Ali, ZeenatZiaei, DorsaSleeman, JenniferYang, ZhifengHalem, Milton2021-04-072021-04-072021-03-22Zeenat 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-http://hdl.handle.net/11603/21293Combining Artificial Intelligence and Machine Learning with Physical Sciences, AAAI-MLPS 2021In 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.5 pagesen-USThis 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.Text