Two Daily Weather Datasets: Chiang Mai International Airport and Theodore Francis Green State Airport

Published: 6 February 2020| Version 6 | DOI: 10.17632/95mr7pr8rj.6
Contributor:
Donlapark Pornnopparath

Description

Two daily weather datasets for experimenting data-driven models on two different weather types: 1. Chiang Mai International Airport, Chiang Mai, Thailand from January 1st 1998 to July 31st 2019. The data were acquired from the station via personal communication. The following files are provided: - chiang_mai_1998-2019_raw.csv : the raw data without any preprocessing. Note that some of the data are missing. - chiang_mai_1998-2019.csv : the preprocessed data: the dates and redundant variables were removed, the missing data were imputed with MICE algorithm and all units were changed to SI units. 2. Theodore Francis Green State Airport, Providence, RI from January 1st 2006 to October 31st 2019. The data were acquired from the National Oceanic and Atmospheric Administration (https://www.ncdc.noaa.gov/cdo-web/datatools/lcd). The following files are provided: - providence_2006-2019_raw.csv : the raw data with redundant variables removed. Some of the data are missing. - providence_2006-2019.csv : the preprocessed data: the dates were removed, the missing data were imputed with MICE algorithm and all units were changed to SI units. Additionally, we provide code in Python and shell scripts for reproducibility of the three autoencoder models in "Short-term Daily Precipitation Forecasting with Seasonally-Integrated Long Short-Term Memory Autoencoder". The code have the following requirements: - Python 3.6 or higher - Keras 2.2 or higher (Python library) - Tensorflow 1.12.0 or higher (Python library) Additionally, we use RAdam for stable learning rate schedules. RAdam can be installed via pip installer. pip install keras-rectified-adam With these requirements, training the proposed model on the data is as easy as running the following command: ./Providence.sh After the training is done, the RMSE and CORR scores will be reported, and the forecast values will be saved in `path/to/data_XXXXXX-xxxxxx.csv`. The README.md file provides additional information on code usage. REFERENCES: - Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., Han, J., 2019. On the variance of the adaptive learning rate and beyond. arXiv preprint arXiv:1908.03265. - Zaytar, M.A., Amrani, C.E., 2016. Sequence to sequence weather forecasting with long short-term memory recurrent neural networks. International Journal of Computer Applications 143, 7–11. doi:10.5120/ijca2016910497.

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Categories

Meteorology, Weather Forecasting, Forecasting, Time Series, Weather, Time Series Forecasting, Deep Learning, Autoencoder (Artificial Neural Networks)

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