GOES-R, the next generation of the National Oceanic and Atmospheric Administration's (NOAA) Geostationary
Operational Environmental Satellite (GOES) System, represents a new technological era in operational geostationary
environmental satellite systems. GOES-R will provide advanced products that describe the state of the atmosphere, land,
oceans, and solar/ space environments over the western hemisphere. The Harris GOES-R Ground Segment team will
provide the software, based on government-supplied algorithms, and engineering infrastructures designed to produce and
distribute these next-generation data products. The Harris GOES-R Team has adopted an integrated applied science and
engineering approach that combines rigorous system engineering methods, with modern software design elements to
facilitate the transition of algorithms for Level 1 and 2+ products to operational software. The Harris Team GOES-R GS
algorithm framework, which includes a common data model interface, provides general design principles and
standardized methods for developing general algorithm services, interfacing to external data, generating intermediate and
L1b and L2 products and implementing common algorithm features such as metadata generation and error handling.
This work presents the suite of GOES-R products, their properties and the process by which the related requirements are
maintained during the complete design/development life-cycle. It also describes the algorithm architecture/engineering
approach that will be used to deploy these algorithms, and provides a preliminary implementation road map for the
development of the GOES-R GS software infrastructure, and a view into the integration of the framework and data
model into the final design.
For daytime water phase clouds, an iterative physical retrieval algorithm is proposed to determine the cloud top pressure, temperature, and height that best matches the window-IR channel radiance to that predicted from the atmospheric/surface state as specified by numerical weather prediction model inputs and other data. The iterative retrieval uses a fast radiative transfer (or forward) model and includes a parameterization of cloud multiple scattering. The optical thickness and effective particle size are used as explicit inputs and so it can account for both optically thin and thick clouds.
The Interactive Algorithm Tool Box (IATB) is a multi-layered architecture designed to aid in the rapid implementation and end-to-end assessment of algorithms for estimating environmental parameters from remote sensing data. This architectural scheme employs a layered approach. The primary layer provides common methods for accessing sensor, ancillary and auxiliary data as well as user configurable parameters. The second layer provides a standard set of tools that can be used in the development of the target remote sensing algorithm and its supporting simulation tools. One of the primary tools provides a self-consistent mechanism for performing radiative transfer calculations over a broad spectral range from the Microwave to the Infrared/Visible. This toolbox also provides standard mechanisms for building first-order sensor models. The final layer provides a platform independent "wrapper" for integrating the target algorithm and its simulation tools with a set of standard and custom analysis tools. This layer provides an end-to-end product that can be used for extended analysis and calibration/validation with either simulated or "real" data.
The testbed architecture has been applied to instruments measuring in spectra from the visible to the microwave. It has been employed during the development of algorithms for existing remote sensing systems (e.g. AMSU and AIRS) as well as sensor suites that will be flown on next generation satellites (e.g. NPOESS and GOES). This paper describes the IATB architecture and presents the implementation for several applications.