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└── README.md
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/README.md:
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1 | 
2 |
3 | # Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations
4 |
5 | 【Accepted】by **the 26th International Conference on Artificial Intelligence and Statistics (AISTATS) 2023**
6 |
7 |
[](https://github.com/Xun-Zhu/weather2k/) [](https://huggingface.co/datasets/BUPT-PRIS-727/Weather2K)
8 |
9 | ## Download the dataset
10 |
11 | This is the open-source version (after necessary adjustments and checks) of the Weather2K dataset: https://huggingface.co/datasets/BUPT-PRIS-727/Weather2K
12 |
13 | The shape of the numpy file of Weather2K-R is (1866, 13, 13632), which means 1,866 groud weather stations, 3 constants for position information and 10 meteorological factors, and 13,632 time steps with 3-hour time resolution (Time coverage range: January 1, 2017- August 31, 2021).
14 |
15 |
16 |
17 | | Numpy Index | **Long Name** | **Short Name** | **Unit** |
18 | | :------------: | :---------------------------------------- | :--------------------- | :------: |
19 | | 0 | Latitude | lat | (°) |
20 | | 1 | Longitude | lon | (°) |
21 | | 2 | Altitude | alt | (m) |
22 | | 3 | Air pressure | ap | hpa |
23 | | 4 | Air Temperature | t | (°C) |
24 | | 5/6 | Maximum / Minimum temperature | mxt / mnt | (°C) |
25 | | 7 | Relative humidity | rh | (%) |
26 | | 8 | Precipitation in 3h | p3 | (mm) |
27 | | 9 | Wind direction | wd | (°) |
28 | | 10 | Wind speed | ws | (ms-1) |
29 | | 11 | Maximum wind direction | mwd | (°) |
30 | | 12 | Maximum wind speed | mws | (ms-1) |
31 |
32 | If you have any quesetions about the data download, please contact wuming@bupt.edu.cn
33 |
34 |
35 |
36 | ## Archive
37 |
38 | - [Weather2K v1.0](https://github.com/Xun-Zhu/weather2k/tree/v1.0.0)
39 |
40 |
41 | ## Cite
42 |
43 | If you are using this dataset please cite
44 | > Zhu X, Xiong Y, Wu M, et al. Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations[C]//International Conference on Artificial Intelligence and Statistics. PMLR, 2023: 2704-2722.
45 |
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