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NASA Power Weather Data over North, Central, and South America from 1984 to 2022

This dataset contains daily solar and meteorological data downloaded from the NASA Power API

Dataset Details

The dataset includes solar and meteorological variables collected from January 1st, 1984, to December 31st, 2022. We downloaded 28 variables directly and estimated an additional 3 from the collected data. The data spans a 5 x 8 grid covering the United States, Central America, and South America. Each grid rectangle contains 160 data points spaced 0.5 degrees apart in latitude and longitude.

Dataset Description

Here are the descriptions of the 31 weather variables with their units:

Parameter Name Symbol Unit
Temperature at 2 Meters T2M C
Temperature at 2 Meters Maximum T2M_MAX C
Temperature at 2 Meters Minimum T2M_MIN C
Wind Direction at 2 Meters WD2M Degrees
Wind Speed at 2 Meters WS2M m/s
Surface Pressure PS kPa
Specific Humidity at 2 Meters QV2M g/Kg
Precipitation Corrected PRECTOTCORR mm/day
All Sky Surface Shortwave Downward Irradiance ALLSKY_SFC_SW_DWN MJ/m^2/day
Evapotranspiration Energy Flux EVPTRNS MJ/m^2/day
Profile Soil Moisture (0 to 1) GWETPROF 0 to 1
Snow Depth SNODP cm
Dew/Frost Point at 2 Meters T2MDEW C
Cloud Amount CLOUD_AMT 0 to 1
Evaporation Land EVLAND kg/m^2/s * 10^6
Wet Bulb Temperature at 2 Meters T2MWET C
Land Snowcover Fraction FRSNO 0 to 1
All Sky Surface Longwave Downward Irradiance ALLSKY_SFC_LW_DWN MJ/m^2/day
All Sky Surface PAR Total ALLSKY_SFC_PAR_TOT MJ/m^2/day
All Sky Surface Albedo ALLSKY_SRF_ALB 0 to 1
Precipitable Water PW cm
Surface Roughness Z0M m
Surface Air Density RHOA kg/m^3
Relative Humidity at 2 Meters RH2M 0 to 1
Cooling Degree Days Above 18.3 C CDD18_3 days
Heating Degree Days Below 18.3 C HDD18_3 days
Total Column Ozone TO3 Dobson units
Aerosol Optical Depth 55 AOD_55 0 to 1
Reference evapotranspiration ET0 mm/day
Reference evapotranspiration ET0 mm/day
Vapor Pressure VAP kPa
Vapor Pressure Deficit VAD kPa

Grid coordinates for the regions

the location indices in the dataset refer to the order of these coordinates. For instance usa_0 refers to the first rectangle of the USA in the list below. For the pytorch data, location indices 0-34 refer to the data from the USA grid, 35-110 refer to the data from the South America grid and the rest refer to the data from the Central America grid.

USA 

((29, -109), (24, -101)),
((29, -101), (24, -93)),
((29, -93), (24, -85)),
((29, -85), (24, -77)),
((34, -125), (29, -117)),
((34, -117), (29, -109)),
((34, -109), (29, -101)),
((34, -101), (29, -93)),
((34, -93), (29, -85)),
((34, -85), (29, -77)),
((34, -77), (29, -69)),
((39, -125), (34, -117)),
((39, -117), (34, -109)),
((39, -109), (34, -101)),
((39, -101), (34, -93)),
((39, -93), (34, -85)),
((39, -85), (34, -77)),
((39, -77), (34, -69)),
((44, -133), (39, -125)),
((44, -125), (39, -117)),
((44, -117), (39, -109)),
((44, -109), (39, -101)),
((44, -101), (39, -93)),
((44, -93), (39, -85)),
((44, -85), (39, -77)),
((44, -77), (39, -69)),
((49, -133), (44, -125)),
((49, -125), (44, -117)),
((49, -117), (44, -109)),
((49, -109), (44, -101)),
((49, -101), (44, -93)),
((49, -93), (44, -85)),
((49, -85), (44, -77)),
((49, -77), (44, -69)),

Central America 

((29, -117), (24, -109)),
((24, -117), (19, -109)),
((24, -109), (19, -101)),
((24, -101), (19, -93)),
((24, -93), (19, -85)),
((24, -85), (19, -77)),
((19, -109), (14, -101)),
((19, -101), (14, -93)),
((19, -93), (14, -85)),
((19, -85), (14, -77)),

South America 

((-51, -77), (-56, -69)),
((-51, -69), (-56, -61)),
((-46, -85), (-51, -77)),
((-46, -77), (-51, -69)),
((-46, -69), (-51, -61)),
((-41, -85), (-46, -77)),
((-41, -77), (-46, -69)),
((-41, -69), (-46, -61)),
((-41, -61), (-46, -53)),
((-36, -85), (-41, -77)),
((-36, -77), (-41, -69)),
((-36, -69), (-41, -61)),
((-36, -61), (-41, -53)),
((-36, -53), (-41, -45)),
((-31, -85), (-36, -77)),
((-31, -77), (-36, -69)),
((-31, -69), (-36, -61)),
((-31, -61), (-36, -53)),
((-31, -53), (-36, -45)),
((-26, -85), (-31, -77)),
((-26, -77), (-31, -69)),
((-26, -69), (-31, -61)),
((-26, -61), (-31, -53)),
((-26, -53), (-31, -45)),
((-26, -45), (-31, -37)),
((-21, -85), (-26, -77)),
((-21, -77), (-26, -69)),
((-21, -69), (-26, -61)),
((-21, -61), (-26, -53)),
((-21, -53), (-26, -45)),
((-21, -45), (-26, -37)),
((-21, -37), (-26, -29)),
((-16, -85), (-21, -77)),
((-16, -77), (-21, -69)),
((-16, -69), (-21, -61)),
((-16, -61), (-21, -53)),
((-16, -53), (-21, -45)),
((-16, -45), (-21, -37)),
((-16, -37), (-21, -29)),
((-11, -85), (-16, -77)),
((-11, -77), (-16, -69)),
((-11, -69), (-16, -61)),
((-11, -61), (-16, -53)),
((-11, -53), (-16, -45)),
((-11, -45), (-16, -37)),
((-11, -37), (-16, -29)),
((-6, -85), (-11, -77)),
((-6, -77), (-11, -69)),
((-6, -69), (-11, -61)),
((-6, -61), (-11, -53)),
((-6, -53), (-11, -45)),
((-6, -45), (-11, -37)),
((-6, -37), (-11, -29)),
((-1, -85), (-6, -77)),
((-1, -77), (-6, -69)),
((-1, -69), (-6, -61)),
((-1, -61), (-6, -53)),
((-1, -53), (-6, -45)),
((-1, -45), (-6, -37)),
((-1, -37), (-6, -29)),
((4, -85), (-1, -77)),
((4, -77), (-1, -69)),
((4, -69), (-1, -61)),
((4, -61), (-1, -53)),
((4, -53), (-1, -45)),
((4, -45), (-1, -37)),
((9, -85), (4, -77)),
((9, -77), (4, -69)),
((9, -69), (4, -61)),
((9, -61), (4, -53)),
((9, -53), (4, -45)),
((14, -85), (9, -77)),
((14, -77), (9, -69)),
((14, -69), (9, -61)),
((14, -61), (9, -53)),

Dataset Structure

raw: unprocessed data dump from NASA Power API in the JSON format.

csvs: Processed data in the CSV format.

pytorch: Pytorch TensorDataset objects ready to be used in training. All of the daily, weekly, and monthly data have been reshaped so that the sequence length is 365. Each sample is a tuple of the following data:

  • weather measurements (shape sequence_length x 31)
  • coordinates (shape 1 x 2)
  • index (1 x 2). the first number is the temporal index of the current row since Jan 1, 1984. The second number is the temporal granularity, or the spacing between indices, which is 1 for daily data, 7 for weekly data, and 30 for monthly data. Note: this means the daily data contains 1 year of data in each row, weekly data contains 7 years of data in each row (7 * 52 = 364) and monthly data contains 12 years of data (12 * 30 = 360).

Dataset Creation

Source Data

NASA Power API daily weather measurements. The data comes from multiple sources, but mostly satellite data.

Data Processing

The raw data is in the JSON format and unprocessed. The csvs and the pytorch data are processed in the following manner:

  • Missing values were backfilled.
  • 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.
  • Data was pivoted. So each measurement has x columns where x is either 365, 52, or 12.
  • pytorch data was standardized using the mean and std of the weather over the continental United States.

Citation

BibTeX:

@misc{hasan2024weatherformerpretrainedencodermodel,
      title={WeatherFormer: A Pretrained Encoder Model for Learning Robust Weather Representations from Small Datasets}, 
      author={Adib Hasan and Mardavij Roozbehani and Munther Dahleh},
      year={2024},
      eprint={2405.17455},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2405.17455}, 
}
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