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.


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.


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.


Forecasting production from wind and solar power plants, and making effective decisions under forecast uncertainty, are essential capabilities in low-carbon energy systems. This competition invites participants to develop state-of-the-art forecasting and energy trading techniques to accelerate the global transition to net-zero and to win a share of $21,000 in prize money. It aims to bridge the gap between academic and industry practice, introduce energy forecasting challenges to new communities, and promote energy analytics and data science education.

Last Updated On: 
Mon, 11/13/2023 - 16:27
Citation Author(s): 
Jethro Browell, Sebastian Haglund, Henrik Kälvegren, Edoardo Simioni, Ricardo Bessa, Yi Wang

Seven years of water consumption data, along with population data, were manually collected in collaboration with the local municipality office. This data was then combined with climatic data to model the proposed machine learning algorithm. The weather data was recorded for a period of 7 years using precise meteorological instruments installed in Islamabad at coordinates 33.64° N and 72.98° E, with an elevation of 500 meters above sea level.



This dataset corresponds to the paper Calibration of a Hail-Impact Energy Electroacoustic Sensor, submitted to IEEE Transactions in Instrumentation and Measurement by Florencia Blasina, Andrés Echarri, and Nicolás Pérez. 

The dataset corresponds to the voltage signals acquired regarding several steel-ball impacts on the proposed hail-sensor plate to calibrate it. 


10 year simulation of GCM plasim on grid of 32x64 and 6 hour resolution 


This data set was generated and used in determining the workability of a homemade Intelligent IoT Weather Station Using an Embedded System.


This dataset contains actual field/experimental data for the following environmental engineering applications, namely:

  • Concentration data generated from filtration systems which treat influents, having contaminant materials, via adsorption process.
  • Streamflow height data collated for 50 states/cities in America for the historical period between 1900-2018.

This dataset contains the collection of temperature, humidity, heat index, thermal discomfort index and temperature and humidity index of five indoor environments in São Paulo, Brazil. Data were collected using the ESP32 microcontroller and the DHT11 temperature and humidity sensor.