--- license: mit --- # 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](https://power.larc.nasa.gov/) ## 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}, } ```