Weather
This dataset contains a collection of images documenting various natural disasters. The images are sourced from multiple events worldwide, capturing the impact, devastation, and aftermath of these disasters. Each image is labeled with relevant metadata, such as the type of disaster, location, and date of occurrence. The dataset is designed to support research and development in fields such as disaster management, image recognition, machine learning, and humanitarian aid.
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This dataset presents daily meteorological data collected from 2004 to 2024 by BMKG (Meteorology, Climatology, and Geophysical Agency) at the Sleman Station, Yogyakarta, Indonesia. The data includes several key weather parameters: minimum, maximum, and average temperatures, relative humidity, rainfall, sunshine duration, wind speed, and wind direction. This comprehensive dataset serves as a valuable resource for research on climate variability, environmental changes, and weather forecasting over a period of 20 years.
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203 Views
This dataset includes 30 hyperspectral cloud images captured during the Summer and Fall of 2022 at Auburn University at Montgomery, Alabama, USA (Latitude N, Longitude W) using aResonon Pika XC2 Hyperspectral Imaging Camera. Utilizing the Spectronon software, the images were recorded with integration times between 9.0-12.0 ms, a frame rate of approximately 45 Hz, and a scan rate of 0.93 degrees per second. The images are calibrated to give spectral radiance in microflicks at 462 spectral bands in the 400 – 1000 nm wavelength region with a spectral resolution of 1.9 nm.
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The atmospheric radio propagation data set includes atmospheric environment data and radio frequency propagation phase detection data. Atmospheric environment data is collected by temperature, humidity and pressure sensors with a sampling rate of 1. The test time lasts for 10,000 seconds. RF propagation phase jitter was measured by the phase comparison method using a digital multimeter (Keysight 34411A) with a sampling rate of 1. The measurement time is 40,000 seconds.
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SETCD (Satellite and ERA5-based Tropical Cyclone Dataset), a comprehensive dataset encompassing satellite imagery data and ERA5 data for all TCs recorded between 1980 and 2022. Our dataset is derived from two publicly available data sources: GridSat-B1 and ERA5. To capture relevant information associated with TC, SETCD adopts the latitude and longitude positions provided by IBTrACS as the center points. The satellite data within the SETCD dataset consists of three channels from GridSat-B1: infrared, water vapor, and visible.
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This data is the reanalysis of sea surface temperature provided by Extended Reconstruction Sea Surface Temperature version 5 (ERSST v.5) from January 1854 to December 2022, Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) from January 1870 to December 2022, and COBE-SST2 Sea Surface Temperature and Ice (COBE-SST2) from January 1854 to December 2022. All data is re-gridded to have the same spatial resolution of 2.0° × 2.0°, and the grid spans from 88°N to 88°S and 0°E to 358°E via bilinear interpolation from the initial grid. This dataset is in NetCDF4 format.
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This data repository comprises three distinct datasets tailored for different predictive modeling tasks. The first dataset is a synthetic dataset designed to simulate multivariate time series patterns, incorporating both linear and non-linear dependencies among input and target features. The second dataset, the Beijing Air Quality PM2.5 dataset, consists of PM2.5 measurements alongside meteorological data like temperature, humidity, and wind speed, with the objective of predicting PM2.5 concentrations.
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348 Views
As the harmful effects of climate change on human society increase, the analysis of abnormal weather is becoming an important issue. Therefore, this work provides the Korean weather dataset, including the anomaly score measurements by using seven different methods. In this dataset, seven types of weather data for each day in 64 Korean cities from 2010 to 2020 are provided by Weather Radar Center in Korea Meteorological Administration.
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196 Views
The first Algerian forest fires dataset consist of data on forest fires occurrence in Algeria related to meteorological observations and the fire weather indices. Our dataset includes mainly the daily meteorological observations and the Fire Weather Index (FWI) system components. Given the lack of publically available datasets on data on forest fires occurrence in Algeria we have created this one to study the feasibility of the appliance of machine learning algorithms as models for forest fires prediction in the context of Algeria.
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863 Views
Since meteorological satellites can observe the Earth’s atmosphere from a spatial perspective at a large scale, in this paper, a dust storm database is constructed using multi-channel and dust label data from the Fengyun-4A (FY-4A) geosynchronous orbiting satellite, namely, the Large-Scale Dust Storm database based on Satellite Images and Meteorological Reanalysis data (LSDSSIMR), with a temporal resolution of 15 minutes and a spatial resolution of 4 km from March to May of each year during 2020–2022.
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712 Views