Dan Crichton

A Reusable Process Control System Framework for the Orbiting Carbon Observatory and NPP Sounder PEATE Missions

We describe a reusable architecture and implementation framework for managing science processing pipelines for mission ground data systems. Our system, dubbed ``PCS, for Process Control System, improves upon an existing software component, the OODT Catalog and Archive (CAS), which has already supported the QuikSCAT, SeaWinds and AMT earth science missions. This paper focuses on PCS within the context of two current earth science missions: the Orbiting Carbon Observatory (OCO), and NPP Sounder PEATE projects.

Evaluation of the Surface Climatology over the Conterminous United States in the North American Regional Climate Change Assessment Program Hindcast Experiment Using a Regional Climate Model Evaluation System

AbstractSurface air temperature, precipitation, and insolation over the conterminous United States region from the North American Regional Climate Change Assessment Program (NARCCAP) regional climate model (RCM) hindcast study are evaluated using the Jet Propulsion Laboratory (JPL) Regional Climate Model Evaluation System (RCMES). All RCMs reasonably simulate the observed climatology of these variables. RCM skill varies more widely for the magnitude of spatial variability than the pattern. The multimodel ensemble is among the best performers for all these variables. Systematic biases occur across these RCMs for the annual means, with warm biases over the Great Plains (GP) and cold biases in the Atlantic and the Gulf of Mexico (GM) coastal regions. Wet biases in the Pacific Northwest and dry biases in the GM/southern Great Plains also occur in most RCMs. All RCMs suffer problems in simulating summer rainfall in the Arizona–New Mexico region. RCMs generally overestimate surface insolation, especially in the...

Evaluating cloud computing in the NASA DESDynI ground data system

The proposed NASA Deformation, Ecosystem Structure and Dynamics of Ice (DESDynI) mission would be a first-of-breed endeavor that would fundamentally change the paradigm by which Earth Science data systems at NASA are built. DESDynI is evaluating a distributed architecture where expert science nodes around the country all engage in some form of mission processing and data archiving. This is compared to the traditional NASA Earth Science missions where the science processing is typically centralized. Whats more, DESDynI is poised to profoundly increase the amount of data collection and processing well into the 5 terabyte/day and tens of thousands of job range, both of which comprise a tremendous challenge to DESDynIs proposed distributed data system architecture. In this paper, we report on a set of architectural trade studies and benchmarks meant to inform the DESDynI mission and the broader community of the impacts of these unprecedented requirements. In particular, we evaluate the benefits of cloud computing and its integration with our existing NASA ground data system software called Apache Object Oriented Data Technology (OODT). The preliminary conclusions of our study suggest that the use of the cloud and OODT together synergistically form an effective, efficient and extensible combination that could meet the challenges of NASA science missions requiring DESDynI-like data collection and processing volumes at reduced costs.

A cloud-enabled regional climate model evaluation system

The climate research community is increasingly interested in utilizing direct, observational measurements to validate model output in an effort to tune those models to better approximate our planets dynamic climate. The current emphasis on performing these comparisons at regional, as opposed to global, scales presents challenges both scientific and technical, since regional ecosystems are highly heterogeneous and the available data is not readily consumed on a regional basis. If provided with a common approach for efficiently accessing and utilizing the existing observational datasets, climate researchers have the potential to effect lasting societal, economic and political benefits. A key challenge, however, is that model-to-observational comparison requires massive quantities of data and significant computational capabilities. Further complicating matters is the fact that, currently, observational data and model outputs exist in a variety of data formats, utilize varying degrees of specificity and resolution, and reside in disparate, highly heterogeneous data systems. In this paper we present a software architectural approach that leverages the advantages of cloud computing and modern open-source software technologies to address the regional climate modeling problem. Our system, dubbed RCMES, is highly scalable and elastic, allows for both local and distributed management of the satellite observations and generated model outputs, and delivers this information to climate researchers in a way that is easily integrated into existing climate simulations and statistical tools.

A Science Data System approach for the DESDynI mission

Amongst the many key challenges to the Science Data System (SDS) for the DESDynI (Deformation, Eco-system Structure, and Dynamics of Ice) mission is the exceptionally large data volume (on the order of 5 tera-byte per day) acquired by the radar and the consequent huge volume of data products produced (on the order of 16 peta-bytes per year). This paper presents an SDS conceptual approach to effectively and efficiently support the mission. The features of this SDS approach include: 1) A modular functional architecture that is based on the proven Object Oriented Data Technology (OODT) based framework, 2) the application of a Testbed Concept that facilitates the morphing of scientific algorithms to operational codes, and 3) innovative data staging, storage and backup strategies. This SDS approach is expected to form a strong basis for helping DESDynI achieve its many science goals and objectives.