LSTMs for Inferring Planetary Boundary Layer Height (PBLH)
| dc.contributor.author | Ali, Zeenat | |
| dc.contributor.author | Ziaei, Dorsa | |
| dc.contributor.author | Sleeman, Jennifer | |
| dc.contributor.author | Yang, Zhifeng | |
| dc.contributor.author | Halem, Milton | |
| dc.date.accessioned | 2021-04-07T17:58:16Z | |
| dc.date.available | 2021-04-07T17:58:16Z | |
| dc.date.issued | 2021-03-22 | |
| dc.description | Combining Artificial Intelligence and Machine Learning with Physical Sciences, AAAI-MLPS 2021 | en_US |
| dc.description.abstract | In this paper, we describe new work which is part of a larger study to understand how machine learning could be used to augment existing methods for calculating and estimating the Planetary Boundary Layer Height (PBLH). We describe how a Long Short-Term Memory (LSTM) Network could be used to learn PBLH changes over time for different geographical locations across the United States, used in conjunction with the WRF-Chem model. If the machine learning method could achieve accuracy levels similar to the model-based calculations, then it is feasible for the deep learning model to be used as an embedded method for the WRF-Chem model. The paper shows promising results that warrant more exploration. We describe results for two experiments in particular. The first experiment used 20 geographical locations for a two-month period of hourly WRF-Chem calculated PBLH. In this experiment, we evaluated how well the LSTM could learn PBLH by using limited data across a set of nearby locations. This model achieved RMSE of .11 on predicted PBLH. The second experiment used one year of hourly PBLH calculations from the WRF-Chem model to evaluate the LSTM prediction for a selection of three locations with separate LSTM models, achieving RMSE scores of 0.04, 0.01 and 0.05, respectively. We describe these results and the future plans for this work. | en_US |
| dc.description.sponsorship | This work has been funded by the following grants: NASA grant NNH16ZDA001-AIST16-0091 and NSF CARTA grant 17747724. | en_US |
| dc.description.uri | https://ebiquity.umbc.edu/paper/html/id/972/LSTMs-for-Inferring-Planetary-Boundary-Layer-Height-PBLH- | en_US |
| dc.format.extent | 5 pages | en_US |
| dc.genre | conference papers and proceedings | en_US |
| dc.genre | preprints | |
| dc.identifier | doi:10.13016/m2md2b-sbnc | |
| dc.identifier.citation | Zeenat Ali, Dorsa Ziaei, Jennifer Sleeman, Zhifeng Yang, and Milton Halem, LSTMs for Inferring Planetary Boundary Layer Height (PBLH),https://ebiquity.umbc.edu/paper/html/id/972/LSTMs-for-Inferring-Planetary-Boundary-Layer-Height-PBLH- | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/21293 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | AAAI | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.relation.ispartof | UMBC Physics Department | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.title | LSTMs for Inferring Planetary Boundary Layer Height (PBLH) | en_US |
| dc.type | Text | en_US |