Data repository for measurements from 3 wind masts in Papua New Guinea. Data transmits daily reports for wind speed, wind direction, air pressure, relative humidity and temperature. Please refer to the country project page for additional outputs and reports, including installation reports: https://www.esmap.org/re-mapping/papua-new-guinea For access to maps and GIS layers, please visit the Global Wind Atlas: https://globalwindatlas.info/ Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP).

Maps with wind speed, wind rose and wind power density potential in Vietnam. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/).

GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).

Maps with wind speed, wind rose and wind power density potential in Poland. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).

This dataset contains daily histograms of wind speed at 100m ("WS100"), wind direction at 100 m ("WD100") and an atmospheric stability proxy ("STAB") derived from the ERA5 hourly data on single levels [1] accessed via the Copernicus Climate Change Climate Data Store [2]. The dataset covers six geographical regions (illustrated in regions.png) on a reduced 0.5 x 0.5 degrees regular grid and covers the period 1994 to 2023 (both years included). The dataset is packaged as a zip folder per region which contains a range of monthly zip folders following the convention of zarr ZipStores (more details here: https://zarr.readthedocs.io/en/stable/api/storage.html). Thus, the monthly zip folders are intended to be used in connection with the xarray python package (no unzipping of the monthly files needed).Wind speed and wind direction are derived from the U- and V-components. The stability metric makes use of a 5-class classification scheme [3] based on the Obukhov length whereby the required Obukhov length was computed using [4]. The following bins (left edges) have been used to create the histograms:Wind speed: [0, 40) m/s (bin width 1 m/s)Wind direction: [0,360) deg (bin width 15 deg)Stability: 5 discrete stability classes (1: very unstable, 2: unstable, 3: neutral, 4: stable, 5: very stable)Main Purpose: The dataset serves as minimum input data for the CLIMatological REPresentative PERiods (climrepper) python package (https://gitlab.windenergy.dtu.dk/climrepper/climrepper) in preparation for public release).References:[1] Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 (Accessed Nov. 2024)[2] Copernicus Climate Change Service, Climate Data Store, (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 (Accessed Nov. 2024)'[3] Holtslag, M. C., Bierbooms, W. A. A. M., & Bussel, G. J. W. van. (2014). Estimating atmospheric stability from observations and correcting wind shear models accordingly. In Journal of Physics: Conference Series (Vol. 555, p. 012052). IOP Publishing. https://doi.org/10.1088/1742-6596/555/1/012052[4] Copernicus Knowledge Base, ERA5: How to calculate Obukhov Length, URL: https://confluence.ecmwf.int/display/CKB/ERA5:+How+to+calculate+Obukhov+Length, last accessed: Nov 2024

This dataset contains 350mb winds NCEP GFS forecast imagery taken during the HIPPO project. The data is in PNG format. The data covers the time span from 2008-12-31 12:00:00 to 2009-01-09 12:00:00.

Maps with wind speed, wind rose and wind power density potential in The United States of America. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).

Maps with wind speed, wind rose and wind power density potential in Denmark. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).

This data set consists of true wind speed and wind direction plots from the SBI U.S. Coast Guard Cutter (USCGC) Healy Summer 2002 Cruise (HLY-02-03). Files are in .png format.

Maps with worldwide wind speed and wind power density potential. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). The link provides poster size (.pdf) and midsize maps (.png).

Maps with wind speed, wind rose and wind power density potential in Pakistan. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).

Maps with wind speed and wind power density potential for South Asia. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). The link provides poster size (.pdf) and midsize maps (.png).

Maps with worldwide wind speed and wind power density potential. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). The link provides poster size (.pdf) and midsize maps (.png).

Maps with wind speed and wind power density potential in East Asia and Pacific. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). The link provides poster size (.pdf) and midsize maps (.png).

Maps with wind speed and wind power density potential in Latin America and Caribbean. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). The link provides poster size (.pdf) and midsize maps (.png).

Weather Long-term Time Series Forecasting (2020)

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8734253%2F832430253683be01796f74de8f532b34%2Fweather%20forecasting.png?generation=1730602999355141&alt=media" alt="">

Dataset Description

Weather is recorded every 10 minutes throughout the entire year of 2020, comprising 20 meteorological indicators measured at a Max Planck Institute weather station. The dataset provides comprehensive atmospheric measurements including air temperature, humidity, wind patterns, radiation, and precipitation. With over 52,560 data points per variable (365 days × 24 hours × 6 measurements per hour), this high-frequency sampling offers detailed insights into weather patterns and atmospheric conditions. The measurements include both basic weather parameters and derived quantities such as vapor pressure deficit and potential temperature, making it suitable for both meteorological research and practical applications. You can find some initial analysis using this dataset here: "Weather Long-term Time Series Forecasting Analysis".

File Structure

The dataset is provided in a CSV format with the following columns:

Potential Use Cases

This high-resolution meteorological dataset enables applications across multiple domains. For weather forecasting, the frequent measurements support development of prediction models, while climate researchers can study microclimate variations and seasonal patterns. In agriculture, temperature and vapor pressure deficit data aids crop modeling and irrigation planning. The wind and radiation measurements benefit renewable energy planning, while the comprehensive atmospheric data supports environmental monitoring. The dataset's detailed nature makes it particularly suitable for machine learning applications and educational purposes in meteorology and data science.

Credits

• This data was provided by the Max Planck Institute, and acc...

This dataset contains (a) a script “R_met_integrated_for_modeling.R”, and (b) associated input CSV files: 3 CSV files per location to create a 5-variable integrated meteorological dataset file (air temperature, precipitation, wind speed, relative humidity, and solar radiation) for 19 meteorological stations and 1 location within Trail Creek from the modeling team within the East River Community Observatory as part of the Watershed Function Scientific Focus Area (SFA). As meteorological forcings varied across the watershed, a high-frequency database is needed to ensure consistency in the data analysis and modeling. We evaluated several data sources, including gridded meteorological products and field data from meteorological stations. We determined that our modeling efforts required multiple data sources to meet all their needs. As output, this dataset contains (c) a single CSV data file (*_1981-2022.csv) for each location (20 CSV output files total) containing hourly time series data for 1981 to 2022 and (d) five PNG files of time series and density plots for each variable per location (100 PNG files). Detailed location metadata is contained within the Integrated_Met_Database_Locations.csv file for each point location included within this dataset, obtained from Varadharajan et al., 2023 doi:10.15485/1660962. This dataset also includes (e) a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and (f) a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. Review the (g) ReadMe_Integrated_Met_Database.pdf file for additional details on the script, methods, and structure of the dataset. The script integrates Northwest Alliance for Computational Science and Engineering’s PRISM gridded data product, National Oceanic and Atmospheric Administration’s NCEP-NCAR Reanalysis 1 gridded data product (through the RCNEP R package, Kemp et al., doi:10.32614/CRAN.package.RNCEP), and analytical-based calculations. Further, this script downscales the input data into hourly frequency, which is necessary for the modeling efforts.

A team of consultants conducted a review of Pacific Regional Meteorological Services as commissioned by the Secretariat of the Pacific Regional Environment Programme (SPREP) in November 2009. This was in response to a directive from Pacific Islands Forum Leaders. Over the period November 2009-April 2010, the team reviewed relevant documentation, consulted with SPREP member countries and other organisations, and considered feedback on a draft report before presenting its final report and recommendations.Available onlineCall Number: [EL]Physical Description: 155 p.

Maps with wind speed, wind rose and wind power density potential in The Philippines. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).

Context

Predicting wildfire spread is critical for land management and disaster preparedness. To this end, we present `Next Day Wildfire Spread,' a curated, large-scale, multivariate data set of historical wildfires aggregating nearly a decade of remote-sensing data across the United States. In contrast to existing fire data sets based on Earth observation satellites, this data set combines 2D fire data with many explanatory variables (e.g., topography, vegetation, weather, drought index, population density) aligned over 2D regions, providing a feature-rich data set for machine learning applications. This data set can be used as a benchmark for developing wildfire propagation models based on remote sensing data for a lead time of one day.

Content

We aggregate the data across the contiguous United States from 2012 to 2020. The data set has a total of 18,445 samples. Each sample is a 64 km x 64 km region at 1 km resolution from a location and time at which a fire occurred. We represent the fire information as a fire mask over each region, showing the locations of ‘fire’ versus ‘no fire’, with an additional class for uncertain labels (i.e., cloud coverage or other unprocessed data). To capture the fire spreading pattern, we include both the fire mask at time t (which we call ‘previous fire mask’) and at time t + 1 day (which we call ‘fire mask’). Using Google Earth Engine (GEE), we aggregate data from different data sources and overlay the fire data in location and time with other variables relevant to wildfire predictions. In addition to the fire data, this data set contains the following features: elevation, wind direction and wind speed, minimum and maximum temperatures, humidity, precipitation, drought index, normalized difference vegetation index (NDVI), energy release component (ERC), and population density.

The following figure shows examples from this data set. In the fire masks, red corresponds to fire, while grey corresponds to no fire. Black indicates uncertain labels (i.e., cloud coverage or other unprocessed data).

https://i.postimg.cc/bYxHNVVV/data-visualization.png" alt="data-visualization.png">

The published notebook provides an example of how to read and plot the data.

A detailed description of this data set is provided here: Arxiv paper.

Data Source

• The historical wildfire data are from the MOD14A1 V6 data set [1].
• Topography data are from the Shuttle Radar Topography Mission (SRTM) [2].
• Weather data are from the Gridded Surface Meteorological data set (GRIDMET) [3].
• Drought data are from the GRIDMET Drought data set [4].
• Vegetation data are from the NASA VIIRS Vegetation Indices (VNP13A1) data set [5].
• Population density data are from the Gridded Population of World Version 4 (GPWv4) data set [6].

Inspiration

Some potential questions that this data set can be used to answer include: - Given a fire on a given day, where will the fire spread the following day? - What are the main variables related to fire spreading?

Citation

[1] L. Giglio and C. Justice, “Mod14a1 modis/terra thermal anomalies/fire daily l3 global 1km sin grid v006,” 2015, https://doi.org/10.5067/MODIS/MOD14A1.006.

[2] T. G. Farr, P. A. Rosen, E. Caro, R. Crippen, R. Duren, S. Hensley, M. Kobrick, M. Paller, E. Rodriguez, L. Roth, D. Seal, S. Shaffer, J. Shimada, J. Umland, M. Werner, M. Oskin, D. Burbank, and D. Alsdorf, “The shuttle radar topography mission,” Reviews of Geophysics, vol. 45, no. 2, 2007, https://doi.org/10.1029/2005RG000183.

[3] J. T. Abatzoglou, “Development of gridded surface meteorological data for ecological applications and modelling,” International Journal of Climatology, vol. 33, no. 1, pp. 121–131, 2013, https://doi.org/10.1002/joc.3413.

[4] J. T. Abatzoglou, D. E. Rupp, and P. W. Mote, “Seasonal climate variability and change in the pacific northwest of the united states,” Journal of Climate, vol. 27, no. 5, pp. 2125–2142, 2014, https://doi.org/10.1002/joc.3413.

[5] K. Didan and A. Barreto, “Viirs/npp vegetation indices 16-day l3 global 500m sin grid v001,” 2018, [https://doi.o...

Summarizes the findings to date, and places them in a regional and historical context. Discusses the SEAFRAME gauge in Manus Island, Papua New Guinea, which records sea level, air and water temperature, atmospheric pressure, wind speed and direction. It is one of an array designed to monitor changes in sea level and climate in the Pacific.