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Daniel M. Ricciuto
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2020 – today
- 2023
- [j6]Fengming Yuan, Dali Wang
, Shih-Chieh Kao, Michele Thornton, Daniel M. Ricciuto, Verity Salmon, Colleen Iversen, Peter Schwartz, Peter E. Thornton:
An ultrahigh-resolution E3SM land model simulation framework and its first application to the Seward Peninsula in Alaska. J. Comput. Sci. 73: 102145 (2023) - 2022
- [j5]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, Jitendra Kumar, Charles D. Koven, Li Li, Mario Melara, Lavanya Ramakrishnan, Daniel M. Ricciuto
, Anthony P. Walker
, Wei Zhi, Qing Zhu
, Charuleka Varadharajan:
Guidelines for Publicly Archiving Terrestrial Model Data to Enhance Usability, Intercomparison, and Synthesis. Data Sci. J. 21(1): 3 (2022) - [c8]Siyan Liu, Dan Lu, Daniel M. Ricciuto, Anthony P. Walker
:
Improving net ecosystem CO2 flux prediction using memory-based interpretable machine learning. ICDM (Workshops) 2022: 1111-1119 - 2020
- [c7]Dan Lu, Daniel M. Ricciuto
:
Efficient Distance-based Global Sensitivity Analysis for Terrestrial Ecosystem Modeling. ICDM (Workshops) 2020: 324-332
2010 – 2019
- 2019
- [c6]Zhuo Yao, Dali Wang
, Daniel M. Ricciuto
, Fengming Yuan
, Chunsheng Fang:
Parallel Computing for Module-Based Computational Experiment. ICCS (2) 2019: 377-388 - [c5]Dan Lu, Siyan Liu
, Daniel M. Ricciuto
:
An Efficient Bayesian Method for Advancing the Application of Deep Learning in Earth Science. ICDM Workshops 2019: 270-278 - [c4]Dan Lu, Daniel M. Ricciuto
:
Learning-Based Inversion-Free Model-Data Integration to Advance Ecosystem Model Prediction. ICDM Workshops 2019: 279-287 - [i1]Dan Lu, Daniel M. Ricciuto:
Efficient surrogate modeling methods for large-scale Earth system models based on machine learning techniques. CoRR abs/1901.05125 (2019) - 2016
- [j4]Dali Wang, Jens Domke, Jiafu Mao
, Xiaoying Shi, Daniel M. Ricciuto
:
A scalable framework for the global offline community land model ensemble simulation. Int. J. Comput. Sci. Eng. 12(1): 73-85 (2016) - 2015
- [j3]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
:
Development of mpi_EPIC model for global agroecosystem modeling. Comput. Electron. Agric. 111: 48-54 (2015) - 2014
- [c3]Roisin Langan, Richard Archibald
, Matthew Plumlee
, Salil Mahajan
, Daniel M. Ricciuto
, Chengen Yang
, Rui Mei, Jiafu Mao
, Xiaoying Shi, Joshua S. Fu
:
Stochastic Parameterization to Represent Variability and Extremes in Climate Modeling. ICCS 2014: 1146-1155 - 2013
- [j2]Chad A. Steed
, Daniel M. Ricciuto
, Galen M. Shipman, Brian E. Smith, Peter E. Thornton
, Dali Wang, Xiaoying Shi, Dean N. Williams:
Big data visual analytics for exploratory earth system simulation analysis. Comput. Geosci. 61: 71-82 (2013) - [c2]Brian E. Smith, Daniel M. Ricciuto
, Peter E. Thornton
, Galen M. Shipman, Chad A. Steed
, Dean N. Williams, Michael F. Wehner
:
ParCAT: Parallel Climate Analysis Toolkit. ICCS 2013: 2367-2375 - 2012
- [c1]Chad A. Steed
, Galen M. Shipman, Peter E. Thornton
, Daniel M. Ricciuto
, David Erickson, Marcia L. Branstetter:
Practical Application of Parallel Coordinates for Climate Model Analysis. ICCS 2012: 877-886 - 2011
- [r1]Dali Wang, Daniel M. Ricciuto, Wilfred M. Post, Michael W. Berry:
Terrestrial Ecosystem Carbon Modeling. Encyclopedia of Parallel Computing 2011: 2034-2039
2000 – 2009
- 2006
- [j1]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, Hyojung Kwon, Hongyan Luo, Steven C. Wofsy, Allison L. Dunn, J. William Munger
, Dennis D. Baldocchi
, Liukang Xu, David Y. Hollinger, Andrew D. Richardson, Paul C. Stoy
, Mario B. S. Siqueira, Russell K. Monson, Sean P. Burns
, Lawrence B. Flanagan
:
Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations. IEEE Trans. Geosci. Remote. Sens. 44(7-1): 1908-1925 (2006)
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