Chris A. Mattmann

Bio: Chris A. Mattmann is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Software system & Software architecture. The author has an hindex of 21, co-authored 143 publications receiving 2028 citations. Previous affiliations of Chris A. Mattmann include University of Southern California & University of California, Los Angeles.

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

Abstract: 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.

Computing: A vision for data science.

Abstract: To get the best out of big data, funding agencies should develop shared tools for optimizing discovery and train a new breed of researchers, says Chris A Mattmann

Apache airavata: a framework for distributed applications and computational workflows

Abstract: In this paper, we introduce Apache Airavata, a software framework to compose, manage, execute, and monitor distributed applications and workflows on computational resources ranging from local resources to computational grids and clouds. Airavata builds on general concepts of service-oriented computing, distributed messaging, and workflow composition and orchestration. This paper discusses the architecture of Airavata and its modules, and illustrates how the software can be used as individual components or as an integrated solution to build science gateways or general-purpose distributed application and workflow management systems.

Biology: The big challenges of big data

Abstract: As they grapple with increasingly large data sets, biologists and computer scientists uncork new bottlenecks.

Machine Learning and Data Mining Methods in Diabetes Research.

Abstract: The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble

Abstract: . EURO-CORDEX is an international climate downscaling initiative that aims to provide high-resolution climate scenarios for Europe. Here an evaluation of the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) ensemble is presented. The study documents the performance of the individual models in representing the basic spatiotemporal patterns of the European climate for the period 1989–2008. Model evaluation focuses on near-surface air temperature and precipitation, and uses the E-OBS data set as observational reference. The ensemble consists of 17 simulations carried out by seven different models at grid resolutions of 12 km (nine experiments) and 50 km (eight experiments). Several performance metrics computed from monthly and seasonal mean values are used to assess model performance over eight subdomains of the European continent. Results are compared to those for the ERA40-driven ENSEMBLES simulations. The analysis confirms the ability of RCMs to capture the basic features of the European climate, including its variability in space and time. But it also identifies nonnegligible deficiencies of the simulations for selected metrics, regions and seasons. Seasonally and regionally averaged temperature biases are mostly smaller than 1.5 °C, while precipitation biases are typically located in the ±40% range. Some bias characteristics, such as a predominant cold and wet bias in most seasons and over most parts of Europe and a warm and dry summer bias over southern and southeastern Europe reflect common model biases. For seasonal mean quantities averaged over large European subdomains, no clear benefit of an increased spatial resolution (12 vs. 50 km) can be identified. The bias ranges of the EURO-CORDEX ensemble mostly correspond to those of the ENSEMBLES simulations, but some improvements in model performance can be identified (e.g., a less pronounced southern European warm summer bias). The temperature bias spread across different configurations of one individual model can be of a similar magnitude as the spread across different models, demonstrating a strong influence of the specific choices in physical parameterizations and experimental setup on model performance. Based on a number of simply reproducible metrics, the present study quantifies the currently achievable accuracy of RCMs used for regional climate simulations over Europe and provides a quality standard for future model developments.

A direct localization of a fast radio burst and its host

Abstract: Subarcsecond localization of the repeating fast radio burst FRB 121102 shows that its source is co-located with a faint galaxy with a low-luminosity active galactic nucleus, or a previously unknown type of extragalactic source. Shami Chatterjee et al. report the subarcsecond localization of the Arecibo-discovered fast radio burst FRB 121102, the only known repeating burst source, using high-time-resolution radio interferometric observations that directly image the bursts. FRBs are radio flashes of unknown physical nature with durations of milliseconds. Previous observations have lacked the resolution to uniquely identify a host or multi-wavelength counterpart. The localization of FRB 121102 reveals a persistent radio and optical source that is coincident with the bursts to within 100 milliarcseconds. The enigmatic persistent source could be a neutron star within its nebula in a distant host galaxy, a low-luminosity active galactic nucleus, or a previously unknown type of extragalactic source. Fast radio bursts1,2 are astronomical radio flashes of unknown physical nature with durations of milliseconds. Their dispersive arrival times suggest an extragalactic origin and imply radio luminosities that are orders of magnitude larger than those of all known short-duration radio transients3. So far all fast radio bursts have been detected with large single-dish telescopes with arcminute localizations, and attempts to identify their counterparts (source or host galaxy) have relied on the contemporaneous variability of field sources4 or the presence of peculiar field stars5 or galaxies4. These attempts have not resulted in an unambiguous association6,7 with a host or multi-wavelength counterpart. Here we report the subarcsecond localization of the fast radio burst FRB 121102, the only known repeating burst source8,9,10,11, using high-time-resolution radio interferometric observations that directly image the bursts. Our precise localization reveals that FRB 121102 originates within 100 milliarcseconds of a faint 180-microJansky persistent radio source with a continuum spectrum that is consistent with non-thermal emission, and a faint (twenty-fifth magnitude) optical counterpart. The flux density of the persistent radio source varies by around ten per cent on day timescales, and very long baseline radio interferometry yields an angular size of less than 1.7 milliarcseconds. Our observations are inconsistent with the fast radio burst having a Galactic origin or its source being located within a prominent star-forming galaxy. Instead, the source appears to be co-located with a low-luminosity active galactic nucleus or a previously unknown type of extragalactic source. Localization and identification of a host or counterpart has been essential to understanding the origins and physics of other kinds of transient events, including gamma-ray bursts12,13 and tidal disruption events14. However, if other fast radio bursts have similarly faint radio and optical counterparts, our findings imply that direct subarcsecond localizations may be the only way to provide reliable associations.