IEEE Geoscience and Remote Sensing Magazine - June 2014 - 94

Architecture/Engineering
Development Environment

Standard Design Protocol
Initialize

Data Model
Interface
Production
Algorithm Core

Get Data

Compute
Product
Production
Algorithm Core

Put Data

Data Model
Interface
Operational/Production
Environment

Terminate

Figure 6. Illustration of two-tiered development model that

enable seamless transition between algorithm development and
operation/production environment based on common abstract
data model interface (DMI) and standardized algorithm framework.
of all parties throughout the entire development life cycle
and beyond, and a common set of software tools that facilitate the transition of algorithms from inception to 24/7
production. Well-defined production processes are key to
implementing a successful transition of science research to
operations. While there is not one exact formula for implementing such, there are several key elements that help
facilitate success. These elements are: 1) The incorporation
of both science and software expertise into the production team, 2) Pre-defined mechanisms for resolving issues
encountered during the development with both the algorithm developers and customer, and 3) Standardized and
configuration-managed documents that are peer reviewed
at formal steps along the process.
The successful transition of remote sensing algorithms
to ground processing production not only requires an
open production development process but also the implementation of standard tools to help facilitate collaboration
between the all parties. In particular, collaboration is essential between the algorithm developers and the production
teams. These tools should also be extended beyond the
use of standard collaboration tools for documentation and
configuration management, documentation development
and software development/testing, to mission specific tools
that directly facilitate the migration algorithm science to
production. One such set of tools is the development and
implementation of a common two-tiered software development environment that not only aids in the development
of production quality algorithm implementation, but in an
optimal environment can also benefit the initial science
algorithm development.
94

The basic elements of such a design are illustrated in
Fig. 6. This figure shows the two product development
environments. At the top of the figure is the development
environment that facilitates rapid production development cycles and the development of unit test environments
that employ standard data interfaces that provide identical functionality through a common data model interface
(DMI), and the use of a common algorithm framework that
can employed seamlessly in deployment in the operational
environment. This architecture/engineering development
environment provide an environment that facilitates ingest
of both internally developed and externally provided test
data, and supports the analysis of resulting diagnostic,
intermediate and output data/products while minimizing the implementation overhead often associated with
production environments. The lower left-hand side of the
diagram illustrates the production environment. While
this implementation is far more complicated and addresses
a number of real-time processing issues not implemented
in the algorithm development environment, it employs
the same mechanism for instantiating an algorithm and
provide the DMIs with identical interface specifications as
those deployed in the development environment illustrated
at the top of the figure. In adopting this process the production team inherits the ability to transition an algorithm or
suite of core science software back and forth between its
development and operational environments without altering the core algorithm code base. This "buy back" process
enables a single code base to be tested both environments
as augmented where appropriate without software re-work.
The right-hand side of the diagram illustrates an example algorithm "shell" that provides implementation examples using the provided DMIs and in many case standard
mechanisms for constructing an algorithm instance and
associated interfaces to the data and other services, e.g.
error handling and logging facilities. This use of a data/
model interface promotes software re-use/re-engineering,
and its modular design practices ensure efficient algorithm
migration from the start. In the optimal environment these
tools are deployed at the algorithm development facility
and are employed during the science exploration phase of
the process, making the transition and production testing
as seamless as possible.
AER has developed, employed and matured the processes
and tools outlined above both from an algorithm development perspective as well as from the production point
of view on a number of programs [30]-[31]. This includes
the development and/or co-development of several algorithm frameworks and data model interfaces to meet program needs and constraints; these have been incorporated
in numerous sensor test beds for operational systems and
national programs such as the algorithm developer of key
components of United States Air Force Weather Agency's
Cloud Depiction and Forecast System (CDFS-II) [32], as
both the algorithm development and production team for
the Air Force space weather Communication/ Navigation
ieee Geoscience and remote sensing magazine

june 2014



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