Award Abstract # 1942714
CAREER: Big Data Climate Causality Analytics

NSF Org: OAC
Office of Advanced Cyberinfrastructure (OAC)
Recipient: UNIVERSITY OF MARYLAND BALTIMORE COUNTY
Initial Amendment Date: April 1, 2020
Latest Amendment Date: September 12, 2023
Award Number: 1942714
Award Instrument: Continuing Grant
Program Manager: Juan Li
jjli@nsf.gov
 (703)292-2625
OAC
 Office of Advanced Cyberinfrastructure (OAC)
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: April 15, 2020
End Date: March 31, 2026 (Estimated)
Total Intended Award Amount: $542,294.00
Total Awarded Amount to Date: $583,384.00
Funds Obligated to Date: FY 2020 = $317,620.00
FY 2021 = $41,090.00

FY 2022 = $110,650.00

FY 2023 = $114,024.00
History of Investigator:
  • Jianwu Wang (Principal Investigator)
    jianwu@umbc.edu
Recipient Sponsored Research Office: University of Maryland Baltimore County
1000 HILLTOP CIR
BALTIMORE
MD  US  21250-0001
(410)455-3140
Sponsor Congressional District: 07
Primary Place of Performance: University of Maryland, Baltimore County
1000 Hilltop Circle
Baltimore
MD  US  21250-0001
Primary Place of Performance
Congressional District:
07
Unique Entity Identifier (UEI): RNKYWXURFRL5
Parent UEI:
NSF Program(s): CAREER: FACULTY EARLY CAR DEV
Primary Program Source: 01002021DB NSF RESEARCH & RELATED ACTIVIT
01002122DB NSF RESEARCH & RELATED ACTIVIT

01002223DB NSF RESEARCH & RELATED ACTIVIT

01002425DB NSF RESEARCH & RELATED ACTIVIT

01002324DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 026Z, 1045, 9179, 019Z
Program Element Code(s): 104500
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

A fundamental problem in climate science is climate causality analysis that studies the cause-effect relationship among climate variables, such as temperature and humidity. By studying how the climate system works from a causality perspective, the findings could be used for many research areas including climate variability, climate dynamics, climate simulation, and extreme climate prediction. Nowadays, climate causality study faces many computing challenges, such as processing very large and high-dimensional datasets, and the complexity of modern computing resources. To tackle these challenges, this project targets novel causality discovery algorithms and related scalable computing techniques. The project is expected to greatly aid Earth System scientists and climate scientists to explore new hypotheses and use cases related to climate causality. The project includes an integrated program of research, education and outreach to help better understand and evaluate climate simulation, fostering workforce development for a multidisciplinary research community on "Data + Computing + Climate Science", and raising interest in both IT technology and climate studies among K-12 students, and various underrepresented groups. The project thus serves the national interest, as stated in NSF's mission, by promoting the progress of science and advancing national prosperity and welfare.

The goal of this CAREER project is to study efficient and reproducible causality analytics for large-scale climate data, so that climate scientists can easily test their causal hypotheses, reproduce existing studies and compare different causality analytics results. To handle the increasing dimensionality and resolution of spatiotemporal climate datasets, the project will study incremental causality discovery algorithms for large-scale climate datasets and parallel causality discovery for spatiotemporal climate data. To address the variety of both causal discovery algorithms and climate simulation/observation datasets, the project will study how to effectively measure climate causality results from different causality algorithms and different climate datasets, and integrate causality results through ensemble techniques. To cope with difficulties in conducting and reproducing causality analytics with large-scale climate datasets, the project will study cloud computing for big data climate analytics pipeline construction and execution optimization. The project will be evaluated from two perspectives. From the computing perspective, the research will be evaluated in terms of algorithm computation complexity, algorithm accuracy and algorithm scalability. From the climate perspective, the applicability of the research will be evaluated by collaborating with climate scientists in their specific research programs.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 24)
Lapp, Louis and Ali, Sahara and Wang, Jianwu "Integrating Fourier Transform and Residual Learning for Arctic Sea Ice Forecasting" , 2023 https://doi.org/10.1109/ICMLA58977.2023.00266 Citation Details
Ali, Sahara and Faruque, Omar and Huang, Yiyi and Gani, Md Osman and Subramanian, Aneesh and Schlegel, Nicole-Jeanne and Wang, Jianwu "Quantifying Causes of Arctic Amplification via Deep Learning Based Time-Series Causal Inference" , 2023 https://doi.org/10.1109/ICMLA58977.2023.00101 Citation Details
Ali, Sahara and Faruque, Omar and Wang, Jianwu "Estimating Direct and Indirect Causal Effects of Spatiotemporal Interventions in Presence of Spatial Interference" , 2024 Citation Details
Ali, Sahara and Huang, Yiyi and Huang, Xin and Wang, Jianwu "Sea Ice Forecasting using Attention-based Ensemble LSTM" Proceedings of Tackling Climate Change with Machine Learning Workshop at International Conference on Machine Learning (ICML 2021) , 2021 Citation Details
Ali, Sahara and Mostafa, Seraj and Li, Xingyan and Khanjani, Sara and Wang, Jianwu and Foulds, James and Janeja, Vandana "Benchmarking Probabilistic Machine Learning Models For Arctic Sea Ice Forecasting" The International Geoscience and Remote Sensing Symposium (IGARSS 2022) , 2022 Citation Details
Ali, Sahara and Wang, Jianwu "MT-IceNet - A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting" 9th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT 2022) , 2023 https://doi.org/10.1109/BDCAT56447.2022.00009 Citation Details
Ali, Sahara and Wang, Jianwu "Tutorial on Causal Inference with Spatiotemporal Data" , 2024 https://doi.org/10.1145/3681778.3698786 Citation Details
Al_Mahmud_Mostafa, Seraj and Wang, Jinbo and Holt, Benjamin and Wang, Jianwu "YOLO based Ocean Eddy Localization with AWS SageMaker" , 2024 https://doi.org/10.1109/BigData62323.2024.10825286 Citation Details
Faruque, Omar and Li, Xingyan and Khan, Md Azim and Alam, Homayra and Wang, Jianwu "Comparative Evaluation of Causal Discovery and Inference Approaches on Arctic Sea Ice Time Series Data" , 2024 https://doi.org/10.1109/BigData62323.2024.10825273 Citation Details
Guo, Pei and Huang, Yiyi and Wang, Jianwu "Scalable and Flexible Two-Phase Ensemble Algorithms for Causality Discovery" Big Data Research , v.26 , 2021 https://doi.org/10.1016/j.bdr.2021.100252 Citation Details
Guo, Pei and Ofonedu, Achuna and Wang, Jianwu "Scalable and Hybrid Ensemble-Based Causality Discovery" 2020 IEEE International Conference on Smart Data Services (SMDS) , 2020 https://doi.org/10.1109/SMDS49396.2020.00016 Citation Details
(Showing: 1 - 10 of 24)

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