Understanding Dominant Factors for Precipitation over the Great Lakes Region
Authors: Soumyadeep Chatterjee, Stefan Liess, Arindam Banerjee, Vipin Kumar
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that global climate indices, computed at different temporal lags, offer predictive information for winter precipitation. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering., 2Department of Soil, Water, and Climate University of Minnesota, Twin Cities Minneapolis, MN 55455 {chat0129, liess}@umn.edu, {banerjee, kumar}@cs.umn.edu |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a link to 'Supplementary material' (http://www-users.cs.umn.edu/chatter/papers/15/supplement.pdf), but it does not explicitly state that this material includes the open-source code for the methodology described in the paper. |
| Open Datasets | Yes | We compiled datasets from two sources: (1) United States Historical Climatological Network (USHCN) (Menne, Williams Jr, and Vose 2010), and (2) North American Regional Reanalysis (NARR) (Mesinger, Di Mego, and others 2006). |
| Dataset Splits | Yes | We divided the data into two sets. The first, comprising of 22 years data, was used for finding dominant factors. The second set, with the remaining 10 years data, was used to test predictive performance. ... We conducted leave-one-year-out cross-validation on held out test set described earlier. |
| Hardware Specification | No | The paper acknowledges technical support from the 'University of Minnesota Supercomputing Institute (MSI)', but it does not provide specific hardware details (e.g., CPU/GPU models, memory, or number of machines) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9, CPLEX 12.4). It describes methodologies like LASSO and Ordinary Least Squares regression. |
| Experiment Setup | Yes | For choosing the regularization parameter λ, we selected 2% of the training set as a validation set and selected λ that provides the smallest prediction mean square error (MSE) on this validation test. |