Andrew F. Hart

Evaluation of the CORDEX-Africa multi-RCM hindcast: systematic model errors

Monthly-mean precipitation, mean (TAVG), maximum (TMAX) and minimum (TMIN) surface air temperatures, and cloudiness from the CORDEX-Africa regional climate model (RCM) hindcast experiment are evaluated for model skill and systematic biases. All RCMs simulate basic climatological features of these variables reasonably, but systematic biases also occur across these models. All RCMs show higher fidelity in simulating precipitation for the west part of Africa than for the east part, and for the tropics than for northern Sahara. Interannual variation in the wet season rainfall is better simulated for the western Sahel than for the Ethiopian Highlands. RCM skill is higher for TAVG and TMAX than for TMIN, and regionally, for the subtropics than for the tropics. RCM skill in simulating cloudiness is generally lower than for precipitation or temperatures. For all variables, multi-model ensemble (ENS) generally outperforms individual models included in ENS. An overarching conclusion in this study is that some model biases vary systematically for regions, variables, and metrics, posing difficulties in defining a single representative index to measure model fidelity, especially for constructing ENS. This is an important concern in climate change impact assessment studies because most assessment models are run for specific regions/sectors with forcing data derived from model outputs. Thus, model evaluation and ENS construction must be performed separately for regions, variables, and metrics as required by specific analysis and/or assessments. Evaluations using multiple reference datasets reveal that cross-examination, quality control, and uncertainty estimates of reference data are crucial in model evaluations.

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...

Cloud computing and virtualization within the regional climate model and evaluation system

The Regional Climate Model Evaluation System (RCMES) facilitates the rapid, flexible inclusion of NASA observations into climate model evaluations. RCMES provides two fundamental components. A database (RCMED) is a scalable point-oriented cloud database used to elastically store remote sensing observations and to make them available using a space time query interface. The analysis toolkit (RCMET) is a Python-based toolkit that can be delivered as a cloud virtual machine, or as an installer package deployed using Python Buildout to users in order to allow for temporal and spatial regridding, metrics calculation (RMSE, bias, PDFs, etc.) and end-user visualization. RCMET is available to users in an “offline”, lone scientist mode based on a virtual machine dynamically constructed with model outputs and observations to evaluate; or on an institution’s computational cluster seated close to the observations and model outputs. We have leveraged RCMES within the content of the Coordinated Regional Downscaling Experiment (CORDEX) project, working with the University of Cape Town and other institutions to compare the model output to NASA remote sensing data; in addition we are also working with the North American Regional Climate Change Assessment Program (NARCCAP). In this paper we explain the contribution of cloud computing to RCMES’s specifically describing studies of various cloud databases we evaluated for RCMED, and virtualization toolkits for RCMET, and their potential strengths in delivering user-created dynamic regional climate model evaluation virtual machines for our users.

Experiments with Storage and Preservation of NASA's Planetary Data via the Cloud

The computing and storage demands force to optimize and manage complex and often conflicting software engineering challenges. Several domain-specific, independent software solutions have been developed to manage large amounts of data, including grid computing platforms- specifically, data-grid software packages such as the Globus Toolkit, DSpace, and OODT (ObjectOriented Data Technology). In addition, several computationally focused software products are geared toward executing large numbers of jobs, including workflow technologies such as Condor and Pegasus, and batch submission systems like the Portable Batch System (PBS) and Torque. The use of cloud computing in NASAs Planetary Data System for large-volume data storage and preservation illustrates how clouds can help researchers meet modern data backup demands, which are approaching the petabyte scale.

Enabling effective curation of cancer biomarker research data

The dramatic increase in data in the area of cancer research has elevated the importance of effectively managing the quality and consistency of research results from multiple providers. The U.S. National Cancer Institutes Early Detection Research Network (EDRN) is a prime example of a virtual organization, sponsoring distributed, collaborative work at dozens of institutions around the country. As part of a comprehensive informatics infrastructure, The NASA Jet Propulsion Laboratory, in collaboration with Dartmouth Medical School, has developed a web application for the curation of cancer biomarker research results. In this paper, we describe and evaluate the application in the context of the EDRN content management process, and detail our experience using the tool in an operational environment to capture and annotate biomarker research data generated by the EDRN.

Time-bound analytic tasks on large datasets through dynamic configuration of workflows

Domain experts are often untrained in big data technologies and this limits their ability to exploit the data they have available. Workflow systems hide the complexities of high-end computing and software engineering by offering pre-packaged analytic steps combined into multi-step methods commonly used by experts. A current limitation of workflow systems is that they do not take into account user deadlines: they run workflows selected by the user, but take their time to do so. This is impractical when large datasets are at stake, since users often prefer to see an answer faster even if it has lower precision or quality. In this paper, we present an extension to workflow systems that enables them to take into account user deadlines by automatically generating alternative workflow candidates and ranking them according to performance estimates. The system makes these estimates based on workflow performance models created from workflow executions, and uses semantic technologies to reason about workflow options. Possible workflow candidates are presented to the user in a compact manner, and are ranked according to their runtime estimates. We have implemented this approach in the WOOT system, which combines and extends capabilities from the WINGS semantic workflow system and the Apache OODT Object Oriented Data Technology and workflow execution system.

Exploring a graph theory based algorithm for automated identification and characterization of large mesoscale convective systems in satellite datasets

Mesoscale convective systems are high impact convectively driven weather systems that contribute large amounts to the precipitation daily and monthly totals at various locations globally. As such, an understanding of the lifecycle, characteristics, frequency and seasonality of these convective features is important for several sectors and studies in climate studies, agricultural and hydrological studies, and disaster management. This study explores the applicability of graph theory to creating a fully automated algorithm for identifying mesoscale convective systems and determining their precipitation characteristics from satellite datasets. Our results show that applying graph theory to this problem allows for the identification of features from infrared satellite data and the seamlessly identification in a precipitation rate satellite-based dataset, while innately handling the inherent complexity and non-linearity of mesoscale convective systems.

Developing an open source strategy for NASA earth science data systems

We have found open source to be an effective platform for software reuse. Within the NASA Earth science data systems community, there are a number of distinct applications, ranging from interactions amongst science investigator led processing systems (SIPS), which focus on active data processing, algorithm experimentation and evaluation, and the reuse of instrument processing approaches; to NASAs Distributed Active Archive Centers (DAACs) that are responsible for outward facing data dissemination to the public, and where long term preservation of data and reuse are distinctly important; all the way to downstream proposal led systems, where investigators are funded by NASA to reuse data and software to produce fused data products, and to aggregate and reuse NASA data systems in a systems-of-systems manner. Recognizing the need for a coordinated effort to inform the reuse of components within the NASA ecosystem, we are developing a strategic approach for the development and reuse of open source software. The NASA open source strategy builds on a set of dimensions involving legal, architectural, community, and redistribution areas that are of prime importance to the agency as a whole.

Understanding Open Source Software at NASA

To provide a framework for comparing and understanding open source software at NASA, the authors describe a set of relevant dimensions and decision points that NASA and other government agencies can use in formulating an open source strategy.

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.