| Memory matters: A case for Granger causality in climate variability studies MC McGraw, EA Barnes Journal of climate 31 (8), 3289-3300, 2018 | 104 | 2018 |
| Daily to decadal modulation of jet variability T Woollings, E Barnes, B Hoskins, YO Kwon, RW Lee, C Li, E Madonna, ... Journal of Climate 31 (4), 1297-1314, 2018 | 79 | 2018 |
| Seasonal sensitivity of the eddy-driven jet to tropospheric heating in an idealized AGCM MC McGraw, EA Barnes Journal of Climate 29 (14), 5223-5240, 2016 | 52 | 2016 |
| A study of links between the Arctic and the midlatitude jet stream using Granger and Pearl causality SM Samarasinghe, MC McGraw, EA Barnes, I Ebert‐Uphoff Environmetrics 30 (4), e2540, 2019 | 37 | 2019 |
| Reconciling the observed and modeled Southern Hemisphere circulation response to volcanic eruptions MC McGraw, EA Barnes, C Deser Geophysical Research Letters 43 (13), 7259-7266, 2016 | 30 | 2016 |
| A cyclone-centered perspective on the drivers of asymmetric patterns in the atmosphere and sea ice during Arctic cyclones R Clancy, CM Bitz, E Blanchard-Wrigglesworth, MC McGraw, SM Cavallo Journal of Climate 35 (1), 73-89, 2022 | 24 | 2022 |
| Creating and evaluating uncertainty estimates with neural networks for environmental-science applications K Haynes, R Lagerquist, M McGraw, K Musgrave, I Ebert-Uphoff Artificial Intelligence for the Earth Systems 2 (2), 220061, 2023 | 17 | 2023 |
| New insights on subseasonal Arctic–midlatitude causal connections from a regularized regression model MC McGraw, EA Barnes Journal of Climate 33 (1), 213-228, 2020 | 17 | 2020 |
| Changes in Arctic moisture transport over the North Pacific associated with sea ice loss MC McGraw, CF Baggett, C Liu, BD Mundhenk Climate dynamics 54, 491-506, 2020 | 5 | 2020 |
| Understanding the forecast skill of rapid Arctic sea ice loss on subseasonal time scales MC McGraw, E Blanchard-Wrigglesworth, RP Clancy, CM Bitz Journal of Climate 35 (4), 1179-1196, 2022 | 3 | 2022 |
| Rapid dynamical evolution of ITCZ events over the east Pacific AO Gonzalez, I Ganguly, MC McGraw, JG Larson Journal of Climate 35 (4), 1197-1213, 2022 | 3 | 2022 |
| Classifying and Addressing Bias in AI/ML for the Earth Sciences A McGovern, A Bostrom, DJ Gagne, I Ebert-Uphoff, K Musgrave, ... 103rd AMS Annual Meeting, 2023 | 2 | 2023 |
| Creating and evaluating uncertainty estimates with neural networks for environmental-science applications K Haynes, R Lagerquist, M McGraw, K Musgrave, I Ebert-Uphoff Authorea Preprints, 2022 | 2 | 2022 |
| AStudy OF CAUSAL LINKS BETWEEN THE ARCTIC AND THE MIDLATITUDE JET-STREAMS S Samarasinghe, M McGraw, EA Barnes, I Ebert-Uphoff Proceedings of the 7th International Workshop on Climate Informatics, 2017 | 2 | 2017 |
| Using AI to Quantify Uncertainty in Tropical Cyclone Genesis MR Baldwin, C Slocum, M McGraw 103rd AMS Annual Meeting, 2023 | 1 | 2023 |
| What Can Machine Learning Methods Tell Us About the Tropical Cyclone Intensity Forecasting Problem? M McGraw, K Musgrave, J Knaff, C Slocum, I Ebert-Uphoff 35th Conference on Hurricanes and Tropical Meteorology, 2022 | 1 | 2022 |
| A New Machine Learning Model for Estimating Tropical Cyclone Track and Intensity Forecast Uncertainty M DeMaria, EA Barnes, G Chirokova, SN Stevenson 35th Conference on Hurricanes and Tropical Meteorology, 2022 | 1 | 2022 |
| Approaching Arctic-Midlatitude Dynamics from a Two-Way Feedback Perspective MC McGraw Colorado State University, 2019 | 1 | 2019 |
| Exploring Tropical Cyclone Structure and Evolution with AI-based Synthetic Passive Microwave Data M McGraw, K Haynes, KD Musgrave, I Ebert-Uphoff, C Slocum, J Knaff 104th AMS Annual Meeting, 2024 | | 2024 |
| Causal Feature Selection for Tropical Cyclone Intensity Forecasting TG Beucler, SG SUDHEESH, FIH Tam, MS Gomez, M McGraw, ... 104th AMS Annual Meeting, 2024 | | 2024 |
Imme Ebert-UphoffColorado State UniversityE-mail confirmado em colostate.edu
