Solar Energy Index fell to 36.76 USD on July 31, 2025, down 1.45% from the previous day. Over the past month, Solar Energy Index's price has risen 3.32%, but it is still 12.46% lower 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 July 27 of 2025.

Daily index of solar flare activity.

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).

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 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 March. 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)

Geomagnetic and solar data during 16 - 26 August 2017

China PPI: MoM: Solar Power data was reported at 101.400 Prev Mth=100 in Jun 2023. China PPI: MoM: Solar Power data is updated monthly, averaging 101.400 Prev Mth=100 from Jun 2023 (Median) to Jun 2023, with 1 observations. The data reached an all-time high of 101.400 Prev Mth=100 in Jun 2023 and a record low of 101.400 Prev Mth=100 in Jun 2023. China PPI: MoM: Solar Power data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IE: Producer Price Index: Previous Month=100.

Solar Index for estimation of Potential evaporation October. 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 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.

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.

Solar Index for estimation of Potential evaporation November. 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).

Graph and download economic data for Producer Price Index by Industry: Electric Power Generation: Primary Products (PCU221110221110P) from Dec 2003 to Jun 2025 about power transmission, primary, electricity, PPI, industry, inflation, price index, indexes, price, and USA.

The Mg II Index is a proxy for solar chromospheric variability. This composite data record is based on the work of Viereck et al. (2004) Space Weather, vol 2, CiteID S10005 for measurements from 1978 through 2003. For this time range, the Upper Atmosphere Research Satellite (UARS) Solar Ultraviolet Spectral Irradiance Monitor (SUSIM), UARS Solar Stellar Irradiance Comparison Experiment (SOLSTICE), ERS-2/Global Ozone Monitoring Experiment (GOME) and five NOAA solar backscatter ultraviolet datasets were used. Initially, the best datasets were selected. Then the gaps in the record were filled with data from various other Mg II datasets. Where no alternate data were available, a cubic spline function was used to bridge the missing data. In some cases the data gaps were too long for reasonable spline fits (more than 5 days), and for these gaps the F10.7 cm flux data were scaled to fill the gaps.

Starting in 2003, the data from SORCE SOLSTICE is used exclusively. The SOLSTICE spectra have been convolved with a 1.1 nm triangular response function to improve the long-term agreement with other measurements. All of the datasets have been normalized to a common scale to create a single long-term record.

A new daily composite of the solar flare index (SFI) and the hemispherically-resolved versions (hSFI) is presented for 1937 to 2024. We offer this new dataset to the space weather community in daily, monthly, and annual resolutions. The highest hSFI value occurred during the superstorm of May 2024, representing the maximum value recorded in nearly a century, and this event registered in the Northern Hemisphere. The dataset confirms that the northern hemisphere dominated solar flare activity during Solar Cycles 17 to 21, but that the southern hemisphere has dominated from Solar Cycle 22 to present. In sunspot activity, the Gnevyshev-Ohl rule’’ shows that the sum of sunspot numbers during even-numbered cycles is greater than that of the subsequent odd-numbered cycle. However, for SFI, the equivalent rule seems to be different: SFI activity during even-numbered cycles is greater than that of the preceding odd-numbered cycle. This suggests different pairing rules apply for the photosphere and chromosphere. This cross-cycle interplay might offer a potential mechanism for multi-decadal changes in solar activity. TheGnevyshev gap’’ phenomenon where solar maximum activity sometimes has two peaks separated by up to 1-2 years of a gap is confirmed for SFI. A theoretical explanation for this phenomenon is discussed in terms of the asymmetry between the two hemispheres and slight differences in the peaks and intensities of the ``Mid-term Periodicities’’ (MTP). Although our dataset represents the longest continuous daily dataset for solar flare activity to-date, it is known that stronger solar flare events occurred before 1937. Therefore, a brief discussion of earlier solar flare events in the historical record is also provided for context.

Smoke Impact Solar Irradiance Index Market Outlook

As per our latest research, the global Smoke Impact Solar Irradiance Index market size is valued at USD 412.5 million in 2024, reflecting a robust foundation for this rapidly emerging sector. With a projected compound annual growth rate (CAGR) of 11.2% from 2025 to 2033, the market is expected to reach USD 1,090.7 million by 2033. This significant growth is primarily attributed to rising awareness regarding the adverse effects of smoke and particulate matter on solar energy yield and the increasing need for precise solar irradiance data across various industries.

The growth trajectory of the Smoke Impact Solar Irradiance Index market is underpinned by several pivotal factors. The escalating frequency and severity of wildfires globally have intensified the demand for advanced tools that can accurately assess and forecast the impact of smoke on solar irradiance. Industries such as solar energy, agriculture, and environmental monitoring are increasingly adopting these solutions to optimize energy production, crop yield, and environmental management strategies. Additionally, governmental regulations mandating air quality monitoring and the integration of renewable energy sources are compelling stakeholders to invest in sophisticated indices and analytical platforms. The convergence of these trends is fostering a conducive environment for market expansion, with technological advancements further enhancing the accuracy and usability of smoke impact indices.

Another key driver is the evolution of data analytics and remote sensing technologies. The integration of satellite imagery, advanced sensors, and machine learning algorithms has revolutionized the way smoke impact on solar irradiance is measured and analyzed. These innovations enable real-time monitoring and predictive modeling, providing stakeholders with actionable insights for decision-making. The proliferation of cloud-based platforms has democratized access to high-quality data, making it feasible for a broader range of users, from government agencies to private enterprises, to leverage these solutions. This technological leap is not only improving the reliability of indices but also reducing operational costs, thereby accelerating market penetration across diverse verticals.

Furthermore, the global transition towards renewable energy and sustainable agricultural practices is amplifying the relevance of the Smoke Impact Solar Irradiance Index. As solar power becomes a cornerstone of the global energy mix, understanding and mitigating the impact of atmospheric particulates on solar yield is critical for maximizing return on investment. Similarly, precision agriculture relies heavily on accurate solar irradiance data to optimize crop growth and resource utilization. The synergy between environmental sustainability goals and the adoption of advanced monitoring tools is expected to sustain market momentum over the forecast period, with collaborative initiatives between public and private sectors further catalyzing innovation and adoption.

From a regional perspective, North America currently dominates the Smoke Impact Solar Irradiance Index market, accounting for a significant share of global revenue. This leadership is attributed to the region’s advanced technological infrastructure, stringent environmental regulations, and high incidence of wildfire events, particularly in the United States and Canada. Europe follows closely, driven by robust renewable energy policies and a strong focus on climate change mitigation. The Asia Pacific region is anticipated to witness the fastest growth, fueled by rapid industrialization, increasing investments in solar energy, and rising awareness about environmental monitoring. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a relatively nascent stage of adoption.

Component Analysis

The component segment of the Smoke Impact Solar Irradiance Index market is categorized into hardware, software, and services. Hardware<

Solar Index for estimation of Potential evaporation September. 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).

Solar Index for estimation of Potential evaporation January. 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. 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 NOAA National Geophysical Data Center (NGDC) and co-located World Data Center for Solar-Terrestrial Physics (Boulder) acquires and maintaines historical solar observations from a large number of ground and space-based solar observatories. Archives include extensive datasets for solar indices (sunspot index, flare index, solar radio, etc), features (flare reports, sunspot regions, etc) and images (composite drawings, photospheric, chromospheric, and corona, etc). Component support space weather.