Datasets:
ArXiv:
License:
clarification
Browse files
README.md
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license: mit
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---
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#
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<!-- Provide a quick summary of the dataset. -->
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@@ -36,7 +36,7 @@ Here are the descriptions of the 31 weather variables with their units:
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| Profile Soil Moisture (0 to 1) | GWETPROF | 0 to 1 |
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| Snow Depth | SNODP | cm |
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| Dew/Frost Point at 2 Meters | T2MDEW | C |
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| Cloud Amount | CLOUD_AMT |
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| Evaporation Land | EVLAND | kg/m^2/s * 10^6 |
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| Wet Bulb Temperature at 2 Meters | T2MWET | C |
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| Land Snowcover Fraction | FRSNO | 0 to 1 |
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| Precipitable Water | PW | cm |
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| Surface Roughness | Z0M | m |
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| Surface Air Density | RHOA | kg/m^3 |
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| Relative Humidity at 2 Meters | RH2M |
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| Cooling Degree Days Above 18.3 C | CDD18_3 | days |
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| Heating Degree Days Below 18.3 C | HDD18_3 | days |
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| Total Column Ozone | TO3 | Dobson units |
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**csvs:** Processed data in the CSV format.
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**pytorch:** Pytorch TensorDataset objects ready to be used in training. All of daily, weekly, and monthly data have been reshaped
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so that the **sequence length is 365**. Each sample is a tuple of the following data:
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* weather measurements (shape `sequence_length x 31`)
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* coordinates (shape `1 x 2`)
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<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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- Missing values were backfilled.
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- Leap year extra day was omitted. So, each year of the daily dataset has 365 days. Similarly, each year of the weekly dataset has 52 weeks, and the monthly dataset has 12 columns.
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- Data was pivoted. So each measurement has x columns where x is either 365, 52, or 12.
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license: mit
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---
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# NASA Power Weather Data over North, Central, and South America from 1984 to 2022
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<!-- Provide a quick summary of the dataset. -->
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| Profile Soil Moisture (0 to 1) | GWETPROF | 0 to 1 |
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| Snow Depth | SNODP | cm |
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| Dew/Frost Point at 2 Meters | T2MDEW | C |
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| Cloud Amount | CLOUD_AMT | 0 to 1 |
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| Evaporation Land | EVLAND | kg/m^2/s * 10^6 |
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| Wet Bulb Temperature at 2 Meters | T2MWET | C |
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| Land Snowcover Fraction | FRSNO | 0 to 1 |
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| Precipitable Water | PW | cm |
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| Surface Roughness | Z0M | m |
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| Surface Air Density | RHOA | kg/m^3 |
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| Relative Humidity at 2 Meters | RH2M | 0 to 1 |
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| Cooling Degree Days Above 18.3 C | CDD18_3 | days |
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| Heating Degree Days Below 18.3 C | HDD18_3 | days |
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| Total Column Ozone | TO3 | Dobson units |
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**csvs:** Processed data in the CSV format.
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+
**pytorch:** Pytorch TensorDataset objects ready to be used in training. All of the daily, weekly, and monthly data have been reshaped
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so that the **sequence length is 365**. Each sample is a tuple of the following data:
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* weather measurements (shape `sequence_length x 31`)
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* coordinates (shape `1 x 2`)
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<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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The `raw` data is in the JSON format and unprocessed. The `csvs` and the `pytorch` data are processed in the following manner:
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+
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- Missing values were backfilled.
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- Leap year extra day was omitted. So, each year of the daily dataset has 365 days. Similarly, each year of the weekly dataset has 52 weeks, and the monthly dataset has 12 columns.
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- Data was pivoted. So each measurement has x columns where x is either 365, 52, or 12.
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