Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations. - Faith Ann Heinsch; Maosheng Zhao; Steven W. Running; John S. Kimball; Ramakrishna R. Nemani; Kenneth J. Davis; Paul V. Bolstad; Bruce D. Cook; Ankur R. Desai; Daniel M. Ricciuto; Beverly E. Law; Walt C. Oechel; et al. (IEEE Trans. Geosci. Remote. Sens., 2006)

Terrestrial Ecosystem Carbon Modeling. - Dali Wang; Daniel M. Ricciuto; Wilfred M. Post; Michael W. Berry (Encyclopedia of Parallel Computing, 2011)

Practical Application of Parallel Coordinates for Climate Model Analysis. - Chad A. Steed; Galen M. Shipman; Peter E. Thornton; Daniel M. Ricciuto; David Erickson; Marcia L. Branstetter (ICCS, 2012)

ParCAT: Parallel Climate Analysis Toolkit. - Brian E. Smith; Daniel M. Ricciuto; Peter E. Thornton; Galen M. Shipman; Chad A. Steed; Dean N. Williams; Michael F. Wehner (ICCS, 2013)

Big data visual analytics for exploratory earth system simulation analysis. - Chad A. Steed; Daniel M. Ricciuto; Galen M. Shipman; Brian E. Smith; Peter E. Thornton; Dali Wang; Xiaoying Shi; Dean N. Williams (Comput. Geosci., 2013)

Stochastic Parameterization to Represent Variability and Extremes in Climate Modeling. - Roisin Langan; Richard Archibald; Matthew Plumlee; Salil Mahajan; Daniel M. Ricciuto; Chengen Yang; Rui Mei; Jiafu Mao; Xiaoying Shi; Joshua S. Fu (ICCS, 2014)

Development of mpi_EPIC model for global agroecosystem modeling. - Shujiang Kang; Dali Wang; Jeffrey A. Nichols; Joseph Schuchart; K. L. Kline; Yaxing Wei; Daniel M. Ricciuto; Stan D. Wullschleger; Wilfred M. Post; Roberto César Izaurralde (Comput. Electron. Agric., 2015)

A scalable framework for the global offline community land model ensemble simulation. - Dali Wang; Jens Domke; Jiafu Mao; Xiaoying Shi; Daniel M. Ricciuto (Int. J. Comput. Sci. Eng., 2016)

Parallel Computing for Module-Based Computational Experiment. - Zhuo Yao; Dali Wang; Daniel M. Ricciuto; Fengming Yuan; Chunsheng Fang (ICCS (2), 2019)

An Efficient Bayesian Method for Advancing the Application of Deep Learning in Earth Science. - Dan Lu 001; Siyan Liu; Daniel M. Ricciuto (ICDM Workshops, 2019)

Learning-Based Inversion-Free Model-Data Integration to Advance Ecosystem Model Prediction. - Dan Lu 001; Daniel M. Ricciuto (ICDM Workshops, 2019)

Efficient surrogate modeling methods for large-scale Earth system models based on machine learning techniques. - Dan Lu 001; Daniel M. Ricciuto (CoRR, 2019)

Efficient Distance-based Global Sensitivity Analysis for Terrestrial Ecosystem Modeling. - Dan Lu 001; Daniel M. Ricciuto (ICDM (Workshops), 2020)

Improving net ecosystem CO2 flux prediction using memory-based interpretable machine learning. - Siyan Liu; Dan Lu 001; Daniel M. Ricciuto; Anthony P. Walker (ICDM (Workshops), 2022)

Guidelines for Publicly Archiving Terrestrial Model Data to Enhance Usability, Intercomparison, and Synthesis. - Maegen B. Simmonds; William J. Riley; Deborah A. Agarwal; Xingyuan Chen; Shreyas Cholia; Robert Crystal-Ornelas; Ethan T. Coon; Dipankar Dwivedi; Valerie C. Hendrix; Maoyi Huang; Ahmad Jan; Zarine Kakalia; et al. (Data Sci. J., 2022)

An ultrahigh-resolution E3SM land model simulation framework and its first application to the Seward Peninsula in Alaska. - Fengming Yuan; Dali Wang; Shih-Chieh Kao; Michele Thornton; Daniel M. Ricciuto; Verity Salmon; Colleen Iversen; Peter Schwartz; Peter E. Thornton (J. Comput. Sci., 2023)

Kilometer-Scale E3SM Land Model Simulation over North America. - Dali Wang; Chen Wang 004; Qinglei Cao; Peter Schwartz; Fengming Yuan; Jayesh Krishna; Danqing Wu; Daniel M. Ricciuto; Peter E. Thornton; Shih-Chieh Kao; Michele Thornton; Kathryn M. Mohror (CoRR, 2025)