on monitoring, modeling, and
forecasting of processes that occur at the ecosystem scale. Past work
using TOPS has shown that changes in these processes can be detected at
resolutions of 8-km and higher. Anomalies in key parameters such as LAI,
soil moisture, and NPP are often caused by processes that affect entire
ecosystems such as climate change, nutrient deposition from pollution,
and invasive species. Furthermore, given the large geographic areas
contained within national parks and the often constrained resource
management budgets, NPS staff and scientists can only respond to changes
which occur or are predicted to occur over sustained time periods and
that affect relatively large portions of the park (multiple square
kilometers).

It is anticipated that 1-km will be the appropriate resolution for
continuous monitoring of park-wide ecosystem conditions. As such,
initial products developed for a prototype system in Yosemite National
Park will be generated at a resolution of 1-km, which will provide over
3000 data points daily for a suite of ecosystem parameters for the
entire park. Higher resolution data from NASA sensors and ecosystem
models, as well as data from commercial satellite platforms will also be
included in the prototype to evaluate the comparative utility of the
moderate and high resolution data products for decision support in
protected area management.

## **Technology Development**

### **Description of TOPS technology**

The technology development component of TOPS is focused on developing a
software system to support the capabilities outlined in the previous
sections, specifically aimed at supporting access, delivery and
interoperability among heterogeneous data sources and software
components.

As outlined in previous sections, we will be deploying an AI planning
and scheduling technology that was developed during the first two years
of this project and which is described in more detail in section
4.3.2.1. We will be using this new technology as a foundation for our
flexible framework on top of which much of our system and applications
will be built.

The technology development will be focused in the areas of data fusion,
metadata, distributed and parallel processing, component architecture
and interoperability, QA/QC, and user interface.

TOPS will build upon ongoing work in the development of a flexible
framework for data processing and integration. We will provide a user
interface for data queries, manipulation, and visualization, in addition
to supporting our goal of continuous awareness. In order to provide the
best possible datasets to our users, we will continue to improve our
data fusion technologies together with new metadata and QA tracking
capabilities. Because TOPS will be also operating in near-real-time
mode, we need to ensure that we provide answers within user-specified
timeframes. To support this requirement, we will continue development on
our direct broadcast system for real-time satellite data acquisition and
we will parallelize our existing applications to speed-up the processing
time. This will also enable us to process much more efficiently climate
datasets (which in some cases span the last 100 years) that are key
inputs to our system. We will continue to develop our existing flexible
application framework based on the (Java) Distributed Application
Framework (jDAF), for improvements in interoperability, and model and
data integration.

### **Current technology status**

This section describes the current status of the individual components
of the TOPS system with respect to technology development. Several of
the TOPS components will be using a planner-based software agent
designed for data-processing domains and applied to an ecosystem
forecasting system. Other technology components are jDAF (a flexible
application framework for data and model integration and
interoperability), SOGS (a framework for integration, fusion and
processing of both gridded and station-observed climate data), metadata
(descriptors of our current and future data holdings), QA/QC
(definition, tracking and control of the quality of both ours and
integrated data products) and parallelization (an effort to improve
efficiency of not only new but also legacy software components).

#### AI Planner and Scheduler

DoPPLER (Data Processing PLannER) is a planner for data processing
domains, which was developed under the CICT/IS program and enhanced
greatly during the first two years of this project. For TOPS purposes,
planning is a restricted form of automatic program synthesis, in which a
*plan*, typically a loop-free sequence of actions, such as
data-processing operations, is generated in response to a *goal*, a set
of conditions that the plan must bring about. The goals DoPPLER accepts
are descriptions of desired data products, and the plans it generates
are dataflow programs*,* which output the requested data. A dataflow
program is composed of data-processing operations, each of which can
have multiple inputs and outputs, in which outputs of one action can be
connected to inputs of another. Actions are eligible for execution as
soon as their inputs are available, and multiple actions can be executed
in parallel.

This approach is well suited to supporting interoperability among even
legacy systems, because the planner can be used to connect these systems
together in whatever way needed to achieve a particular goal. All that
is needed is descriptions, in the form of planner actions, of the
systems to be integrated. The approach leads to a design that is
*modular* and *evolvable*, since any new component can be brought in by
providing only a description of that component. Similarly, descriptions
of individual components can be modified, and descriptions can exist for
different versions of components, leading to a system that is
*maintainable*. Libraries of such descriptions, which we call *domain
libraries*, can be distributed along with the components they describe,
leading to a *plug-and-play* architecture.

Some of the key technologies underlying the planner are:

-   A rich language for describing data and data-processing operations,
    called DPADL \[2\]. DPADL is a declarative language with syntax
    similar to Java and C++. Unlike other planning domain description
    languages, DPADL is specialized for data-processing domains; actions
    can have multiple inputs and outputs and can explicitly create new
    objects. DPADL provides support for complex data types and
    constraints over all primitive types and Java objects, which can be
    specified from a constraint library or defined by embedded Java
    code. DPADL is a new language developed to express the unique
    aspects of data-processing domains, but has foundations in a
    substantial body of prior work in action representation.

-   A constraint reasoning system \[3\] capable of solving the
    constraints introduced in DPADL files. The constraint reasoning
    system is taken from the Europa planner, a second-generation version
    of the Remote Agent planner that flew aboard the Deep Space 1
    spacecraft \[4\].

-   A planning algorithm optimized for data-processing domains. Such
    domains are atypical compared to benchmark planning domains, in that
    the number of "objects," and hence the number of possible ground
    actions, is huge, and plans tend to be "shallow" but "bushy," that
    is, having potentially large numbers of actions that don't interfere
    with each other and can all be executed in parallel, but fairly
    short sequences that must be executed sequentially. Most modern
    planning algorithms are optimized for exactly the opposite
    circumstances -- relatively few objects, but long plans with many
    destructive interactions. The planning problem is ultimately reduced
    to a constraint network, which is solved by the constraint reasoning
    system. The planning algorithm is new technology developed under
    CICT/IS and enhanced during the first two years of this project.

#### JDAF: Java Distributed Application Framework

The Java Distributed Application Framework (JDAF) \[21\] provides a
generic framework that will serve as "glue" and merge together the data,
models and distributed environment in a flexible and extensible fashion.
JDAF is the architecture that was developed for the Terrestrial
Observation and Prediction System (TOPS). It provides a common interface
and execution environment for all of the models and data processing
operations needed for TOPS. The DoPPLER planner exploits this common
interface to invoke operations in TOPS. JDAF can also be used as a
stand-alone system, driven by a database specifying what operations to
invoke. Like the planner, JDAF is not fundamentally tied to the TOPS
application, and we plan to generalize it. JDAF was partially developed
under CICT/IS.

In order to facilitate interoperation of the planner with the Earth
science processing algorithms, as well as general extensibility and
flexibility of the overall system, we are implementing the Java
Distributed Application Framework (JDAF). Using this framework we are
able to easily integrate existing algorithms written in several
different languages (C, C++, Fortran) into a complex application. While
the algorithm integration is an important feature of the system, there
is a provision for another, equally important, integration of the
acquired data needed for the processing. There has been an enormous
increase in the data volume and the number of data sources over the past
several years, and while some data are being duplicated (for example we
can obtain FPAR/LAI data from MODIS-Terra, MODIS-Aqua, AVHRR, or MISR),
they usually come in variety of formats ranging from simple binary to
HDF-EOS. The different data formats often introduce another level of
complexity into the system integration process, because the system will
require new modules that can read these new formats. With these facts in
mind we are building our framework in a way that accommodates for both
data and algorithm fusion, so that we can add new algorithms and new
data streams seamlessly to the existing system while minimizing the
integration efforts.

Since most of the Earth science algorithms are written in C or C++, we
take advantage of Java Native Interface (JNI) facilities provided by the
standard Java distribution. There is a single point of entry in and out
of the native code and we only use the Java interface for parameter
passing between the processing algorithm and the rest of the system.
This leads to a very simple design and a fast and efficient integration.
On the Java side of the system, we provide a set of common API\'s, which
are implemented by each of the active objects (data pre-processing
objects, processing algorithms, data analyzers). This makes it simple to
form processing pipelines in a flexible manner, by either an application
programmer, or by the planner. The simplicity of integration,
flexibility, and fast deployment, makes JDAF a good candidate for
prototyping of new algorithm processing systems, competing with
scripting languages like Perl. Even though scripts are very suitable for
fast prototypes, JDAF adds the flexibility and the distributed execution
component not often available in common scripting languages.

In order to take advantage of new available data streams, we will use
FGDC-compliant metadata formats as well as Earth Science Markup Language
(ESML), an XML-based \[27\] description language that eases
significantly the data fusion process, and supports on of our design
goals - separating of the data from the processing algorithms. The
algorithms obtain their inputs through a data translator that has a
detailed knowledge about the structure of the data, while the processing
component does not have to know anything about the source or the format
of the data. The knowledge of the data translator comes from external
XML descriptions of the data, and this external description is often the
only thing needed to integrate a new data stream with no code change
required!

#### SOGS

The Surface Observation Gridding System (SOGS) is a flexible framework
for automatic retrieval, intelligent storage and gridding of surface
observations \[28\]. The system provides TOPS with, but is not
restricted to, meteorological and climate grids over any region at any
reasonable spatial scale. The generated grids may be used solely or in
conjunction with other related information by different ecosystem or
hydrology models, such as Biome-BGC \[29\] and RHESSyS \[30\], or any
system being executed within JDAF.

SOGS produces maximum and minimum temperature, vapor pressure deficit
(VPD) \[31,36\], precipitation and solar radiation \[31,36\] grids from
ground observations in near real time and or retrospectively. The input
datasets used to generate these fields may come from the Climate
Prediction Center (CPC) \[32\], California Irrigation Management
Information System (CIMIS) \[33\], National Climatic Data Center (NCDC)
\[34\], SNOTEL \[35\] and many other regional data networks. The system
may \"merge\" these datasets when appropriate, creating a unified input
to produce the desired outputs. The gridding algorithms may also
leverage ancillary inputs, such as topographic layers derived from SRTM
\[38\] and NEXRAD radar data \[37\].

Within the framework there are a number of gridding interpolation
algorithms that can be utilized depending on the situation and
appropriateness \[28\]. Given the flexibility of SOGS, routines may
quickly be added or modified to meet the requirements of any particular
model being executed within JDAF.

SOGS underlying storage mechanism is a relational database. The
structured query language (SQL) database provides a simple, common
interface to allow spatial and temporal selection and summarization of
data with ease and efficiency, allowing application to quickly do a
multitude or analysis on the surface observations themselves.

#### Metadata

The metadata development efforts will concentrate on developing a
metadata model that will easily describe both the data that are inputs
and outputs of the TOPS system. The goal of this effort is to improve
automation in the data and model integration. This will also be
important for our customers in the NPS since we are planning on
providing these metadata descriptions together with our data. This
effort will be based upon the requirements and specifications provided
by the NPS park managers as customers. The metadata development project
is in its initial phase and we are planning to leverage our extensive
knowledge with the ECS and FGDC metadata standards, as well as
experience with the ESML language.

#### QA/QC

The development of a detailed QA/QC plan for our products is one of the
most important aspects of providing scientific datasets to the public.
The problem is that many of our inputs come without any QA information.
However, we will have to consider the quality of the data otherwise they
could introduce an unknown bias into our results. We will take advantage
of the fact that we are bringing in large number of datasets that cover
the same region and same variables -- some from ground stations,
satellites and other models. We will use the QA information from
products that provide it and in combination with ground truth derive a
measure of QA for undocumented data products. We will additionally
develop a strategy for QC and for QA propagation through our system and
will provide documentation to the users. This is the initial phase of
the QA/QC development, which will be based on our extensive knowledge of
the QA/QC development on the MODIS instruments on both Terra and Aqua.

#### Parallel Processing

In order to be able to better provide services to the NPS, and in order
to improve scalability, we are planning to develop a suite of parallel
processing algorithms that achieve this goal. One of the biggest
obstacles to deploying these techniques is the vast amount of legacy
code that comprises some of the libraries that we use. In order to take
advantage of these techniques while minimizing the need for code
re-writes, we will use existing methodologies in combination with new
components currently in development. One of the main goals of this
technology is to improve the throughput of the system to take advantage
of resources that are available to us, e.g., the Columbia supercomputer
at NASA Ames. Success in this development is critical, because it will
enable us to process far greater amounts of data in much finer
resolution, and thus will improve greatly the output of our system --
both nowcasts and forecasts. This is a general capability applicable to
other regions of interest and users, but during this project we will
only focus on the regions of interest for NPS and the SERVIR projects.

### **TOPS improvements**

The TOPS project will mature the existing technology from its current
TRL of 4 to 7, as detailed in Section 4.3.4. This will consist of
integration of the major components and testing the integrated system,
improving the overall design and performance.

#### AI Planner and Scheduler

The applications that this technology can provide automation for are
determined by the domain libraries, *i.e*., the descriptions of file
formats and data-processing operations associated with particular
application domains, written in DPADL. Thus, to extend the scope of this
work and test the technology towards the applications needed by the NPS,
the TOPS project will write new domain libraries and JDAF interfaces.
Since many data formats and data-processing programs are widely used
across different problem domains, and the description/interface for any
given format or program need only be written once, the project expects a
large domain library to greatly simplify the task of applying the
technology to new application domains. While it is possible for anybody
to extend this domain library, it requires a certain degree of
proficiency in the DPADL language and can be a highly technical
undertaking. It is outside the scope of this project to develop domain
libraries outside the area of interest of our users.

#### JDAF: Java Distributed Application Framework

We will extend the JDAF framework in a way that will facilitate access
to all databases, processing components and models that are part of the
TOPS system. We will additionally improve the automation of model and
data integration by developing automatic code generator for generating a
standard interface between the JDAF framework and external components.
We will fully integrate both the high-resolution RHESSyS modeling
framework and the climate gridding system (SOGS) into the components
accessible by JDAF. Furthermore, we will build on our previous work in
developing a Web service interface to access certain aspects of the JDAF
framework.

In order to take advantage of new available data streams, we will use
FGDC-compliant metadata formats as well as Earth Science Markup Language
(ESML), an XML-based description language that eases significantly the
data fusion process, and supports one of our design goals - separation
of the data from the processing algorithms. The algorithms obtain their
inputs through a data translator that has a detailed knowledge about the
structure of the data, while the processing component does not have to
know anything about the source or the format of the data. The knowledge
of the data translator comes from external XML descriptions of the data,
and this external description is often the only thing needed to
integrate a new data stream with no code change required.

#### SOGS

We will complete fully automated spatial autocorrelation, theoretical
variogram modeling and incorporate it into the product stream. We will
also extend the I/O capabilities of the SOGS system to include bzip,
GeoTIFF, HDF5, and shapefiles for both input and output. To improve some
of our output data streams, we will add GOES satellite data, which will
enable us to provide better estimates of the meteorological variables,
particularly for precipitation. We will improve our radiation algorithm
and will also go through several reprocessing campaigns in order to
improve our historic data holdings, which will have significant impact
on both nowcasts and forecasts. Finally, in order to take advantage of
the processing power of the Columbia supercomputer at NASA Ames, we will
parallelize the code so we can improve both temporal and spatial
resolution of our historic runs. Last, but not least we will re-design
parts of our database system that holds all station data, in order to
improve performance and we will provide a clean interface to this new
database.

#### Metadata, QA/QC, Parallel Processing

All of these components are in their initial stages and we will develop
them in a way to best accommodate requirements both of our internal
subsystems, but also the requirements of our collaborators in the NPS.
The improvements described should substantially increase our processing
capabilities as well as the ability of the users to manipulate and
understand our data. We will build on extensive experience with
parallelization, ECS metadata models, and QA/QC development on MODIS
instruments on both Terra and Aqua platforms.

#### User Interface

User interface development will primarily be focused on the automated
integration of data products from TOPS into the NPS system. To
accomplish this goal, we will first extend the existing planner
capabilities within TOPS to automate the extraction and temporal
compositing of regional subsets of data (e.g., Yosemite National Park
plus a buffer zone) from existing data archives using shapefiles
provided by NPS collaborators. We will also implement tools to automate
the conversion of data from binary to GIS compatible formats (e.g.,
GeoTIFF) and convert existing TOPS metadata structures into formats
compatible with the I&M database templates. Concurrent with verification
of these tools, we will work with NPS staff to design and build
client-side tools that will reside on NPS servers and interact with the
TOPS system to retrieve, subset and composite data, and simplify the
integration process.

In addition to these tools for automated machine-to-machine transfer and
integration of data products into the NPS system, we will provide a
user-friendly interface to the TOPS data holdings using the following
strategies:

1.  Expansion of the existing data browsing tools contained within the
    TOPS website (http://ecocast.arc.nasa.gov) to allow browsing of the
    full data archive, data subsetting, data query, and data retrieval;

2.  Implementation of a web-based data visualization tools (e.g.,
    ArcIMS) to facilitate distribution of TOPS data products related to
    U.S. National Parks and the SERVIR project's areas of interest in
    Central America, and facilitate data visualization and query;

3.  Distribution of data via a WMS server for visualization using the
    NASA WorldWind and/or Google Earth applications.

The primary purpose of these interfaces would be to facilitate use of
data developed for NPS decision making for outreach and education, and
to support use of the system by non-NPS users.

### **Technology Readiness Level (TRL) Assessment** 

The main components of the system are the JDAF framework, SOGS
subsystem, model integration system, and AI planner. We discuss the
current TRL of the system, then the plan for advancing the TRL in each
year of the project.

#### Current Status

1.  Planner. The planner is composed of three primary elements: A parser
    for reading in domain descriptions, a constraint-reasoning system
    and a high-level planning algorithm. All of these elements are
    implemented and integrated. The planner has been tested on small
    representative problems from the TOPS domain, including reprojection
    and mosaic generation from MODIS data, as well as general
    file-manipulation problems. The planner has also been partially
    integrated with the JDAF environment, demonstrating the ability to
    close the loop from sensing to execution.

2.  JDAF. The JDAF framework is implemented and supports a significant
    subset of the target TOPS capabilities. The key efforts in the JDAF
    framework will be to improve stability and performance with among
    others improvements in parallelization and exception handling.

3.  Model integration: use of models to integrate observations from
    satellites and ground-based networks to provide a capability for
    continuous monitoring and validation. This subproject is currently
    in a prototype stage. While we are able to perform the model
    integration, the process is not yet fully automated and there is
    additional need for development of metadata descriptions and QA/QC
    strategy in order to achieve the desired results.

4.  SOGS: climate data integration, fusion and gridding together with
    cross-validation. This subproject is currently operational. The key
    development efforts of the SOGS subsystem will deal with improved
    efficiency of execution, fine-tuning of our interpolation methods,
    and QA/QC development. We will also integrate additional sources of
    climate data into the system, such as local meteorological
    monitoring networks located within individual parks.

These basic subsystems have been integrated, however there is still a
lack of integration automation, which is important for additional
development. All have been demonstrated on representative problem
domains. Thus, the overall TRL of this effort is 4.

#### Year 1 (FY06): TRL 5

The goal of the FY06 is to advance to TRL 5. This will involve
integrating all of the components in a more stable way and completing
improvements in system automation. We will test the integrated system in
a set of realistic simulations with the NPS.

#### Year 2 (FY07): TRL 6

The goal of the FY07 will be to reach TRL 6, which will require us to
mature the work done in the first year, improve stability, efficiency
and capabilities, and deploy the system live in a series of tests
directly with the components of the NPS's operational environment.

#### Year 3 (FY08): TRL 7

The goal of the FY08 will be to deploy an end-to-end system at the NPS's
site. We will deliver data and demonstrate the system in the operational
environment. We will also provide end-user documentation to accompany
the programmer documentation that is maintained in the course of
software development. We will also provide training on the operations of
our system for the NPS. We will also focus our effort on evaluating the
design of the system from the perspectives of stability and efficiency,
making any necessary improvements.

## **Deliverables**

This section provides a description of TOPS deliverables according to
its Work Breakdown Structure (WBS). The TOPS project will be completed
by the end of FY 2008.

WBS 3.0 Baseline System Documentation *(12/31/2005)*

WBS 4.0 NPS Coordination Management

> *Meeting documentation and minutes first on 10/28/05; other following
> telecoms and meetings*

WBS 5.0 NPS Data Requirements *(Data Requirements Document -- 7/1/2006)*

WBS 5.1 Product/variable requirements *(7/1/2006)*

WBS 5.2 Format requirements *(7/1/2006)*

WBS 5.3 Volumes constraints *(7/1/2006)*

WBS 5.4 Metadata requirements *(7/1/2006)*

WBS 6.0 NPS Service Requirements *(Service Requirements Document --
7/1/2006)*

WBS 6.1 Anomaly and warning system requirements *(8/1/2006)*

WBS 6.2 Monitoring/Continuous awareness system requirements *(8/1/2006)*

WBS 7.0 AI Technology Integration

WBS 7.1 Temporal composition *(4/1/2006)*

WBS 7.2 Data acquisition *(7/1/2006)*

WBS 8.0 Remote Sensing and Climate Integration

WBS 8.1 Direct Broadcast

WBS 8.1.1 EDC dataset integration *(3/1/2006)*

WBS 8.1.2 OSU dataset integration *(9/1/2006)*

WBS 8.2 GOES data integration *(10/1/2006)*

WBS 8.3 AMSR-E data integration (*1/1/2007*)

WBS 8.4 SSMI data integration (*3/1/2007*)

WBS 8.5 AIRS data integration (*9/1/2007*)

WBS 8.6 OMI data integration (*1/1/2008*)

WBS 8.7 DMSP data integration (*5/1/2008*)

WBS 9.0 TOPS core system redesign/refactoring *(10/30/2007)*

WBS 10.0 Climate Module Development/Enhancement *(5/1/2008)*

WBS 10.1 Algorithm Improvements (*1/1/2007*)

WBS 10.2 Parallelization (*4/1/2008*)

WBS 10.3 Automation (*8/1/2007*)

WBS 11.0 QA/QC

WBS 11.1 QA/QC Approach development *(12/15/2006)*

WBS 11.2 QA/QC implementation *(5/25/2007)*

WBS 12.0 TOPS/NPS System Integration

WBS 12.1 Data converter development (*10/30/2006*)

WBS 12.2 NPS client development *(10/1/2007)*

WBS 12.3 Anomaly module integration (*1/1/2007*)

WBS 12.4 Monitoring module integration (*6/1/2007*)

WBS 12.5 Data visualization (*5/1/2008*)

WBS 12.6 Integration with other NPS I&M Networks (TBD)

WBS 13.0 Subsystem Integration

WBS 14.1 JDAF/Planner integration *(7/1/2006)*

WBS 14.2 Full JDAF/SOGS integration *(1/31/2007)*

WBS 14.0 Documentation *(Final documentation 9/30/2008)*

WBS 15. 0 Field Test/Demonstration

WBS 15.1 NPS Interface demonstration *(6/1/2008)*

WBS 16.0 Testing and Validation

WBS 16.1 End-to-end test design (*5/1/2007*)

WBS 16.2 Climate validation (*9/1/2007*)

WBS 16.3 TOPS model output validation (*1/1/2008*)

WBS 16.4 Climate software testing

WBS 16.5 TOPS software testing

WBS 17.0 Security

WBS 18.0 SERVIR Project Integration

WBS 18.1 Climate data integration (depends on access to ground stations)

WBS 18.2 Satellite data integration (*6/1/2006*)

WBS 18.3 Automation

WBS 19.0 Final Demonstration *(8/29/2008)*

## **TOPS Schedule & Milestones** 

### **Schedule**

  -------------------------- --------------- ---------- -------- --- -------- -------- --- ---------- -------- -------- -------- ---------- -------- -------- -------- --------
  **Task**                   **Personnel**   **FY06**                                      **FY07**                              **FY08**                              

                                             **Q1**     **Q2**       **Q3**   **Q4**       **Q1**     **Q2**   **Q3**   **Q4**              **Q1**   **Q2**   **Q3**   **Q4**

  Baseline system document   Melton, Votava                                                                                                                             

  NPS Data Requirements      Melton, Votava                                                                                                                             

  *Product/Variable          Melton, Votava                                                                                                                             
  Requirements*                                                                                                                                                        

  *Format Requirements*      Votava                                                                                                                                     

  *Volume Constraints*       Votava                                                                                                                                     

  *Metadata Requirements*    Votava, Melton                                                                                                                             

  NPS Service Requirements   Melton, Votava                                                                                                                             

  *Anomaly and warning       Votava,Melton                                                                                                                              
  system req*                                                                                                                                                          

  *Monitoring/Cont.          Melton,Votava                                                                                                                              
  awareness reqs*.                                                                                                                                                     

  AI Technology Integration  Votava, Pang                                                                                                                               

  *Temporal Composition*     Votava, Pang                                                                                                                               

  *Data Acquisition*         Votava, Pang                                                                                                                               

  EDC DB Integration         Votava                                                                                                                                     

  OSU DB Integration         Votava                                                                                                                                     

  GOES Data Integration      Votava                                                                                                                                    

  AMSR-E Data Integration    Votava                                                                                                                                     

  SSMI Data Integration      Votava                                                                                                                                     

  AIRS Data Integration      Votava                                                                                                                                     

  OMI Data Integration       Votava                                                                                                                                     

  DMSP Data Integration      Votava                                                                                                                                     

  Code redesign/refactoring  Votava,                                                                                                                                    
                             Michaelis                                                                                                                                 

  Climate Module Enhancement Michaelis                                                                                                                                  

  *Climate Alg.              Michaelis                                                                                                                                  
  Enhancements*                                                                                                                                                        

  *Climate Module            Michaelis                                                                                                                                 
  Automation*                                                                                                                                                          

  *Clim. Module              Michaelis                                                                                                                                 
  Parallelization*                                                                                                                                                     

  QA/QC Approach             Votava, Nemani                                                                                                                             

  QA/QC Implementation       Votava,                                                                                                                                    
                             Michaelis                                                                                                                                 

  TOPS/NPS Integration       Votava,                                                                                                                                    
                             Michaelis                                                                                                                                 

  *Data Coverter*            Michaelis,                                                                                                                                 
                             Votava                                                                                                                                    

  *Anomaly Module*           Votava,                                                                                                                                   
                             Michaelis                                                                                                                                 

  *Monitoring Module*        Votava,                                                                                                                                   
                             Michaelis                                                                                                                                 

  *NPS Client*               Votava,                                                                                                                                   
                             Michaelis                                                                                                                                 

  *NPS Vis./User Interface*  Votava,                                                                                                                                   
                             Michaelis                                                                                                                                 

  Testing and Validation     Votava, Melton                                                                                                                            

  *End-to-end test design*   Melton, Votava                                                                                                                            

  *Climate Validation*       Michaelis                                                                                                                                 

  *TOPS Output Validation*   Votava                                                                                                                                    

  *Climate Software Testing* Michaelis,                                                                                                                                
                             Melton                                                                                                                                    

  *TOPS Software Testing*    Votava, Melton                                                                                                                            

  JDAF/Planner Integration   Votava, Pang                                                                                                                               

  JDAF/SOGS Integration      Michaelis,                                                                                                                                 
                             Votava                                                                                                                                    

  Documentation              Melton                                                                                                                                     

  QA/QC Documentation        Votava, Nemani                                                                                                                             

  AI Planner/TOPS            Pang, Votava                                                                                                                               
  Demonstration                                                                                                                                                        

  NPS Interface              Votava, Melton                                                                                                                             
  Demonstration                                                                                                                                                        

  SERVIR Project Integration Melton, Votava                                                                                                                             

  Final Demonstration        All                                                                                                                                        
  -------------------------- --------------- ---------- -------- --- -------- -------- --- ---------- -------- -------- -------- ---------- -------- -------- -------- --------

### **Milestones**

The timeline for the following tasks and milestones is shown in Section
4.5.1. Milestones are indicated in *italics*. All years will include
participation in SEEDS activities. Security, management, and testing are
activities that are ubiquitous throughout the project execution phase.
Additionally, the following activities are broken down by year.

#### FY06

-   **Baseline system document.** Determine the baseline of the current
    DSS capabilities in the National Park Service. *Deliver a NPS DSS
    Baseline document.*

-   **NPS Data requirements.** Determine what data TOPS could provide to
    the National Park Service to improve its DSS. *Requirements document
    providing precise definition of data requirements from the NPS
    including variables, format(s), projection(s), resolution(s), and
    volume constraints.*

-   **NPS metadata requirements.** Determine NPS metadata requirements
    on the data that are introduced in their DSS. *Deliver metadata
    requirements document.*

-   **NPS service requirements.** Determine what service we will be
    providing to the NPS. *Deliver document specifying the service
    requirements including interface options and constraints on
    turnaround time.*

-   **AI technology integration (temporal composition).** Integrate
    temporal composition capabilities with the AI planner.

-   **AI technology integration (data acquisition).** Integrate data
    acquisition capabilities with the AI planner.

-   **EDC Direct Broadcast integration.** Integrate Direct Broadcast
    datastream from EDC DAAC into our automated system. *Automated
    acquisition, archiving and processing of DB data from EDC DAAC
    through the TOPS system*.

-   **OSU Direct Broadcast integration.** Integrate Direct Broadcast
    datastream from OSU into our automated system. *Automated
    acquisition, archiving and processing of DB data from OSU through
    the TOPS system*.

-   **JDAF/Planner integration.** Finish integration of the AI planner
    with the JDAF framework.

[]{.mark}

#### FY07

*.*

-   **GOES data integration.** Integrate GOES-10 satellite data from
    MSFC/UAH into our automated system. *Automated acquisition,
    archiving and processing of GOES-10 data from MSFC/UAH through the
    TOPS system*.

-   **AMSR-E data integration.** Integrate AMSR-E satellite data into
    our automated system. *Automated acquisition, archiving and
    processing of AMSR-E data through the TOPS system*.

-   **SSMI data integration**. Integrate SSMI datastream into our
    automated system. *Automated acquisition, archiving and processing
    of SSMI satellite data through the TOPS system*.

-   **AIRS data integration.** Integrate AIRS datastream from into our
    automated system. *Automated acquisition, archiving and processing
    of AIRS satellite data through the TOPS system*.

```{=html}
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-   **Code redesign, refactoring.** Make necessary improvements in the
    design of the code to increase robustness and efficiency and
    facilitate future development. *Fewer bugs. Reduction in lines of
    code and other objective measures of complexity, with no reduction
    in capabilities.*

-   **What-if scenario application prototype.** Prototype and
    application for what-if scenarios in the context of the Yosemite
    National Park. *Test of the prototype and receive feedback from
    NPS.*

-   **Climate module enhancement implementation.** General efficiency,
    automation and algorithmic improvements in the Climate (SOGS)
    component of our system. *Improved system checkpointed.*

-   **JDAF/SOGS integration.** Full integration of the climate gridding
    system with the JDAF framework. *Test of the integrated system.*

-   **QA/QC approach development.** Develop a methodology for handling
    QA/QC in both inputs and outputs. *Document describing the QA/QC
    strategy for our project.*

-   **QA/QC implementation.** Implementation of the QA/QC methodology
    within our system. *QA/QC information integrated into our system so
    that it takes effect in both production and data delivery*

-   **QA/QC documentation.** *Provide comprehensive document describing
    the approach and the explanation of the QA/QC information
    distributed with our datasets.*

-   **NPS Client implementation.** Implement client to be run on NPS
    side that will include data converter, anomaly module and
    monitoring/awareness module. *Deliver NPS Client software.*

-   **SERVIR Project Integration.** *Provide data integration services
    with the SERVIR project (while this is in FY07, the schedule will
    depend highly on timely access to the ground station data in central
    America).*

#### FY08

-   **NPS Visualization and User Interface**. *Implementation of user
    interface for the NPS*.

-   **OMI data integration.** Integrate OMI datastream into our
    automated system. *Automated acquisition, archiving and processing
    of Aura satellite data through the TOPS system*.

-   **DMSP data integration.** Integrate DMSP datastream into our
    automated system. Automated acquisition, archiving and processing of
    DMSP satellite data through the TOPS system.

-   **Final documentation.** *Deliver comprehensive documentation of the
    project, API, QA/QC, methodologies and technologies developed.*

-   **Final demonstration.** *A demonstration is conducted, in which the
    final implementation is shown to be usable by the intended
    customers.*

-   **Handover to NPS.** *Application is under primary control of
    customer, though it may still depend on data or services provided by
    NASA Ames.*

## **Risk Management**

The objectives of the TOPS project are ambitious. New developments and
applications proposed for TOPS are well beyond the current state of the
art, and although the technology components have been demonstrated on a
small scale, integrating them and scaling the system up will be a
challenge.

Building and deploying the TOPS software applications will be
labor-intensive and expensive. The primary challenge will be to complete
all the required software development using a small number of
programmers. The schedule and milestones listed in sections 4.4.1 and
4.4.2, respectively are based on careful estimates of what can be
accomplished with the amount of labor available. The focus of the
project is also subject to change due to interaction with the user
community, and as such, new challenges may arise.

Achieving the desired Technology Readiness Levels (TRLs) will also be a
challenge. Development costs tend to increase rapidly with TRL, but the
budget for the project is constant. Thus, the challenge of reaching the
next TRL will increase every year.

Budgetary constraints have caused a number of milestones from the
original TOPS concept to be removed and/or modified. The main reason for
these modifications is both change in personnel and a new partnership
with the NPS. The risk of modifying these tasks is that a final
application delivered to the user community may be less flexible, but
given the budgetary constraints we are planning to focus on the needs of
the NPS with extensibility and flexibility in mind. If additional
funding becomes available, we will be able to target the new audience
quickly, but even without additional funding, we will deliver a complete
set of capabilities to our partners in the NPS. Capabilities added for
this project include satellite integration milestones, Direct Broadcast,
QA/QC milestones, and additional tools for data visualization.

Additionally, current MODIS-based products are too coarse for many
applications and the project requires a commitment from NASA HQ for
lower-level data, better standardized products or the ability to manage
the data itself for on-demand processing. To mitigate this risk, it may
be necessary that TOPS benchmark MODIS data first before moving onto
other data suites of data products.

Finally, given the uncertainties of implementation of the
full-cost-accounting at NASA Ames, the budget may be further reduced at
which point all the milestones and success criteria of this project
would have to re-evaluated.

## **Budget** 

For period from: [10/1/2005]{.underline} to: [9/30/2008]{.underline}

[FY06 FY07 FY08]{.underline}

1.  Direct Labor \$224,726 \$253,034 \$258,095

2.  Other Direct Costs: \$243,653 \$199,915 \$206,468

    a.  *Subcontracts* \$235,557 \$191,819 \$198,372

*(UARC -- Pang,*

*CSUMB -- Votava, Melton, Michaelis)*

b.  *Consultants* \$0 \$0 \$0

c.  *Equipment* \$0 \$0 \$0

d.  *Supplies* \$0 \$0 \$0

e.  *Travel* \$8,096 \$8,096 \$8,096

f.  *Other* \$0 \$0 \$0

```{=html}
<!-- -->
```
3.  Facilities and Administrative Costs \$44,945 \$50,607 \$51,619

4.  Other Applicable Costs: \$0 \$0 \$0

5.  SUBTOTAL \$513,324 \$503,556 \$516,182

6.  Less Proposed Cost Sharing (if any) \$0 \$0 \$0

7.  **Total Costs \$513,324 \$503,556 \$516,182**

**[Notes:]{.underline}**

+-----------------------------------------------------------------------+
| From FY04 onward, Ames Research Center will be preparing full cost    |
| accounting budget submissions that include civil service direct       |
| labor, benefits, travel, and center general and administrative costs. |
| Full cost accounting represents the transfer of personnel, travel,    |
| and center operations dollars from separate labor, travel, and        |
| operation budgets to the research projects performing the work. This  |
| represents a new method of accounting for costs and adds no           |
| additional cost to the R&A funding requirements.                      |
|                                                                       |
| General and Administrative (G&A) Costs are those not attributable to  |
| any one project, but benefiting the entire organization. G&A is       |
| calculated by dividing the ARC Institutional costs by the assigned    |
| direct workforce. Functions funded from G&A include Salary, Mail      |
| Services, Fire, Security, Envirnomental, Center Management and Staff, |
| Medical Services, and Administrative IT.                              |
|                                                                       |
| Allocated Service Pool (ASP) charges are not immediately identified   |
| to a project but can be assigned based on usage or comsumption.       |
| Functions funded include Computer Security, Network Replacement,      |
| Utilities, Photo & Imaging, Data Communications, Maintenance, and     |
| Instrumentation.                                                      |
|                                                                       |
| Other include support costs identified are for Directorate, Division, |
| Branch, and Earth Science Facilities such as the Ecosystem            |
| Computational Facility (ECF).                                         |
|                                                                       |
| CSUMB Coop budget includes salaries for Mr. Votava at 0.6FTE, Mr.     |
| Michaelis at 0.6FTE and Mr. Melton at 0.4FTE                          |
+-----------------------------------------------------------------------+
|                                                                       |
+-----------------------------------------------------------------------+
|                                                                       |
+-----------------------------------------------------------------------+
|                                                                       |
+-----------------------------------------------------------------------+
|                                                                       |
+-----------------------------------------------------------------------+

##  {#section .unnumbered}

## **Exit Strategy/Project Termination/Transferability**

The development and implementation of TOPS will be completed by fiscal
year 2008. At that time, it is likely that specific parts of TOPS, or
specific applications built on TOPS, would be migrated to the NPS. Our
exit strategy for the project is focused on providing tools to automate
the integration of TOPS products in the NPS I&M network, which is
intended to be the central repository for monitoring data pertaining to
park vital signs. While national I&M standards and protocols have been
developed, implementation of regional networks (including the Sierra
Nevada Network) is an ongoing process and data standards are still being
defined. A plan for integration of the TOPS data products into the
Sierra Nevada I&M network is currently being developed in cooperation
with NPS staff and will be updated as development of the Sierra Nevada
Network proceeds. Likewise, integration of TOPS data with the SERVIR
network is still being scoped. At minimum, we will deliver a software
package to the NPS that will provide tools for automated data retrieval
and integration of TOPS data into the NPS I&M and SERVIR systems, an API
for linking data from TOPS to local NPS tools and models, and
functionality for anomaly detection and continuous awareness.

TOPS is designed to compile and integrate data for any geographic
location, and all technology development conducted under this project
will be focused on achieving the goal of system transferability. While
TOPS provides a number of tools for automated data retrieval and
delivery of data products for any geographic location, implementation of
certain products is limited by the need for some manual processing
during the initialization phase. In order to set-up TOPS for a new
location, it is necessary to:

-   Obtain climate data from networks within the new region of interest

-   Obtain historic climate data for the region to perform long-term
    spin-up runs

-   Obtain both current and historic satellite data for the region

-   Create ancillary soils and landcover datasets for the new region

-   Obtain terrain information needed for climate data gridding

-   Perform historic climate gridding (this can be extremely time
    consuming and not possible for desired resolution)

-   Perform historic model spin-up runs

At resolutions of 1 km or coarser, technology and data sets developed
under this project will be easily transferable to any location in the
U.S. and most international locations. For data resolutions finer than 1
km, manual intervention is required to locate and obtain data from
meteorological station networks (if available), obtain historic climate
datasets, and create ancillary datasets including soil and vegetation
maps. These tasks require some level of expertise and can involve both
technical and negotiation activities, especially for international
protected areas. As such, transferability of the high resolution
modeling components developed under this project will be dependent upon
customer resources available to assist with system implementation.

## **Performance Measures, Metrics and Success Criteria**

In addition to achievement of the project milestones listed in Section
4.5.2, the evaluation of overall project performance will be guided by
the following questions:

-   Does the use of TOPS in the NPS's I&M DSS increase visibility of
    NASA data from satellites and Earth science models among the NPS
    community, as measured by presentations at park science meetings and
    articles in park journals, newsletters, and websites?

-   Has TOPS enabled delivery of NASA data to improve NPS risk
    assessment capabilities?

-   Is TOPS able to reliably (i.e., \> 90% success rate) provide data
    and information on current ecosystem conditions for the entire park
    in time intervals required by the NPS?

-   Have TOPS products been incorporated into at least two I&M networks
    and/or the SERVIR data network?

-   Are TOPS products being used to address at least one critical
    management issue in at least two different protected areas?

The TOPS project will also use GPRA metrics for evaluating project
progress and will report the metrics as required. In addition to metrics
related to the broad societal impact of the project, the project will
adopt narrower metrics for the technology development that are
relatively easy to measure. These metrics include:

-   The number of tasks (e.g., answering questions about the Earth
    system) a user can perform successfully using the technology.
    Enabling naïve users to use data in ways they otherwise could not
    extends the value and impact of the data.

-   The time taken to perform those tasks. Time taken is correlated with
    cost and inversely correlated with productivity, so we would expect
    a significant decrease in time to enable users to do more, e.g.,
    science, for less money.

To evaluate the relative advantage of TOPS technology according to these
metrics, the project will conduct user trials, giving users tasks to
perform, using either the technology the project develops or a baseline
technology. The project will evaluate the system as a whole, in addition
to individual components. For example, the project will evaluate the NPS
decision-making process with and without TOPS to measure the impact of
that component.

TOPS will also be evaluated by the following information technology
challenges:

-   Ability to adapt to new geographic locations;

-   Ease of integration of new data sources from sensors;

-   Whether or not improvements from new data sources can be determined;

-   Ease of use and accessibility of the user interface

# References

1.  Chien, S., Fisher, F., Lo, E., Mortensen, H. and Greeley, R. 1997.
    **Using artificial intelligence planning to automate science data
    analysis for large image database.** *Proc. 1997 Conference on
    Knowledge Discovery and Data Mining.*

2.  Golden, K. 2002. **DPADL: An action language for data processing
    domains.** *Proceedings of the 3^rd^ International NASA Workshop on
    Planning and Scheduling for Space.*

3.  Golden, K., Frank, J. 2002. **Universal quantification in a
    constraint-based planner. Universal quantification in a
    constraint-based planner.** *Proceedings of the 6th International
    Conference on AI Planning and Scheduling*

4.  Jönsson, A., Morris, P., Muscettola, N. and Rajan, K. 2000.
    **Planning in Interplanetary Space: Theory and Practice.**
    *Proceedings of the 5th International Conference on AI Planning and
    Scheduling*

5.  Lansky, A. L., and Philpot, A. G. 1993. **AI-based planning for data
    analysis tasks.** *Proceedings of the Ninth IEEE Conference on
    Artificial Intelligence for Applications (CAIA-93).*

6.  Nemani, R.,Votava, P., Roads, J., White, M., Thornton, P. and
    Coughlan, J. 2002. **Terrestrial observation and predition system:
    Integration of satellite and surface weather observations with
    ecosystem models.** *Proceedings of the 2002 International
    Geoscience and Remote Sensing Symposium (IGARSS).*

7.  Nemani, R., White, M., Votava, P., Glassy, J., Roads, J. and
    Running, S. 2000. **Biospheric forecast system for natural resource
    management.** *Procceedings of GIS/EM --4*

8.  King, M., and R. Greenstone. 1999. **1999 EOS Reference Handbook, A
    Guide to NASA's Earth Science Enterprise and the Earth Observing
    System**. [*http://eos.nasa.gov*](http://eos.nasa.gov/)

9.  McGuffie, K., and A. Henderson-Sellers. 1997. **A Climate Modeling
    Primer**. *John Wiley & Sons.*

10. Justice, C., at al. 1998. **The Moderate Resolution Imaging
    Spectroradiometer (MODIS): Land remote sensing for global change
    research**. *IEEE Transactions on Geoscience and Remote Sensing,
    36(4), 1228-1249, 1998.*

11. Herring, D., et al. 1998. The First Earth Observing Satellite,
    NASA's Earth Observing **System EOS AM-1**.
    [*http://terra.nasa.gov/Brochure*](http://terra.nasa.gov/Brochure)

12. Running, S.W., R. Nemani, J. Glassy, and P. Thornton. 1999. MODIS
    Daily **Photosynthesis (PSN) and Annual Net Primary Production (NPP)
    Product (MOD17) Algorithm Theoretical Basis Document.**
    [*http://modis.gsfc.nasa.gov/data/atbd/land_atbd.html*](http://modis.gsfc.nasa.gov/data/atbd/land_atbd.html)

13. Knyazikhin, Y., et al. 1999. **MODIS Leaf Area Index (LAI) and
    Fraction of Photosynthetically Active Radiation Absorbed by
    Vegetation (FPAR) Product (MOD15) Algorithm Theoretical Basis
    Document.** *http://modis.gsfc.nasa.gov/data/atbd/land_atbd.html*

14. Running, S.W., and S.T. Gower. 1991. **FOREST-BGC, a General Model
    of Forest Ecosystem Processes for Regional Applications II. Dynamic
    Carbon Allocation and Nitrogen Budgets.** *Tree Physiology **9**:
    147-160.*

15. Running, S.W. and E.R. Hunt Jr. 1993. **Generalization of a forest
    ecosystem process model for other biomes, BIOME-BGC, and an
    application for global-scale models.** *Pp 141-158, IN: Scaling
    Processes Between Leaf and Landscape Levels. J.R.Ehleringer and
    C.Field eds. Academic Press.*

16. Pitt, E., and K. McNiff. 2001. **java.rmi: The Remote Method
    Invocation Guide**. *Addison Wesley*.

17. Ramachandran, R., et al. 2001. **Earth Science Markup Language: A
    Solution for Generic Access to Heterogeneous Data Sets**. *In
    Proceedings of Earth Science Technology Conference*

18. The National Center for Supercomputing Applications. 1999. **HDF5
    abstract data model.** *Presented to NASA EOS/HDF Workshop.*

19. Federal Geographic Data Committee. 2000. **Content Standard for
    Digital Geospatial Metadata Workbook version 2.0.** *FGDC.*

20. Diner, D.J. 1990. **EOS Multi-angle Imaging SpectroRadiometer**. *In
    Remote Sensing of the Biosphere, Proc. SPIE 1300, April, p.
    163-171*.

21. Votava, P., et al. 2002. **Distributed Application Framework for
    Earth Science Data Processing.** *In Proceedings of IGARSS 2002.*

22. Olsen, L. and G. Major. 2000. **NASA\'s Global Change Master
    Directory: Discovery Source for Earth Science Data.** *The Earth
    Observer, Vol. 12, No.1, January/February 2000.*

23. Benjamin, S.G., J.M. Brown, K.J. Brundage, B.E. Schwartz, T.G.
    Smirnova, T.L. Smith, L.L. Morone. 1998. **RUC-2 - The Rapid Update
    Cycle Version 2.** *NWS Technical Procedure Bulletin No. 448.
    NOAA/NWS, 18 pp.*

24. Bergès, J.C. 2002. **Support of WMO binary format (BUFR and GRIB).**
    *In Proceedings of Open source GIS - GRASS user conference 2002*

25. NOAA. 1998. **NOAA Polar Orbiter Data User\'s Guide.**
    [*http://www2.ncdc.noaa.gov/docs/podug/*](http://www2.ncdc.noaa.gov/docs/podug/)

26. Parkinson, L.C. 2001. **Monitoring the Earth's Water Cycle and
    Associated Variables from the Vantage of Space.**
    [*http://aqua.nasa.gov/publications.html*](http://aqua.nasa.gov/publications.html)

27. World Wide Web Consortium. **XML: Extensible Markup Language**.
    *W3C, <http://www.w3.org/XML>*

28. Jolly, M.W., Graham, J.M., Michaelis, A., Nemania, R., Running,
    S.W., 2004**. A flexible, integrated system for generating
    meteorological surfaces derived from point sources across multiple
    geographic scales**. *Environmental Modeling & Software.*

29. <http://www.ntsg.umt.edu/ecosystem_modeling/BiomeBGC>

30. <http://geography.sdsu.edu/Research/Projects/RHESSYS/>

31. Thornton, P.E., Running, S.W., 1999. **An improved algorithm for
    estimating incident daily solar radiation from measurements of
    temperature, humidity, and precipitation**. *Agricultural and Forest
    Meteorology 93 (1999) 211-228*

32. <http://www.cpc.ncep.noaa.gov/products/monitoring_and_data/us_prec.shtml>

33. <http://wwwcimis.water.ca.gov/cimis/welcome.jsp>

34. <http://www4.ncdc.noaa.gov/cgi-win/wwcgi.dll?wwAW~MP~F>

35. <http://www.wcc.nrcs.usda.gov/snow/snotel-geninfo.htm>

36. Thornton, P.E., Running, S.W., White, M.A., 1997. **Generating
    surfaces of daily meteorological variables over large regions of
    complex terrain**. *Journal of Hydrology 190, 241-251.*

37. <http://www.ncdc.noaa.gov/oa/radar/radarresources.html>

38. <http://srtm.usgs.gov/>

39. Stratton, Richard D. 2004. **Assessing the Effectiveness of
    Landscape Fuel Treatments on Fire Growth and Behavior**. *Journal of
    Forestry, Oct./Nov., vol. 102, no. 7, pp. 32-40.*

40. Kahle, A.B.; F.D. Palluconi; S.J. Hook; V.J. Realmuto; and G.
    Bothwell (1991)*.* **The Advanced Spaceborne Thermal Emission and
    Reflectance Radiometer (ASTER***), International Journal of Imaging
    Systems and Technology, 3: 144-156.*

41. Huete, A., Justice, C., Leeuwen, V. 1999. **MODIS Vegetation Index
    (MOD13) Algorithm Theoretical Basis Document**.
    [*http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf*](http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf)

42. Wan, Z. 1999. MODIS Land-Surface Temperature Algorithm Theoretical
    Basis (ATBD).
    [*http://modis.gsfc.nasa.gov/data/atbd/atbd_mod11.pdf*](http://modis.gsfc.nasa.gov/data/atbd/atbd_mod11.pdf)

43. <http://modis-land.gsfc.nasa.gov/fire.htm>

44. Hall, D.K., G.A. Riggs, and V.V. Salomonson. 2000, updated weekly.
    **MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid V004, January to
    March 2003**. *Boulder, CO, USA:* *National Snow and Ice Data
    Center. Digital media.*

45. Pagano, T. S., M. T. Chahine, H. H. Aumann, D. A. Elliott, E. M.
    Manning, V. J. Realmoto, C. K. Thompson, B. H. Lambrigtsen, S.-Y.
    Lee, S. E. Broberg, E. T. Olsen, E. J. Fetzer, L. L. Strow, 2003:
    **AIRS/AMSU/HSB on EOS Aqua: First Year Post Launch Assessment**.
    *Proc. SPIE Earth Observing Systems VIII, San Diego, CA, 5151,
    201-210.*

46. <http://aura.gsfc.nasa.gov/science/index.html>

47. <http://www.ssmi.com/ssmi/ssmi_description_details.html>

48. <http://www.goes.noaa.gov/>

49. http://www.ghcc.msfc.nasa.gov/AMSR/

Signature Page

+------------------------------------------------+----+---------------+
|                                                |    |               |
+------------------------------------------------+----+---------------+
|                                                |    |               |
+------------------------------------------------+----+---------------+
| Ramakrishna R. Nemani                          |    | Date          |
|                                                |    |               |
| Research Scientist                             |    |               |
|                                                |    |               |
| Ames Research Center                           |    |               |
|                                                |    |               |
| Moffett Field, CA. 94035                       |    |               |
+------------------------------------------------+----+---------------+
|                                                |    |               |
+------------------------------------------------+----+---------------+
| Woody Turner                                   |    | Date          |
|                                                |    |               |
| Ecological Forecasting Manager                 |    |               |
|                                                |    |               |
| Applied Sciences Program                       |    |               |
|                                                |    |               |
| Science Mission Directorate                    |    |               |
|                                                |    |               |
| NASA Headquarters, Washington, DC.             |    |               |
+------------------------------------------------+----+---------------+
|                                                |    |               |
+------------------------------------------------+----+---------------+
| Ronald J. Birk                                 |    | Date          |
|                                                |    |               |
| Program Manager, Applied Sciences Program      |    |               |
|                                                |    |               |
| Science Mission Directorate                    |    |               |
|                                                |    |               |
| NASA Headquarters, Washington, DC              |    |               |
+------------------------------------------------+----+---------------+

#  {#section-1 .unnumbered}