Solar Energy Index fell to 47.66 USD on December 1, 2025, down 3.17% from the previous day. Over the past month, Solar Energy Index's price has fallen 2.97%, but it is still 27.26% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Solar Energy Index.

Prices for Solar Energy Index - Precio De Mercado including live quotes, historical charts and news. Solar Energy Index - Precio De Mercado was last updated by Trading Economics this November 22 of 2025.

Collection includes a variety of indices related to solar activity contributed by a number of national and private solar observatories located worldwide. This metadata record is currently under construction.[SolarIndices A]

Collection includes a variety of indices related to solar activity contributed by a number of national and private solar observatories located worldwide. This metadata record is currently under construction.[SolarIndices A]

Daily forecast of the amount of solar irradiance across thirteen forecast zones within the SDG&E service territory, given as a fraction of the peak solar irradiance at solar noon on the summer solstice under clear-sky conditions.

This product gives the solar noon UV index (UVI) for a given day. The solar spectral irradiance arriving on a horizontal Earth's surface at solar noon and a given location is weighted by the erythemal action spectrum and integrated over wavelength to yield the erythemal dose rate, and then converted to UVI by simple multiplication. The erythemal action spectrum describes the reddening of human skin after exposure to ultraviolet radiation. Input total column ozone data are obtained from GOME-2/Metop measurements while daily cloud data are collected from multiple overpasses of AVHRR/3 instrument onboard both Metop and NOAA satellites. The inputs are interpolated to solar noon time.

This dataset simulates factors influencing solar ultraviolet (UV) radiation and includes target labels indicating the UV risk levels. The data is designed to support predictive modeling of UV radiation and risk assessment using tree-based or other machine learning approaches.

Features: Month: Integer (1–12), representing the month of the year. Day: Integer (1–28), representing the day of the month (simplified for uniformity). Hour: Integer (0–23), representing the time of day in hours. Solar_Radiation: Continuous variable representing solar radiation intensity (in W/m²). Solar radiation impacts UV intensity, with higher values typically leading to increased UV exposure. Cloud_Cover: Continuous variable (0 to 1), representing the fraction of cloud cover, where 0 indicates clear skies and 1 indicates fully overcast conditions. Higher cloud cover tends to reduce UV radiation reaching the surface. Ozone_Level: Continuous variable (200–400 Dobson Units), representing atmospheric ozone thickness. The ozone layer absorbs UV radiation, so thicker ozone generally lowers UV exposure. Altitude: Continuous variable (in km), representing altitude above sea level. UV radiation increases with altitude due to reduced atmospheric shielding. Target Variables: UV_Index: Continuous variable (0 to 11), calculated as an index to represent UV intensity levels based on environmental conditions. UV_Risk_Level: Categorical variable, indicating UV exposure risk based on the UV index: Low: UV index 0–2.9 Moderate: UV index 3–5.9 High: UV index 6–7.9 Very High: UV index 8–10.9 Extreme: UV index 11+

The KNMI provides a daily solar irradiation forecast. The sun's power is a measure for the amount of ultraviolet radiation (UV) in sunlight that reaches the earth. The higher the sun is in the sky, the more solar radiation enters the atmosphere. Part of that radiation is invisible ultraviolet light (UV). It gives you a tan, but too much UV leads to sunburn and can eventually cause skin problems. The total amount of UV on the ground at noon is called solar power, internationally also known as the UV Index. The data is published in two different formats: txt and xml.

ABOUT

The Global UV Index Dataset provides a detailed record of UV exposure levels across different locations and times. It includes information such as latitude, longitude, date, time, UV index (UVI), and time of day classification. This dataset is useful for researchers, environmental scientists, health professionals, and individuals concerned with sun exposure and skin protection.

Key Features of the Dataset:

✅ Covers global latitude and longitude ranges
✅ Includes UV index data across different times of the day
✅ Categorizes time periods into morning, afternoon, and evening
✅ Provides structured data for trend analysis and visualization

This dataset serves as a valuable resource for studying UV exposure patterns, developing health guidelines, and supporting climate research.

FILE INFORMATION

The dataset consists of structured records related to UV index measurements, including geographic coordinates, time-based observations, and classification labels. It is compiled from global environmental monitoring systems.

• File Type: CSV
• Data Fields: Latitude, Longitude, Date, Time, Hour, Time of Day, and UVI

COLUMNS DESCRIPTION

The chromosphere is a highly dynamic outer plasma layer of the Sun. Its physical processes accounting for the variability are poorly understood. We reconstructed the solar chromospheric flare index (SFI) to study the solar chromospheric variability from 1937 to 2020. The new SFI database is a composite record of the Astronomical Institute Ondrˇejov Obser- vatory of the Czech Academy of Sciences from 1937 – 1976 and the records of the Kandilli Observatory of Istanbul, Turkey from 1977 – 2020. The SFI records are available in daily, monthly, and yearly resolutions. We carried out the time-frequency analyses of the new 84-year long SFI records using the wavelet transform. We report the periodicities of 21.88 (Hale cycle), 10.94 (Schwabe cycle), 5.2 (quasi-quinquennial cycle), 3.5, 1.7, 1, 0.41 (or 149.7 days, Rieger cycle), 0.17 (62.1 days), 0.07 (25.9 days, solar rotational modulation) years. All these periodicities seem always present and persistent throughout the observational interval. Thus, we suggest that there is no reason to assume these solar periodicities are absent from other solar cycles. Time variations of the amplitude of each oscillation or periodicity were also studied using the inverse wavelet transform. We found that for the SFI the most active flare cycles over the record were Cycles 17, 19, and 21, while Cycles 20, 22, 23, and 24 were the weakest ones with Cycle 18 was intermediate in flare activity. This shows several differences to the equivalent relationships for solar activity implied by sunspot number records. Furthermore, this confirms that solar activity trends and variability in the chromosphere as captured by SFI are not necessarily the same as those of the Sun’s photosphere, as implied by the sunspot number activity records, for instance. We have also introduced a new signal/noise wavelet coherence metric to analyze two different chromo- spheric indices available (i.e. the SFI and the disk-integrated chromospheric Ca II K activity indices) and to quantify the differences and similarities of the oscillations within the solar chromosphere. Our findings suggest the importance of carrying out additional co-analyses with other solar activity records to find physical inter-relations and connections between the different solar layers from the photosphere, the chromosphere to the corona.

This dataset includes software for representing solar EUV irradiance using Generalized Additive Models (GAMs). These GAMs represent solar EUV irradiance in multiple wavelength bands using smooth functions of three solar indices: the F10.7 solar index, revised sunspot number (SSN), and the Lyman-alpha solar index. The performance of the GAMs are fit in two steps: (1) GAMs are fit between the solar indices and outputs of the FISM2 empirical solar EUV model in each band (Yi), and (2) GAMs are fit between the residuals of Yi and the native measurements of FISM2 in each band ((\zeta_i)). The resulting GAMs (F_i=Y_i-\zeta_i) are used to model solar EUV irradiance in Solar Cycle 24. The GAMs are driven with known historical inputs (models (F^K_i)) and inputs hindcasted with (a) an autoregressive model and (b) the novel Dynamic Superposed Epoch Analysis (DSEA) technique (models (F^P_i)). The performance of models (F^K_i), (F^P_i), and uncalibrated FISM2 estimates are evaluated in terms of relative error, skew normal distribution parameters, and assessed for dependency on solar activity and season.

All of the code for this project is written in Python 3.8.

Solar Index for estimation of Potential evaporation May. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).

Collection includes a variety of indices related to solar activity contributed by a number of national and private solar observatories located worldwide. This metadata record is currently under construction.[SolarIndices A]

The graph shows the changes in the g-index of ^ and the corresponding percentile for the sake of comparison with the entire literature. g-index is a scientometric index similar to g-index but put a more weight on the sum of citations. The g-index of a journal is g if the journal has published at least g papers with total citations of g2.

FLARE INDEX

Note: It is recommended that it would be appropriate courtesy to acknowledge in publications that the flare index data were calculated by the staff of Kandilli Observatory. The following statement is suggested: "Flare Index Data used in this study were calculated by T.Atac and A.Ozguc from Bogazici University Kandilli Observatory, Istanbul, Turkey" Website: http://www.koeri.boun.edu.tr/eng/topeng.htm

Kleczek (1952) first introduced the quantity "Q = i x t" to quantify the daily flare activity over 24 hours per day. He assumed that this relationship gives roughly the total energy emitted by the flares. In this relation, "i" represents the intensity scale of importance and "t" the duration (in minutes) of the flare. Some reviews of flare activity using Kleczek's method are given for each day from 1936 to 2000 by Kleczek (1952), Knoska and Letfus (unpublished), Knoska and Petrasek (1984), Atac (1987) and Atac and Ozguc (1998). The daily flare index of the 21,22,23 Solar Cycles was determined by using the final grouped solar flares which are compiled by NGDC (National Geophysical Data Center). It is calculated for each flare using the formula: Q = (i x t)

To obtain final daily values, the daily sums of the index for the northern and southern hemispheres and for the total surface are divided by the total time of observation of that day calculated from Solar- Geophysical Data, Comprehensive Reports. 1986-2000 flare index data are produced by: Dr. Tamer Atac Bogazici University Kandilli Observatory and Earthquake Research Institute Cengelkoy-81220 Istanbul Turkey e-mail: atac@boun.edu.tr fax: 90-216-3321711 phone: 90-216-3080514 References Dr. Atila Ozguc Bogazici University Kandilli Observatory and Earthquake Research Institute Cengelkoy-81220 Istanbul Turkey e-mail: ozguc@boun.edu.tr fax: 90-216-3321711 phone: 90-216-3080514Draft: 09 October 2012 (WFD) Atac, T.: 1987, Astrophys. Space Sci. 135, 201. Ozguc, A. and Atac, T.: 1989, Solar Phys., 123, 357-365 Ozguc, A. and Atac, T.: 1994, Solar Phys., 150, 339-346, 1994 Ozguc, A. and Atac, T.: 1996, Solar Phys., 163, 183-191, 1996 Atac, T.,and Ozguc, A.: 1996, Solar Phys., 166, 201-208, 1996 Atac, T.,and Ozguc, A.: 1998, Solar Phys., 180, 397-407 Kleczek, J.: 1952, Publ. Inst. Centr. Astron., No. 22, Prague. Knoska, S and Petrasek, J.: 1984, Contr. Astron. Obs. Skalnate Pleso 12, 165. Acknowledgements The authors would like to thank to H.E.Coffey and E.H. Erwin of WDC-A for Solar-Terrestrial Physics, NOAA E/GC2, Broadway 325, Boulder, CO, who made available the grouped flare lists.

Collection includes a variety of indices related to solar activity contributed by a number of national and private solar observatories located worldwide. This metadata record is currently under construction.[SolarIndices A]

Solar Index for estimation of Potential evaporation June. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

Monthly average peak UV index values at Invercargill, Lauder (Otago region), Christchurch, Paraparaumu (Wellington region), and Leigh (Auckland region). The strength of UV light is expressed as a solar UV index, starting from 0 (no UV) to 11+ (extreme). Exposure to the sun's ultraviolet (UV) light helps our bodies make vitamin D, which we need for healthy bones and muscles. However, too much exposure to UV light can cause skin cancer. New Zealand has naturally high UV levels, and monitoring UV levels helps us understand the occurrence of skin cancer. Ozone in the upper atmosphere absorbs some of the sun’s UV light, protecting us from harmful levels. The amount of UV radiation reaching the ground varies in relation to changes in the atmospheric ozone concentrations. The Antarctic ozone hole lies well to the south of New Zealand and does not have a large effect on New Zealand’s ozone concentrations. More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Collection includes a variety of indices related to solar activity contributed by a number of national and private solar observatories located worldwide. This metadata record is currently under construction.[SolarIndices A]