Research: Dr. Tung's Earth System Science Data Lab: Purdue University Skip to main content

Active Research Themes

WCERES: Research in the nexus of Weather, Climate, Environment, Resources, Energy, and Society

Climate change, growth in world population, and extreme weather are increasingly taxing the Earth’s limited natural resources. For society to absorb and counter this strain, it is critical to integrate multi-disciplinary knowledge in the nexus of the intricately coupled systems of weather, climate, environment, resources, energy, and society (WCERES). As exemplified by the integrated framework of the United Nations (UN) Sustainable Development Goals (SDGs,, solutions to the “wicked” problems---the complex global issues spanning the natural and social sciences and beyond---demand an interdisciplinary approach. 

Professors Tung and Cleveland are the organizers of a growing group of Purdue faculty from across the campus: Science, Engineering, Agriculture, Technology, and Business, who are conducting collaborative research, developing data-science methods and performing data analysis in the WCERES nexus. The group's research activities are supported computationally by two HPC clusters, named WCERES and WSC, each running the DeltaRho software ( with R & DeltaRho's datadr at the front end and Hadoop at the back end.

Harnessing the Persistent Temporal Correlation or Long-Range Dependence (Long Memory) in Climate Data

Persistence is characterized by a power-law decay in the autocorrelation of a time series, implying that the influence of past events in a time series extends into the distant future. Prof. Tung's group works on various aspects of temporal persistence in climate data, including phenomenological studies of tropical deep convection, precipitation, and tree-ring data as well methodologies for analyzing climate data with temporal persistence.

Recently, Bowers and Tung (J. Climate, 2018) created an adaptive procedure for estimating the variability and determining error bars as confidence intervals for climate mean states by accounting for both short-and long-range dependence. While the prevailing methods for quantifying the variability of climate means account for shortrange dependence, they ignore long memory, which is demonstrated to lead to underestimated variability and hence artificially narrow confidence intervals. To capture both short- and long-range correlation structures, climate data are modeled as fractionally integrated autoregressive moving-average processes. The preferred model can be selected adaptively via an information criterion and a diagnostic visualization, and the estimated variability of the climate mean state can be computed directly from the chosen model.

Physical and Impact Studies of US West-Coast and Gulf-Coast Atmospheric Rivers

Atmospheric rivers (ARs), the long, narrow filaments of enhanced water vapor transport in the lower troposphere, have significant hydrological impacts on western North America and the central United States. We are undertaking an observational climatology study of ARs that either reached the western coast or intruded into the Gulf of Mexico from the cloud-radiative effects perspective. The differences between the ARs’ hydrological properties and the related aerosol and cloud-radiation processes are examined by distributed-parallel computing on a Hadoop cluster using data from the ERA-interim reanalysis, Global Precipitation Climatology Project, A-Train CALIPSO, and the Clouds and the Earth’s Radiant Energy System products. The linkage between the West-Coast ARs and the Gulf-Coast ARs are under study, so are their collective cloud-radiative effects over the contiguous US.