A High Performance Remote Sensing Product Generation System Based on a Service Oriented Architecture for the Next Generation of Geostationary Operational Environmental Satellites

Satya Kalluri,Paul Van Rompay,James Gundy,Allan Weiner,David Vititoe,Anthony Paullin,Brian Haman

doi:10.3390/rs70810385

Satya Kalluri, Paul Van Rompay + Show 5 more

Open Access

https://doi.org/10.3390/rs70810385

Copy DOI

Journal: Remote sensing	Publication Date: Aug 13, 2015
Citations: 12	License type: CC BY 4.0

Affiliation: National Oceanic and Atmospheric Administration

Abstract

The Geostationary Operational Environmental Satellite (GOES) series R, S, T, U (GOES-R) will collect remote sensing data at several orders of magnitude compared to legacy missions, 24 × 7, over its 20-year operational lifecycle. A suite of 34 Earth and space weather products must be produced at low latency for timely delivery to forecasters. A ground system (GS) has been developed to meet these challenging requirements, using High Performance Computing (HPC) within a Service Oriented Architecture (SOA). This approach provides a robust, flexible architecture to support the operational GS as it generates remote sensing products by ingesting and combining data from multiple sources. Test results show that the system meets the key latency and availability requirements for all products.

Highlights

Satellite data provide vital information needed to predict severe meteorological events, such as hurricanes, severe storms and tornadoes
This paper describes the architecture of the Geostationary Operational Environmental Satellite (GOES)-R ground system (GS), the High Performance Computing (HPC) architecture of the Product Generation and Product Distribution subsystems
The Product Generation (PG) Subsystem will process over two terabytes of data per day with 34 unique products, each with stringent latency requirements

Summary

Introduction

Satellite data provide vital information needed to predict severe meteorological events, such as hurricanes, severe storms and tornadoes. Timely processing of raw satellite data into higher-level products, such as radiances, imagery, cloud properties and soundings, is critical for operational meteorology. The GOES-R series represents a generational change in geostationary meteorological observation to meet forecasting and environmental monitoring requirements. The Earth and space observation technology improvements embodied in GOES-R will enable a higher science data rate compared to the current GOES satellites. A high-performance, high-availability Ground System (GS) has been designed and implemented to accommodate the real-time data processing demands of this much higher data rate.

Mission Overview

Solar Flux

GS Performance Test Results

Conclusions